Search

The expansion of oil palm plantations has become of global concern. Jambi Province in Indonesia is one of the regions experiencing a rapid shift in land use, and oil palm plantation, settlement, and agriculture have emerged as the primary force behind this shift. This study aims to project scenarios of future land-use change and expansion of oil palm plantations in Jambi Province in 2030, 2060, and 2100. This study uses land-use map Jambi Province and driving factor data, including elevation, slope, river, road, and forest. Projections of land-use change scenarios are conducted using the Land Change Modeler (LCM), in which Cellular Automata Markov (CA-Markov) is the model base. The validation of the model against historical land-use changes showed an overall kappa value of 0.97. In the no-conservation scenario, assuming a continuation of the trends from 1990 to 2011, the forest area continues to decrease. Oil palm will replace forest as the dominant landuse cover in Jambi Province in 2100. Driving factors explaining the spatial distribution of oil palm plantation expansion include distance from forest, distance from road, and elevation. Our study shows the importance of government regulations' importance in slowing or stopping deforestation, especially for forests near plantations. [ABSTRACT FROM AUTHOR]

Jambi covers various land uses with different characteristics related to biogeophysical cycle. Land use plays an important role in the atmosphere-surface interaction and energy balance partition, which influenced rainfall pattern. Two proxies widely used to differentiate various land uses are albedo and normalized difference vegetation index (NDVI). However, study on albedo and NDVI relationship with rainfall in Jambi is still limited. This study aims to analyze the correlation of NDVI and albedo with rainfall and their distribution in Jambi and Muaro Jambi in 2013 and 2017. The research used Landsat 8 OLI TIRS satellite image data to derived NDVI and albedo, and CHIRPS data for rainfall. A simple linear regression was used to calculate the correlation of NDVI and albedo with rainfall. The results showed that the distribution of albedo for each land use class from the lowest to the highest was forest, plantation, cropland, shrubs, and settlements, respectively. On the contrary, the distribution of NDVI and rainfall is the inverse to albedo. Albedo and NDVI had a strong influence on rainfall through surface energy balance partition. This was indicated by the high R-square between albedo and rainfall (0.99) and between NDVI and rainfall (0.97). Increasing upward latent heat flux from the land surface to atmosphere leads to a rainfall increase. In other words, rainfall may also increase with the decrease in albedo, increase in NDVI, or land use change. [ABSTRACT FROM AUTHOR]

Deforestation plays an essential role in land cover changes in tropical area. It resulted in changing surface biophysical characteristics such as albedo and surface roughness altering the climate, heat fluxes as well as CO2 fluxes. Thus, this study aims to analyse the biogeophysical and biogeochemical processes in forest, shrub, grass, and soybean. Also, the study analyses the coupling strength in the terrestrial segment. This analysis is conducted using the Community Land Model Version 5 (CLM5). The results showed that land-use changes alter surface heat fluxes. Forest changing into shrub, grass, and soybean decreased latent heat fluxes and increased sensible heat fluxes. Land-use changes also alter carbon uptake, although it did not necessarily reduce the net primary production (NPP) and efficiency. Forest changing into soybean will increase NPP, while its changes to grass and shrub will decrease NPP. Among all land covers, soybean had higher carbon use efficiency (CUE) due to the availability of water and soil nutrient supply. In comparison, the forest had lower CUE due to its high respiration to maintain its biomass. Coupling strength between soil moisture and latent heat fluxes in all land covers was positive, but the R-squared was relatively low. This result indicated that there was another factor impacting surface energy balance partition such as vegetation cover. [ABSTRACT FROM AUTHOR]