Increasing prices and demand for biofuel and cooking oil from importer countries have caused a remarkable expansion of oil palm plantations in Indonesia. In this paper, we attempt to monitor the expansion of oil palm plantations on peat land and in tropical forests. We measure the GHG emissions from the land conversion activities at provincial scale. Using Landsat images from three different periods (1990s, 2000s and 2012), we classified LULC of the Riau Province, which is the largest oil palm producing region in Indonesia. A hybrid method of integration, generated by combining automatic processing and manual analysis, yields the best results. We found that the tropical rainforest cover decreased from ∼63% in the 1990s to ∼37% in the 2000s. By 2012, the remaining tropical rainforest cover was only ∼22%. From the 1990s to the 2000s, conversion of forests and peat lands was the primary source of emissions, total CO2 emitted to the atmosphere was estimated at ∼26.6 million tCO2.y-1, with 40.62% and 59.38% of the emissions from conversion of peat lands and forests, respectively. Between 2000 and 2012, the total CO2 emitted to the atmosphere was estimated at ∼5.2 million tCO2. y-1, with 69.94% and 27.62% of the emissions from converted peat lands and converted forests, respectively. The results show that in the Riau Province, the oil palm industry boomed in the period from 1990 to 2000, with transformation of tropical forest and peat land as the primary source of emissions. The decrease of CO2 emissions in the period from 2000 to 2012 is possibly due to the enforcement of a moratorium on deforestation.

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Mapping and assessment of mangrove environment are crucial since the mangrove has an important role in the process of human-environment interaction. In Indonesia alone, 25% of South East Asia's mangroves available are under threat. Recognizing the availability and the ability of new sensor of Landsat data, this study investigates the use of Landsat ETM + 7 and Landsat 8, acquired in 2002 and 2013 respectively, for assessing the extent of mangroves along the South Sulawesi's coastline. For each year, a supervised classification of the mangrove was performed using open source GRASS GIS software. The resulting maps were then compared to quantify the change. Field work activities were conducted and confirmed with the changes that occurred in the study area. Considering the accuracy assessment, our study shows that the RGB composite color-supervised classification is better than band ratio-supervised classification methods. By linking the open source software with the Landsat data and Google Earth satellite imagery that is available in public domain, mangroves forest conversion and changes can be observed remotely. Ground truth surveys confirmed that, decreases of mangroves forest is due to the expansion of fishpond activity. This technique could potentially allow rapid, inexpensive remote monitoring of cascading, indirect effects of human activities to mangroves forest.

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