Investigating forest fragmentation through earth observation datasets and metric analysis in the tropical rainforest area

• Main Authors: Osaka Ruandache Pyngrope, Mukesh Kumar, Rocky Pebam, Sudhir Kumar Singh, Arnab Kundu, Deepak Lal
• Format: Article
• Language: English
• Published: Springer 2021-06-01
• Series: SN Applied Sciences
• Subjects: Forest cover; FRAGSTATS; Forest fragmentation; Class metrics; Deforestation; Soil erosion
• Online Access: https://doi.org/10.1007/s42452-021-04683-5

Abstract Extensive mining operations, deforestation, jhumming, and soil erosion coupled with population stress in the study area have put an adverse effect on its forest resources. This study investigates the transition in forest cover classes and its fragmentation in the Jaiñtia Hills District of Meghalaya (India). Satellite data (multispectral images from Landsat 5 and 8) for 1995, 2001, 2007, and 2015 were classified using the supervised classification method. Landscape metrics from the classified images were calculated using FRAGSTATS. The overall accuracy of classification was found to be 87.50% (1995), 87.50% (2001), 85.00% (2007) and 91.67% (2015), respectively. The results revealed an increase in dense forest with an increase in the patch number from 1995 to 2007. Additionally, a decrease in non-forest cover with an increase in the number of patches from 2001 to 2015 was observed which further suggests fragmentation. It has been reported that 8.13% of the dense forest increased and 19.47% of non-forested areas decreased during the study period. Overall, this study highlights the changes in the distribution of forest area which could aid policy makers to adopt appropriate forest conservation strategies.