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Surface Water Extent Dynamics from Three Decades of Seasonally Continuous Landsat Time Series at Subcontinental Scale in a Semi-Arid Region
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... Spatiotemporal information from Earth Observation (EO) Satellites has opened a new era for assessing qualitative and quantitative dynamic attributes of freshwater resources (Bhaduri et al., 2016;Petropoulos et al., 2015). EO data covering large geographic areas represent an important milestone for sustainable water management, especially in water-stressed areas such as arid and semiarid regions (Heimhuber et al., 2016;Tulbure et al., 2016;Wickens, 1998). In this context, most research has been devoted to inferring water characteristics and dynamics over the long term through quantitative remote sensing methods. ...
... However, its spatial resolution (250 m) is relatively coarse, hampering its application for monitoring small and seasonally unstable water bodies. In an attempt to overcome this limitation and improve surface water mapping, substantial progress has been made in employing advanced instruments, such as public sensors affiliated with Landsat missions and their derived products, ensuring higher spatial, radiometric, and spectral resolution (Pekel et al., 2016;Tulbure et al., 2016;Zou et al., 2018). ...
... Recently, the high-quality data provided by Sentinel missions have gained ground over Landsat-derived data for delineating and mapping small water bodies, given their better compromise between spectral, temporal, and spatial resolution . Another emerging practice to assess seasonal dynamics in small-sized water bodies relies on the use of multi-temporal multi-sensor imageries (Ozesmi and Bauer, 2002;Vanderhoof et al., 2023;Tulbure et al., 2016;Tulbure et al., 2022), with focus on Sentinel-1 and Sentinel-2 (Jiang et al., 2022;Mayer et al., 2021;Radoux et al., 2016;Huang et al., 2018), or a combination of both (Bioresita et al., 2019;Chen and Zhao, 2022;Vanderhoof et al., 2023) for classification purposes. Notably, while the combination of Sentinel-1 and Sentinel-2 has the potential to enhance the comprehension of surface water distribution and seasonality, particularly in small water bodies, this remains poorly investigated in the current literature, making it still unclear whether Sentinel-derived products offer higher performance compared to other high-resolution approaches (e.g., Landsat) for detecting and quantifying small-sized water bodies. ...
Inland freshwaters are essential in maintaining ecological balance and supporting human development. However, comprehensive water data cataloguing remains insufficient, especially for small water bodies (i.e., ponds), which are overlooked despite their ecological importance. To address this gap, remote sensing has emerged as a possible solution for understanding ecohydrological characteristics of water bodies, particularly in water-stressed areas. Here, we propose a novel framework based on a Sentinel-1&2 local surface water (SLSW) model targeting very small (<0.5 ha, Mdn ≈ 0.031 ha) and seasonal water bodies. We tested this framework in three semiarid regions in SW Iberia, subjected to distinct seasonality and bioclimatic changes. Surface water attributes, including surface water occurrence and extent, were modelled using a Random Forests classifier, and SLSW time series forecasts were generated from 2020 to 2021. Model reliability was first verified through comparative data completeness analyses with the established Landsat-based global surface water (LGSW) model, considering both intra-annual and inter-annual variations. Further, the performance of the SLSW and LGSW models was compared by examining their correlations for specific periods (dry and wet seasons) and against a validation dataset. The SLSW model demonstrated satisfactory results in detecting surface water occurrence (μ ≈ 72 %), and provided far greater completeness and reconstructed seasonality patterns than the LGSW model. Additionally, SLSW model exhibited a stronger correlation with LGSW during wet seasons (R2 = 0.38) than dry seasons (R2 = 0.05), and aligned more closely with the validation dataset (R2 = 0.66) compared to the LGSW model (R2 = 0.24). These findings underscore the SLSW model’s potential to effectively capture surface characteristics of very small and seasonal water bodies, which are challenging to map over broad regions and often beyond the capabilities of conventional global products. Also, given the vulnerability of water resources in semiarid regions to climate fluctuations, the present framework offers advantages for the local reconstruction of continuous, high-resolution time series, useful for identifying surface water trends and anomalies. This information has the potential to better guide regional water management and policy in support of Sustainable Development Goals, focusing on ecosystem resilience and water sustainability.
... An analysis of millions of Landsat scenes [13] illustrates inter-annual and intra-annual global inland water dynamics, noting that improved spatial resolution is necessary to accurately characterize mixed land and water cover within a single Landsat pixel (30 m resolution). Other research has focused on continental and sub-continental dynamic change mapping [14,15] and the development of global water body maps and datasets [16]. ...
... accurately characterize mixed land and water cover within a single Landsat pixel (30 m resolution). Other research has focused on continental and sub-continental dynamic change mapping [14,15] and the development of global water body maps and datasets [16]. ...
... This study encompasses fourteen (14) areas within a 0.30 • × 0.30 • bounding box ( Figure 2b). These areas include thirteen (13) rivers, seven lakes and reservoirs, and five paddy field areas. ...
Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling.
... Satellite imagery is a reliable data source (Hermas et al. 2021) that is regularly used to map surface water and flood dynamics across various scales (Ayanu et al. 2012;Jones 2019;Pekel et al. 2016;Tulbure et al. 2016). Machine learning is an effective method for classifying floods in satellite imagery . ...
... The RF inputs included all Sentinel-2 surface reflectance bands, two vegetation indices and six water indices, all previously shown to be important when mapping floods with satellite data (Goffi et al. 2020;Tulbure et al. 2016Tulbure et al. , 2022 (Table 1). We produced all the indices in GEE. ...
... In addition to Sentinel-2 surface reflectance bands and derived indices, several other datasets readily available in GEE were incorporated into the RF model (Table 2). A digital elevation model (DEM) can be used to derive data (e.g., slope) that influences where floods occur (Tulbure et al. 2016). In our model, we used the United States Geological Survey (USGS) 3DEP 10 m National Map (U.S. Geological Survey 2023) in GEE to calculate slope, aspect, and hillshade. ...
The risk of floods from tropical storms is increasing due to climate change and human development. Maps of past flood extents can aid in planning and mitigation efforts to decrease flood risk. In 2021, Hurricane Ida slowed over the Mid-Atlantic and Northeast United States and released unprecedented rainfall. Satellite imagery and the Random Forest algorithm are a reliable combination to map flood extents. However, this combination is not usually applied to urban areas. We used Sentinel-2 imagery (10 m), along with derived indices, elevation, and land cover data, as inputs to a Random Forest model to make a new flood extent for southeastern Pennsylvania. The model was trained and validated with a dataset created with input from PlanetScope imagery (3 m) and social media posts related to the flood event. The overall accuracy of the model is 99%, and the flood class had a user’s and producer’s accuracy each over 97%. We then compared the flood extent to the Federal Emergency Management Agency flood zones at the county and tract level and found that more flooding occurred in the Minimal Hazard zone than in the 500-year flood zone. Our Random Forest model relies on publicly available data and software to efficiently and accurately make a flood extent map that can be deployed to other urban areas. Flood extent maps like the one developed here can help decision-makers focus efforts on recovery and resilience.
Graphical abstract
... Satellite imagery is a reliable data source (Hermas et al., 2021) that is regularly used to map surface water and ood dynamics across various scales (Ayanu et al., 2012;Jones, 2019;Pekel et al., 2016;Tulbure et al., 2016). Machine learning is an effective method for classifying oods in satellite imagery . ...
... The RF inputs included all Sentinel-2 surface re ectance bands, two vegetation indices and six water indices, all previously shown to be important when mapping oods with satellite data (Go et al., 2020;Tulbure et al., 2016Tulbure et al., , 2022 (Table 1). We produced all the indices in GEE. ...
... In addition to Sentinel-2 surface re ectance bands and derived indices, several other datasets readily available in GEE were incorporated into the RF model (Table 2). A digital elevation model (DEM) can be used to derive data (e.g., slope) that in uences where oods occur (Tulbure et al., 2016). In our model, we used the United States Geological Survey (USGS) 3DEP 10 m National Map (U.S. Geological Survey, 2023) in GEE to calculate slope, aspect, and hillshade. ...
The risk of floods from tropical storms is increasing due to climate change and human development. Maps of past flood extents can aid in planning and mitigation efforts to decrease flood risk. In 2021, Hurricane Ida slowed over the Mid-Atlantic and Northeast United States and released unprecedented rainfall. Satellite imagery and the Random Forest algorithm are a reliable combination to map flood extents. However, this combination is not usually applied to urban areas. We used Sentinel-2 imagery (10 m), along with derived indices, elevation, and land cover data, as inputs to a Random Forest model to make a new flood extent for southeastern Pennsylvania. The model was trained and validated with a dataset created with input from PlanetScope imagery (3 m) and social media posts related to the flood event. The overall accuracy of the model is 99%, and the flood class had a user’s and producer’s accuracy each over 99%. We then compared the flood extent to the Federal Emergency Management Agency (FEMA) flood zones at the county and tract level and found that more flooding occurred in the Minimal Hazard zone than in the 500-year flood zone. Our Random Forest model relies on publicly available data and software to efficiently and accurately make a flood extent map that can be deployed to other urban areas. Flood extent maps like the one developed here can help decision-makers focus efforts on recovery and resilience.
... and increasing demands from agricultural, industrial and domestic water uses. Understanding the spatiotemporal patterns of surface water dynamics in water scarce regions like Bundelkhand can lead to droughts and water shortages, with significant consequences to agricultural systems and food security (Tulbure et al., 2016). ...
... This approach of manually selecting a threshold is time consuming and is limited by subjectivity of the image analyst. An alternative approach is to use a combination of spectral bands and other variables and classification trees to develop models based on a range of predictor variables rather than selected indices (Tulbure & Broich, 2013), with ensemble models proven to be more successful than a single classification tree (Tulbure et al., 2016). ...
Surface water is essential for agricultural, domestic and industrial production worldwide. Monitoring surface dynamics is crucial for sustainable ecosystems and global water resources. Importance of monitoring surface water dynamics is even more pronounced in the semi‐arid regions worldwide. An analysis of surface water extent and volume change patterns has been conducted, comparing these dynamics with alterations in precipitation patterns within a basin in Central Bundelkhand, a semi‐arid region in the Central India prone to droughts. To map the waterbodies, we leveraged Sentinel‐1 SAR data using an automated mapping framework and utilised DEM dataset to extract bathymetry using interpolation with modifications using water persistence. Analysis revealed a lag in surface water peak water level with respect to accumulated rainfall by 2–3 months. Furthermore, we have categorised the water bodies into small, medium and large by surface area and found that smaller water bodies show a higher intra‐annual variance, while medium and large water bodies show a lower intra‐annual variance. The findings suggest that smaller communities reliant on smaller water bodies are at a higher risk from climate variability in the region and a delay in attaining peak surface storage across the basin causes further challenges to water management.
... Multi-spectral satellite remote sensing images have the advantages of large scale, low cost, and repeated observation, providing valuable data sources for the dynamic monitoring of surface water [7,8]. Compared to traditional site monitoring, remote sensing technology is more conducive to continuous surface water monitoring from a spatial perspective [6]. ...
... Machine learning classification methods, including supervised classification (such as K-Nearest Neighbors [21], Support Vector Machines [22][23][24], and Decision Trees/Random Forests [7,[24][25][26]), and unsupervised classification (such as K-Means [27], Iterative Self-Organizing Data Analysis Technique [28], Density-Based Clustering [29], and Hierarchical Dynamic Clustering [30,31]), are another widely used approach for surface water extraction. Compared to unsupervised classification methods, supervised classification methods achieve higher water extraction accuracy. ...
Surface water is a crucial part of terrestrial ecosystems and is crucial to maintaining ecosystem health, ensuring social stability, and promoting high-quality regional economic development. The surface water in the Yellow River Basin (YRB) has a high sediment content and spatially heterogeneous sediment distribution, presenting a significant challenge for surface water extraction. In this study, we first analyze the applicability of nine water indexes in the YRB by using the Landsat series images (Landsat 5, 7, 8) and then examine the correlation between the accuracy of the water indexes and suspended particulate matter (SPM) concentrations. On this basis, we propose a surface water extraction method considering the SPM concentrations (SWE-CSPM). Finally, we examine the dynamic variations in the surface water in the YRB at four scales: the global scale, the secondary water resource zoning scale, the provincial scale, and the typical water scale. The results indicate that (1) among the nine water indexes, the MBWI has the highest water extraction accuracy, followed by the AWEInsh and WI2021, while the NDWI has the lowest. (2) Compared with the nine water indexes and the multi-index water extraction rule method (MIWER), the SWE-CSPM can effectively reduce the commission errors of surface water extraction, and the water extraction accuracy is the highest (overall accuracy 95.44%, kappa coefficient 90.62%). (3) At the global scale, the maximum water area of the YRB shows a decreasing trend, but the change amount is small. The permanent water area shows an uptrend, whereas the seasonal water area shows a downtrend year by year. The reason may be that the increase in surface runoff and the construction of reservoir projects have led to the transformation of some seasonal water into permanent water. (4) At the secondary water resource zoning scale, the permanent water area of other secondary water resource zonings shows an increasing trend in different degrees, except for the Interior Drainage Area. (5) At the provincial scale, the permanent water area of all provinces shows an uptrend, while the seasonal water areas show a fluctuating downtrend. The maximum water area of Shandong, Inner Mongolia Autonomous Region, and Qinghai increases slowly, while the other provinces show a decreasing trend. (6) At the typical water scale, there are significant differences in the water area variation process in Zhaling Lake, Eling Lake, Wuliangsuhai, Hongjiannao, and Dongping Lake, but the permanent water area and maximum water area of these waters have increased over the past decade. This study offers significant technical support for the dynamic monitoring of surface water and helps to deeply understand the spatiotemporal variations in surface water in the YRB.
... The water index method is widely applied in the extraction of surface water bodies [29][30][31][32]. Common water indices include the normalized difference water index (NDWI) [33], modified normalized difference water index (MNDWI) [34], automated water extraction index (AWEI) [35], and others. ...
... Common water indices include the normalized difference water index (NDWI) [33], modified normalized difference water index (MNDWI) [34], automated water extraction index (AWEI) [35], and others. But it is challenging to distinguish water bodies from non-water bodies using a single threshold due to the spatiotemporal heterogeneity of water spectral characteristics [29]. Another commonly used method involves developing classification models using a series of predictor variables, including original spectral bands and water indices, to extract water bodies. ...
The lakes of Jianghan Plain, as an important component of the water bodies in the middle and lower reaches of the Yangtze River plain, have made significant contributions to maintaining the ecological health and promoting the sustainable development of the Jianghan Plain. However, there is a relatively limited understanding regarding the trends of lake area change for different types of lakes and their dominant factors over the past three decades in the Jianghan Plain. Based on the Google Earth Engine (GEE) platform, combined with the water body index method, the changes in area of three different types of lakes (area > 1 km²) in the Jianghan Lake Group from 1990 to 2020 were extracted and analyzed. Additionally, the Partial least squares structural equation model (PLS-SEM) was utilized to analyze the driving factors affecting the changes in water body area of these lakes. The results show that from 1990 to 2020, the area of the lakes of the wet season and level season exhibited a decreasing trend, decreasing by 893.1 km² and 77.9 km², respectively. However, the area of dry season lakes increased by 59.27 km². The areas of all three types of lakes reached their minimum values in 2006. According to the PLS-SEM results, the continuous changes in the lakes’ area are mainly controlled by environmental factors overall. Furthermore, human factors mainly influence the mutation of the lakes’ area. This study achieved precise extraction of water body areas and accurate analysis of driving factors, providing a basis for a comprehensive understanding of the dynamic changes in the lakes of Jianghan Plain, which is beneficial for the rational utilization and protection of water resources.
... NDWI can be defined as a popular and very important indicator used in the field of water resources for monitoring [39], in the case of remote sensitivity measurement in the visible and near-infrared electromagnetic spectrum portions. This NDWI is related to the process of the interaction of the electromagnetic spectrum with the water surface and the absorption and reflection of rays, where the water surface absorbs the ultimate of the red and near-infrared rays equally, whereas, these surfaces reflect the visible spectrum a little amount, particularly in reflecting blue and green spectrum, based on the purity of water, contamination or water impurities besides the water depth impact. ...
... Surface water maps were produced from the time series of Sentinel-2 images at 10-m resolution. The surface water maps were obtained from a supervised classification at pixel level using the random forest algorithm [50], commonly used in surface water detection [51,52]. A training data set was selected for each Sentinel-2 L2A product using a randomized selection of 10,000 pixels, including 2,000 water samples. ...
What remote sensing products can be used to better quantify the water stored in hundreds of thousands Indian Small Reservoirs (SR)? This ungauged hydrological component of the water cycle is intermittently filled with rainwater runoff, constantly reshaped by farmers since last two decades, crucial for upstream irrigated agriculture. Given the small size and shallow depth of those reservoirs, usual remote sensing techniques (Altimeters and LIDAR) used in spatial hydrology to monitor their water level are not adapted. We evaluated the uncertainty of SR volume retrieval methods based on surface water estimates from Sentinel-2 and associated volumes from global available DEM at a medium to coarse resolution. Four pair of stereoscopic images at Very High Resolution (VHR) from Pléiades satellites were acquired during the last two dry hydrological years (2016 and 2019), when SR were totally empty. The Pléiades DEMs produced were cross validated with LIDAR IceSAT-2 products, and used to extract 504 SR bathymetries within an area covering 1,813 km² located in the Telangana state (114,789 km²). We compared Pléiades based retrievals to freely available regional to global DEM to explore the regional volume retrieval Bias: ALOS World 3D-30 m, WorldDEM GLO-30 at 30 m TanDEM-X DEM at 90 m and one Indian DEM (CartoDEM at 30 m). The Mean Absolute Percentage Error (MAPE) of reservoir volumes from global DEMs range from 47% to 78%. MAPE are 17%, 29% and 46% for Pléiades DEM resampled at 12, 30 and 90 m, respectively. In a near future, upcoming stereoscopy satellite missions at lower costs and with larger coverage and shorter revisit such as CO3D will provide 12m or higher resolution DEMs that, if acquired in dry years, will lead to acceptable MAPE (< 20%), to monitor empty SR geometries throughout India and other semi-arid areas in the world.
... We would like to analyze the variability of the surface waters [34]. We expect to perform data fusion to exploit the synergy of multiple complementary satellite observations such as e.g., ref. [35][36][37] Downscaling framework is based on a floodability index, so improving the floodability index is a natural way to improve the downscaling. Note that with the proposed downscaling method, different types of floodability index can be used or even combined. ...
A surface water extent downscaling framework was developed in the past using a floodability index based on topography. We presented here a new downscaling approach including several improvements. (1) The use of a new Floodability Index (FI), including better integration of auxiliary permanent waters (i.e., presence of water during the whole time record). By using this updated FI, the new downscaling became a true data-fusion with permanent water databases originating mainly from visible observations. (2) Some discontinuities between low resolution cells have been reduced thanks to a new smoothing algorithm. (3) Finally, a coastal extrapolation scheme has been presented to deal with coarse resolution data contaminated by the ocean. This new and complex downscaling framework was tested here on the GIEMS (Global Inundation Extent from Multi-Satellite) database but the approach is generalizable and any surface water database could be used instead. It was shown that this new downscaling procedure (including several processing steps, algorithms and data sources) is a significant improvement compared to the previous version thanks to the new floodability index and the downscaling processing chain. Compared to the previous version, the downscaling results (GIEMS-D) were more coherent with the permanent water database and preserved better the original low-resolution information (e.g., mean scare error water fraction (0–1) of 0.0041 for the old version, and 0.0018 for the new version, over flooded areas in the Amazon). GIEMS-D has also been evaluated at the global scale and over the Amazon basin using independent datasets, showing an overall good performance of the downscaling.
... Xinjiang is located in the arid zone of north-western China (insert a map after this paragraph) where surface water shortage is a key constraint to its development (Tulbure et al., 2016). In the context of regional warming and humidification (Tang et al., 2022), drastic changes in the area of lakes and reservoirs water bodies (Chen et al., 2023;Liu et al., 2023a) have posed a great threat to the ecological environment of the basin. ...
... To monitor lake areas, first a comprehensive dataset spanning from 1984 to 2022 was compiled using Landsat mission imagery, chosen for its high spatio-temporal resolution [31][32][33][34][35][36][37][38]. Then Normalized Difference Water Index (NDWI) [39], a well-established method for delineating water bodies from surrounding landcover, was applied to each image (Eq. ...
... Sample based methods rely on training samples and supervised classification. The machine learning based classifier such as random forest [9], decision trees [10], and Supported vector machine (SVM) [11] are widely used. On the contrary, the threshold-based methods formulate the discriminative water body index, such as NDWI [12], MNDWI [13], and LSWI [14], that relies on the different spectral response in various bands and output the binary classification map of water and none-water. ...
Water is an important element in the ecological environment, and different types of water (e.g., rivers, lakes, and ponds) have different impacts on the ecology. The extraction and classification of different types of water bodies has significant implications for the water resource management and water environment monitoring. Current research on the water-body types classification is relatively limited compared to water body extraction. Existing methods typically adopt a two-stage architecture, where the first stage extracts water bodies at the pixel level, and the second stage classifies the water bodies into different types using rule-based thresholds classifier and morphological features at object level. However, methods in the second stage suffer from overfitting, lack of robustness, and confusion in object segmentation. Despite these challenges, the deep learning methods could capture the high-level semantic features, which are effective for the classification of different types of water bodies. In this paper, a novel Water-Scene Enhancement Deep Model (WSEDM) was proposed for identifying multiple types of water bodies. The WSEDM consists of a pixel-wise water body extraction using Edge-Otsu and a patch-wise water-body types classification through deep learning model. In order to improve the accuracy of patch-wise water body classification, a novel multimodal feature fusion network(CASANet) was designed for the fusing of optical and SAR images. The water-body types classification was conducted on three international wetland cities in the urban agglomeration in the middle reaches of the Yangtze River. The 10m water-body types map achieved an overall accuracy of 94.6%. The proposed CASANet is also validated through comparison and transferability experiments, which further confirmed the superior performance.
... It calculates the frequency of water occurrence for each pixel and generates a frequency distribution map of water bodies in Anhui. Based on the principle of seasonal changes in water bodies, surface water with a frequency greater than 75 % is defined as permanent surface water, those with a frequency between 5 % and 75 % are categorized as seasonal water bodies, while water bodies with a frequency less than 5 % are classified as temporary surface water [52]: ...
Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km², accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km², accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.
... Urbanized watersheds construct artificial systems, contrasting sharply with forested rivers [61]. Urban rivers experience few morphological changes [62], and therefore, it is important to quantify long-term surface water information, considering seasonality [63]. In urban areas, rivers are often channelized [64], with residential areas replacing riparian vegetation, narrowing the water surface width [65] and severely restricting fish populations. ...
The present study explores the strategic siting of hydroelectric power plants, focusing on the Miyanaka Intake Dam (MID) and Shinano River Hydroelectric Power Station (SHP). Built in 1939 to support Tokyo’s railway electrification, these facilities demonstrate the complexities of balancing renewable energy production with ecological conservation. Despite the high costs and energy losses associated with transmitting power from the Sea of Japan side, the SHP has effectively powered Tokyo’s rail system for over 80 years, owing to advanced transmission technologies and the region’s abundant water resources. However, river-crossing structures such as dams disrupt fish migration and habitats, necessitating the implementation of fishways. The MID fishway, continually improved since its construction, emphasizes the importance of integrating ecological considerations into hydropower projects. Our findings highlight the higher power generation efficiency on the Sea of Japan side and stress the need for careful site selection to ensure sustainable hydroelectric power while preserving river ecosystems. In conclusion, hydropower sites should be chosen based on both environmental impacts and future development potential to maintain the ecological balance and support long-term renewable energy goals.
... Other approaches have used adaptive thresholding techniques to classify water and inundated vegetation [28], while simple thresholds applied to near-infrared and shortwave infrared data have also proven effective [26]. Machine learning techniques have also been used, combining water and vegetation indices to predict inundation extent [29,30]. However, validation of these approaches has involved comparisons to existing Landsat-derived inundation maps [28,29] and the use of interpolated depth information [26], which may present uncertainties in assessing accuracy. ...
Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.
... [7] introduced a new index called the automatic water extraction index (AWEI), which is able to detect water bodies from time-series Landsat images using a single threshold. AWEI has been adopted in many recent studies [10] to extract water bodies from Landsat images. The formula used to determine the calculation of AWEI can be seen in the following equation. ...
Monitoring shoreline change is one of the efforts in the management and protection of the coastal environment. Through digital image processing as the utilization of shoreline extraction index can be used as a reference in measuring the rate of shoreline change. Galur District is one of the sub-districts in Kulon Progo Regency that experiences the highest abrasion process, the worst abrasion that has occurred in recent years is located at Trisik Beach where abrasion threatens the existence of stalls, turtle conservation sites and also coastal vegetation that grows along the beach. The purpose of this study is to determine changes in the coastline starting in 2013, 2018, and 2023 and also to analyze numerically through DSAS the rate of change in the coastline both the influence of abrasion and accretion processes in Galur District. The method used in this research is Quantitative Descriptive assisted by Landsat 8 image transformation using Automated Water Extraction Index (AWEI) and measuring the rate of shoreline change using DSAS based on EPR and NSM values. The results of this study show that Banaran Village has the highest shoreline retreat changes where in 2013 - 2018 the shoreline retreat reached 70 meters and in 2018 - 2023 the coastal retreat reached -75.7 meters, while for the rate of change in Karang Sewu Village in 2013 - 2018 the shoreline retreat reached -33.2 meters and in 2018 - 2023 showed the highest abrasion of -33.5 meters.
... Furthermore, effective reservoir monitoring serves as a deterrent to transboundary water conflicts and promotes international collaboration and also providing timely information by analyzing surface water dynamics as demonstrated for Turkey (Donchyts et al., 2022). The insights provided by surface water monitoring is needed for informed decision-making in environmental, agricultural, and urban water use, as for example shown for semi-arid Australia where it plays a crucial role in shaping water policy and management strategies (Tulbure et al., 2016). Overall, the integrated monitoring of surface water resources emerges as a support in addressing water challenges on a global scale, contributing not only to resource sustainability but also fostering international cooperation and informed governance. ...
Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km² (WI) and 86,530 km² (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.
... They used three algorithms: decision tree, random forest (RF), and gradientboosted decision trees. Zhou et al. [46] use a support vector machine (SVM); Tulbure et al. [47] and Schumann et al. [48] RF for the analysis of water bodies. Pech-May et al. [5] analyze the behavior of land cover and water bodies of floods in the rainy season using multispectral images and RF, SVM, and classification and regression trees algorithms. ...
Floods are the most common phenomenon and cause the most significant economic and social damage to the population. They are becoming more frequent and dangerous. Consequently, it is necessary to create strategies to intervene effectively in the mitigation and resilience of the affected areas. Different methods and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth's surface, and geospatial information processing tools help manage different natural disasters. Likewise, deep learning is an approach capable of forecasting time series that can be applied to satellite images for flood prediction and mapping. This paper presents an approach for flood segmentation and visualization using the UNet architecture and Sentinel-1 SAR satellite imagery. The UNet architecture can capture relevant features in SAR images. The approach comprises various phases, from data loading and preprocessing to flood inference and visualization. For the study, the georeferenced dataset Sen1Floods11 is used to train and validate the model through different epochs and training. A study area in southeastern Mexico that presents frequent floods was chosen. The results demonstrate that the segmentation model achieves high accuracy in detecting flooded areas, with promising metrics regarding loss, precision, and F1-score.
... Zhou [18] analyzed the change process of the temperature field of permafrost subgrades in the case of roadside ponding and determined that roadside ponding caused an uneven decrease in the upper limit of the permafrost in the subgrade, which led to an increase in the thawing depth of the subgrade. The Qinghai-Tibet Plateau is known as the "Water Tower of Asia" due to its abundance of rivers, lakes, and glaciers [19][20][21], and climate change greatly affects the distribution of surface water in this region [22][23][24][25]. Luo et al. ...
Due to climate change and seasonal precipitation, water conditions in the Qinghai–Tibet region are a significant factor affecting the stability of subgrades. The accumulation of large amounts of surface water leads to subgrade diseases along the Qinghai–Tibet Highway. Based on remote sensing photos obtained from Google Earth Engine and processing the photos using ENVI 5.6.3 and CAD 2019 software, this paper analyzed the distribution characteristics of surface water and studied the impact of roadside ponding on subgrade diseases. The results showed that the total area of surface water was more than 3.7 million m2, and the surface water was most widely distributed in large river areas such as the Tuotuo River and Buqu River. The subgrade diseases of the Qinghai–Tibet Highway could be categorized into three types: settlement, longitudinal crack, and frost boiling, which accounted for 71.09%, 17.13%, and 11.78% of the total number of subgrade diseases, respectively. Additionally, the ground mean annual temperature was an important factor affecting the distribution of surface water, with the surface water area showing an increasing trend with the increase in ground mean annual temperature, and roadside ponding was most likely to form in the high-temperature extremely unstable permafrost area.
... When employing a single threshold for extracting water bodies in complex terrain areas, using a sole water index approach becomes challenging due to difficulties in determining an ideal threshold to distinguish between water and non-water regions. Consequently, the applicability and accuracy of this method are constrained by intricate backgrounds [18,19]. ...
Surface water is a critical natural resource, but its mapping accuracy is vulnerable to cloud cover, snow, shadows, and diverse roofing materials. Recognizing the limitations of a single threshold segmentation method that fails to achieve high-precision extraction of surface water in complex terrain areas, this study introduces a multiple threshold water detection rule (MTWDR) method to improve water extraction results. This method uses the multi-band reflectance characteristics of ground features to construct a water index and combines brightness features with the Otsu algorithm to eliminate interference from highly reflective ground features like ice, snow, bright material buildings, and clouds. The Yunan–Guizhou Plateau was selected as the study area due to its complex terrain and multiple types of surface water, and experiments were conducted using Sentinel-2 data on the Google Earth Engine (GEE). The results demonstrate that: (1) The proposed method achieves an overall accuracy of 94.08% and a kappa coefficient of 0.8831 in mountainous areas. In urban areas, the overall accuracy reaches 95.15%, accompanied by a kappa coefficient of 0.8945. (2) Compared to five widely used water indexes and rules, the MTWDR method improves accuracy by more than 3%. (3) It effectively overcomes interference from highly reflective ground features while maintaining the integrity and accuracy of water boundary extraction. In conclusion, the proposed method enhances extraction accuracy across different types of surface water within complex terrain areas, and can provide significant theoretical implications and practical value for researching and applying surface water resources.
... Besides, the accuracy of such approaches depends on the quantity and quality of training data, which are still limited for surface water dynamics over large areas. Although numerous studies derived surface water dynamics from satellite datasets at regional or national scales 20,[29][30][31][32] , such data are limited at the integrating continental and global scales 14,15 , and the spatial coverage of monthly wetland dynamics is very poor (Supplementary Fig. 1). ...
Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.
... Entre los índices más utilizados destacan el índice de agua de diferencia normalizada (NDWI, por sus siglas en inglés), que ha sido aplicado para evaluar la variabilidad interanual y estacional de la extensión del agua superficial (Tulbure et al., 2016); el índice de agua modificado de diferencia normalizada (MNDWI, por sus siglas en inglés), que se ha utilizado para observaciones a largo plazo de los cambios en el agua en lagos (Yudha, 2023); el índice de aguas superficiales terrestres (LSWI, por sus siglas en inglés), que ha sido empleado para medir la humedad del suelo (Nadeem et al., 2023); y el índice de extracción de agua automatizado con sombra (AWEIsh, por sus siglas en inglés) y sin sombra (AWEInsh, por sus siglas en inglés), usado principalmente para el monitoreo eficiente y continuo de las aguas superficiales (Yue et al., 2023). Estos índices se han utilizado en diversas investigaciones sobre cuerpos de agua alrededor del mundo, algunos de ellos han sido probados de forma simultánea en un solo estudio. ...
La sobreexplotación de los cuerpos de agua aunado a las sequías y el impacto del cambio climático reducen el agua disponible para actividades humanas, lo cual genera serios problemas económicos y sociales. Por tanto, una tarea imprescindible es el monitoreo del estado de los cuerpos de agua superficiales, y una alternativa rápida, precisa y económica es hacerlo mediante técnicas de teledetección usando sensores remotos satelitales. Estas técnicas ayudan a obtener información a distancia de un determinado objeto situado sobre la superficie terrestre. El objetivo de este estudio fue, mediante el método PRISMA, realizar una revisión de las aplicaciones de los sensores remotos en el monitoreo de cuerpos de agua para dar alternativas de uso de los índices de agua. El índice de agua modificado de diferencia normalizada (MNDWI, por sus siglas en inglés) y el índice de extracción de agua automatizado (AWEI, por sus siglas en inglés) son los más adecuados debido a que son fáciles de construir e interpretar, además de que tienen alta precisión.
... Terrestrial open-surface water bodies, such as rivers, lakes, and reservoirs, are essential components of the hydrosphere and vital resources for both human and ecological systems (Huang et al., 2018). The impacts of climate change on these water bodies have far-reaching consequences for agriculture, industrial production, and terrestrial ecosystems (Brown and Lall, 2006;Hall et al., 2014;Tulbure et al., 2016). Notably, seasonal and small water bodies, which intermittently appear throughout the year, are particularly vulnerable to climate change (Jaeger et al., 2014;Marcé et al., 2019;Pi et al., 2022;Pumo et al., 2016). ...
... Random Forest modelling is an ensemble classification technique (Breiman, 2001) and has been extensively used in the classification of remote sensing data (e.g., Yu et al., 2011;Rodriguez-Galiano et al., 2012). Random Forest models excel at recognizing regional variations in threshold values, surpassing the capabilities of traditional index thresholding methods (Tulbure et al., 2016). Notably, Random ...
Glacial Lake Outburst Floods (GLOFs) are widely recognized as one of the most devastating natural hazards in the Himalaya, which may catastrophic consequences including substantial loss of lives. To effectively mitigate these risks and enhance regional resilience, it is imperative to conduct an objective and holistic assessment of GLOFs hazards and their potential impacts of GLOFs over a large spatial scale. However, this is challenged by the limited availability of data and the inaccessibility to most of the glacial lakes in high-altitude areas. The data challenge is exacerbated when dealing with multiple lakes across an expansive spatial area. This study aims to exploit remote sensing techniques, well-established Bayesian regression models for estimating glacial lake conditions, cutting-edge flood modelling technology, and open data from various sources to innovate a framework for assessing the national exposure and impact of GLOFs. In the innovative framework, multi-temporal imagery is utilized with a Random Forest model to extract glacial lake water surfaces. Bayesian models, derived from previous research, are employed to estimate a plausible range of glacial lake water volumes and associated GLOF peak discharges, while accounting for the uncertainty stemming from the limited size of available data and outliers within the data. A significant amount of GLOF scenarios is subsequently generated based on this estimated plausible range of peak discharges. A GPU-based hydrodynamic model is then adopted to simulate the resulting flood hydrodynamics in different GLOF scenarios. Necessary socio-economic information is collected and processed from multiple sources including OpenStreetMap, Google Earth, local archives, and global data products to support exposure analysis. Established depth-damage curves are used to assess the GLOF damage extents to different exposures. The evaluation framework is applied to 21 glacial lakes identified potentially dangerous in the Nepal Himalaya. The results indicate that Tsho Rolpa Lake, Lower Barun Lake and Thulagi Lake bear the most serious impacts of GLOFs on buildings and roads, and influence existing hydropower facilities, while Lower Barun Lake, Tsho Rolpa Lake and Lumding Lake will experience the most impacts of GLOFs on agriculture areas. Four anonymous lakes (located at 85°37′51″ E, 28°09′44″ N; 87°44′59″ E, 27°48′57″ N; 87°56′05″ N, 27°47′26″ E; 86°55′41″ E, 27°51′00″ N) have the potential to impact more than 100 buildings, and the first three lakes may even submerge existing hydropower facilities.
... The perception of increasing distances to water sources was also linked to climate change. This notion is also corroborated by other studies which indicate that climate change and increased climate variability strongly impact surface water resources directly (Aherne et al., 2006, Ferguson and Maxwell, 2012, Tulbure et al., 2016Poudel et al., 2017). Moreover, land users mentioned that when it was extremely hot, some of the water resources are negatively affected. ...
In Lesotho, the alpine wetlands and sponges are the sources of drinking water for humans and livestock and environmentally critical for sustainable perennial flow of our streams. However, these environments are some of the most threatened ecosystems in Lesotho. Both ground and surface water are exposed to increasing and unprecedented threats from anthropogenic stressors that degrade water quality, reduce water quantity and availability. The net effects are compounded by the climate change impacts creating extreme precipitation conditions of drought and floods, both of which collude to destroy habitat and harm aquatic life. Climate change is the compounding factor, the effects of which are exacerbated by anthropogenic activities which cause land degradation. Since the knowledge, attitudes and practices of the people to a large extend influence how they manage the environment, the purpose of the study was to assess the land users' perceptions on their water quality, availability and distribution. A structured open-ended questionnaire was used to illicit information from focus group discussions with community groups and interviews with key informants. The questions were based on LADA framework of Driving Forces, Pressures, State, Impacts and Responses (DPSIR), which sought to assess the state (S) of the water resources, the driving forces and pressure factors (DP) bearing on water resources, the impacts (I) impacts of the anthropogenic factors on the ecosystem as well as to the households and then the response (R) surface of the community and policy makers. The status of the water resources were found to have decreased due to lack of proper management of the resources, high population caused water shortage and infrastructures decrease water sources as result, the water becomes low both in quality and quantity and their crop production decreased. In respone, the community had started making dams and covering their wells. However, no policies were made regarding water resources management.
... The extraction of the surface water area from the remote sensing images was implemented in a supervised manner, with RF employed as the classifier. As a popular statistical classifier, RF has been widely used for surface water mapping and change monitoring, and has showed stable performances (Li and Xu 2021;Rodriguez-Galiano et al. 2012;Tulbure et al. 2016). RF is an integrated predictor that builds a set of decision trees, which can effectively reduce the single model's sensitivity to data noise and anomalies (Breiman 2001). ...
Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets.
... The widely utilized water indices include the Normalized Difference Water Index (NDWI) [20], the Modified Normalized Difference Water Index (MNDWI) [21], and the Automated Water Extraction Index (AWEI) [11]. However, since the spectral signature of the same ground objects varies in different external environments, it is challenging to distinguish water bodies using a single ideal threshold [22]. The Otsu algorithm solves this problem by automatically determining the optimal threshold [23,24], but this algorithm is difficult to ensure the accuracy of local water extraction in large watersheds [25]. ...
Surface water bodies exhibit dynamic characteristics, undergoing variations in size, shape, and flow patterns over time due to numerous natural and human factors. The monitoring of spatial-temporal changes in surface water bodies is crucial for the sustainable development and efficient utilization of water resources. In this study, Landsat series images on the Google Earth Engine (GEE) platform, along with the HydroLAKES and China Reservoir datasets, were utilized to establish an extraction process for surface water bodies from 1986 to 2021 in the Yellow River Basin (YRB). The study aims to investigate the dynamics of surface water bodies and the driving factors within the YRB. The findings reveal an overall expansion tendency of surface water bodies in the YRB between 1986 and 2021. In the YRB, the total area of surface water bodies, natural lakes, and artificial reservoirs increased by 2983.8 km2 (40.4%), 281.1 km2 (11.5%), and 1017.6 km2 (101.7%), respectively. A total of 102 natural lakes expanded, while 23 shrank. Regarding artificial reservoirs, 204 expanded, and 77 shrank. The factors that contributed most to the increase in the surface water bodies were increasing precipitation and reservoir construction, whose contribution rates could reach 47% and 32.6%, respectively. Additionally, the rising temperatures melted permafrost, ice, and snow, positively correlating with water expansion in the upper reaches of the YRB, particularly natural lakes.
... Moreover, the widely used threshold-based binary classification based on water indices (such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)) falls within the first category (Acharya et al., 2018;Xu, 2006). Among various methods, thresholding on the satellite-derived water indices has the advantages of rapid extraction, high accuracy, simplicity, robustness, and good repeatability when extracting large areas of water (Han & Niu, 2020;Tulbure et al., 2016;R. Wang et al., 2020;X. ...
The Landsat mission has captured images of the Earth’s surface for over 50 years, and the data have enabled researchers to investigate a vast array of different change phenomena using machine learning models. Landsat-based monitoring research has been influential in geography, forestry, hydrology, ecology, agriculture, geology, and public health. When monitoring Earth's surface change using Landsat data and machine learning, it is essential to consider the implications of the size of the study area, specifics of the machine learning model, and image temporal density. We found that there are two general approaches to Landsat change detection analysis with machine learning: post-classification comparison and sequential imagery stack approaches. The two approaches have different advantages, and the design of an appropriate type of Landsat change detection analysis depends on the task at hand and the available computing resources. This review provides an overview of different approaches used to apply machine learning to Landsat change detection analysis, outlines a framework for understanding the relevant considerations, and discusses recent developments such as generative artificial intelligence, explainable machine learning, and ethical analysis considerations.
High-resolution satellite imagery providing long-term, continuous information on surface water extent in highly developed regions is paramount for elucidating the spatiotemporal dynamics of water bodies. The landscape of water bodies is a key indicator of water quality and ecological services. In this study, we analyzed surface water dynamics, including rivers, lakes, and reservoirs, using Landsat images spanning from the 1980s to 2020, with a focus on the highly developed Coastal Chinese Mainland (CCM) region. Our objectives were to investigate the temporal and spatial variations in surface water area extent and landscape characteristics, to explore the driving forces behind these variations, to gain insights into the complex interactions between water bodies and evolving environmental conditions, and ultimately to support sustainable development in coastal regions. Our findings revealed that reservoirs constitute the largest proportion of surface water, while lakes occupy the smallest share. Notably, a trend of expansion in surface water extent in the CCM was observed, mainly from the construction of new reservoirs. These reservoirs primarily gained new areas from agricultural land and river floodplains in the early stages (1980s–2000), while a greater proportion of construction land was encroached upon by reservoirs in later periods (2001–2020). At the landscape level, a tendency toward fragmentation and complexity in surface water, particularly in reservoirs, was evident. Human interference, particularly urbanization, played a pivotal role in driving the expansion of water surfaces. While reservoir construction benefits water resource assurance, flood control, and prevention, it also poses eco-hydrological challenges, including water quality deterioration, reduced hydrological connectivity, and aquatic ecosystem degradation. The findings of this study provide essential data support for sustainable water resource development. These insights underscore the urgency and importance of integrated water resource management strategies, particularly in efforts aimed at conservation and restoration of natural water bodies and the scientific regulation of artificial water bodies. Balancing human development needs with the preservation of ecological integrity is crucial to facilitating a water resource management strategy that integrates climatic and socio-economic dimensions, ensuring sustainable water use and protection for future generations.
Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications.
Wetland ecosystems are experiencing rapid degradation due to human activities, particularly the diversion of natural flows for various purposes, leading to significant alterations in wetland hydrology and their ecological functions. However, understanding and quantifying these eco-hydrological changes, especially concerning inundation dynamics, presents a formidable challenge due to the lack of long-term, observation-based spatiotemporal inundation information. In this study, we classified wetland areas into ten equal-interval classes based on inundation probability derived from a dense, 30-year time series of Landsat-based inundation maps over an Australian dryland riparian wetland, Macquarie Marshes. These maps were then compared with three simplified vegetation patches in the area: river red gum forest, river red gum woodland, and shrubland. Our findings reveal a higher inundation probability over a small area covered by river red gum forest, exhibiting persistent inundation over time. In contrast, river red gum woodland and shrubland areas show fluctuating inundation patterns. When comparing percentage inundation with the Normalized Difference Vegetation Index (NDVI), we observed a notable agreement in peaks, with a lag time in NDVI response. A strong correlation between NDVI and the percentage of inundated area was found in the river red gum woodland patch. During dry, wet, and intermediate years, the shrubland patch consistently demonstrated similar inundation probabilities, while river red gum patches exhibited variable probabilities. During drying events, the shrubland patch dried faster, likely due to higher evaporation rates driven by exposure to solar radiation. However, long-term inundation probability exhibited agreement with the SAGA wetness index, highlighting the influence of topography on inundation probability. These findings provide crucial insights into the complex interactions between hydrological processes and vegetation dynamics in wetland ecosystems, underscoring the need for comprehensive monitoring and management strategies to mitigate degradation and preserve these vital ecosystems.
The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.
For many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10-30 m), which are also referred to fine or medium resolutions in the literature. To facilitate the development of more high-spatial resolution products, this paper systematically reviews the state-of-the-art progress on inversion algorithms and publicly available regional and global products. We begin with an inventory of available high-resolution satellite data, and then present different algorithms for determining cloud masks, estimating aerosol optical depth, and performing atmospheric correction and topographic correction for land surface reflectance retrieval. The majority of this paper reviews the inversion algorithms and existing regional to global products of 18 variables in four major categories: 1) Land surface radiation, including broadband albedo, land surface temperature, and all-wave net radiation; 2) Terrestrial ecosystem variables, including leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, fractional forest cover, tree height, forest above-ground biomass gross primary production, net primary production, and agricultural crop yield; 3) Water cycle and cryosphere, including soil moisture, evapotranspiration, and snow cover; and 4) Land surface types, such as global land cover, impervious surface, inland water, crop type, and fire. Since the existing products over large regions are usually spatially discontinuous due to cloud contamination, different data fusion and data assimilation algorithms and some products for producing spatially seamless and temporally continuous products are presented. In the end, we discuss a variety of challenges in generating global high spatial resolution satellite products.
Water level monitoring in lakes and reservoirs is essential for effective water resource management, especially in remote areas where traditional ground sensors are costly and difficult to maintain. Remote sensing offers an alternative, but improving the quality, resolution, and accuracy of satellite data remains crucial. This paper introduces MoRLa (Measurement of Reservoir Level from Altimetry), a data filtering procedure designed to enhance satellite altimetry retrievals. MoRLa increases the acceptance of satellite observations and improves the quality of water level estimates by using physical characteristics of water bodies to exclude non-conforming measurements. Unlike previous studies with static masks, MoRLa employs a dynamic filter adaptable to actual water levels at specific times. Tested on reservoirs in the Korean Peninsula, including the Hwang-Gang dam, MoRLa shows significant improvements in water level measurements using Cryosat-2, ICESat-2, and Sentinel-3A and B satellites.
Changes in climate and land‐use/land‐cover will impact surface water dynamics throughout the 21st century and influence global surface water availability. However, most projections of surface water dynamics focus on climate drivers using local‐scale hydrological models, with few studies accounting for climate and human drivers such as land‐use/land‐cover change. We used a data‐driven, machine learning model to project seasonal surface water areas (SWAs) in the southeastern U.S. from 2006 to 2099 that combined land‐cover and climate projections under eight different development and emissions scenarios. The model was fitted with historic Landsat imagery, land‐use/land‐cover, and climate observation data (mean squared error 0.14). We assessed the change in SWA for each scenario, and we compared the surface water projections from our data‐driven model and a process‐based model. We found that the scenario with the largest forest‐dominated land cover loss and most extreme climate change had watersheds with the greatest projected increases (in the South Atlantic Gulf) and decreases (in the Lower Mississippi) in SWA. When compared to the increase or decrease in surface water projected by the process‐based model, most of the watersheds across scenarios agreed on the direction of change. Our findings highlight the importance of forest‐dominated land cover in maintaining stable surface water availability throughout the 21st century, which can inform land‐use management policies for adaptation and water‐stress mitigation as well as strategies to prepare for future flood and drought events.
Hamburg will only achieve the transformation to blue–green urban water management if all urban development processes consequently address the changing requirements of urban water management right from the very beginning. In order to achieve this, water management, open space, urban development, transport planning and other relevant fields must collaborate from the start.1 The exchange of urban stakeholders must be supported by good public communication and public participation. In Hamburg, integrated storm water management is seen as a collaborative local task for these groups. In this respect, HAMBURG WASSER and the Authority for the Environment, Climate, Energy and Agriculture (BUKEA) are implementing the RISA Structure Plan 20302 concepts and solutions for a sustainable management of storm water in Hamburg within the framework of the RainwaterInfraStrutureAdaptation (RISA) since 2015. The goal is to bundle resources across institutions, to develop unconventional approaches to new challenges in stormwater management, and to implement and realise them.
The following examples show components for urban water management of the future. They are based on lessons learned from projects that have been realised so far, as well as on the need to increase the resilience of urban water management. That means, on the one hand, preserving and precautionary strategies are needed to ensure the robustness of urban water management by qualifying the existing central drainage and purification systems.
On the other hand, buffering strategies are also necessary that allow urban water management to become more flexible by integrating modular, redundant and decentralised elements. Furthermore, rapid transformation in the sense of resilience is only possible if creative solutions can be accomplished with a high level of innovation and cooperation.
Lakes are essential components of the terrestrial water cycle. Their size and quantity reflect natural climate change and anthropogenic activities in time. Lakes on the Inner-Mongolian Plateau (IMP) have experienced significant changes in recent decades, but the current situation remains elusive. In this study, we conducted multi-decadal intensive monitoring of lake area and performed comprehensive variation analysis on the IMP. The study involved pre-processing, lake area extraction, post-processing, and lake area analysis procedures using multi-source satellite images. The results reveal the detailed variation in the lake from various aspects. The temporal analysis indicates that the lake area has undergone two distinct periods of decline followed by subsequent increase, and the variation pattern from January to December was diverse. The spatial analysis suggests that the lake area expanded slightly in the western and southern parts, while the area shrank in the other two. Variations in lakes of different sizes exhibited spatial heterogeneity. To some extent, effective actions have led to lake rehabilitation, but it has not yet reached or surpassed the level of 2000. This study provides a substantial data basis and technological guidance for conducting lacustrine surveys. The results can play important roles in fostering further analyses of the water cycle and the carbon cycle.
Rivers in arid regions serve as crucial freshwater resources for local communities and play an essential role in global hydrological and biogeochemical cycles. The Aral Sea Basin (ASB) in Central Asia is characterized by an arid climate and river dynamics that are sensitive to climate change and human activities. Monitoring the spatiotemporal variations in river water extent in the ASB is essential to maintain an ecological balance and ensure water security. In this study, we extracted data regarding monthly river water bodies in the ASB from 2017 to 2022 by synthesizing monthly Sentinel-2 images. The water extents on the Sentinel images were automatically mapped using the Otsu method, and the river widths for all river channels were calculated using the RivWidth algorithm. We investigated the relationships between the river dynamics and the geomorphology, climatic change, human activities, and the annual and interannual variations in the river width in different reaches of the basin. The results show a seasonal variability in the river width, with most rivers reaching the largest width in the warm season and a few rivers in the middle and lower areas reaching the valley value in the warm season. Compared to their tributaries, the mainstem in the middle/lower regions showed less seasonal variability. According to interannual analysis, most of the rivers in the ASB significantly narrowed between 2017 and 2022, a phenomenon which is generally impacted by temperature and evapotranspiration variations. Comparisons show that our results provide improved information about the narrow river reaches and denser river networks compared to the previous global dataset, demonstrating the advantageous properties of high spatial resolution in Sentinel-2 imagery.
Intermittent and ephemeral channels are a critical component of the global hydrologic network. The dominant feature in dryland environments, ephemeral channel transmission loss facilitates aquifer recharge. Characterizing flow intermittency improves groundwater storage estimates; however, limited gauging of intermittent systems impedes this understanding. This research develops an improved classification for surface flow, optimized for ephemeral systems using linear discriminant function analysis and remotely sensed imagery. It further applies this methodology to assess temporal and spatial flow patterns across the Souss channel, an ungauged, ephemeral system in central Morocco. Linear discriminant function analysis demonstrates high predictive accuracy for Landsat imagery, with significantly improved classification success as compared to the Modified Normalized Difference Water Index. Application to the Souss channel from 1984 to 2022 points to a decreasing trend in flow frequency. Despite this change, flow events remain concentrated within the wet season, critical for regional aquifer recharge. Spatial flow characteristics further support sustained infiltration, with the majority of events focused within the upstream channel section during both dry and wet seasons. Decreased occurrence moving downstream highlights the likely impact of additional factors such as transmission loss, evapotranspiration, and agricultural abstraction contributing to channel intermittency.
A novel method for assessing spectral index efficiencies for landscape mapping in tropical wetlands was formulated: spectral indices assessment for mapping tropical wetlands (SIA_MW). SIA_MW consists of three stages: (1) identification of covers that make up the landscape, (2) feature selection consistency assessment, and (3) result validation. These stages are evaluated based on six criteria, each of which contains a decision rule (DR) with their respective rating alternatives. The DRs are integrated into two equations: efficiency of an index for landscape mapping in tropical wetlands (EIM_W) and efficiency of an index for water body mapping in tropical wetlands (EIM_Ww). SIA_MW has been proposed as a novel instrument that allows each of the stages of supervised classification to be developed and evaluated in an orderly and coherent manner. This ensures that the final decision to select an index is supported by a robust process that integrates qualitative and quantitative methods of spectral evaluation. SIA_MW is applicable to multiple remote sensing products and can be used in environments other than wetlands. This is because it is independent of factors such as landscape cover categories, the type of sensor product from which spectral indices are derived, and spectral classification algorithms. For the formulation of SIA_MW, the Bajo Sinú Wetland Complex (BSWC), located in northern Colombia, was selected as a pilot site, and 9 vegetation indices derived from a PlanteScope image were compared and evaluated. The soil-adjusted vegetation and water-adjusted vegetation indices (SAVI and WAVI, respectively) yielded the best results with values for EMI_W of 0.94 and 0.89, respectively. These results indicate SIA_MW was consistent because the covariance between the two best indices was 0.88. Additionally, the correlation between the DR scores of the evaluated indices was low, thus, indicating criteria complementarity.
Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km2 occurred in 2005, and the maximum water area of 2614.96 km2 occurred in 2020, with an annual average variation of 193.92 km2. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture.
This paper assesses the hydrological response to scenarios of climate change in the Okavango River catchment in Southern Africa. Climate scenarios are constructed representing different changes in global mean temperature from an ensemble of 7 climate models assessed in the IPCC AR4. The results show a substantial change in mean flow associated with a global warming of 2 °C. However, there is considerable uncertainty in the sign and magnitude of the projected changes between different climate models, implying that the ensemble mean is not an appropriate generalised indicator of impact. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation. There is also a clear need to evaluate the physical mechanisms associated with the model projected changes in this region. The implications for water resource management policy are considered.
As this edition of The World’s Water goes to press in early 2011, eastern Australia is recovering from devastating floods that claimed more than 20 lives and destroyed hundreds of homes. The heavy rains of 2009 and 2010 that caused so much destruction also marked the end of Australia’s decade-long Millennium Drought. Beginning in about 1997, declines in rainfall and runoff had contributed to widespread crop failures, livestock losses, dust storms, and bushfires. Such are the vagaries of water on the continent with the world’s most uncertain and variable climate.
Following extreme flooding in eastern Australia in 2011, the Australian Government established a programme to improve access to flood information across Australia. As part of this, a project was undertaken to map the extent of surface water across Australia using the multi-decadal archive of Landsat satellite imagery. A water detection algorithm was used based on a decision tree classifier, and a comparison methodology using a logistic regression. This approach provided an understanding of the confidence in the water observations. The results were used to map the presence of surface water across the entire continent from every observation of 27years of satellite imagery. The Water Observation from Space (WOfS) product provides insight into the behaviour of surface water across Australia through time, demonstrating where water is persistent, such as in reservoirs, and where it is ephemeral, such as on floodplains during a flood. In addition the WOfS product is useful for studies of wetland extent, aquatic species behaviour, hydrological models, land surface process modelling and groundwater recharge. This paper describes the WOfS methodology and shows how similar time-series analyses of nationally significant environmental variables might be conducted at the continental scale.
The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.
Ecologists and space agencies must forge a global monitoring strategy, say Andrew K. Skidmore, Nathalie Pettorelli and colleagues.
Context: Landscape-scale research quantifying ecological connectivity is required to maintain the viability of populations in dynamic environments increasingly impacted by anthropogenic modification and environmental change.
Objective: To evaluate how surface water network structure, landscape resistance to movement, and flooding affect the connectivity of amphibian habitats within the Murray–Darling Basin (MDB), a highly modified but ecologically significant region of south-eastern Australia.
Methods: We evaluated potential connectivity network graphs based on circuit theory, Euclidean and least-cost path distances for two amphibian species with different dispersal abilities, and used graph theory metrics to compare regional- and patch-scale connectivity across a range of flooding scenarios.
Results: Circuit theory graphs were more connected than Euclidean and least-cost equivalents in floodplain environments, and less connected in highly modified or semi-arid regions. Habitat networks were highly fragmented for both species, with flooding playing a crucial role in facilitating landscape-scale connectivity. Both formally and informally protected habitats were more likely to form important connectivity “hubs” or “stepping stones” compared to non-protected habitats, and increased in importance with flooding.
Conclusions: Surface water network structure and the quality of the intervening landscape matrix combine to affect the connectivity of MDB amphibian habitats in ways which vary spatially and in response to flooding. Our findings highlight the importance of utilising organism-relevant connectivity models which incorporate landscape resistance to movement, and accounting for dynamic landscape-scale processes such as flooding when quantifying connectivity to inform the conservation of dynamic and highly modified environments.
The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2 of inland water globally—nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal “inland water” biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer (MODIS) water mask and Landsat-based national land cover datasets was very high, with commission errors < 4% and omission errors < 14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds—effects which were compounded by differences in image-acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset (GIW) is available for open access at the GLCF website (http://www.landcover.org).
Wetlands play an important role in the provision of ecosystem services, ranging from the regulation of hydrological systems to carbon sequestration and biodiversity habitat. This paper reports the mapping of Indonesia’s wetland cover as a single thematic class, including peatlands, freshwater wetlands, and mangroves. Expert-interpreted training data were used to identify wetland formations including areas of likely past wetland extent that have been converted to other land uses. Topographical indices (Shuttle Radar Topography Mission-derived) and optical (Landsat) and radar (PALSAR) image inputs were used to build a bagged classification tree model based on training data in order to generate a national-scale map of wetland extent at a 60 m spatial resolution. The resulting wetland map covers 21.0% (39.6 Mha) of Indonesia’s land, including 25.2% of Sumatra (11.9 Mha), 22.9% of Kalimantan (12.2 Mha), and 28.9% of Papua (11.8 Mha). Results agree with existing image-interpreted products from Indonesia’s Ministries of Forestry and Agriculture and Wetlands International (89% overall agreement), and with the Ministry of Forestry forest inventory data for Sumatra and Kalimantan (91% overall agreement). An internally consistent algorithm-derived national wetland extent map can be used to quantify changing rates of land conversion inside and outside of wetlands. Additionally, wetlands extent can be used to efficiently allocate field resources in national assessments of wetland sub-types such as peatlands, which are a current focus of policies aiming to reduce carbon emissions from land use change.
Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN).
We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans.
The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999–2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified 'stepping stone' water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as 'rich get richer' algorithm. The 'stepping stone' water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as 'stepping stone' over time may help prioritize surface water bodies that are essential for maintaining regional scale connectivity.
Malaria is a major global public health problem, particularly in Sub‐Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district‐level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single‐sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.
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.
Changes in land cover affect the global climate by absorbing and reflecting solar radiation, and by altering fluxes of heat, water vapour, carbon dioxide and other trace gases. Detailed assessments — regional, global, daily and seasonal — of land use and land cover are needed to monitor biodiversity loss and ecosystem dynamics and to aid in reducing emissions from deforestation and forest degradation.
Satellite imagery is the best source of such data, especially over large areas. Observations need to be extensive, regular and consistent to establish baselines and trends. But today, most satellite observations have limited coverage and compatibility, because they are controlled by the diverse objectives of national space programmes. In many cases, satellite data are restricted or charged for.
A new era of open-access satellite data has arrived. In 2008, the US Geological Survey (USGS) released for free to the public its Landsat archive, which dates back to the 1970s and is the world's largest collection of Earth imagery. Greater computing power is also enabling scientists to manipulate big data representing larger areas and with greater sophistication, to produce multi-billion-pixel composite maps of land cover and change across regions, continents and the globe. Monitoring land-cover change in near-real time is now a reality.
http://www.nature.com/news/satellites-make-earth-observations-open-access-1.15804
An accurate description of the abundance and size distribution of lakes is critical to quantifying limnetic contributions to the global carbon cycle. However, estimates of global lake abundance are poorly constrained. We used high-resolution satellite imagery to produce a GLObal WAter BOdies database (GLOWABO), comprising all lakes greater than 0.002 km2. GLOWABO contains geographic and morphometric information for ~117 million lakes with a combined surface area of about 5 million km2, which is 3.7% of the Earth’ non-glaciated land area. Large and intermediate-sized lakes dominate the total lake surface area. Overall, lakes are less abundant, but cover a greater total surface area relative to previous estimates based on statistical extrapolations. The GLOWABO allows for the global-scale evaluation of fundamental limnological problems, providing a foundation for improved quantification of limnetic contributions to the biogeochemical processes at large scales.
Context
Frog species are now targets for delivery of high-value managed environmental flows on floodplains. Information on the drivers of frog presence and abundance is required to support adaptive management, including analysis of the roles of flood frequency, flood timing and habitat type.
Aims
This paper describes frog species richness and abundance responses to flooding and habitat type in the Barmah Forest, part of the largest river red gum forest in the world.
Methods
Surveys were conducted at 22 sites over six years, to determine species presence, relative abundance, and evidence of breeding. Data were then used to examine temporal patterns within and between wet and dry years and spatial relationships with site geomorphology, vegetation form, and wetting frequency.
Key results
Six species were common and widespread, and three were rare. The seasonal timing of peak numbers of calling males differed between species. The seasonal pattern of calling for each species did not differ between wet and dry years, however significantly lower numbers of frogs were recorded calling in dry years. The number of frogs calling was significantly higher in well-vegetated grassy wetlands. Evidence of a positive relationship between wetting frequency and numbers of calling males was found for Limnodynastes fletcheri, Crinia signifera and Limnodynastes dumerilii. The abundance of tadpoles was significantly higher in wet years.
Conclusions
The seasonal timing of flooding in Barmah Forest will influence the breeding success of individual species with different preferences. Flooding from September to December is required to cover most preferred breeding seasons, but longer durations may be required to maximise recruitment. This, together with regular flooding of well-vegetated grassy wetland habitat, will increase the likelihood of species persistence and maximise diversity. Insufficient flooding frequency will result in reduced frog species richness and abundance.
Implications
Managed flooding is important for frog abundance and species richness. This study emphasises the value of key habitats such as well-vegetated grassy wetlands and reinforces the need to make their preservation a priority for management. It has identified knowledge gaps to drive future data collection for improved modelling, including a need for further research on flow regime change and frog communities.
Before being used in scientific investigations and policy decisions, thematic maps constructed from remotely sensed data should be subjected to a statistically rigorous accuracy assessment. The three basic components of an accuracy assessment are: 1) the sampling design used to select the reference sample; 2) the response design used to obtain the reference land-cover classification for each sampling unit; and 3) the estimation and analysis procedures. We discuss options available for each of these components. A statistically rigorous assessment requires both a probability sampling design and statistically consistent estimators of accuracy parameters, along with a response design determined in accordance with features of the mapping and classification process such as the land-cover classification scheme, minimum mapping unit, and spatial scale of the mapping.
Temporal stability, defined as the change of accuracy through time, is one of the validation aspects required by the Committee on Earth Observation Satellites’ Land Product Validation Subgroup. Temporal stability was evaluated for three burned areaproducts: MCD64, Globcarbon, and fire_cci. Traditional accuracy measures, such as overall accuracy and omission and commission error ratios, were computed from reference data for seven years (2001–2007) in seven study sites, located in Angola, Australia, Brazil,Canada, Colombia, Portugal, and South Africa. These accuracy measures served as the basis for the evaluation of temporal stability of each product. Nonparametric tests were constructed to assess different departures from temporal stability, specifically a monotonic trend in accuracy over time (Wilcoxon test for trend), and differences in median accuracy among years (Friedman test). When applied to the three burned area products, these tests did not detect a statistically significant temporal trend or significant differences among years, thus, based on the small sample size of seven sites, there was insufficient evidence to claim these products had temporal instability. Pairwise Wilcoxon tests comparing yearly accuracies provided a measure of the proportion of year-pairs with significant differences and these proportions of significant pairwise differences were in turn used to compare temporal stability between BA products. The proportion of year-pairs with different accuracy (at the 0.05 significance level) ranged from 0% (MCD64) to 14% (fire_cci), computed from the 21 year-pairs available. In addition to the analysis of the three real burned area products, the analyses were applied to the accuracy measures computed for four hypothetical burned area products to illustrate the properties of the temporal stability analysis for different hypothetical scenarios of change in accuracy over time. The nonparametric tests were generally successful at detecting the different types of temporal instability designed into the hypothetical scenarios. The current work presents for the first time methods to quantify the temporal stability of BA product accuracies and to alert product end-users that statistically significant temporal instabilities exist. These methods represent diagnostic tools that allow product users to recognize the potential confounding effect of temporal instability on analysis of fire trends and allow map producers to identify anomalies in accuracy over time that may lead to insights for improving fire products. Additionally, we suggest temporal instabilities that could hypothetically appear, caused by for example by failures or changes in sensor data or classification algorithms.
Major rivers worldwide have experienced dramatic changes in flow, reducing their natural ability to adjust to and absorb disturbances. Given expected changes in global climate and water needs, this may create serious problems; including loss of native biodiversity and risks to ecosystems and humans from increased flooding or water shortages. Here, we project river discharge under different climate and water withdrawal scenarios and combine this with data on the impact of dams on large river basins to create global maps illustrating potential changes in discharge and water stress for dam-impacted and free-flowing basins. The projections indicate that every populated basin in the world will experience changes in river discharge and many will experience water stress. The magnitude of these impacts is used to identify basins likely and almost certain to require proactive or reactive management intervention. Our analysis indicates that the area in need of management action to mitigate the impacts of climate change is much greater for basins impacted by dams than for basins with free-flowing rivers. Nearly one billion people live in areas likely to require action and approximately 365 million people live in basins almost certain to require action. Proactive management efforts will minimize risks to ecosystems and people and may be less costly than reactive efforts taken only once problems have arisen.
Timely and accurate monitoring of forest disturbance is essential to help us understand how the Earth system is changing. MODIS (Moderate Resolution Imaging Spectroradiometer) imagery and subsequent MODIS products provide near-daily global coverage and have transformed the ways we study and monitor the Earth. To monitor forest disturbance, it is necessary to be able to compare observations of the same place from different times, but this is a challenging task using MODIS data as observations from different days have varying view angles and pixel sizes, and cover slightly different areas. In this paper, we propose a method to fuse MODIS and Landsat data in a way that allows for near real-time monitoring of forest disturbance. The method is based on using Landsat time-series images to predict the next MODIS image, which forms a stable basis for comparison with new MODIS acquisitions. The predicted MODIS images represent what the surface should look like assuming no disturbance, and the difference in the spectral signatures between predicted and observed MODIS images becomes the “signal” used for detecting forest disturbance. The method was able to detect subpixel forest disturbance with a producer's accuracy of 81% and a user's accuracy of 90%. Patches of forest disturbance as small as 5 to 7 ha in size were detected on a daily basis. The encouraging results indicate that the presented fusion method holds promise for improving monitoring of forest disturbance in near real-time.
The accuracy of a land cover classification is the degree to which the map land cover agrees with the reference land cover classification (i.e. ground condition). The basic sampling designs historically implemented for map accuracy assessment have served well for the error matrix based analyses traditionally used. But contemporary applications of land cover maps place greater demands on accuracy assessment, and sampling designs must be constructed to target objectives such as accuracy of land cover composition and landscape pattern. Sampling designs differ in their suitability to achieve different objectives, and trade-offs among desirable sampling design criteria must be recognized and accommodated when selecting a design. An overview is presented of the sampling designs used in accuracy assessment, and the status of these designs is appraised for meeting current needs. Sampling design features that facilitate multiple-objective accuracy assessments are described.
We review the human actions, proximal stressors and ecological responses for floodplain forests Australia's largest river system---the Murray-Darling Basin. A conceptual model for the floodplain forests was built from extensive published information and some unpublished results for the system, which should provide a basis for understanding, studying and managing the ecology of floodplains that face similar environmental stresses. Since European settlement, lowlands areas of the basin have been extensively cleared for agriculture and remnant forests heavily harvested for timber. The most significant human intervention is modification of river flows, and the reduction in frequency, duration and timing of flooding, which are compounded by climate change (higher temperatures and reduced rainfall) and deteriorating groundwater conditions (depth and salinity). This has created unfavorable conditions for all life-history stages of the dominant floodplain tree (Eucalyptus camaldulensis Dehnh.). Lack of extensive flooding has led to widespread dieback across the Murray River floodplain (currently 79% by area). Management for timber resources has altered the structure of these forests from one dominated by large, widely spreading trees to mixed-aged stands of smaller pole trees. Reductions in numbers of birds and other vertebrates followed the decline in habitat quality (hollow-bearing trees, fallen timber). Restoration of these forests is dependent on substantial increases in the frequency and extent of flooding, improvements in groundwater conditions, re-establishing a diversity of forest structures, removal of grazing and consideration of these interacting stressors.
The assessment of the role of lakes and impoundments at regional and global scales, e.g., in biogeochemical cycles, requires good estimates of the areal extent and shape of water bodies. Upscaling to large regions, except in limited areas where precise maps are available, so far depends on statistical estimates of the number and size of lakes, which explains why estimates are poor. We present an automated procedure that allows mapping of the actual number, size, and distribution of lakes at large scale. Landsat 7 Enhanced Thematic Mapper Plus (ETM +) mosaics from the GeoCover Circa 2000 dataset covering the Earth land surfaces with 14.25 m spatial resolution were used as input data. We developed an approach called GWEM (GeoCover (TM) Water bodies Extraction Method) that combines remote sensing and GIS to extract water bodies and study their abundance and morphometry. All water bodies greater than 0.0002 km(2) were taken into account as lakes. The accuracy of the method was tested on Sweden, where detailed maps of lakes, based on in situ data and orthophotos, exist for the whole country. The proposed method produced accurate results. The largest sources of errors are shadows of mountains and clouds, since the GeoCover mosaics are not absolutely cloud free.
Growing an ensemble of decision trees and allowing them to vote for the most popular class produced a significant increase in classification accuracy for land cover classification. The objective of this study is to present results obtained with the random forest classifier and to compare its performance with the support vector machines (SVMs) in terms of classification accuracy, training time and user defined parameters. Landsat Enhanced Thematic Mapper Plus (ETM+) data of an area in the UK with seven different land covers were used. Results from this study suggest that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time. This study also concludes that the number of user‐defined parameters required by random forest classifiers is less than the number required for SVMs and easier to define.
Relatively few large-scale inventories of the world's wetlands exist because of the difficulties of spatial scale, associated cost and multiple objectives, often temporally confounded, that drive classification. The extent of wetlands across a large part of Australia (New South Wales, 80.6 million ha) was determined using satellite image analyses. These data allowed analyses of the distribution of wetlands, their conservation status and potential threats at different spatial scales; that is, State, coastal and inland, and catchment. Approximately 5.6% of New South Wales is wetland (4.5 million ha), mostly (96%) in inland river catchments. Broad classification allowed identification of the extent of wetland types: (i) floodplains (89%); (ii) freshwater lakes (6.6%); (iii) saline lakes (< 1%); (iv) estuarine wetlands (2.5%); and (v) coastal lagoons and lakes (1.5%). Conservation reserves protect only 3% of wetland area. The analyses identified the north-west as the key area for wetland conservation as most other catchments have lower wetland extent and more potential threatening processes. The first stage of a large-scale inventory is to determine the extent and location of wetlands, with immediate benefits for strategic conservation and management. Other objectives (e.g. classification, biotic composition, hydrology and threats) seldom have sufficient data available for large-scale inventories but can be completed later with resources.
River regulation has led to a decline in the condition of Australia's dominant riverine tree species, Eucalyptus camaldulensis Dehnh., in the Murray-Darling Basin. A quantitative method of assessing the condition of these important riparian forests is required for effective monitoring and management. A range of stand structural, morphological and physiological variables was measured in stands of contrasting condition along the Murray River in south-eastern Australia. Percentage live basal area, plant area index and crown vigour were found to be reliable, objective indicators of stand condition. Little difference was detected in the physiological performance of trees in terms of water potential and chlorophyll fluorescence among stands of good and poor condition.
1. Ecologists have long sought to distinguish relationships that are general from those that are idiosyncratic to a narrow range of conditions. Conventional methods of model validation and selection assess in- or out-of-sample prediction accuracy but do not assess model generality or transferability, which can lead to overestimates of performance when predicting in other locations, time periods or data sets.
2. We propose an intuitive method for evaluating transferability based on techniques currently in use in the area of species distribution modelling. The method involves cross-validation in which data are assigned non-randomly to groups that are spatially, temporally or otherwise distinct, thus using heterogeneity in the data set as a surrogate for heterogeneity among data sets.
3. We illustrate the method by applying it to distribution modelling of brook trout (Salvelinus fontinalis Mitchill) and brown trout (Salmo trutta Linnaeus) in western United States. We show that machine-learning techniques such as random forests and artificial neural networks can produce models with excellent in-sample performance but poor transferability, unless complexity is constrained. In our example, traditional linear models have greater transferability.
4. We recommend the use of a transferability assessment whenever there is interest in making inferences beyond the data set used for model fitting. Such an assessment can be used both for validation and for model selection and provides important information beyond what can be learned from conventional validation and selection techniques.