Forest Cover Change Detection Over North Eastern Ghat Zone of Odisha, India Using Multi-Year Landsat Data
Rashmirekha Senapati
Department of Agricultural Meteorology, Odisha University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, India.
Bama Shankar Rath
Department of Agronomy, Odisha University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, India.
Fawaz Parapurath
*
Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore - 641003, Tamil Nadu, India.
Meera Mahanty
AMFU, RRTTS, Ranital, Odisha University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, India.
Argha Ghosh
Department of Agricultural Meteorology, Odisha University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, India.
Ankit Kumar Meena
Department of Agricultural Meteorology, Odisha University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, India.
Ritoban Pandit
Department of Agricultural Meteorology and Physics, Faculty of Agriculture, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur - 741252, West Bengal, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: The current study's objective is to compute the forest cover dynamics using Land Use and Land Cover (LULC) change detection.
Place and Duration of Study: North Eastern Ghat Zone (NEGZ) of Odisha, India over 1990 to 2020.
Methodology: Through the use of Landsat images and the Supervised & Unsupervised technique of classification, five main categories were established under LULC, viz., Agriculture, Barren Lands, Forest, Settlements, and Water Bodies.
Results: The results infer that the forest cover reduced by 20%. On the contrary, the settlements area increased by about 130%. From this we could infer that the expansion of settlements due to population hike is the primary driver of deforestation and forest fragmentation because the population growth and increased settlements accounted for 97% and 93% of the variability in forest cover dynamics, as illustrated by the coefficient of determination (R2 = 0.971** for population and R2 = 0.9271** for settlement areas). Moreover, the LULC classification achieved high accuracy, with an overall accuracy and kappa coefficient of 87.5% and 0.84 respectively.
Conclusion: Therefore, by placing special focus on the aforementioned findings, we may conclude that the current study may contribute to research on forest management, climate change mitigation, and sustainable development for emphasizing the critical need to address deforestation and forest fragmentation driven by population growth.
Keywords: Forest dynamics, GEE, LULC, Odisha, Population growth