Efficient Recognition of Forest Species Biodiversity by Inventory-Based Geospatial Approach Using LISS IV Sensor

Tropical forest is one of the great biodiversity repositories of the world ecosystem. Biodiversity is depleting very fast due to conversion of forest region into agricultural or other land use. Here comes the role of biodiversity assessment and evaluation of spatial data of species to prioritize the conservation purposes. Traditionally, ground-based plots were used to assess different biodiversity. Later on, remote sensing approaches were also incorporated along with field-based studies to quantify the results accurately. Assessment of biodiversity constitutes estimation of various indices that were obtained using ground-based plot or survey. With the advancement of the remote sensing technology, spatial information about tree species was collected using field sample and satellite data and field sample plots within the Sariska Tiger Reserve. Different diversity indices were calculated like α, β, diversity, and others, i.e., Pilot´s index (J), Shannon-Wiener index (SR), Margalef index (E_w), and Whittaker´s index (H´). The multistage statistical techniques, which integrate high spatial resolution and spectral characteristics of satellite data (LISS IV), will help in providing precise information about tree species. Regression analysis provides better results to identify forest species among different bands. A positive correlation has been found in the infrared band even negative correlation has been found in other bands. This paper incorporates field-based surveys along with remote sensing technologies using a regression model (r² = 0.636) to estimate and recognize different species diversity in Sariska Tiger Reserve.