Remote sensing satellite data and spectral indices: an initial evaluation for the sustainable development of an urban area

Spectral indices help in modeling, predicting, or infer surface processes. Indices are derived for many different combinations of satellite spectral bands. There are various applications of the satellite indices that are explored in the past studies includes agriculture, water resources, urban development, forest ecology, geology, soil sciences, vegetation, and other applications. In the literature, a few studies have been conducted majorly from the perspective to evaluate/compare/explore Landsat-8 OLI and Sentinel-2 images. Spectral, statistical, and the image analysis techniques have been used. However, there has not been any study focusing on Nava Raipur Atal Nagar, India till date. Not many studies have used GIS-based tools even though neither considered the spectral nor spatial consistency of Landsat-8 and Sentinel-2A data using criteria sets methodology performed as in this work. In the current research, the ensuing indices were derived: Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, Soil Adjusted Vegetation Index, Normalized Difference Built-up Index, Bare Soil Index and Built-up Index. The comparative evaluation of the spectral and spatial consistency of Landsat-8 and Sentinel-2 and the correlation coefficients for each pair of indices are derived, and plotted the outcomes with scatter plots. In addition, linear model is compared with several alternative fitting curvilinear models such as Reciprocal-x, Square root-y reciprocal-x, Squared-y reciprocal-x, S-curve model, Squared-y logarithmic-x, Logarithmic-x, Squared-y square root-x, Square root-y logarithmic-x, Square root-x, Squared-y, Double reciprocal, Multiplicative, Double square root, Logarithmic-y square root-x, Square root-y, Double squared, Reciprocal-y logarithmic-x, Exponential, Squared-x, Reciprocal-y square root-x, Square root-y squared-x, Reciprocal-y, Logarithmic-y squared-x, Reciprocal-y squared-x, Logistic, and Log probit. The Modified Normalized Difference Water Index, Normalized Difference Built-up Index and Normalized Difference Vegetation Index indices have got the better correlation coefficients of 72.02%, 70%, and 69.81%, and Bare Soil Index takes the minimum at 54.4%. Outcomes establish a good correlation between derived satellite indices of Sentinel-2 and Landsat-8. Comparing this research to other research in the Indian region with satellite-based indices demonstrates that our model accuracies are significant although the difference between them is low. These data have multiple applications that are essential in sustainable development as much research are focusing attention on indices from multiple sensors and more research is underway. Present paper will come in handy for the initial evaluation for the sustainable development of an urban area.