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Deforestation in peatland areas such as Kalimantan, Indonesia has been going on for decades. The deforestation has indirectly increased peatlands to become degraded and flammable. The Synthetic Aperture Radar (SAR) interferometry approach for identification of degraded peatlands can be performed using ALOS-2 PALSAR-2 data by converting land deformation data generated from SAR interferometry analysis into water table (WT) depth data using Wosten models. Peatlands with WT depth conditions of more than 40 cm are classified as degraded peatlands which are flammable. By using fire data from previous studies, this research confirms that identification of degraded peatlands using SAR interferometry approach by ALOS-2 PALSAR-2 is more reliable with high precision related to forest fires, with a precision level of 88% compared to 5% precision level using the WT depth monitoring system that has been installed in Central Kalimantan. The highest wavelength of ALOS-2 PALSAR-2 (L-Band) data can resolve the limitation due to temporal and volumetric decorrelation, compared to C-Band and X-Band satellite data. The combination methods of SAR interferometry approach and the real-time WT depth monitoring system to identify degraded peatlands can be more efficient, faster, and accurate. The advantage of this research result shows that SAR interferometry analysis can reach blank spot areas that are not covered by the observation station of WT depth monitoring system. It also gives a benefit as a guide to select precise locations of observation stations related to degraded peatland and forest fire.

Forest fire in Indonesia occurs mostly in peatland area. Dry peatland areas with groundwater table (GWT) more than 40 cm from the soil surface have become degradation areas with high potentials to fire. This paper presents a new novel to detect a peat fire risk area by incorporating two methods: the impedance model and the differential interferometric SAR (DInSAR) technique which is based on the knowledge of annual subsidence rate associated with the GWT. The previous impedance model is modified in this paper by integrating the surface roughness information in the model as a part of novelty. The proposed method was then validated with ground truth data of GWT. By using an impedance model, this paper successfully detected peat fire risk area based on the backscattering coefficient simulation of dry peatland. Based on the simulation model, the average, minimum, and maximum of backscattering coefficient of dry peat are −13.97, −11.5, and −17.29 dB, respectively. The correlation coefficient between the simulated backscattering coefficient and backscattering from ALOS-2/PALSAR-2 data is 0.8 with root mean square error of 1.4. By using the DInSAR method, detection of dry peatland area was successful. The significant relationships confirmed between GWT measurement and model are 0.71 for Pair A and 0.85 for Pair B. Both methods showed that peat fire risk areas were identified successfully. The dielectric constant of the peat soil also revealed that the soil condition of the area of interest is very dry indicating the potential to peat fire risk. Employing two models, respectively, were recommended to get precision of detection analysis. INDEX TERMS Detection of peat fire risk area, impedance model, DInSAR, ALOS-2 PALSAR-2 data. I. INTRODUCTION Indonesia has the largest peatland area in Southeast Asia (47%), besides Malaysia (6%), Papua New Guinea (3%), and smaller area amounting 1% spreading in Brunei, Myanmar, Thailand, and Vietnam. Conservation and rehabilitation of peat-forest area in Indonesia have been conducted however The associate editor coordinating the review of this manuscript and approving it for publication was Weimin Huang. it remains the largest area because of (1) human impact such as plantations of oil palms, rubber, pulp trees, and food production, and (2) the great impact of climate changes including El Nino, La Nina, and ENSO [1]. Forest fire is the main problem in Indonesia starting in 1982 when 75% of the forest fire is occurred in peatland area, mainly in open area [2]. Between 1990 and 2015, almost 27.5 million ha of forest had changed into logging, fires, timber, pulpwood, and palm oil plantations, and now of the 75%,