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Mapping potential, existing and efficient wetlands using free remote sensing data
Abstract
Although wetlands remain threatened by human pressures and climate change, monitoring and managing them are challenging due to their high spatial and temporal dynamics within a fine-grained pattern. New satellite time-series at high temporal and spatial resolutions provide a promising opportunity to map and monitor wetlands. The objective of this study was to develop an operational method for managing valley bottom wetlands based on available free remote sensing data. The Potential, Existing, Efficient Wetlands (PEEW) approach was adapted to remote sensing data to delineate three wetland components: (1) potential wetlands, mapped from a digital terrain model derived from LiDAR data; (2) existing wetlands, delineated from land cover maps derived from Sentinel-1/2 time-series; and (3) efficient wetlands, identified from functional indicators (i.e. annual primary production, vegetation phenology, seasonality of carbon flux) derived from MODIS annual time-series. Soil and vegetation samples were collected in the field to calibrate and validate classification of remote sensing data. The method was applied to a 113 000 ha watershed in northwestern France. Results show that potential wetlands were successfully delineated (82% overall accuracy) and covered 21% of the watershed area, while 44% of existing wetlands had been lost. Small wetlands along headwater channels, which are considered as ordinary, cover 56% of wetland area in the watershed. Efficient wetlands were identified as contiguous pixels with a similar temporal functional trajectory. This method, based on free remote sensing data, provides a new perspective for wetland management. The method can identify sites where restoration measures should be prioritized and enables better understanding and monitoring of the influence of management practices and climate on wetland functions.
... Fourthly, interlinkages between natural and anthropogenic pressures and wetlands are often complex, making it difficult to reveal their relationships (Thamaga et al., 2022). Relevant EO data can be translated into meaningful information for wetland management for the prioritization of wetlands for conservation , restoration (Rapinel et al., 2019), agricultural development (Dossou-Yovo et al., 2017;Gumma et al., 2016), and impact scenario building (Gabiri et al., 2019;Näschen et al., 2019). Improving, but still incomplete databases reflect the continued need to enhance mapping, characterization, and monitoring of wetlands in Africa. ...
... The efficient wetland category describes ecosystem properties (Merot et al., 2006). In wetland monitoring and management, this categorization can deliver information on wetland loss and on changes in wetland functional categories and was adopted in a remote sensing-based study of wetlands in north-eastern France (Rapinel et al., 2019). Accordingly, the basis of our wetland characterization framework consists of a potential wetlands map (potential wetlands), from which actual wetland boundaries are derived (existing wetlands). ...
... The delineation approach proposed in Chapter 2 is based on topographic wetland probability, spectral indices that reflect water and wetland vegetation, and image segmentation. Such approaches are frequently used in large-scale wetland delineation with optical , or optical and radar remote sensing (Ludwig et al., 2019;Muro et al., 2020;Rapinel et al., 2019), as they work across various wetland types and minimize confusion with upland vegetation. ...
Africa’s wetlands are diverse and highly productive ecosystems that hold significant potential to enhance food production. However, sustainable management is essential to mitigate negative impacts from agricultural use. While considerable progress has been made in understanding wetland ecosystems, knowledge gaps persist, particularly for small and intermittent wetlands. Advances in Earth observation (EO), especially through the Copernicus Program, offer opportunities to address these gaps with high-resolution satellite data.
This thesis explores how EO can support sustainable management of African wetlands to improve food security. It investigates aspects of wetland ecosystems and management along the terrestrial-aquatic continuum. Firstly, key requirements were identified and a framework of four information layers designed—Wetland Delineation, Land Use/Land Cover (LULC), Surface Water Occurrence (SWO), and Wetland Use Intensity (WUI)—based on Copernicus imagery. SWO and WUI captured wetlands dynamics, while LULC and WUI revealed drivers like land conversion and intensification. Secondly, the WUI layer was further analysed for Rwanda, demonstrating its utility in monitoring pressures on wetlands at national and local scales. Cloud computing enhanced the efficiency of WUI calculation, increasing applicability for wetland management. Thirdly, Sentinel-2 imagery was used to model water quality by assessing small reservoir turbidity in dammed wetlands in Kenya. The models were calibrated using both laboratory-grade equipment and low-cost sensors as affordable alternatives for in-situ measurements. While the laboratory-based calibrations demonstrated high accuracy, the low-cost sensors achieved moderate agreement. The study underscores the potential of EO-based assessments for efficient turbidity monitoring in small, often unmonitored water bodies. Fourthly, an analysis of small reservoirs in Kenya further explored the use of modelled turbidity time series as an indicator of natural and human impacts on wetlands using machine learning. Key drivers of annual turbidity included windspeed and topography, while long-term turbidity was influenced by land cover and WUI, highlighting the importance of effective land management strategies.
Overall, EO provides relevant information and enables integration across scales, and quantification of interlinkages. Future research should enhance understanding of small, seasonally inundated wetlands and scalable water quality assessments while leveraging high-resolution data, cloud computing, and in-situ measurements to promote sustainable wetland management for improved food security.
... In terms of data sources, remote-sensing data used for wetland information extraction can be broadly categorized into optical data and Synthetic Aperture Radar (SAR) data (Deng, Jiang, Ling, et al. 2023;Rapinel et al. 2019;. Optical data, which includes multispectral data from various platforms such as Landsat (Halabisky et al. 2016;Kovács, Horion, and Fensholt 2022;Reschke and Hüttich 2014;X. ...
... Sentinel-1 carries a C-band synthetic aperture radar capable of providing surface backscatter coefficients, while Sentinel-2, equipped with a multispectral instrument, offers not only visible and near-infrared bands but also red-edge and shortwave infrared bands. Furthermore, data from the Sentinel series are openly accessible and freely available (M. Wang et al. 2023), leading to an increasing number of researchers undertaking wetland mapping studies using Sentinel-1/2 data (Ashourloo et al. 2022;Rapinel et al. 2019). For example, Slagter et al. (2020) established a wetland stratification extraction framework utilizing the Random Forest algorithm, applying temporally dense Sentinel-1/2 data for information extraction from the St. Lucía wetlands in South Africa. ...
... Therefore, employing feature selection algorithms to eliminate redundant features is crucial (Schratz et al. 2021). Although combining multi-source features and feature selection for wetland mapping has been extensively studied (Rapinel et al. 2019;Slagter et al. 2020;Xing et al. 2023), the inclusion of H-Alpha (α) features derived from Sentinel-1 Dual-Pol Decomposition has received less attention in terms of its contribution to mapping accuracy. Thus, incorporating H-α features into the construction of a multi-source, multi-feature ensemble for wetland mapping merits further exploration. ...
... Wetlands are among the most biologically productive natural ecosystems that play critical role in maintaining and improving water quality, mitigating floods, reaching aquifers, and providing habitat for wildlife (Kaplan and Avdan 2017;Pal and Talukdar 2018;Singh and Sinha 2022). Also, as a transitional between terrestrial and open-water aquatic ecosystems, they contain open water bodies, vegetation, and mixture (Kaplan and Avdan 2017;Xu et al. 2020), that adapted to flooding or waterlogging and associated conditions of restricted aeration (Perennou et al. 2018;Rapinel et al. 2019). The functions that wetlands provide are widely recognized by scientists, citizens and stakeholders (Salomaa et al. 2018). ...
... However, environmental and human factors threatening the wetland ecosystems (Jamal and Ahmad 2020). In this regard, human activities such as intensive agriculture and urbanization continue to threaten these valuable ecosystems (Baker et al. 2006;Maltby and Acreman 2011;Heintzman and McIntyre 2019;Rapinel et al. 2019). Therefore, maintain their services, wetlands need to be protected from rapid transformation and overuse (Amler et al. 2015). ...
... Although wetlands remain threatened by human pressures and climate change, monitoring and managing them is challenging due to their high spatial and temporal dynamics within a fine-grained pattern (Haidary et al. 2013;Rapinel et al. 2019). Generally, land use land cover change (LULCC) is one of the major driving factors for regional and global environmental change that has a significant effect on water resources (Li et al. 2016). ...
Wetlands in warm Mediterranean regions are strongly influenced by land use land cover change (LULCC). Studying the effects of LULCC on wetlands can prevent further destruction of these valuable ecosystems in the future. Here, LULCC effect on hydrological changes in Ajigol, Almagol and Alago international wetlands in northern Iran, was investigated using Long-term hydrological impact assessment (L-THIA) model and satellite images (Landsat 5, 7 and 8) during 1987–2018. The L-THIA model estimates direct runoff based on the CN method for LULCC classes using multi-time satellite images. Wetland water volume calibrated based on inflow and outflow, and its balance was investigated using STELLA method. The results showed that the most LULCC was observed in agricultural lands and residential areas with an overall accuracy and kappa coefficient of 0.93 and 0.8, respectively. During the evaluation period, the area of Almagol, Alagol and Ajigol wetlands decreased by 78.0, 74.0 and 23.0%, respectively. The L-THIA model showed that LULCCs have directly affected the three wetlands over time. A correlation coefficient (R-squared) of 0.70, 0.64 and 0.60 was observed between modeled and actual water volume in Almago, Ajogol and Alagol wetlands, respectively, under LULCCs. Also, the water volume of Almagol, Alagol and Ajigol wetlands decreased by 51.0, 42.0 and 52.0%, respectively. Overall, ecological land management can lead to physical protection of wetlands.
... These variables can be derived from either satellite [39] or airborne [40] remote sensing data. This approach is particularly appropriate for supporting national mapping since it identifies most wetlands, including those covered by common types of vegetation or those that have been damaged and where restoration could be initiated [41]. This second approach was first applied at spatial resolutions of 1 km over Europe [42] and 50 m over France [43], and more recently 500 m over the entire globe [35], 30 m over Rwanda [44] and Albania [45], and up to 25 m over Europe [46]. ...
... Several studies have emphasized the need to use airborne DTMs with vertical accuracy of ca. 0.2 m and spatial resolution of 1-5 m to identify small wetlands, including those under forest cover [14,41,[48][49][50]. These studies were conducted at the local scale and should be extended to the national scale, especially since open-access national airborne DTMs are increasingly available [41]. ...
... 0.2 m and spatial resolution of 1-5 m to identify small wetlands, including those under forest cover [14,41,[48][49][50]. These studies were conducted at the local scale and should be extended to the national scale, especially since open-access national airborne DTMs are increasingly available [41]. ...
While wetland ecosystem services are widely recognized, the lack of fine-scale national inventories prevents successful implementation of conservation policies. Wetlands are difficult to map due to their complex fine-grained spatial pattern and fuzzy boundaries. However, the increasing amount of open high-spatial-resolution remote sensing data and accurately georeferenced field data archives, as well as progress in artificial intelligence (AI), provide opportunities for fine-scale national wetland mapping. The objective of this study was to map wetlands over mainland France (ca. 550,000 km2) by applying AI to environmental variables derived from remote sensing and archive field data. A random forest model was calibrated using spatial cross-validation according to the precision-recall area under the curve (PR-AUC) index using ca. 135,000 soil or flora plots from archive databases, as well as 5 m topographical variables derived from an airborne DTM and a geological map. The model was validated using an experimentally designed sampling strategy with ca. 3000 plots collected during a ground survey in 2021 along non-wetland/wetland transects. Map accuracy was then compared to those of nine existing wetland maps with global, European, or national coverage. The model-derived suitability map (PR-AUC 0.76) highlights the gradual boundaries and fine-grained pattern of wetlands. The binary map is significantly more accurate (F1-score 0.75, overall accuracy 0.67) than existing wetland maps. The approach and end-results are of important value for spatial planning and environmental management since the high-resolution suitability and binary maps enable more targeted conservation measures to support biodiversity conservation, water resources maintenance, and carbon storage.
... This index is a reliable indicator of wetland ecosystem functioning, such as carbon storage and fluxes [26] or habitat support [27]. ANPP has high spatial and temporal variability in wetlands and responds to human and climate disturbances [28,29]. ANPP can be derived from continuous satellite-based time series of the NDVI, as the latter, which is strongly correlated with aboveground net primary productivity, is a linear estimator of the fraction of photosynthetically active radiation absorbed by vegetation, the main driver of primary production [30]. ...
... To investigate the influence of LULC on NDVI-I accuracy, we used the 2021 ESA WorldCover map [39] instead of the CCDC classification output to ensure independence between LULC and NDVI-I values. The cumulative RMSE of the synthetic NDVI images for 2021, the LULC classes, and the number of Landsat cloud-free observations available in 2021 (divided into five classes using the Jenks natural breaks method: very small (0-6), small (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19), moderate (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), large , and very large (64-113)) were extracted for each Landsat pixel at each site. Under-represented (<100 pixels per site) combinations of LULC class and observation number (e.g., grasslands and very large) were removed from the analysis. ...
The long-term monitoring of wetland ecosystem functioning is critical because wetlands, which provide multiple services, can be affected by human activities and climate change. The aim of this study was to monitor wetland ecosystem functioning in the long term using the Landsat archive. Four contrasting, Ramsar wetlands were selected in boreal, temperate, arid, and tropical areas. First, the annual sum of the normalized difference vegetation index (NDVI-I) was calculated as an indicator of annual net primary productivity for the period 1984–2021 using the continuous change detection and classification (CCDC) algorithm. Next, the influence of the number of Landsat images and class of land use and land cover (LULC) on the accuracy of the CCDC was investigated. Finally, correlations between annual NDVI-I and climate were analyzed. The results revealed that NDVI-I accuracy was influenced mainly by the LULC class and to a lesser extent by the number of cloud-free Landsat observations. Infra- and inter-site variations in NDVI-I were high and showed an overall increasing trend. NDVI-I was positively correlated with the mean temperature. This study shows that this approach applied in contrasting sites is robust for the long-term monitoring of wetland ecosystem functioning and can be used to improve the implementation of international biodiversity conservation policies.
... Multi-source geospatial data have been widely applied in potential wetland distribution modeling research at various scales. Currently, Lidar data has been successfully utilized in potential wetland simulation studies at small scales (Hird et al. 2017;Rapinel et al. 2019), while large-scale potential wetland distribution models often rely on various terrain indices derived from DEM data. Zhu and Gong (2014) employed the Compound Topographic Index (CTI) along with hydrological and climatic data to obtain the first high-resolution (1km) global potential wetland distribution dataset, excluding Antarctica. ...
... Previously, Mao et al. (2020) found that wetlands in China are mainly distributed below 200 m (42.8%) and above 3000 m (28.1%) in altitude. In addition, Rapinel et al. (2019) also found that most wetlands are in low-lying lands with poor drainage and ponding and relatively flat terrain. Other studies also show that hydrological factors are the largest contributors to potential wetland distribution, with factors including surface temperature, wind speed, air temperature, wetland, precipitation, and other climate factors. ...
Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China. To protect and restore wetlands, it is urgent to predict the spatial distribution of potential wetlands. In this study, the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms. Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic, soil, vegetation, and topographic factors, a simulation model was constructed by machine learning algorithms. The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good, with an area under the receiver operating characteristic curve value of 0.851. The area of potential wetlands was 332,702 km², with 39.0% of potential wetlands in Northeast China. Geographic features were notable, and potential wetlands were mainly concentrated in areas with 400–600 mm precipitation, semi-hydric and hydric soils, meadow and marsh vegetation, altitude less than 700 m, and slope less than 3°. The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.
... The popularity of remote sensing-based approaches for monitoring land features is on the rise due to their capacity to furnish detailed information about the land surface across extensive areas (Kafy et al., 2021;Liu et al., 2020). Such monitoring methods are commonly utilized to detect changes in land cover/use within wetlands and to assess their overall health (Aghsaei et al., 2020;Assefa et al., 2021;Tu and Baykal, 2023;Thamaga et al., 2022;Rapinel et al., 2019). ...
Wetlands provide biodiversity conservation, carbon sequestration, recreation services, and contribute to the health and resilience of the overall ecosystem. It is of great importance to protect wetlands to ensure that these benefits are sustainable and to mitigate the effects of climate change. Overall, monitoring environmental changes in wetlands plays a vital role in understanding, managing, and safeguarding wetland environments and resources. Accessible platforms providing diverse RS&GIS integrated methodological approaches to continental/national land-cover changes provide important information about the direction and causes of land-cover changes as well as their possible effects on ecosystems, biodiversity, and human societies. Open-source platforms and data are frequently used to collect and evaluate data quickly and efficiently. This has successfully implemented land monitoring and provided fresh opportunities for Earth observation applications. According to the 2020 official data, 93 wetlands in Türkiye are recognized as being of national/international significance (and 14 of these wetlands are protected under the Ramsar Convention) and were selected as the study area. The focus of our evaluation is on the LULC classes and conversions of the Türkiye Wetland, as well as the 22-year (2000–2022) LULC vegetation gain and loss land determination. This article examines Türkiye Wetland environment monitoring using Collect Earth (CE), a free open-source and user-friendly software tool developed by the Food and Agriculture Organization of the United Nations (FAO), a land monitoring platform powered by Google Earth Engine (GEE). The primary objectives of this study are threefold: (1) to ascertain Wetland land cover/use classes; (2) to identify land cover/use conversion and direction in wetland areas with different statuses; and (3) to delve into potential factors driving shifts in wetland land cover/use, particularly the loss-gain areas. The anticipated outcomes of the study, an enhanced comprehension of factors influencing regional wetlands in the context of environmental conservation, water management, climate change mitigation, and economic benefits, will empower planners and policymakers to formulate appropriate strategies for the long-term conservation of these vital ecosystems. Furthermore, this study example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring, can be used to rapidly and sustainably build capacity for land monitoring, and to substantively improve our collective understanding of Wetland land use and land cover.
... Compared to Landsat-1, MODIS has a higher temporal resolution, which can retrieve seasonal changes in large wetlands. The C-band radar is often used to detect water bodies that are hidden beneath vegetation, particularly for the purpose of identifying marshes [12], for example, Sentinel-1 is equipped with a C-band Synthetic Aperture Radar (SAR) to capture polarization characteristics, which is commonly used to distinguish different land cover types in wetlands [13]. A nationwide inventor was established IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING in 1975 by the U.S. Fish and Wildlife Service and mainly conducted through manual interpretation by aerial photographs [14]. ...
Highly sensitive to climate change and glacier melt, alpine wetlands on the Tibetan Plateau have important implications for carbon storage and animal habitat. However, long-term accurate mapping and changes of wetlands on the Tibetan Plateau remain knowledge gaps. Here, we qualify wetlands' spatial and temporal changes between 1990 and 2020 using Landsat imagery with an optimized random forest model. We find that the total area of marsh, floodplain, and swamp wetlands was approximately 8.25×10
4
(2.68% of the plateau area), 6.99×104 (2.27%), and 8.47×10
4
km
2
(2.75%) in 1990, 2000, and 2020, respectively. The total area of wetlands decreased by about 1.29×104 km2 (-15.7%) between 1990 and 2000 but increased by about 1.52×10
4
km
2
(21.7%) between 2000 and 2020. Marsh and floodplain wetlands show a decrease followed by an increase, while swamps continue to increase. The high-density wetlands are mainly distributed in the Yangtze and Yellow River basins, especially with marsh and floodplain wetlands >40%, and Brahmaputra high-density swamps ∼40%. Conversion of wetland types occurred in a relatively high proportion of all periods, but there was a gradual downward trend. The proportion of conversions from wetlands to non-wetlands decreased and the proportion of conversions within wetlands increased. This study elucidates the distribution characteristics, area change, and type conversion of wetlands on the Tibetan Plateau, which provides scientific support for the development and use of alpine wetlands and governmental risk decision-making.
... To better understand the dynamics of these changes, over the last two decades, earth observation data from remotely sensed platforms has been widely used to quantify land system changes around wetlands (Kandus et al., 2018;Rapinel et al., 2019;Shukla et al., 2023). This technological advancement allows researchers to gather extensive data, and the integration of this information with field-based observations has provided valuable inputs for geospatial models, offering new insights into wetland dynamics (Mondal et al., 2017;Saha & Pal, 2019). ...
... Na bacia selecionada, nota-se uma frequência significativa de feições do relevo que remetem às atividades tectônicas recentes, como cristas alinhadas, cotovelos ou trechos retilíneos de drenagem (ETCHEBEHERE et al, 2004;SORDI et al., 2018). Assim, se faz possível pressupor a ações de falhas e zonas de cisalhamento e suas eventuais reativações neotectônicas no rearranjo do sistema fluvial, nos moldes do relatado por Heilbron et al (2004), Tupinambá et al (2007), Tupinambá, Teixeira e Heilbron (2012), Rezende (2013) e Marques Neto et al (2022). Logo, o recorte espacial escolhido se mostra como um cenário instigador para o estudo de pequenas áreas úmidas. ...
Wetlands are hydrogeomorphological systems formed by the prolonged saturation of topographic surfaces with water, either temporarily or permanently. The study of small wetlands in Brazil is perceived as a gap to be filled, given the barriers to understanding these systems and the geomorphological, geological, and hydrological contexts that surround them. The overall objective of this work is to elucidate the environmental factors that contribute to the formation of wetlands in the 'Mares de Morros' morphoclimatic domain.
... To extend the amount of usable data and thus potentially further refine the spatiotemporal resolution, there is the possibility to use active satellite sensors. An example is the study of Rapinel et al. (2019), who have used a combination of data and data products from active and passive sensors to derive information about potential, existing and efficient wetlands. The authors utilized LiDAR Digital Terrain Models (DTM) for defining potential wetlands based on topographical characteristics. ...
This work was produced upon request of Sovon, Dutch centre of Field Ornithology as part of the project ‘Innovations for Migratory Bird Monitoring Along the East Atlantic Flyway’.
The ‘East Atlantic flyway’ (EAF) is one of the world's most prominently used migratory routes and serves as a critical pathway for numerous avian migrants. Since 2014 a flyway-wide simultaneous monitoring is conducted every three years, providing estimates for total population numbers. The most recent data (2020) shows that 30% of the EAF water-bird populations showed a long-term and 29% a short-term declining trend. Staging sites along the flyway may be exposed to a range of pressures, which may contribute to these negative trends. To gain insights into the global and local effects of the environment on bird populations, it is important to gather spatially explicit data along the complete flyway.
Novel developments in sensors, platforms and analytical tools open a range of opportunities for monitoring these spatiotemporal dimensions via remote sensing. In this report, we assessed the possibilities and opportunities to apply remote sensing solutions to monitoring of habitats and anthropogenic pressures along the EAF. One promising approach is the utilisation of satellite data, which offers global coverage and extends to poorly accessible areas. We give an overview of available satellite data and data products, indicating their main technical characteristics and limitations (technical overview). Taking the technical specifications and the monitoring demands into account, we then formulate recommendations that can guide implementation of remote-sensing based monitoring of habitats along the flyway.
... Regarding the biodiversity indicators, Fractional Vegetation Cover (FVC) is an NDVI derived indicator serving as proxy to vegetation structure and primary productivity [32]. Moreover, the NDVI-based indicators of the Integrated NDVI (NDVIIntegral), Intra-Annual Relative Range (NDVIIARR) and Date of annual Mode (NDVIDoM), were selected as proxies to net primary production [33], seasonality of carbon fluxes [34], and vegetation phenology and diversity [35], respectively [36]. Additionally, Plant Phenology Index (PPI) is a DVI based indicator, using Red and NIR bands, and is was selected to model phenological diversity [37]. ...
Conservation and management of biological diversity is central to human health and well-being. The EU-funded LIFE project "hELlenic BIOodiversity Information System: An innovative tool for biodiversity conservation - LIFE EL-BIOS" project aims to contribute in EU and national policies by designing, developing and implementing a central biodiversity information system, "EL-BIOS", operated by the Greek Natural Environment and Climate Change Agency (NECCA). In this work, we present the development framework of the EL-BIOS EO Data Cube, a service that provides access to biodiversity indicators and variables in analysis ready form, based on Open Data Cube framework for big data management and web service applications. Using freely available Copernicus Sentinel-2 imagery, six national-scale and four local-scale biodiversity indicators are generated in a spatial and temporal systematic framework for assessing and monitoring biodiversity state and condition.
... Table 1 highlights some of the algorithms reviewed and applicable to GDEs. Depending on the objectives of their study, several studies (e.g., Laba et al., 2010;Farda, 2017;Mahdavi et al., 2018;Han et al., 2018;Rapinel et al., 2019;Ghosh and Das, 2020) have employed machine-learning algorithms to elucidate GDE dynamics. The most often utilized methods in the evaluation and monitoring of ecosystems such as wetlands are support vector machine (SVM) and random forest (RF) ( Table 1). ...
Groundwater-dependent ecosystems (GDEs) are important ecological systems that provide a range of supporting, cultural, provisioning, and regulating services. However, GDEs are increasingly vulnerable to climate variability and change, which can lead to changes in groundwater levels, vegetation structure, and ecosystem functioning. Remote sensing techniques offer a powerful tool for monitoring the effects of climate variability on GDEs, and the data can be used to map and monitor GDEs, assess their health and condition, and identify changes over time. By collecting data on vegetation health, water availability, and other environmental factors, remote sensing can help to identify GDEs at risk, track changes over time, and develop strategies to protect these valuable ecosystems. This chapter reviews the application of remote sensing techniques to monitor climate variability effects on GDEs. It discusses the different types of remote sensing data that can be used as well as the various methods that can be applied to analyze the data, including challenges and opportunities. These challenges include the need for ground-based data to calibrate and validate remote sensing data, the difficulty of distinguishing GDEs from other types of ecosystems, and the need to develop new methods for analyzing the complex relationships between GDEs, climate variability, and other environmental factors. Thus, by using remote sensing, we can better understand the impacts of climate change on these important ecosystems and develop effective management strategies to protect them. Overall, the chapter concludes that remote sensing has the potential to play a vital role in monitoring and protecting GDEs in the face of climate variability and change. By providing regular and comprehensive data on these ecosystems, remote sensing can help researchers and managers to make informed decisions about how to conserve and protect GDEs for future generations.
... Vegetation in this study is defined as a system of largely spontaneously growing plants ( Van der Maarel, 2005) which refers to any form of plant with green pigment including actively growing crops. Though there was an increase in the availability of satellite data in the twenty-first century (Ochsner et al., 2013), the high cost of fine-resolution satellite data for small wetland mapping hindered the derivation of detailed information on wetland soil moisture over larger geographical areas (Rapinel et al., 2019). The introduction of satellite-based remote sensing, however, led to the development of scientific models for monitoring soil moisture based on various active and passive satellite sensors (Petropoulos et al., 2015;Kirimi et al., 2016). ...
... Existing fine-grained wetland classifications are mainly used in the field of wetland mapping. For example, Rapinel et al. (2019)obtained wetland maps for a 113,000-ha watershed in northwestern France using a digital terrain model of LiDAR data, a Sentinel-1/2 time series and a MODIS annual time series. Hubert-Moy, Fabre, and Rapinel (2020) used SPOT-7 multitemporal imagery to map fine-grained patterns of 470 vegetation classes in 11 ha of fresh marsh (France). ...
Methods for fine-grained sample collection are essential for detecting land cover changes at large scales. The complexity of wetland types increases the difficulty of obtaining training samples for high-precision wetland changes, while existing methods mainly focus on coarse-grained classification of urban areas, ignoring the physical growth cycle of vegetation. To solve the above problems, we propose a method for phenological knowledge transfer-based fine grained land cover change sample collection (PKT). Taking the Yellow River Delta as an example, the experimental results are shown as follows. (1) The overall accuracy of the results of the PKT method is 77.03%, and k is 0.42, which is better than the results of the other methods. (2) The PKT method is able to obtain the area of wetland change more accurately and can identify the wetland type changes in the area of change. (3) Making full use of multisource data and fine-grained category information can effectively improve the accuracy of change training samples. (4) Changes in coastal wetlands are the result of the interaction between natural factors and human activities. (5) Further restoration and management of wetlands can be carried out in terms of appropriate protective measures and restrictions on construction behavior.
... La combinaison de ces indicateurs avec d'autres indicateurs permet d'évaluer et de cartographier l'intensité de fonctions hydrologiques, biogéochimiques et écologiques des MH (Figure 45). Au-delà de la représentation spatiale des fonctions, l'intérêt de cette approche réside dans la possibilité de caractériser les variations inter annuelles des fonctions expliquées par les variations climatiques et/ou la gestion des sites (Rapinel et al., 2019b). L'objectif de ce volet est d'utiliser cette approche pour produire un support d'aide à la décision et de suivi des plans de gestion ou de restauration de sites de quelques hectares. ...
Background and Objectives: While numerous challenges are related to wetlands management, there is an ongoing demand for a robust and comprehensive national map of these ecosystems. In this context, the objectives of this study are to locate, delineate and characterize wetlands across mainland France, with the aim of developing a baseline map layer at 1:10,000 scale (equivalent to 5-meter spatial resolution) for stakeholders and wetlands practitioners. The approach developed to achieve these objectives encompasses three steps: 1) delineation of wetlands over the entire French mainland territory; 2) mapping of semi-natural and human-made habitats within the potential wetlands layer in 10 watersheds; 3) calculation of functional indicators from satellite imagery on the 10 selected watersheds in collaboration with local stakeholders. The originality and innovative aspect of this approach lies in the combination of both exclusive use of open field and remote sensing data, using artificial intelligence (AI) algorithms, and the collaboration between the study authors and local stakeholders involved in wetland management.
Step 1 - location and delineation of wetlands at the national scale: This operational step involves five environmental variables characterizing topography, hydro-system networks, and parental material (geology). Soil and flora field measurements were derived from archive databases and combined with the environmental variables to evaluate the probability of 5-meter resolution pixels belonging to wetlands and their presence/absence using an AI-based random forest model and experts knowledge. The accuracy of the wetland maps was assessed using independent field data collected in 2021 and 2022. The final result was also compared to 10 other existing global, European, or national similar wetland products. The probability map highlights the wetness gradient and the fine spatial pattern of wetlands (PR-AUC of 0.79). The presence/absence map of wetland is significantly more accurate (F1-score of 0.73) than existing similar layers. A combination of our wetland maps with a 2020 land-use / land-cover map and a map of protected areas in France also enables us to determine that approximately half of the wetland area has been degraded or lost due to agricultural intensification and urban sprawl. We also found that 90% of the wetlands impacted by these changes do not benefit from any protection.
Step 2 - Mapping semi-natural and anthropogenic habitats within wetland in 10 pilot watersheds: The five environmental variables used in step 1 were combined with a vegetation height variable and spectral variables derived from satellite time series. These variables were then crossed with field measurements from archive databases to map habitats according to the EUNIS level 3 classification system in the 10 pilot watersheds using a random forest algorithm. The accuracy of the habitat map was evaluated by cross-validation. Results reveal: (1) substantial inter- and intra-habitat variability in modelling accuracy; (2) a decrease in map accuracy as the number of habitats to model increased (R² = 0.51, p-value = 0.02); (3) a minimum of one hundred field measurements per habitat was necessary to achieve satisfactory modelling accuracy (F1-score > 0.75). The probability of presence of some habitats was correctly predicted and spatialized, which enables to define conservation objectives and associated pressures for assessing, monitoring, or managing wetlands at both national or local (i.e. watershed) scales. However, the accuracy of the combined habitat map (10m) is currently insufficient for use at a very local scale (i.e. site), but will be improved following R&D work to be carried out in 2023-2024.
Step 3 - Production of functional indicators: This R&D step is based on a vegetation index (NDVI) calculated from Sentinel-2 time series over two consecutive years. Three functional indicators were derived from this vegetation index across the 10 selected watersheds to emphasize the variability of functions in wetlands over time and space: NDVI-I (or "total NDVI") : primary productivity; RREL (or "NDVI regularity") : seasonality of carbon fluxes ; MMAX (or "maximum NDVI") : phenology. Results analysed at several test sites underscore the impact of meteorological conditions and management practices on the "habitat support" ecological function and the "carbon sequestration" biogeochemical function.
... This first mapping step allows the delineation of areas where wetland ecosystems could occur with a high level of probability, using mainly environmental criteria (Weise et al. 2020;Rapinel et al. 2023). The delineation of PWA often relies on topographic indices or their combination with EObased data (Beven and Kirkby 1979;Bwangoy et al. 2010;Ågren et al. 2014;Hiestermann and Rivers-Moore 2014;Ludwig et al. 2019;Rapinel et al. 2019). The present assessment is based on the methodology developed by the MWO (Weise et al 2020). ...
Mediterranean wetlands are the richest and most productive ecosystems in the region, and are essential for climate change adaptation and mitigation. However, despite their importance, they have suffered significant destruction over time. We estimate that half of the natural wetlands have been lost since the 1970s, and the regional trend shows no signs of slowing down. It is therefore urgent to implement concrete solutions that can preserve the remaining wetlands and restore those that have been lost. The increasing availability of free and open Earth Observation (EO) data and tools has provided a basis for mapping these ecosystems and monitoring their status and trends. In this paper, we show how EO-based data and tools can support the pre-identification of candidate sites for wetlands restoration at large scale through the mapping and delineation of existing and lost wetland habitats, their current land use status, and the estimation of the efforts needed to recreate the lost and transformed ones. We used this approach in the Sebou river basin in Morocco and the transboundary Medjerda watershed between Algeria and Tunisia. The resulting products, i.e., Potential Wetland Areas and Potentially Restorable Wetlands maps, enabled the identification of more than 7000 km ² and 1700 km ² of lost wetland habitats that could be regained in the Sebou and Medjerda basins, respectively. These results hold immense value for water resources management and land planning as they can enhance and assist prioritization efforts for wetland restoration at local, national, and regional scales. They can serve as baseline data to identify candidate sites to implement wetland restoration actions as Nature-based Solutions, regenerate their habitats, and restore the ecosystem services they provide to society.
... It is important to note that the calculation of the topographical moisture index has some limitations, as this index does not take into account the nature of surface deposits, so it is mainly used to pre-locate "potential" wetlands. 65,66 To refine the result, we exclude the areas classified as wetlands that are located in impervious areas (urbanised areas or roads) by using a mask produced beforehand. ...
Wetlands are one of the most important ecosystems in the world. Today, however, their fate is under serious threat, and their alarming decline highlights the urgent need to preserve these areas rich in biodiversity. The aim of this work is to spatially map and mapping the wetlands of the Crozon peninsula in Brittany France. The methodology is divided into two parts; the first part is to map the wetlands as a whole, while the second part is to map the wetlands using a adapted typology. Several databases were used to spatialize the wetlands: 12 Sentinel-2 images in L3A format, 23 Sentinel-1 VV and VH images and the RGE Alti (DTM at 1 metre resolution). The images were processed and stacked alone or in synergy. A Random Forest (RF) machine learning algorithm was then trained to predict wetlands in our study area using binary training data. The training data were obtained from a wetland inventory conducted in Brittany, distributed at the scale of a Sentinel-2 tile (30UUU). Post-processing was then carried out on the best result: binary morphological erosion and thresholding based on the DTM to remove outliers. We carried out two classifications, which we later merged. The classifications were carried out using a Pleiades time series (five dates) to achieve a very fine scale classification. A classification of 13 land cover classes with six different wetland types (mudflats, salt marshes, coastal lagoons, wet meadows, wet forest, swamps/bogs) was performed using three methods: pixel-by-pixel random forest, object-based random forest and Convolutional Neural Network (CNN). The best results obtained was for the pixel-based classification: kappa = 0.89, overall accuracy = 0.90, F1-score = 0.90.
... Remote sensing images at different temporal and spatial resolutions, including Landsat (Sheng et al., 2016), Sentinel (Jia et al., 2021), and MODIS(Feng et al., 2012), are widely used in wetland mapping. In addition, in order to improve the accuracy of wetland extraction, some research used multi-source remote sensing data to identify different wetlands (Zhao and Qin, 2020;Rapinel et al., 2019;Pan et al., 2022). Considering the complexity of wetlands, an appropriate method should be chosen to define the various wetlands (Adam et al., 2010). ...
Urban wetlands play a crucial role in sustainable social development. However, current research mainly focuses on specific wetland types, and fine extraction of urban wetlands remains a challenge. This study proposes a fine extraction framework based on hierarchical decision trees and shape features for urban wetlands, using Sentinel-2 remote sensing data to obtain detailed wetland data of Wuhan and Nanchang from 2016 to 2022. Our framework applies random forests to classify land cover, extracts urban fine wetlands by hierarchical decision trees and shape features, and assesses the dynamics of wetlands in the two cities. We also analyzed and discussed the characteristics of urban wetlands in the two cities. The results show that wetland accuracies of Wuhan and Nanchang are greater than 84.5 % and 82.9 %, respectively. The wetland areas of Wuhan in 2016, 2019, and 2022 are 1969.4 km2, 1713.8 km2, and 1681.1 km2, while those in Nanchang are 1405.9 km2, 1361.6 km2, and 766.9 km2. Inland wetlands are the main wetland types in both regions, with lake wetlands accounting for the highest proportion (over 40 %). The urban wetlands in the two cities exhibit different spatial and temporal evolution patterns, with varying change trends of wetland area and the structural proportions of fine wetlands. Besides, Wuhan's urban wetlands are primarily located in the south, while Nanchang's urban wetlands are concentrated in the east, exhibiting higher spatial and temporal dynamics. Analysis suggests that the reduced urban wetlands from 2016 to 2022 are related to fluctuating decreasing precipitation, growing population, and gross domestic product (GDP). Our study provides support for the conservation of urban wetland resources in Wuhan and Nanchang and highlights the need for targeted management strategies.
... Based on the ability of the Random Forest algorithm to take into account all aspects of input and computation that are repeated as needed [19]. to produce more accurate classification results, both are used for the classification of diverse land cover [10], [20], [21]. ...
The amount of rainfall in a watershed with steep slopes, small cross-sectional areas, and less water catchment areas. This will cause an increase in water discharge in the river which can cause flooding. These characteristics can be found in Mata Allo River, Enrekang Regency. To identify the most flood-hit areas, the simulating model can be done utilizing the HEC-RAS program. Use of Satellite Imagery Data such as Sentinel-2 for extracting land use data information, and Sentinel-1 for data extraction of actual water bodies/rivers. The analysis is carried out by integrating the interpretation results from multi-sensor images with the results of modeling the flood inundation area using HEC-RAS. Based on the analysis results, the land use classification accuracy is 82.9% for Sentinel-2 data using the random forest algorithm. While for the actual extraction of water bodies using Sentinel-1 imagery was 89.6%. Approaching the threshold value between water and non-water bodies is taken using -13.39. The inundation area in the study area reached 87.66ha at the largest discharge model. The most affected land use after integrating each data is built-up land, most of which are settlements covering an area of 47.26ha.
... Soil is one of the main factors that determine the formation of wetlands (Tiner, 2017). The results of this study confirm that the ST was very important for the formation of wetlands, which was consistent with the results of Horvath et al. (2017) and Rapinel et al. (2019). In addition, Mitsch and Gosselink (2007) also emphasized that hydric soil is one of the key characteristics of wetlands. ...
Mapping potential wetlands provides a promising approach to get such information rapidly, and thus is of great significance to understanding ecosystem sustainability and support wetland conservation and restoration. This study proposed a new processing pipeline to map potential wetlands in the Yangtze River Basin, the largest basin in China, by combining a random forest (RF) algorithm and an indicator system constituted by several indicators, including vegetation, soil, terrain, and climatic features. Results reveal that slope, annual precipitation (APRE), digital elevation model (DEM), normalized difference vegetation index (NDVI), and annual mean temperature (AMT) are the most important variables affecting the distribution of potential wetlands, with a relative importance value of 7.5 %, 5.9 %, 5.5 %, 5.2 %, and 5.2 %, respectively. Mapping potential wetlands in the Yangtze River Basin was achieved using the RF model with overall accuracy of 79.31 % and Kappa coefficient of 0.58. The estimated total area of potential wetlands in this basin is approximately 39,677 km2, mainly distributed in the Yalong River watershed, the Dongting Lake watershed, and the regions bordering main streams of the Yangtze River. The proposed approach in this study evidenced its generalizability in terms of the good accuracy and distribution consistency with the natural wetlands observed from satellites and field investigation. We expect that this approach can be further used to generate potential wetland datasets at a broader scale in a long time series and benefit the evaluation of Sustainable Development Goals (SDGs).
... Wetlands were classified as grasslands in CLUD, natural wetlands in Mao's study, and wetlands in this study did not include paddy field, which resulted those differences in the estimation of wetland area. Furthermore, the spatial resolution of the wetland maps has an impact on the estimation of wetland area, and there is more uncertainty in the image data with lower spatial resolution (Rapinel et al., 2019). Consequently, the differences in data sources and wetland classification systems result in differences in the findings of different scholars, and the estimation of wetland areas in China can provide an important data base for wetland research and management (Table 3). ...
Wetlands are one of the most productive ecosystems on earth and play an important role in maintaining ecological balance and regulating climate. In this paper, 30 m resolution land use/land cover (LULC) data (2000, 2005, 2010, 2015, 2020) were employed to analyze the characteristics of spatial and temporal changes in the distribution of wetlands in China and their evolution patterns in the last two decades. The results indicated that the total area of wetlands in China showed an increasing trend during 2000–2020. During the study period, provinces with more increase in wetland area were concentrated in Qinghai Province, Tibet Autonomous Region and Xinjiang Uygur Autonomous Region. And the provinces with more reduction in wetland area were mainly in Inner Mongolia Autonomous Region. The analysis of wetland change driving mechanism by introducing PLS-SEM model and GWR model found that good climate conditions as well as agricultural and economic conditions are favorable for wetland conservation in 2000–2020, while accelerated urbanization and population growth showed negative effects on wetland change. The spatial distribution pattern of wetland and NPP (Net Primary Production) changes showed spatial consistency in the extent of NPP changes and wetland transfer. The results of this study are intended to provide a basis for wetland conservation, rational use of wetland resources and scientific restoration in China.
... Furthermore, random forest classifiers are able to better integrate detailed and data complex vectors, similar to the segments our approach generates, in comparison to other non-parametric classifiers such as support vector machines [39]. While community-level classifications of coastal vegetation communities have been achieved with freely accessible sensors, such as Sentinel-2, these yielded accuracies that were lower than our approach with the pay-for-service Worldview-2 sensor, which is an important consideration for wetland restoration projects that are limited by funding constraints [90]. ...
Coastal wetlands are restored to regenerate lost ecosystem services. Accurate and frequent representations of the distribution and area of coastal wetland communities are critical for evaluating restoration success. Typically, such data are acquired through laborious, intensive and expensive field surveys or traditional remote sensing methods that can be erroneous. Recent advances in remote sensing techniques such as high-resolution sensors (<2 m resolution), object-based image analysis and shallow learning classifiers provide promising alternatives but have rarely been applied in a restoration context. We measured the changes to wetland communities at a 200 ha restoring coastal wetland in eastern Australia, using remotely sensed Worldview-2 imagery, object-based image analysis and random forest classification. Our approach used structural rasters (digital elevation and canopy height models) and a multi-temporal technique to distinguish between spectrally similar land cover. The accuracy of our land cover maps was high, with overall accuracies ranging between 91 and 95%, and this supported early detection of increases in the area of key ecosystems, including mixed she-oak and paperbark (10 ha), mangroves (0.91 ha) and saltmarsh (4.31 ha), over a 5-year monitoring period. Our approach provides coastal managers with an accurate and frequent method for quantifying early responses of coastal wetlands to restoration, which is essential for informing adaptive management in the regeneration of ecosystem services.
... Support Vector Machines (SVM) proved their performance when utilised on Sentinel-2 and WorldView-2 (Araya-L opez et al. 2018) as well as Sentinel-2, Landsat-8, and RapidEye data (Jakovljevi c et al. 2019). DTM thresholding, Random Forest (RF) classification, and index exploitation were gathered as the Potential, Existing, Efficient Wetlands (PEEW) approach by Rapinel et al. (2019) to detect different types of wetlands using LiDAR, Sentinel-1, Sentinel-2, and MODIS annual time series, respectively. Ludwig et al. (2019) presented tile-based dynamic thresholding of water/wetness indices created from the Sentinel-2 time series as another alternative. ...
Wetlands are of great importance to the diversity of biota and ecology, thereby to humans. Monitoring such valuable areas is essential for sustainable development. When the sizes, geographic distribution, and total coverage of wetlands across the earth are taken into account, remote sensing shines out as the most economically and technically feasible method to realise the monitoring task. Concerning the utilisation of medium resolution satellite images as the input, the pixel-level approach falls short of understanding the wetland dynamics since vast amounts of pixels in such areas have mixed content. This study proposes a framework for determining the extent of wetlands and extracting their ground characteristics at the sub-pixel level. In the extent determination part, Tasselled Cap Water Index (TCWI) values are calculated on time series, and their variations throughout the year are modelled by fitting a double-sided sigmoid function. This information is coupled with Digital Terrain Model (DTM) thresholding to extract the final extent. A sub-pixel analysis is proposed for the latter part, which includes adopting a systematic approach using a three-element (soil, vegetation, water) scheme for establishing wetland ontology and implementing supervised spectral unmixing enhanced by band weight optimisation. Balıkdamı, one of the most impressive wetlands of Turkey, is chosen as the test area. Open-access optical satellite data acquired by the Sentinel-2 constellation are utilised as the primary data input. Since the abundance values of land cover classes in each Sentinel-2 pixel are estimated, reference abundance data with a 10 m ground sampling distance (GSD) are generated using four-band aerial images having a 30 cm GSD for the verification stage. A new method entitled ‘Abundance Confusion Matrix’ is introduced for comparison and detailed assessment of fractional land cover. Experimental results demonstrate that the extent determination is addressed with a precision of 99.21% and a miss rate of 5.75%. In addition, the abundance values of land cover classes are identified with an overall accuracy of 66.17% after the optimisation step. The proposed method proves to be a valuable tool for the detailed monitoring of wetlands.
... In the Mediterranean, wetlands include different section i.e. freshwater lakes, karstic cave systems, temporary ponds etc. and play an important ecological role in these areas (Taylor et al. 2021 Although wetlands remain threatened by human pressures and climate change, monitoring and managing them are challenging due to their high spatial and temporal dynamics within a ne-grained pattern (Haidary et al., 2013;Rapinel et al., 2019). Generally, land-use/cover change (LUCC) is one of the major driving factors for regional and global environmental change that has a signi cant in uence on water resources (T. ...
Wetland hydrology in warm Mediterranean regions is strongly influenced by land use/cover change (LUCC) and accurate modeling plays an important role in the maintenance of these valuable ecosystems. Here, LUCC effect on hydrological changes in Ajigol, Almagol and Alago international wetlands in northern Iran, was investigated using Long-term hydrological impact assessment (L-THIA) model and satellite images (Landsat 5, 7 and 8) during 1987–2018. The results showed that the most LUCC was observed in agricultural lands and residential areas with an overall accuracy and kappa coefficient of 0.93 and 0.8, respectively. The L-THIA model showed that LUCCs have directly affected the three wetlands under different scenarios. The model showed a relatively high accuracy of changes in wetland water volume under LUCC. A correlation coefficient (R-squared) of 0.70, 0.64 and 0.60 was observed between modeled and actual water volume in Almago, Ajogol and Alagol wetlands, respectively, under LUCCs.
... The accurate identification of physical settings comprising of peat is pertinent to digital soil mapping and a variety of associated land management activities. These applications can conform to the ascertainment of areas with higher soil carbon and organic matter contents (Vitt et al., 2009), as well as detecting wetlands (Rapinel et al., 2019;Whitcomb et al., 2009) and evaluating the extent of environmental impact for these respective regions (Sulla-Menashe et al., 2018). The recent availability and enhanced resolution of remotely-sensed imagery from multiple platforms, has facilitated digital soil mapping research for peatlands (Minasny et al., 2019). ...
Neural networks were explored to achieve a binary classification for determining land corresponding to peat for a study area in the boreal forest of northern Ontario, Canada. Environmental covariates were employed as predictors and obtained from multiple sources, which included multispectral imagery, LiDAR, SAR, and aeromagnetic data. A dense neural network (DNN), as well as a convolutional neural network (CNN), were each implemented. Logistic regression, support vector machine (SVM) and random forest (RF) approaches were also modelled. Neighboring pixels surrounding the soil sampling sites were incorporated as input into the CNN, that permitted training on additional information that was not exploited by other methods. Preliminary results indicate that a CNN can attain improved accuracies for peat classification, when compared against other approaches.
... Values ranging from 5 to 50, up in fives, were assessed for both parameters. Gini index was used as a tree node splitting criterion (Rapinel et al., 2019). The classification was performed by the random forest algorithm in ArcGIS Pro 2.5 (Learn ArcGIS) software. ...
Resilience is the ability of a system to absorb disturbances, rearrange itself, and adapt in order to maintain its functionality, structure, identity, and feedback. Research involving fire resilience in subtropical wetlands (SW) allows us to understand the dynamics of these ecosystems, measure impacts on fauna and flora, and promote policies for the management and protection. The aim of the present study is to assess the fire resilience of SW. The study was divided into three steps: (i) burned area classification, (ii) vegetation pattern classification, and (iii) temporal analysis of SW fire resilience based on NDVI calculation. Our results show that (a) high resilience potential of emerging plants, which developed green leaves in less than 90 days after the fire; (b) poor recovery of peatlands with underground fire history. Daily coverage of high spatial resolution PlanetScope images has great potential for classification and monitoring of land use in areas where there are rapid changes, such as after a fire event, explosions, and dam ruptures with ore tailings, for example.
... To perform periodic wetlands monitoring, remote sensing has become an essential tool, as it is shown in references [9] [10]. Over the years, remote sensing platforms have been successfully tested for this application, from satellites [11] to UAVs [12] [13] .However, every remote sensing technique presents different constraints depending on the operating conditions, in the case of high altitude wetlands (Andean), there are many limitations for their implementation due to the cloudiness, accessibility, and wind gusts of the region. ...
Remote sensing using satellites and unmanned aerial vehicles (UAVs) has become an important tool for wetland delimitation and saturation assessment since they enable patterns identification and wetland saturation data collection in an agile and optimum way. However, their deployment and operative costs limit their implementation in harsh environments, such as the ones presented in the high Andean wetlands. In this context, this work presents a framework to monitor cost-effectively high Andean wetlands using a multi-agent approach based on: field testing, UAV orthomosaics, and satellite imagery. The method developed comprises two stages: i) definition of the monitoring agent (field testing, satellite, UAV) and ii) image processing. For these stages, semi-empirical and statistical models, which were developed in previous works are incorporated in an open-source framework to tailor each monitoring approach accordingly to the seasonality of a representative Andean wetland. The application of the method and its results highlight the suitability of using visual spectrum low-cost remote sensing approach to compute wetlands saturation percentage. In addition, the methodology proposed allowed the development of a temporal monitoring scheme, where the viability of each monitoring agent is examined. In order to validate the method, field data and multispectral imagery were employed using as case of study the Pugllohuma wetland located in the Antisana Reserve. Thus, the main contribution of this work lies in establishing a technified monitoring framework for the Ecuadorian high Andean wetlands, which can be scaled up and extrapolated to other wetlands with similar harsh environmental conditions, helping to their management and protection policies decision-making.
... Sentinel-1A is broad in scope, and it is possible to extract information on partially or wholly wet meadows using the Shannon Entropy method. Rapinel et al. (2019) developed an alternative method of wetland mapping based on free remote sensing data. The data used are Sentinel-1, Sentinel-2 image data, validated MODIS data with LIDAR data. ...
Ogan Komering Ilir (OKI) Regency area, dominated by wetlands, requires appropriate land use planning to support welfare and environmental conservation programs. This article explores the utilization of Synthetic Aperture Radar (SAR) Sentinel-1A satellite data to identify wetland areas with various land covers to provide a medium scale wetland map quickly. The soil moisture maps were captured from the SEPAL platform to monitor soil moisture patterns throughout 2015–2017. The backscattering profiles of VV (δVV) polarization and VH polarization (δVH) were then analyzed for data sentinel-1 from 2015 to 2018. The ratio between VV and VH (δVV/δVH) values and textures of VV and VH is used as a reference to determine the wetland area; determined on an experimental analysis of soil moisture profile, dielectric constants, and backscattering coefficient values from other studies. The low δVH/δVV ratio (ranges between 1.3 and 1.6) is located in the northern OKI regency and is mainly covered by acacia plantations. These estimations align with the Peat Hydrology Unit maps and ground check as validation data. Overall, the δVH/δVV ratio is relatively the same yearly, with a small variation value. This study found the capability of Sentinel-1 for the accurate classification of wetland vegetation types, primarily herbaceous or shrubby vegetated wetlands, but not for high-vegetated wetlands.
Wilayah Kabupaten Ogan Komering Ilir (OKI) yang didominasi lahan basah memerlukan perencanaan tata guna lahan yang tepat untuk mendukung program kesejahteraan dan pelestarian lingkungan. Program tersebut perlu disusun berdasarkan data lahan basah yang tepat dalam penetapan tata guna lahan. Artikel ini mengeksplorasi pemanfaatan data satelit Synthetic Aperture Radar (SAR) Sentinel-1A untuk mengidentifikasi area lahan basah dengan berbagai tutupan lahan untuk menyediakan peta lahan basah skala menengah secara cepat. Peta kelembaban tanah diambil dari platform SEPAL untuk memantau pola kelembaban tanah sepanjang 2015–2017. Profil hamburan balik polarisasi VV (?VV) dan polarisasi VH (?VH) dianalisis untuk data sentinel-1 dari tahun 2015 hingga 2018. Rasio antara nilai VV dan VH (?VV/?VH) serta tekstur VV dan VH digunakan sebagai acuan untuk menentukan lahan basah; ditentukan pada analisis eksperimental profil kelembaban tanah, konstanta dielektrik, dan nilai koefisien hamburan balik dari penelitian lain. Rasio VH/?VV yang rendah (berkisar antara 1,3 dan 1,6) terletak di bagian utara Kabupaten OKI dan sebagian besar ditutupi oleh tanaman akasia. Estimasi ini sejalan dengan peta kesatuan hidrologis gambut dan data validasi berdasarkan ground check lapangan. Secara keseluruhan, rasio ?VH/?VV tahunan bernilai relatif sama, dengan nilai variasi yang kecil. Studi ini menemukan kemampuan Sentinel-1A untuk keperluan klasifikasi tipe vegetasi lahan basah yang akurat, terutama lahan basah bervegetasi herba atau semak, tetapi tidak untuk lahan basah bervegetasi tinggi.
Kata kunci : SAR Sentinel-1A, lahan basah, backscatter, program konservasi, keberlanjutan
... For example, products such as the National Agriculture Imagery Program (NAIP), GlobeLand30 and Sentinel-2 are evolving and improving rapidly in terms of spatial resolution, revisit time, and sensor capability to enhance identification of land by type (Drusch et al., 2012;Knight and Kvaran, 2014;Maxwell et al., 2017). Studies from recent years show the importance of NAIP in tracking wetlands Xie et al., 2019) and also demonstrate the potential of the Sentinel-2 data in mapping and detecting grassland (Griffiths et al., 2019b;Kolecka et al., 2018;Rapinel et al., 2019b), wetlands (Araya-López et al., 2018;Arroyo-Mora et al., 2018;Ludwig et al., 2019;Rapinel et al., 2019a), and cropland (Defourny et al., 2019;Griffiths et al., 2019a). Of course, sensors with higher spatial resolution like those in Sentinel-2 can increase image processing and computational resource demand which is a factor that must be considered. ...
Transparent, consistent, and statistically reliable land use/ land cover area estimates are needed to assess land use change and greenhouse gas emissions associated with biofuel production and other land uses that are influenced by policy. As relevant studies have increased rapidly during past decades, the methods used to combine data extracted from land use land cover (LULC) surveys and remote sensing-based products and track or report sources of uncertainty vary notably. This paper reviews six data sources that are most commonly used to investigate LULC and change in the contiguous U.S. by highlighting the main characteristics, strengths and weaknesses and considering how uncertainty is assessed by the June Area Survey (JAS), the Census of Agriculture (COA), the Farm Survey Agency (FSA) acreage, the National Resources Inventory (NRI), the National Wetlands Inventory (NWI), and the Forest Inventory and Analysis (FIA); and two remote sensing-based data products, the Cropland Data Layer (CDL) and the National Land Cover Database (NLCD). The summary and conclusion identify important research gaps or challenges limiting current land use/land cover and change studies (e.g., lack of high-quality reference data and uncertainty quantification, etc.) and opportunities and emerging techniques (data fusion and machine learning) that will improve reliability of land use/land cover assessments and associated policies. Blended approaches that marry high quality ground truth data that are more finely resolved than data supplied by government surveys with multitemporal imagery are needed track use of non-agricultural lands vulnerable to agricultural expansion. These considerations are notably important as the U.S. considers the renewal and possibly revision of its Renewable Fuel Standard, which includes provisions that require monitoring of agricultural land expansion.
... The efficient wetland category describes ecosystem properties (Merot et al. 2006). In wetland monitoring and management, this categorization can deliver information on wetland loss and on changes in wetland functional categories and was adopted in a remote sensing based study of wetlands in north-eastern France (Rapinel et al. 2019). Accordingly, the basis of our wetland characterization framework consists of a potential wetlands map (potential wetlands), from which actual wetland boundaries are derived (existing wetlands). ...
Wetlands are abundant across the African continent and provide a range of ecosystem services on different scales but are threatened by overuse and degradation. It is essential that national governments enable and ensure the sustainable use of wetland resources to maintain these services in the long run. As informed management decisions require reliable, up-to-date, and large coverage spatial data, we propose a modular Earth observation-based framework for the geo-localisation and characterization of wetlands in East Africa. In this study, we identify four major challenges in spatial data supported wetland management and present a framework to address them. We then apply the framework comprising Wetland Delineation, Surface Water Occurrence, Land Use/Land Cover classification and Wetland Use Intensity for the whole of Rwanda and evaluate the ability of these layers to meet the identified challenges. The layers' spatial and temporal characteristics make them combinable and the information content, of each layer alone as well as in combination, renders them useful for different wetland management contexts.
... The high-precision classification and rapid monitoring of wetland vegetation is an important basis for the systematic study of the structure and ecological function of wetlands, and provides necessary reference information for the protection and rational development of wetlands. The existence of remote sensing technology not only solves the obstacle of field investigation caused by the inaccessibility of wetland environment, but also makes the monitoring of wetland vegetation more timely and effective (Taddeo et al., 2019;Abeysinghe et al., 2019;Rapinel et al., 2019;Jia et al., 2021;Shen et al., 2021). As more and more sensors were deployed on satellite and airborne remote sensing platforms, remote sensing images of the same geographic location have also increased over time, and at the same time, more and more algorithms for identifying and classifying remote sensing images were used. ...
The verification of the transfer learning ability of the convolutional neural network in the classification of natural vegetation is relatively lacking. In this paper, 16 combination scenarios of multispectral images in marsh vegetation were constructed. The influence of the combination with different spatial resolution gradients and spectral dimensions on the classification accuracy of marsh vegetation was systematically studied. Multi-sensor images were used to evaluate the transfer learning ability of DeepLabV3+ and HRNet algorithms in marsh vegetation, and analyse the transfer learning effect of two algorithms in different spatial resolution and spectral ranges. The majority voting method was used to fuse the classifications of high, medium and low spatial resolution images. Based on the largest area method, the fusion results were integrated with multi-scale segmentation to explore the classification ability of the integration of pixel-based classification and object-based classification. The average accuracies of different spatial resolutions to vegetation in multispectral images were statistically analysed in order to quantitatively study the classification ability of spatial resolution to marsh vegetation. The results indicated that: (1) image combination improved the classification accuracy of marsh vegetation in low-resolution images, and decreased the classification accuracy of vegetation in high- and medium-resolution images based on DeepLabV3+ and HRNet algorithms; (2) when GF-1 and Sentinel-2A images were used for combination, the spectral range increased by 1565–1655 nm and 2100–2280 nm from 450–900 nm, the classification accuracy of GF-1 image improved by 0.93–1.77%, and the classification accuracy of Sentinel-2A image decreased by 2.34–4.15%; (3) DeepLabV3+ and HRNet algorithms both have good transfer learning capabilities in the classification of marsh vegetation, but the transfer learning ability was better in images with different spatial resolutions in similar spectral ranges than in images with different spectral ranges; (4) the integration of object-based segmentation and pixel-based classifications (DeepLabV3+ and HRNet algorithms) improved the accuracy, and the growth rate of overall accuracy reached 4.45–5%; (5) the classification of water in images with different spatial resolution gradients had the largest difference, and the accuracy of deep-water marsh vegetation was lower than that of shrub and shallow-water marsh vegetation.
... In recent years, remote sensing technology has become a vital tool for large-scale wetland research because of its high efficiency, wide spatial coverage, and small restrictions caused by topographical conditions McCarthy et al., 2018). Currently, the remote sensing data used for wetland identification and classification mainly include optical images, synthetic aperture radar (SAR) images, LiDAR images, and multi-source remote sensing data (Rapinel et al., 2019;Kumar et al., 2013). Because of the rich spectral information and diverse spatial resolution from low (>1 km) to high (<1 m), optical remote sensing data have been widely used in wetland classification studies carried out at different scales (Mahdianpari et al., 2018). ...
Wetland vegetation is susceptible to climate change and human disturbance, and has experienced significant losses and degradation. However, the spatial patterns of dynamics for China’s inland lake wetlands remain unknown. In this paper, an adaptive-stacking algorithm based on Google Earth Engine was proposed to map the vegetation distribution of Dongting Lake wetland using Sentinel-1/2 and DEM data. Subsequently, LandTrendr was utilized to analyze vegetation dynamics over the 1999–2018 based on Landsat normalized combustion ratio time-series. By overlaying the latest vegetation types and spatial distribution of vegetation dynamics, the main types of vegetation change were examined. Results showed that the overall accuracy and kappa coefficient of adaptive-stacking classification were 94.59% and 0.92, respectively, which were higher than those of support vector machine and random forest. The overall accuracy of change detection for three types (vegetation gain, vegetation loss, and no changes) was 83.67%. In the past 20 years, 2,604.43 and 5,458.84 km² land has experienced vegetation loss and gain, respectively. The increase in the areas of forest, reed, and sedge in wetland were 1330.39, 86.42, 136.97 km², respectively. We found that the overall recovery condition of the wetland vegetation around Dongting Lake was good, which demonstrated the key role played by the national wetland ecological protection policy.
... However, due to the remote and difficult access of most wetlands, the traditional data collection method is labor-intensive, costly, and dangerous (Mahdianpari et al., 2018a(Mahdianpari et al., , 2018bMohammadimanesh et al., 2018). Remote sensing technology allows for large-scale synchronous observation at a high time resolution, which solves the limitations of traditional technology and has become the most effective means to obtain spatiotemporal information on wetlands (Taddeo et al., 2019;Abeysinghe et al., 2019;Rapinel et al., 2019;Lane et al., 2014;Franklin et al., 2017). ...
The accurate classification of wetland vegetation is essential for rapid assessment and management. The Honghe National Nature Reserve (HNNR), located in Northeast China, was studied. The multi-scale remote sensing data of a new generation of Chinese high-spatial-resolution earth observation satellites Gaofen-1 (GF-1), Gaofen-2 (GF-2), Ziyuan-3 (ZY-3), and international earth observation satellites Sentinel-2A and Landsat 8 OLI were selected as sources. Based on the DeepLabV3 Plus deep learning model, 12 intelligent marsh vegetation classification models were constructed. We quantitatively analyzed the applicability and identification ability of DeepLabV3 Plus for classifying complex marsh vegetation. We discuss the differences in accuracy of marsh vegetation classification with different remote sensing data sets. The spatial resolution of remote sensing data sets ranges from 30 m to 0.8 m, and spectral bands range from blue bands (450 nm) to shortwave infrared bands (2280 nm). The specific conclusions of this study are as follows: (1) The DeepLabV3 Plus model better identified marsh vegetation, but there were significant differences in the classification accuracy of 12 DeepLabV3 Plus intelligent identification models. (2) Under the same conditions of the spectral bands (four Blue ~ NIR bands), the accuracy of deep-water marsh vegetation classification gradually increased as spatial resolution improved. For shallow-water marsh vegetation, when the accuracy of vegetation classification increased to a certain level, the classification accuracy decreased with the improvement of spatial resolution, which indicated that high-resolution images reduced pixel mixing to a certain extent, but for some vegetation types, the internal spectral difference increased, which made classification more difficult. (3) The increase of spectral bands improved the classification of marsh vegetation, while the classification accuracy of models with spectral indices was better than that of models only including spectral bands. (4) The accuracy of marsh vegetation classification was greatly improved by combining spectral bands and spectral indices. (5) The classification of the five sensor satellite images had statistical differences between models with different spatial resolutions and models with different spectral ranges.
... They have many unique functions such as erosion control, coastal protection, flood prevention, regulation of water regime, biodiversity conservation, maintain and sustain of wildlife, water quality improvement, control of climate change, and use for ecotourism and recreation (Bwangoy et al. 2010;Chen et al. 2014a;Debanshi and Pal 2020). On the other hand, mistaken agricultural policies, industrialization, urbanization, and inconvenient land use decisions seriously harm those environments, and so, total areas of lakes are decreasing (Rapinel et al. 2019). Hence, it is an exceedingly considerable issue to monitor the lakes and extract their surface areas in order to protect natural heritage zones and transfer them to posterity. ...
The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen’s KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.
... For both parameters, values between 5 and 50 were assessed, varying by 5. The Gini index was used as the tree node division criterion [44]. k-NN is based on the determination of training samples that are closer to unclassified data, classifying them in relation to the most frequent class of the selected neighbors [30]. ...
The classification of vegetation species is a fundamental technical task, necessary for the sustainable management of wetland ecosystems. GEographic-Object-Based Image Analysis (GEOBIA) and data mining have enabled classification and monitoring of wetlands with higher accuracies and lower costs. The objective of this study is to evaluate the performance of Random Forest (RF) and k-Nearest Neighbor (k-NN) data mining methods in vegetation species object-based classification in a subtropical wetland, integrating Sentinel-1 and Sentinel-2A images. In this work, 91.3% accuracy was reached for the object-based classification of vegetation in wetlands with the RF method, and 81.1% accuracy was reached with the k-NN method. Synthetic aperture radar (SAR) features obtained the two major importances, 9.7% (VHM) and 8.9% (VVM). The optical features, red edge and the two short-wavelength infrared bands resulted in values greater than 6%. We conclude that the integration of optical satellite images and SAR, together with the use of GEOBIA and data mining, was successful in classifying vegetation classes of wetlands.
... Due to the non-existence of superficial connection with the river, the functionality between soil-water and aquifer in the geographically isolated wetlands is influenced by the structure of hydric soils and internal topography, both determining the lateral and subsurface flows, controlling downstream flow and acting as discharge and recharge areas [58]. Figure 3 shows the internal pattern of the studied wetland: (1) shrub and tall grass vegetation that dominate in the border of wetland and (2) short-grass vegetation organized in circular patches located exclusively in the Each pattern represents hydrologic soils influenced by distinct microtopography, vegetation, and local distribution of water [59][60][61]. In the Cerrado biome, vegetation structure is strongly related to groundwater depth [19]. ...
In seasonal flooding isolated wetlands, the degree of wetness suggests a close synergy between soil processes, landscape evolution and hydrology along space and time. Until now, that subject has received insufficient attention despite natural wetlands supply essential environmental services to society and are surrounded by intensive agriculture that uses agro-chemicals and fertilizers in their management. The objectives of this study were to propose an infiltration architecture model based on local surface and subsurface water-fluxes in isolated wetland embedded in lateritic plateau covered by savanna and qualify the environmental sensitivity as an area of aquifer recharge. Grain size, soil bulk density, and hydraulic conductivity were determined in five profiles in a soil catena. Unmanned Aerial Vehicle high-resolution images were obtained to generate a digital elevation model and discriminate areas with different vegetation, water accumulation, and environmental sensitivity. Electrical tomography was performed to unveil the soil architecture and infiltration. The soils (Plinthosols) developed on aquic conditions determine the linkage between the surface-subsurface hydrodynam-ics with the soil's physical properties. We have identified vertical and lateral water-flows in the soil architecture. Vertical flow occurs exclusively at the center, where the wetland is characterized as a recharge zone. Lateral flow towards the borders characterizes a discharge zone. The recharge zone is a depression surrounded by crops; therefore, it is a point of high environmental sensitivity. This hydrodynamic model is essential to support studies related to the dispersion of contaminants since soybean agriculture dominates the whole area of well-drained soils in the Brazilian Cerrado.
The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.
Wetlands are important and valuable ecosystems in the landscape of the extreme south of Brazil. However, they are among the ecosystems threatened by human pressures and climate change. Monitoring and managing these environments is a challenge due to their high spatial and temporal dynamics. The use of remote sensing techniques, supervised classification, and machine learning algorithms offers a promising opportunity to map and monitor wetlands. The objective of this work is to develop a method to map Potential Wetlands (PW) from the integration of images from different satellites, sensor systems, spectral, topographic, climatological and hydrological features, made available on the Google Earth Engine (GEE) cloud user platform. Supervised classification was performed on the geomorphological units Coastal Plain (CP) and Central Depression (CD) considering two classes: Potential Wetlands, which encompasses all types of wetlands, and No Wetlands, for the remaining land use and occupation classes. The supervised pixel-by-pixel classification, individual for each geomorphological unit, allowed more accurate results consistent with previous, consolidated mapping. The PW were mapped with a global accuracy higher than 88% and consumer and producer accuracy higher than 81% in the Coastal Plain and Central Depression. These results allow us to affirm that the proposed methodology enabled the identification of 22% to 24% increase in potential wetlands in geomorphological regions, from spectral signatures, and pixel-by-pixel supervised classification of PW using different image collections and sensor systems.
A novel method for assessing spectral index efficiencies for landscape mapping in tropical wetlands was formulated: spectral indices assessment for mapping tropical wetlands (SIA_MW). SIA_MW consists of three stages: (1) identification of covers that make up the landscape, (2) feature selection consistency assessment, and (3) result validation. These stages are evaluated based on six criteria, each of which contains a decision rule (DR) with their respective rating alternatives. The DRs are integrated into two equations: efficiency of an index for landscape mapping in tropical wetlands (EIM_W) and efficiency of an index for water body mapping in tropical wetlands (EIM_Ww). SIA_MW has been proposed as a novel instrument that allows each of the stages of supervised classification to be developed and evaluated in an orderly and coherent manner. This ensures that the final decision to select an index is supported by a robust process that integrates qualitative and quantitative methods of spectral evaluation. SIA_MW is applicable to multiple remote sensing products and can be used in environments other than wetlands. This is because it is independent of factors such as landscape cover categories, the type of sensor product from which spectral indices are derived, and spectral classification algorithms. For the formulation of SIA_MW, the Bajo Sinú Wetland Complex (BSWC), located in northern Colombia, was selected as a pilot site, and 9 vegetation indices derived from a PlanteScope image were compared and evaluated. The soil-adjusted vegetation and water-adjusted vegetation indices (SAVI and WAVI, respectively) yielded the best results with values for EMI_W of 0.94 and 0.89, respectively. These results indicate SIA_MW was consistent because the covariance between the two best indices was 0.88. Additionally, the correlation between the DR scores of the evaluated indices was low, thus, indicating criteria complementarity.
Wetlands fulfil a number of functions in the landscape, especially non-productive ones. Information on landscape and biotope changes is important not only from a theoretical point of view for understanding the forces and pressures that cause changes in the landscape, but also from a practical point of view, as we can take inspiration from history when planning the landscape. The main goal of this study is to analyse the dynamics and trajectories of changes in wetlands, including testing the influence of the main natural conditions (climate, geomorphology) on their changes, for a large area of 141 cadastral territories (1315 km2), which will allow the results to be sufficiently generalized. The results of our study confirmed the global trend of rapid wetland loss, with almost three quarters of wetlands disappearing, mostly on arable land (37 %). The results of the study are of great importance in the field of the ecology of landscapes and wetlands, both in the national and international context, not only because they make it possible to understand the regularities and forces that affect changes in wetlands and landscapes, but also have significance due to the methodology. The specific methodology and procedure are based on the application of advanced GIS functions (Union and Intersect functions) to identify the location and area of individual change dynamics and types of wetland (new, extinct, continuous), using accurate old large-scale maps and aerial photographs. The proposed and tested methodological procedure can generally be used for wetlands in other locations, but also for studying the dynamics of changes and trajectories of other biotopes in the landscape. The greatest potential for using the results of this work in the field of environmental protection is the possibility of using the places of extinct wetlands for their restoration.
Wetlands are among the most important ecosystems on Earth. They provide innumerable direct and indirect ecosystem services. Over the past 50 years, wetlands have been polluted and are in rapid decline – three times faster than tropical forests. Light detection and ranging (LiDAR) technology has been in use for some time for atmospheric, bathymetry, and geoscience, but there has been limited application to wetland ecosystems. Past application includes data fusion with optical, microwave, and hyperspectral approaches, and the use of unmanned aerial vehicle datasets. Different types of LiDAR have their own advantages and their suitability can be assessed according to application. Wetland research themes include mapping, classification, water level estimation, hydrological modeling, biomass estimation, nutrient efficiency estimation, and vegetation level classification. There is ample future scope for advanced LiDAR methods applied to wetland research.
Identifying and delineating groundwater-dependent ecosystems (GDEs) is critical in understanding their location, distribution and groundwater allocation. However, this information is inadequately understood due to limited available data for most areas where they occur. Thus, this study aims to address this gap using remotely sensed, analytical hierarchy process (AHP) and in situ data to identify and delineate GDEs in the Khakea–Bray transboundary aquifer region. The study tested various spatial-explicit GDE indices that integrates environmental factors that predict occurrence of GDEs. These include the normalized difference vegetation index as a proxy for vegetation productivity and modified normalized difference water index as proxy for moisture availability, land-use and landcover, topographical factors such as slope, topographic wetness index, flow accumulation and curvature. The GDEs were delineated using the weighted overlay tool in a Geographic Information System (GIS) environment. The thematic output layer was then spatially classified into two classes, namely, GDEs and non-GDEs. The results showed that only 1.34% of the area is characterised by GDEs covering 721,908 ha. Overall, identified GDEs were found mostly on a gentle slope on the large portion of shrubland and grassland. The derived GDEs map was then statistically compared with groundwater level (GWL) data from 22 boreholes that occur in the area. Our results indicated that: GDEs are concentrated at the northern, central and south-western part of the study area. The validation results showed significant overlapping of GDEs classes with both the groundwater level (GWL) and rainfall in the study area. The results show a possible delineation of GDEs in the study area using remote sensing and GIS techniques along with AHP and is transferable to other arid and semiarid environments. The results of this study contributes to identifying and delineating priority areas where appropriate water conservation programmes for sustainable groundwater development can be implemented.
Remote sensing of groundwater-dependent ecosystems (GDEs) has increased substantially in recent years. Of significant prominence, is the delineation and mapping of groundwater-dependent vegetation (GDV), species diversity, and water quality in these ecosystems. Groundwater-dependent ecosystems provide several ecological services such as habitat for wildlife fauna, carbon sequestration and water purification. The recent technological advancements and readily accessibility of new satellite sensors with improved sensing characteristics have
resulted in numerous state-of-the-art applications for GDEs assessment and monitoring. These studies were done at varying scales, essentially in light of global climate change and variability. In this study, we review and assess the progress on the remote sensing of GDEs in semi-arid environments. We present the key trends in GDEs remote sensing that underpin many of the recent scientific research milestones and application developments. In
addition, we observed a considerable shift towards the use of advanced spatial modelling techniques, using highresolution
remotely sensed data to further improve the characterisation and understanding of GDEs. Thus, literature shows the successful use of freely available remotely sensed data in mapping GDEs. We conclude that the advancement in remote sensing provides unique opportunities for the assessment and monitoring of GDEs in environments currently influenced by climate change and other anthropogenic impacts. Although remarkable progress has been made, this review revealed the need for further remote sensing and geospatial analysis studies to map other GDEs aspects including water quality and species diversity. Furthermore, the mapping and characterizing of the seasonal and yearly variability and changes in GDEs is required, mainly in the face of climate change and human impacts as well as water scarcity, particularly in data-limited tropical environments.
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.
Wetlands are important ecosystems, and detecting changes in wetlands plays a very important role in the management of wetland resources. Therefore, we selected Sanjiang National Nature Reserve as the study area and proposed a method in which bi-temporal GaoFen (GF) remote sensing data was combined with a spatial–temporal attention neural network (STANet) model, coupled with the selection of spectral variables. We used the bi-temporal images from GF-1 and GF-6 and compared three band selection methods, Relief F, principal component analysis, and RGB combination, and two STANet-based change detection methods (BAM: basic spatial–temporal attention module and PAM: pyramid spatial–temporal attention module) to improve the change detection of the wetland restoration. The results show that the accuracy of wetland change detection is related to different combinations of spectral variable selection and STANet models. The combination of Relief F feature selection and the BAM model led to the highest accuracy of the wetland change detection, 71.72 % for F1-Score and 76.92 % for MIou. The vector results of the change detection statistics show that the area of the Sanjiang National Nature Reserve cultivated lands converted to wetlands is 46.05 km². This study implies that the STANet method has the capacity of detecting complex wetland restoration. Red, Bule, NIR and Red Edge1 bands of GaoFen data can provide effective information in the detection of changes in wetlands. The results of the study can be used as a reference for rational planning of wetland restoration, seeking a balance between cultivated lands and wetlands, which is important for the sustainable development of wetland.
Coastal wetlands play a vital role in coastline protection. Coastal landscape of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has changed drastically. In order to explore the process and law of coastal landscape dynamics in GBA, in this research, multi-temporal Landsat images and proposed hybrid object-based image analysis and multi-level decision tree classification were applied to monitor the changing trends and current status of the coastal landscape in GBA from 1988 to 2018. There were 5818.58 km² of natural wetlands in 2018, including mangroves (39.62 km²), tidal flats (73.64 km²), rivers (161.65 km²), and shallow seas (5543.67 km²). Due to the influence of anthropogenic activities, natural wetlands showed a decreasing trend, while artificial wetlands and non-wetlands have been increasing. Results indicated that the landscape in the study area showed a trend of fragmentation, the degree of landscape aggregation weakened, the dominant patches were scattered, the shape became more and more complex, and the landscape heterogeneity increased. Then, a future land use simulation model was used to simulate the coastal landscape change trends of GBA in 2028 under four scenarios. The mangrove has the fastest growth rate of 1.47 km²/a under wetland protection scenario. The tidal flat and paddy field would increase under scenarios of the wetland protection and farmland protection, respectively. The construction land has the fastest growth rate under the economic development scenario and would be 47.56 km²/a. Woodland, dry land, and culture ponds are further transformed into other landscape types, while rivers, shallow seas, and reservoirs/ponds have no obvious change.
Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.
Peatlands that are close to a natural state are rich in biodiversity and are significant carbon storages. Simultaneously, peat resources are of interest to industry, which leads to competing interests and tensions regarding the use and management of peatlands. In this case study, we studied knowledge-management interactions through the development of participation and the resulting representation of nature (how nature was described), as well as the proposed and implemented conservation policy instruments. We focused on the years 2009-2015, when peatland management was intensively debated in Finland. We did an interpretative policy analysis using policy documents (Peatland Strategy; Government Resolution; Proposal for Conservation Programme) and environmental legislation as central data. Our results show how the representation of nature reflected the purpose of the documents and consensus of participants' values. The representation of nature changed from skewed use of ecosystem services to detailed ecological knowledge. However, simultaneously, political power changed and the planned supplementation programme for peatland conservation was not implemented. The Environment Protection Act was reformulated so that it prohibited the use of the most valuable peatlands. Landowners did not have the chance to fully participate in the policy process. Overall, the conservation policy instruments changed to emphasize voluntariness but without an adequate budget to ensure sufficient conservation.
Wetlands play a key role in controlling flooding and non-point-source (diffuse) pollution. They are therefore an important tool for mitigating diffuse water pollution from farms. However, to use this tool, it is necessary to obtain detailed assessments and identification of potential wetland restoration or creation sites. This is complicated by the diversity of landscapes, environmental conditions, and land ownership. Site suitability for wetland restoration or creation depends on many factors: the underlying geology, soils, topography, hydrology, drainage, and land ownership. Local hydrology and soils are among the most important factors. However, the inventory and characterization of a site’s soils and hydrology often requires extensive, expensive, and time-consuming ground surveys, and it is therefore limited to small areas. Another possibility would be to consider topography, which strongly determines water movement patterns. Light detection and ranging (LiDAR) data provides detailed topographic information and can be acquired by remote sensing. Our study showed that terrain analysis using high-resolution topographical data can produce suitability maps for wetlands that can be easily used by decision makers and planners in watershed management. The rapid methodology reveals potential wetland creation or restoration sites at a reasonable cost; with the resulting spatially explicit suitability map, managers can plan for wetland creation or restoration without having to wait for field-data collection.
Wetlands are valuable natural resources that provide many benefits to the environment.
Therefore, mapping wetlands is crucially important. Several review papers on remote
sensing (RS) of wetlands have been published thus far. However, there is no recent review
paper that contains an inclusive description of the importance of wetlands, the urgent need
for wetland classification, along with a thorough explanation of the existing methods for
wetland mapping using RS methods. This paper attempts to provide readers with an
exhaustive review regarding different aspects of wetland studies. First, the readers are
acquainted with the characteristics, importance, and challenges of wetlands. Then, various
RS approaches for wetland classification are discussed, along with their advantages and
disadvantages. These approaches include wetland classification using aerial, multispectral,
synthetic aperture radar (SAR), and several other data sets. Different pixel-based and objectbased
algorithms for wetland classification are also explored in this study. The most
important conclusions drawn from the literature are that the red edge and near-infrared
bands are the best optical bands for wetland delineation. In terms of SAR imagery, large
incidence angles, short wavelengths, and horizontal transmission and vertical reception
polarization are best for detecting of herbaceous wetlands, while small incidence angles,
long wavelengths, and horizontal transmission and reception polarization are appropriate for
mapping forested wetlands.
Over the past decades, remote sensing has been repeatedly identified as a promising and powerful tool to aid nature conservation. Many methods and applications of remote sensing to monitor biodiversity have indeed been published, and continue to be at an increasing rate. As such, remote sensing is seemingly living up to its expectations; yet, its actual use in nature conservation (or rather the lack thereof) contradicts this. We argue that, at least for the practical implementation of regular vegetation monitoring, including within protected areas (e.g., European Natura 2000 sites), a lack of transferability of remote sensing methods is an overlooked factor that hinders its effective operational use for nature conservation. Among the causes of poor method transferability is the large variation in objects of interest, user requirements, ground reference data, and image properties, but also the lack of consideration of transferability during method development. To stimulate the adoption of remote sensing based techniques in vegetation monitoring and conservation, we recommend that a number of actions are taken. We call upon remote sensing scientists and nature monitoring experts to specifically consider and demonstrate method transferability by using widely available image data, limiting ground reference data dependence, and making their preferably open-source programming code publicly available. Furthermore, we recommend that nature conservation specialists are open and realistic about potential outcomes by not expecting the replacement of current in-place methodologies, and actively contributing to the thought process of generating transferable and repeatable methods.
Societal, economic and scientific interests in knowing where biodiversity is, how it is faring and what can be done to efficiently mitigate further biodiversity loss and the associated loss of ecosystem services are at an all-time high. So far, however, biodiversity monitoring has primarily focused on structural and compositional features of ecosystems despite growing evidence that ecosystem functions are key to elucidating the mechanisms through which biological diversity generates services to humanity. This monitoring gap can be traced to the current lack of consensus on what exactly ecosystem functions are and how to track them at scales beyond the site level. This contribution aims to advance the development of a global biodiversity monitoring strategy by proposing the adoption of a set of definitions and a typology for ecosystem functions, and reviewing current opportunities and potential limitations for satellite remote sensing technology to support the monitoring of ecosystem functions worldwide. By clearly defining ecosystem processes, functions and services and their interrelationships, we provide a framework to improve communication between ecologists, land and marine managers, remote sensing specialists and policy makers, thereby addressing a major barrier in the field.
Orfeo ToolBox is an open-source project for state-of-the-art remote sensing, including a fast image viewer, applications callable from command-line, Python or QGIS, and a powerful C++ API. This article is an introduction to the Orfeo ToolBox’s flagship features from the point of view of the two communities it brings together: remote sensing and software engineering.
Temporal streams are vitally important for hydrology and riverine ecosystems. The identification of wet channel networks and spatial and temporal dynamics is essential for effective management, conservation, and restoration of water resources. This study investigated the temporal dynamics of stream networks in five watersheds under different climate conditions and levels of human interferences, using a systematic method recently developed for extracting wet channel networks based on light detection and ranging elevation and intensity data. In this paper, thresholds of canopy height for masking densely vegetated areas and the ‘time of forward diffusion’ parameter for filtering digital elevation model are found to be greatly influential and differing among sites. The inflection point of the exceedance probability distribution of elevation differences in each watershed is suggested to be used as the canopy height threshold. A lower value for the ‘time of forward diffusion’ is suggested for watersheds with artificial channels. The properties of decomposed and composite probability distribution functions of intensity and the extracted intensity thresholds are found to vary significantly among regions. Finally, the wet channel density and its variation with climate for five watersheds are found to be reasonable and reliable according to results reported previously in other regions.
Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R ² = 0.65). Estimators based on a single MODIS NDVI image performed worse (R ² ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems. View Full-Text
A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result.
The objective of this study is to test a cost-effective, physically based Light Detection and Ranging (LiDAR) classification methodology for wetland and upland land cover types within an area exceeding 1,000 km² in the Boreal Plains, Alberta, Canada. Decision criteria are based on physical attributes of the landscape that influence maintenance of land cover types. Results are compared with 38 geolocated measurement plots at land cover boundaries and transition zones, manual delineation of 2,337 wetlands using photogrammetric methods and publicly available land cover classifications. Results suggest that 57% of LiDAR-based wetland classes correspond with delineated wetlands, whereas 37% occur as errors of commission due to excluded wetlands in the manual delineation and confusion with harvested areas. Comparison of classified edges with plot shows that all classifications underestimate wetland area. Residual differences of the LiDAR-based classification are −0.3 m, on average (compared with measured), and have reduced range of error compared with other methods. Multispectral classifications misclassify up to 2/3 of wetland boundaries as a result of lower-resolution mixed pixels. Therefore, high-resolution maps of terrain morphology and vegetation structure provide an accurate, cost-effective means for characterizing wetland vs. upland forest in areas where LiDAR data are available. 2016
IntroductionUnderstanding Wetlands: A Moving TargetAccounting for Spatial and Temporal Variability in WetlandsCompensating for Influences from the Surrounding LandscapeRestoring and Creating WetlandsDepending on WetlandsConclusion
AcknowledgementsReferences
Wetlands are highly productive and support a wide variety of ecosystem goods and services. Various forms of global change impose compelling needs for timely and reliable information on the status of wetlands worldwide, but several characteristics of wetlands make them challenging to monitor remotely: they lack a single, unifying land-cover feature; they tend to be highly dynamic and their energy signatures are constantly changing; and steep environmental gradients in and around wetlands produce narrow ecotones that often are below the resolving capacity of remote sensors. These challenges and needs set the context for a special issue focused on wetland remote sensing. Contributed papers responded to one of three overarching questions aimed at improving remote, large-area monitoring of wetlands: (1) What approaches and data products are being developed specifically to support regional to global long-term monitoring of wetland landscapes? (2) What are the promising new technologies and sensor/multisensor approaches for more accurate and consistent detection of wetlands? (3) Are there studies that demonstrate how remote long-term monitoring of wetland landscapes can reveal changes that correspond with changes in land cover and land use and/or changes in climate?
Comprehensive wetland inventories are an essential tool for wetland management, but developing and maintaining an inventory is expensive and technically challenging. Funding for these efforts has also been problematic. Here we describe a large-area application of a semi-automated process used to update a wetland inventory for east-central Minnesota. The original inventory for this area was the product of a labor-intensive, manual photo-interpretation process. The present application incorporated high resolution, multi-spectral imagery from multiple seasons; high resolution elevation data derived from lidar; satellite radar imagery; and other GIS data. Map production combined image segmentation and random forest classification along with aerial photo-interpretation. More than 1000 validation data points were acquired using both independent photo-interpretation as well as field reconnaissance. Overall accuracy for wetland identification was 90 % compared to field data and 93 % compared to photo-interpretation data. Overall accuracy for wetland type was 72 and 78 % compared to field and photo-interpretation data, respectively. By automating the most time consuming part of the image interpretations, initial delineation of boundaries and identification of broad wetland classes, we were able to allow the image interpreters to focus their efforts on the more difficult components, such as the assignment of detailed wetland classes and modifiers.
The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aerosol type. As a first step, this study compares the performances of two kinds of AOT estimation methods applied to FormoSat-2 and LandSat time series of images: a multi-spectral method that assumes a constant relationship between surface reflectance measurements and a multi-temporal method that assumes that the surface reflectances are stable with time. In a second step, these methods are combined to obtain more accurate and robust estimates. The estimated AOTs are compared to in situ measurements on several sites of the AERONET (Aerosol Robotic Network). The methods, based on either spectral or temporal criteria, provide accuracies better than 0.07 in most cases, but show degraded accuracies in some special cases, such as the absence of vegetation for the spectral method or a very quick variation of landscape for the temporal method. The combination of both methods in a new spectro-temporal method increases the robustness of the results in all cases.
Satellite remote sensing is an important tool for monitoring the status of biodiversity and associated environmental parameters, including certain elements of habitats. However, satellite data are currently underused within the biodiversity research and conservation communities. Three factors have significant impact on the utility of remote sensing data for tracking and understanding biodiversity change. They are its continuity, affordability, and access. Data continuity relates to the maintenance of long-term satellite data products. Such products promote knowledge of how biodiversity has changed over time and why. Data affordability arises from the cost of the imagery. New data policies promoting free and open access to government satellite imagery are expanding the use of certain imagery but the number of free and open data sets remains too limited. Data access addresses the ability of conservation biologists and biodiversity researchers to discover, retrieve, manipulate, and extract value from satellite imagery as well as link it with other types of information. Tools are rapidly improving access. Still, more cross-community interactions are necessary to strengthen ties between the biodiversity and remote sensing communities. (C) 2014 Published by Elsevier Ltd.
Over the last 30 years, ecological networks have been deployed to reduce global biodiversity loss by enhancing landscape connectivity. Bird species dwelling in woodland habitats that are embedded in agriculture-dominated landscapes are expected to be particularly sensitive to the loss of connectivity. This study aimed to determine the role of landscape connectivity in woodland bird species richness, abundance, and community similarity in north-east Brittany (north-west France). An exhaustive woodland selection protocol was carried out to minimize the effects of woodland size on the response variables. Connectivity of the woodland and forest network in the study area was evaluated using graph-theory, accounting for matrix permeability, and a characteristic median natal dispersal distance at the community level based on the bird species pool recorded in the sampled woodlands. Information-theoretic model selection, controlling for woodland size in all the cases, depicted the response of woodland birds at the community level to the connectivity of agriculture-dominated landscapes.
On average, the sampled woodlands (n = 25) contained 15.5 ± 2.4 bird species, with an abundance of 25.1 ± 3.9, and had highly similar bird communities (species composition and proportion); eight species represented 57% of total abundance and were present in at least 22 woodlands. The performance of models improved when using effective, rather than Euclidean, interpatch distances in the connectivity assessment. Landscape connectivity was only significantly related to similarity of proportional species composition. Large woodlands contained communities with more similar species proportions in an inhospitable agricultural landscape matrix than in a more permeable one. Woodland size was the most relevant factor determining species abundance, indicating that the bird population sizes are primarily proportional to the local habitat availability. Connectivity in relation to landscape matrix permeability did not seem to induce the flow of woodland-dependent bird species that are dominant in the community but rather of matrix-dwelling bird species that are less dependent on woodland patch area. In conclusion, both habitat conservation and restoration (i.e., amount and quality), in combination with permeable landscape structures (such as heterogeneous land cover mosaics), are advocated for community level conservation strategies.
Wetlands perform functions that deliver benefits to society, often referred to as ecosystem services. These ecosystem services include water supply, flood regulation, water purification, climate regulation, biodiversity, agriculture (e.g. grazing land), and amenity. A functional approach to wetland assessment enables a holistic view to be taken of the wide range of services wetlands can provide. The functional assessment procedures (FAPs) in this volume translate best available scientific knowledge into reasonable predictions of how component parts of wetlands function in different landscape contexts. They can be used to indicate the potential and priorities for management options in such areas as flood control, pollution reduction and biodiversity conservation. Functional assessment enables the user to predict the functioning of a wetland area without the need for comprehensive and expensive empirical research The FAPs therefore provide a methodology that can be used by both experts and non-experts to assess wetland functioning relatively rapidly. The volume includes an electronic version of the FAPs on CD which automates aspects of the assessment once the initial recording stage is completed. It is anticipated that the FAPs will be used by a range of individuals or organisations concerned with wetland management who wish to gain a better understanding of the processes, functions, services or benefits and potential of the wetlands for which they have responsibility. Provides a systematic methodology to evaluate how wetlands function. Allows on-experts to assess wetland functioning rapidly and cost-effectively. Automates aspects of the functional assessment through the accompanying CD-ROM.
With increasing scarcity of natural resources, there is a need to provide resource managers and planners with maps that reliably inform about areas vulnerable to hydrological risks, including areas with ephemeral to intermittent flows. This paper demonstrates that the newly developed Wet-Areas Mapping (WAM) process using LiDAR-based point cloud data addresses some of these needs. This is done by portraying local flow patterns, soil drainage, soil moisture regimes and natural vegetation type across mapped areas in a numerically robust and consistent manner. As a result, WAM-derived maps are useful for “surprise-free” operations planning in several areas of natural resource planning (forestry, parks and recreation, oil and gas extraction, land reclamation), and also serve as field guides for locating and delineating flow channels, road-stream crossings, wet areas and wetlands.
Low saltmarshes are subjected to variability between sites and tidal cycles in terms of erosive forces by current and waves, the frequency and duration of flooding and soil salinity. The establishment of vegetation in pioneer zones is directly related to sedimentary dynamics but few data are available concerning the effects of plants on sediment dynamics. In the Mont Saint Michel Bay (France), the low saltmarshes, including pioneer zones, are characterized by a micro-topography composed of hummocks with vegetation dominated by Puccinellia maritima, mudflats with a low sparse vegetation of Spartina anglica, Salicornia fragilis and Puccinellia maritima and a few erosion zones. The aim of this study was to (1) investigate the sediment deposition and soil elevation patterns, between tidal cycles and between sites; (2) look for a relationship between the development and dynamics of the micro-topography and the different plant species; and (3) evaluate whether Puccinellia maritima plays any role in enhancing sediment deposition and therefore plant succession in these lower marshes.
Ecosystem services are natural assets produced by the environment and utilized by humans -- such as clean air, water, food and materials -- and contribute to social and cultural well-being. This concept, arguably, has been developed further in wetlands than any other ecosystem. Wetlands were historically important in producing the extensive coal deposits of the Carboniferous period; key steps in human development took place in communities occupying the wetland margins of rivers, lakes and the sea; and wetlands play a key role in the hydrological cycle influencing floods and river droughts. In this paper we examine three pillars that support the wetland research agenda: hydrology, wetland origins and development, and linkages to society. We investigate these through an overview of the evolution of wetland science and assessment of the wide range of topics relating to ecosystem services covered in this Special Issue. We explain the seminal change in how modern society values the benefits of natural ecosystems and highlight the pathfinder role that wetland research has played in the paradigm shift.Co-editors D. Koutsoyiannis and Z.W. KundzewiczCitation Maltby, E. and Acreman, M.C., 2011. Ecosystem services of wetlands: pathfinder for a new paradigm. Hydrological Sciences Journal, 56 (8), 1341–1359.
An important goal of conservation biology is the maintenance of ecosystem processes. Incorporating quantitative measurements of ecosystem functions into conser-vation practice is important given that it provides not only proxies for biodiversity patterns, but also new tools and criteria for management. In the satellite era, the translation of spectral information into ecosystem functional variables expands and complements the more traditional use of satellite imagery in conservation biology. Remote sensing scientists
Aim To examine the geographical patterns of the interception of photosynthetically active radiation by vegetation and to describe its spatial heterogeneity through the definition of ecosystem functional types (EFTs) based on the annual dynamics of the Normalized Difference Vegetation Index (NDVI), a spectral index related to carbon gains.
Location The Iberian Peninsula.
Methods EFTs were derived from three attributes of the NDVI obtained from NOAA/AVHRR sensors: the annual integral (NDVI-I), as a surrogate of primary production, an integrative indicator of ecosystem functioning; and the intra-annual relative range (RREL) and month of maximum NDVI (MMAX), which represent key features of seasonality.
Results NDVI-I decreased south-eastwards. The highest values were observed in the Eurosiberian Region and in the highest Mediterranean ranges. Low values occurred in inner plains, river basins and in the southeast. The Eurosiberian Region and Mediterranean mountains presented the lowest RREL, while Eurosiberian peaks, river basins, inner-agricultural plains, wetlands and the southeastern part of Iberia presented the highest. Eurosiberian ecosystems showed a summer maximum of NDVI, as did high mountains, wetlands and irrigated areas in the Mediterranean Region. Mediterranean mountains had autumn–early-winter maxima, while semi-arid zones, river basins and continental plains had spring maxima. Based on the behaviour in the functional traits, 49 EFTs were defined.
Main conclusions The classification, based on only the NDVI dynamics, represents the spatial heterogeneity in ecosystem functioning by means of the interception of radiation by vegetation in the Iberian Peninsula. The patterns of the NDVI attributes may be used as a reference in evaluating the impacts of environmental changes. Iberia had a high spatial variability: except for biophysically impossible combinations (high NDVI-I and high seasonality), almost any pattern of seasonal dynamics of radiation interception was represented in the Peninsula. The approach used to define EFTs opens the possibility of monitoring and comparing ecosystem functioning through time.
Question: What is the influence of management on the functioning of vegetation over time in Mediterranean ecosystems under different climate conditions?
Location: Mediterranean shrublands and forests in SE Iberia (Andalusia).
Methods: We evaluated the Normalized Difference Vegetation Index (NDVI) for the 1997-2002 time series to determine phenological vegetation patterns under different historical management regimes. Three altitudinal ranges were considered within each area to explore climate × management interactions. Each phenological pattern was analysed using time series statistics, together with precipitation (monthly and cumulative) and temperature.
Results: NDVI time series were significantly different under different management regimes, particularly in highly transformed areas, which showed the lowest NDVI, weakest annual seasonality and a more immediate phenological response to precipitation. The NDVI relationship with precipitation was strongest in the summer-autumn period, when precipitation is the main plant growth-limiting factor.
Conclusions: NDVI time series analyses elucidated complex influences of land use and climate on ecosystem functioning in these Mediterranean ecosystems. We demonstrated that NDVI time series analyses are a useful tool for monitoring programmes because of their sensitivity to changes, ease of use and applicability to large-scale studies.
Local species coexistence is the outcome of abiotic and biotic filtering processes which sort species according to their trait values. However, the capacity of trait-based approaches to predict the variation in realized species richness remains to be investigated. In this study, we asked whether a limited number of plant functional traits, related to the leaf-height-seed strategy scheme and averaged at the community level, is able to predict the variation in species richness over a flooding disturbance gradient. We further investigated how these mean community traits are able to quantify the strength of abiotic and biotic processes involved in the disturbance–productivity–diversity relationship. We thus tested the proposal that the deviation between the fundamental species richness, assessed from ecological niche-based models, and realized species richness, i.e. field-observed richness, is controlled by species interactions. Flooding regime was determined using a detailed hydrological model. A precise vegetation sampling was performed across 222 quadrats located throughout the flooding gradient. Three core functional traits were considered: specific leaf area (SLA), plant height and seed mass. Species richness showed a hump-shaped response to disturbance and productivity, but was better predicted by only two mean community traits: SLA and height. On the one hand, community SLA that increased with flooding, controlled the disturbance-diversity relationship through habitat filtering. On the other hand, species interactions, the strength of which was captured by community height values, played a strong consistent role throughout the disturbance gradient by reducing the local species richness. Our study highlights that a limited number of simple, quantitative, easily measurable functional traits can capture the variation in plant species richness at a local scale and provides a promising quantification of key community assembly mechanisms.
En conditions climatiques tempérées, dans des contextes géomorphologiques avec substrat à faible profondeur et faible perméabilité, à pentes modérées, la nappe est généralement proche de la surface du sol en bas de versant.. Ces conditions conduisent de façon saisonnière à la présence de petites zones humides ripariennes de quelques hectares au plus. Ces zones sont insérées et dispersées au sein de paysages agricoles. Elles sont souvent oubliées des inventaires des zones humides bien qu'elles jouent un rôle important dans le contrôle de l'hydrologie et de la qualité des eaux des bassins versants. Une typologie hydrologique de ces petites zones humides est proposée ici pour accompagner la réflexion sur leur gestion raisonnée, confrontée à des objectifs antagonistes de maintien de biodiversité et de lutte contre la pollution.
As one of the most important ecosystems, wetlands are threatened from both natural and anthropogenic activities. Mapping wetland is one of the curtail needs in order to prevent further loss. Since the beginning of the Remote Sensing revolution, different approaches using satellite images have been used for mapping and monitoring wetlands.
In this paper we investigate the potential of the recently launched Sentinel satellites, both separate and in combination, for accurately mapping of different wetland classes using Support Vector Machines (SVMs) learning classifier. For investigating the influence of the Sentinel-2 red-edge bands, and the radar bands from Sentinel-1, three different datasets have been analyzed.
The results showed that for more accurate mapping of different wetland classes, different datasets should be used. Thus, the red-edge bands have significant influence over the intensive vegetated wetland classes such as swamps, and the radar bands have significant influence over partially decayed vegetated wetland areas such as bogs. For future studies, in addition to the analyzed datasets, we recommend adding and investigating several vegetation indices for mapping and monitoring wetland areas.
Dry grasslands are species rich and ecologically valuable habitats that have experienced a massive decline in Switzerland during the last century due to agricultural intensification and land abandonment. Appropriate management is a key factor in maintaining habitat quality of the remaining most valuable sites and should thus be an essential part of monitoring studies. However, information on management is often missing and fine-scale patterns are difficult to assess, especially over large areas and for past decades. The aim of this study was to predict habitat quality of protected dry grasslands in Switzerland. Using a nation-wide in-situ vegetation data set with plot-based species lists, we derived six habitat quality indicators (management tolerance, light availability, nutrient content, moisture content and species richness). We then tested how well satellite-based phenology metrics, in combination with environmental and climate data, can predict these dry grassland habitat quality indicators. We expected that the seasonal pattern of vegetation activity, based on the Normalized Difference Vegetation Index (NDVI), would represent local productivity and management patterns, two crucial indicators of dry grassland habitat quality. Linear regression analysis was conducted to assess the relative importance and ecological relationship of different NDVI metrics and other environmental and climate predictors for habitat quality. Variance partitioning was applied to assess model contributions of the three variable groups which represent different data sources for productivity and management. Accuracies for the habitat quality prediction models ranged between 34% and 57% and significant correlations with multiple NDVI metrics were found. Including NDVI phenology improved all models by 7–12%. Single contributions of NDVI phenology were highest for management tolerance and nutrient content. However, we found high variation of contributions between management types. NDVI metrics were highly informative for the habitat qualities of abandoned sites, but grazing and mowing reduced or even cancelled their predictive power. Moreover, our results demonstrate the limitation of single-date NDVI values in predicting habitat quality of dry grasslands, in particular pastures and meadows. For monitoring applications of dry grasslands, we propose using a combination of NDVI metrics, as our results showed that they greatly improve prediction results of essential habitat qualities. The Landsat legacy dataset facilitates the assessment of habitat changes during past decades and can be complemented in the future with higher resolution data, such as Sentinel-2, to increase the temporal and spatial resolution so analyses are more appropriate for the typically limited size of dry grassland habitat sites in Switzerland.
Mapping semi-natural grassland has become increasingly important with regard to climate variability, invasive species, and the intensification of land use. At the same time, adequate field data collection is of pivotal importance for national and international reporting obligations, such as the European Habitats Directive. We present a remote-sensing-based monitoring framework for a Natura 2000 site with a heterogeneous composition of different grassland communities, using the Random Forest algorithm. Automated training data selection was successfully implemented based on the Random Forest proximity measure (Overall Accuracy ranging from 77.5–86.5%). RapidEye acquisitions originating from the onset of vegetation (prespring and first spring) and senescence (late summer and first autumn) were identified as important phenological phases for mapping semi-natural grassland communities. The derived probability maps of occurrences for each grassland class captured transitions between grassland communities and are therefore a better approximation of real-world conditions compared to classical, discrete maps.
Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.
Geosynphytosociology deals with the study of combinations of vegetation series – or geosigmeta – within landscape. Its main advantage is to assess conservation status based on vegetation dynamics. However, this field-based approach has not been widely applied, because local surveys are not representative of spatio-temporal landscape complexity, which leads to uncertainties and errors for geosigmeta structural and functional mapping. In this context, satellite time series appear as relevant data for monitoring vegetation dynamics. This article aims to assess the contribution of an annual satellite time series for geosigmeta structural and functional mapping. The study area, which focuses on the French Atlantic coast (4630 km²), includes salt, brackish, sub-brackish and fresh marshes. A structural vegetation map was derived from the classification of an annual time series of 38 MODIS images validated with field surveys. The functional vegetation map was derived from the annual Integral of Normalized Difference Vegetation Index (NDVI-I), as an indicator of above-ground net primary production. Results show that geosigmeta were successfully mapped at a scale of 1:250,000 with an overall accuracy of 82.9%. The geosigmeta functional map highlights a strong gradient from the lowest NDVI-I values in salt marshes to the highest values in fresh marshes.
Wetland area has decreased in most parts of the world and remains threatened by human pressures. However, wetland loss is difficult to accurately detect, delineate and quantify. While wetland distribution is influenced mainly by landform, LiDAR data provide accurate digital elevation models that can be used to delineate wetlands. Our objective was to map wetland loss at a fine-scale using LiDAR data and historical aerial photographs based on a functional typology that identifies potential, existing and efficient wetlands. The study focused on a 132 km2 site with valley bottom wetlands located in western France. Boundaries of potential wetlands were extracted from a LiDAR-derived Digital Terrain Model that was standardized according to channel network elevation. We identified existing wetlands using interpretation of aerial photographs acquired in 1952, 1978 and 2012. We used multiple correspondence analysis to identify different types of wetland loss. Results show that potential wetlands were successfully delineated at 1:5000 (88–90% overall accuracy) and that 14% of existing wetland area was lost. This highlights the importance of identifying “negotiation areas” where wetland restoration is a priority. The results also reveal two main types of wetland loss based on area, geomorphic context, land cover and period of loss.
Remote sensing is one of the most important tools in ecology and conservation for an effective monitoring of ecosystems in space and time. Hence, a proper training is crucial for developing effective conservation practices based on remote sensing data. In this paper we aim to highlight the potential of open-access data and open-source software and the importance of the inter-linkages between these and remote sensing training, with an interdisciplinary perspective. We will first deal with the importance of open access data by further providing several examples of Free and Open Source Software (FOSS) for a deeper and more critical understanding of remote sensing applications.
National Wetland Inventory (NWI) maps are the most comprehensive wetland maps in the U.S., but NWI maps are now outdated in many regions. A consortium led by Ducks Unlimited is updating the NWI for the Great Lakes/Atlantic Region. Updates are complete for several states but have not been verified extensively with field data. We used wetland maps from 129 on-site wetland delineation projects in three Illinois regions to assess accuracy of original and updated NWI maps. We used ancillary spatial data to characterize areas that were incorrectly classified and identify potential sources of error. Across the three regions, the original NWI omitted 49 % of total wetland area for wetlands greater than 0.2 ha, and 57 % of the area mapped by the NWI was non-wetland. The updated NWI omitted less wetland area (40 % omitted for wetlands larger than 0.2 ha), but only slightly improved errors of commission (55 % of mapped area was non-wetland). Polygons mapped as forested wetlands were less likely to be truly wetlands. Small (<0.06 ha) wetlands were often omitted. Errors reflect ambiguity in the definition of wetlands and technical limitations of the NWI methodology. Due to the high error rates, we recommend further refinement of regional wetland inventories.
This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis
(OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer’s and user’s accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively,
and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods.
A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.
The Prairie Pothole Region of North America is characterized by numerous, small, wetland depressions that perform important ecological and hydrological functions. Recent studies have shown that total wetland area in the region is decreasing due to cumulative impacts related to natural and anthropogenic changes. The impact of wetland losses on landscape hydrology is an active area of research and management. Various spatially distributed hydrologic models have been developed to simulate effects of wetland depression storage on peak river flows, frequently using dated geospatial wetland inventories. We describe an innovative method for identifying wetland depressions and quantifying their nested hierarchical bathymetric/topographic structure using high-resolution light detection and ranging (LiDAR) data. This contour tree method allows identified wetland depressions to be quantified based on their dynamic filling-spilling-merging hydrological processes. In addition, wetland depression properties, such as surface area, maximum depth, mean depth, storage volume, etc., can be computed for each component of a depression as well as the compound depression. We successfully applied the proposed method to map wetland depressions in the Little Pipestem Creek watershed in North Dakota. The methods described in this study will provide more realistic and higher resolution data layers for hydrologic modeling and other studies requiring characterization of simple and complex wetland depressions, and help prioritize conservation planning efforts for wetland resources.
The project VegFrance is presently designed in order to elaborate a national vegetation database covering all vegetation types and regions. Launched in January 2012, this project is commonly led by Research Institutions as National Center for Scientific Research (UMS3468, BBEES & UMR 6553 ECOBIO), the Museum National Histoire Naturelle (Service du Patrimoine naturel), Fédération Conservatoires Botaniques Nationaux, the French association for Phytosociology and the Ministry of Ecology. The projected database will integrate three main types of dataset: syntaxa reflecting the national classification, relevés describing the vegetation at the landscape level (i.e. synphytosociological relevés) and analytical relevés or any plots which properly describe vegetation. This project is developed in strong connection with the European Vegetation Survey and with the national project for producing a vegetation map (CarHab).
Mapping wetlands across both natural and human-altered landscapes is important for the management
of these ecosystems. Though they are considered important landscape elements providing both ecological
and socioeconomic benefits, accurate wetland inventories do not exist in many areas. In this study, a
multi-scale geographic object-based image analysis (GEOBIA) approach was employed to segment three
high spatial resolution images acquired over landscapes of varying heterogeneity due to humandisturbance
to determine the robustness of this method to changing scene variability. Multispectral
layers, a digital elevation layer, normalized-difference vegetation index (NDVI) layer, and a first-order
texture layer were used to segment images across three segmentation scales with a focus on accurate
delineation of wetland boundaries and wetland components. Each ancillary input layer contributed to
improving segmentation at different scales. Wetlands were classified using a nearest neighbor approach
across a relatively undisturbed park site and an agricultural site using GeoEye1 imagery, and an urban
site using WorldView2 data. Successful wetland classification was achieved across all study sites with
an accuracy above 80%, though results suggest that overall a higher degree of landscape heterogeneity
may negatively affect both segmentation and classification. The agricultural site suffered from the greatest
amount of over and under segmentation, and lowest map accuracy (kappa: 0.78) which was partially
attributed to confusion among a greater proportion of mixed vegetated classes from both wetlands and
uplands. Accuracy of individual wetland classes based on the Canadian Wetland Classification system
varied between each site, with kappa values ranging from 0.64 for the swamp class and 0.89 for the
marsh class. This research developed a unique approach to mapping wetlands of various degrees of
disturbance using GEOBIA, which can be applied to study other wetlands of similar settings.
Procéder à la restauration de la dynamique naturelle d'un écosystème passe par un suivi scientifique efficace des opérations et de leurs effets écologiques. Après vingt ans de suivi de la réhabilitation de la tourbière de Landemarais en Bretagne, quels sont les résultats sur le maintien des espèces et des habitats ? Quelles sont les améliorations à envisager dans les pratiques de gestion ?
Appropriate integration of remote sensing technologies into ecosystem services concepts and practices leads to potential practical benefits for the protection of biodiversity and the promotion of sustainable use of Earth's natural assets. The last decade has seen the rapid development of research efforts on the topic of ecosystem services, which has led to a significant increase in the number of scientific publications. This systematic review aims to identify, evaluate and synthesise the evidence provided in published peer reviewed studies framing their work in the context of spatially explicit remote sensing assessment and valuation of ecosystem services. Initially, a search through indexed scientific databases found 5920 papers making direct and/or indirect reference to the topic of “ecosystem services” between the years of 1960 and 2013. Among these papers, 211 make direct reference to the use of remote sensing. During the search we aimed at selecting papers that were peer-reviewed publications available through indexed bibliographic databases. For this reason, our literature search did not include books, grey literature, extended abstracts and presentations. We quantitatively present the growth of remote sensing applications in ecosystem services’ research, reviewing the literature to produce a summary of the state of available and feasible remote sensing variables used in the assessment and valuation of ecosystem services. The results provide valuable information on how remotely sensed Earth observation data are used currently to produce spatially-explicit assessments and valuation of ecosystem services. Using examples from the literature we produce a concise summary of what has been done, what can be done and what can be improved upon in the future to integrate remote sensing into ecosystem services research. The reason for doing so is to motivate discussion about methodological challenges, solutions and to encourage an uptake of remote sensing technology and data where it has potential practical applications.
The advancement of synthetic aperture radar (SAR) technology with high-resolution and quad-polarization data demands better and efficient polarimetric SAR (PolSAR) speckle-filtering algorithms. Two requirements on PolSAR speckle filtering are proposed: 1) speckle filtering should be applied to distributed media only, and strong hard targets should be kept unfiltered; and 2) scattering mechanism preservation should be taken into consideration, in addition to speckle reduction. The purpose of this paper is twofold: 1) to propose an effective algorithm that is an extension of the improved sigma filter developed for single-polarization SAR; and 2) to investigate speckle characteristics and the need for speckle filtering for very high resolution (decimeter) PolSAR data. The proposed filter was specifically developed to account for the aforementioned two requirements. Its effectiveness is demonstrated with Jet Propulsion Laboratory airborne synthetic aperture radar data, and comparisons are made with a boxcar filter, the refined Lee filter, and a Wishart-based nonlocal filter. For very high resolution PolSAR systems, such as the German Aerospace Center F-SAR and Japanese Pi-SAR2, with decimeter spatial resolution, we found that the complex Wishart distribution is still valid to describe PolSAR speckle characteristics of distributed media and that speckle filtering may be needed depending on the size of objects to be analyzed. F-SAR X-band data with 25-cm resolution is used for illustration.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
caret has several functions that attempt to streamline the model building and evaluation process. The train function can be used to • evaluate, using resampling, the effect of model tuning parameters on performance • choose the “optimal ” model across these parameters • estimate model performance from a training set To optimize tuning parameters of models, train can be used to fit many predictive models over a grid of parameters and return the “best ” model (based on resampling statistics). See Table 1 for the models currently available. As an example, the multidrug resistance reversal (MDRR) agent data is used to determine a predictive model for the “ability of a compound to reverse a leukemia cell’s resistance to adriamycin” (Svetnik et al, 2003). For each sample (i.e. compound), predictors are calculated that reflect characteristics of the molecular structure. These molecular descriptors are then used to predict assay results that reflect resistance. The data are accessed using data(mdrr). This creates a data frame of predictors called mdrrDescr and a factor vector with the observed class called mdrrClass. To start, we will: • use unsupervised filters to remove predictors with unattractive characteristics (e.g. distributions or high inter–predictor correlations) spare • split the entire data set into a training and test setThe caret Package • center and scale the training and test set using the predictor means and standard deviations from the training set See the package vignette “caret Manual – Data and Functions ” for more details about these operations.> print(ncol(mdrrDescr)) [1] 342> nzv <- nearZeroVar(mdrrDescr)> filteredDescr <- mdrrDescr[,-nzv]> print(ncol(filteredDescr)) [1] 297> descrCor <- cor(filteredDescr)> highlyCorDescr <- findCorrelation(descrCor, cutoff = 0.75)> filteredDescr <- filteredDescr[,-highlyCorDescr]> print(ncol(filteredDescr)) [1] 50> set.seed(1)> inTrain <- sample(seq(along = mdrrClass), length(mdrrClass)/2)> trainDescr <- filteredDescr[inTrain,]> testDescr <- filteredDescr[-inTrain,]> trainMDRR <- mdrrClass[inTrain]> testMDRR <- mdrrClass[-inTrain]> print(length(trainMDRR)) [1] 264> print(length(testMDRR)) [1] 264> preProcValues <- preProcess(trainDescr)> trainDescr <- predict(preProcValues, trainDescr)> testDescr <- predict(preProcValues, testDescr)
Impacts of climate change on US wetlands will add to those of historical impacts due to other causes. In the US, wetland losses and degradation result from drainage for agriculture, filling for urbanization and road construction. States that rely heavily on agriculture (California, Iowa, Illinois, Missouri, Ohio, Indiana) have lost over 80% of their historical area of wetlands, and large cities, such as Los Angeles and New York City, have retained only tiny remnants of wetlands, all of which are highly disturbed. The cumulative effects of historical and future degradation will be difficult to abate. A recent review of mitigation efforts in the US shows a net loss of wetland area and function, even though ‘no net loss’ is the national policy and compensatory measures are mandatory. US policy does not include mitigation of losses due to climate change. Extrapolating from the regulatory experience, one can expect additional losses in wetland areas and in highly valued functions. Coastal wetlands will be hardest hit due to sea-level rise. As wetlands are increasingly inundated, both quantity and quality will decline. Recognition of historical, current and future losses of wetland invokes the precautionary principal: avoid all deliberate loss of coastal wetland area in order to reduce overall net loss. Failing that, our ability to restore and sustain wetlands must be improved substantially.