Abstract:
Forest plays an important role in regulating carbon sequestration in the ecosystem. Monitoring temporal forest changes can identify management gaps that can be used for policymaking for afforestation initiatives. Many studies have been conducted to monitor the temporal change in forests. However, those studies used coarse spatial resolution data or lack social data to relate the causes of deforestation. This study uses high spatial resolution remote sensing data and socio-economic for temporal analysis of the upper Dir Forest area. The specific objectives of the study were to (1) analyze spatio-temporal long-term forest cover change using satellite data with neural network technique and (2) develop a spatial statistical model to analyze socio-economic drivers’ interaction with forest in the region. Landsat imagery of the years 1990, 2000, 2010, and 2021 was classified using the Artificial Neural Networks (ANN) approach. For ANN classification, more than 400 signatures were collected for forest class and more than 300 signatures for bare land, agriculture, snow, and grass/shrubs. Areas of all classes were calculated and compared to estimate the change in forest cover. A socio-economic survey was conducted with the help of an online questionnaire to analyze the socio-economic situation of people. Statistical modeling was performed using linear regression model stepwise selection method in SAS software to determine most to least significant variable. The result shows the variation in the land cover classes during different study periods. Forest areas showed a decreasing trend from 1990 to 2010. However, in 2021, there was an increase in forest cover due to afforestation. In contrast, agricultural areas increased by 1.37 percent, and built-up areas increased by 7.64 percent. However, bare land, and forest area decreased by 9.1 and 4.17 percent from year 1990 to 2021 respectively. In general, in study district forest cover was 26% of the total area in 1990 and decreased to 20.18% in 2010, in 2021 due to afforestation, it became 21%, whereas agricultural area increased from 0.16% to 7.8% in 2021 respectively. The study shows that most people earn their living by practicing agriculture. However, an increase in population caused an increase in food demand, leading to conversion of forest land in agriculture. The conversion of forest land into residential areas was also the cause of concern. Meanwhile, ineffective management, lack of coordination between government departments, and ignorance of the forest department are major contributing factors in deforestation. The study recommend that we need to develop better energy alternatives and technically reliable inputs to decrease food demand, introduce awareness among organizations for coordination and introduce alternate earning sources for the local public of the district
