Forest productivity and carbon stock analysis from vegetation phenological indices using satellite remote sensing in Indonesia

Indonesia has the highest forest density in the world, and the productivity of its forests can potentially be maximized to minimize CO2 emissions. However, due to anthropogenic activities, phenological properties are subject to risk to ensure productivity and carbon exchange in the different forest ecosystems in Indonesia. Early prediction of carbon values could indicate a declining trend of forest quality with reference to vegetation levels. Thus, the purpose of this research is to evaluate forest productivity and carbon stock using phonological properties for different forests. The vegetation phenology was used to assess the level of forest productivity with different classifications to estimate carbon stock in six types of forest in south Sumatera using gross primary productivity (GPP) approaches. The vegetation phenologies were analyzed to develop a system dynamics model under two scenarios: first, a changing trend of normalized difference vegetation index (NDVI), and second, a changing trend of area, considering either increasing or decreasing solar radiation in both scenarios. This system was run through the GIS environment to develop a database and to simulate results for future predictions. Verification was performed to test the simulation model by comparing the results with the IPCC reference. NDVI showed good correlations with GPP using MODIS MOD13Q1 for convertible production forest (CPF R2 = 0.97), permanent production forest, PPF (R2 = 0.99), limited production forest (LPF, R2 = 0.98), tourism recreation forest (TRF, R2 = 0.95), and wildlife reserve forest (WRF, R2 = 0.95), nature reserve forest (NRF, R2 = 0.99). The explicit differential function was used to estimate net primary productivity (NPP), which was related to the changes in area and productivity over time. Productivity and carbon stock analysis was performed via the proposal of five levels referring to Indonesian forest policy planning, considering resilience classified as high forest productivity (V1), moderate forest productivity (V2), marginal forest productivity (V3), very low forest productivity (N1), and no forest productivity (N2). TRF was found fall below the IPCC levels from 2015 to 2017, and NRF fall below the IPCC standards from 2015 to 2018. Therefore, the satellite-based remote sensing, system dynamics model can be implemented in the Indonesian forest policy system for assessing forest productivity and carbon stocks.