مدل‌سازی اثرات متقابل اقتصاد مرزی و پویایی‌های کاربری اراضی در جنگل‌های زاگرس شمالی (بانه)

Extended Abstract

Background and objectives: Land use and land cover (LULC) change has emerged as one of the most significant manifestations of global environmental change, with profound implications for ecosystem sustainability, biodiversity conservation, carbon cycling, and the provision of ecosystem services. Among Iran's forest ecosystems, the Zagros forests constitute the country's largest semi-arid forest biome, playing a pivotal role in soil and water conservation, biodiversity preservation, climate regulation, and sustaining the livelihoods of local communities. However, over recent decades, these forests have experienced increasing degradation and land use conversion driven by a combination of economic development, urban expansion, infrastructure growth, changing land-use practices, and other anthropogenic pressures. Baneh County, located in the northern Zagros region along Iran's western border, represents one of the most environmentally sensitive areas within this ecosystem. Owing to its strategic border location, rapid commercial development, and expanding tourism sector, the region has undergone substantial land use transformation during the past two decades. Although numerous studies have monitored LULC changes using remote sensing techniques, relatively few have simultaneously examined the influence of socioeconomic drivers through the integration of remote sensing data with econometric analysis, particularly in border regions such as Baneh. Therefore, the present study aimed to monitor forest cover dynamics in Baneh County during 2004–2025 and to investigate the effects of selected socioeconomic variables on forest cover changes using satellite imagery and the Autoregressive Distributed Lag (ARDL) modeling approach.

Methodology: Landsat satellite imagery acquired between 2004 and 2025 was employed to generate land use and land cover maps following standard image preprocessing procedures. The study area was classified into five major land cover categories: forest, agricultural land, rangeland, barren land, and built-up areas. Land use transitions and the magnitude of changes among different classes were quantified using transition matrix analysis and gain–loss analysis. Classification accuracy was evaluated using Overall Accuracy (OA) and the Kappa coefficient. To investigate the socioeconomic determinants of forest cover change, an ARDL model was developed in which annual forest area change served as the dependent variable. The explanatory variables included the number of natural resource violation cases, exchange rate, number of incoming visitors, and number of business licenses issued annually. Stationarity of the variables was first assessed using the Augmented Dickey–Fuller (ADF) unit root test. Subsequently, the optimal ARDL specification was estimated to evaluate both short-run and long-run relationships among the variables. Long-run cointegration was examined using the Bounds Test, while a series of diagnostic tests—including serial correlation, heteroscedasticity, normality of residuals, and the CUSUM stability test—were performed to assess the validity and stability of the estimated model.

Results: The classification results demonstrated satisfactory mapping accuracy, with Overall Accuracy ranging from 76.67% to 82.16% and Kappa coefficients varying between 0.669 and 0.748. Analysis of LULC dynamics revealed a persistent decline in forest area throughout the study period, accompanied by continuous expansion of built-up and barren lands. Gain–loss analysis further indicated that forest loss consistently exceeded forest recovery during most observation periods, resulting in a net decline in forest cover. The Bounds Test confirmed the existence of a long-run equilibrium relationship between the socioeconomic variables and forest cover change. The estimated ARDL model indicated that the number of business licenses exerted a significant negative effect on forest area in both the short and long run, suggesting that increased economic activity and commercial development contributed to forest decline. In contrast, the exchange rate exhibited a significant positive relationship with the dependent variable, highlighting the influence of macroeconomic fluctuations on forest cover dynamics. Natural resource violation cases showed only marginal significance in the long run, whereas the number of incoming visitors had no statistically significant effect on forest area change. Furthermore, the diagnostic tests confirmed the absence of serial correlation and heteroscedasticity, while the CUSUM test verified the structural stability and robustness of the estimated ARDL model.

Conclusion: The findings demonstrate that forest cover dynamics in Baneh County cannot be explained solely by natural environmental processes but rather result from complex interactions among economic, managerial, and anthropogenic factors. The significant effects of exchange rate fluctuations and business licensing indicate that macroeconomic conditions can directly or indirectly influence forest degradation and land use change in border regions. Moreover, the continuous decline in forest cover and the expansion of built-up land underscore the need to reconsider regional development policies, strengthen land use regulations, and improve the management of economic activities in environmentally sensitive border areas. From a methodological perspective, integrating remote sensing techniques with econometric modeling provides an effective framework for simultaneously monitoring spatial patterns of land use change and identifying their underlying socioeconomic drivers. This integrated approach offers valuable scientific evidence to support land-use planning, environmental policymaking, and the sustainable management of the Zagros forests.