Abstract
In various studies, such as meteorology, agriculture and ecology, quantitative estimation of biophysical variables is very important, and thus information about the spatial and temporal distribution of these variables are highly useful. Remote sensing is meanwhile regarded as an important source of knowledge in broad areas for the estimation of fractional vegetation coverage. In the remote sensing, estimation of vegetation characteristics using spectral indices have become very common today, but soil and rocks reflections are also much more than the reflection in these areas of sparse vegetation, which makes it difficult to distinguish plant signals. In this analysis, a variety of spectral indices have been considered to estimate biophysical vegetation parameters to boost vegetation signal in remotely-sensed data and provide an estimated measurement of living green vegetation using Landsat4,5 Thematic Mapper (TM) and Landsat8 Operational Land Imager (OLI) sensor data. To identify the best vegetation index for sparsely vegetated semi-arid and arid region of Chhattisgarh state using four vegetation indexes; Normalized Difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI). The TNDVI indices showed the best fractional vegetation cover to estimate the highest precision.
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Bramha, S., Bhunia, G.S., Kamlesh, S.R., Shit, P.K. (2021). Comparative Assessment of Forest Deterioration through Remotely Sensed Indices—A Case Study in Korba District (Chhattisgarh, India). In: Shit, P.K., Pourghasemi, H.R., Das, P., Bhunia, G.S. (eds) Spatial Modeling in Forest Resources Management . Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-56542-8_6
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