Untitled

Shevyrnogov, A.; Kartushinsky, Alexei V.; Vysotskaya, G.

doi:10.1023/a:1015658927683

Cited by 8 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial distribution of water and its changes over time are central to many agricultural, environmental, and ecological issues and are important factors for human socioeconomic development (Shevyrnogov, Kartushinsky, and Vysotskaya 2002;Famiglietti and Rodell 2013). Mapping the distribution of water in space and time is thus crucial for scientific research as well as for adaptive and sustainable ecosystem management (Cole et al 2007;Carroll et al 2011;Craglia et al 2012).…”

Section: Introductionmentioning

confidence: 99%

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Feng

Sexton

Channan

et al. 2015

International Journal of Digital Earth

281

190

View full text Add to dashboard Cite

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 km 2 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 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 is available for open access at the GLCF website (http://www.landcover.org).

show abstract

Section: Introductionmentioning

confidence: 99%

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Feng

Sexton

Channan

et al. 2015

International Journal of Digital Earth

281

190

View full text Add to dashboard Cite

show abstract

“…As an essential component of water resources, surface water bodies (i.e., lakes, reservoirs, rivers, streams, and ponds) provide a series of ecosystem services, such as water supply and regulation, climate regulation, and food production [2,3]. Therefore, the spatial distribution of surface water and its changes over time are central to many agricultural, environmental, and ecological issues, and are important factors for human socioeconomic development [4][5][6]. Global climate change and anthropogenic activities can have dramatic impacts on the inter-annual and intra-annual variations in surface water bodies, which can have profound influences on human society and natural ecosystems [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Continuous Monitoring of the Surface Water Area in the Yellow River Basin during 1986–2019 Using Available Landsat Imagery and the Google Earth Engine

Wang

2022

IJGI

View full text Add to dashboard Cite

The Yellow River Basin (YRB) has been facing severe water shortages; hence, the long-term dynamic monitoring of its surface water area (SWA) is essential for the efficient utilization of its water resources and sustainable socioeconomic development. In order to detect the changing trajectory of the SWA of the YRB and its influencing factors, we used available Landsat images from 1986 through to 2019 and a water and vegetation indices-based method to analyze the spatial–temporal variability of four types of SWAs (permanent, seasonal, maximum and average extents), and their relationship with precipitation (Pre), temperature (Temp), leaf area index (LAI) and surface soil moisture (SM).The multi-year average permanent surface water area (SWA) and seasonal SWA accounted for 46.48% and 53.52% in the Yellow River Basin (YRB), respectively. The permanent and seasonal water bodies were dominantly distributed in the upper reaches, accounting for 70.22% and 48.79% of these types, respectively. The rate of increase of the permanent SWA was 49.82 km2/a, of which the lower reaches contributed the most (34.34%), and the rate of decrease of the seasonal SWA was 79.18 km2/a, of which the contribution of the source region was the highest (25.99%). The seasonal SWA only exhibited decreasing trends in 13 sub-basins, accounting for 15% of all of the sub-basins, which indicates that the decrease in the seasonal SWA was dominantly caused by the change in the SWA in the main river channel region. The conversions from seasonal water to non-water bodies, and from seasonal to permanent water bodies were the dominant trends from 1986 to 2019 in the YRB. The SWA was positively correlated with precipitation, and was negatively correlated with the temperature. Because the permanent and seasonal water bodies were dominantly distributed in the river channel region and sub-basins, respectively, the change in the permanent SWA was significantly affected by the regulation of the major reservoirs, whereas the change in the seasonal SWA was more closely related to climate change. The increase in the soil moisture was helpful in the formation of the permanent water bodies. The increased evapotranspiration induced by vegetation greening played a significant positive role in the SWA increase via the local cooling and humidifying effects, which offset the accelerated water surface evaporation caused by the atmospheric warming.

show abstract

“…The formation, expansion, shrinkage and disappearance of surface water are important factors influencing the environment and regional climate changes. Water is also an important factor in socioeconomic development, because it affects many agricultural, environmental and ecological issues over time [2,3]. Hence the rapid and accurate extraction of water resource information can provide necessary data, which is of great significance for water resource investigation [4][5][6], flood monitoring [7,8], wetland protection [9,10] and disaster prevention and reduction [11,12].…”

Section: Introductionmentioning

confidence: 99%

Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks

Wang

Wei

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.

show abstract

Untitled

Cited by 8 publications

References 18 publications

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Continuous Monitoring of the Surface Water Area in the Yellow River Basin during 1986–2019 Using Available Landsat Imagery and the Google Earth Engine

Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks

Contact Info

Product

Resources

About