Abstract
AbstractDespite advancements in remote sensing, satellite imagery is underutilized in conservation research. Multispectral data from various sensors have great potential for mapping landscapes, but distinct spectral and spatial resolution capabilities are crucial for accurately classifying wildlife habitats. Our study aimed to develop a technique for precisely discerning habitat categories for the Himalayan Ibex (Capra sibirica) using different satellite imagery. To address both spectral and spatial challenges, we utilized LISS IV and Sentinel 2A data and integrated the LISS IV data with Sentinel 2A data along with their corresponding geometric information. Employing multiple supervised classification algorithms, we found the Random Forest (RF) algorithm to outperform others. The integrated (LISS IV-Sentinel 2A) classified image achieved the highest accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84.To map the suitable habitat of the Ibex, we conducted ensemble modeling using the Land Cover Land Use (LCLU) of all three image types (LISS IV, Sentinel 2A, Integrated) and other predictors such as topographical, soil type, vegetation, and water radiometric indices. The integrated model provided the most accurate prediction of suitable habitat for the Ibex, surpassing the other two LCLU classes derived from individual images. The Soil Adjusted Vegetation Index (SAVI) and elevation were identified as crucial factors in identifying suitable habitatsThese findings hold valuable implications for the development of effective conservation strategies, as accurate classification schemes enable the identification of vital landscape elements. By precisely classifying LULC satellite images and identifying crucial habitats for the Ibex, this pilot study provides a new and valuable strategy for conservation planning. It enhances our ability to preserve and protect the habitat of wildlife species in the mountain ecosystem of the Himalayas.
Publisher
Cold Spring Harbor Laboratory
Reference120 articles.
1. Satellite image classification methods and techniques: A review;International journal of computer applications,2015
2. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)
3. Anders Karlsson , 2003. Classification of high resolution satellite images, August 2003, available at http://infoscience.epfl.ch/record/63248/files/TPD_Karlsson.pdf.
4. Anderson, R.P. , Martínez-Meyer, E. , Nakamura, M. , Araújo, M.B. , Peterson, A.T. , Soberón, J. and Pearson, R.G ., 2011. Ecological niches and geographic distributions (MPB-49). Princeton University Press.
5. Ensemble forecasting of species distributions;Trends in ecology & evolution,2007