Investigating Land Cover Changes and Their Impact on Land Surface Temperature in Khyber Pakhtunkhwa, Pakistan

Abstract

Restoration of degraded land is a significant concern in the 21st century in order to combat the impacts of climate change. For this reason, the provisional government of Khyber Pakhtunkhwa (KPK), Pakistan, initialized a Billion Tree Tsunami Project (BTTP) in 2013 and finished it in 2017. Although a few researchers have investigated the land use transitions under BTTP in the short term by merging all the vegetation types into one, analysis of the long-term benefits of the project and future persistence were missing. Furthermore, the previous studies have not discussed whether the prime objective of the BTTP was achieved. Considering the existing gaps, this research mainly involves analyzing (i) fluctuations in the green fraction by employing a land change modeler (LCM), along with the spatial location of gain-loss and exchange analysis using a high-resolution dataset (GLC30); (ii) forest cover changes under the influence of the BTTP; (iii) impacts of green fraction changes towards land surface temperature (LST) by utilizing the less-explored technique of curve fit linear regression modeling (CFLR); and finally, (iv) assessing the persistence of the NDVI and LST trends by employing the Hurst exponent. Research findings indicate that as an output of BTTP, despite the government’s claim of increasing the forest cover by 2%, a significant gain of grassland (3904.87 km2) was observed at the cost of bare land. In comparison, the overall increase in forest cover was only 0.39%, which does not satisfy the main objective of this project. On the other hand, the CFLRM-based actual contributions of land cover change (LCC) transition to LST indicate a significant decline in LST in the areas with gains in green fraction for both grassland and forest. At the same time, an increase was observed with reverse transitions. Although the results appear positive for climatic impacts in the short term, the HURST model-based persistence analysis revealed that the spatial locations of increasing vegetation and decreasing LST trends fall under the weakly persistent category, therefore these trends may not continue in the near future. Despite some positive impact on LST attributed to the green fraction increase, this project cannot be regarded as a complete success due to its failure to achieve its prime objective.