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Flood Mapping, Monitoring, and Damage Assessment
Published in Saeid Eslamian, Faezeh Eslamian, Flood Handbook, 2022
Vaibhav Garg, Shivani Pathak, Jyoti Rathour, Saeid Eslamian
An example of mapping floodwater using Landsat data is provided here. In the present analysis, the cloud-free Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of October 20, 2008, was used. The image belongs to the frequently flooded Kosi River, Bihar in India. In the year 2008, the most devastating flood occurred in this region due to a breach of river embankment near the India–Nepal border on August 18, 2008. The river changed its course and flooded areas that had never been flooded in the many decades as seen even in the image of the month of October 2008. The area remained flooded over more than four months. Using a simple NDWI band ratio technique the water pixels were enhanced as shown in Figure 17.11. The NDWI values theoretically range from −1 to +1; however, in the present image, the range of value is −0.54 to +0.68. Again, theoretically, it is considered that the pixels having an NDWI value of more than 0 are water; however, it may end up in overestimation/underestimation of water pixels. Therefore, the user has to select the appropriate threshold using the trial-and-error approach. In the present analysis, the value of 0.35 and more has been taken as a threshold for extracting the water pixels.
Analyzing the Relationship of LST with MNDWI and NDBI in Urban Heat Islands of Hyderabad City, India
Published in Shruti Kanga, Suraj Kumar Singh, Gowhar Meraj, Majid Farooq, Geospatial Modeling for Environmental Management, 2022
Normalized Difference Water Index (NDWI) is frequently used for water area extraction. The NDWI proposed by McFeeters (1996) is expressed by the equation as follows: (Green Band−NIR Band)/(Green Band+NIR Band)
Environmental Application of Medium to High Resolution Remotely Sensed Data
Published in Alexandra Gemitzi, Nikolaos Koutsias, Venkat Lakshmi, Advanced Environmental Monitoring with Remote Sensing Time Series Data and R, 2019
NDWI proposed by (Gao 1996) or SIWSI is a spectral vegetation index sensitive to water content of plant leaves (Fensholt and Sandholt 2003). NDWI is calculated using the spectral channels that correspond to Near Infrared (NIR) and the Shortwave Infrared (SWIR). The formula to calculate NDWI from remote sensing spectral data is as follows: NDWI=(NIR−SWIR)/(NIR+SWIR) Sentinel-2 data are provided by Copernicus Open Access Hub (https://scihub.copernicus.eu/dhus/#/home) either as Level-1C (S2MSI1C) Top-of-Atmosphere reflectances or as Level-2A (S2MSI2A) Bottom-of-Atmosphere reflectances which are ˜600 and ˜800 MB respectively in size and 100 km ´ 100 km. Level-1C can be transformed to Level-2A by using the Sen2Cor software which is a processor for Sentinel-2 Level-2A product generation and formatting developed by ESA. The software performs the atmospheric, terrain, and cirrus correction of Top-of-Atmosphere Level-1C input data and creates Bottom-of-Atmosphere, optionally terrain and cirrus corrected reflectance images. Additionally, it creates Aerosol Optical Thickness, Water Vapor, Scene Classification Maps, and Quality Indicators for cloud and snow probabilities (https://step.esa.int/main/third-party-plugins-2/sen2cor/). The image data are provided in GML-JPEG2000 format. To read them in R (R Core Team 2017) the “rgdal” package (Bivand, Keitt, and Rowlingson 2018) can be used along with the “raster” package (Hijmans 2019). An example of the R-script is provided in Table 1.5.
Impact of urban growth on the natural drainage network of the Srinagar city
Published in Urban Water Journal, 2023
Bilquis Shah, M. Sultan Bhat, Akhtar Alam, Hilal Ahmad Sheikh, Noureen Ali
The most used technique for extracting water bodies from remote sensing data is the normalised difference water index (NDWI) (McFeeters 1996). The GREEN band and the NIR band are the bands that are used in this index. The range of the NDWI value is from -1 to +1, with positive values denoting features of the surface water and negative values denoting features other than water. Equation (1) contains the NDWI index formula. NDWI has been updated to MNDWI (modified normalised difference water index), which displays more accurate findings by enhancing the water information (Xu 2005). The MNDWI employs the Green and SWIR bands, which have a range of −1 to +1. The NDWI is computed using Eq. (2).
An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data
Published in Geomatics, Natural Hazards and Risk, 2020
Subhanil Guha, Himanshu Govil, Monika Besoya
The current reserach work applied NDWI (McFeeters 1996, 2013) as a robust normalized difference spectral index for determining the relationship with LST. NDWI is determined by the green and near-infrared (NIR) bands. For, TM and ETM + data, band 2 is used as green band and band 4 is used as NIR band, respectively. For OLI/TIRS data, band 3 and band 5 are used as green and NIR bands, respectively (Table 3). The value of NDWI ranges between −1 and +1. Generally, the negative value of NDWI indicates the built-up area and bare land those have no water surfaces, whereas the positive NDWI value shows water and vegetation surface (McFeeters 1996, 2013).
Analysis of flow modifications and stress in the Tangon river basin of the Barind tract
Published in International Journal of River Basin Management, 2019
Swades Pal, Arpita Saha, Tithi Das
The NDWI image cannot offer definite water depth, but qualitatively it represents the equivalent. Generally, the values of NDWI vary from 0 to 1. ‘1’ represents high-level water presence and a higher depth of water within the water bodies. Considering these issues, the segment-wise (pre and post discharge regulation for post monsoon phases) average NDWI score was computed to represent the transforming nature of water availability.where ; Ix = Image of the xth period; s = number of images taken into consideration.