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... In drylands, the occurrence of water is intermittent and difficult to predict, and earth observations represent the only way of quantifying and monitoring these dynamics across large spatial extents and through time (Leblanc et al., 2012). The availability of public domain, temporally deep satellite data archives, together with improvements in algorithms and available computing power , have led to huge leaps in recent years in mapping surface water dynamics at the regional (Carroll et al., 2017;Tulbure and Broich, 2013;Tulbure et al., 2016;Zou et al., 2018), continental (Jones, 2019(Jones, , 2015, and global scales (Pekel et al., 2016;Pickens et al., 2020). Most of these mapping efforts have been based on optical satellite data such as MODIS (Klein et al., 2017) at daily intervals but coarser resolution (250 m) or the entire Landsat archive at low temporal resolution (16-day repeat frequency) and medium spatial resolution (30 m). ...
... In prior work where accuracy assessment was conducted for the entire time monitored with Landsat time series, the only reference data source was the Landsat archive itself (Pickens et al., 2020;Tulbure et al., 2016). The standard for reference data is that the reference classification must be of higher quality than the map itself and cover the entire time period monitored. ...
... Our overall accuracy was 99% with user's accuracy (complement of commission error) for the water class of 80% (±3.6%) and producer's accuracy (complement of omission error) for the water class of 76% (±5.6%), similar to other studies mapping surface water/wetlands (Bwangoy et al., 2010;Midekisa et al., 2014;Tulbure et al., 2016;Tulbure and Broich, 2013;Wright and Gallant, 2007) and to what is expected of remote sensing accuracy assessments (Foody, 2008). In addition, our work here mapped not only open water, but also floods, which are more challenging to detect than open water because their spectral signature can be different from that of permanent water or seasonally inundated areas due to sediment load, turbidity, dissolved matter, algal content, depth, and bottom reflectance signal (Pekel et al., 2016). ...
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Spatiotemporal quantification of surface water and flooding is essential given that floods are among the largest natural hazards. Effective disaster response management requires near real-time information on flood extent. Satellite remote sensing is the only way of monitoring these dynamics across vast areas and over time. Previous water and flood mapping efforts have relied on optical time series, despite cloud contamination. This reliance on optical data is due to the availability of systematically acquired and easily accessible optical data globally for over 40 years. Prior research used either MODIS or Landsat data, trading either high temporal density but lower spatial resolution or lower cadence but higher spatial resolution. Both MODIS and Landsat pose limitations as Landsat can miss ephemeral floods, whereas MODIS misses small floods and inaccurately delineates flood edges. Leveraging high temporal frequency of 3–4 days of the existing Landsat-8 (L8) and two Sentinel-2 (S2) satellites combined, in this research, we assessed whether the increased temporal frequency of the three sensors improves our ability to detect surface water and flooding extent compared to a single sensor (L8 alone). Our study area was Australia’s Murray-Darling Basin, one of the world’s largest dryland basins that experiences ephemeral floods. We applied machine learning to NASA’s Harmonized Landsat Sentinel-2 (HLS) Surface Reflectance Product, which combines L8 and S2 observations, to map surface water and flooding dynamics. Our overall accuracy, estimated from a stratified random sample, was 99%. Our user’s and producer’s accuracy for the water class was 80% (±3.6%, standard error) and 76% (±5.8%). We focused on 2019, one of the most recent years when all three HLS sensors operated at full capacity. Our results show that water area (permanent and flooding) identified with the HLS was greater than that identified by L8, and some short-lived flooding events were detected only by the HLS. Comparison with high resolution (3 m) PlanetScope data identified extensive mixed pixels at the 30 m HLS resolution, highlighting the need for improved spatial resolution in future work. The HLS has been able to detect floods in cases when one sensor (L8) alone was not, despite 2019 being one of the driest years in the area, with few flooding events. The dense optical time-series offered by the HLS data is thus critical for capturing temporally dynamic phenomena (i.e., ephemeral floods in drylands), highlighting the importance of harmonized data such as the HLS.
... This dataset provides the global surface water location, extent, and variation data from 1984 to 2019, with a resolution of 30 m. Generated from 4,185,439 scenes of Landsat 5, 7, and 8 from 16 March 1984 to 31 December 2019, each pixel was separately divided into water/non-water using an expert system, and the results were collated into month and year records. All Landsat images over multiple decades were used to map seasonal changes of the surface water at continental [38] and sub-continental [39] scales with high accuracy. The JRC dataset extends previous work by using the entire multi-temporal orthorectified Landsat 5, 7 and 8 data [40]. ...
... This dataset provides the global surface water location, extent, and variation data from 1984 to 2019, with a resolution of 30 m. Generated from 4,185,439 scenes of Landsat 5, 7, and 8 from 16 March 1984 to 31 December 2019, each pixel was separately divided into water/non-water using an expert system, and the results were collated into month and year records. All Landsat images over multiple decades were used to map seasonal changes of the surface water at continental [38] and subcontinental [39] scales with high accuracy. The JRC dataset extends previous work by using the entire multi-temporal orthorectified Landsat 5, 7 and 8 data [40]. ...
... Landsat images are widely used in lake water area extraction [37][38][39]44,45]. JRC data were generated from Landsat 5, 7, and 8, and a consistent algorithm was applied to all 32 years of Landsat observations to produce a validated dataset that documents global surface water dynamics with the best levels of spatial detail and accuracy to date [40]. ...
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Taitema Lake, located in the lower reaches of the Tarim River and the Cherchen River, is one of the most important ecological barriers in Ruoqiang County. The amount of water in Taitema Lake plays an important role in maintaining a healthy cycle within the ecosystem, curbing sandstorms, and improving salinization and desertification. The aim of this study was to reasonably determine the volume of ecological water conveyance by calculating the ecological water demand. We systematically analyzed the spatial and temporal variation characteristics of Taitema Lake during 21 ecological water conveyance processes from 2000 to 2020. The results showed that the area of Taitema Lake increased at a rate of 144% per year because of the Ecological Water Conveyance Project (EWCP). The areas of water in dry, normal, and high flow years were 30.35, 57.76, and 103.5 km2, respectively. The corresponding ecological water demand was 1.58 × 108, 3.09 × 108, and 5.66 × 108 m3, respectively. We calculated that the Cherchen River and the Tarim River carried 0.87 × 108–3.11 × 108 m3 and 0.71 × 108–2.55 × 108 m3 of water, respectively, under different inflow frequencies. This study has significance as a reference for estimates of the ecological water demand of terminal lakes under the condition of artificial water transport in arid inland river basins, and provides the basis for the rational allocation of water resources in the Tarim River Basin.
... Satellite remote sensing offers capacity to continuously monitor and produce surface water maps at various spatio-temporal scales (Alsdorf et al., 2007;Halabisky et al., 2016;Li et al., 2018;Pekel et al., 2016;Seiler et al., 2009;Tulbure et al. 2016) which can fill the gaps in poorly ungauged basins. The benefits of integrating spatio-temporal information in local planning processes are well recognised. ...
... In Australia, for example, the 2011 extreme floods caused severe socio-economic damages and triggered the government to initiate a country-wide multi-year mapping of surface water using earth observation data and obtain accurate spatio-temporal information on surface water change and dynamics to support local planning (Mueller et al., 2016). With the opening of Landsat archives in 2008 (Wulder et al., 2012), temporally consistent and free-to-use satellite imagery spanning over 40 years are now available, and has since been instrumental in improving our understanding of global surface water dynamics (Feng et al., 2016;Pekel et al., 2016;Pickens et al., 2020) and in many local settings (Deng et al., 2019;Mueller et al., 2016;Tulbure et al., 2016;Tulbure et al., 2014;Xia et al., 2019). The 10 m resolution imagery from Copernicus Sentinel-2A and-2B satellites provide even more spatially enhanced capability than Landsat to map surface water (Yang et al., 2020) but data from these satellites are only available since 2015, and do not yet provide elaborate historical information on changes and dynamics of surface water. ...
... Cloud cover is also a key obstacle to accurate mapping of flash floods, but recent availability of multi-temporal optical satellite data and free Synthetic Aperture Radar (SAR) data has enhanced the capability to map surface water despite cloud cover (DeVries et al., 2020;Hardy et al., 2019;De Groeve, 2010). Despite recent methodological advancements in surface water mapping (Feyisa et al., 2014;Isikdogan et al., 2017;Tulbure et al., 2016;Isikdogan et al., 2017;Lary et al., 2016) coupled with the availability of free-to use satellite data covering several decades (Loveland and Dwyer, 2012;Loveland and Irons 2016;Wulder et al., 2012Wulder et al., , 2015, accurate spatio-temporal information on surface water changes and dynamics is still lacking for many humaninhabited basins which experience extreme floods. In such basins, publicly available global surface water datasets (e.g. ...
... Satellite remote sensing offers capacity to continuously monitor and produce surface water maps at various spatio-temporal scales (Alsdorf et al., 2007;Halabisky et al., 2016;Li et al., 2018;Pekel et al., 2016;Seiler et al., 2009;Tulbure et al. 2016) which can fill the gaps in poorly ungauged basins. The benefits of integrating spatio-temporal information in local planning processes are well recognised. ...
... In Australia, for example, the 2011 extreme floods caused severe socio-economic damages and triggered the government to initiate a country-wide multi-year mapping of surface water using earth observation data and obtain accurate spatio-temporal information on surface water change and dynamics to support local planning (Mueller et al., 2016). With the opening of Landsat archives in 2008 (Wulder et al., 2012), temporally consistent and free-to-use satellite imagery spanning over 40 years are now available, and has since been instrumental in improving our understanding of global surface water dynamics (Feng et al., 2016;Pekel et al., 2016;Pickens et al., 2020) and in many local settings (Deng et al., 2019;Mueller et al., 2016;Tulbure et al., 2016;Tulbure et al., 2014;Xia et al., 2019). The 10 m resolution imagery from Copernicus Sentinel-2A and-2B satellites provide even more spatially enhanced capability than Landsat to map surface water (Yang et al., 2020) but data from these satellites are only available since 2015, and do not yet provide elaborate historical information on changes and dynamics of surface water. ...
... Cloud cover is also a key obstacle to accurate mapping of flash floods, but recent availability of multi-temporal optical satellite data and free Synthetic Aperture Radar (SAR) data has enhanced the capability to map surface water despite cloud cover (DeVries et al., 2020;Hardy et al., 2019;De Groeve, 2010). Despite recent methodological advancements in surface water mapping (Feyisa et al., 2014;Isikdogan et al., 2017;Tulbure et al., 2016;Isikdogan et al., 2017;Lary et al., 2016) coupled with the availability of free-to use satellite data covering several decades (Loveland and Dwyer, 2012;Loveland and Irons 2016;Wulder et al., 2012Wulder et al., , 2015, accurate spatio-temporal information on surface water changes and dynamics is still lacking for many humaninhabited basins which experience extreme floods. In such basins, publicly available global surface water datasets (e.g. ...
Article
Spatio-temporal characterization of surface water dynamics with Landsat in the Cuvelai basin of Namibia
... Satellite remote sensing offers capacity to continuously monitor and produce surface water maps at various spatio-temporal scales (Alsdorf et al., 2007;Halabisky et al., 2016;Li et al., 2018;Pekel et al., 2016;Seiler et al., 2009;Tulbure et al. 2016) which can fill the gaps in poorly ungauged basins. The benefits of integrating spatio-temporal information in local planning processes are well recognised. ...
... In Australia, for example, the 2011 extreme floods caused severe socio-economic damages and triggered the government to initiate a country-wide multi-year mapping of surface water using earth observation data and obtain accurate spatio-temporal information on surface water change and dynamics to support local planning (Mueller et al., 2016). With the opening of Landsat archives in 2008 (Wulder et al., 2012), temporally consistent and free-to-use satellite imagery spanning over 40 years are now available, and has since been instrumental in improving our understanding of global surface water dynamics (Feng et al., 2016;Pekel et al., 2016;Pickens et al., 2020) and in many local settings (Deng et al., 2019;Mueller et al., 2016;Tulbure et al., 2016;Tulbure et al., 2014;Xia et al., 2019). The 10 m resolution imagery from Copernicus Sentinel-2A and-2B satellites provide even more spatially enhanced capability than Landsat to map surface water (Yang et al., 2020) but data from these satellites are only available since 2015, and do not yet provide elaborate historical information on changes and dynamics of surface water. ...
... Cloud cover is also a key obstacle to accurate mapping of flash floods, but recent availability of multi-temporal optical satellite data and free Synthetic Aperture Radar (SAR) data has enhanced the capability to map surface water despite cloud cover (DeVries et al., 2020;Hardy et al., 2019;De Groeve, 2010). Despite recent methodological advancements in surface water mapping (Feyisa et al., 2014;Isikdogan et al., 2017;Tulbure et al., 2016;Isikdogan et al., 2017;Lary et al., 2016) coupled with the availability of free-to use satellite data covering several decades (Loveland and Dwyer, 2012;Loveland and Irons 2016;Wulder et al., 2012Wulder et al., , 2015, accurate spatio-temporal information on surface water changes and dynamics is still lacking for many humaninhabited basins which experience extreme floods. In such basins, publicly available global surface water datasets (e.g. ...
Article
Socio-economic damages caused by extreme floods have been increasing rapidly in recent years, mainly driven by changes in the climate and modulated by increasing human population in deltic areas and floodplains. The Cuvelai-Etosha Basin (CEB) in southern Africa, covering southern Angola and northern Namibia, experiences socially and economically devastating extreme floods. Yet, accurate information on past and current surface water changes and dynamics is lacking. Here, we estimate and map the surface water extents in the CEB and its surroundings (CEB + S) for 32 years (1990–2021) from Landsat data using random forest models to provide long-term baseline information on surface water changes and dynamics. Based on the reference data, a total of 15,677 ± 1080 km2 have been inundated by surface water in the CEB + S during 1990–2021. This extent was accurately mapped by our local water extent product (mapped area = 16,273 km2, user’s accuracy = 91.5 ± 2.5%, producer’s accuracy = 91.1 ± 6%). With user’s and producer’s accuracy of 91%, our overall water extent provides the first most accurate long-term baseline information on surface water inundation in CEB + S necessary for local spatial planning processes to minimise future negative impacts of floods in the basin. Interannual variability of surface water extent is, however, high, with water extent ranging from 520.8 ± 375.7 km2 to 12372.3 ± 1154.7 km2 during the 1990–2021 period. The largest annual water extents (>10,000 km2) were recorded in 2006, 2008, 2009, 2011, and 2017, whereas the smallest extents (<1000 km2) were recorded in 1992 and 2019. We found that over 40% of the area inundated in the CEB + S during 1990–2021 was inundated less than 9 times. With human population increasing rapidly in the CEB + S, rarely inundated areas with short water residence could become a prime target for human settlements, which may lead to huge socio-economic damages during extreme floods if no preventive measures are put in place. Globally available surface water maps from the Global Land Analysis and Discovery (GLAD) and European Commission’s Joint Research Centre (JRC) did not provide realistic surface water extent for CEB + S, especially during years with extreme floods. Therefore, locally adopted product for operational monitoring of surface water in the CEB + S is needed to provide accurate information for informing spatial planning processes and surface water resource management strategies in this endorheic basin and help minimise future negative impacts of floods.
... Several factors have come together to now allow for systematically mapping land cover at medium to high spatial resolution (10s of metres) across regional (Broich et al., 2011;Ghorbanian et al., 2020;Tulbure & Broich, 2013;Tulbure et al., 2016), national (Griffiths et al., 2019;Yang et al., 2020) and global extents (Buchhorn et al., 2020;Chen et al., 2017;GLanCE, 2021;Hansen et al., 2013;Pesaresi et al., 2016;Pickens et al., 2020;Potapov et al., 2021;Worldcover, 2021;Zhang et al., 2020Zhang et al., , 2021. Among the most important of these factors is the availability of freely accessible multi-temporal remote sensing datasets in a user-friendly format as 'Analysis Ready Data', ARD (Claverie et al., 2018;Dwyer et al., 2018;Frantz, 2019;Truckenbrodt et al., 2019;Wulder et al., 2016), such as the Committee on Earth Observation Satellites ARD for Land (CARD4L, https://ceos.org/ard/). ...
... For the reasons outlined above, studies have either used image interpretation of classes of interest, based on spectral-temporal information (Broich et al., 2011;Tulbure et al., 2016) or made use of existing layers . Finally, the assignment of class labels requires regional knowledge to properly label the classes of interest, highlighting the need for regional efforts for such data collection. ...
... A key difference between regional and global products is that at regional scales, validation according to best practices (Olofsson et al., 2014) is routinely done and expected (Griffiths et al., 2018;Tulbure et al., 2016). At the global scale, however, this is much more challenging, and not many maps are validated according to best practices, which precludes their usage, with notable exceptions (Pickens et al., 2020). ...
Article
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Unprecedented amounts of analysis‐ready Earth Observation (EO) data, combined with increasing computational power and new algorithms, offer novel opportunities for analysing ecosystem dynamics across large geographic extents, and to support conservation planning and action. Much research effort has gone into developing global EO‐based land‐cover and land‐use datasets, including tree cover, crop types, and surface water dynamics. Yet there are inherent trade‐offs between regional and global EO products pertaining to class legends, availability of training/validation data, and accuracy. Acknowledging and understanding these trade‐offs is paramount for both developing EO products and for answering science questions relevant for ecology or conservation studies based on these data. Here we provide context on the development of global EO‐based land‐cover and land‐use datasets, and outline advantages and disadvantages of both regional and global datasets. We argue that both types of EO‐derived land‐cover datasets can be preferable, with regional data providing the context‐specificity that is often required for policy making and implementation (e.g., land‐use and management, conservation planning, payment schemes for ecosystem services), making use of regional knowledge, particularly important when moving from land cover to actors. Ensuring that global and regional land‐cover and land‐use products derived based on EO data are compatible and nested, both in terms of class legends and accuracy assessment, should be a key consideration when developing such data. Open access high‐quality training and validation data derived as part of such efforts are of utmost importance. Likewise, global efforts to generate sets of essential variables for climate change, biodiversity, or eventually land use, which often require land‐cover maps as inputs, should consider regionalized, hierarchical approaches to not sacrifice regional context. Global change impacts manifest in regions, and so must the policy and planning responses to these challenges. EO data should embrace that regions matter, perhaps more than ever, in an age of global data availability and processing.
... There are many methods available for mapping surface water using surface reflectance from remote sensing data [16][17][18] including the Landsat series [19][20][21][22]. Some of the more complex methods use machine learning or other classification techniques [23][24][25][26][27]. Spectral indices are typically used for mapping surface water due to their simplicity and ease of application [28][29][30][31][32]. ...
... One example is for the Barmah-Millewa Forest (whose extent can be seen in Figure 7a) located in the southern section of the MDB, and which is listed as a Ramsar wetland site. This floodplain area consists of open canopy forest (~70%) and woodlands (~29%) [25]. Figure 7b, c, and d show the multi-index method, TCW np035 , and mNDWI np3 from a Landsat image acquired in December 2010 during a flooding period. ...
... The magenta line shows the major river, and orange shows the extent of ANAE wetlands. mNDWI np3 (Figure 7d) shows much less flood extent within this forested wetland area compared to TCW np035 (Figure 7c [25] for the flood event for the ANAE wetland area, making the extent shown in the multi-index method within the wetland area plausible. However, less water is identified using the multi-index method in the northern section of the floodplain (Figure 7b) compared to Tulbure et al. (2016) [25], and TCW np035 (Figure 7c) compares much better. ...
Article
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Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.
... Meanwhile, an efficient water extraction method is critical to obtaining accurate water surface extent from remote sensing images [24]. The water index followed by a threshold was widely used to extract surface water body for its convenience [25]. At present, the most widely used water body indexes include the normalized differential water index (NDWI), the modified normalized differential water index (MNDWI) [26]- [27]. ...
... AWEInsh is an index formulated to effectively eliminate nonwater pixels, including dark built surfaces in areas with urban background and AWEIsh is primarily formulated for further improvement of accuracy by removing shadow pixels that AWEInsh may not effectively eliminate [28]. However, an ideal single threshold to distinguish between water bodies and non-water bodies is difficult to determine because the spectral signature of a water body varies in space and time [25]. We aim to extract the water surface extent of Dongting Lake in the Yangtze River Basin through a relatively simple, fast and accurate method, so this study extracts the water area of Dongting Lake with a new water detection rule developed by Deng et al. [14]. ...
Article
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Understanding the variation regularity of water extent can provide insights into lake conservation and management. In this study, inter- and inner-annual variations of water extent during the period of 19872020 were analyzed to understand the temporal and spatial distribution characteristics of Dongting Lake. We applied the Multiple Index Water Detection Rule (MIWDR) to extract the Dongting Lake water extent quickly and accurately based on Google Earth Engine (GEE) platform, and then assessed the extraction accuracy. The water surface analysis results showed that: (1) Based on SDG 6.6.1, the trend of water extent showed the downward fluctuating trend from 1987 to 2020, with the overall average water extent being 1894.48km. (2) Among the monthly average water area, the largest extent was 2477.14km (July), and the smallest was 848.14km (January). Among the seasonal mean water area, summer was the largest, with an area of 2438.06km, and winter was the smallest at 967.34km. (3) For the Water Inundation Frequency, seasonal water body accounted for the largest proportion, with 1577.85 km; the non-water area was the smallest, with the area of 573.02 km; and the permanent water area was 1086.21 km. Through the analysis of the historical water body extent of the long time series of Dongting Lake, this study reflected support for SDG, for which the research idea and design can help us understand the importance and feasibility of the SDG 6.6.1 Indicator.
... Compared to the VI method, the automatic extraction method has higher extraction efficiency. Generally, it can be divided into index analysis-based method [8][9][10][11][12], edge detection-based method [7,[13][14][15], threshold segmentation-based method [16][17][18][19][20], region growth-based method [7], neural network-based method [21][22][23][24] and sub-pixel method [16,[25][26][27][28][29]. ...
... The recent decades have seen the driving advance of neural networks in various visual recognition fields such as object detection [37,38], image classification [39], and semantic segmentation [40,41]. However, the calculation process of the neural network method is complicated and it needs pixel samples as pure as possible [23,24]. The sub-pixel method can obtain waterline results better than pixel level by reducing the influence of mixed pixels on waterline extraction. ...
Article
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Coastline is an important geographical element of the boundary between ocean and land. Due to the impact of the ocean-land interactions at multiple temporal-spatial scales and the intensified human activities, the waterline of muddy coast is undergoing long-term and continuous dynamic changes. Using traditional remote sensing-based waterline extraction methods, it is difficult to achieve ideal results for muddy coast waterlines, which are faced with problems such as limited algorithm stability, weak algorithm migration, and discontinuous coastlines extraction results. In response to the above challenges, three different types of muddy coasts, Yancheng, Jiuduansha and Xiangshan were selected as the study areas. Based on the Sentinel-2 MSI images, we proposed an adaptive remote sensing extraction algorithm framework for the complex muddy coast waterline, named AEMCW (Adaptive Extraction for Muddy Coast Waterline), including main procedures of high-pass filtering, histogram statistics and adaptive threshold determination, which has the capability to obtain continuous and high-precision muddy coastal waterline. NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index) and ED (Edge Detection) methods were selected to compare the extraction effect of AEMCW method. The length and spatial accuracy of these four methods were evaluated with the same criteria. The accuracy evaluation presented that the length errors of ED method in all three study areas were minimum, but the waterline results were offset more to the land side, due to spectral similarity, turbid water and tidal flats having similar values of NDWI and MNDWI. Therefore, the length and spatial accuracies of NDWI and MNDWI methods were lower than AEMCW method. The length errors of the AEMCW algorithm in Yancheng, Jiuduansha, and Xiangshan were 14.4%, 18.0%, and 7.7%, respectively. The producer accuracies were 94.3%, 109.6%, and 94.2%, respectively. The user accuracies were 82.4%, 92.9%, and 87.5%, respectively. These results indicated that the proposed AEMCW algorithm can effectively restrain the influence of spectral noise from various land cover types and ensure the continuity of waterline extraction results. The adaptive threshold determination equation reduced the influence of human factors on threshold selection. The further application on ZY-1 02D hyperspectral images in the Yancheng area verified the proposed algorithm is transferable and has good stability.
... In addition, optical remote sensing is limited by cloud cover and topography, particularly in mountainous areas with high relief. Although we used spatial data that had been subjected to a statistically rigorous validation process, the presence of cloud cover may not have been completely eliminated [27,43]. As the vegetation cover in the Zoigê area is relatively high, and waterbodies with a vegetation cover were easily classified as vegetation, it could have affected the results of the connectivity evaluation. ...
... There are a large number of artificial ditches in the northeastern part of the Zoigê area, which facilitate the drainage of groundwater. However, the role of connected artificial ditches in draining water from peatland mainly occurs during the dry season of the year in the Zoigê area, which means that we may not be able to monitor some flooding events [43]. ...
Article
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The sustainability of wetlands is threatened by the past and present land use practices. Hydrological connectivity is one of the most important aspects to consider for wetland rehabilitation planning purposes. Circuit theory and connectivity indices can be used to model and assess hydrological connectivity. The aim of this study was to assess spatiotemporal variation in the hydrological connectivity of the Zoigê area from 2000–2019 using both methods. The study area contains a Ramsar wetland of international importance, namely the Sichuan Ruoergai Wetland National Nature Reserve. We used a global surface water observation product as the major input for both methods, and then analyzed the temporal and spatial characteristics, in terms of important components and patches. We found that the overall connectivity has increased slightly in the last 20 years, while the probability of connection between patches of surface water has increased significantly. Important components and patches represent steppingstone habitat for the dispersal of organisms in the landscape. The main determinants of hydrological connectivity are mostly human oriented, predominantly a decrease in large livestock population size and population increase.
... SWD is another Landsatbased water body time series product. It follows a slightly different approach, using a random forest classifier and spans the timeframe of 1986-2011 at a temporal resolution of ~3 months [42,177]. There are also global Landsat-based products and approaches that try to advance on GSW. ...
... The majority of studies concentrating on Australia (56%) have a focus on the anthroposphere, particularly the assessment of drought-and flood-related surface water dynamics [42,165,174,177,252]. The rest focus on surface water area dynamics in general [124,253], or specifically concentrate on certain water bodies like lakes and reservoirs [175] or estuaries [90]. ...
Article
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Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.
... The Landsat mission, including the satellites and multi-spectral instruments from Landsat 4, 5, 7, and 8, represents the longest, continuous remote sensing monitoring dataset and is ideally suited for tracking multi-decadal lake dynamics (Wulder et al., 2016) as illustrated by its past and present use in numerous long-term water detection studies (Busker et al., 2019;Mueller et al., 2016;Pekel et al., 2016;Tang et al., 2016;Tulbure & Broich, 2013;Tulbure et al., 2016;Verpoorter et al., 2014;Weekley & Li, 2019;Yao et al., 2019). Despite possessing a modest 16-day to revisit period, overlap along scene edges will provide additional measurements for several water bodies, and the 30-m spatial resolution will enable observation of smaller water bodies with greater accuracy than possible with other sensors featuring better temporal resolution but decreased spatial resolution, like MODIS (Keys & Scott, 2018;Khandelwal et al., 2017;Moradi et al., 2014). ...
... In comparison to techniques like supervised classifications and decision trees, which can be difficult to develop and/or computationally expensive (Huang et al., 2018), water indices, which are calculated from two or more bands based on the spectral characteristics of water and non-water targets, are computationally efficient which makes them ideal for time-series analysis if a suitable segmentation threshold can be identified. This research analyzes several common water indices which have been used in similar large-scale studies (Fisher et al., 2016;Tulbure et al., 2016;Zhou et al., 2017Zhou et al., , 2019 including Normalized Difference Water Index (NDWI; McFeeters, 1996), Modified Normalized Difference Water Index (MNDWI; Xu, 2006), Automated Water Extraction Index (AWEIsh and Figure 2. General processing procedure for estimating water surface elevation for any given lake with merged DEM/ bathymetry data. As mentioned, the accuracy of water indices is highly dependent upon selecting an optimum segmentation threshold which can be a difficult process. ...
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Identifying patterns and trends in long‐term lake dynamics is essential to establish effective water management procedures and boost our understanding of inland water's role in the global water cycle. This research leverages Google Earth Engine to estimate multi‐decadal water surface elevations for 52 lakes and reservoirs with varying physical properties. Water surface elevation was estimated using the entire Landsat 4, 5, 7, and 8 Landsat Top‐of‐Atmosphere Tier‐1 Collection‐1 archive from August 1982 through December 2017 via shoreline boundary statistics extracted from the National Elevation Dataset merged with lake bathymetry. Image contamination was identified and removed to provide elevation estimates for images with varying levels of image contamination. To improve accuracy, data filtering techniques were identified which retained over 70% of images with detectable water boundaries producing 26 lakes with sub‐meter root‐mean‐square‐error accuracy and 40 lakes with sub‐meter mean‐absolute‐error‐accuracy using a general overall parameter model. Additionally, lake‐specific locally optimized models were also determined with 45 of the 52 lakes producing sub‐meter root‐mean‐square‐error accuracies and 49 with sub‐meter mean‐absolute‐errors with individual lake accuracy as low as 0.191 m RMSE CI95%[0.129, 0.243]. In general, individual lake accuracy is highly correlated with the mean slope of the surrounding terrain with low‐slope shorelines having greater accuracy than high‐slope shorelines. Seasonal patterns in estimate accuracy were also identified. This research extends our ability to track lake dynamics over long time periods to lakes lacking traditional in‐situ monitoring, enables rapid assessment of lake dynamics across large areas, and balances a need for both high‐accuracy measurements and maximum temporal resolution.
... Most of the existing water body mapping methods are based on a single image and are calculated at the pixel scale. In practice, to achieve long-term and continuous water body mapping, medium-or low-spatial-resolution remotely sensed images with a high temporal resolution, such as Moderate Resolution Imaging Spectroradiometer (MODIS) [18][19][20][21] and Landsat [22][23][24] images, are commonly used. Based on the continuous observation of these medium-or low-spatial-resolution images, time series datasets of surface water bodies have been generated. ...
... where c 1 (·) and c 2 (·) are the class labels of an arbitrary pixel in the previous fraction image and the current fraction image, respectively. P is calculated as [23] P(c 2 (·) = ω ι | c 1 (·) = ω κ ) = ...
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Water body mapping is an effective way to monitor dynamic changes in surface water, which is of great significance for water resource management. Super-resolution mapping is a valid method to generate high-resolution dynamic water body maps from low-spatial-resolution images. However, the accuracy of existing super-resolution mapping methods is not high due to the low accuracy of fraction images and the insufficiency of spatial pattern information. To solve this problem, this paper proposes a spectral similarity scale-based multiple-endmember spectral mixture analysis (SSS-based MESMA) and a multiscale spatio-temporal dependence method based on super-resolution mapping (MESMA_MST_SRM) for water bodies. SSS-based MESMA allows different coarse pixels to have different endmember combinations, which can effectively improve the accuracy of spectral unmixing and then improve the accuracy of fraction images. Multiscale spatio-temporal dependence adopts both pixel-based and subpixel-based spatial dependence. In this study, eight different types of water body mappings derived from the Landsat 8 Operational Land Imager (OLI) and Google Earth images were employed to test the performance of the MESMA_MST_SRM method. The results of the eight experiments showed that compared with the other four tested methods, the overall accuracy (OA) value, as well as the overall distribution and detailed information of the water map generated by the MESMA_MST_SRM method, were the best, indicating the great potential and efficiency of the proposed method in water body mapping.
... In this paper we are particularly interested in the detection of surface water. Many studies around the world have applied Landsat imagery in the detection of surface water dynamics [15][16][17][18][19]. As a result of the importance of surface water maps, as well as the large spatio-temporal variability of water bodies, many ways to detect the location and amount of surface water from space have been developed [20]. ...
... 19 WRS Path/Row tiles covering Armenia, Azerbaijan, and Georgia in the South Caucasus region, with validation points located in the areas where tiles overlap. ...
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The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.
... Besides mapping surface water using spectral water indices, another category of supervised methods have exploited the potential of ML algorithms to classify water bodies among other LULC classes. The robust classification capability of ML methods, for example SVM and RF, have been confirmed in conducting pixel-wise classification with handlabelled features based on spectral water indices (Zhou et al., 2014;Tulbure et al., 2016), which can be especially helpful when dealing with data sources from disparate satellite missions. However such a model may become insufficient when generalizing to a global or continental scale, since the diversities and characteristics of water and background pixels vary tremendously across different climate regions (Karpatne et al., 2016;Zhu et al., 2020). ...
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Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning (ML). High-resolution maps of inland surface water bodies are important for water supply and natural disaster mitigation as well as for monitoring, managing, and preserving landscapes and ecosystems. In this paper, we propose an automatic surface water mapping workflow by training a deep residual neural network (ResNet) based on OpenStreetMap (OSM) data and Sentinel-2 multispectral data, where the Simple Non-Iterative Clustering (SNIC) superpixel algorithm was employed for generating object-based training samples. As a case study, we produced an open surface water layer for Germany using a national ResNet model at a 10 m spatial resolution, which was then harmonized with OSM data for final surface water products. Moreover, we evaluated the mapping accuracy of our open water products via conducting expert validation campaigns, and comparing to existing water products, namely the WasserBLIcK and Global Surface Water Layer (GSWL). Using 4,600 validation samples in Germany, the proposed model (ResNet+SNIC) achieved an overall accuracy of 86.32% and competitive detection rates over the WasserBLIcK (87.47%) and GSWL (98.61%). This study provides comprehensive insights into how to best explore the synergy of VGI and ML of EO data in a large-scale surface water mapping task.
... Hu [72] extracted the area of four lakes, Zhuonai Lake, Kusai Lake, Heidinor Lake, and Salt Lake, from 1989 to 2018 using Landsat imagery but selected the area of the lakes for only one day of the year to represent the area for the whole year, resulting in inaccurate results as the surface of water bodies varies significantly due to change of seasons [73]. ...
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Lakes are important natural resources closely related to human survival and development. Based on PIE cloud computing platform, the study uses Landsat images and the empirical normalized water body index (ENDWI) to extract water body information of the large lakes in Sichuan province from 2000 to 2020 in the drought and rainy seasons, respectively, and uses the Mann–Kendall test to obtain the long-term trends of their area and climate. On this basis, the evolution of the lakes and their correlation with climate and human activities are analyzed. The results show that (1) In the past 20 years, the area of Lugu Lake, Qionghai Lake, and Luban Reservoir represent a decreasing trend, with Lugu Lake being the most affected. The area of Ma Lake, Three Forks Lake, and Shengzhong Reservoir increased, with the area of Shengzhong Reservoir increasing significantly; (2) During the drought season, all six lakes showed a decreasing trend in precipitation, with the most apparent decreasing trend for Lugu Lake (Slope = −0.8). Only Lugu Lake showed a decreasing trend in precipitation (Slope = −0.15) during the rainy season. The precipitation of Ma Lake, Three Forks Lake, Luban Reservoir and Shengzhong Reservoir showed a significant increasing trend (Slope value was greater than 1.96); (3) The temperatures of the remaining lakes all decreased in the drought season and increased in the rainy season, except that the temperature of Shengzhong Reservoir decreases throughout the year; (4) The area change of plain lakes is greatly affected by human activities, but the area of plateau lakes is are more impacted by climate. Our study improved the accuracy of long-term water body change monitoring with PIE-Engine Studio. Besides, the findings would provide reference for the implementation of sustainable water resources management in Sichuan Province.
... Water indices' techniques (Acharya et al., 2018) such as Normalized Difference Water Index (NDWI) (Xu, 2006), Automated Water Extraction Index (AWEI) (Feyisa et al., 2014), Modified NDWI (MNDWI) (Xu, 2006), Automated Water extraction Index (AWEI) (Feyisa et al., 2014), Normalized Difference Moisture Index (NDMI) (Gang and Dong-sheng, 2012;Gao, 1996;Hardisky et al., 1984), New Water Index (NWI) (Ding et al., 2018;Tian et al., 2017) and Water Ratio Index (WRI) (Shen and Li, 2010) are used widely. Classification methods including decision trees (DTs) (Acharya et al., 2016;Baker et al., 2006;Mueller et al., 2016;Tulbure and Broich, 2013;Tulbure et al., 2016), maximum likelihoods (MLs) (Frazier and Page, 2000;, statistical pattern recognition techniques (Acharya et al., 2018;Ji et al., 2015) are also used to acquire water bodies information. Whereas, recent advancement in automation various machine learning algorithms have been applied to extract water bodies from remote sensing images such as neural networks (NNs) (Rokni et al., 2015), artificial neural networks (ANN) (Skakun, 2012), Support vector machines (SVM) (Sun et al., 2014;Zhang et al., 2013), naive Bayes (NB), random forest (RF), gradient boosted machine (GBM), recursive partitioning and regression trees (RPART), and constraint energy minimizations (CEMs) (Acharya et al., 2019b;Ji et al., 2015;Wu et al., 2008). ...
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Waterbody extraction in the high-elevated region of the Tibetan Plateau from remote sensing imagery is an efficient way to investigate and monitor water resources. The intrusion of shadows, snow/ice, and other impediments are still unconvincing in cryospheric regions. Landsat 8 OLI images were used to extract water bodies by applying various spectral indices such as; Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), Automated Water Extraction Index (AWEI_sh), New Water Index (NWI), Water Ratio Index (WRI), and LST based Water Extraction Index (LBWEI). The result showed that the LBWEI index has high accuracy in all weather conditions from 93.66% to 97.63% and improved consistency around 3% to 7% as compared to other techniques. The study suggests the LBWEI technique may be used in the cryospheric region for quick evaluation and provide the baseline information for researchers in algorithm developments.
... For example, Bodele Depression, part of a dry lake bed at the southern edge of the Sahara Desert, is the world's greatest source of mineral dust, contributing between 6 and 18% to the global Remote sensing imagery products have been invaluable resources for analyzing and monitoring LULC changes, hydrological responses, water erosion, and wind erosion [45][46][47][48]. Given their high temporal coverage, optical satellite remote sensing products have been widely employed for investigating playas' long-term variability of inundation [9,[49][50][51], water surface extent and area [52][53][54], water level [55,56], evaporative water loss [57], and spectral reflectance parameters [58]. However, few studies have been conducted to link the impact of hydrological changes to dust generation from playa surfaces [8,18,59,60]. ...
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Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and meteorological parameters, windblown dust events initiated from Lordsburg Playa, New Mexico, United States, threaten public safety and health through low visibility and exposure to dust emissions. Combining optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas—a potent landform for wind erosion. The optical images provide a long-term data record, while radar images can observe land surface irrespective of clouds, darkness, and precipitation. As a home for optical and radar imagery, powerful algorithms, cloud computing infrastructure, and application programming interface applications, Google Earth Engine (GEE) is an invaluable resource facilitating acquisition, processing, and analysis. In this study, the fractional abundance of soil, vegetation, and water endmembers were determined from pixel mixtures using the linear spectral unmixing model in GEE for Lordsburg Playa. For this approach, Landsat 5 and 8 images at 30 m spatial resolution and Sentinel-2 images at 10–20 m spatial resolution were used. Employing the Interferometric Synthetic Aperture Radar (InSAR) techniques, the playa’s land surface changes and possible sinks for sediment loading from the surrounding catchment area were identified. In this data recipe, a pair of Sentinel-1 images bracketing a monsoon day with high rainfall and a pair of images representing spring (dry, windy) and monsoon seasons were used. The combination of optical and radar images significantly improved the effort to identify long-term changes in the playa and locations within the playa susceptible to hydrological stresses and LULC changes. The linear spectral unmixing algorithm addressed the limitation of Landsat and Sentinel-2 images related to their moderate spatial resolutions. The application of GEE facilitated the study by minimizing the time required for acquisition, processing, and analysis of images, and storage required for the big satellite data.
... Based on the 148 images of surface water distribution (the 42 images contaminated by clouds were discarded to maximize extraction accuracy), we calculated the following: (1) the annual surface water distribution from 1990 to 2020, and (2) the relative frequency of surface water from 1990 to 2020. Relative frequency of surface water was defined as the number of times a pixel was flagged as flooded divided by the number of cloud-free observations per pixel; it was expressed as ranging from 0 to 100% [42]. ...
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Surface water is an important factor affecting vegetation change in desert areas. However, little research has been conducted on the effects of surface water on vegetation expansion. In this study, the annual spatial distribution range of vegetation and surface water in the Daliyabuyi Oasis from 1990 to 2020 was extracted using Landsat time-series images. Based on multi-temporal and multi-scale remote sensing images, several plots were selected to demonstrate the process of landform change and vegetation expansion, and the influence of surface water on vegetation expansion was analyzed. The results show that the vegetation distribution and surface water coverage have increased from 1990 to 2020; and surface water is a critical factor that drives the expansion of vegetation. On the one hand, surface water in the study area was essential for reshaping the riparian landform, driving the transformation of dunes into floodplains, and increasing the potential colonization sites for vegetation. However, landform changes ultimately changed the redistribution of surface water, ensuring that enough water and nutrients provided by sediment were available for plant growth. Our study provides a critical reference for the restoration of desert vegetation and the sustainable development of oases.
... It is worth mentioning that the new Duolun route is located in semi-arid region, where climatic conditions are highly unpredictable [13]. Moreover, the predicted climate change is likely to increase the inter-and intra-annual variations in semi-arid regions [59]. As animal migrations are relatively regular events, of which many features, such as the onset, temporal patterns, and seasonal energy stores, are endogenously programmed [60], resource synchrony and predictability is critical for successful completion of the migratory journey [61]. ...
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In the last 15 years, the west population of white-naped crane (Antigone vipio) decreased dramatically despite the enhanced conservation actions in both breeding and wintering areas. Recent studies highlighted the importance of protecting the integrity of movement connectivity for migratory birds. Widespread and rapid landcover changes may exceed the adaptive capacity of migrants, leading to the collapse of migratory networks. In this study, using satellite tracking data, we modeled and characterized the migration routes of the white-naped crane at three spatial levels (core area, migratory corridor, and migratory path) based on the utilization distribution for two eras (1990s and 2010s) spanning 20 years. Our analysis demonstrated that the white-naped crane shifted its migratory route, which is supported by other lines of evidences. The widespread loss of wetlands, especially within the stopover sites, might have caused this behavioral adaptation. Moreover, our analysis indicated that the long-term sustainability of the new route is untested and likely to be questionable. Therefore, directing conservation effects to the new route might be insufficient for the long-term wellbeing of this threatened crane and large-scale wetland restorations in Bohai Bay, a critical stopover site in the East Asian-Australasian flyway, are of the utmost importance to the conservation of this species.
... Spatial variations studies of shallow lakes are highlighted in large surfaces such as Sweden, Siberia, United States, Canada, among others; related to their resources and physical processes (Hein et al., 2012;McDonald et al., 2012;Karlsson et al., 2014, Mohsen et al., 2018. Landsat imagery is one of the most common types of data employed for mapping surface water (Tulbure et al., 2016). Spatial dynamics of shallow lakes on the Canadian plains were studied using Landsat images with different classification methods and periods. ...
Article
p>The Pampean region in Argentina is an extensive plain characterized by abundant shallow lakes that fulfill many environmental, ecological, and social functions. This study aims to detect the multiannual lake area changes in this region during 2001-2009 using remote sensing, including lakes as small as ≥10,000 m<sup>2</sup> or 1 ha. Landsat scenes of the wet (2008-2009), normal (2006), and dry (2008-2009) seasons were obtained, and using remote sensing techniques, the number and area of shallow lakes were calculated. The spatiotemporal variation of shallow lakes was studied in different climate periods in eight singular subregions. Spatial associations between annual precipitation and lake number and area were analyzed through the development of a Geographic Information System (GIS) at a subregional scale. During the study period the total lake area in the Pampean region decreased by 5257.39 km<sup>2 </sup>(62 %), but each subregion showed different responses to climatic events. In seven of them, the differences between climate periods prove to be statistically significant (P>0.01). The relationship between precipitation and lake number and area revealed the domain of positive association. We conclude that climate factors play a dominant role in lake changes across the Pampean plains. However, other factors such as origin, topographic and edaphic characteristics intensify or mitigate changes in surface hydrology.</p
... Currently, water monitoring via remote sensing technology is mainly focused on optical satellite and synthetic aperture radar (SAR) satellite. Optical data such as Landsat and GF-2 can obtain multispectral images and NDVI indices, and have become commonly used in waterline mapping with its good spatial and temporal resolution [10][11][12][13][14]. However, due to the interference of cloud and light intensity, it is difficult for optical satellites to provide adequate time-series data to continuously monitor the change of water dynamically. ...
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Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds.
... The prevalence of internet access and mobile devices has revolutionized geographic information systems (GIS) promoting web-enabled geo-visualizations. The advent of cloud-computing facilitates incorporating spatio-temporal analysis of remotely-sensed data provides a new platform for expressing various approaches to visualization, interaction, and analysis of scientific data of complex Earth systems (Kehrer & Hauser 2013;Tulbure et al. 2016). The move to web-based geo-visualization enables a visualized narrative for communicating spatio-temporal characteristics of multiple data sets and is essential for making comparisons between data sets. ...
Article
The freeware DiscoverFramework provides new tools to build spatial and temporal data visualization applications accessible to stakeholders, policy makers, scientists, and educators. By focusing on environmental data and supporting applications accessible via laptops, tablets, and cell phones, the DiscoverFramework can be used to increase public awareness and inspire responsible use of complex environmental systems upon which human society depends. DiscoverFramework enables computer-savvy domain scientists to develop interactive applications using “Elements” and workflows defined to make visualization easy and address common problems such as spatio-temporal scales and user engagement. Two applications are used to demonstrate DiscoverFramework: DiscoverWater and DiscoverHABs. DiscoverWater uses Map, Chart, and Text Elements to relate streamflow changes to groundwater withdrawals. DiscoverHABs uses the Scenario Element to aid stakeholders, such as resource managers and users, struggling to identify when and where harmful algal blooms (HABs) are likely given that causal relations in these systems remain poorly understood.
... Surface water changes over time due to factors of natural or human impacts (Huang et al. 2018). Understanding these changes, also known as river dynamics, can offer optimal solutions to address severe issues, i.e., drought, floods, and water shortages (Tulbure et al. 2016). Recently, surface water mapping is a simple and effective method to classify land and water covers, which can be applied widely in water management (Kumar & Reshmidevi 2013). ...
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Monitoring surface water provides vital information in water management; however, limited data is a fundamental challenge for most developing countries, such as Vietnam. Based on advanced remote sensing technologies, the authors proposed a methodology to process satellite images and use their outcomes to extract surface water in water resource management of Quang Nam province. Results of the proposed study show good agreement with in situ measurement data when the obtained Overall Accuracy and Kappa Coefficient were greater than 90% and 0.99, respectively. Three potential applications based on the surface water results are selected to discuss sustainable water management in Quang Nam province. Firstly, reservoir operating processes can be examined, enhanced, and even developed through long-term extracted water levels, which are the interpolation results between the extracted surface water area and the water level–area–volume curve. Secondly, the long-term morphological change for the Truong Giang river case between 1990 and 2019 can also be detected from the Water Frequency Index performance and provided additional information regarding permanent and seasonal water changes. Lastly, the flood inundation extent was extracted and separated from permanent water to assess the damage of the Mirinae typhoon on 2 November 2009 in terms of population and crop aspects. HIGHLIGHTS Excellent performance of extractions for surface water features, i.e., reservoirs and rivers, from water indices and satellite images.; Reservoir's dynamic assessment for water level monitoring and operations.; Long-term morphological changes in the Truong Giang river and its adjacent areas for sustainable management.; Flood extent detection for flood hazard and risk assessment.;
... The common optical image-based methods of surface water mapping can generally be divided into the following four categories (Ji et al., 2009): (a) thematic classification (Davranche et al., 2010;Abou EL-Magd and Tanton, 2003); (b) linear unmixing (Xie et al., 2016), (c) single-band thresholding (Jain et al., 2005); and (d) two-band spectral water indices (Xu, 2006). Combinations of different methods have also been proposed to improve surface water mapping, and specific examples have been given by Yang et al. (2017), Zhang et al. (2014), Sun et al. (2012) and Tulbure et al. (2016). Surface water mapping errors are usually due to the high similarity of the surface water and nonwater features. ...
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Earth’s surface water plays an important role in the global water cycle, environmental processes, and human society, and it is necessary to dynamically capture the distribution and extent of surface water on Earth. However, due to the high complexity of the surface environment of Earth, the current surface water mapping methods are limited in applicability and precision. In this study, to explore an automatic and applicable model for surface water mapping, particularly for the regions with highly heterogenous backgrounds, we adopted state-of-the-art deep learning techniques and structured a new model, namely, WatNet, for surface water mapping. Specifically, we combined a state-of-the-art image classification model and a semantic segmentation model into an improved deep learning model. For the fine-scale identification of small water bodies, the combined model was further improved with surface water mapping-tailored design. To learn the surface water features of worldwide regions, a surface water knowledge base that consists of worldwide satellite images was built in this study. The newly structured WatNet model was tested on three highly heterogeneous regions, and as demonstrated by the results, 1) the trained WatNet model achieved the highest accuracies, which were above 95%, for all the selected test regions; 2) the new structured WatNet model yields significant improvements through state-of-the-art model combinations and the surface water-tailored design; and 3) unlike conventional methods, which usually require parameterization in accordance with the specific surface environment, trained WatNet can be directly applied for highly accurate surface water mapping, and, thus, no human labor is required.
... The data were used as a baseline to choose other images according to the sea-level position on the variable beach morphology (Pourkerman et al. 2018). The Automated Water extraction Index (AWEI) was used to detect shoreline positions from the satellite images (Tulbure et al. 2016, Supplementary methods Appendix 1). ...
Article
The western Makran subduction zone is capable of producing considerable tsunami run-up heights that penetrate up to 5 km inland. In this study, we show how climate change has affected urbanization along the tsunami-prone Makran coastline during the past 35 years. To address this issue, we have employed climate data, satellite altimeter radar, geomorphology and historical shoreline changes in order to shed light on the factors leading to a decline in access to freshwater resources and also rapid urbanization. We furthermore consider the interactions between environmental changes and human-induced coastal and catchment modifications in increasing socioeconomic vulnerabilities of littoral areas. The results of this study show that agricultural and freshwater management methods along the Chabahar coastal plain date back to at least 1808 CE, when wetter climate conditions characterized the area. Severe climate changes have been pronounced since 2000. Within this context, the majority of agricultural lands have been abandoned due to increasing drought intensity and duration. Decreasing cultivation and limited access to freshwater resources have led to extensive urbanization particularly for the two cities of Konarak and Chabahar. Enhanced soil erosion, increasing summer monsoon wind speed, sea-level rise and the growing number of strong storm events are some of the climate change-related hazards for high to very high socially vulnerable zones. In addition to environmental risks, poor urban planning has increased damage to coastal infrastructures such as ports and desalination plants. Furthermore, industrial and urban growth in the northwest of the Makran could further enhance socioeconomic damage by earthquakes and tsunamis.
... When coupling three methods, the accuracy of water extraction can be improved significantly. For example, Tulbure et al. (2016) comprehensively used water indices and vegetation indices as semantic features to highlight water information, which was then input to RF model. The seasonal water bodies in the Murray Darling Basin were extracted with high accuracy, and the overall classification accuracy, production accuracy and user accuracy reached 99.9%, 87% and 96%, respectively. ...
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Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state‐of‐the‐art methods for water extraction, including threshold‐based methods, water indices, and machine learning‐based methods; (c) state‐of‐the‐art models for quantitative estimation of water quality, including empirical models, semi‐empirical/semi‐analytical models, and machine learning‐based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to “low spatiotemporal resolution,” and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring.
... It includes two indices, AWEI nsh , which works well when there are no shadows, and AWEI sh , which further distinguishes water pixels from shadow pixels. AWEI has been adopted in many recent studies for extracting water bodies from Landsat images [97,98]. ...
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This study aims to assess the impact of climate change on water resources as a case study, Mosul Dam Lake by detecting the changes in Water Surface Area (WSA) and studying the relationship between climatological variables and lake area through employing Remote Sensing (RS) and Geographic Information System techniques (GIS).
... Numerous algorithms and datasets have been developed over the last decade focusing on the accurate estimation of surface water dynamics from optical satellite imagery 18,[58][59][60][61][62][63] . A key requirement to use those algorithms that can classify water where it is occluded due to clouds 24,64,65 . ...
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... Recently, hydrological analyses of large areas have been trending toward more data-driven empirical approaches, because satellite imagery is the only way to assess water systematically over large spatial and temporal scales (Palazzoli & Ceola, 2020;Pekel et al., 2016;Perin et al., 2021;Tulbure & Broich, 2019;Wada et al., 2017;Walker et al., 2020). Surface-water-specific data sets derived from moderate resolution (30 m, Landsat) satellite imagery over 30-40 years are a relatively new development at the regional (Tulbure et al., 2016;Tulbure & Broich, 2013), national (Jones, 2015(Jones, , 2019, and global scales (Pekel et al., 2016;Pickens et al., 2020). The spatial and temporal scale of these surface water data sets, and the similar spatial and temporal scales of LULC (the Cropland Data Layer, CDL; "CropScape -NASS CDL Program"), population (LandScan; Rose et al., 2020), and climate data (Gridded Surface Meteorological data set; Abatzoglou, 2013), enable us to tackle the critical task of assessing the impact of climate and anthropogenic drivers on surface water. ...
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... Surface water bodies (including rivers, lakes, reservoirs and ponds, etc.) are the core elements in agriculture, aquaculture, industrial production and aquatic and terrestrial eco-systems [1], and the changes they undergo are important indicators reflecting the impact of climate change and human activities on surface water resources [2]. In recent decades, important components of surface water, such as artificial water bodies comprising reservoirs, canals, fish farming ponds, mines, quarries, etc., have been increasing in several regions of the world due to the construction of new reservoirs, climate change and flood irrigation [3]. ...
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... River morphology has been changing frequently over the last few decades due to climate change and anthropogenic factors (van Vliet et al. 2013;Grill et al. 2019). Particularly in ecologically fragile and densely populated areas, intensive human activity has severely affected and restricted the evolution of river morphology, and river ecology is facing increasing pressure (Tulbure et al. 2016;Yousefi et al. 2017;Zhang et al. 2019). In these areas, river morphology exhibits a high degree of nonlinearity and complexity (Agnihotri et al. 2020;Boota et al. 2021), and there is still no unified solution on how to accurately represent its morphological characteristics. ...
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The manuscript discusses the problem of water scarcity engendered by climate change impact through our test area, the Ain Zada Dam (North-East Algeria). The latter has witnessed a noticeable water-level decrease during the last years. Therefore, the status of the water surface is in a critical condition and the dam is in need of continuous follow-up survey. The present paper aims to model the spatiotemporal changes of the Ain Zada Dam’s water surface in the period 2015–2018 using remote sensing. To reach this end, NDWI (Normalized Difference Water Index) algorithm, which is a reference for water body extraction techniques on Sentinel-2 Satellite, is used for detecting spatiotemporal changes of the dam under study. The results obtained showed that the use of the NDWI provides an accurate water body mapping in the region for the time-series Sentinel-2 images, with an average overall accuracy of 98.57%. Also, the findings demonstrated that the dam’s surface area has steadily decreased between 2015 and 2018, where the dam has lost about two thirds of its water surface area, which corresponds to about 77% of the water size since the 2015 winter. These major repercussions are due mainly to the climate change and human factors that have affected the water supply as well. Considering that water shortage is one of the biggest future environmental issues in Algeria, the findings of the present study should be taken into account vis-à-vis any future potential development in the area, and they could be further enhanced or complemented by future research on the environmental impact of water shortage, which is relevant to the Algerian context.
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Surface water is experiencing significant changes due to anthropogenic activities and climate change. Recent studies have explored long-term surface water dynamics with the development of multi-source remote sensing data and mapping technology. However, cost-effective and time-saving reliable references for mapping surface water from remote sensing images at multiple times are still lacking. Further, negligence of abnormal changes in time-series surface water maps seriously affects surface water mapping accuracy. In this study, we propose a high-accuracy method for long-term mapping of surface water by automatic update of training samples and temporal consistency modification in surface water sequences. Taking the surface water in Huizhou from 1986 to 2020 as a case study, the method is applied and tested. Temporal training samples were updated through Robust Mahalanobis distances and statistical filtering. Combined with the random forest algorithm and the Google Earth Engine platform, annual surface water maps were generated. A method for temporal consistency detection in moving window and abnormal changes modification rule with intra-annual information are proposed to improve annual surface water mapping accuracy. The accuracy of surface water dynamics and abnormal water modifications were quantified as 89.8% and 95.7%, respectively. With our proposed method, the resultant maximum annual water areas of Huizhou were detected, with a statistically significant upward trend and net increase of 103.1 km² over 35 years. Reservoir construction and aquaculture expansion constitute the main sources of increased waters in this case study. It is demonstrated that our proposed method offers important advantages in terms of the accuracy of annual surface water maps and especially change monitoring.
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The Yellow River Basin (YRB) has been facing severe water shortages; hence, the long-term dynamic monitoring of its surface water area (SWA) is essential for the efficient utilization of its water resources and sustainable socioeconomic development. In order to detect the changing trajectory of the SWA of the YRB and its influencing factors, we used available Landsat images from 1986 through to 2019 and a water and vegetation indices-based method to analyze the spatial–temporal variability of four types of SWAs (permanent, seasonal, maximum and average extents), and their relationship with precipitation (Pre), temperature (Temp), leaf area index (LAI) and surface soil moisture (SM).The multi-year average permanent surface water area (SWA) and seasonal SWA accounted for 46.48% and 53.52% in the Yellow River Basin (YRB), respectively. The permanent and seasonal water bodies were dominantly distributed in the upper reaches, accounting for 70.22% and 48.79% of these types, respectively. The rate of increase of the permanent SWA was 49.82 km2/a, of which the lower reaches contributed the most (34.34%), and the rate of decrease of the seasonal SWA was 79.18 km2/a, of which the contribution of the source region was the highest (25.99%). The seasonal SWA only exhibited decreasing trends in 13 sub-basins, accounting for 15% of all of the sub-basins, which indicates that the decrease in the seasonal SWA was dominantly caused by the change in the SWA in the main river channel region. The conversions from seasonal water to non-water bodies, and from seasonal to permanent water bodies were the dominant trends from 1986 to 2019 in the YRB. The SWA was positively correlated with precipitation, and was negatively correlated with the temperature. Because the permanent and seasonal water bodies were dominantly distributed in the river channel region and sub-basins, respectively, the change in the permanent SWA was significantly affected by the regulation of the major reservoirs, whereas the change in the seasonal SWA was more closely related to climate change. The increase in the soil moisture was helpful in the formation of the permanent water bodies. The increased evapotranspiration induced by vegetation greening played a significant positive role in the SWA increase via the local cooling and humidifying effects, which offset the accelerated water surface evaporation caused by the atmospheric warming.
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Monitoring the spatiotemporal dynamics of surface water from remote sensing imagery is essential for understanding water's impact on the global ecosystem and climate change. There is often a tradeoff between the spatial and temporal resolutions of imagery acquired from current satellite sensors and as such various spatiotemporal image fusion methods have been explored to circumvent the challenges this situation presents (e.g., STARFM). However, some challenges persist in mapping surface water at the desired fine spatial and temporal resolution. Principally, the spatiotemporal changes of water bodies are often abrupt and controlled by topographic conditions, which are usually unaddressed in current spatiotemporal image fusion methods. This paper proposes the SpatioTemporal Surface Water Mapping (STSWM) method, which aims to predict Landsat-like, 30 m, surface water maps at an 8-day time step (same as the MODIS 8-day composite product) by integrating topographic information into the analysis. In addition to MODIS imagery acquired on the date of map prediction and a pair of MODIS and Landsat images acquired temporally close to the date of prediction, STSWM also uses the surface water occurrence (SWO, which represents the frequency with which water is present in a pixel) and DEM data to provide, respectively, topographic information below and above the water surface. These data are used to translate the coarse spatial resolution water distribution representation observed by MODIS into a 30 m spatial resolution water distribution map. The STSWM was used to generate an 8-day time series surface water maps of 30 m resolution in six inundation regions globally, and was compared with several other state-of-the-art spatiotemporal methods. The stratified random sampling design was used, and unbiased estimators of the accuracies were provided. The results show that STSWM generated the most accurate surface water map in which the spatial details of surface water were well-represented.
Chapter
Near‐real‐time flood maps derived from satellite data are invaluable to river forecasters and decision makers for disaster monitoring and relief efforts. Combining utilization of the low earth orbiting (LEO) and geostationary (GEO) satellite imagery shows great advantages in flood mapping. Under clear‐sky coverage, the floodwater detected in LEO satellite imagery shows rich inundation detail; while under nonclear‐sky conditions, composition from multiple GEO satellite images provides more clear‐sky coverage for flood detection and the clear‐sky information can be used to fill the gaps of clouds and cloud shadows in LEO imagery. With support from the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration Joint Polar Satellite System and Geostationary Operational Environmental Satellites (GOES) R programs, the flood mapping software has been developed to derive flood maps from Suomi National Polar‐orbiting Partnership and NOAA‐20/Visible Infrared Imaging Radiometer Suite imagery, and GOES‐16 and 17/Advanced Baseline Imager imagery. These flood maps are distributed via the Unidata Local Data Manager, reviewed by river forecasters in the second generation of the Advanced Weather Interactive Processing System and applied in flood operations. Initial feedback from operational forecasters on the product accuracy and performance has been largely positive. Offline evaluation efforts include visual inspection, an intercomparison with the Moderate‐Resolution Imaging Spectroradiometer automatic flood products, and a quantitative validation using Landsat imagery. The steady performance indicates encouraging feasibility of these flood maps to be provided at the product level.
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Natural conditions of surface water bodies and groundwater aquifers in the North China Plain (NCP) have been altered to meet the ever-growing human water demands. Several water resources management measures have been implemented in recent decades to alleviate groundwater depletion, maintain ecological resilience, and sustain agricultural production. This study aims to investigate their impacts on land water storage, and thus obtain a picture of the spatio-temporal variation of water resources over the NCP. Based on multi-mission earth observation datasets, i.e., altimetry (Sentinel-3), spectral and synthetic aperture radar (SAR) imagery (Sentinel-1/2), gravimetry (GRACE/-FO), and microwave sensors (IMERG), as well as reanalysis datasets, we investigate surface water storage (SWS), soil moisture water storage (SMS), and total water storage (TWS) changes. Groundwater storage (GWS) change is subsequently estimated as the residual of the total storage equation. Results show that TWS declined significantly over the past decades (-1.04 ± 0.05 cm/yr in 2004 to 2020), while SMS rebounded after a decreasing trend from 2004 to 2014. The spatial pattern of TWS variations depicts a particularly severe depletion along provincial boundaries. The SWS dynamics reveal that the volumes of three major NCP reservoirs (Guanting, Miyun, and Danjiangkou) increased significantly since around 2014 when the operation of the South-to-North Water Diversion Middle Route project (SNWDP-MR) started. Moreover, GWS maintained a depletion rate of -1.05 ± 0.08 cm/yr during 2004-2014 over the whole NCP, while the depletion rate accelerated during 2015-2020 (-1.88 ± 0.38 cm/yr). We also found that the GWS depletion in Beijing (-1.20 ± 0.10 cm/yr during 2004-2014 and -0.79 ± 0.44 cm/yr during 2015-2020) and its surrounding areas has been lowered possibly because of the SNWDP-MR. This study shows how multi-mission satellite earth observation products can be combined to monitor water resources at a regional scale and provide spatio-temporally resolved estimates of the impacts of human-induced changes in the inland water cycle.
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Dynamics in surface water area (SWA) and its impacts on desertification are poorly understood in the desertification-prone region (DPR) of China. We defined 2440 sub-watersheds at the fifth-order level in China’s DPR, and investigated the spatiotemporal variations in SWA and their substantial driving factors (such as temperature, precipitation, evaporation, and irrigation activity). The results showed that the average annual maximum SWA was 7165 km² in DPR from 2000 to 2019, of which 84% was the seasonal SWA. The seasonal and permanent SWA increased by 188 km² yr⁻¹ and 46 km² yr⁻¹, respectively. The SWA in non-DPR was the dominant driving factor for SWA dynamics in most basins except for HunlunBuir, Otindag and Ordos, where the irrigation activity showed significant contributions to SWA variations. Climate had a relatively low contribution to SWA variations in DPR. The role of SWA variations on desertification processes in DPR of China showed significant spatiotemporal differences from 2000 to 2019. The increased SWA promoted reversal of desertification in watersheds along the Tarim River, northwestern Junggar, eastern Ordos, and parts of Otindag, however, it did not help in northwestern Qinghai. Our findings provide a scientific insight into the characteristics of surface water resources and the relationship between SWA dynamics and desertification in the DPR of China.
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Abstract - Surface water is the most readily accessible water resource and provides an array of ecosystem services, but is stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000-2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the Southeastern U.S. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean -0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land use policies can be highly effective tools in controlling surface water variations in the Southeastern U.S.
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Seasonal changes of temperature and precipitation cause inland open surface water and ice cover extents to vary dramatically through the year from local to global scales. These dynamics of land, water, and ice have a significant impact on climate and often are critical to natural ecosystem functioning. However, global seasonal dynamics of both water and ice extent have not been well quantified. Here, we present the quantification of monthly surface water and ice areas for 2019 with associated uncertainties. Time-series reference data were created for a probability sample of 10 m grid cells by interpreting the entire 2019 time-series of 10 m Sentinel-2 data and a subset of 3 m PlanetScope data in selected places with a mix of land and water. From the probability sample reference data, we estimate that 4.86 ± 0.16 million km² had inland water presence at some point during the year. Globally, only 23% of the total area with water was permanent water that remained open year-round (1.13 ± 0.19 million km²). Permanent water with seasonal ice cover extended 1.97 ± 0.21 million km², comprising 41% of the total area with water. Seasonal water-land transitions (both with and without ice/snow cover) covered the remaining 36% of the total area with water (1.76 ± 0.19 million km²). February had the maximum extent of ice over areas of inland permanent and seasonal water, totaling 2.49 ± 0.25 million km², and January – March had a larger global extent of ice cover than of open water. To investigate the spatiotemporal distribution of ice cover and the suitability of Landsat, prototype maps of surface water ice cover phenology were created by integrating the ice/snow and no data labels from the quality assurance layer of the GLAD ARD of Potapov et al. (2020) with the monthly surface water layers of Pickens et al. (2020), both of which are Landsat-based. While limited by data availability, these maps reveal the high spatiotemporal variability of ice phenology. The near-daily observations near the poles and the 10 m resolution bands of Sentinel-2 provide unprecedented potential to examine surface water and ice dynamics for 2016 forward and to investigate the drivers and impacts of this variability.
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As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km2 and 6129.55 km2, respectively. The maximal surface water extent fell rapidly from 7897.96 km2 in 2001 to 5087.46 km2 in 2020, with a loss speed of 155.41 km2 per year (R2 = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km2 and 2064.99 km2 during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R2 = 0.98, slope = 36.41 km2 per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R2 = 0.86, slope = 0.99 km2 per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide.
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As this edition of The World’s Water goes to press in early 2011, eastern Australia is recovering from devastating floods that claimed more than 20 lives and destroyed hundreds of homes. The heavy rains of 2009 and 2010 that caused so much destruction also marked the end of Australia’s decade-long Millennium Drought. Beginning in about 1997, declines in rainfall and runoff had contributed to widespread crop failures, livestock losses, dust storms, and bushfires. Such are the vagaries of water on the continent with the world’s most uncertain and variable climate.
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The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2 of inland water globally—nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal “inland water” biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer (MODIS) water mask and Landsat-based national land cover datasets was very high, with commission errors < 4% and omission errors < 14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds—effects which were compounded by differences in image-acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset (GIW) is available for open access at the GLCF website (http://www.landcover.org).
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Wetlands play an important role in the provision of ecosystem services, ranging from the regulation of hydrological systems to carbon sequestration and biodiversity habitat. This paper reports the mapping of Indonesia’s wetland cover as a single thematic class, including peatlands, freshwater wetlands, and mangroves. Expert-interpreted training data were used to identify wetland formations including areas of likely past wetland extent that have been converted to other land uses. Topographical indices (Shuttle Radar Topography Mission-derived) and optical (Landsat) and radar (PALSAR) image inputs were used to build a bagged classification tree model based on training data in order to generate a national-scale map of wetland extent at a 60 m spatial resolution. The resulting wetland map covers 21.0% (39.6 Mha) of Indonesia’s land, including 25.2% of Sumatra (11.9 Mha), 22.9% of Kalimantan (12.2 Mha), and 28.9% of Papua (11.8 Mha). Results agree with existing image-interpreted products from Indonesia’s Ministries of Forestry and Agriculture and Wetlands International (89% overall agreement), and with the Ministry of Forestry forest inventory data for Sumatra and Kalimantan (91% overall agreement). An internally consistent algorithm-derived national wetland extent map can be used to quantify changing rates of land conversion inside and outside of wetlands. Additionally, wetlands extent can be used to efficiently allocate field resources in national assessments of wetland sub-types such as peatlands, which are a current focus of policies aiming to reduce carbon emissions from land use change.
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Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN). We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans.
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Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region. Key Points Remote sensing produced an accurate wetland map for the Ethiopian highlands Wetlands were associated with spatial variability in malaria risk Mapping and monitoring wetlands can improve malaria spatial decision support
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Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing. To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continent-wide investigation and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.
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An accurate description of the abundance and size distribution of lakes is critical to quantifying limnetic contributions to the global carbon cycle. However, estimates of global lake abundance are poorly constrained. We used high-resolution satellite imagery to produce a GLObal WAter BOdies database (GLOWABO), comprising all lakes greater than 0.002 km2. GLOWABO contains geographic and morphometric information for ~117 million lakes with a combined surface area of about 5 million km2, which is 3.7% of the Earth’ non-glaciated land area. Large and intermediate-sized lakes dominate the total lake surface area. Overall, lakes are less abundant, but cover a greater total surface area relative to previous estimates based on statistical extrapolations. The GLOWABO allows for the global-scale evaluation of fundamental limnological problems, providing a foundation for improved quantification of limnetic contributions to the biogeochemical processes at large scales.
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Context Frog species are now targets for delivery of high-value managed environmental flows on floodplains. Information on the drivers of frog presence and abundance is required to support adaptive management, including analysis of the roles of flood frequency, flood timing and habitat type. Aims This paper describes frog species richness and abundance responses to flooding and habitat type in the Barmah Forest, part of the largest river red gum forest in the world. Methods Surveys were conducted at 22 sites over six years, to determine species presence, relative abundance, and evidence of breeding. Data were then used to examine temporal patterns within and between wet and dry years and spatial relationships with site geomorphology, vegetation form, and wetting frequency. Key results Six species were common and widespread, and three were rare. The seasonal timing of peak numbers of calling males differed between species. The seasonal pattern of calling for each species did not differ between wet and dry years, however significantly lower numbers of frogs were recorded calling in dry years. The number of frogs calling was significantly higher in well-vegetated grassy wetlands. Evidence of a positive relationship between wetting frequency and numbers of calling males was found for Limnodynastes fletcheri, Crinia signifera and Limnodynastes dumerilii. The abundance of tadpoles was significantly higher in wet years. Conclusions The seasonal timing of flooding in Barmah Forest will influence the breeding success of individual species with different preferences. Flooding from September to December is required to cover most preferred breeding seasons, but longer durations may be required to maximise recruitment. This, together with regular flooding of well-vegetated grassy wetland habitat, will increase the likelihood of species persistence and maximise diversity. Insufficient flooding frequency will result in reduced frog species richness and abundance. Implications Managed flooding is important for frog abundance and species richness. This study emphasises the value of key habitats such as well-vegetated grassy wetlands and reinforces the need to make their preservation a priority for management. It has identified knowledge gaps to drive future data collection for improved modelling, including a need for further research on flow regime change and frog communities.
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Before being used in scientific investigations and policy decisions, thematic maps constructed from remotely sensed data should be subjected to a statistically rigorous accuracy assessment. The three basic components of an accuracy assessment are: 1) the sampling design used to select the reference sample; 2) the response design used to obtain the reference land-cover classification for each sampling unit; and 3) the estimation and analysis procedures. We discuss options available for each of these components. A statistically rigorous assessment requires both a probability sampling design and statistically consistent estimators of accuracy parameters, along with a response design determined in accordance with features of the mapping and classification process such as the land-cover classification scheme, minimum mapping unit, and spatial scale of the mapping.
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Major rivers worldwide have experienced dramatic changes in flow, reducing their natural ability to adjust to and absorb disturbances. Given expected changes in global climate and water needs, this may create serious problems; including loss of native biodiversity and risks to ecosystems and humans from increased flooding or water shortages. Here, we project river discharge under different climate and water withdrawal scenarios and combine this with data on the impact of dams on large river basins to create global maps illustrating potential changes in discharge and water stress for dam-impacted and free-flowing basins. The projections indicate that every populated basin in the world will experience changes in river discharge and many will experience water stress. The magnitude of these impacts is used to identify basins likely and almost certain to require proactive or reactive management intervention. Our analysis indicates that the area in need of management action to mitigate the impacts of climate change is much greater for basins impacted by d