Monitoring of large-scale forest restoration: Evidence of vegetation recovery and reversing chronic ecosystem degradation in the mountain region of Pakistan

Monitoring of large-scale forest restoration: Evidence of vegetation recovery and reversing chronic ecosystem degradation in the mountain region of Pakistan

BACKGROUND:

Global forest area has decreased by 4.2% over the past three decades, highlighting the urgent need for effective restoration efforts. In response, Pakistan launched the Billion Tree Tsunami Afforestation Program (BTAP) in 2014 to restore depleted forest ecosystems in Khyber Pakhtunkhwa Province through extensive afforestation and community involvement. This study evaluates the success of BTAP in reversing forest degradation by analyzing satellite imagery and vegetation growth trends from 2014 to 2021.

GOALS AND METHODS:

The aim of the study is to evaluate the performance of large-scale forest restoration efforts under the BTAP in reversing chronic ecosystem degradation. The study employs a bi-temporal change analysis and a time series analysis of satellite images to assess historic deforestation trends and recent forest restoration efforts. Regression and trend analysis of the annual normalized difference vegetation index (NDVI) composites from 2014 to 2021 are used to evaluate vegetation growth and ecosystem restoration across various afforestation sites.

CONCLUSIONS AND TAKEAWAYS: 

The study concludes that BTAP significantly improves forest cover and vegetation growth in the targeted regions, with robust success in 50% of the area and modest improvement in 39%. The findings highlight the effectiveness of large-scale afforestation projects in reversing deforestation and emphasize the importance of continuous monitoring and community involvement for sustainable forest management.

Reference: 

Abbas S, Qamer FMueen, Ali H, et al. Monitoring of large-scale forest restoration: Evidence of vegetation recovery and reversing chronic ecosystem degradation in the mountain region of Pakistan. Ecological Informatics. 2023;77:102277. doi:10.1016/j.ecoinf.2023.102277.