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Spatiotemporal regression combining Theil-Sen median trend and Man-Kendall tests was applied to MODIS time-series data to quantify the trend and rate of change to forest cover in the Central Highlands, Vietnam from 2001 to 2019. Several MODIS data products, including Percent Tree Cover (PTC), Evapotranspiration (ET), Land Surface Temperature (LST), and Gross Primary Productivity (GPP) were selected as indicators for forest cover and climate and carbon cycle patterns. Emerging hot spot analysis was applied to identify patterns of long-term deforestation. Spatial regression analysis using Geographically Weighted Regression (GWR) was performed to understand variations in the relationship between vegetation changes and trends in LST, ET, and GPP. Our analysis reveals that deforestation occurred significantly in the study area with a total decrease of 14.5% in PTC and a total of 7314 deforestation hot spots were identified. Results indicate that forest cover loss explains 72.9%, 67.7%, and 89.4% of the changes in ET, GPP, and LST, respectively, and the levels of influence are heterogenous across space and dependent on the types of deforestation hot spots. The approach introduced in our study can be performed worldwide to address complex research questions about environmental challenges that emerge from deforestation.

Spatiotemporal analysis of forest cover change and associated environmental challenges: a case study in the Central Highlands of Vietnam