Mapping and Modelling of Forest Cover Change in Kohima District, Nagaland through Remote Sensing and Support Vector Machine approach
This article is a preprint and is currently undergoing peer review by UCL Open: Environment.
Abstract
Northeast India, known for its rugged terrain, high rainfall and rich biodiversity, contains some of the country’s most dynamic forest ecosystems. Nagaland, situated within this region, experiences a predominantly subtropical climate with steep mountains and deep valleys that shape its unique vegetation patterns. Kohima District, the capital city, is characterized by complex topography and a mosaic of dense, moderate and open forests interspersed with scrubland and non-forest areas. Given ongoing pressures from urban expansion, shifting cultivation and resource use, monitoring long-term forest-cover change is essential for understanding ecological transformations in the district. This study examines forest-cover dynamics in Kohima District over a 24-year period(2000–2024) using pixel-based classification performed through the Support Vector Machine (SVM) technique. Classified maps from three time periods were validated with high-resolution Google Earth Pro imagery, enabling accurate detection of both large-scale and small, localized forest-cover transitions. The results indicate a measurable increase in Dense Forest, which expanded by29.94 km² (3.03%), along with a substantial rise in Open Forest, which grew by218.79 km² (22.11%). Conversely, Moderate Forest experienced a significant decline of –137.62 km² (–13.91%), suggesting widespread canopy degradation. Scrubland also decreased markedly by –122.44 km² (–12.37%). Overall, the analysis reveals a landscape undergoing both regeneration and degradation, with notable transitions from moderate to open forest classes. The findings underscore the effectiveness of SVM-based classification for forest monitoring and highlight the need for sustainable land-management strategies to maintain ecological stability in this environmentally sensitive region.
Keywords: Forest cover, Moderate forest, regeneration, forest management, urbanization