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Surface Water Extent Dynamics from Three Decades of Seasonally Continuous Landsat Time Series at Subcontinental Scale in a Semi-Arid Region
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... Satellite remote sensing offers capacity to continuously monitor and produce surface water maps at various spatio-temporal scales (Alsdorf et al., 2007;Halabisky et al., 2016;Li et al., 2018;Pekel et al., 2016;Seiler et al., 2009;Tulbure et al. 2016) which can fill the gaps in poorly ungauged basins. The benefits of integrating spatio-temporal information in local planning processes are well recognised. ...
... In Australia, for example, the 2011 extreme floods caused severe socio-economic damages and triggered the government to initiate a country-wide multi-year mapping of surface water using earth observation data and obtain accurate spatio-temporal information on surface water change and dynamics to support local planning (Mueller et al., 2016). With the opening of Landsat archives in 2008 (Wulder et al., 2012), temporally consistent and free-to-use satellite imagery spanning over 40 years are now available, and has since been instrumental in improving our understanding of global surface water dynamics (Feng et al., 2016;Pekel et al., 2016;Pickens et al., 2020) and in many local settings (Deng et al., 2019;Mueller et al., 2016;Tulbure et al., 2016;Tulbure et al., 2014;Xia et al., 2019). The 10 m resolution imagery from Copernicus Sentinel-2A and-2B satellites provide even more spatially enhanced capability than Landsat to map surface water (Yang et al., 2020) but data from these satellites are only available since 2015, and do not yet provide elaborate historical information on changes and dynamics of surface water. ...
... Cloud cover is also a key obstacle to accurate mapping of flash floods, but recent availability of multi-temporal optical satellite data and free Synthetic Aperture Radar (SAR) data has enhanced the capability to map surface water despite cloud cover (DeVries et al., 2020;Hardy et al., 2019;De Groeve, 2010). Despite recent methodological advancements in surface water mapping (Feyisa et al., 2014;Isikdogan et al., 2017;Tulbure et al., 2016;Isikdogan et al., 2017;Lary et al., 2016) coupled with the availability of free-to use satellite data covering several decades (Loveland and Dwyer, 2012;Loveland and Irons 2016;Wulder et al., 2012Wulder et al., , 2015, accurate spatio-temporal information on surface water changes and dynamics is still lacking for many humaninhabited basins which experience extreme floods. In such basins, publicly available global surface water datasets (e.g. ...
Socio-economic damages caused by extreme floods have been increasing rapidly in recent years, mainly driven by changes in the climate and modulated by increasing human population in deltic areas and floodplains. The Cuvelai-Etosha Basin (CEB) in southern Africa, covering southern Angola and northern Namibia, experiences socially and economically devastating extreme floods. Yet, accurate information on past and current surface water changes and dynamics is lacking. Here, we estimate and map the surface water extents in the CEB and its surroundings (CEB + S) for 32 years (1990–2021) from Landsat data using random forest models to provide long-term baseline information on surface water changes and dynamics. Based on the reference data, a total of 15,677 ± 1080 km2 have been inundated by surface water in the CEB + S during 1990–2021. This extent was accurately mapped by our local water extent product (mapped area = 16,273 km2, user’s accuracy = 91.5 ± 2.5%, producer’s accuracy = 91.1 ± 6%). With user’s and producer’s accuracy of 91%, our overall water extent provides the first most accurate long-term baseline information on surface water inundation in CEB + S necessary for local spatial planning processes to minimise future negative impacts of floods in the basin. Interannual variability of surface water extent is, however, high, with water extent ranging from 520.8 ± 375.7 km2 to 12372.3 ± 1154.7 km2 during the 1990–2021 period. The largest annual water extents (>10,000 km2) were recorded in 2006, 2008, 2009, 2011, and 2017, whereas the smallest extents (<1000 km2) were recorded in 1992 and 2019. We found that over 40% of the area inundated in the CEB + S during 1990–2021 was inundated less than 9 times. With human population increasing rapidly in the CEB + S, rarely inundated areas with short water residence could become a prime target for human settlements, which may lead to huge socio-economic damages during extreme floods if no preventive measures are put in place. Globally available surface water maps from the Global Land Analysis and Discovery (GLAD) and European Commission’s Joint Research Centre (JRC) did not provide realistic surface water extent for CEB + S, especially during years with extreme floods. Therefore, locally adopted product for operational monitoring of surface water in the CEB + S is needed to provide accurate information for informing spatial planning processes and surface water resource management strategies in this endorheic basin and help minimise future negative impacts of floods.
... There are many methods available for mapping surface water using surface reflectance from remote sensing data [16][17][18] including the Landsat series [19][20][21][22]. Some of the more complex methods use machine learning or other classification techniques [23][24][25][26][27]. Spectral indices are typically used for mapping surface water due to their simplicity and ease of application [28][29][30][31][32]. ...
... One example is for the Barmah-Millewa Forest (whose extent can be seen in Figure 7a) located in the southern section of the MDB, and which is listed as a Ramsar wetland site. This floodplain area consists of open canopy forest (~70%) and woodlands (~29%) [25]. Figure 7b, c, and d show the multi-index method, TCW np035 , and mNDWI np3 from a Landsat image acquired in December 2010 during a flooding period. ...
... The magenta line shows the major river, and orange shows the extent of ANAE wetlands. mNDWI np3 (Figure 7d) shows much less flood extent within this forested wetland area compared to TCW np035 (Figure 7c [25] for the flood event for the ANAE wetland area, making the extent shown in the multi-index method within the wetland area plausible. However, less water is identified using the multi-index method in the northern section of the floodplain (Figure 7b) compared to Tulbure et al. (2016) [25], and TCW np035 (Figure 7c) compares much better. ...
Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.
... Monthly, seasonal, and annual observations can be made in extracting the water surface area. As far as is known, monthly analyses have only been considered by [4][5][6][7][8][9], seasonality analyses have been evaluated by [7,[10][11][12][13][14][15][16][17][18], and annual analyses have been assessed by [2,3,11,[19][20][21][22][23][24][25]. ...
... Currently, various machine-learning (ML) algorithms, such as decision trees (DT) [18], support vector machines (SVM) [44,45], and random forests (RFs) [12,23,46], have been utilized for wetland information extraction research. In the ref. ...
Lakes and reservoirs, comprising surface water bodies that vary significantly seasonally, play an essential role in the global water cycle due to their ability to hold, store, and clean water. They are crucial to our planet’s ecology and climate systems. This study analyzed Harmonized Sentinel-2 images using the Google Earth Engine (GEE) cloud platform to examine the short-term changes in the surface water bodies of Çivril Lake from March 2018 to March 2023 with meteorological data and lake surface water temperature (LSWT). This study used the Sentinel-2 Level-2A archive, a cloud filter, the NDVI (normalized difference vegetation index), NDWI (normalized difference water index), MNDWI (modified NDWI), and SWI (Sentinel water index) methods on lake surfaces utilizing the GEE platform and the random forests (RFs) method to calculate the water surface areas. The information on the water surfaces collected between March 2018 and March 2023 was used to track the trend of changes in the lake’s area. The seasonal (spring, summer, autumn, and winter) yearly and monthly changes in water areas were identified. Precipitation, evaporation, and temperature are gathered meteorological parameters that impact the observed variation in surface water bodies for the same area. The correlations between the lake area reduction and the chosen meteorological parameters revealed a strong positive or negative significant association. Meteorological parameters and human activities selected during different seasons, months, and years have directly affected the shrinkage of the lake area.
... O algoritmo de aprendizado de máquina (machine learning) para classificação baseado em árvores de decisão Random Forest (Breiman, 2001) tem apresentado bons resultados e alta acurácia global ao combinar e integrar diferentes tipos de variáveis para o mapeamento da água superficial permanente (Ko et al., 2015) ou temporária associada a inundações, onde há maior mistura da resposta espectral dos alvos , Tulbure et al., 2016. O Random Forest, em relação a outros algoritmos de aprendizado de máquina, é simples de parametrizar, informa a contribuição relativa de cada variável para a classificação e fornece a exatidão do modelo (Tyralis et al., 2019). ...
... O algoritmo de aprendizado de máquina (machine learning) para classificação baseado em árvores de decisão Random Forest (Breiman, 2001) tem apresentado bons resultados e alta acurácia global ao combinar e integrar diferentes tipos de variáveis para o mapeamento da água superficial permanente (Ko et al., 2015) ou temporária associada a inundações, onde há maior mistura da resposta espectral dos alvos , Tulbure et al., 2016. O Random Forest, em relação a outros algoritmos de aprendizado de máquina, é simples de parametrizar, informa a contribuição relativa de cada variável para a classificação e fornece a exatidão do modelo (Tyralis et al., 2019). ...
Traditional classifications present limitations for mapping floods due to mixing the spectral response of water with adjacent non-aquatic targets or similar spectral response of non-aquatic targets with water. Furthermore, in general, these classificationsare evaluated only in terms of overall accuracy without considering the uncertainties in the classification process. Thus, this study aimed to integrate uncertainty in the Random Forest (RF) classification process for flood mapping, which guided the sampling process. The classification used 21 variables including indices and spectral bands from the Operational Land Imager sensor of the Landsat-8 satellite. Sampling was performed initially with the selection of points from the visual interpretation of the satellite image and later by collecting samples with high Shannon entropy values in the uncertainty map. The variables with the greatest importance for classification were selected by the Recursive Feature Elimination (RFE) algorithm. The final RF classification using samples collected based on the uncertainty map and with the four selected variables by the RFE presented an accuracy of 98.0% and a reduction of uncertainty, which indicates a greater confidence in the spatial representation and quantification of water permanent and temporary surface associated with floods.
... Remote sensing is an efficient technique for continuous monitoring of surface water dynamics, but a major challenge of such methods is the trade-off between the temporal and spatial resolutions (Li et al., 2021b). Moderate Resolution Imaging Spectroradiometer (MODIS) data collected at daily temporal resolution and typical spatial resolution at 250 m to 500 m have been used in rapid surface water change detections and flood extractions (Huang et al., 2014;Tellman et al., 2021); Landsat sensors have been widely used to capture surface water dynamics on monthly or annual scales with their low temporal resolutions (16 days) and high spatial resolutions (30 m) (Pekel et al., 2016;Tulbure et al., 2016;Olthof and Rainville, 2022). The low spatial resolution for MODIS limits its ability to participate in water monitoring, especially in heterogeneous landscapes where the presence of mixed pixels can cause many small water bodies to be lost in the results. ...
... Additionally, it is hard to eliminate the influence of mixed ground objects when using only one water index to estimate the surface water extent (Zhou et al., 2019). Inspired by this concern, various machine learning methods, like multilayer perceptrons (Jiang et al., 2018), support vector machines (Nandi et al., 2017), convolutional neural networks (Li et al., 2021a), decision trees (DTs) (Tulbure and Broich, 2013), and random forests (RFs) (Mueller et al., 2016;Tulbure et al., 2016), have been used to map surface water because such algorithms allow the use of multiple water indices. Among these methods, treebased ensemble machine learning algorithms such as RFs have been broadly adopted in surface water mapping owing to their robustness. ...
... SWD is another Landsatbased water body time series product. It follows a slightly different approach, using a random forest classifier and spans the timeframe of 1986-2011 at a temporal resolution of ~3 months [42,177]. There are also global Landsat-based products and approaches that try to advance on GSW. ...
... The majority of studies concentrating on Australia (56%) have a focus on the anthroposphere, particularly the assessment of drought-and flood-related surface water dynamics [42,165,174,177,252]. The rest focus on surface water area dynamics in general [124,253], or specifically concentrate on certain water bodies like lakes and reservoirs [175] or estuaries [90]. ...
Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.
... Most of the existing water body mapping methods are based on a single image and are calculated at the pixel scale. In practice, to achieve long-term and continuous water body mapping, medium-or low-spatial-resolution remotely sensed images with a high temporal resolution, such as Moderate Resolution Imaging Spectroradiometer (MODIS) [18][19][20][21] and Landsat [22][23][24] images, are commonly used. Based on the continuous observation of these medium-or low-spatial-resolution images, time series datasets of surface water bodies have been generated. ...
... where c 1 (·) and c 2 (·) are the class labels of an arbitrary pixel in the previous fraction image and the current fraction image, respectively. P is calculated as [23] P(c 2 (·) = ω ι | c 1 (·) = ω κ ) = ...
Water body mapping is an effective way to monitor dynamic changes in surface water, which is of great significance for water resource management. Super-resolution mapping is a valid method to generate high-resolution dynamic water body maps from low-spatial-resolution images. However, the accuracy of existing super-resolution mapping methods is not high due to the low accuracy of fraction images and the insufficiency of spatial pattern information. To solve this problem, this paper proposes a spectral similarity scale-based multiple-endmember spectral mixture analysis (SSS-based MESMA) and a multiscale spatio-temporal dependence method based on super-resolution mapping (MESMA_MST_SRM) for water bodies. SSS-based MESMA allows different coarse pixels to have different endmember combinations, which can effectively improve the accuracy of spectral unmixing and then improve the accuracy of fraction images. Multiscale spatio-temporal dependence adopts both pixel-based and subpixel-based spatial dependence. In this study, eight different types of water body mappings derived from the Landsat 8 Operational Land Imager (OLI) and Google Earth images were employed to test the performance of the MESMA_MST_SRM method. The results of the eight experiments showed that compared with the other four tested methods, the overall accuracy (OA) value, as well as the overall distribution and detailed information of the water map generated by the MESMA_MST_SRM method, were the best, indicating the great potential and efficiency of the proposed method in water body mapping.
... Compared to the VI method, the automatic extraction method has higher extraction efficiency. Generally, it can be divided into index analysis-based method [8][9][10][11][12], edge detection-based method [7,[13][14][15], threshold segmentation-based method [16][17][18][19][20], region growth-based method [7], neural network-based method [21][22][23][24] and sub-pixel method [16,[25][26][27][28][29]. ...
... The recent decades have seen the driving advance of neural networks in various visual recognition fields such as object detection [37,38], image classification [39], and semantic segmentation [40,41]. However, the calculation process of the neural network method is complicated and it needs pixel samples as pure as possible [23,24]. The sub-pixel method can obtain waterline results better than pixel level by reducing the influence of mixed pixels on waterline extraction. ...
Coastline is an important geographical element of the boundary between ocean and land. Due to the impact of the ocean-land interactions at multiple temporal-spatial scales and the intensified human activities, the waterline of muddy coast is undergoing long-term and continuous dynamic changes. Using traditional remote sensing-based waterline extraction methods, it is difficult to achieve ideal results for muddy coast waterlines, which are faced with problems such as limited algorithm stability, weak algorithm migration, and discontinuous coastlines extraction results. In response to the above challenges, three different types of muddy coasts, Yancheng, Jiuduansha and Xiangshan were selected as the study areas. Based on the Sentinel-2 MSI images, we proposed an adaptive remote sensing extraction algorithm framework for the complex muddy coast waterline, named AEMCW (Adaptive Extraction for Muddy Coast Waterline), including main procedures of high-pass filtering, histogram statistics and adaptive threshold determination, which has the capability to obtain continuous and high-precision muddy coastal waterline. NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index) and ED (Edge Detection) methods were selected to compare the extraction effect of AEMCW method. The length and spatial accuracy of these four methods were evaluated with the same criteria. The accuracy evaluation presented that the length errors of ED method in all three study areas were minimum, but the waterline results were offset more to the land side, due to spectral similarity, turbid water and tidal flats having similar values of NDWI and MNDWI. Therefore, the length and spatial accuracies of NDWI and MNDWI methods were lower than AEMCW method. The length errors of the AEMCW algorithm in Yancheng, Jiuduansha, and Xiangshan were 14.4%, 18.0%, and 7.7%, respectively. The producer accuracies were 94.3%, 109.6%, and 94.2%, respectively. The user accuracies were 82.4%, 92.9%, and 87.5%, respectively. These results indicated that the proposed AEMCW algorithm can effectively restrain the influence of spectral noise from various land cover types and ensure the continuity of waterline extraction results. The adaptive threshold determination equation reduced the influence of human factors on threshold selection. The further application on ZY-1 02D hyperspectral images in the Yancheng area verified the proposed algorithm is transferable and has good stability.
... Zhou et al. [37] use the support vector machine (SVM). Merela [38] and Schmitt [23] use random forest (RF) for the analysis of water bodies. Pech-May et al. [29] analyze the behavior and land cover of water bodies during floods in the rainy season using multispectral images and RF, SVM, and classification and regression tree (CART) algorithms. ...
Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.
... Landsat images from Collection 2 have been subjected to geometric and atmospheric correction, as well as cross-calibration between different sensors [25,26], which improves the detection and characterization of surface changes. Landsat images were chosen for this study because they offer high spatial and temporal resolution data, open and free access, and having the longest time series [27][28][29][30] allowing the monitoring of wetlands. ...
Spatial and temporal changes in the water surfaces of the Nakanbe-Wayen watershed influence, at the local and national levels, sustainable economic development. However, these changes in the basin remain poorly characterized. The present study, which aims at analyzing the spatio-temporal dynamics of the water surfaces of lakes Bam and Dem, two wetlands of international importance in the said watershed, exploited the processing power of the Google Earth Engine (GEE) platform to analyze 2121 Landsat image scenes over the period from 2000 to 2020. The water surfaces extraction algorithm, based on a combination of water and vegetation index, has been tested and adopted to rapidly extract the water surfaces of said wetlands. The results indicate that: (1) the water surfaces extraction method is well suited to that of Bam and Dem lakes; (2) the areas of water surfaces have a significant shrinking trend respectively-22.80 ha/year (P-value=0.0006) and-4.44 ha/year (P-value=0. 009) of the permanent surfaces of Bam Lake and Dem Lake from 2000 to 2020; (3) changes in the water surfaces of these lakes may be associated with climate change and human activities and should be studied in more detail. In view of the significant loss of water surfaces areas and the importance of lakes Bam and Dem for the communities and the environment, taking into account strong and concerted actions for restoration and conservation is urgent in order to perpetuate these natural spaces.
... In the hydric context, researches are evidenced in the evaluation of changes in superficial waters dynamics, and these analyses are applied on regional or global scale (El-Asmar and Hereher, 2011;Song et al., 2014;Verpoorter et al., 2014;Feng et al., 2015;Tulbure et al., 2016;Wan et al., 2016;Klein et al., 2017). ...
Remote sensing techniques are of fundamental importance to investigate the changes occurred in the terrestrial mosaic over the years and contribute to the decision-making by increasing efficient environmental and water management. This article aimed to detect, demarcate and quantify the hydric area of Poço da Cruz reservoir, located in Ibimirim, Pernambuco, semiarid region of Brazil, with modeling based on Landsat 8/OLI satellite multispectral images from 2015 to 2020, and to relate it with data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellites average rainfall. For this purpose, the Modified Normalized Difference Water Index (MNDWI) was modeled, being produced georeferenced theme maps and extracted only the pixels represented by positive spectral values, which represent water targets. The open-access software Quantum Geographic Information System (QGIS, version 2.18.16) was used for all stages of digital image processing and connection with complementary databases on the theme maps elaboration. In the results, changes in the spatial distribution of Poço da Cruz were evidenced and analyzed using precipitation data from the CHIRPS product, allowing a better understanding of the rainfall behavior in the region and its influence. The MNDWI was lined with the CHIRPS product, in which the spatial correlation between the rainy event and the water area’s delimitation is documented, especially in October 2017 (minimum values) and October 2020 (maximum values).
... Remote-sensing datasets, such as Landsat and MODIS data, have been widely used to extract lake areas and monitor the spatio-temporal variations of lakes [13][14][15]. Furthermore, the extracted lake areas can be combined with meteorological data (e.g., precipitation, temperature, and evaporation) to reveal the climatic driving factors of lake variations by using statistical methods [16][17][18]. Though the development of remote-sensing technology has improved data availability for natural scientific research in large-scale regions [19][20][21][22], the quality and quantity of the data are highly susceptible to climate conditions and record frequency [23,24]. Therefore, using only remote-sensing data may not be sufficient to obtain continuous daily variations of lakes and reveal the hydrological processes associated with the variations. ...
Lakes are key factors in maintaining ecosystems in semi-arid regions. However, due to data shortage, most studies used remote-sensing data and water-balance models to analyze lake variations in semi-arid ungauged closed watersheds, resulting in the oversimplified assessment of lake variations and their associated hydrologic processes. This study aimed to enhance the understanding of the mechanisms behind the water supplement and consumption of lakes and reveal the influences of hydrological processes on lake variations in such watersheds. Physically based and lake-oriented hydrologic modeling, remote-sensing technology, and results from previous studies were comprehensively integrated to achieve the research objective. The Hongjiannao (HJN) watershed in Northwest China was selected as the study area of this research. The calibration and validation results demonstrated that remote-sensing data and results from previous studies indeed guaranteed the accuracy of the lake-oriented model. Further hydrologic and statistical analyses revealed the linkage between lake variations and their associated hydrologic processes, and the mechanisms behind the linkage. Specifically, rainfall and snowmelt were found to be the most stable sources of HJN Lake, particularly in dry years. Due to the differences in recession rates, groundwater inflow was more stable than upstream inflow and inflow from the contributing area of HJN Lake. The correlations between hydrologic processes and the storage variation of HJN Lake varied significantly at daily and monthly time scales, which can be explained by the generation mechanisms of these processes. This study provided valuable guidance for water resources management and ecosystem protection in the HJN watershed and can be further applied for hydrologic simulations in other similar watersheds.
... The critical success index was also used to quantify the accuracy of surface water [87]. The use of better input data (such as images with finer resolution) and the use of more advanced interpretation methods (such as visual interpretation by using expert knowledge) are effective to quantify the land cover or surface water map [37,[88][89][90][91]. As the sample data were interpreted from the 2 m GF-1 and GF-6 images, the accuracy of the pixel scale classification map outputted from NDWI_OTSU, MNDWI_OTSU, SVM, and UNet at the 10 m resolution was not assessed using these 8000 samples. ...
Mapping high-spatial-resolution surface water bodies in urban and suburban areas is crucial in understanding the spatial distribution of surface water. Although Sentinel-2 images are popular in mapping water bodies, they are impacted by the mixed-pixel problem. Sub-pixel mapping can predict finer-spatial-resolution maps from the input remote sensing image and reduce the mixed-pixel problem to a great extent. This study proposes a sub-pixel surface water mapping method based on morphological dilation and erosion operations and the Markov random field (DE_MRF) to predict a 2 m resolution surface water map for heterogeneous regions from Sentinel-2 imagery. DE_MRF first segments the normalized difference water index image to extract water pixels and then detects the mixed pixels by using combined morphological dilation and erosion operations. For the mixed pixels, DE_MRF considers the intra-pixel spectral variability by extracting multiple water endmembers and multiple land endmembers within a local window to generate the water fraction images through spectral unmixing. DE_MRF was evaluated in the Jinshui Basin, China. The results suggested that DE_MRF generated a lower commission error rate for water pixels compared to the comparison methods. Because DE_MRF considers the intra-class spectral variabilities in the unmixing, it is better in mapping sub-pixel water distribution in heterogeneous regions where different water bodies with distinct spectral reflectance are present.
... Waterbody occurrence, also known as inundation frequency, is typically represented by the dynamics of surface water, which shows the proportion of reliable observations when water is found at the surface [13,45]. Generally, water pixels with an inundation frequency (WIF) ≥ 75% are defined as permanent water, and those in the range of 25% ≤ WIF < 75% are classified as seasonal water; the sum of seasonal water and permanent water constitutes maximum water. ...
Surface water dynamics are sensitive to climate change and anthropogenic activity, and they exert important feedback to the above two processes. However, it is unclear how climate and human activity affect surface water variation, especially in semi-arid regions, such as Horqin Sandy Land (HQSL), a typical part of the fragile region for intensive interaction of climate and land use change in northern China. We investigated the changes of spatiotemporal distribution and the influence of climatic and anthropogenic factors on Surface Water Area (SWA) in HQSL. There are 5933 Landsat images used in this research, which were processed on the Google Earth Engine cloud platform to extract water bodies by vegetation index and water index method. The results revealed that the area and number of water bodies showed a significant decrease in HQSL from 1985 to 2020. Spatially, the SWA experienced different amplitudes of variation in the Animal Husbandry Dominated Region (AHDR) and in the Agriculture Dominated Region (ADR) during two periods; many water bodies even dried up and disappeared in HQSL. Hierarchical partitioning analysis showed that the SWA of both regions was primarily influenced by climatic factors during the pre-change period (1985–2000; the mutation occurred in 2000), and human activity has become more and more significantly important during the post-change period (2001–2020). Thus, it is predictable that SWA variation in the following decades will be influenced by the interaction of climate change and human activity, even more by the later in HQSL, and the social sectors have to improve their ability to adapt to climate change by modifying land use strategy and techniques toward the sustainable development of water resources.
... The second reason was to focus on recent lake area dynamics. Landsat 7 Collection 1 Tier 1 and Real-Time data calibrated the TOA reflectance that was used in this study and covered the period from 1999 to date; however, the products were characterized by a scan line corrector (SLC) failure between 2003 and 2011 [37]. We avoided the SLC characteristic periods and used the SLC year of 2012 to bridge Landsat-5 and Landsat-8, which started in 2013 and covered the study period up to 2021. ...
The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics.
... Thus, it was not over-fitted when the number of trees increased, and coped better with outliers and noise [55,56]. Therefore, the RF algorithm was widely used in long time-series remote sensing datasets [57,58]. ...
The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning models has become one of the most novel techniques for OHE target extraction in recent years. Its performance is mainly influenced by machine learning models, target features, and regional differences. However, there is still a lack of internal comparative studies on the different influencing factors in this framework. Therefore, based on this framework, we selected four different typical experimental regions within the hydrocarbon basins in the South China Sea to validate the extraction performance of six machine learning models (the classification and regression tree (CART), random forest (RF), artificial neural networks (ANN), support vector machine (SVM), Mahalanobis distance (MaD), and maximum likelihood classification (MLC)) using time-series VIIRS night light remote sensing images. On this basis, the influence of the regional differences and the importance of the multi-features were evaluated and analyzed. The results showed that (1) the RF model performed the best, with an average accuracy of 90.74%, which was much higher than the ANN, CART, SVM, MLC, and MaD. (2) The OHE targets with a lower light radiant intensity as well as a closer spatial location were the main subjects of the omission extraction, while the incorrect extractions were mostly caused by the intensive ship activities. (3) The coefficient of variation was the most important feature that affected the accuracy of the OHE target extraction, with a contribution rate of 26%. This was different from the commonly believed frequency feature in the existing research. In the context of global warming, this study can provide a valuable information reference for studies on OHE target extraction, carbon emission activity monitoring, and carbon emission dynamic assessment.
... The water index method was used in this study to determine the range of water mass distribution efficiently. This will be achieved through the calculation of bands and processing of threshold values (Feyisa et al., 2014;Tulbure et al., 2016;Wang et al., 2020Wang et al., , 2021. However, the disadvantage of this approach is that it can be challenging to identify an optimal single threshold due to the variability of spectral signatures over time and space (Li and Ding 2013). ...
The rapid and precise extraction of water information through satellite remote sensing has become a crucial technology in the study of water resources due to its ability to provide substantial and repeatable coverage instantly. This technology is particularly useful in monitoring wetlands, among other applications. It in this perceptive, this research aims to study the surface water extent (SWE) variation of the Great Sebkha of Oran (GSO). A wetland area classified as Ramsar, bordering Oran city. Extended over nearly 40,000 ha, the water extent is influenced by climatic, geological, and hydrological factors, which are typical of endorheic regions in semi-arid and arid climates.However, over the last decade, these natural effects have been overtaxed by anthropogenic impacts caused by the strong urban expansion of the city of Oran. This is particularly evident with the commissioning of the large El Kerma sewage wastewater treatment plant (WWTPK) in 2009. Located at NE edge of the GSO. The function of this plant is to treat a large proportion of the wastewater volume generated by the city of Oran. Nevertheless, for various reasons, a lag in the implantation of the various execution phases of a development project extended to the entire GSO basin has led to nearly a decade; the WWTPK has discharged partially treated wastewater directly into GSO. This had a major impact on the mode of surface water extent change of the sebkha and led to major pollution of the latter. To better understand the (SWE) dynamics, post and pre-discharges from the WWTPK in the sebkha were monitored monthly over 33 years (1987–2019). A consistent database of Landsat sensor images (TM, ETM+, and OLI) was collected and processed in the Google Earth Engine (GEE) platform to extract the SWE in the GSO, which was achieved using spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI1 and NDWI2), Modified Normalized Difference Water Index (MNDWI) and Dryness/Wetness index (DWI). Subsequently, the SWE, correlated with precipitation over the entire GSO watershed, was analyzed and discussed at different time steps: monthly, annual, and seasonal. Before 2009, the precipitation regime study concerning the SWE shows the complexity of the functional dynamics of the endorheic system of the GSO watershed. After 2009, the commissioning of WWTPK had disrupted the SWE variation of the GSO. The maximum
average of the SWE during the 2009–2019 sub-period was 169 km2, while it was 122 km2 during the undisturbed sub-period (1987–2008). The use of remote sensing data analysis contributes effectively to this work, representing proven results to protect this sensitive natural environment. They remain necessary to help decision-makers to ensure and preserve the sustainable ecological viability of the GSO and the natural environment of wetlands of the entire Oran sublittoral furrow of Gdyel-Arzew: Daiet Morcelly, Telamine Lake, Sebkha golf Arzew.
Keywords: Wetlands, Great Sebkha of Oran (GSO), Surface water extent (SWE), Spectral indices, Landsat, Wastewater treatment plant of El Kerma (WWTPK), Anthropogenic impacts
... Our area estimations indicate that water resources are relatively scarce across the landscape, which aligns with our knowledge of these semiarid systems. Further, our area estimations validate the importance of mesic vegetation and suggest that it should be considered in the monitoring of water availability in semi-arid mountain systems, as in aggregate these slowly wane as the water year progresses (Petersen et al., 2012;Tulbure et al., 2016). The exclusion of mesic vegetation and monitoring of only surface water using data at the 10 m scale, results in a different pattern of water availability, where June appears to have less water than in July. ...
Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product in (94 % producer’s accuracy (PA) vs 84 %) and comparable user’s accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments.
... Climate change can greatly affect surface water resources, resulting in drastic changes in interannual water quantities, which in turn have extensive impacts on human society and ecosystems [7], [8], [9]. Studies have shown that precipitation [5], [10], glacial meltwater, and permafrost degradation induced by temperature changes [11], [12], [13] are the main climate factors for surface water changes. ...
The Qinghai–Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai–Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai–Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km2, and there was a significant increasing trend (=322 km2/year,
$p < 0.01$
) during 2000–2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.
... Supervised classification was also found to be one of the most widely used approaches for flood extent delineation and/or land cover/land use classification to assist with water detection. This method includes maximum likelihood (e.g., [66,75]), random forest (e.g., [42,[76][77][78]), and support vector machine (e.g., [79,80]). Despite the large use of supervised classifiers, their use requires a priori knowledge of the classes to be identified. ...
The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium-high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites .
... However, some promising results are emerging. Multi-temporal remote sensing platforms like Landsat, Sentinel-1, and Sentinel-2 provide insights into streamflow dynamics but are necessarily focused on efforts in large rivers (Tulbure et al., 2016;Miller et al., 2014;Isikdogan et al., 2017;DeVries et al., 2020;Yang et al., 2020), particularly in intermittent rivers in arid regions (Hou et al., 2019;Pereira et al., 2019;Kostianoy et al., 2020). For example, Hou et al. (2019) used Landsat imagery over 27 years to track river dynamics within rivers with a width > 25 m in Australia. ...
Headwater streams and inland wetlands provide essential functions that support healthy watersheds and downstream waters. However, scientists and aquatic resource managers lack a comprehensive synthesis of national and state stream and wetland geospatial datasets and emerging technologies that can further improve these data. We conducted a review of existing United States (US) federal and state stream and wetland geospatial datasets, focusing on their spatial extent, permanence classifications, and current limitations. We also examined recent peer-reviewed literature for emerging methods that can potentially improve the estimation, representation, and integration of stream and wetland datasets. We found that federal and state datasets rely heavily on the US Geological Survey's National Hydrography Dataset for stream extent and duration information. Only eleven states (22%) had additional stream extent information and seven states (14%) provided additional duration information. Likewise, federal and state wetland datasets primarily use the US Fish and Wildlife Service's National Wetlands Inventory (NWI) Geospatial Dataset, with only two states using non-NWI datasets. Our synthesis revealed that LiDAR-based technologies hold promise for advancing stream and wetland mapping at limited spatial extents. While machine learning techniques may help to scale-up these LiDAR-derived estimates, challenges related to preprocessing and data workflows remain. High-resolution commercial imagery, supported by public imagery and cloud computing, may further aid characterization of the spatial and temporal dynamics of streams and wetlands, especially using multi-platform and multi-temporal machine learning approaches. Models integrating both stream and wetland dynamics are limited, and field-based efforts must remain a key component in developing improved headwater stream and wetland datasets. Continued financial and partnership support of existing databases is also needed to enhance mapping and inform water resources research and policy decisions.
... The study found that a number of multi-source remote sensing data with different spatial and temporal resolution are available. Moderate Resolution Imaging Spectrometer (MODIS) [21,22], Landsat Thematic Mapper (TM) [23][24][25], Synthetic Aperture Radar [13,26] and other passive and active remote sensors with visible and microwave bands have all been employed to estimate inundation areas and delineate water borders. The Deltares Aqua Monitor [27] and GEE [28] were used to examine changes in the Earth's surface water over the last 30 years. ...
Understanding the effects of global change and human activities on water supplies depends greatly on surface water dynamics. A comprehensive examination of the hydroclimatic variations at the transboundary level is essential for the development of any adaptation or mitigation plans to deal with the negative effects of climate change. This research paper examines the hydro-climatic factors that contribute to the desiccation of the Doosti Dam's basin in the transboundary area using multisensor satellite data from the Google Earth Engine (GEE) platform. The Mann-Kendall and Sens slope estimator test was applied to the satellite datasets to analyse the spatial and temporal variation of the hydroclimate variables and their trend over the transboundary area for 18 years from 2004 to 2021 (as the dam began operating in 2005). Statistical analysis results showed decreasing trends in temperature and an increase in rainfall with respect to station-observed available data. Evapotranspiration and irrigated area development followed the increasing pattern and a slight decrease in snow cover. The results confirmed a large expansion of the irrigated area, especially during the winter growing season. The increase in irrigated cultivated areas during both winter and summer seasons is possibly the main reason for the diversion of water to meet the irrigation requirements of the developed agriculture areas. The approach followed in this study could be applied to any location around the globe to evaluate the hydrological conditions and spatiotemporal changes in response to climate change, trend analysis and human activities.
... The AWEI is divided into two indices: AWEInsh for no shadows and AWEIsh for differentiating water pixels from shadow pixels. Many recent investigations, for example, Tulbure et al. (2016), have used the AWEI to extract water bodies from Landsat imagery. Fisher et al. (2016) developed a new water index, WI2015, utilising linear discriminant analysis using surface reflectance on visible, NIR, and SWIR channels. ...
Globally, water is acknowledged as indispensable. It is essential for both human life and environmental needs. However, surface water resources are threatened by human and climatic influences, which may result in changes in size and density. This study aimed to evaluate the effectiveness of the normalised difference water index (NDWI), modified normalised difference water index (MNDWI) and automated water extraction index (AWEI) in detecting land surface water changes using Landsat satellite data. The results showed that the AWEI performed considerably better than the MNDWI and NDWI for extracting water surface area in the Upper Mzingwane sub-catchment, with an overall accuracy of 0.93 and a kappa coefficient of 0.82. The MNDWI and NDWI, had overall accuracy/kappa values of 0.88/0.74 and 0.89/0.73, respectively. The AWEI can enhance surface water features while effectively suppressing or eliminating pollution and noise from surrounding vegetation and muddy soil. NDWI/MDWI water information is often mixed with pollution noise, vegetation and muddy soil, overestimating the area of water. All the applied indices indicate a progressive loss in the surface area of the water bodies in the sub-catchment. The decrease in water surface area could be a result of degradation, as the decreasing patterns of water surface area coincide with a decrease in vegetation cover and an increase in degraded areas. Future research needs to investigate the hydrological response of the sub-catchment to the potential influence of climate, variability, change, and LULC changes.
... Hu [72] extracted the area of four lakes, Zhuonai Lake, Kusai Lake, Heidinor Lake, and Salt Lake, from 1989 to 2018 using Landsat imagery but selected the area of the lakes for only one day of the year to represent the area for the whole year, resulting in inaccurate results as the surface of water bodies varies significantly due to change of seasons [73]. ...
Lakes are important natural resources closely related to human survival and development. Based on PIE cloud computing platform, the study uses Landsat images and the empirical normalized water body index (ENDWI) to extract water body information of the large lakes in Sichuan province from 2000 to 2020 in the drought and rainy seasons, respectively, and uses the Mann–Kendall test to obtain the long-term trends of their area and climate. On this basis, the evolution of the lakes and their correlation with climate and human activities are analyzed. The results show that (1) In the past 20 years, the area of Lugu Lake, Qionghai Lake, and Luban Reservoir represent a decreasing trend, with Lugu Lake being the most affected. The area of Ma Lake, Three Forks Lake, and Shengzhong Reservoir increased, with the area of Shengzhong Reservoir increasing significantly; (2) During the drought season, all six lakes showed a decreasing trend in precipitation, with the most apparent decreasing trend for Lugu Lake (Slope = −0.8). Only Lugu Lake showed a decreasing trend in precipitation (Slope = −0.15) during the rainy season. The precipitation of Ma Lake, Three Forks Lake, Luban Reservoir and Shengzhong Reservoir showed a significant increasing trend (Slope value was greater than 1.96); (3) The temperatures of the remaining lakes all decreased in the drought season and increased in the rainy season, except that the temperature of Shengzhong Reservoir decreases throughout the year; (4) The area change of plain lakes is greatly affected by human activities, but the area of plateau lakes is are more impacted by climate. Our study improved the accuracy of long-term water body change monitoring with PIE-Engine Studio. Besides, the findings would provide reference for the implementation of sustainable water resources management in Sichuan Province.
... Compared with NDWI, the modified normalized difference water index (MNDWI) proposed by Xu et al. has a higher accuracy in extracting the water boundary of eutrophic lakes and can easily distinguish shadows from water bodies [20]. Landsat series satellites have provided long-term observations of the Earth since 1972, which present a unique opportunity to provide long-term surface water monitoring at a fine spatial resolution (30~78 m) [21][22][23]. ...
The spatial–temporal characteristics of water bodies and their response to climatic factors are significant for the study of the water budget and the ecological environment. As Asia's water tower, the Qinghai–Tibet Plateau (QTP) has abundant surface water bodies, whose distribution and duration are sensitive to climate change. In this article, the surface water of the QTP in the recent 20 years was extracted from Landsat series TM/ETM+/OLI data combined with the Joint Research Centre of European Commission water body dataset. In addition, the temporal and spatial variation characteristics of the surface water area over the QTP were analyzed, and the climatic factors driving its dynamic change were explored based on 13 climatic variables. Results showed the following: First, from 2002 to 2020, the area of permanent water body in QTP increased from about 46 300 km2 to about 54 700 km2, showing the characteristics of increase in the north and decrease in the south. The area of seasonal water area decreased from about 8900 km2 to about 6600 km2, which were characterized by increase in the west and decrease in the east. Second, with 13 climatic variables reflecting the overall, seasonal, and extreme values of climate changes, the annual total precipitation had the strongest effect on the permanent water area over the total QTP, and the autumn mean air temperature was most relevant with the seasonal water area. Third, the predicted permanent water area of the QTP showed a trend of first slow and then fast increasing during 2021–2025, and expanded by 294 km2 in 2025 compared with 2021, whereas the seasonal water area first increased by 131 km2 and then decreased by 203 km2. These results will help provide important references for water resource management and ecological environment protection in QTP.
... For example, missing floodwater due to cloud cover/cloud shadow could be reduced by using microwave frequencies where clouds are more transparent. Additionally, small water fractions may be fixed using these satellite flood products with higher native spatial resolution Tulbure et al., 2016;Fisher et al., 2016;Chang et al., 2020). ...
As one of the costliest and most frequent natural disasters, flood is a major threat to human lives and property. Flood forecasting, simulation, and monitoring plays an important role in disaster relief and mitigation. With the support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, the VIIRS (Visible Infrared Imaging Radiometer Suite) global 375-m flood products in near real-time, daily composition, and 5-day composition have been developed and derived flood extent represented in water fractions from Suomi-NPP and NOAA-20. The products have shown good quality for the spatial distribution of floodwater. However, the moderate spatial resolution is a limiting factor when obtaining inundation detail. Moreover, no vertical information on the floodwater is included in the products. Based on water’s self-leveling nature, it is feasible to construct the vertical structure of floodwater using the VIIRS 375-m water fractions and high-resolution Digital Elevation Model (DEM) such as the 30-m Shuttle Radar Topography Mission (SRTM)/DEM through a downscaling process (Li et al., 2013). With impact factors including topography, land cover, tree cover, river network and watershed, the downscaling process has been integrated into a model called the Downscaling Model. By using the 30-m SRTM/DEM, the model can downscale the VIIRS 375-m floodwater fraction products into 30-m flood extent and water depth products. These products include flood information in both horizontal and vertical directions and thus are called 3-D flood products in this study. This paper presents a comprehensive introduction to the model with the results evaluated against high-resolution satellite images from Landsat-8 Operational Land Imager (OLI), Sentinel-2, and GeoEye, aerial photos, and river gauge observations. Evaluation results indicate reliable quality and promising performance of the model and the downscaled flood products. A routine system has been set up at the Cooperative Institute for Satellite Studies (CIMSS) at University of Wisconsin-Madison to generate experimental VIIRS downscaled flood extent and water depth products over the Continental USA (CONUS). The routinely generated VIIRS 30-m floodwater depth maps are available at: https://floods.ssec.wisc.edu/?products=VIIRS-3Dflood.
... With 30 m spatial resolution, the Landsat data offer greater potential than Moderate Resolution Imaging Spectroradiometer (MODIS, a coarser satellite sensor with 500 m spatial resolution) data for mapping and monitoring surface water bodies of almost all sizes (Huang et al., 2018). Recently, the Landsat data have been used for spatiotemporal mapping of surface water area at a global scale (Pekel et al., 2016;Pickens et al., 2020;Yao et al., 2018), national scales (Krause et al., 2021;Mueller et al., 2016;Sheng et al., 2016) and regional scales (Fuentes et al., 2020;Heimhuber et al., 2016;Tulbure et al., 2016;Zou et al., 2016). ...
Spatiotemporal dynamic information on surface water area and level is a prerequisite for effective wetland conservation and management. However, such information is either unavailable or difficult to obtain. In this study, for the first time, we leverage Landsat imagery, ICESat-2 and airborne LiDAR data to develop time series of water body dynamics over the last 35 years (1987–2021) using machine learning method on a cloud computing platform for lakes identified as international importance in the Western District Lakes Ramsar site in Victoria, Australia. Our results reveal distinct seasonal (dry and wet) variation patterns and long-term changes in trends of lake water areas and levels in response to seasonal rainfall variations and regional climate changes for the periods of before, during and after the Millennium Drought when southeast Australia experienced unprecedented dry conditions. Lake water bodies have not recovered to the status of pre-Millennium Drought, and many permanent Ramsar-listed lakes in the region have become to ephemeral lakes due to climate change. The outcome of this study provides a baseline to help understand the historical and ongoing status of the Ramsar-listed lakes in a warming and drying climate in support of the development of strategic plan to implement international obligations for wetlands protection under the Ramsar Convention.
... Numerous algorithms and datasets have been developed over the last decade focusing on the accurate estimation of surface water dynamics from optical satellite imagery 18,[58][59][60][61][62][63] . A key requirement to use those algorithms that can classify water where it is occluded due to clouds 24,64,65 . ...
Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.
... Surface water bodies (including rivers, lakes, reservoirs and ponds, etc.) are the core elements in agriculture, aquaculture, industrial production and aquatic and terrestrial eco-systems [1], and the changes they undergo are important indicators reflecting the impact of climate change and human activities on surface water resources [2]. In recent decades, important components of surface water, such as artificial water bodies comprising reservoirs, canals, fish farming ponds, mines, quarries, etc., have been increasing in several regions of the world due to the construction of new reservoirs, climate change and flood irrigation [3]. ...
Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China’s surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration.
... River morphology has been changing frequently over the last few decades due to climate change and anthropogenic factors (van Vliet et al. 2013;Grill et al. 2019). Particularly in ecologically fragile and densely populated areas, intensive human activity has severely affected and restricted the evolution of river morphology, and river ecology is facing increasing pressure (Tulbure et al. 2016;Yousefi et al. 2017;Zhang et al. 2019). In these areas, river morphology exhibits a high degree of nonlinearity and complexity (Agnihotri et al. 2020;Boota et al. 2021), and there is still no unified solution on how to accurately represent its morphological characteristics. ...
The morphological expression of rivers provides a primary medium for human understanding of river geomorphology and the transmission of geographical information. In an ever-changing environment, constantly updated river monitoring data and products offer considerable potential for an explicit expression of river morphological characteristics and associated processes. This paper reviewed the advances in river morphology expression and examines how the various approaches can be utilized to interpret changing geomorphic features of rivers. First, taking alluvial rivers as the research object, river morphology is classified into three types of expression data and four categories of expression models. Then, the limitations of current river morphology models, such as uncertainty, inconsistency, and poor joint application, are analyzed. Finally, four outlooks are offered for improving river morphology expression, including stimulating the expression of river morphology with big data of rivers, redefining different river types, promoting multidisciplinary and interdisciplinary integration, and serving scientific management and decision-making. HIGHLIGHTS
Constantly updated data and models offer great potential for expressing river morphology.;
The expression of river morphology faces the challenges of uncertainty, inconsistency, and insufficient joint application.;
Four outlooks are expected to enhance the future expression of river morphology.;
A systematic research framework for the morphological expression of rivers is recommended.;
... Recently, hydrological analyses of large areas have been trending toward more data-driven empirical approaches, because satellite imagery is the only way to assess water systematically over large spatial and temporal scales (Palazzoli & Ceola, 2020;Pekel et al., 2016;Perin et al., 2021;Tulbure & Broich, 2019;Wada et al., 2017;Walker et al., 2020). Surface-water-specific data sets derived from moderate resolution (30 m, Landsat) satellite imagery over 30-40 years are a relatively new development at the regional (Tulbure et al., 2016;Tulbure & Broich, 2013), national (Jones, 2015(Jones, , 2019, and global scales (Pekel et al., 2016;Pickens et al., 2020). The spatial and temporal scale of these surface water data sets, and the similar spatial and temporal scales of LULC (the Cropland Data Layer, CDL; "CropScape -NASS CDL Program"), population (LandScan; Rose et al., 2020), and climate data (Gridded Surface Meteorological data set; Abatzoglou, 2013), enable us to tackle the critical task of assessing the impact of climate and anthropogenic drivers on surface water. ...
Surface water is the most readily accessible water resource and provides an array of ecosystem services, but its availability and access are stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000–2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the southeastern United States. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean −0.17%) and the highest explained variance (R² 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land‐use policies can be highly effective tools in controlling surface water variations in the Southeast.
... It includes two indices, AWEI nsh , which works well when there are no shadows, and AWEI sh , which further distinguishes water pixels from shadow pixels. AWEI has been adopted in many recent studies for extracting water bodies from Landsat images [97,98]. ...
This study aims to assess the impact of climate change on water resources as a case study, Mosul Dam Lake by detecting the changes in Water Surface Area (WSA) and studying the relationship between climatological variables and lake area through employing Remote Sensing (RS) and Geographic Information System techniques (GIS).
Single-sensor monitoring of flood events at high spatial and temporal resolutions is difficult because of the lack of data owing to instrument defects, cloud contamination, imaging geometry. However, combining multisensor data provides an impressive solution to this problem. In this study, 11 synthetic aperture radar (SAR) images and 13 optical images were collected from the Google Earth Engine (GEE) platform during the Sardoba Reservoir flood event to constitute a time series dataset. Threshold-based and indices-based methods were used for SAR and optical data, respectively, to extract the water extent. The final sequential flood water maps were obtained by fusing the results from multisensor time series imagery. Experiments show that, when compare with the Global Surface Water Dynamic (GSWD) dataset, the overall accuracy and Kappa coefficient of the water body extent extracted by our methods range from 98.8% to 99.1% and 0.839 to 0.900, respectively. The flooded extent and area increased sharply to a maximum between May 1 and May 4, and then experienced a sustained decline over time. The flood lasted for more than a month in the lowland areas in the north, indicating that the northern region is severely affected. Land cover changes could be detected using the temporal spectrum analysis, which indicated that detailed temporal information benefiting from the multisensor data is highly important for time series analyses.
In water‐stressed hyperarid basins, questions mount over the impacts of anthropogenic groundwater extraction and climate‐driven perturbations on groundwater‐surface water interactions and the resilience of ecosystem‐critical surface water. Coupling groundwater with surface water observations from Sentinel‐2 data provides an unprecedented opportunity to evaluate surface water connectivity with local aquifers following intense precipitation events in arid basins. Surface water area and groundwater level data were analyzed for trends following precipitation, including peak lag time, post‐peak recession rates, and changes in hydraulic gradients. Results indicate variable connectivity following large precipitation events between surface water change and groundwater level fluctuations in the upgradient freshwater aquifer, whereas the downgradient brine‐to‐brackish area of the aquifer indicated virtually no connectivity with the aquifer. Comparison between precipitation and surface water response indicate distinct responses based on the physical relationship of the surface water body with the brine‐to‐brackish area of the aquifer. Lumped parameter modeling of surface water inundation also constrains the possible hydrologic dynamics of the post‐precipitation response. While modeled influx to surface water seems primarily controlled by watershed hydraulics rather than direct hydraulic connectivity of the aquifers, the relationship between surface water and adjacent groundwater levels coupled with surface water area indicates that local aquifers are primarily connected to the surface water bodies through discharge via subsurface infiltration. Modeling results imply that the existence of brine‐adjacent surface water in arid basins relies on upgradient discharge from freshwater aquifers. Our results further support that marginal surface water systems can serve as a critical recharge mechanism to local aquifers.
Australian inland riparian wetlands located east of the Great Dividing Range exhibit unique, hydroecological characteristics. These flood-dependent aquatic systems located in water-limited regions are declining rapidly due to the competitive demand for water for human activities, as well as climate change and variability. However, there exist very few reliable data to characterize inundation change conditions and quantify the impacts of the loss and deterioration of wetlands. A long-term time record of wetland inundation maps can provide a crucial baseline to monitor, assess, and assist the management and conservation of wetland ecosystems. This study presents a random forest-based multi-index classification algorithm (RaFMIC) on the Google Earth Engine (GEE) platform to efficiently construct a temporally dense, three-decadal time record of inundation maps of the southeast Australian riparian inland wetlands. The method was tested over the Macquarie Marshes located in the semiarid region of NSW, Australia. The results showed a good accuracy when compared against high-spatial resolution imagery. The total inundated area was consistent with precipitation and streamflow patterns, and the temporal dynamics of vegetation showed good agreement with the inundation maps. The inundation time record was analysed to generate inundation probability maps, which were in a good agreement with frequently flooded areas simulated by a hydrodynamic model and the distribution of flood-dependent vegetation species. The long-term, time-dense inundation maps derived from the RaFMIC method can provide key information to assess the condition and health of wetland ecosystems and have the potential to improve wetland inventory with spatially explicit water regime information. RaFMIC can be adapted over other dryland wetlands, as an effective semiautomated method of mapping long-term inundation dynamics.
The Shapotou National Nature Reserve in the Ningxia Hui Autonomous Region is a typical arid region in China. There is an exceptionally serious problem of surface water resource conservation, and dynamic monitoring of surface water with the help of water indices can help to elucidate its change patterns and impact mechanisms. Here, we analysed the characteristics of interannual variation in surface water area in the study area from 1992–2021. The correlation coefficients of the surface water area in the previous year and the contemporaneous water bodies of the Yellow River with the total surface water area (TSWA) were calculated. The results show the following: ① In terms of the classification accuracy of the two methods, water indices and support vector machine classification, water indices are more suitable for water body extraction in the study area. In particular, the three water indices, NDWI, MNDWI and AWEIsh, were more effective, with average overall accuracies of 90.38%, 90.33% and 90.36% over the 30-year period, respectively. ② From the TSWA extraction results from the last 30 years, the TSWA showed an increasing trend with an increase of 368.28 hm2. Among the areas, Tenggeli Lake contributed the most to the increase in TSWA. ③ The highest correlation between the TSWA and the previous year’s TSWA was 0.89, indicating that the better way to protect the water body is to maintain water surface stability year-round. The surface water area of the Yellow River and TSWA also showed a strong correlation, indicating that the rational use of Yellow River water is also an important direction for the future conservation of water resources in the study area.
Optical remote sensing images are a common data sources for surface water monitoring, while they are easily contaminated by clouds, cloud shadows, terrain shadows, etc., resulting in spatial gaps in surface water images. This paper proposes a surface water gap-filling method based on Naive Bayes classification. It uses the historical cloud-free binary (water, non-water) surface water images as prior data and the uncontaminated pixels in the partially contaminated ternary (water, non-water, contaminated pixels) surface water image as evidence to identify the category of gap pixels to achieve the purpose of gap-filling. This method considers the relationship between disconnected water bodies and does not depend on terrain data. When the image is heavily covered by clouds, this method can also reconstruct the complete water extent accurately. Five study areas with different scenarios including rivers, lakes or reservoirs, are selected to evaluate the method. Results show that the average gap-filling accuracy in all five study areas is over 90%. After gap-filling, the time series of surface water area presents a good correlation with the time series of water level (e.g., the coefficient of determination R2=0.95 in the Dartmouth reservoir). The proposed method is proved effective in filling gaps caused by clouds, cloud shadows and terrain shadows in surface water image, and it would be suitable for high-frequency surface water monitoring and near real-time surface water mapping.
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.
The presence of clouds and shadows and the low temporal resolution of the sensors canbe limiting factors for several analyses using satellite images. Considering this, the presentstudy aimed to verify if the integration of the MSI (Sentinel/2) and OLI (Landsat/8) images, aiming at the expansion of the data set, can improve the mapping of the surface waterextents of the artificial reservoirs. For this, spectral indices were calculated from images ofthe OLI and MSI and compared with useful water volume and water depth data collectedin situ in the Ceraíma reservoir (BA). The correlation (r) between the surface water extentvalues obtained by MSI images and the in situ variables useful water volume and relativewater depth are 0.80 and 0.78. While, between OLI and the useful water volume and rela-tive water depth variables, the correlations values are 0.59 and 0.58. And, between MSIand OLI, the correlation value is 0.89 and the index of agreement is 0.88. This study con-cluded that differences in spatial and temporal resolutions have a relevant influence onthe ability to integrate images from different satellites for quick and simple results. Thelow spatial resolution makes it difficult to accurately extract the reservoir contours, whilethe temporal one limits the number of images for extracting clouds and shadows.
The monitoring of floodplain productivity and dynamics in freshwater habitats, be it, riverine (e.g., rivers streams), lacustrine (large water holes in low elevation areas), or palustrine (swamps, intermittent floodplain water channels) systems is required to predict the impacts of hydro-meteorological fluctuations and the consequences of changes in flows on floodplain wetland ecosystems. This is essential among other things, for proactive water resources planning and the development of climate change mitigation and resource management strategies. However, such monitoring and assessment are complicated by the inaccessibility of many large wetland systems during times of inundation, particularly in remote parts of the world. This can make in-situ sampling difficult at a time when high levels of aquatic primary producers are generating food and energy sources for higher order consumers. This chapter provides some highlights on the concept and satellite applications in eco-hydrology, leveraging the outcome from the work undertaken by Ndehedehe et al. (2020a) over a large river floodplain in the Gulf region of northern Australia. This chapter also highlights critical aspects of mapping and monitoring changes in freshwater habitats and floodplains using satellite remote sensing. The overarching goal is to improve understanding of the link between ecological implications resulting from human intervention and climate change on wetland hydrology.
Knowledge of the location of surface water in time and space is critical to inform policy on the environment, wildlife, and human welfare. Dynamic surface water maps have been created at continental to global scales from medium and coarse resolution satellite image archives, most notably by Pekel et al. (2016), who mapped global water dynamics from 1984 to 2015 Landsat data. Their occurrence layer, depicting the percentage of time that water was observed in each 30 m resolution cell, has been relied upon heavily by Natural Resources Canada's (NRCan) Emergency Geomatics Service (EGS) to map surface water during flood events. To generate dynamic surface water maps that are optimized for Canada's unique geography, the fully automated EGS surface water mapping methodology was applied in a cloud environment to the 1984–2019 Landsat archive over Canada. National-scale surface water maps and derived inundation frequency akin to Pekel's occurrence, as well as inter-annual wetting and drying trends calculated using per-pixel logistic regression, were produced to form the complete dataset. Separate comparisons of our frequency layer with Pekel's occurrence layer and Canada's water base layer from the National Hydrographic Network (NHN) were conducted. The comparison with Pekel's occurrence showed that our product contains a large number of unique water objects, the majority of which are correct when assessed against Google Earth (GE) imagery. Comparison with the NHN indicated that the NHN contained a large number of permanent water objects that were mapped as ephemeral water objects in our product, with the majority of these being verified in GE as true ephemeral water objects such as floodplains and wetlands. Wetting trends were found to be more than five times greater than drying trends across Canada, with notable wetting in the Prairie Pothole region and Low Arctic verified with examples of statistically significant wetting and drying features. The dataset will enhance EGS flood and river ice mapping operations, provide information on floodplain location and extent, and give insight into the effects of climate change on surface water availability.
Surface water, which refers to water stored in rivers, streams, lakes, reservoirs, ponds, and wetlands, is a precious resource in terms of biodiversity, ecology, water management, and economics. As a significant hydrological parameter, surface water storage (SWS) influences the exchange of water and energy between the land/water surface and atmosphere. The quantification of SWS and its dynamics is crucial for a better understanding of global hydrological and biogeochemical processes. For more than 30 years, Earth observation (EO) technology has shown that SWS can be measured to some degree, and a variety of techniques have been proposed to facilitate this purpose.
The Yellow River Basin (YRB) has been facing severe water shortages; hence, the long-term dynamic monitoring of its surface water area (SWA) is essential for the efficient utilization of its water resources and sustainable socioeconomic development. In order to detect the changing trajectory of the SWA of the YRB and its influencing factors, we used available Landsat images from 1986 through to 2019 and a water and vegetation indices-based method to analyze the spatial–temporal variability of four types of SWAs (permanent, seasonal, maximum and average extents), and their relationship with precipitation (Pre), temperature (Temp), leaf area index (LAI) and surface soil moisture (SM).The multi-year average permanent surface water area (SWA) and seasonal SWA accounted for 46.48% and 53.52% in the Yellow River Basin (YRB), respectively. The permanent and seasonal water bodies were dominantly distributed in the upper reaches, accounting for 70.22% and 48.79% of these types, respectively. The rate of increase of the permanent SWA was 49.82 km2/a, of which the lower reaches contributed the most (34.34%), and the rate of decrease of the seasonal SWA was 79.18 km2/a, of which the contribution of the source region was the highest (25.99%). The seasonal SWA only exhibited decreasing trends in 13 sub-basins, accounting for 15% of all of the sub-basins, which indicates that the decrease in the seasonal SWA was dominantly caused by the change in the SWA in the main river channel region. The conversions from seasonal water to non-water bodies, and from seasonal to permanent water bodies were the dominant trends from 1986 to 2019 in the YRB. The SWA was positively correlated with precipitation, and was negatively correlated with the temperature. Because the permanent and seasonal water bodies were dominantly distributed in the river channel region and sub-basins, respectively, the change in the permanent SWA was significantly affected by the regulation of the major reservoirs, whereas the change in the seasonal SWA was more closely related to climate change. The increase in the soil moisture was helpful in the formation of the permanent water bodies. The increased evapotranspiration induced by vegetation greening played a significant positive role in the SWA increase via the local cooling and humidifying effects, which offset the accelerated water surface evaporation caused by the atmospheric warming.
The manuscript discusses the problem of water scarcity engendered by climate change impact through our test area, the Ain Zada Dam (North-East Algeria). The latter has witnessed a noticeable water-level decrease during the last years. Therefore, the status of the water surface is in a critical condition and the dam is in need of continuous follow-up survey. The present paper aims to model the spatiotemporal changes of the Ain Zada Dam’s water surface in the period 2015–2018 using remote sensing. To reach this end, NDWI (Normalized Difference Water Index) algorithm, which is a reference for water body extraction techniques on Sentinel-2 Satellite, is used for detecting spatiotemporal changes of the dam under study. The results obtained showed that the use of the NDWI provides an accurate water body mapping in the region for the time-series Sentinel-2 images, with an average overall accuracy of 98.57%. Also, the findings demonstrated that the dam’s surface area has steadily decreased between 2015 and 2018, where the dam has lost about two thirds of its water surface area, which corresponds to about 77% of the water size since the 2015 winter. These major repercussions are due mainly to the climate change and human factors that have affected the water supply as well. Considering that water shortage is one of the biggest future environmental issues in Algeria, the findings of the present study should be taken into account vis-à-vis any future potential development in the area, and they could be further enhanced or complemented by future research on the environmental impact of water shortage, which is relevant to the Algerian context.
Dynamics in surface water area (SWA) and its impacts on desertification are poorly understood in the desertification-prone region (DPR) of China. We defined 2440 sub-watersheds at the fifth-order level in China’s DPR, and investigated the spatiotemporal variations in SWA and their substantial driving factors (such as temperature, precipitation, evaporation, and irrigation activity). The results showed that the average annual maximum SWA was 7165 km² in DPR from 2000 to 2019, of which 84% was the seasonal SWA. The seasonal and permanent SWA increased by 188 km² yr⁻¹ and 46 km² yr⁻¹, respectively. The SWA in non-DPR was the dominant driving factor for SWA dynamics in most basins except for HunlunBuir, Otindag and Ordos, where the irrigation activity showed significant contributions to SWA variations. Climate had a relatively low contribution to SWA variations in DPR. The role of SWA variations on desertification processes in DPR of China showed significant spatiotemporal differences from 2000 to 2019. The increased SWA promoted reversal of desertification in watersheds along the Tarim River, northwestern Junggar, eastern Ordos, and parts of Otindag, however, it did not help in northwestern Qinghai. Our findings provide a scientific insight into the characteristics of surface water resources and the relationship between SWA dynamics and desertification in the DPR of China.
Seasonal changes of temperature and precipitation cause inland open surface water and ice cover extents to vary dramatically through the year from local to global scales. These dynamics of land, water, and ice have a significant impact on climate and often are critical to natural ecosystem functioning. However, global seasonal dynamics of both water and ice extent have not been well quantified. Here, we present the quantification of monthly surface water and ice areas for 2019 with associated uncertainties. Time-series reference data were created for a probability sample of 10 m grid cells by interpreting the entire 2019 time-series of 10 m Sentinel-2 data and a subset of 3 m PlanetScope data in selected places with a mix of land and water. From the probability sample reference data, we estimate that 4.86 ± 0.16 million km² had inland water presence at some point during the year. Globally, only 23% of the total area with water was permanent water that remained open year-round (1.13 ± 0.19 million km²). Permanent water with seasonal ice cover extended 1.97 ± 0.21 million km², comprising 41% of the total area with water. Seasonal water-land transitions (both with and without ice/snow cover) covered the remaining 36% of the total area with water (1.76 ± 0.19 million km²). February had the maximum extent of ice over areas of inland permanent and seasonal water, totaling 2.49 ± 0.25 million km², and January – March had a larger global extent of ice cover than of open water. To investigate the spatiotemporal distribution of ice cover and the suitability of Landsat, prototype maps of surface water ice cover phenology were created by integrating the ice/snow and no data labels from the quality assurance layer of the GLAD ARD of Potapov et al. (2020) with the monthly surface water layers of Pickens et al. (2020), both of which are Landsat-based. While limited by data availability, these maps reveal the high spatiotemporal variability of ice phenology. The near-daily observations near the poles and the 10 m resolution bands of Sentinel-2 provide unprecedented potential to examine surface water and ice dynamics for 2016 forward and to investigate the drivers and impacts of this variability.
As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km2 and 6129.55 km2, respectively. The maximal surface water extent fell rapidly from 7897.96 km2 in 2001 to 5087.46 km2 in 2020, with a loss speed of 155.41 km2 per year (R2 = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km2 and 2064.99 km2 during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R2 = 0.98, slope = 36.41 km2 per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R2 = 0.86, slope = 0.99 km2 per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide.
This paper assesses the hydrological response to scenarios of climate change in the Okavango River catchment in Southern Africa. Climate scenarios are constructed representing different changes in global mean temperature from an ensemble of 7 climate models assessed in the IPCC AR4. The results show a substantial change in mean flow associated with a global warming of 2 °C. However, there is considerable uncertainty in the sign and magnitude of the projected changes between different climate models, implying that the ensemble mean is not an appropriate generalised indicator of impact. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation. There is also a clear need to evaluate the physical mechanisms associated with the model projected changes in this region. The implications for water resource management policy are considered.
As this edition of The World’s Water goes to press in early 2011, eastern Australia is recovering from devastating floods that claimed more than 20 lives and destroyed hundreds of homes. The heavy rains of 2009 and 2010 that caused so much destruction also marked the end of Australia’s decade-long Millennium Drought. Beginning in about 1997, declines in rainfall and runoff had contributed to widespread crop failures, livestock losses, dust storms, and bushfires. Such are the vagaries of water on the continent with the world’s most uncertain and variable climate.
Historical sources of information were examined to develop a picture of the structure of River Red Gum Eucalyptus camaldulensis forests of the southern Murray-Darling Basin prior to European settlement. We sought information on the density and distribution of fallen timber (grounded logs and limbs ≥ 10 cm diameter). None of these potential sources yielded much useable information to estimate fallen timber loads prior to European settlement. There is good evidence that the structure and demography of red gum forests has been significantly altered since the 1830s, with the former parklands of large, veteran trees > 500 yr being replaced by ranks of smaller, younger trees. Large trees are more likely to produce larger amounts of fallen timber, so that the landscape-scale changes in demographics coupled with the massive reduction of the area of floodplain forest are likely to have produced a much lower total fallen timber load across the whole Murray-Darling basin. Alterations of flooding and wildfire regimes, and the incessant demands for large amounts of firewood are likely to maintain the paucity of fallen timber compared with the early part of the 19th century. The current status of fallen timber in the Barmah-Millewa forest is also described.
Following extreme flooding in eastern Australia in 2011, the Australian Government established a programme to improve access to flood information across Australia. As part of this, a project was undertaken to map the extent of surface water across Australia using the multi-decadal archive of Landsat satellite imagery. A water detection algorithm was used based on a decision tree classifier, and a comparison methodology using a logistic regression. This approach provided an understanding of the confidence in the water observations. The results were used to map the presence of surface water across the entire continent from every observation of 27years of satellite imagery. The Water Observation from Space (WOfS) product provides insight into the behaviour of surface water across Australia through time, demonstrating where water is persistent, such as in reservoirs, and where it is ephemeral, such as on floodplains during a flood. In addition the WOfS product is useful for studies of wetland extent, aquatic species behaviour, hydrological models, land surface process modelling and groundwater recharge. This paper describes the WOfS methodology and shows how similar time-series analyses of nationally significant environmental variables might be conducted at the continental scale.
The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.
Ecologists and space agencies must forge a global monitoring strategy, say Andrew K. Skidmore, Nathalie Pettorelli and colleagues.
Context: Landscape-scale research quantifying ecological connectivity is required to maintain the viability of populations in dynamic environments increasingly impacted by anthropogenic modification and environmental change.
Objective: To evaluate how surface water network structure, landscape resistance to movement, and flooding affect the connectivity of amphibian habitats within the Murray–Darling Basin (MDB), a highly modified but ecologically significant region of south-eastern Australia.
Methods: We evaluated potential connectivity network graphs based on circuit theory, Euclidean and least-cost path distances for two amphibian species with different dispersal abilities, and used graph theory metrics to compare regional- and patch-scale connectivity across a range of flooding scenarios.
Results: Circuit theory graphs were more connected than Euclidean and least-cost equivalents in floodplain environments, and less connected in highly modified or semi-arid regions. Habitat networks were highly fragmented for both species, with flooding playing a crucial role in facilitating landscape-scale connectivity. Both formally and informally protected habitats were more likely to form important connectivity “hubs” or “stepping stones” compared to non-protected habitats, and increased in importance with flooding.
Conclusions: Surface water network structure and the quality of the intervening landscape matrix combine to affect the connectivity of MDB amphibian habitats in ways which vary spatially and in response to flooding. Our findings highlight the importance of utilising organism-relevant connectivity models which incorporate landscape resistance to movement, and accounting for dynamic landscape-scale processes such as flooding when quantifying connectivity to inform the conservation of dynamic and highly modified environments.
The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2 of inland water globally—nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal “inland water” biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer (MODIS) water mask and Landsat-based national land cover datasets was very high, with commission errors < 4% and omission errors < 14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds—effects which were compounded by differences in image-acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset (GIW) is available for open access at the GLCF website (http://www.landcover.org).
Wetlands play an important role in the provision of ecosystem services, ranging from the regulation of hydrological systems to carbon sequestration and biodiversity habitat. This paper reports the mapping of Indonesia’s wetland cover as a single thematic class, including peatlands, freshwater wetlands, and mangroves. Expert-interpreted training data were used to identify wetland formations including areas of likely past wetland extent that have been converted to other land uses. Topographical indices (Shuttle Radar Topography Mission-derived) and optical (Landsat) and radar (PALSAR) image inputs were used to build a bagged classification tree model based on training data in order to generate a national-scale map of wetland extent at a 60 m spatial resolution. The resulting wetland map covers 21.0% (39.6 Mha) of Indonesia’s land, including 25.2% of Sumatra (11.9 Mha), 22.9% of Kalimantan (12.2 Mha), and 28.9% of Papua (11.8 Mha). Results agree with existing image-interpreted products from Indonesia’s Ministries of Forestry and Agriculture and Wetlands International (89% overall agreement), and with the Ministry of Forestry forest inventory data for Sumatra and Kalimantan (91% overall agreement). An internally consistent algorithm-derived national wetland extent map can be used to quantify changing rates of land conversion inside and outside of wetlands. Additionally, wetlands extent can be used to efficiently allocate field resources in national assessments of wetland sub-types such as peatlands, which are a current focus of policies aiming to reduce carbon emissions from land use change.
Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN).
We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans.
The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999–2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified 'stepping stone' water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as 'rich get richer' algorithm. The 'stepping stone' water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as 'stepping stone' over time may help prioritize surface water bodies that are essential for maintaining regional scale connectivity.
Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.
Key Points
Remote sensing produced an accurate wetland map for the Ethiopian highlands
Wetlands were associated with spatial variability in malaria risk
Mapping and monitoring wetlands can improve malaria spatial decision support
Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing.
To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continent-wide investigation and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia.
Results showed high inter- and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.
An accurate description of the abundance and size distribution of lakes is critical to quantifying limnetic contributions to the global carbon cycle. However, estimates of global lake abundance are poorly constrained. We used high-resolution satellite imagery to produce a GLObal WAter BOdies database (GLOWABO), comprising all lakes greater than 0.002 km2. GLOWABO contains geographic and morphometric information for ~117 million lakes with a combined surface area of about 5 million km2, which is 3.7% of the Earth’ non-glaciated land area. Large and intermediate-sized lakes dominate the total lake surface area. Overall, lakes are less abundant, but cover a greater total surface area relative to previous estimates based on statistical extrapolations. The GLOWABO allows for the global-scale evaluation of fundamental limnological problems, providing a foundation for improved quantification of limnetic contributions to the biogeochemical processes at large scales.
Context
Frog species are now targets for delivery of high-value managed environmental flows on floodplains. Information on the drivers of frog presence and abundance is required to support adaptive management, including analysis of the roles of flood frequency, flood timing and habitat type.
Aims
This paper describes frog species richness and abundance responses to flooding and habitat type in the Barmah Forest, part of the largest river red gum forest in the world.
Methods
Surveys were conducted at 22 sites over six years, to determine species presence, relative abundance, and evidence of breeding. Data were then used to examine temporal patterns within and between wet and dry years and spatial relationships with site geomorphology, vegetation form, and wetting frequency.
Key results
Six species were common and widespread, and three were rare. The seasonal timing of peak numbers of calling males differed between species. The seasonal pattern of calling for each species did not differ between wet and dry years, however significantly lower numbers of frogs were recorded calling in dry years. The number of frogs calling was significantly higher in well-vegetated grassy wetlands. Evidence of a positive relationship between wetting frequency and numbers of calling males was found for Limnodynastes fletcheri, Crinia signifera and Limnodynastes dumerilii. The abundance of tadpoles was significantly higher in wet years.
Conclusions
The seasonal timing of flooding in Barmah Forest will influence the breeding success of individual species with different preferences. Flooding from September to December is required to cover most preferred breeding seasons, but longer durations may be required to maximise recruitment. This, together with regular flooding of well-vegetated grassy wetland habitat, will increase the likelihood of species persistence and maximise diversity. Insufficient flooding frequency will result in reduced frog species richness and abundance.
Implications
Managed flooding is important for frog abundance and species richness. This study emphasises the value of key habitats such as well-vegetated grassy wetlands and reinforces the need to make their preservation a priority for management. It has identified knowledge gaps to drive future data collection for improved modelling, including a need for further research on flow regime change and frog communities.
Before being used in scientific investigations and policy decisions, thematic maps constructed from remotely sensed data should be subjected to a statistically rigorous accuracy assessment. The three basic components of an accuracy assessment are: 1) the sampling design used to select the reference sample; 2) the response design used to obtain the reference land-cover classification for each sampling unit; and 3) the estimation and analysis procedures. We discuss options available for each of these components. A statistically rigorous assessment requires both a probability sampling design and statistically consistent estimators of accuracy parameters, along with a response design determined in accordance with features of the mapping and classification process such as the land-cover classification scheme, minimum mapping unit, and spatial scale of the mapping.
Temporal stability, defined as the change of accuracy through time, is one of the validation aspects required by the Committee on Earth Observation Satellites’ Land Product Validation Subgroup. Temporal stability was evaluated for three burned areaproducts: MCD64, Globcarbon, and fire_cci. Traditional accuracy measures, such as overall accuracy and omission and commission error ratios, were computed from reference data for seven years (2001–2007) in seven study sites, located in Angola, Australia, Brazil,Canada, Colombia, Portugal, and South Africa. These accuracy measures served as the basis for the evaluation of temporal stability of each product. Nonparametric tests were constructed to assess different departures from temporal stability, specifically a monotonic trend in accuracy over time (Wilcoxon test for trend), and differences in median accuracy among years (Friedman test). When applied to the three burned area products, these tests did not detect a statistically significant temporal trend or significant differences among years, thus, based on the small sample size of seven sites, there was insufficient evidence to claim these products had temporal instability. Pairwise Wilcoxon tests comparing yearly accuracies provided a measure of the proportion of year-pairs with significant differences and these proportions of significant pairwise differences were in turn used to compare temporal stability between BA products. The proportion of year-pairs with different accuracy (at the 0.05 significance level) ranged from 0% (MCD64) to 14% (fire_cci), computed from the 21 year-pairs available. In addition to the analysis of the three real burned area products, the analyses were applied to the accuracy measures computed for four hypothetical burned area products to illustrate the properties of the temporal stability analysis for different hypothetical scenarios of change in accuracy over time. The nonparametric tests were generally successful at detecting the different types of temporal instability designed into the hypothetical scenarios. The current work presents for the first time methods to quantify the temporal stability of BA product accuracies and to alert product end-users that statistically significant temporal instabilities exist. These methods represent diagnostic tools that allow product users to recognize the potential confounding effect of temporal instability on analysis of fire trends and allow map producers to identify anomalies in accuracy over time that may lead to insights for improving fire products. Additionally, we suggest temporal instabilities that could hypothetically appear, caused by for example by failures or changes in sensor data or classification algorithms.
Major rivers worldwide have experienced dramatic changes in flow, reducing their natural ability to adjust to and absorb disturbances. Given expected changes in global climate and water needs, this may create serious problems; including loss of native biodiversity and risks to ecosystems and humans from increased flooding or water shortages. Here, we project river discharge under different climate and water withdrawal scenarios and combine this with data on the impact of dams on large river basins to create global maps illustrating potential changes in discharge and water stress for dam-impacted and free-flowing basins. The projections indicate that every populated basin in the world will experience changes in river discharge and many will experience water stress. The magnitude of these impacts is used to identify basins likely and almost certain to require proactive or reactive management intervention. Our analysis indicates that the area in need of management action to mitigate the impacts of climate change is much greater for basins impacted by dams than for basins with free-flowing rivers. Nearly one billion people live in areas likely to require action and approximately 365 million people live in basins almost certain to require action. Proactive management efforts will minimize risks to ecosystems and people and may be less costly than reactive efforts taken only once problems have arisen.
Timely and accurate monitoring of forest disturbance is essential to help us understand how the Earth system is changing. MODIS (Moderate Resolution Imaging Spectroradiometer) imagery and subsequent MODIS products provide near-daily global coverage and have transformed the ways we study and monitor the Earth. To monitor forest disturbance, it is necessary to be able to compare observations of the same place from different times, but this is a challenging task using MODIS data as observations from different days have varying view angles and pixel sizes, and cover slightly different areas. In this paper, we propose a method to fuse MODIS and Landsat data in a way that allows for near real-time monitoring of forest disturbance. The method is based on using Landsat time-series images to predict the next MODIS image, which forms a stable basis for comparison with new MODIS acquisitions. The predicted MODIS images represent what the surface should look like assuming no disturbance, and the difference in the spectral signatures between predicted and observed MODIS images becomes the “signal” used for detecting forest disturbance. The method was able to detect subpixel forest disturbance with a producer's accuracy of 81% and a user's accuracy of 90%. Patches of forest disturbance as small as 5 to 7 ha in size were detected on a daily basis. The encouraging results indicate that the presented fusion method holds promise for improving monitoring of forest disturbance in near real-time.
The accuracy of a land cover classification is the degree to which the map land cover agrees with the reference land cover classification (i.e. ground condition). The basic sampling designs historically implemented for map accuracy assessment have served well for the error matrix based analyses traditionally used. But contemporary applications of land cover maps place greater demands on accuracy assessment, and sampling designs must be constructed to target objectives such as accuracy of land cover composition and landscape pattern. Sampling designs differ in their suitability to achieve different objectives, and trade-offs among desirable sampling design criteria must be recognized and accommodated when selecting a design. An overview is presented of the sampling designs used in accuracy assessment, and the status of these designs is appraised for meeting current needs. Sampling design features that facilitate multiple-objective accuracy assessments are described.
We review the human actions, proximal stressors and ecological responses for floodplain forests Australia's largest river system---the Murray-Darling Basin. A conceptual model for the floodplain forests was built from extensive published information and some unpublished results for the system, which should provide a basis for understanding, studying and managing the ecology of floodplains that face similar environmental stresses. Since European settlement, lowlands areas of the basin have been extensively cleared for agriculture and remnant forests heavily harvested for timber. The most significant human intervention is modification of river flows, and the reduction in frequency, duration and timing of flooding, which are compounded by climate change (higher temperatures and reduced rainfall) and deteriorating groundwater conditions (depth and salinity). This has created unfavorable conditions for all life-history stages of the dominant floodplain tree (Eucalyptus camaldulensis Dehnh.). Lack of extensive flooding has led to widespread dieback across the Murray River floodplain (currently 79% by area). Management for timber resources has altered the structure of these forests from one dominated by large, widely spreading trees to mixed-aged stands of smaller pole trees. Reductions in numbers of birds and other vertebrates followed the decline in habitat quality (hollow-bearing trees, fallen timber). Restoration of these forests is dependent on substantial increases in the frequency and extent of flooding, improvements in groundwater conditions, re-establishing a diversity of forest structures, removal of grazing and consideration of these interacting stressors.
The assessment of the role of lakes and impoundments at regional and global scales, e.g., in biogeochemical cycles, requires good estimates of the areal extent and shape of water bodies. Upscaling to large regions, except in limited areas where precise maps are available, so far depends on statistical estimates of the number and size of lakes, which explains why estimates are poor. We present an automated procedure that allows mapping of the actual number, size, and distribution of lakes at large scale. Landsat 7 Enhanced Thematic Mapper Plus (ETM +) mosaics from the GeoCover Circa 2000 dataset covering the Earth land surfaces with 14.25 m spatial resolution were used as input data. We developed an approach called GWEM (GeoCover (TM) Water bodies Extraction Method) that combines remote sensing and GIS to extract water bodies and study their abundance and morphometry. All water bodies greater than 0.0002 km(2) were taken into account as lakes. The accuracy of the method was tested on Sweden, where detailed maps of lakes, based on in situ data and orthophotos, exist for the whole country. The proposed method produced accurate results. The largest sources of errors are shadows of mountains and clouds, since the GeoCover mosaics are not absolutely cloud free.
Growing an ensemble of decision trees and allowing them to vote for the most popular class produced a significant increase in classification accuracy for land cover classification. The objective of this study is to present results obtained with the random forest classifier and to compare its performance with the support vector machines (SVMs) in terms of classification accuracy, training time and user defined parameters. Landsat Enhanced Thematic Mapper Plus (ETM+) data of an area in the UK with seven different land covers were used. Results from this study suggest that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time. This study also concludes that the number of user‐defined parameters required by random forest classifiers is less than the number required for SVMs and easier to define.