An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

Jiang, Hao; Feng, Min; Zhu, Yunqiang; Lu, Ning; Huang, Jiazhang; Tong, Xiao

doi:10.3390/rs6065067

Cited by 237 publications

(159 citation statements)

References 23 publications

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“…However, the optimal threshold of each index for separating water varies regionally and over time due to mixing and local similarities with other cover types (Ji et al 2009;Jiang et al 2014). …”

Section: Landsat-based Predictorsmentioning

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

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294

190

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The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 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).

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Section: Landsat-based Predictorsmentioning

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

Self Cite

294

190

View full text Add to dashboard Cite

show abstract

“…In order to address the shadow problem, some researchers have combined water indices with additional shadow detection methods, such as using DEM data to detect the shadow units [25], digital image processing techniques to remove the shadow noises [24] and manual shadow mask [26].…”

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confidence: 99%

High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery

et al. 2015

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Accurate information of urban surface water is important for assessing the role it plays in urban ecosystem services under the content of urbanization and climate change. However, high-resolution monitoring of urban water bodies using remote sensing remains a challenge because of the limitation of previous water indices and the dark building shadow effect. To address this problem, we proposed an automated urban water extraction method (UWEM) which combines a new water index, together with a building shadow detection method. Firstly, we trained the parameters of UWEM using ZY-3 imagery of Qingdao, China. Then we verified the algorithm using five other sub-scenes (Aksu, Fuzhou, Hanyang, Huangpo and Huainan) ZY-3 imagery. The performance was compared with that of the Normalized Difference Water Index (NDWI). Results indicated that UWEM performed significantly better at the sub-scenes with kappa coefficients improved by 7.87%, 32.35%, 12.64%, 29.72%, 14.29%, respectively, and total omission and commission error reduced by 61.53%, 65.74%, 83.51%, 82.44%, and 74.40%, respectively. Furthermore, UWEM has more stable performances than NDWI's in a range of thresholds near zero. It reduces the OPEN ACCESS Remote Sens. 2015, 7 12337 over-and under-estimation issues which often accompany previous water indices when mapping urban surface water under complex environmental conditions.

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“…For the MODIS images, we can utilize bands 4 and 6 to extract water bodies by constructing Modified Normalized Difference Water Index (MNDWI) [13]. However, snow has similar reflectance difference as water, the TP2000 Lake Inventory dataset is applied to provide lake information and generate the lake seeds, which can expand water to lakes and remove noise [39].…”

Section: Water Detection Methodsmentioning

confidence: 99%

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

et al. 2017

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Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km 2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R 2 ) (0.89-0.957 for SR and 0.79-0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R 2 : 0.598-0.728 and 0.61-0.735, respectively).

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An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

Cited by 237 publications

References 23 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

High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

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