No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Landcover classification of the Lower Nhecolândia subregion of the Brazilian Pantanal Wetlands using ALOS/PALSAR, RADARSAT-2 and ENVISAT/ASAR imagery
... Scientists have successfully applied remote sensing technologies in hydrogeological, environmental, and geological research using optical and radar data from various satellites: Sentinel-1 [1][2][3][4][5][6][7][8][9][10]; Sentinel-2 [2,3,[7][8][9][10][11][12][13][14][15][16][17][18][19][20]; Landsat-1, -2, and -3--8 [10,12,14,15,[21][22][23][24][25][26][27][28][29][30][31][32][33]; MODIS (Terra/Aqua) [24,[34][35][36][37][38][39][40][41][42][43]; ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) [19,24,44]; ASAR (Advanced synthetic Aperture Radar) [1,36,[45][46][47][48]; AVHRR (Advanced Very High Resolution Radiometer) [36,49]; NOAA (National Oceanic and Atmospheric Administration); LIDAR [21,36,50]; and ENVISAR [36,45,48]. Researchers often use Sentinel-1/2-5 data products, which have been open access on the Copernicus Open Access Hub platform since 2014, as well as Landsat-1/2, 3-8, MODIS (Terra/Aqua), and NOAA, ones also available to consumers. ...
... Scientists have successfully applied remote sensing technologies in hydrogeological, environmental, and geological research using optical and radar data from various satellites: Sentinel-1 [1][2][3][4][5][6][7][8][9][10]; Sentinel-2 [2,3,[7][8][9][10][11][12][13][14][15][16][17][18][19][20]; Landsat-1, -2, and -3--8 [10,12,14,15,[21][22][23][24][25][26][27][28][29][30][31][32][33]; MODIS (Terra/Aqua) [24,[34][35][36][37][38][39][40][41][42][43]; ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) [19,24,44]; ASAR (Advanced synthetic Aperture Radar) [1,36,[45][46][47][48]; AVHRR (Advanced Very High Resolution Radiometer) [36,49]; NOAA (National Oceanic and Atmospheric Administration); LIDAR [21,36,50]; and ENVISAR [36,45,48]. Researchers often use Sentinel-1/2-5 data products, which have been open access on the Copernicus Open Access Hub platform since 2014, as well as Landsat-1/2, 3-8, MODIS (Terra/Aqua), and NOAA, ones also available to consumers. ...
... When using radar data to identify water surfaces, it is imperative to use noise reduction filters. The use of speckle filtering (Lee filter) to identify water surfaces is common in modern research when processing satellite images containing radar data in the SNAP program [1,3,4,6,48]. Speckle filters are moving window filters that change the value of the central pixel based on all pixel values in the window. There are the different window sizes-3 × 3, 5 × 5, and 7 × 7-and, in this research, we used a 5 × 5 window. ...
Using remote sensing data to accurately record water surface changes over large areas is crucial in monitoring water resources. However, mapping water surfaces from remote sensing data has its advantages and disadvantages. This study presents a method for mapping water surfaces and wetlands based on Sentinel-1/-2 data over a study area of more than 26,000 km² in three river basins, the Bug, Dniester, and San, located along the Polish–Ukrainian border. To achieve this goal, an image processing algorithm with additional options was developed (special filters, type classification, and post-classification), which minimized the shortcomings and increased the accuracy of the method. As a result, by using optical and radar data, it was possible to create maps of water bodies in the study area in the driest month of the year from 2018 to 2021. The results were evaluated numerically and graphically. The accuracy of the method was assessed using the Kappa coefficient. For optical data, the lowest value was 76.28% and the highest was 88.65%; for radar data, these values were 87.61% and 97.18%, respectively. When assessing accuracy, the highest values were achieved for overall accuracy (OA), with a maximum of 0.95 (for SAR) and 0.91 (for optical data). The highest values were in user accuracy (UA), with a maximum value of 1 for both SAR and optical data.
... Long wavelengths have shown to be more adequate to map forested wetland (Li and Chen, 2005), whereas short wavelengths have been reported to be favored for mapping herbaceous vegetation. In contrast, for long wavelengths such a L-band herbaceous vegetation is partially transparent (Brisco et al., 2009, Evans and Costa, 2013, Mahdianpari et al., 2017b. Shorter wavelengths have also been described to distinguish well between non-forested wetland classes (Li and Chen, 2005). ...
... For non-forested wetlands all polarizations of X-band (HH, VV, cross-pol) as well as C-band (HH, VV and crosspol) were summarized to be important contributors, alongside with L-HH (Schmullius and Evans, 1997). As reported in some further studies L-band SAR is well suited for identifying marshland and causes lower backscatter values due to a combination of surface and volume scattering in L-band versus only volume scattering in shorter wavelengths such as C-band (Silva et al., 2008, Evans and Costa, 2013, Mahdianpari et al., 2017b. ...
... Dual-co-polarized (HH-VV) SAR data were mainly retrieved from Terra SAR-X (e.g., Betbeder et al., 2014b, Betbeder et al., 2015, Moser et al., 2016b, Moser et al., 2016a, Heine et al., 2016, Klingebiel et al., 2021 or previously, from a combination of different sensors at the same wavelength, such as JERS-1 and ALOS PALSAR (e.g., Milne and Tapley, 2010), or RADARSAT-1 and ERS-1 (e.g., Townsend, 2002). Dual-cross-pol data are available form a multitude of sensors, and were used for wetland studies with ASAR (e.g., Na et al., 2013a, Evans andCosta, 2013), RADARSAT-2 (e.g., Costa, 2013, Moser et al., 2016a), ALOS PALSAR (e.g., Evans and Costa, 2013, Ferreira-Ferreira et al., 2015, Mahdianpari et al., 2017b, Mahdavi et al., 2017, ALOS-2 PALSAR-2 (e.g., Mahdavi et al., 2017) and lately, from Sentinel-1 (e.g., Schmitt et al., 2016, Muro et al., 2016, Cazals et al., 2016, Chatziantoniou et al., 2017, Mahdavi et al., 2017, Tsyganskaya et al., 2018a, Mahdianpari et al., 2019, Slagter et al., 2020, Mahdianpari et al., 2020. For dual-co-pol data, decomposition techniques are still a new research field, and for cross-pol data options are limited. ...
Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa.
... Space borne satellite remote sensing has revolutionised monitoring capabilities of the natural environment. Satellite imagery provides a powerful technology for mapping surface water and wetlands as it captures instantaneous information across wide, remote, potentially inaccessible areas such as floodplains (Frazier et al., 2000;Roshier et al., 2001;Li and Chen, 2005;Evans and Costa, 2013;Dabrowska-Zielinska et al., 2014;Mueller et al., 2015;Tulbure et al., 2016 (Thomas et al., 2011 and. ...
... Earth observation data enables broad scale, instantaneous mapping of landcover, and captured temporally provides the opportunity for ongoing monitoring and analysis of land cover change in a consistent and robust manner (Gomez et al., 2016). Earth observation data from orbital satellites provides a powerful technology for mapping surface water and wetlands, capturing rapid information across wide, remote, potentially inaccessible areas such as large swamplands and river floodplains (Frazier et al., 2000;Roshier et al., 2001;Li and Chen, 2005;Evans and Costa, 2013;Dabrowska-Zielinska et al., 2014;Mueller et al., 2015;Tulbure et al., 2016). Both optical and SAR satellite data have a demonstrated capacity for detection of a wide range of wetland attributes, capturing information on chemical (Lillesand et al., 2014) and dielectric (Singh, 1989;Horn et al., 2000) properties of surface features, at appropriate scales for mapping of wetland vegetation communities, braided channels associated with floodplains, lakes and waterholes. ...
... Different SAR wavelengths may increase its application (e.g. Evans and Costa, 2013), particularly in relation to new spaceborne C-and L-band SAR sensors with short repeat cycles. These could provide additionally useful datasets for flood and wetland inventory and monitoring. ...
Rivers and wetlands are under considerable threat around the world from climate
change, pollution, invasive species and overharvesting of water. Such complex
problems demand development of tools to improve wetland management and
recognize their ecological and ecosystem service values. Understanding the interrelationships between freshwater ecosystems and human uses necessitates
analyses of water resources at large spatial and long temporal scales, possible
using classification of satellite imagery. The inundation regime directly influences
wetland type, critical for understanding long-term trajectories of change which
affect the ecosystem. Landsat optical imagery is routinely used to map inundation
and wetlands across Australia, but cannot penetrate cloud cover or vegetation to
detect all inundated areas. Contrastingly, synthetic aperture radar (SAR) imagery
overcomes these shortcomings, but remains largely untested for arid areas. To
address this gap in knowledge, I tested the effectiveness of using L-band SAR to
map inundation and wetland types in the Paroo and Warrego River floodplains
within the Murray-Darling Basin, arid Australia. Data from the Japanese Advanced
Land Observing Satellite (ALOS) Phase Arrayed L-band SAR (PALSAR) sensor were
provided through the Kyoto & Carbon (K&C) Initiative to meet requirements of the
RAMSAR convention on wetlands. I successfully detected inundation in arid land
cover types using segmentation and change detection of SAR L-HH/HV data to high
accuracy. I used SAR data mining in a classification and regression tree analysis to
differentiate arid wetland types with a high accuracy, validated with field data.
Overall, the use of L-band SAR satellite data was effective for rapid flood mapping
and discrimination of different vegetated wetlands, and clearly has wider
applicability. I then investigated the flooding regime of the largest floodplain
iii
wetland in the system, Yantabulla Swamp, by mapping inundation using a
combination of ALOS PALSAR, Landsat, and aerial surveys; obtaining historic river
flow records, local rainfall and evaporation data; and modelling flood volume to
hindcast flooding regime over 4.5 decades (1970-2015). Yantabulla Swamp has a
highly variable inundation regime. On average, large episodic ‘boom’ events (>90%
extent) occurred about every 3.1 years, medium-high events (>40%) every 1.6
years, medium-low events (<40%) every year, with a mean flood extent of 14.82%
(SD=25.57). Some years were dry while others had multiple low spells within a year
and the longest period between episodic (>90%) events was 7.21 years, reflecting
the unpredictability of the system. Rainfall contributed occasionally as the sole
contributor to some low extent events, while a combination of rainfall and flow
from Paroo River and Cuttaburra Creek produced the largest and longest events.
Overall there was a higher mean rate of rise (1.67%/day) compared to fall
(0.33%/day) for individual events, showing slower recession. The combination of
SAR and Landsat data successfully enabled modelling to characterise multidecadal inundation regimes of Yantabulla Swamp.
... L-band is the second most used frequency band, with 39 studies employing L-band in their analysis, which is approximately half the use of the C-band [90]. Among the current sensors operating in L-band, ALOS-PALSAR (24 studies) and JESR (6 studies) were the most applied in wetland studies [56,58,70,[91][92][93][94][95][96][97][98][99]. Data from TerraSAR-X was applied in the 11 X-band studies. ...
... Most studies were conducted for wetland mapping (84 studies), water level monitoring (27 studies) or change detection (25 studies). Wetland mapping includes classification and monitoring of wetlands, where the main goal is to produce a wetland map [91]. The water-level monitoring studies were mostly conducted in coastal wetlands, where the InSAR technique was particularly used. ...
... Despite the low temporal resolution of the dataset, water levels were monitored over a 4 year period with cm level accuracy [93]. Additionally, although C-band has shorter wavelength (5.66 cm) and less penetration, numerous studies have demonstrated an increase in backscatter in cases of flooded vegetation [41,111,115], C-band RADARSAT-2 data have proven to be highly sensitive to plant structural parameters [38,39,49,91,108,[116][117][118]. In particular, in wetlands where the density of herbaceous vegetation is low, vertically polarized C-band data achieved good classification accuracy [119]. ...
Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on the roughness and moisture content of the surface, SAR offers unique potential for wetland monitoring. However, there are still challenges in applying SAR for mapping complex wetland environments. The backscattering similarity of different wetland classes is one of the challenges. Choosing the appropriate SAR specifications (incidence angle, frequency and polarization), based on the wetland type, is also a subject of debate and should be investigated more thoroughly. The geometric distortion of SAR imagery and loss of coherency are other remaining challenges in applying SAR and its processing techniques for wetland studies. Hence, this study provides a systematic meta-analysis based on compilation and analysis of indexed research studies that used SAR for wetland monitoring. This meta-analysis reviewed 172 papers and documented an upward trend in usage of SAR data, increasing usage of multi-sensor data, increasing integration of C-and L-bands over other configurations and higher classification accuracy with multi-frequency and multi-polarized SAR data. The highest number of wetland research studies using SAR data came from the USA, Canada and China. This meta-analysis highlighted the current challenges and solutions for wetland monitoring using SAR sensors.
... Additionally, images of spring and autumn are able to optimize the separability between vegetation types (Deventer et al., 2019). MODIS, Landsat and SPOT images have been widely and successfully used for monitoring the wetland vegetation and detecting the presence and extent of flood (Evans and Costa, 2013;Hou et al., 2018;Zhou et al., 2016). However, their spectral and spatial resolutions may compromise detail wetland vegetation type identification. ...
... According to the scatter mechanisms of ground targets captured by Polarimetric Synthetic Aperture Radar (PolSAR), a range of studies have been conducted for discriminating various land cover types with different shapes, structures as well as roughness and permittivity. For instance, dual-polarization (vertical transmit-vertical receive (VV) / vertical transmit-horizontal receive (VH)) data have been used for the identification of the water body and vegetation (Mahdianpari et al., 2017;Jahncke et al., 2018;Evans et al., 2013). Frequency (or wavelength) is another SAR parameter, which related to the penetration depths through ground targets, reflecting the land surface structure. ...
... On the other hand, the huge calculation load of Sentinel-1/2 time series cause difficulties in application. Additionally, many studies focused on seeking a single classifier for high accuracy wetland mapping (Dronova et al., 2011;Jahncke et al., 2018;Evans and Costa, 2013). However, they may ignore the realistic ecology situation of landscape units in heterogeneous area. ...
Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.
... Previous studies have performed manifold classifications of the Nhecolândia lakes using passive and active sensors at short-term periods or single images (see [11,12,14,26]). Nowadays, Google Earth Engine (GEE) offers a new suitable cloud-based platform for environmental data analysis from local to planetary scales, with rapid access and processing of multiple orbital data from different missions [27]. The data catalogue contains a variety of standard Earth Science raster datasets consisting of imagery, geophysical, climate and weather, and demographic data collections with widely used geospatial datasets, such as the entire Landsat archive. ...
... The area is subject to seasonal flooding, with peaks usually occurring from February to April, and a dry period from August Remote Sens. 2020, 12, 1090 4 of 21 to October [36]. Landscape units in the Nhecolândia Region comprise: (i) Forest woodlands, (ii) open wood savanna, (iii) open grass savanna, (iv) swampy grasslands, and (v) lakes [26]. The region presents a complex hydrographic network with water usually flowing along the hundred-meter-wide shallow waterways locally known as vazantes. ...
... Baías and salobras are water bodies with pH < 9 and electrical conductivity (EC) below 750 µS cm −1 , connected to shallow waterways (vazantes) during the wet season and shrinking in the dry season, leaving the floor of some lakes totally exposed and occupied by herbaceous vegetation [26,47]. The salobras are usually deeper, less connected to the major drainage systems, and have higher pH values and total dissolved solids (TDS) concentrations than most baías [47]. ...
The Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere, the water pH being one of the key factors in controlling the biogeochemical functioning and, consequently, production and emission of GHGs in these lakes. Here, we present a new field-validated classification of the Nhecolândia lakes using water pH values estimated based on a cloud-based Landsat (5 TM, 7 ETM+, and 8 OLI) 2002-2017 time-series in the Google Earth Engine platform. Calibrated top-of-atmosphere (TOA) reflectance collections with the Fmask method were used to ensure the usage of only cloud-free pixels, resulting in a dataset of 2081 scenes. The pH values were predicted by applying linear multiple regression and symbolic regression based on genetic programming (GP). The regression model presented an R 2 value of 0.81 and pH values ranging from 4.69 to 11.64. A lake mask was used to extract the predicted pH band that was then classified into three lake classes according to their pH values: Freshwater (pH < 8), oligosaline (pH 8-8.9), and saline (≥9). Nearly 12,150 lakes were mapped with those with saline waters accounting for 7.25%. Finally, a trend surface map was created using the ALOS PRISM Digital Surface Model (DSM) to analyze the correlation between landscape features (topography, connection with the regional drainage system, size, and shape of lakes) and types of lakes. The analysis was in consonance with previous studies that pointed out that saline lakes tend to occur in lower positions compared to freshwater lakes. The results open a relevant perspective for the transfer of locally acquired experimental data to the regional balances of the Nhecolândia lakes.
... Previous studies have performed manifold classifications of the Nhecolândia lakes using passive and active sensors at short-term periods or single images (see [11,12,14,26]). Nowadays, Google Earth Engine (GEE) offers a new suitable cloud-based platform for environmental data analysis from local to planetary scales, with rapid access and processing of multiple orbital data from different missions [27]. The data catalogue contains a variety of standard Earth Science raster datasets consisting of imagery, geophysical, climate and weather, and demographic data collections with widely used geospatial datasets, such as the entire Landsat archive. ...
... The area is subject to seasonal flooding, with peaks usually occurring from February to April, and a dry period from August Remote Sens. 2020, 12, 1090 4 of 21 to October [36]. Landscape units in the Nhecolândia Region comprise: (i) Forest woodlands, (ii) open wood savanna, (iii) open grass savanna, (iv) swampy grasslands, and (v) lakes [26]. The region presents a complex hydrographic network with water usually flowing along the hundred-meter-wide shallow waterways locally known as vazantes. ...
... Baías and salobras are water bodies with pH < 9 and electrical conductivity (EC) below 750 µS cm −1 , connected to shallow waterways (vazantes) during the wet season and shrinking in the dry season, leaving the floor of some lakes totally exposed and occupied by herbaceous vegetation [26,47]. The salobras are usually deeper, less connected to the major drainage systems, and have higher pH values and total dissolved solids (TDS) concentrations than most baías [47]. ...
... Some authors report mean backscatter values around −12 dB for emergent herbaceous (Martinez and Le Toan. 2007;Arnesen et al. 2013;Evans and Costa 2013), similar to the ones found for dense marshes in this study. White et al. (2015) mention that emergent flooded vegetation does not have a unique backscatter mechanism response: a double bounce backscatter mechanism occurs when vegetation is surrounded by a smooth water surface, a combination of double bounce and volume backscatter mechanism occurs when vegetation is dense, and specular reflection occurs when vegetation is dispersed, patchy or short. ...
... Moreover, TFW are usually spatially located between PFW and NW, often occupying narrow areas. Some authors found the same kind of confusions between some of their classes (Hess et al. 2003;Evans et al. 2010;Rebelo 2010;Evans and Costa 2013;Pistolesi, Ni-Meister, and McDonald 2015;Mahdianpari et al. 2017;Mohammadimanesh et al. 2018;Mahdianpari et al. 2019). The confusion between TFW and NW may be related to the fact that the backscatter values of short grasses, bare soil and shallow water may overlap, especially during the dates with hydrologic deficit. ...
... Thus, some rough water bodies can be confounded with other rough surfaces. The selected threshold is in agreement with other research (Evans et al. 2010;Chapman et al. 2015;Yuan, Lee, and Jung 2015;Zhang et al. 2016;Mahdianpari et al. 2017) although there are also higher values (Arnesen et al. 2013;Ward et al. 2014) and lower values (Evans and Costa 2013) informed in the literature. The problem in differentiating open water, bare soil and short herbaceous vegetation has also been found by other authors (Martinez and Le Toan. ...
Although regional wetland mapping studies have mostly relied on optical sensors, synthetic aperture radar (SAR) sensors are being increasingly applied. The aim of this study is to analyse the ability of the Phased Array type L-band Synthetic Aperture Radar on board of the Advanced Land Observing Satellite (ALOS/PALSAR-1) data to identify, delineate and monitor wetlands, and to evaluate the importance of scene selection in a highly unpredictable wetland. Three SAR scene sets (Year A, Year B and Inter-annual) were built for this purpose, considering the intra-annual and inter-annual hydrologic variability and the phenologic variability of the studied coastal wetland. Seven land cover types were defined, including three permanently flooded wetland classes, three temporarily flooded wetland classes and one non-wetland class. An object-based unsupervised classification approach was applied on each multi-temporal set. The obtained clusters were characterized by a temporal signature and assigned to the seven land cover types using a decision tree with user-defined thresholds. The accuracy assessment of each product was performed using a set of 258 data sites, including field collected data and data retrieved from Landsat 8 Operational Land Imager (OLI) imagery acquired during the dates of the field campaign. The Year B set showed the best accuracy (83.4% overall, 75% Kappa coefficient (κ)) and the lowest omission and commission mean errors (16.6% and 16.1% respectively). The classes that were best differentiated are permanently flooded wetlands (PFW) and non-wetlands (NW) in all sets.
... Differences exist in the performance of individual C-band or L-band data in its application [33,[37][38][39]. Given the increased ability at longer wavelengths to penetrate vegetation canopies, the ...
... Differences exist in the performance of individual C-band or L-band data in its application [33,[37][38][39]. Given the increased ability at longer wavelengths to penetrate vegetation canopies, the pixels in vegetation should be more concentrated at high entropy for the C-band data because of the predominating canopy volume-scattering mechanisms [25]. ...
... Overall, the experimental results based on a three-hierarchy framework indicated that polarimetric distortions of most GF-3 PolSAR images were similar to ALOS-2 and RADARSAT-2. ALOS-2 and RADARSAT-2 have been widely applied in Earth observations for many years, and their quality is confirmed to meet the users' requirements [11,12,15,33,37,[43][44][45]. The crosstalk accuracy of RADARSAT-2 of −30 dB, the channel imbalance of 0.5 dB in amplitude, and 5 degrees in phase are reported [46]. ...
GaoFen-3 (GF-3) is the first Chinese civilian multi-polarization synthetic aperture radar (SAR) satellite, launched on 10 August of 2016, and put into operation at the end of January 2017. The polarimetric SAR (PolSAR) system of GF-3 is able to provide quad-polarization (quad-pol) images in a variety of geophysical research and applications. However, this ability increases the complexity of maintaining image quality and calibration. As a result, to evaluate the quality of polarimetric data, polarimetric signatures are necessary to guarantee accuracy. Compared with some other operational space-borne PolSAR systems, such as ALOS-2/PALSAR-2 (ALOS-2) and RADARSAT-2, GF-3 has less reported calibration and image quality files, forcing users to validate the quality of polarimetric imagery of GF-3 before quantitative applications. In this study, without the validation data obtained from a calibration infrastructure, an innovative, three-hierarchy strategy was proposed to assess PolSAR data quality, in which the performance of GF-3 data was evaluated with ALOS-2 and RADARSAT-2 data as references. Experimental results suggested that: (1) PolSAR data of GF-3 satisfied backscatter reciprocity, similar with that of RADARSAT-2; (2) most of the GF-3 PolSAR images had no signs of polarimetric distortion affecting decomposition, and the system of GF-3 may have been improved around May 2017; and (3) the classification accuracy of GF-3 varied from 75.0% to 91.4% because of changing image-acquiring situations. In conclusion, the proposed three-hierarchy approach has the ability to evaluate polarimetric performance. It proved that the residual polarimetric distortion of calibrated GF-3 PolSAR data remained at an insignificant level, with reference to that of ALOS-2 and RADARSAT-2, and imposed no significant impact on the polarimetric decomposition components and classification accuracy.
... Li et al. [4] proposed mapping tea plantations based on the phenological characteristics from Vegetation and Environmental New micro Spacecraft (VENµS) images and achieved an OA of 95%. However, tea plantations are widespread in subtropical areas [9], for which it is challenging to obtain high-quality time-series data due to clouds and weather [10,11]. Consequently, multi-source data continue to play a significant role in mapping tea plantations. ...
... SAR data have the potential for land cover classification because they are not affected by clouds and weather [10,[35][36][37]. In addition, SAR data can reflect the geometric and dielectric properties of scattering, improving classification accuracy [38,39]. ...
The accurate mapping of tea plantations is significant for government decision-making and environmental protection of tea-producing regions. Hyperspectral and Synthetic Aperture Radar (SAR) data have recently been widely used in land cover classification, but effective integration of these data for tea plantation mapping requires further study. This study developed a new feature-level image fusion method called LPPSubFus that combines locality preserving projection and subspace fusion (SubFus) to map tea plantations. Based on hyperspectral and SAR data, we first extracted spectral indexes, textures, and backscattering information. Second, this study applied LPPSubFus to tea plantation mapping with different classification algorithms. Finally, we compared the performance of LPPSubFus, SubFus, and pixel-level image fusion in tea plantation mapping. Feature-level image fusion performed better than pixel-level image fusion. An improvement of about 3% was achieved using feature-level image fusion compared to hyperspectral data alone. Regarding feature-level image fusion, LPPSubFus improved the overall accuracy by more than 3% compared to SubFus. In particular, LPPSubFus using neural network algorithms achieved the highest overall accuracy (95%) and over 90% producer and user accuracy for tea plantations and forests. In addition, LPPSubFus was more compatible with different classification algorithms than SubFus. Based on these findings, it is concluded that LPPSubFus has better and more stable performance in tea plantation mapping than pixel-level image fusion and SubFus. This study demonstrates the potential of integrating hyperspectral and SAR data via LPPSubFus for mapping tea plantations. Our work offers a promising tea plantation mapping method and contributes to the understanding of hyperspectral and SAR data fusion.
... As most tropical anurans have the highest activity levels during the peak of the rainy season (Duellman & Trueb, 1994), our sampling was concentrated at the end of January 2017, which corresponded to the month of highest precipitation for that season (231 mm, Fick & Hijmans, 2017). Landscapes of this southernmost region are influenced by the neighbouring Cerrado (tropical savanna) and include grasslands, open wood savanna and forested woodland (Evans & Costa, 2013). Permanent bodies of standing water used by anurans are embedded among F I G U R E 1 Conceptual scheme depicting (a) temporal resolution: incidence of vocal active species registered for 2 min each 20 min over the early, mid and late periods of the night (19:00-04:00 hr, UTC-4) and summarized with two temporal resolutions: 1-and 3-hr intervals. ...
... Second, we addressed the potential role of distinct ecological processes on the variation of species composition in nightly activity by fitting relationships on (c.1) the differences in species composition from the early-late night period and (c.2) the variance of such differences across the different monitored days patches of these vegetation formations and have their low-lying adjacent areas composed of seasonally flooded grasslands that often inundate during the rainy season (Prado et al., 2005). The aquatic vegetation of these freshwater water bodies comprises erectophile grass-like plants from Cyperaceae and Typhaceae families, and floating emergent plants from Pontederiaceae, Araceae, Salviniaceae and Nymphaceae families (Delatorre et al., 2020;Evans & Costa, 2013;Pott & Pott, 2000). ...
Temporal scale in animal communities is often associated with seasonality, despite the large variation in species activity during a diel cycle. A gap thus remains in understanding the dynamics of short‐term activity in animal communities.
Here we assessed calling activity of tropical anurans and addressed how species composition varied during night activity in assemblages along gradients of local and landscape environmental heterogeneity.
We investigated 39 anuran assemblages in the Pantanal wetlands (Brazil) with passive acoustic monitoring during the peak of one breeding season, and first determined changes in species composition between night periods (early, mid and late) using two temporal resolutions (1‐ and 3‐hr intervals). Then, we addressed the role of habitat structure (local and landscape heterogeneity variables from field‐based and remote sensing metrics) and ecological context (species richness and phylogenetic relatedness) in determining changes in species composition (a) between night periods and (b) across days.
Nocturnal calling activity of anuran assemblages varied more within the 1‐hr resolution than the 3‐hr resolution. Differences in species composition between early‐ and late‐night periods were related to local habitat structure and phylogenetic relatedness, while a low variation in compositional changes across days was associated with low‐heterogeneous landscapes. None of these relationships were observed using the coarser temporal resolution (3 hr).
Our findings on the variation of calling activity in tropical anuran assemblages suggest potential trade‐offs mediated by fine‐temporal partitioning. Local and landscape heterogeneity may provide conditions for spatial partitioning, while the relatedness among co‐signalling species provides cues on the ecological overlap of species with similar requirements. These relationships suggest a role of niche dimensional complementarity on the structuring of these anuran assemblages over fine‐temporal scales. We argue that fine‐temporal differences between species in breeding activity can influence the outcome of species interaction and thus, addressing temporal scaling issues can improve our understanding of the dynamics of animal communities.
... An object-based image analysis and decision tree classification for the mapping of vegetation in a late Quaternary landform in the Amazonian wetlands was performed by Cordeiro and Rossetti [131]. In South America, L-band studies were also carried out by Evans et al. [132], Evans and Costa [133], and Evans et al. [134] who performed object-based image analysis using dual season and dual polarization L-band ALOS/PALSAR and C-band RADARSAT-2 data to map ecosystems of the Brazilian Pantanal wetlands showing the spatial distribution of terrestrial and aquatic habitats. ...
... They indicated that L-band data performed better than C-band data when coarse land cover classification system was applied; however, both radar datasets could not effectively separate finer vegetation classes. Furthermore, Evans and Costa [133] used multi-temporal L-band ALOS/PALSAR, C-band RADARSAT-2, and ENVISAT-ASAR data to map ecosystems and lake distribution in a tropical wetland and revealed that the combination of dual-season, high spatial resolution C-and L-band imagery was essential for proper separation among the land cover classes. Jin et al. [217] examined the synergetic use of numerous metrics derived from multitemporal dual-polarized ALOS/PALSAR imagery for vegetation and land cover mapping. ...
The coastal zone offers among the world's most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land-and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and subtropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.
... Due to higher topographical position of the sand hill/saline lake complex (3 to 5 m above the surroundings), these two geomorphological features are not typically reached by the seasonal freshwaters of the floods, allowing the development of forested savanna vegetation on the sand hills. Intermittent watercourses are formed by the coalescence of freshwater lakes during the seasonal inundation and are generally covered by open grass savanna or swampy grasslands during the dry season (Barbiero et al., 2002;Evans and Costa, 2013) The saline waters, mainly fed by rain precipitation (Freitas et al., 2019), are currently formed in the region by lake shrinkage due to the high evapotranspiration rates, producing waters with pH of 8.5 to 10.1 and EC from 6 to 68 dS m −1 . The freshwater from the floods, on the other hand, commonly presents lower pH < 6.0 and EC < 0.30 dS m −1 (Barbiero et al., 2002;Almeida et al., 2003). ...
... This process has been apparently active in the last 3000 years in the region (Furquim et al., 2017), confirmed by a set of evidence. In the aerial images, the preserved saline lakes are totally surrounded by forested savanna, whereas the brackish lakes are mainly surrounded by open grass savanna and/or grasslands, which typically occur in areas subjected to flood (Almeida et al., 2003;Fernandes, 2007;Evans and Costa, 2013). The aerial images also reveal that incipient watercourse channels link brackish lakes characterized by waters that are more similar to the saline lakes, whereas more developed channels link brackish lakes with waters that are more similar to the freshwaters (Almeida et al., 2003). ...
Some of the saline lakes occurring in the Nhecolândia, a sub-region of the Pantanal wetland, have transformed into brackish lakes due to atypical freshwater input from seasonal flooding. Consequently, the Saline-Sodic soils formed around the saline lakes, previously submitted to salinization and solonization, have been converted into Sodic, Solodized-Solonetz and Solod soils around the brackish lakes, under the action of solonization and/or solodization. In this research, fine clay fractions (< 0.2 μm size) of B natric horizons of Saline-Sodic soils surrounding a saline lake and Sodic and Solod soils surrounding brackish lakes were studied in order to understand the genesis of clay minerals with the gradual transformation of these salt-affected soils. Fine clay mineralogy was studied by experimental XRD, full XRD profile modelling using NEWMOD 3.2.1, STEM/HAADF and ICP-OES. In the Saline-Sodic soils, illitic mixed-layered R0 kaolinite-illite (K-I) and R0 illite-smectite (I-S) comprise most of the samples, the percentage of illite (in K-I and IS), kaolinite in R0 kaolinite smectite (K-S and K-I), and smectite (in K-S and IS) layers was 73-76%, 14-16% and 10-11%, respectively. In the Sodic soils, illitic K-I and/or IS still dominate the samples, but the percentage of illite layers (in K-I, IS and/or illite-vermiculite) is smaller (52-68%), with an increase of kaolinite (19-35% in end member kaolinite, K-S and K-I) and smectite (7-21% in K-S and IS) layers. Finally, the Solod soil shows dominance of smectitic IS and illitic IS , with a significant decrease in illite layers (36-41% in K-I and IS), the maintenance of kaolinite layers (21-31% in pure kaolinite, K-S and K-I) and significant increase in smectite layers (31-37% in K-S and IS). These progressive changes in mineral assemblages from the most alkaline (Saline-Sodic) to the most acidic soil (Solod) is probably due to gradual transformations, especially from the illitic phases, neoformed around the saline lake, to other 2:1 (mixed-layered smectite) and 1:1 (mixed-layered kaolinite) clay minerals under the new geochemical conditions of the brackish lakes. The mineral range observed in the samples suggest that the transformation from one clay mineral into another takes place without the complete dissolution and consequent precipitation, but as progressive mixed-layering reactions. This model explains the existence of several mixed-layered minerals, which is in agreement with the geochemical evolution of soils under progressive solonization and solodization.
... The Nhecolândia relict landscape zone is a transitional area with the Taquari megafan, an open plain with a complex of northeast oriented relict ponds, which are being superimposed by fluvial channels presently ( Figure 3B, see also Figure S2 high-resolution image in the Supporting Information). The Nhecolândia region represents the southern limit of the Taquari megafan, a unique lacustrine system where more than 10 000 saline-alkaline and freshwater lakes and ponds co-exists in close proximity (Por, 1995;Assine and Soares, 2004;Evans and Costa, 2013;Oliveira et al., 2018). The elongated northeast ponds of this zone are inherited morphologies of the Nhecolândia region, currently under fluvial reshaping, which favors the connection of ponds and loss of their morphologies. ...
... The lake is almost completely covered by macrophytes, growing on or below the water surface line and forming floating mats ('camalotes') and floating meadows ('baceiros') (Pivari et al., 2008;Evans and Costa, 2013;Almeida et al., 2015). Floating meadows and floating mats are commonly freefloating vegetation clusters that form macrophytes islands, which can move downslope by currents. ...
... The Nhecolândia relict landscape zone is a transitional area with the Taquari megafan, an open plain with a complex of northeast oriented relict ponds, which are being superimposed by fluvial channels presently ( Figure 3B, see also Figure S2 high-resolution image in the Supporting Information). The Nhecolândia region represents the southern limit of the Taquari megafan, a unique lacustrine system where more than 10 000 saline-alkaline and freshwater lakes and ponds co-exists in close proximity (Por, 1995;Assine and Soares, 2004;Evans and Costa, 2013;Oliveira et al., 2018). The elongated northeast ponds of this zone are inherited morphologies of the Nhecolândia region, currently under fluvial reshaping, which favors the connection of ponds and loss of their morphologies. ...
... The lake is almost completely covered by macrophytes, growing on or below the water surface line and forming floating mats ('camalotes') and floating meadows ('baceiros') (Pivari et al., 2008;Evans and Costa, 2013;Almeida et al., 2015). Floating meadows and floating mats are commonly freefloating vegetation clusters that form macrophytes islands, which can move downslope by currents. ...
Wetlands are permanently or seasonally flooded areas which support countless species of plants and animals. The Pantanal, in central‐west Brazil is one of the largest freshwater wetlands in the world covering an area of ~150,000 km2. The relationships between geomorphology, hydrology, sedimentation, and vegetation cover are critical for understanding how the landscape constrains the dynamics of wetlands. We provide a detailed study of the geomorphology and surface hydrology of the Negro River Interfan System (NRIS), in the southern Pantanal, by applying multiples approaches (i.e., remote sensing analysis, geomorphological zonation and hydrosedimentological surveys). A multitemporal analysis of Landsat imagery produced an inundation frequency map (2000–2011 period) that revealed a permanently flooded area in the central portion of the NRIS. A hidden fluvial lake was previously undetected due to the accumulation of floating mats and floating meadows of macrophytes. The Negro and Aquidauana feeder rivers exhibit remarkable differences in channel planform, water discharge, and sediment load. The Negro River presents a distributary pattern with marginal levees with decreasing elevation as it progrades into the lake and remains as a subaqueous landform conditioning the water flow downstream. The lake outflow to the Paraguay River occurs mainly by sheet flow during flood seasons and through small tributary channels during dry months. The lake's geometry is outlined by ENE and WNW straight borders, suggesting that the area is tectonically controlled. A cloud‐based worldwide water surface database (1984 to 2015) revealed frequent channel changes within the NRIS. Recent channel avulsions in the lower course of the Negro River are noteworthy mainly because the former river channel at the confluence with the Paraguay River is no longer connected with the Negro River channel.
... Knowledge of the spatial distribution of these valuable ecosystems is crucial in order to characterize ecosystem processes and to monitor the subsequent changes over time [2]. However, the remoteness, vastness, and seasonally dynamic nature of most wetland ecosystems make conventional methods of data acquisition (e.g., surveying) labor-intensive and costly [3,4]. Fortunately, remote sensing, as a cost-and time-efficient tool, addresses the limitations of conventional techniques by providing valuable ecological data to characterize wetland ecosystems and to monitor land cover changes [5,6]. ...
... The main goals of this study were, therefore, to: (1) Eliminate the limitation of the number of input bands by developing a pipeline in Python with the capacity to operate with multi-layer remote sensing imagery; (2) examine the power of deep CNNs for the classification of spectrally similar wetland classes; Remote Sens. 2018, 10, 1119 4 of 21 (3) investigate the generalization capacity of existing CNNs for the classification of multispectral satellite imagery (i.e., a different dataset than those they were trained for); (4) explore whether full-training or fine-tuning is the optimal strategy for exploiting the pre-existing convnets for wetland mapping; and (5) compare the efficiency of the most well-known deep CNNs, including DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in a comprehensive and elaborate analysis. Thus, this study contributes to the use of the state-of-the-art classification tools for complex land cover mapping using multispectral remote sensing data. ...
Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.
... OBIA facilitates the fusion of multi-sensor data for improved classification relative to per-pixel analyses (Blaschke, 2010). While OBIA is widely used to classify features with clearly defined boundaries (Hossain & Chen, 2019), it can be used as an analytical tool to classify land cover (Evans & Costa, 2013;Jabs-Sobocińska et al., 2021;Onojeghuo & Onojeghuo, 2017), and we extend this to conceptualize bird species' habitat (Glad et al., 2020). A key difference here is that, rather than using remote sensing spectral or structural similarity for image segmentation, objects were created using presence and pseudo-absence locations. ...
Remote sensing data capture ecologically important information that can be used to characterize, model and predict bird habitat. This study implements fusion techniques using Random Forests (RF) with spectral Landsat data and structural airborne laser scanning (ALS) data to scale habitat attributes through time and to characterize habitat for four bird species in dynamic young forest environments in the United Kingdom. We use multi-temporal (2000, 2005, 2012/13, 2015) multi-sensor (Landsat and ALS) data to (i) predict structural attributes via pixel-level fusion at 30 metre spatial resolution, (ii) model bird habitat via object-level fusion and compare with models based on ALS, Landsat and predicted structural attributes, and (iii) predict bird habitat through time (i.e., predict 2015 habitat based on 2000-2012 data). First, we found that models predicting mean height from spectral information had the highest accuracy, whilst maximum height, standard deviation of heights, foliage height diversity, canopy cover and canopy relief ratio had good accuracy, and entropy had low accuracy. The green band and the normalized burn ratio (NBR) were consistently important for prediction, with the red and shortwave infrared (SWIR) 1 bands also important. For all structural variables, high values were underpredicted and low values were overpredicted. Second, for Blue Tit ( Cyanistes caeruleus ) and Chaffinch ( Fringilla coelebs ), the most accurate model employed Landsat data, while object-level fusion performed best for Chiffchaff ( Phylloscopus collybita ) and Willow Warbler ( Phylloscopus trochilus ). ALS mean, maximum and standard deviation of heights and Landsat tasseled cap transformations (TCT) (i.e., wetness, greenness and brightness) were ranked as important to all species across various models. Third, we used our models to predict presence in 2015 and implemented a spatial intersection approach to assess the predictive accuracy for each species. Blue Tit and Willow Warbler presences were well predicted with the Landsat, ALS, and objectlevel fusion models. Chaffinch and Chiffchaff presences were best predicted with the ALS model. Predictions based on pixel-level predicted structure surfaces had low accuracy but were acceptable for Chaffinch and Willow Warbler. This study is significant as it provides guidance for Landsat and ALS data application and fusion in habitat modelling. Our results highlight the need to use appropriate remote sensing data for each study species based on their ecology. Object-level data fusion improved habitat characterization for all species relative to ALS, but not to Landsat for Blue Tit and Chaffinch. Pixel-level fusion for predicting structural attributes in years where ALS data are note available is increasingly being used in modelling but may not adequately represent within-patch wildlife habitat. Finally, incorporating predicted surfaces generated through pixel-level fusion in our habitat models yielded low accuracy.
Highlights
We used object- and pixel-level fusion with ALS and Landsat to examine bird habitat
Pixel-level fusion predicted surfaces yielded low accuracy in habitat models
Best models: Landsat (Blue Tit, Chaffinch); fusion (Chiffchaff, Willow Warbler)
Best prediction: ALS (Chaffinch, Chiffchaff)
Best prediction: ALS, Landsat, object-level fusion (Blue Tit, Willow Warbler)
Graphical abstract
... In addition to the Humid and Dry Chaco, the study area includes a vast tropical mosaic of wetlands, grasslands and forests called the Pantanal. The Pantanal is located at the NE of the Gran Chaco, extending across the south of the Mato Grosso in Brazil, Bolivia and Paraguay (Evans and Costa, 2013). Known for its high diversity and richness of aquatic, wetland and many terrestrial species, the Pantanal is a large alluvial plain with various ecosystems characterized by periodic flooding. ...
... Visual comparison between the SAR images shows that in L HH polarization the double bounce contributes to medium-high backscatter with a ratio (L HH /L HV ) that is high (shades of blue) in contrast to shades of green of the C VH polarization. L HH polarization is less attenuated by the vertical structure of the fluvial system compared also to C VV polarization, due to the sensitivity of L HH polarization to double-bounce scattering (Evans and Costa 2013). Likewise, the L HH polarization produces the greatest contrast between terraces and floodplains. ...
In the past few decades, attempts have been made to map and monitor disturbances affecting white-sand ecosystems in the Amazonian lowlands. Status and dynamics of the plant communities of these ecosystems are related to a variety of factors (soil type, flooding, nutrient availability, and wildfire), and full characterization of these unique ecosystems is lacking to this date. Hence, we have selected a study area that encompasses the lowlands lying between the Orinoco and the Rio Negro rivers to the west and the borders of the Guyana highlands to the east, between 5°N and 2°N, in the Amazonas state of Venezuela. The objective of the study was to obtain an accurate white-sand land-cover and land-use change (LC-LUC) map through a synergistic integration of Global PALSAR-2 and Sentinel-1 satellite products, with the Normalized Difference Vegetation Index (NDVI) derived from Landsat data. Special attention was given to the mapping of herbaceous formations (meadows) and flooded vegetation. An interpretation key was developed combining an improved Expectation-Maximization (EM) algorithm classification with post-classification refinement (Bayesian Information Criterion), as well as the integration of contextual spatial information and high-resolution imagery (Google Earth and Bing images). The supervised classification differentiates forest (closed to open), inundated forest, dry or flooded open white-sand scrublands, shrubby meadows, shrubby or herbaceous cover regularly flooded, mosaics of plant communities, sparse vegetation, bare rocky areas, and sandbanks/sandridges. The approach of combining two different microwave sensors is a sound one as an orbital sensor can acquire multi-frequency and multi-polarization data. The implementation of integrated image classification (L- and C-band with NDVI data) allows grouping spectral combinations of classes, offering an operational way for efficient mapping of complex units of the white-sand ecosystems. The results provide novel information not only on the canopy structure but also on the vegetation greenness.KeywordsVenezuelaAmazonas statewhite-sand ecosystemsC-band SARL-band SARNDVICanopy structureGreennessComplex units
... Air temperatures can exceed 40 C in the summer and drop to 0 C in the winter (Alvares et al., 2013). The landscape is naturally fragmented (Fig. 1), composed of a mosaic of semideciduous forests, scrub forests, scrub grasslands and open grasslands partially flooded with permanent and temporary salty and freshwater lakes (Evans & Costa, 2013). ...
Mammals use thermoregulatory behavioural strategies to reduce the cost of physiological thermoregulation. Environmental temperatures should, therefore, impact their decisions. We investigated the effect of environmental temperature on the movement decisions of a large mammal with low capacity for physiological thermoregulation: the giant anteater, Myrmecophaga tridactyla. We GPS-tracked 14 giant anteaters in the Brazilian Pantanal wetland over 5 years. We used hidden Markov models to identify two behavioural states (encamping, as a proxy of resting, and moving, as a proxy of being active) across individuals' trajectories. Then, we estimated the effect of environmental temperature on the probability of moving across the hours of the day in open and forested habitats. We also used integrated step selection analysis to understand how environmental temperature drives giant anteater's habitat selection across the day. Giant anteaters showed three important behavioural thermoregulatory strategies in response to environmental temperature changes: they modulated activity duration, completely shifted activity period on a scale of days and selected forests as thermal shelters. With increasing environmental temperature, giant anteaters increased activity duration, nocturnality and diurnal selection for forests, increasing energy intake while avoiding heat gain by solar radiation. With decreasing environmental temperature, they decreased activity duration, increased diurnality and increased nocturnal selection for forests, thus gaining heat from solar radiation when active and taking shelter in milder microclimates when resting. Besides their high short-term behavioural plasticity regarding activity, giant anteaters also used forests to thermoregulate. These results provide insights into how other mammals could respond to climate change. In particular, we highlight the importance of forests as thermal shelters, offering milder temperatures than adjacent open areas during both hot and cold weather spells. Thermal shelters will become more and more indispensable to animal thermoregulation as the frequency and intensity of extreme weather events increase.
... No matter what imaging sensor has been used, the accuracy of the end products has been mainly bound either by the spatial resolution of the sensor of by the processing algorithm used to build the land-cover land-used preferences (Karakus¸et al. 2017, Comert et al. 2019. Better image resolution provides more successful classification accuracies, especially within more heterogeneous land cover-land use schemes (Chen et al. 2004) and diverse ecosystems (Evans and Costa 2013). With the accumulated knowledge of 40þ years, a considerable amount of natural forest cover loss was detected with remote sensing technologies (Rock et al. 1986, Vogelmann 1988). ...
TanDEM-X Forest/Non-Forest (FNF) map(s) have been one such data focusing on the status of global forest coverage, which has played an essential role in combating climate change. Although the producers have carried out verification and comparison analyses , the need for accuracy assessments in a broader sense creates uncertainties for the users to approve the new data. For this purpose , TanDEM-X 50 m FNF maps were exclusively examined visually through 66,000 test grids within 30 geocells selected from temperate, boreal, and tropical forest zones. Thus, it was aimed to provide product accuracy utilizing visual inspections to the end users of TanDEM-X FNF maps. In addition, Collect Earth (CE) software was used to evaluate the dataset visually, and its advantages or disadvantages were compared with similarly designed studies. Consequently, even though the producers' data sets were found to have an accuracy of around 85%, it was observed that there were some issues, especially in the definition of the "non-forest" class. CE software was found to be helpful in such studies. However, the dependence of the analyses on geo-browser supplied imagery had some limitations in estimating the accuracy of a new dataset. ARTICLE HISTORY
... Current research on crop distribution monitoring using remote sensing technology can be divided into two main categories based on the sensor's working mode: using synthetic aperture radar (SAR) to obtain the electromagnetic radiation and scattering properties of targets [4,5], and using optical satellites to acquire the spectral information on targets [6,7]. Evans et al. [8] used a dual-season set of fine-spatial-resolution C-band (RA-DARSAT-1) and L-band (JERS-1) SAR imagery to effectively avoid the problem of cloud cover and the influence of dense vegetation canopy. In such a way, they determined the distribution of the variety of habitats in the Lower Nhecolândia subregion of the Pantanal at a regional scale. ...
The quick and precise assessment of rice distribution by remote sensing technology is important for agricultural development. However, mountain rice is limited by the complex terrain, and its distribution is fragmented. Therefore, it is necessary to fully use the abundant spatial, temporal, and spectral information of remote sensing imagery. This study extracted 22 classification features from Sentinel-2 imagery (spectral features, texture features, terrain features, and a custom spectral-spatial feature). A feature selection method based on the optimal extraction period of features (OPFSM) was constructed, and a multitemporal feature combination (MC) was generated based on the separability of different vegetation types in different periods. Finally, the extraction accuracy of MC for mountain rice was explored using Random Forest (RF), CatBoost, and ExtraTrees (ET) machine learning algorithms. The results show that MC improved the overall accuracy (OA) by 3–6% when compared to the feature combinations in each rice growth stage, and by 7–14% when compared to the original images. MC based on the ET classifier (MC-ET) performed the best for rice extraction, with the OA of 86%, Kappa coefficient of 0.81, and F1 score of 0.95 for rice. The study demonstrated that OPFSM could be used as a reference for selecting multitemporal features, and the MC-ET classification scheme has high application potential for mountain rice extraction.
... The study area is 6,880,354 km 2 in extent (33°45′00" S to 3°40′12" N; 73°32′24" W to 38°52′48" W; Figure 1 with moist evergreen dense forest comprising the largest portion of the native vegetation cover (Câmara et al., 2015). The Pantanal is the world's largest tropical wetland, being formed by permanent aquatic, periodically flooded, and permanently dry habitats, and includes forest woodland, open wood savanna, and grasslands (Assine et al., 2015;Evans & Costa, 2013;Junk et al., 2006). The Cerrado has a high landscape heterogeneity, including savanna, semideciduous, and deciduous forests (Silva et al., 2006). ...
Climate and land‐use changes are expected to negatively affect many species and ecological processes, leading to biodiversity loss. However, some species can adapt to these changes. Wide‐ranging species are expected to be less impacted by such changes, but they can occur in different domains with contrasting environmental conditions, resulting in different conservation statuses along their range. To understand whether a species will overall benefit or lose with global change, we evaluated the responses of a wide‐ranging but a vulnerable bird ( Crax fasciolata ) to separate and combined effects of climate and land‐use changes under different environmental policies in Brazil. Using ecological niche modeling and a land‐use model within the Brazilian political context, we quantified climatic, habitat, and environmental suitability for Crax fasciolata under historical (2000) and future (2050) scenarios. Our findings showed that environmental suitability can increase for Crax fasciolata in Brazil in future, but these effects vary according to the domain and the specific future scenario considered. Climatically suitable areas will increase in all scenarios, and those environmental scenarios that include better habitat conditions will provide more environmentally suitable areas for Crax fasciolata . However, this increase comes from newly suitable areas in the Atlantic Forest and the Amazon, while the Pantanal, the Caatinga, and the Cerrado will lose environmental suitability due to native vegetation loss. Despite the availability of these new areas, reduced landscape permeability may hinder Crax fasciolata from reaching them. This reinforces the urgent call for public policies for native vegetation protection, reforestation, and effective deforestation control.
Abstract in Portuguese is available with online material
... Channel aggradation, crevassing, avulsion, rechannelization and new landscape development are interlinked processes that, depending upon spatiotemporal scales and discharge intensity, may take decades to centuries to occur (Overeem and Brakenridge, 2009;Paola et al., 2011;Nienhuis et al., 2018). A growing understand of these interlaced processes in Pantanal has been gradually achieved by multispectral and microwave remote sensing and Geographic Information System (GIS) (Souza et al., 2011;Evans and Costa, 2013;Miranda et al., 2018;Lisenby et al., 2019). In turn, this work aims to shed new light about the underlying nexus between SDG and ES (Costanza et al., 2014;Costanza et al., 2017;Yang et al., 2020) changes via decadal mapping of land cover changes in the active depositional lobe of the Taquari River megafan Louzada et al. (2020). ...
The Pantanal wetland is a mosaic of landscapes that brings together rich biodiversity with the valuable activities of fishing, tourism and ranching. Human occupation and land use in the headwaters have intensified the rate of channel avulsions in the lower reaches of the largest megafan on the Taquari River. This study evaluates the long-term changes of landscapes in the active depositional lobe of the Taquari megafan from the perspective of local communities of pantaneiros. Maps derived from multiple decades of multispectral Landsat data have proven useful for studying land cover changes through the relationship between dry (terrestrial vegetation and soil/dry pastures) and humid landscapes (open waters, aquatic macrophytes and wet soils), as well as through Sankey diagrams and spatiotemporal mapping with Boolean operations according to the rate of dryland recovery. We found that dryland recovery associated with an older and smaller avulsion (known as Zé da Costa) is analogous to that of a most recent and much larger avulsion (known as Caronal), which is still ongoing and has greater importance due to the scale of the impacts. Land value and fish capture depreciate as the partial Caronal avulsion still evolves, increasing the likelihood of environmental conflicts. While pantaneiros no longer profit from ecosystem services of provision (e.g., livestock or fishing), dryland recovery may deliver quantifiable ecosystem services of regulation. The strengthening of partnerships among stakeholders and the implementation of environmental compensation mechanisms are central for the best management of the Pantanal's megafans that ensure quality of life for all pantaneiros.
... The Nhecolândia lobes are currently experiencing degradation through erosional tributary intermittent channels that drain floodwaters to the interfan system of the Negro River (Assine et al. 2015b). Two distinct geomorphologic zones characterize the Nhecolândia region: the upper Nhecolândia, characterized by distributary paleochannels with apexes on proximal areas of the megafan; and the myriads of ponds composing the Lower Nhecolândia Soares 2004, Evans andCosta 2013) (Fig. 2). ...
The Pantanal wetland is an active sedimentary basin representing a relevant depositional setting for alluvial sedimentation studies. However, sedimentation homogeneity and the lack of outcrops makes sedimentary analysis more difficult. The Lower Nhecolândia is located at the Southern edge of the Taquari river megafan, whose genetic origin has been disputed as fluvial or eolian deposition. GPR analysis was used to characterize the subsurface stratigraphy and understand the region’s geomorphic evolution. The 100 MHz GPR provided continuous good quality sections up to a depth of 8 m. Two continuous reflections are disconformities that bound three depositional sequences characterized by distinct radar facies. The lower facies presents an upper erosional truncation followed by reflections presenting ∼1.5 m deep channelized forms and concave-up low amplitude reflections. The intermediate facies (∼4 m thick) presents a base with erosional truncation followed by concave-upward forms, ∼10 m wide, 1–3 m deep, separated by 1–2 m, and offlapping geometry. The upper facies has a flat base and thickness of 2–4 m, with parallel reflections; it shows a strong correlation between the radar facies and the forms preserved in the landscape, suggesting that channelized fluvial streams did not form them. The results obtained indicate that GPR use in the Pantanal is an important method to elucidate its geologic evolution.
... This tropical savanna region has a wet season from October to April and a dry season from May to September. Landscapes are characterized by permanent and seasonal ponds of fresh and brackish water embedded in a natural mosaic of grassland savannas, woody savannas, and forested areas (Evans and Costa 2013). ...
Acoustic signaling is key in mediating mate choice, which directly impacts individual fitness. Because background noise and habitat structure can impair signal transmission, the acoustic space of mixed‐species assemblages has long been hypothesized to reflect selective pressures against signal interference and degradation. However, other potential drivers that received far less attention can drive similar outputs on the acoustic space. Phylogenetic niche conservatism and allometric constraints may also modulate species acoustic features, and the acoustic space of communities could be a side‐effect of ecological assembly processes involving other traits (e.g., environmental filtering). Additionally, the acoustic space can also reflect the sorting of species relying on public information through extended communication networks. Using an integrative approach, we revisit the potential drivers of the acoustic space by addressing the distribution of acoustic traits, body size, and phylogenetic relatedness in tropical anuran assemblages across gradients of environmental heterogeneity in the Pantanal wetlands. We found the overall acoustic space to be aggregated compared with null expectations, even when accounting for confounding effects of body size. Across assemblages, acoustic and phylogenetic differences were positively related, while acoustic and body size similarities were negatively related, although to a minor extent. We suggest that acoustic partitioning, acoustic adaptation, and allometric constraints play a minor role in shaping the acoustic output of tropical anuran assemblages and that phylogenetic niche conservatism and public information use would influence between‐assemblage variation. Our findings highlight an overlooked multivariate nature of the acoustic dimension and underscore the importance of including the ecological context of communities to understand drivers of the acoustic space.
... Different sensors have already been used to identify the spatial distribution of different land cover and vegetation characteristics of the many habitats present in the Pantanal complex system. Evans and Costa (2013) use in their study multi-temporal L-band ALOS/ PALSAR, C-band RADARSAT-2, and ENVISAT/ASAR data to map ecosystems and create a lake distribution map of the Lower Nhecolândia subregion in the Brazilian Pantanal, achieving a satisfactory result showing the spatial distribution of aquatic, terrestrial and transitional habitats. ...
This paper evaluates the potential of false-color composite images, from 3 different remote sensing satellites, for the identification of continental wetlands. Landsat 8, Sentinel-2, and CBERS-4 scenes from three different Ramsar sites (i.e., sites designated to be of international importance) two sites located within the Mato-Grossense Pantanal, and one within the Sul-mato-grossense were used for analyses. For each site, images from both the dry and rainy seasons were analyzed using Near-Infrared (NIR), Shortwave Infrared (SWIR), and visible (VIS) bands. The results show that false-color composite images from both the Landsat 8 and the Sentinel-2 satellites, with both SWIR 2-NIR-BLUE and NIR-SWIR-RED spectral band combinations, allow the identification of wetlands.
... Wright and Gallant [48] distinguished five classes: aquatic bed (vegetation on or below the water surface), emergent (herbaceous hydrophytes), forested, scrub-shrub (height < 6 m), and unconsolidated shore, applying Landsat TM imagery. Recently, the application of microwave data for the classification of vegetation habitats has been expanded by Evans et al. [49,50]. Santoro et al. [51] who studied ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) data found that better classification results were obtained from HV than from HH polarization. ...
The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.
... In this respect, the expansion of the orbital sensor constellation, and the advances in remote sensing data analyses and techniques, have enabled the generation of highly useful information at a reasonable time-frequency and lower costs (e.g. Evans and Costa, 2013;Junk et al., 2014;Silio-Calzada et al., 2017). Additional advances in mapping validation can be achieved via high spatial resolution images from Google Earth™, complementing the ground truth points (Adade et al., 2017;Zhai et al., 2017;Chen et al., 2018). ...
Copy this link to your browser before May 16, 2020-->>
https://authors.elsevier.com/c/1aop5B8ccoB~P
The Pantanal is an important active sedimentary basin in central South America where highly diverse flora and fauna are sustained by seasonal floods. Intense land use in the catchment areas enhanced sediment load and destabilized avulsive river systems in the plains. A well-known avulsion in the Taquari River during the 1980–90s, called “Zé da Costa”, has shifted the river mouth and drastically changed the nearby landscapes, making them difficult to map because of the hard access and the large variations in spectral and spatial attributes of raster data like Landsat images. Therefore, we developed a useful method to map and explore landscape changes in “Zé da Costa” avulsion that combines geotagged field pictures, randomly selected high-resolution orbital truths, normalized difference vegetation index, digital elevation models, linear spectral mixture models and Landsat historical imagery in pixel-based and object-oriented supervised classifications. We found that bands in green, red, and near-infrared spectra provide better mapping results with object-oriented algorithms for deriving and studying temporal dry/wet ratio dynamics. The temporal analyses of the dry/wet ratio showed that avulsions in the Taquari River have the potential to change permanently the “Zé da Costa” area into a dry landscape, making it susceptible for land use (deforestation and fire), except areas seasonally inundated by the floods of the Paraguay River. Overall, our method might be also useful for long-term studies of land use and climate change in avulsive rivers in wetlands around the world.
... It can be found several investigations, e.g. (Evans and Costa 2013;Hamilton et al. 2002;Girard et al. 2010;Matos et al. 2012), but they study the water extension areas based on in situ derived-water level time series in sparse stations in particular. This work attempts to make a contribution to the existing emptiness mainly in the studies with satellite gravimetry in the Pantanal area. ...
The continental water storage is significantly in-fluenced by wetlands, which are highly affected by climate change and anthropogenic influences. The Pantanal, located in the Paraguay river basin, is one of the world’s largest and most important wetlands because of the environmental biodiversity that represents.
The satellite gravity mission GRACE (Gravity Recovery And Climate Experiment) provided until 2017 time-variable Earth’s gravity field models that reflected the variations due to mass transport processes-like continental water storage changes-which allowed to study environments such as wetlands, at large spatial scales.
The water storage variations for the period 2002-2016, by using monthly land water mass grids of Total Water Storage (TWS) derived from GRACE solutions, were evaluated in the Pantanal area.
The capability of the GRACE mission for monitoring this particular environment is analyzed, and the comparison of the water mass changes with rainfall and hydrometric heights data at different stations distributed over the Pantanal region was carried out. Additionally, the correlation between the TWS and river gauge measurements, and the phase differences for these variables, were also evaluated. Results show two distinct zones: high correlations and low phase shifts at the north, and smaller correlation values and consequently significant phase differences towards the south. This situation is mainly related to the hydrogeological domains of the area.
... This can lead to classification errors in regions with high vegetation growth variability or with different vegetation types. A few advanced techniques, among other machine learning techniques [11], decision tree [41], or rule-based classification [13,42] use satellite time series [4,40,[43][44][45][46][47] or multi-dates [11,38,[48][49][50][51][52], which allow the inclusion of multi-temporal, -polarized or/and ancillary information for the extraction of temporary open water (TOW) and TFV classes. Thereby, seasonal or annual fluctuations of backscatter and multiple observations of the same area can be used to improve the reliability of mapping the flood extent or even the flood dynamics [43]. ...
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer’s/User’s accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use.
... Polarization feature-based method: Synthetic Aperture Radar (SAR) satellites penetrate clouds, rain and snow and are less affected by weather than other methods. The SAR images have rich polarization information, and Evans and Costa (2013) used the information to classify landscapes. Fan et al. (2015) used the joint sparse representation classification method to extract aquaculture areas from high-resolution SAR satellite remote sensing data. ...
... Land use and land cover (LULC) data are essential in several activities, including urban and regional planning [1,2] natural resources inventories [3,4], global environmental modeling processes [5], and monitoring of greenhouse gas emissions related to deforestation and forest degradation [6,7]. Although most of the LULC mappings in Brazil have been produced using optical remote sensing data [8,9], they present limitations in tropical regions because of these regions' persistent cloud coverage. ...
This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman-Durden, Yamaguchi, and Cloude-Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.
... Almeida-Filho and Shimabukuro [30] demonstrated that the L Band from the JERS-1 synthetic-aperture radar (SAR) can be used to detect cover changes in forested and non-forested areas in the Cerrado biome. Evans and Costa [31] also mapped six vegetation habitats in Brazil using L and C Bands using the backscattering information from the surface. In the same country, Saatchi et al. [32] mapped five land cover types using the JERS-1 mosaic, using texture measurement. ...
Mapping vegetation types through remote sensing images has proved to be effective, especially in large biomes, such as the Brazilian Cerrado, which plays an important role in the context of management and conservation at the agricultural frontier of the Amazon. We tested several combinations of optical and radar images to identify the four dominant vegetation types that are prevalent in the Cerrado area (i.e., cerrado denso, cerradão, gallery forest, and secondary forest). We extracted features from both sources of data such as intensity, grey level co-occurrence matrix, coherence, and polarimetric decompositions using Sentinel 2A, Sentinel 1A, ALOS-PALSAR 2 dual/full polarimetric, and TanDEM-X images during the dry and rainy season of 2017. In order to normalize the analysis of these features, we used principal component analysis and subsequently applied the Random Forest algorithm to evaluate the classification of vegetation types. During the dry season, the overall accuracy ranged from 48 to 83%, and during the dry and rainy seasons it ranged from 41 up to 82%. The classification using Sentinel 2A images during the dry season resulted in the highest overall accuracy and kappa values, followed by the classification that used images from all sensors during the dry and rainy season. Optical images during the dry season were sufficient to map the different types of vegetation in our study area.
... During the peak of seasonal inundation, the non-alkaline lakes merge and form intermittent watercourses, which can have 30 m of width and long extensions (Barbiero et al. 2002). Open grass savanna and swampy grasslands are the dominant vegetation in the complex non-alkaline lakes/intermittent watercourses (Evans and Costa 2013). ...
The Nhecolândia subregion (area ~ 26,921 km²), in central-southern Pantanal, is marked by the presence of hundreds of alkaline–saline lakes, located on the top of sand hills, and thousands of non-alkaline lakes adjacent to the sand hills. This study aimed to provide a better understanding of the hydrological dynamics in the Nhecolândia, which is scarce and controversial, based on piezometric monitoring, isotopic data and chemical analyses of the surface water and groundwater of an alkaline–saline lake, a non-alkaline lake and the sand hill between them. The potentiometric data indicated that both alkaline–saline and non-alkaline lakes act as recharge zones, the former in all seasons and the later only in the wet season. These results are corroborated by downward flow near the zones of recharge, indicated by hydraulic head measurements in multilevel wells around the non-alkaline lake and in mini-piezometers installed in the studied alkaline–saline lake and two more lakes in the region. Also, δ¹⁸O and δ²H values of the alkaline–saline lake surface water became more depleted than the surrounding groundwater in the wet season, confirming that the inflow to the alkaline–saline lake in this season was by the more depleted rainwater and not by the isotopically enriched groundwater. The water chemistry data is also in agreement with recharge from the alkaline–saline lake, even though this recharge is limited by a low-K layer. Because of this layer, the non-alkaline lake does not dry off during the dry season. This set of evidence demonstrated that the groundwater recharge in the study area occurs in the topographical lows, through a process known as depression-focused recharge, which disagrees with previous studies of the area.
... A patchy landscape of freshwater and soda lakes has developed in the lower Nhecolândia ( Fig. 1) (Assine and Soares, 2004;Barbiero et al., 2008;Bergier et al., 2016;Costa et al., 2015;McGlue et al., 2015). Topographic and flooding variations have led to a wide diversity of aquatic environments, making lower Nhecolândia one of the most biodiverse areas in South America (Por, 1995;Evans and Costa, 2013;Costa et al., 2015). In Nhecolândia, soda lakes are termed salinas by the traditional Brazilian community of pantaneiro ranchers, who have been colonizing the region for more than two centuries (Abreu et al., 2010). ...
The Pantanal is the most conserved biome in Brazil and among the last wild refuges in South America, but intensification of agriculture and other land use changes present challenges for protecting this exceptionally biodiverse wetland ecosystem. Recent studies have shed new light on the origins and biogeochemistry of a suite of >600 small saline-alkaline lakes in Nhecolândia, a floodplain setting located south of the Taquari River in south-central Pantanal. These soda lakes form a unique aquatic environment in Pantanal and nascent research on their geomicrobiology suggests that their biota may be analogous to early life, and extreme life in Earth's deep biosphere. We argue that the conservation of the soda lakes in the lower Nhecolândia region should be an important strategic component of any conservation plan that aims to mitigate the advance of unsustainable land-use change in the Pantanal. Soda lake conservation has important implications for the carbon cycle, as these landforms sequester carbon dioxide and transmit considerably lower concentrations of methane in comparison to macrophyte-rich freshwater lakes in the region. Further, minerals precipitated in the saline-alkaline lakes are leveraged for cattle consumption, and therefore the continued presence of the lakes is critical for allowing pantaneiro ranchers to pursue certified organic, sustainable beef production systems. Beyond protecting soda lakes and their surrounding forests (mata de cordilheiras) for food systems security, the conservation strategy would also allow further research of little studied extremophile biodiversity and biogeochemistry, with potential for biotechnological innovations attendant to UN Sustainable Development Goals. © 2018 Associação Brasileira de Ciência Ecológica e Conservação
... Controlling floods, improving water-quality, supporting wildlife habitat for several unique species of flora and fauna, and shoreline stabilization are some of the advantages associated with wetlands [1]. Wetlands are considered as one of the most arduous regions to investigate using traditional methods (e.g., ground survey) given their inaccessibility and size, and also the everchanging nature of most wetland ecosystems [2]. However, satellite remote sensing data have significantly facilitated wetland mapping and monitoring by addressing main drawbacks of traditional approaches. ...
In this study, a new hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a study area located in the north eastern portion of the Avalon Peninsula, Newfoundland and Labrador province, Canada. Specifically, multi-polarization and multi-frequency SAR data, including single polarized TerraSAR-X (HH), dual polarized L-band ALOS-2 (HH/HV), and fully polarized C-band RADARSAT-2 images, were applied in three different classification levels. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. Importantly, an overall accuracy of 94.82% was obtained for the final classified map in this study.
... The effect of different SAR incidence angles on interferometric coherence has been investigated by several researchers who reported that small incidence angles are preferred for wetland InSAR applications Zhang et al. 2016). This is because steep incidence angles allow a deeper penetration of the canopy by the SAR signal and less energy degradation along the radiation path, which enhances the chance of doublebounce scattering between water surface and flooded vegetation (Grings et al. 2006;Li et al. 2007;Kim et al. 2013;Evans and Costa 2013;Wdowinski and Hong 2015). ...
The production of spatially detailed quantitative maps of water level variations in flooded vegetation, and the detection of flow patterns and discontinuities in both managed and natural wetland ecosystems provide valuable information for monitoring these unique environments. Hydrological monitoring of wetlands is also critical for maintaining and preserving the habitat of various plant and animal species. Over the last two decades, advances in remote sensing technologies have supported wetland monitoring and management in several aspects, including classification, change detection, and water level monitoring. In particular, Interferometric Synthetic Aperture Radar (InSAR) has emerged as a promising tool for hydrological monitoring of wetland water bodies. However, a comprehensive review of the status, trends, techniques, advances, potentials, and limitations of this technique is lacking. In this study, we evaluate the use of InSAR for hydrological monitoring of wetlands, discuss the main challenges associated with this technique, recommend possible solutions to mitigate the main problems identified in the literature, and present opportunities for future research.
RÉSUMÉ
La production de cartes quantitatives détaillées de la variation du niveau d’eau de la végétation submergée et la détection des régimes d’écoulement et des discontinuités fournissent des renseignements importants sur les milieux humides, tant à l’état naturel que dans les zones aménagées. La surveillance hydrologique des milieux humides est essentielle au maintien et à la préservation de l’habitat de la faune et de la flore. Au cours des deux dernières décennies, les progrès techniques en télédétection ont contribué à la surveillance et à la gestion des milieux humides à plusieurs égards, dont la classification, la détection des changements et le suivi du niveau d’eau. En particulier, l’interférométrie par radar à synthèse d'ouverture (InSAR) s’est avérée un instrument prometteur pour la surveillance hydrologique des étendues d’eau en milieu humide. Il manque cependant une revue exhaustive de l'état, des tendances, des techniques, des avancés, du potentiel et des limitations de cette approche. Dans la présente étude, nous évaluons l’utilisation de l’InSAR pour la surveillance hydrologique des milieux humides, nous discutons des défis liés à cette technique, nous recommandons des solutions potentielles pour mitiger les problèmes principaux identifiés dans la littérature et nous présentons des possibilités de recherche future.
... At present, all-polarized data has been widely used in land use/cover [45]- [47], forestry [48], rice [49]- [50], and soil moisture [51]. Full polarization can also be combined with optical data, but there is less research on vegetation monitoring. ...
Coastal wetlands are of great importance in protecting biodiversity, mitigating climate change, and providing natural resources. Using deep learning methods for the classification and mapping of coastal wetlands with optical remote sensing data can effectively monitor changes in wetlands, playing a crucial role in their protection. However, most current wetland classification methods focus on single-temporal data, with relatively few studies addressing multi-temporal data. Therefore, for the wetland classification task in the Bohai Rim region of China, an improved Swin-MTNet model based on the state-of-the-art deep learning model Swin-UNet is proposed in this study to better capture temporal feature variations with multi-temporal Sentinel-2 imagery. The Swin-MTNet is compared with Swin-UNet and DeepLabV3+, and the results indicate that Swin-MTNet achieves overall accuracy improvements of 5.12% and 2.85% and Kappa coefficient improvements of 6.85% and 3.86% over Swin-UNet and DeepLabV3+, respectively, when utilizing multi-temporal data. The classification improvement for Spartina alterniflora is the most significant, with F1 scores increasing by 0.45 and 0.47 compared to Swin-UNet and DeepLabV3+, respectively. These results demonstrate that the proposed Swin-MTNet model can effectively leverage the temporal features of multi-temporal data, significantly improving the accuracy of coastal wetland classification.
The use of freely-available multi-source imagery for mapping vegetation in montane terrain is important for many developing countries that do not have the funding for high-resolution data capture. Radar images are also now freely available and include Sentinel-1 in dual polarisation, and PALSAR-2. These images can penetrate cloud cover and provide the advantage of acquiring data in a cloudy tropical region. This research evaluated whether the addition of radar with optical and topographic data improves classification accuracy in a montane region in Sri Lanka. Six classification experiments were designed based on different combinations of image data to test whether radar data improved land cover classification accuracy compared with optical data alone. Random forest classifier in the Google Earth Engine has been utilised to classify the tropical montane vegetation. The results indicate that radar or optical data alone cannot obtain satisfactory results. However, when combining radar with optical data the overall accuracy increased by approximately 5%, and by an additional 2% when topography data were added. The highest accuracy (92%) was achieved with multiple imagery, and adding the vegetation indices improved the model slightly by 0.3%. In addition, feature importance analysis showed that radar data makes a significant contribution to the classification. These positive outcomes demonstrate that freely-accessible multi-source remotely-sensed data have impressive capability for vegetation mapping, and support the monitoring and managing of forest ecological resources in tropical montane regions.
Despite savannas being known for their relatively sparse vegetation coverage compared to other vegetation ecosystems, they harbour functionally diverse vegetation forms. Savannas are affected by climate variability and anthropogenic factors, resulting in changes in woody plant species compositions. Monitoring woody plant species diversity is therefore important to inform sustainable biodiversity management. Remote sensing techniques are used as an alternative approach to labour-intensive field-based inventories, to assess savanna biodiversity. The aim of this paper is to review studies that applied remote sensing to assess woody plant species diversity in savanna environments. The paper first provides a brief account of the spatial distribution of savanna environments around the globe. Thereafter, it briefly defines categorical classification and continuous-scale species diversity assessment approaches for savanna woody plant estimation. The core review section divides previous remote sensing studies into categorical classification and continuous-scale assessment approaches. Within each division, optical, Radio Detection And Ranging (RADAR) and Light Detection and Ranging (LiDAR) remote sensing as applied to savanna woody species diversity is reviewed. This is followed by a discussion on multi-sensor applications to estimate woody plant species diversity in savanna. We recommend that future research efforts should focus strongly on routine application of optical, RADAR and LiDAR remote sensing of physiologically similar woody plant species in savannas, as well as on extending these methodological approaches to other vegetation environments.
Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images.
Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands’ ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA‐ISRO SAR (NISAR) mission. In this study, the capability of L‐band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana, is investigated using two object‐based machine learning approaches: Support vector machine (SVM) and random forest (RF). L‐band Unmanned Aerial Vehicle SAR (UAVSAR) data are exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean‐shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L‐band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy.
Based on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type mapping, soil moisture estimation and phenology estimation are reviewed, as they are ones with a clear benefit of full polarimetry over dual or single polarimetry. For crop type mapping, supervised or partially unsupervised classification schemes are used. Phenology estimation is treated as a classification problem as well, by regarding the different stages as different classes. Soil moisture estimation makes intensive use of scattering models, in order to separate soil and vegetation scattering and to invert for soil moisture from the isolated ground component. Then, applications of SAR polarimetry to wetland monitoring are considered that include the delineation of their extent and their characterisation by means of polarimetric decompositions. In the last section of the chapter, the use of a SAR polarimetric decomposition is shown for the assessment of the damages consequential to earthquakes and tsunamis.
The Pantanal, one of the largest wetlands of the world, has a high incidence of salt-affected soils due to favorable past and current conditions, such as high evapotranspiration rates and restriction of leaching. According to maps produced during the 1980s, these salt-affected soils are mainly classified as Gleyic Solonetz, Eutric Planosols, and Eutric Vertisols, the former being the dominant soil class in the wetland. More recent and detailed studies performed mainly in two Pantanal subregions, Nhecolândia and Barão de Melgaço, revealed important occurrence of salt-affected soils in areas that have not been previously mapped. In the Nhecolândia, Saline–Sodic soils, currently submitted to salinization and solonization, occur around hundreds of alkaline–saline lakes. In the Barão de Melgaço subregion, Sodic and Solodized Solonetz occur in the highest positions of the landscape, such as paleolevees and paleochannels, whereas Solods are encountered in the lower geomorphic units, specifically depressions and intermittent watercourses. Although the genesis of Saline–Sodic soil is still observed in Nhecolândia, in consonance with the favorable environmental conditions for the development of salt-affected soils in the wetland, the salinity of the soils in the highest areas of Barão the Melgaço seems to be mostly a heritage from more arid conditions in the past.
This study presents a preliminary assessment of vegetation and inundation characteristics in the Amazon basin using ALOS-2 PALSAR-2 L-band SAR and Sentinel-1 C-band SAR backscatter combined with ICESat-2 Lidar photon height data. Key Findings:-Photon height from ICESat-2 found useful to capture seasonal water level variations along major rivers and open water areas.-Comparisons between Lidar photon height and Land C-band backscatter along forest-river transects show good agreement for the wet and dry seasons.
Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the 'wise use of wetlands', following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.
The Nhecolândia region covers the southern wetlands of the Pantanal basin in Brazil. Characterized by myriad shallow freshwater and alkaline–saline lakes, the distinct natural features of the Nhecolândia wetland make it highly sensitive to climate change and the effects of human activities. This paper summarizes the natural and social aspects that have affected this delicate wetland and potential future impacts. We analysed the response of the wetland to historical changes in climate and human activity and used this understanding to forecast the response of the wetland to future changes. The data presented here show that this region is particularly sensitive to alterations in the flood regime, droughts and deforestation, which are intrinsically related to both global and local changes in climate and the intensification of cattle-ranching activities, which include deforestation and the introduction of cultivated pastures.
Gran parte de los estudios relacionados con humedales afirman que estos ecosistemas presentan una alta productividad, la cual se ve reflejada en la riqueza biológica que exhiben, de igual forma, afirman que los humedales presentan una alta capacidad de regulación hídrica, mitigan la erosión y retienen cargas contaminantes, por tal motivo, los humedales son considerados en la actualidad como ecosistemas estratégicos para la conservación y el mantenimiento de servicios ecosistémicos (Mitsch y Gosselink 2000; Ministerio de Medio Ambiente 2001; Álvarez-Mieles, Irvine, Griensven, Arias-Hidalgo, Torres y Mynett, 2013; Flórez Estupiñán-Suárez, Rojas, Aponte, Quiñones, Acevedo y Jaramillo, 2016; García-Muñoz, 2016). Debido al gran interés que se tiene sobre estos ecosistemas, existen diversos estudios dedicados a la identificación y evaluación de ambientes asociados al término humedal (Mitsch y Gosselink, 2000; Flórez et al., 2016; Betancur, 2015; Schmidt-Mumm, 1998; Neiff, 1999; Wanda, Mamba, Msagati, y Msilimba, 2016), la cantidad de estudios enfocados a este tipo de ambientes ha generado múltiples conceptos, creando confusiones en la interpretación del término. La complejidad de formas en las que se presentan los humedales hace que se dificulte establecer una expresión universal sobre estos ecosistemas (Tiner 1991). No solo se tienen diferentes definiciones sobre el término humedal, existen diversos conceptos en referencia a su nomenclatura de clasificación y las metodologías para su estudio (Dronova, Gong, Wang y Zhong, 2015; Wanda, 2016; Flórez et al., 2016; Sieben, Nyambeni, Mtshali, Corry, Venter, MacKenzie y Kotze, 2016). Según Mitsch y Gosselink (2015) el propósito principal por el cual se clasifican los humedales es el de proporcionar una terminología común para describir las similitudes y diferencias entre estos sitios. La clasificación no solo permite la comparación de las particularidades de cada hábitat, también proporciona las bases para llevar a cabo estudios sobre el estado y funciones de los humedales en áreas geográficas determinadas. En general, se observa que los criterios utilizados en la clasificación dependen generalmente del enfoque de cada investigación y de las necesidades de información para cada proyecto. Algunos estudios desarrollados a escalas continentales, consideran la clasificación como el primer paso para la consolidación de información y el desarrollo de programas de conservación (Costa, Farinha, Heckery Vives, 1996; Cowardin, Carter, Golet y LaRoe, 1976). Teniendo en cuenta que el manejo de ecosistemas requiere de la unificación de conceptos, de tal modo que no exista duda o se presenten malas interpretaciones por la inexactitud de estos (Mitsch y Gosselink 2015), el presente apartado busca abordar una discusión en la cual se genere un reporte de las diferentes definiciones y clasificaciones de los humedales, de igual forma, se busca identificar aspectos relevantes en la caracterización y evaluación de estos ecosistemas, de manera que se genere un marco conceptual adecuado a las necesidades del altiplano del Oriente antioqueño y que al mismo tiempo sea consecuente con los trabajos desarrollados a nivel nacional e internacional.
ct APPLIED REMOTE SENSING IN THE LAKES OF LOW NHECOLÂNDIA WETLANDS: CLASSIFICATION ANDGENESIS DISCUSSION The Low Nhecolândia wetland lakes constitute a constant scientific challenge. The main morphologicalunits of this area are the cordilheiras, small elevations covered by arboreal vegetation, vazantes, a drainage feature distributed overmain part of the area with subtly defined channels and corixos, perennial watercourses other than the vazantes. Landsat 5 TM imagessupply a synoptic vision of the main elements in this environment as well as changes in biological activity and physical-chemical lakecharacteristics. Field data on pH and electric conductivity of the water table was obtained after manual drilling in seventy-seven lakes.The Landsat images allowed correlation on a broad scale of lake alkalinity/salinity with respect to cordilheiras, which provideimportant information on the classification and evolution of the lakes. The results suggest classification of the investigated lakes intwo great groups: alkaline and non-alkaline lakes. In the former, field data revealed at least two groups of lakes, i.e., hyper-alkaline andalkaline. Each has physical-chemical peculiarities evident in the data on the phreatic aquifer. From a geochemical point of view, spatialrelationship of the cordilheiras and lakes is suggestive of acidification and filling of the alkaline lakes, simultaneous with the erosionof the cordilheiras.Keywords: Remote sensing, alkaline lackes, Pantanal da Baixa Nhecolândia, classification, genesisResumo As lagoas do Pantanal da Baixa Nhecolândia, constituem desafio científico constante. As denominações locais dos principaiselementos morfológicos deste ambiente são as cordilheiras, pequenas elevações com vegetação arbórea, as vazantes, as drenagensparcamente definidas que ocupam grande parte da área no período das cheias, e os corixos, cursos d’água definidos e mais perenes queas vazantes. As imagens TM do satélite Landsat 5, fornecem visão sinóptica dos principais elementos do meio e, indiretamente, ascaracterísticas físico-químicas dos corpos d’água, ao menos em parte por condicionarem a atividade biológica. Dados de campoconsistiram da amostragem de água em furos de trado em setenta e seis lagoas, com medidas de pH e condutividade elétrica da água dofreático e de algumas águas de superfície, quando presentes, pois a coleta realizou-se no período de estiagem, com muitas lagoas secas.As imagens permitiram correlacionar, em larga escala, a alcalinidade/salinidade dos corpos d’água com a disposição das cordilheiras evazantes, fornecendo subsídios ao estudo da gênese deste ambiente e evidenciando a possibilidade de classificar o universo investigadode lagoas em dois grandes grupos: alcalinas e não alcalinas. Nas primeiras, os dados de campo permitem distinguir pelo menos doisgrupos, a saber: lagoas hiperalcalinas e lagoas alcalinas. Cada grupo exibe peculiaridades quanto a seus parâmetros físico-químicos desuas águas. A relação espacial das cordilheiras e lagoas, considerando-se seus aspectos geoquímicos, sugere acidificação e assoreamentodas lagoas alcalinas, em paralelo à erosão das cordilheiras.
We studied the optimal ranges of water and soil characteristics for wetland plants, particularly Scirpus tabernaemontani (softstem bulrush) and Typha latifolia (broadleaf cattail), which are dominant species with potential for restoration of Korean wetlands. We observed vegetation in S. tabernaemontani and T. latifolia communities from the mid to late June, 2005, and measured characteristics of water environments such as water depth (WD), temperature (WT), conductivity (WC), and concentration of several ions (NO_3{^-}-N,\;Ca^{2 },\;Na^ ,\;Mg^{2 },\;and\;K^ ), and characteristics of soil environments such as soil texture, organic matter (loss on ignition, LOI), conductivity, and pH. The S. tabernaemontani community was accompanied by Zizania latifolia (Manchurian wildrice), Persicaria thunbergii (Korean persicary), Actinostemma lobatum (lobed actinostemma), and Beckmannia syzigachne (American slough grass), while the T. latifolia community was accompanied by P. thunbergii, T. angustifolia (narrowleaf cattail), and Glycine soja (wild soybean). We defined the optimal range for distribution (ORD) as the range that each plant was crowded. The optimal range of water characteristics for the S. tabernaemontani community was a WD\;10{\sim}50cm,\;WT\;24.0{\sim}32.0^{\circ}C,\;WC\;100{\sim}500?S/cm,\;{NO_3}{^-}-N\;0{\sim}60ppb,\;K^ \;0.00{\sim}1.50ppm,\;Ca^{2 }\;7.50{\sim}17.50ppm,\; Na^ \;2.50{\sim}12.50ppm,\;and\;Mg^{2 }\;3.00{\sim}7.00ppm. In addition, the optimal range of soil characteristics for the S. tabernaemontani community was a soil texture of loam, silty loam, and loamy sand, LOI\;8.0{\sim}16.0%,\;pH\;5.25{\sim}6.25, and conductivity 10{\sim}70?S/cm. The optimal range of water characteristics for the T. latifolia community was a WD\;10{\sim}30cm,\;WT\;22.5{\sim}27.5^{\circ}C,\;WC\;100{\sim}400?S/cm,\;{NO_3}{^-}-N\;0{\sim}60ppb,\;K^ \;0.00{\sim}1.50ppm,\;Ca^{2 }\;0.00{\sim}17.50ppm,\;Na^ \;0.00{\sim}12.50ppm,\;and\;Mg^{2 }\;0.00{\sim}5.00ppm, and the optimal range of soil characteristics for the T. fatifolia community was a soil texture of loam, sandy loam, and silty loam, LOI 3.0{\sim}9.0%,\;pH\;5.25{\sim}7.25, and conductivity 0{\sim}70?S/cm.
Seasonal inundation determines the structure and function of tropical floodplain ecosystems, and therefore information on the spatial and temporal variability of inundation is fundamental to understand and manage these ecosystems. This study uses the 37-GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer (SMMR: Nimbus-7 satellite) to reveal inundation patterns in the Pantanal, a vast savanna floodplain located largely in Brazil. We calculated inundation area separately for 10 subregions using mixing models that account for the major landscape units with distinctive microwave emission characteristics. Maximum inundation occurred as early as February in the northern subregions and as late as June in the south, reflecting the delayed drainage of the region. As much as 131,000 km2 was inundated annually during the 9 years of SMMR observations (1979-87). Monthly estimates of the total area inundated in the region varied from 11,000-110,000 km2, averaging 53,000 km2. We reconstructed regional inundation patterns over the past 95 years from the correlation between the Paraguay River stage and total inundation area during the SMMR period. Both the maximum and minimum inundation area showed large interannual variability.
Studies on greenhouse gases (GHG) emitted by hydroelectric reservoirs have shown until now that the fate of carbon, following impoundment, seems to reach the fate of carbon in natural aquatic ecosystems after a decade or so. To adequately assess this assumption and then obtain the net GHG emissions from the Eastmain-1 hydroelectric reservoir, the carbon stock and GHG emissions from peatlands and different succession stages of forested areas need to be characterized prior to the reservoir impoundment. It is therefore important to characterize the carbon now process (surface fluxes and sequestrated carbon) from these terrestrial systems prior to impoundment. The Canadian Wildlife Service of Environment Canada, Québec region, has developed an approach for mapping wetlands using Landsat/RADARSAT-1 satellite images. The method is based on image segmentation using the Definiens Professional software. The top-down object-based classification is based on the Canadian Wetland Classification System and quickly and precisely identifies ecologically meaningful wetland polygons. The main objective of this study is to produce a wetland map of the Eastmain River watershed, using a SPOT-4 image aimed at identifying five classes of wetlands (bog, fen, marsh, swamp, shallow water) for a geographical unit of at least 1 ha, and to add to the peatland classes a description of their components, such as pool complexes and vegetation structures, to assign measured carbon values to these different peatland classes and scale up the data to obtain a regional carbon budget. The second objective of the study consists in determining whether SPOT-4 images can be used to map wetlands, using the object-based method developed with Landsat/RADARSAT-1 images, and if a finer spatial resolution would improve the wetland mapping results by adding information on. wetland components. The SPOT-4 classification using the object-based method allowed the five main wetland classes to be identified in addition to pool complexes in three density classes (isolated, low density and high density) and "bogs"/"fens" vegetation structure (treed or open) in peatland classes. Validation was done at two levels: (i) between the five classes of wetlands, and (ii) between pool complexes and vegetation structures. The overall accuracy was 81% for the first level and 75% for the second.
The influence of habitat, biomass of herbaceous vegetation, depth and distance from permanent water bodies on the structure of fish assemblages of a seasonal floodplain was evaluated using data collected along 22 transects in an area of 25 km² in the floodplain of Cuiabá River, Pantanal, Brazil. Each transect was sampled for fish using throw traps and gillnets during the flood period of 2006. Multivariate multiple regression analysis and multivariate analysis of covariance indicated that depth was the only variable that affected the structure of the fish assemblage, both for quantitative data (abundance) and qualitative data
(presence-absence). Species such as Neofundulus parvipinnis and Laetacara dorsigera were more abundant in shallower
sites (below 25 cm), while Serrasalmus maculatus and Metynnis mola were found mostly in the deepest areas (over 55 cm). However, species such as Hoplias malabaricus and Hoplerythrinus unitaeniatus occurred at all sampled depths. Although the distribution of most species was restricted to a few sites, there was a positive relationship between species richness and depth of the water body. Surprisingly, the replacement of native vegetation by exotic pasture did not affect the fish assemblage in the area, at the probability level considered.
A comparative view of inundation patterns in the large floodplains of South America was derived by analysis of the 37-GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer (SMMR; Nimbus-7 satellite, 1979-1987). The following floodplains were analyzed: (1) mainstem Amazon River floodplain in Brazil; (2) Llanos de Moxos (Beni and Mamoré rivers) in Bolivia; (3) Bananal Island (Araguaía River) in Brazil; (4) Llanos del Orinoco (Apure and Meta rivers) in Venezuela and Colombia; (5) Roraima savannas (Branco and Rupununi rivers) in Brazil and Guyana; and (6) Pantanal wetland (Paraguay River) in Brazil. The maximum areas subject to inundation in each region, which include permanent open waters of river channels and lakes, were as follows (in km2): mainstem Amazon 97,360, Moxos 92,094, Bananal 58,550, Orinoco 107,530, Roraima 16,530, and Pantanal 130,920. The duration of inundation was correlated with the maximum area inundated. Predictive relationships between flooded area and water levels in the nearby rivers allowed extension of the inundation record over nearly a century for the Amazon and Pantanal and several decades for the other floodplains. Interannual variability in the maximum extent of inundation is greatest in the Pantanal, followed by Roraima and Bananal. Based on these extended records, the long-term mean inundation areas (in km2 and including rivers and lakes) were as follows: mainstem Amazon 46,920, Moxos 29,460, Bananal 13,110, Orinoco 34,700, Roraima 3,480, and Pantanal 34,880. This information is fundamental for hydrological, biogeochemical, and ecological studies of these floodplain-river systems and will improve estimates of methane and other trace gas emissions to the atmosphere from these vast wetlands.
Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999–2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based "mapping zones". Over the course of 2 1 / 2 field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework.
Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on the relevant scientific research. This article attempts to identify the current challenges and opportunities in remote sensing for large-area wildlife habitat mapping, and accordingly provide possible solutions and directions for further research.
The aim of this document is to describe the absolute calibration of high rate ASAR Level 1 products generated by ESA using the ASAR processing Facility (PF-ASAR).
The Ramsar Convention of IUCN held in 1971 in Iran raised global awareness of the conservation and management of wetlands. Wetlands, the trans- itional zones that occupy an intermediate position between dryland and open water, regulate the è ow of water and nutrients, thereby facilitating optimum functioning of the physical and biological cycles of nature. Harike wetland in the Indian State of Punjab has been declared a wetland of international importance. Thus it is felt necessary to reclaim and develop Harike wetland for its optimum potential use, but a reliable and accurate wetland database is not available. This study aimed at the generation of a database in terms of landuse/landcover, extent of waterspread and its seasonal variation, aquatic vegetation status and turbidity levels of lake water, using multidate satellite data. The threats of the Harike wetland have been identié ed and adequate measures for its conservation and management suggested.
This publication reports results of an experiment carried out to examine
the potential of polarimetric C-band Synthetic Aperture Radar (SAR) for
mapping various wetland classes found in the Mer Bleue region (near Ottawa,
Canada). TheMer Bleue region was surveyed by the C-band (5.3GHz) polarimetric
(HH, HV, VH, VV) SAR of the Canada Centre for Remote Sensing (CCRS)
at three times within the vegetation season: 16 June (spring ush for vegetation),
6 July (mature growth stage for vegetation) and 3 October 1995 (senescence).
Signatures of six diOEerent cover types (forested and nonforested peat bog, marsh,
open water, clearing and forests) have been derived as a function of incidence
angle. Separability between various classes was used to determine the relationships
between season(s) and polarization(s) needed to diOEerentiate various wetland
classes. A supervised classi. cation was used for wetlands mapping by means of
multipolarization data. These investigations demonstrate some of the capabilities
of SAR at C-band for mapping wetlands. The cross-polarization data provided
the best separation between the observed classes. The October dataset was better
suited for discriminating between the classes present than the other periods
observed. The overall accuracies of the classi. cation are 73% for June, 73% for
July and 86% for October. Classi. cation using a single polarization has been
investigated and the results have shown that the HH and cross-polarizations are
better than VV polarization. For October, the percentage of all pixels correctly
classi. ed is 74% for HH polarization, 76% for cross-polarization, and 59% for
VV polarization.
Investigations were carried out to determine whether temporal changes can
be used to increase the information content of single polarization C-band SAR
data, which are now available from ERS-2 and RADARSAT satellites. They
demonstrated that the use of multitemporal data acquired in June, July and
October do not provide a substantial amelioration of the classi. cation of wetlands
when the diOEerentiation is not possible in any single period.
Up‐to‐date information on the distribution and changes associated with wetlands is essential for their effective conservation and management. Satellite remote sensing is arguably the only practical way for mapping and monitoring wetlands in a timely manner over a large area, and therefore has been chosen by the Canadian Wetland Inventory (CWI) initiative to map Canada's wetlands and by the Environment and Sustainable Development Indicators (ESDI) initiative to monitor the extent of wetlands indicators. As a contribution to these initiatives, this paper presents a rule‐based wetland mapping method using Landsat‐7/ETM+, two‐season Radarsat‐1/SAR images and DEM data. Three study sites in eastern Canada were selected to test this new method. The test results indicate that the classification accuracy of the new method ranges from 71% to 92%, for open bog, open fen, treed bog, marsh and swamp at the three sites. In comparison, two traditional methods based on Landsat‐7/ETM+ alone or both Landsat‐7/ETM+ and Radarsat‐1/SAR have accuracies of 24-89% and 78-85% respectively.
Boreal forests and wetlands play an important role in the climate system, in particular through biosphere–atmosphere flux exchanges. They are an important pool of carbon and their role as sink or source of greenhouse gases is not fully understood. Accurate mapping of the vegetation of Siberia can therefore contribute to a better understanding of these processes at regional scale and of their effects on the climate through regional biosphere modeling. The potential of the combination of radar data with medium‐resolution optical data to obtain regional‐scale land cover mapping is investigated using multi‐spectral imagery from the MERIS sensor at 300 m resolution and a high resolution radar mosaic (pixel spacing of 100 m) covering Western and Eastern Siberia compiled in the framework of the Global Boreal Forest Mapping project. For this purpose, capabilities of oriented‐object image analysis associated to wavelet multi‐resolution techniques are investigated. Results show that wavelet multi‐resolution textures bring relevant additional information for land cover classification. Suggestions are made for the implementation of an object‐based wavelet multi‐resolution texture estimator.
The marsh deer (Blastocerus dichotomus, Cervidae) inhabits floodplains and marshlands of South America and is threatened with extinction. We conducted two aerial surveys in the central southern region of the Pantanal to evaluate population distribution at the end of the dry season and the peak of the flooding season. Our results show an aggregated distribution during the dry season and a relatively diffused distribution in the flooding season. Population density estimates are 0.382 ± 0.362 deer/km2 in the dry season, and 0.395 ± 0.144 deer/km2 in the flooding season. Population size remained essentially unchanged between the seasons. In the study area, the marsh deer moved up to 20 km between the dry and flooding seasons. The design of protected areas should consider migration distances to offer enough area to protect marsh deer populations throughout the year.
The objective of this paper is to investigate the complementarity of JERS-1 and ERS-1 data for mapping coastal tropical regions. We use a decision tree classii er to classify a coastal region of Gabon and describe the feature contribution using the decision tree diagram. The JERS-1 Global Rain Forest Mapping (GRFM) and ERS-1 Central Africa Mosaic Project (CAMP) datasets are used. The result is a land cover map of the west coast of Gabon. The analysis explicitly shows the complementary characteristics of the L-and C-band Synthetic Aperture Radar (SAR) instruments. We demonstrate the usefulness of combined use of L-and C-band data for large area mapping of coastal regions, especially in ooded areas for discrimination of high and low mangroves as well as grasses and tree swamps. The overall classii cation accuracy increases by 18% over single band classii cation.
The identification of key habitat types for wildlife is an essential step to plan and promote sustainable land management strategies. Private cattle ranches occupy most of the Brazilian Pantanal and the recent intensification in land use practices is thought to threaten wildlife. Using encounter rates from transects, landscape use and habitat selection of the community of medium to large-sized mammals was examined to identify key wildlife habitats. Overall landscapes that had a higher proportion of forested habitats were the most used by wildlife. Within the different landscapes, forested environments can be considered key habitats for most of the native mammals considered in this study. Unfortunately, theses are also the habitats most at risk by the recent changes in land use practices. Results from this study predict that current intensifications of ranching practices will be detrimental to wildlife. In addition to deforestation, other threats such as land degradation, fire, landscape alterations such as fencing and artificial water holes may also impact landscape and habitat quality. The key to conserving biodiversity in the Pantanal is preserving the natural habitat matrix that sustains the diversity of landscapes and to continue integrating cattle ranching into the natural processes that sustain a functioning ecosystem.
The factors effecting fish species richness, evenness and relative abundance in 19 floodplain lagoons in the Pantanal (Brazil) were examined. Richness and evenness were strongly correlated with the lagoon's: (i) isolation time from the main river; (ii) depth; and (iii) distance from the main river. Abundance of the five dominant fish species was correlated mainly with: (a) macrophyte cover; (b) the distance from the main river; and (c) piscivore abundance. These results suggest that the species select lagoons to avoid or minimize their confinement in inappropriate floodplain habitats. A combination of strategies, together with variations in the lagoon's connection period, determines the diversity and abundance of the fish species in the Nhecolândia Pantanal.
The Pantanal, one of the largest wetlands on the planet, comprises 140,000 km² of lowland floodplain of the upper Rio Paraguai basin that drains the Cerrado of central Brazil. The diverse mosaics of habitats resulting from the varied soil types and inundation regimes are responsible for an extraordinarily rich terrestrial and aquatic biota, exemplified by the bird richest wetland in the world—463 birds have been recorded there—and the largest known populations of several threatened mammals, such as Pampas deer (Ozotoceros bezoarticus), marsh deer (Blastocerus dichotomus), giant otter (Pteronura brasiliensis), and jaguar (Panthera onca). Until recently, deforestation of the adjoining Brazilian central plateau was considered the major threat to this area, but now deforestation is a critical problem within the floodplain itself. More than 40% of the forest and savanna habitats have been altered for cattle ranching through the introduction of exotic grasses. And there are other threats that lead to large-scale disruption of ecological processes, severely affecting biodiversity. Although the Pantanal wetland is a Biosphere Reserve and is considered a Wetland of International Importance, only 2.5% of the upper Paraguai River basin is formally protected. To date, protected areas have been created opportunistically and as such, although of undoubted value, protect only a fraction of the Pantanal's wildlife and habitats. Among the conservation initiatives in the area, the private sector is increasingly participating in the establishment of private reserves. The prospects are far from optimistic, however, and the major challenge is to find alternative socioeconomic models that allow for conservation and economic uses of the land in association with the development of specific environmental legislation that reflects the unique characteristics of the region.
Although Brazil's first park was established in 1937, the past two decades have witnessed an explosion of protected areas. Until 1989 federal parks and reserves were created by the Brazilian Forest Development Institute (IBDF) and the Special Secretariat for the Environment (SEMA). In 1989 SEMA and IBDF were united to form the Brazilian Institute for the Environment and Renewable Natural Resources (IBAMA). A formal, unified system for federal, state, and municipal parks, the National System for Protected Areas, was made law in 2000. In 1976 IBDF drew up a biogeographical model for siting protected areas for biodiversity conservation in the Amazon, based on representation of phytogeographic regions. There are three ongoing initiatives to determine the location of new protected areas: (1) protected areas were recommended for the majority of 900 priority areas for biodiversity conservation identified in workshops held for Brazil's major biomes (1998–2000); (2) establishment of biodiversity corridors with parks and reserves as key elements; and (3) the creation of protected areas in the 23 Amazonian ecoregions identified by World Wildlife Fund—Brazil (the ARPA program). Federal and state strictly protected areas now number 478 and total 37,019,697 ha, and there are 436 sustainable-use areas totalling 74,592,691 ha. Other categories of protected areas in Brazil include private natural heritage reserves (RPPNs), which are generally small but proving important for the conservation of restricted-range and highly threatened species, and indigenous reserves, which are increasingly recognized as vital for biodiversity conservation because of their enormous size. Although Brazil has created an enormous number of protected areas over the last two decades, enormous challenges remain—not only for their administration and management—but also to protect the parks themselves as Brazil continues with its ambitious developmental programs for energy, infrastructure, industry, and agriculture.
Resumen: Aunque los el primer parque de Brasil fue establecido en 1937, las dos últimas décadas han atestiguado una explosión de áreas protegidas. Hasta 1989, los parques federales y reservas fueron creados por el Instituto Brasileño de Desarrollo Forestal (IBDF) y la Secretaría Especial del Medio Ambiente (SEMA). En 1989 SEMA e IBDF fueron unidos para formar el Instituto Brasileño para el Ambiente y los Recursos Naturales Renovables (IBAMA). En 2000, se estableció legalmente un sistema formal, unificado para parques federales, estatales y municipales, el Sistema Nacional para Áreas Protegidas. En 1976, IBDF adoptó un modelo biogeográfico para situar áreas protegidas para conservación de biodiversidad en la Amazonía, basado en la representación de regiones fitogeográficas. Actualmente están en curso tres iniciativas para determinar la ubicación de nuevas áreas protegidas: (1) se recomendó el establecimiento de áreas protegidas en la mayoría de las 900 áreas prioritarias para la conservación de la biodiversidad que fueron identificadas en talleres sobre los principales biomas de Brasil (1998-2000); (2) establecimiento de corredores de biodiversidad con parques y reservas como elementos claves; y (3) la creación de áreas protegidas en las 23 ecoregiones amazónicas identificadas por el Fondo Mundial para la Vida Silvestre–Brasil (el programa ARPA). Actualmente, hay 478 áreas federales y estatales estrictamente protegidas que abarcan un total de 37,019,697 ha, y hay 436 áreas de uso sustentable que totalizan 74,592,691 ha. Otras categorías de áreas protegidas en Brasil incluyen la Reserva Privada de Patrimonio Natural (RPPN), generalmente pequeña pero importante para la conservación de especies muy amenazadas y con distribución restringida; reservas indígenas, cada vez más reconocidas como vitales para la conservación de biodiversidad debido a su gran extensión. Aunque Brasil ha protegido una superficie enorme en las dos últimas décadas, hay enormes retos no solo para su administración y manejo, sino especialmente para proteger a los parques mismos ya que Brasil continúa con sus ambiciosos programas de desarrollo para energía, infraestructura, industria y agricultura.
The Pantanal of Mato Grosso, Brazil, is famous for its luxurious plant and animal life. We combine a literature review with
recent work and show that species diversity is large but that most major plant and animal groups contain a large number of
not wetland-specific species that depend on permanently terrestrial habitats within the Pantanal, or are restricted to dry
areas during the low water period. These species occur also in the neighbouring biomes of Cerrado, Amazon Forest or Chaco.
Until now, very few endemic species have been described, however, there are large populations of species in the Pantanal that
are considered rare or endangered in South America. The number of trees adapted to long term flooding is low in comparison
with the Amazon River floodplain. We hypothesize that the reason for the lack of local endemisms and the occurrence of a large
number of species with a large ecological amplitude is the climatic instability of the region of the Pantanal, which suffered
severe drought during glacial periods. The instability of the actual climate, which is characterized by multi-annual wet and
dry periods, has a strong impact on distribution, community structure and population size of many plant and animal species
and hinders spatial segregation of populations. The dependence of the system on the flood pulse makes the Pantanal very vulnerable
to human induced changes in hydrology and the predicted changes in global climate.
The Pantanal wetland is a vast seasonally inundated area of extraordinary landscape and biological diversity and complexity. It is located in the upper portion of the Paraguay River basin in central South America. During the rainy season, increased stream discharge from the surrounding basin produces an annual flood pulse through the Pantanal. Increasing human impact, such as dam construction, deforestation, agricultural related activities, and the Hidrovia project in the Parana-Paraguay waterway, threaten the ecological stability of the Pantanal area. As a result, there is an urgent need to introduce new management practices in the Pantanal Basin. In this paper we present a concept for managing the Pantanal catchment based on the integration of ecological knowledge, institutional organization, and involvement of different stakeholders. We propose approaches for an integrated management of the Pantanal and its catchment based on improving data bases and the empowerment of the stakeholder groups. The latter depends on increasing the level of education and access to information, as well as implementing procedures to improve public involvement and enforcement of environmental regulations.
To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.
A distinctive feature of the Nhecolândia, a sub-region of the Pantanal wetland in Brazil, is the presence of both saline and
freshwater lakes. Saline lakes used to be attributed to a past arid phase during the Pleistocene. However, recent studies
have shown that saline and fresh water lakes are linked by a continuous water table, indicating that saline water could come
from a contemporary concentration process. This concentration process could also be responsible for the large chemical variability
of the waters observed in the area. A regional water sampling has been conducted in surface and sub-surface water and the
water table, and the results of the geochemical and statistical analysis are presented. Based on sodium contents, the concentration
shows a 1: 4443 ratio. All the samples belong to the same chemical family and evolve in a sodic alkaline manner. Calcite or
magnesian calcite precipitates very early in the process of concentration, probably followed by the precipitation of magnesian
silicates. The most concentrated solutions remain under-saturated with respect to the sodium carbonate salt, even if this
equilibrium is likely reached around the saline lakes. Apparently, significant amounts of sulfate and chloride are lost simultaneously
from the solutions, and this cannot be explained solely by evaporative concentration. This could be attributed to the sorption
on reduced minerals in a green sub-surface horizon in the “cordilhieria” areas. In the saline lakes, low potassium, phosphate,
magnesium, and sulfate are attributed to algal blooms. Under the influence of evaporation, the concentration of solutions
and associated chemical precipitations are identified as the main factors responsible for the geochemical variability in this
environment (about 92% of the variance). Therefore, the saline lakes of Nhecolândia have to be managed as landscape units
in equilibrium with the present water flows and not inherited from a past arid phase. In order to elaborate hydrochemical
tracers for a quantitative estimation of water flows, three points have to be investigated more precisely: (1) the quantification
of magnesium involved in the Mg-calcite precipitation; (2) the identification of the precise stoichiometry of the Mg-silicate;
and (3) the verification of the loss of chloride and sulfate by sorption onto labile iron minerals.
The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources. Revision of the land use classification system are presented in U. S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions. Refs.
The ecological conditions of the Pantanal of Mato Grosso change during yearly and multi-years cycles because of pronounced water-level fluctuations. Differences in topography and related hydrological conditions allow the coexistence of specific vegetation units. This paper presents an nalysis of the flora and vegetation types described in the scientific literature and based on our own observations in the Pantanal. It offers a unifying characterization of the vegetation of the Pantanal of Mato Grosso with respect to the characteristics of woody plants of the region. Grasslands and aquatic communities were not considered. Three floristic types with 19 units were distinguished: savannas (5 units), forests (2 sub-types and 10 units), and scrublands (4 units). The environmental parameters used to define these types, subtypes, and units are described, and information on species composition is given. © MPI für Limnologie, AG Tropenökologie, Plön; INPA, Manaus.
Basic wetland ecology textbook in its 4th edition. 14 chapter divided into Introduction, The Wetland Environment, and Wetland Management
The response of polarimetric airborne synthetic aperture radar (SAR) to grassland is investigated. Synthetic aperture radar from the National Aeronautical and Space Administration/ Jet Propulsion Laboratory (NASA/JPL) airborne imaging system was acquired over a diverse grassland site in northern NSW, Australia in September 1993. Grassland and high backscatter targets are classified using images from C, L, and P band for hh, hv, and vv polarizations. The grassland classes cover a wide dynamic range of backscatter from −7 dB to −14 dB in C band and −9 dB to −23 dB in L band. Significant regression relationships are formulated between measurements of grassland height and radar backscatter using site data aggregated by 25 mm height class. The relationship between species composition and grassland classes is explored. Polarization effects include an enhanced range of backscatter across grassland classes for 45° cross polarization at L band and differences in the pedestal height of the C band polarization signature for species classes. The results of drainage modeling suggest that soil moisture is a significant confounding factor influencing radar backscatter from the grassland. Simple models using logistic probability of association between height class and radar backscatter in a Bayesian inference engine, and a simple threshold based on logistic probability of association between wet areas and Pvv are examined. Our results suggest that combined imagery from C and L band satellite-borne SAR sensors have potential for current application in grassland monitoring.
Significant progress has been made in using remote sensing as a means of acquiring information about wetlands. This research provides a brief review of selected previous works, which address the issues of wetland identification, classification, biomass measurement, and change detection. Suggested new research emphases include compiling basic spectral‐reflectance characteristics for individual wetland species by means of close‐range instrumentation, analyzing canopies architectures to facilitate species identification, and assessing the impact on composite spectral signatures of wet soils and variable depths of standing water beneath emergent canopies. These research foci are justifiable when considered in the context of environmental change / variability and the production of trace gases.
Radarsat and JERS-1 imagery were used for mapping zonation of vegetation communities in the Amazon floodplain. Imagery analysis indicates that at periods of minimum water level the backscattering values of both C and L bands are the lowest and as the water level rises, so do the backscattering values. JERS-1 imagery exhibits a larger dynamic range of backscattering in response to the ground cover for the two extremes of water level (10 dB) compared to Radarsat imagery. The backscattering differences from different ground cover allowed the use of a region-based classification that produced seasonal maps with accuracies higher than 95% for vegetated areas of the floodplain. These seasonal maps were used to estimate the spatial distribution and time of inundation and the vegetation cover of the floodplain. It was possible to determine that semi-aquatic vegetation, tree-like aquatic plants, and shrub-like trees colonize regions flooded for at least 300 days year. Secondary colonizers, such as tall well-developed floodplain forest, cover regions flooded for approximately 150 days year, and floodplain climax forest colonize regions inundated for approximately 60 days year.
This study assessed the use of multi-polarized L-band images for the identification of coastal wetland environments in the Amazon coast region of northern Brazil. Data were acquired with a SAR R99B sensor from the Amazon Surveillance System (SIVAM) on board a Brazilian Air Force jet. Flights took place in the framework of the 2005 MAPSAR simulation campaign, a German-Brazilian feasibility study focusing on a L-band SAR satellite. Information retrieval was based on the recognition of the interaction between a radar signal and shallow-water morphology in intertidal areas, coastal dunes, mangroves, marshes and the coastal plateau. Regarding the performance of polarizations, VV was superior for recognizing intertidal area morphology under low spring tide conditions; HH for mapping coastal environments covered with forest and scrub vegetation such as mangrove and vegetated dunes, and HV was suitable for distinguishing transition zones between mangroves and coastal plateau. The statistical results for the classification maps expressed by kappa index and general accuracy were 83.3% and 0.734 for the multi-polarized color composition (R-HH, G-HV, B-VV), 80.7% and 0.694% for HH, 79.7% and 0.673% for VV, and 77.9% and 0.645% for HV amplitude image. The results indicate that use of multi-polarized L-band SAR is a valuable source of information aiming at the identification and discrimination of distinct geomorphic targets in tropical wetlands.
Multiple endmember spectral mixture analysis (MESMA) was applied to the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) imagery of a salt marsh in China Camp at San Pablo Bay, California. A nine‐endmember set representing materials within the scene was used including: two Salicornia and two soils, and Grindelia, Spartina, dry grass, water and shade. The resultant abundance maps were used to investigate the spatial distribution of the marsh vegetation species, Salicornia virginica, Grindelia Stricta and Spartina foliosa. The Spartina abundance map exhibited a well‐defined zone bordering the water and the lower marsh, which is in good agreement with the field observations made in 2002. Comparison of the Salicornia map with all six field global positional system (GPS) polygons indicates Salicornia was classified with high accuracy. The proposed approach did a good job in classifying Spartina and Salicornia which cover 93.81% of the total marsh. The Grindelia fraction image underestimates in some areas, while in other areas it shows false detection. This misclassification is attributed to the spectral similarity between Grindelia and Salicornia and to the small patch size of Grindelia. Further work is required to solve this confusion.
The relationships between field water reflectance spectra and physico-chemical data of seven freshwater and five saltwater lakes from the Brazilian Pantanal wetlands were characterized. Selection of the lakes was based on previous inspection of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Principal component analysis (PCA) was used to identify homogeneous groups of lakes, in which the regression relationships were evaluated. The continuum removal method was applied to characterize minor spectral variations in the depth of the absorption bands present in field and image spectra. The results showed lakes with very distinct spectral characteristics. The transition from the freshwater to the saltwater lakes was characterized by lower values of depth and Secchi depth, larger concentrations of dissolved organic carbon (DOC), total suspended sediments (TSS), calcium (Ca), magnesium (Mg), sodium (Na) and potassium (K), and higher values of pH and electrical conductivity. The saline lakes presented a higher overall reflectance in the 400–900 nm range than the freshwater lakes, as indicated by the first principal component. From the optically active constituents analysed, DOC better explained variations in water reflectance. The discrimination of the saltwater lakes along the second principal component was due to the decrease in the chlorophyll (Chl) and to the increase in the DOC concentrations from the greenish to the bluish saline lakes. The AVIRIS instrument was able to detect the narrow 630 nm absorption band present in field water reflectance spectra.
Recent work, spearheaded by Charles Dunnett (1980a), leads to the conclusion that the Tukey-Kramer (TK) method (popularly known as “Kramer's Method”) is the recommended multiple comparisons procedure for the simultaneous estimation of all pairwise differences of means in an imbalanced one-way ANOVA design with homogeneous variances. Nine other multiple comparisons methods are compared to each other and to the TK method using the criteria of conservativeness, narrowness of confidence intervals, robustness, and ease of use. The degree of superiority of the TK method over these methods, especially over the popular Bonferroni method, is sufficient to warrant recommending its use. Because of the lack of robustness of the TK method in heterogeneous variance cases, other methods are recommended.
Field measurements were combined with Synthetic Aperture Radar (SAR) images to evaluate the use of radar for estimating biomass changes and mapping of aquatic vegetation in the lower Amazon. Field campaigns were conducted concomitant to the acquisition of Radarsat and JERS-1 images at five different stages of the hydrological cycle. The temporal variability of the SAR data for aquatic vegetation shows a dynamic range of 5 dB, however this is due dominantly to the significant differences ( p <0.05) between the low water season when vegetation is small and just emerging and other seasons when vegetation is fully developed. The spatial variability of the above-water biomass is detectable with radar data. Significant correlation ( p <0.05) exist between backscattering coefficients and both above-water dry biomass and height of the plants. The logarithmic relationship between backscattering coefficients and biomass suggests that (1) at low biomass, high transmissivity of the microwave radiation through the vegetation canopy occurs and the backscattering is a result of quasi-specular reflection of both C and L bands and a minor contribution of canopy volume scattering from C band; (2) at intermediate levels of biomass, moderate changes in backscattering values occur and the saturation point of backscattering is reached; and (3) at high biomass, the transmissivity of C and L band radiation is equally attenuated and backscattering approaches similar values for both. A combination of Radarsat and JERS-1 images from high and low water periods were classified using a segmentation algorithm and had an accuracy higher than 97% for vegetated areas of the floodplain. Although further research is needed to better understand the saturation points for Radarsat and JERS-1 data, these findings clearly show that C and L bands can accurately map aquatic vegetation of the Amazon floodplain.
Periodically, reviews of our knowledge of radar–wetland relationships and detection parameters have been provided by various authors. Since the publication of these works, additional research has been completed. Five major remote sensing journals spanning the years 1965–2007 formed the basis of this review. The vast majority of significant material found its way into these mainstream journals in one aspect or another. A short history of Synthetic Aperture Radar (SAR)–wetlands discovery based on earlier reviews is followed by an update on radar‐related wetland research. Although some trends emerged with regard to which wavelengths or polarizations to use, there was variation in optimum season/time of year and selection of multitemporal imagery. What is evident throughout the recent literature is that multidimensional radar data sets are attaining an accepted role in operational situations needing information on wetland presence, extent and conditions.
An inventory of Loboi swamp was undertaken to determine the macrophyte diversity and distribution. A total of 36 vascular plant species in 13 families were recorded, with Cyperaceae forming over 30% of macrophytes. Two vegetation zones were observed, characterised by the presence of Typha and papyrus. The Typha zone, comprising over 70% of the swamp, is dominated by T. domingensis and is species rich with 35 plant species whereas the papyrus zone includes the dominant Cyperus papyrus and only one other macrophyte species. Distribution of macrophytes is correlated with depth and period under water, with the Typha zone seasonally flooded while the papyrus zone is permanently under water at depths over 0.5m. Water chemistry has little influence on the distribution of macrophytes in the swamp, but at the edges there is predominance of Cyperus laevigatus in high alkalinity soils. Current uses of the swamp include dry season grazing, harvesting of papyrus and other plant material for mat making and house thatching, and use of the swamp water for domestic and irrigation agriculture. Further monitoring is needed to evaluate the effect of the resource uses on the swamp.
To obtain data on home range, movements, activity and habitat use by giant anteaters Myrmecophaga
tridactyla, seven animals were captured, radio-collared and monitored from March to December 2001 in a 104-km2 study area in the Pantanal wetland, Brazil. Four of five males used areas that covered 4.0–7.5 km2 (5.7±1.7 km2), and one of two females monitored occupied a larger area (11.9 km2) than the males, but none of the curves of cumulative area unequivocally reached the asymptote. Generally, there was considerable overlap among individual areas used. The home-range estimates were calculated using the 100% minimum convex polygon, and 95% adaptive kernel methods. The areas used by the giant anteaters in the Pantanal wetland were larger than home ranges of giant anteaters in the Serra da Canastra National Park, Brazil. The habitat types were generally used in the same order as they occurred in each home range. Two giant anteaters previously monitored with VHF radio-telemetry were subsequently tracked with a modified global positioning (GPS) system in different periods. The modified GPS acquired data on activity and habitat use for c. 9 days. Giant anteaters did not show a similar pattern of habitat use during the period of study using the modified GPS unit, but their activity patterns were similar. One of the ranges recorded over 9 days with this method was larger than the range obtained over 252 days by standard VHF radio-telemetry.
1.This paper provides an introduction to Synthetic Aperture Radar (SAR) remote sensing and, in particular, the significance of long-wavelength (L-band) SAR for wetland applications relevant to the Ramsar Wetlands Convention.2.The Convention has long been a supporter of effective wetland inventory being used to support management initiatives and the wise use of all wetlands.3.Three major application areas have been identified where SAR data may constitute an important additional information source for wetland inventory and management. These comprise mapping of below-canopy inundation, monitoring of environmental disturbances and wetland inventories based on SAR mosaics. These areas have all previously been supported in general terms by formal resolutions on wetland inventory and assessment through the Convention with recognition that further technique development was required.4.The potential to make further use of remote sensing is increased through wider use of the special features of SAR in situations where other data are less suitable.5.The Japanese Advanced Land Observing Satellite (ALOS) provides an opportunity to support the Convention and its goal of wise use of all wetlands. Copyright © 2007 John Wiley & Sons, Ltd.
The tropical wetland environments of northern Australia have ecolo-gical, social, cultural and economic values. Additionally, these areas are relatively pristine compared to the many other wetland environments in Australia, and around the world, that have been extensively altered by humans. However, as the remote northern coastline of Australia becomes more populated, environmental problems are beginning to emerge that highlight the need to manage the tropical wetland environments. Lack of information is currently considered to be a major factor restricting the eVective management of many ecosystems and for the expans-ive wetlands of the Northern Territory, this is especially the case, as these areas are generally remote and inaccessible. Remote sensing is therefore an attractive technique for obtaining relevant information on variables such as land cover and vegetation status. In the current study, Landsat TM, SPOT (XS and PAN) and large-scale, true-colour aerial photographywere evaluated for mapping the vegeta-tion of a tropical freshwater swamp in Australia's Top End. Extensive ground truth data were obtained, using a helicopter survey method. Fourteen cover types were delineated from 1:15 000 air photos (enlarged to 1:5000 in an image processing system) using manual interpretation techniques, with 89% accuracy. This level of detail could not be extracted from any of the satellite image data sets, with only three broad land-cover types identii ed with accuracy above 80%. The Landsat TM and SPOT XS data provided similar results although superior accuracy was obtained from Landsat, where the additional spectral information appeared to compensate in part for the coarser spatial resolution. Two diVerent classii cation algorithms produced similar results.
1.The Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observing Satellite (ALOS) L-band Phased Array Synthetic Aperture Radar (PALSAR), launched successfully in January 2006, will provide new data sets for coastal ecosystems mapping and change monitoring at local to global scales.2.To evaluate L-band capability for mangrove applications, data acquired by the NASA airborne SAR (AIRSAR) and Japanese Earth Resources Satellite (JERS-1 SAR) over sites in Australia, French Guiana and Malaysia were used to demonstrate benefits for mapping extent and zones, retrieving biomass and structural attributes (e.g. height), and detecting change.3.The research indicates that mapping is most effective where mangroves border non-forested areas and where differences in structure, as a function of species, growth stage and biomass distributions, occur between zones.4.Using L-band SAR, biomass can be retrieved up to ∼100–140 Mg ha−1, although retrieval is complicated by a noticeable decrease in L-band backscattering coefficient within higher (∼>200 Mg ha−1) biomass stands, particularly those with extensive prop root systems.5.Change detection through multi-temporal comparison of data proved useful for mapping deforestation/regeneration and mangrove dynamics associated with changing patterns of sedimentation.6.The research highlights the likely benefits and limitations of using ALOS PALSAR data and supports JAXA's Kyoto & Carbon (K&C) Initiative in promoting the use of these data for regional mangrove assessment.Copyright © 2007 John Wiley & Sons, Ltd.