Estimation Model of Ground Water Table at Peatland in Central Kalimantan, Indonesia
This research is investigating the ground water table at forested peatland in Kalimantan which is expected to be an indicator for better wild fire control. Firstly, modified Keeth-Byram drought index (mKBDI) was computed by incorporating satellite-based precipitation GSMaP and MTSAT land surface temperature (LST). Secondly a regression analysis was carried out between mKBDI and near surface ground water table (GWT) measurements at drained forest (DF), un-drained forest (UF) and drained burnt forest (DB) respectively. Overall a modeled GWT at forested peatland showed very good time-series of behaviors along with that of in-situ measurement. A modeled GWT was more sensitive to precipitation resulting in a drastic water table rise-up and more calibration is indispensable to get a better result. A comparison of GWT and hotpot detected by MODIS showed that lower GWT areas clearly have more fire occurrences. It was found that mKBDI was well calibrated with GWT at the above mentioned three measurements sites and a very good indicator for peat fire risk zone mapping at forested peatland. These modeling results are updated in a near-real time fashion and all the database are open to public at http://jica-jst.lapanrs.com/GWT/.
KeywordsEvapotranspiration Precipitation and hotspots
This study is partially supported by SATREPS (Science and Technology Research Partnership for Sustainable Development) project entitled as “Wild fire and carbon management in peat-forest in Indonesia” founded by JST (Japan Science and Technology Agency) and JICA (Japan International Cooperation Agency).
- Hirano T et al (2005) Energy balance of a tropical peat swamp forest in Central Kalimantan, Indonesia fires. Phyton 45(4):67–71Google Scholar
- Keetch JJ, Byram GM (1968) A drought index for forest fire control. Res. Paper SE-38. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, AshevilleGoogle Scholar
- Takeuchi W, Gonzalez L (2009) Blending MODIS and AMSR-E to predict daily land surface water coverage. In proceed. International Remote Sensing Symposium (ISRS), Busan, South KoreaGoogle Scholar