scholarly journals Extraction of Water-body Area from High-resolution Landsat Imagery

2018 ◽  
Vol 8 (6) ◽  
pp. 4111
Author(s):  
B. Chandrababu Naik ◽  
B. Anuradha
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Body ◽  
Water Area ◽  
Water Bodies ◽  
Landsat 8 ◽  
Indexing Methods ◽  
Water Index ◽  
Surface Water Body ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

scholarly journals ASSESSMENT OF SURFACE WATER DYNAMICS USING MULTIPLE WATER INDICES AROUND ADAMA WOREDA, ETHIOPIA

2018 ◽  
Vol IV-5 ◽  
pp. 181-188
Author(s):  
M. Sathianarayanan
Keyword(s):  
Surface Water ◽  
Surface Area ◽  
Rapid Change ◽  
Water Bodies ◽  
Water Dynamics ◽  
Landsat 8 ◽  
Water Index ◽  
Surface Water Resources ◽  
Normalized Difference Water Index ◽  
Water Indices

<p><strong>Abstract.</strong> Rapid change of Adama wereda during the last three decades has posed a serious threat to the existence of ecological systems, specifically water bodies which play a crucial part in supporting life. Role of Satellite images in Remote Sensing could be more important in investigation, monitoring dynamically and planning of natural surface water resources. Landsat-5(TM) &amp;amp; Landsat 8 (OLI) has high spatial, temporal and multispectral resolution and therefore provides consistent and perfect data to detect changes in surface changes of water bodies. In this paper, a study was conducted to detect the changes in water body extent during the period of 1984, 2000 and 2017 using various water indices such as namely Water Ratio Index (WRI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), supervised classification and wetness component of K-T transformation and the results are Presented. NDWI has been adopted for this study as compared with other indices through ground survey. The results showed an intense decreasing trend in the lakes of chelekleka, kiroftu, lake 1 and lake 3 of surface area in the period 1984–2017, especially between 2000 and 2017 when the lake lost about 1.309<span class="thinspace"></span>km<sup>2</sup> (one third) of its surface area compared to the year 2000, which is equivalent to 76%, 18%, 0.03% and 96%. Interestingly koka lake has shown very erratic changes in its area coverage by losing almost 3.5<span class="thinspace"></span>km<sup>2</sup> between 1984 and 2000 and then climbing back up by 14.8<span class="thinspace"></span>km<sup>2</sup> in 2017. Percentage of increment was observed that 10.6% as compared with previous year.</p>

scholarly journals Downscaling MODIS Surface Reflectance to Improve Water Body Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xianghong Che ◽  
Min Feng ◽  
Hao Jiang ◽  
Jia Song ◽  
Bei Jia
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Terrestrial Ecosystems ◽  
Water Area ◽  
Water Bodies ◽  
Tibet Plateau ◽  
Surface Reflectance ◽  
Discrimination Ability ◽  
Automated Method ◽  
Landsat 7

Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

scholarly journals Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

2020 ◽  
Vol 12 (17) ◽  
pp. 2675
Author(s):  
Qianqian Han ◽  
Zhenguo Niu
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Temporal Parameter ◽  
Modis Ndvi ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Spatio Temporal ◽  
Global Surface ◽  
Global Water

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

scholarly journals Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Yan Zhou ◽  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Tong Xiao ◽  
Zhiqi Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Landsat 8 ◽  
Open Surface ◽  
Body Mapping ◽  
Water Index ◽  
Landsat 7 ◽  
Surface Water Bodies ◽  
Water Indices ◽  
Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

scholarly journals DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2

2020 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Suwarsono Suwarsono ◽  
Fajar Yulianto ◽  
Hana Listi Fitriana ◽  
Udhi Catur Nugroho ◽  
Kusumaning Ayu Dyah Sukowati ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Water Index ◽  
Index Model ◽  
Normalized Difference Water Index ◽  
Sentinel 2 ◽  
Water Vegetation

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir.

scholarly journals Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kshitij Mishra ◽  
P. Rama Chandra Prasad
Keyword(s):  
Satellite Imagery ◽  
Water Body ◽  
Landsat Imagery ◽  
Threshold Value ◽  
Water Bodies ◽  
Limit Function ◽  
Linear Predictor ◽  
Water Index ◽  
Feature Vectors ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been widely explored in the recent past. Several approaches have been developed to delineate water bodies from different satellite imagery varying in spatial, spectral, and temporal characteristics. The current study puts forward an automatic approach to extract the water body from a Landsat satellite imagery using a perceptron model. Perceptron involves classification based on a linear predictor function that merges few characteristic properties of the object commonly known as feature vectors. The feature vectors, combined with the weights, sum up to provide an input to the output function which is a binary hard limit function. The feature vector in this study is a set of characteristic properties shown by a pixel of the water body. Low reflectance of water in SWIR band, comparison of reflectance in different bands, and a modified normalized difference water index are used as descriptors. The normalized difference water index is modified to enhance its reach over shallow regions. For this study a threshold value of 2 has been proved as best among the three possible threshold values. The proposed method accurately and quickly discriminated water from other land cover features.

scholarly journals A high-resolution inland surface water body dataset for the tundra and boreal forests of North America

2021 ◽  
Author(s):  
Yijie Sui ◽  
Min Feng ◽  
Chunling Wang ◽  
Xin Li
Keyword(s):  
Surface Water ◽  
North America ◽  
Water Body ◽  
Boreal Forests ◽  
Water Bodies ◽  
Permafrost Region ◽  
Morphological Attributes ◽  
Complete Representation ◽  
Surface Water Body ◽  
Sentinel 2

Abstract. Inland surface waters are abundant in the tundra and boreal forests in North America, essential to environments and human societies but vulnerable to climate changes. These high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper we present an inland surface water body inventory (SWBI) dataset for the tundra and boreal forests of North America. Nearly 6.7 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2. The dataset provides geometry coverage and morphological attributes for every water body. During this study we developed an automated approach for detecting surface water extent and identifying water bodies in the 10 m resolution Sentinel-2 multispectral satellite data to enhance the capability for delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America, providing a more complete representation of the region than existing regional datasets, e.g., Permafrost Region Pond and Lake (PeRL). Total accuracy of the detected water extent by SWBI dataset was 96.36 % by comparing to interpreted data for locations randomly sampled across the region. Compared to the 30 m or coarser resolution water datasets, e.g., JRC GSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database (GLWD), the SWBI provided an improved ability on delineating water bodies, and reported higher accuracies in the size, number, and perimeter attributes of water body by comparing to PeRL and interpreted regional dataset. This dataset is available on the National Tibetan Plateau/Third Pole Environment Data Center (TPDC, http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271021 (Feng et al., 2020).

scholarly journals A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 323-328 ◽  
Author(s):  
S. L. K. Reddy ◽  
C. V. Rao ◽  
P. R. Kumar ◽  
R. V. G. Anjaneyulu ◽  
B. G. Krishna
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Additional Information ◽  
Water Canal ◽  
Water Canals

<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>

scholarly journals Estimation of changes in the Dead Sea surface water area through multiple water indices and geospatial techniques

2021 ◽  
Keyword(s):  
Surface Water ◽  
Surface Area ◽  
Unsupervised Classification ◽  
Dead Sea ◽  
Water Area ◽  
Sea Surface ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
The Dead ◽  
Sea Area

<p>The study calculated the changes in the Dead Sea surface area from 1984 to 2019. The satellite images of 1985, 1990, 2000, 2010, and 2019 were classified by applying four different methods to estimate the changes in the Dead Sea surface water area. The methods included normalized difference water index (NDWI), modified normalized differences water index (MNDWI), automated water extraction Index (AWEI), and ISO cluster unsupervised classification. The results revealed a decrease of 76.63 sq. km area that accounts for an average of 11.27% sea area. The statistical model predicted that the Dead Sea surface area will shrink by half within the next 143 years, and the sea will be completely dried by 2305 if appropriate measures are not taken by decision-makers to avoid further reduction of the surface area.</p>

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