Hyperspectral data application for peat forest monitoring in Central Kalimantan, Indonesia

Peatland is a large CO2 reservoir which accumulates 2000Gt of CO2, which is equal to 30% of global soil carbon. However, it has been becoming a large CO2 emission source because of peat decomposition and fire due to drainage water. This is caused by social activities such as canalizing. Especially, in Indonesia, peat swamp forests cover considerable portions of Kalimantan and 37.5% of CO2 emission source is peatland (DNPI, 2010). To take measures, it is necessary to conduct appropriate assessment of CO2 emission in broad peat swamp forest. Because hyperspectral data possess higher spectral resolutions, it is expected to evaluate the detailed forest conditions. We develop a method to assess carbon emission from peat swamp forest by using hyperspectral data in Central Kalimantan, Indonesia. Specifically, we estimate 1) forestry biomass and 2) underground water level expected as an indicator of CO2 emission from peat. In this research, we use the image taken by HyMAP which is one of the airborne hyperspectral sensors. Since the research area differs in forest types and conditions due to the past forest fire and disturbance, forest types are classified with the sparse linear discriminant analysis. Then, we conduct a biomass estimation using Normalized Difference Spectral Index (NDSI). We also analyze the relationship between underground water level and Normalized Difference Water Index (NDWI), and find the possibility of underground water level estimation with hyperspectral data. We plan to establish a highly developed method to apply hyperspectral sensor to peatland monitoring system.