Beyond Traditional Methods: Innovative Integration of LISS IV and Sentinel 2A Imagery for Unparalleled Insight into Himalayan Ibex Habitat Suitability

Abstract

Abstract Despite the progress made in remote sensing technology, the application of satellite imagery is predominantly limited to the field of conservation study. The utilisation of multispectral data from diverse sensors holds significant promise in the field of landscape mapping. However, it is imperative to consider the varied spectral and spatial resolution capabilities in order to achieve precise classification of wildlife habitats. The objective of our study was to provide a methodology for accurately classifying habitat types for the Himalayan Ibex (Capra sibirica) by utilising various satellite data. In order to tackle the issues related to both spectral and spatial aspects, we employed LISS IV and Sentinel 2A data. We then proceeded by integrating the LISS IV data with the Sentinel 2A data, taking into account their respective geometric information. By utilising a variety of supervised classification techniques, it was shown that the Random Forest (RF) approach had superior performance compared to the other algorithms. The classified image obtained by the integration of LISS IV and Sentinel 2A sensors demonstrated the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. In order to delineate the suitable habitat for the Himalayan Ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use (LCLU) data from three distinct image types (namely LISS IV, Sentinel 2A, and Integrated image). Additionally, we incorporated other predictors including topographical features, vegetation types, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the Himalayan Ibex, compared to the other two LULC classes that were obtained from the other two mentioned images. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The consequences of these findings are significant for the advancement of conservation measures, as the utilisation of precise classification methods facilitates the recognition of crucial landscape components. This pilot study offers a novel and important approach to conservation planning by accurately categorising LULC and identifying critical habitats for the Ibex. The utilisation of this technology significantly improves our capacity to conserve and safeguard the natural environment inhabited by many wildlife species within the mountainous ecosystem like the Himalayas.