Simulating different scenarios of vegetation dynamics under the influence of human and climatic factors based on the residual trend analysis and machine learning

Although vegetation, as a significant part of all terrestrial ecosystems, is influenced by both climatic and anthropogenic factors, identifying the relative contribution of these factors to vegetation variation remains a challenge. This article aims to assess the relative contribution of these factors to vegetation dynamics in the Gavkhuni watershed, Iran, using the residual trend analysis and machine learning algorithm, considering the Enhanced Vegetation Index (EVI) and Standardized Precipitation Evapotranspiration Index (SPEI). To achieve this goal, a time series of the EVI over the period 2001-2023 was obtained from MOD13Q1 through the Google Earth Engine platform. Also, a time series of 03, 06, 09, and 12-month SPEI was calculated using the meteorological data related to 2001-2023. Based on SPEI and EVI, the residual trend analysis was done in TerrSet 19.0.6 software, and the relative contribution of human and climatic factors to EVI dynamics was assessed. Then, considering environmental parameters besides climatic and human factors, the random forest algorithm was utilized to model the contribution of humans and climate to vegetation variation and specify the importance of variables in model efficiency. The results demonstrated that climate was responsible for the decrease in vegetation in approximately 20% of the watershed area, and human factors were the major driver for the increase in vegetation in about 38% of the Gavkhuni watershed area due to agricultural and gardening activities. According to the machine learning outcomes, climatic parameters played a significant role in vegetation decline, mostly in the northwest of the study area, and humans played an important role in vegetation increase in the west, southwest, and southeast parts of the watershed. These findings can be useful in guiding environmental policies and regional vegetation restoration projects.