Use of Landsat ETM+ data for delineation of water bodies in hilly zones

Abstract

The remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM þ ) dataset is used for the detection and delineation of water bodies in hilly zones. The water bodies were detected using Surface Wetness Index (SWI), Normalised Difference Vegetation Index (NDVI) and a slope map.The assessment of areas under dense vegetation in water bodies is omitted in the combined map prepared using classified raster images showing (1) the distribution of 'water' and 'non-water' based on SWI and (2) the distribution of 'vegetation' and 'non-vegetation' based on NDVI. The shadows' effect in estimated areas under water bodies is detected and delineated using the combination of (1) a combined raster image (classified SWI and NDVI) and (2) a slope map. About 3.8% (1370 ha) of the total area reviewed is estimated under water bodies with 91.74% overall accuracy. The water bodies include (1) major and minor dams, (2) watered streams, (3) springs distributed in foothill zones and (4) small dams on minor streams. The relatively smaller water body objects, i.e. streams and springs, have estimated less producer's (92-96%) and user's (85-92%) accuracy than the major water bodies, i.e. 96.77% producer's and 100% for user's accuracy.

Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach

“…Therefore, wet soils are appeared darker than dry surface in RS images (Zhang and Voss, 2006). Some of the studies like Gao (1996), Jain et al (2005), Xu (2006), Bhagat (2009), Bhagat and Sonawane (2011), Zolekar and Bhagat (2014), etc. have detected water bodies and soil moisture, successfully using calculated Soil Wetness Index (SWI) and Normalized Difference Water Index (NDWI).…”

Small farm dams: impact on river flows and sustainability in a context of climate change

“…Several studies have succeeded in inferring the number of dams and the area they cover using satellite data and GIS (geographic information system), (Bhagat and Sonawane, 2011;Rodrigues et al, 2012;Shao et al, 2012). Since the present study is more a prospective than retrospective study, the future location of the dams cannot be observed, and the choice was made to build simple hypotheses that could fit both the present day and the coming decades.…”

“…One issue for such modelling is the determination of the geolocalization and volumes stored in the numerous reservoirs. Several studies have focused on determining the number and extent of small farm reservoirs using satellite data (Bhagat and Sonawane, 2011;Casas et al, 2011;Rodrigues et al, 2012;Shao et al, 2012). A few authors have been able to conduct detailed studies of the impact on hydrology, based on the simulation of a large number of small farm dams.…”

Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation

“…Equations (8) and (10) suggest that when the magnitude of R is small, the measurements are accurate, and then the state estimate depends mostly on the measurements and vice versa. The new measurements add to knowledge of the system; therefore, the estimated error decreases in Equation (9). The posterior uncertainty or covariance is always less than the prior as it is reduced by the multiple of the Kalman gain (Equation (10)).…”

“…In the last few decades, satellite remote sensing has evolved as a promising alternative for regular global monitoring of water resources [1][2][3]. Satellite altimetry is now a well-established tool for inland water level estimation [4][5][6][7] and Landsat, with its long archive, free availability and relatively high-resolution database, delivers one of the most frequently used remote sensing data sets [8][9][10][11].…”