Abstract-Knowledge of peat depth distribution is vitally important for accurately estimating carbon stock within tropical peatlands. These estimates aid in understanding the role of tropical peatlands in global environmental change processes. This study evaluates the potential of C-band dual-polarization synthetic aperture radar (SAR) data for peat depth classification on oil palm plantations in Siak Regency, Riau Province, Indonesia. Specifically, features derived after the ground-range radar cross section (sigma-naught or σ 0 ) and slant-range perpendicular radar cross section (gamma-naught or γ 0 ) for both polarization channels of C-band Sentinel-1 data were compared and evaluated on monthly basis, during 2015, for discriminating peat depth classes using the decision tree classifier. Overall, γ 0 features yielded a higher value of distance factors (DF) for peat depth classes, for both polarization channels, than those produced by the σ 0 , indicating a better performance in discriminating peat depth classes. Moreover, the seasonal variation of rainfall intensity was discovered to be influencing feature selection for peat depth classification. Thus, the combination of γ 0 features derived in the much rain months was selected for separating the shallow-and medium-peat classes, whereas those derived in the less rain months was selected for discriminating the deep-and very deep-peat classes. In addition, the developed methodology gave the best accuracy for the very deep-peat class, with 76% and 67.86%, producer's accuracy (PA) and user's accuracy (UA), respectively, followed by the shallow-peat class that yielded a PA of 64% and UA of 80%. Subsequently, the deep-peat class produced a PA of 58% and UA of 59.18%, whereas the medium-peat class yielded the lowest PA and UA, of 54% and 49.09%, respectively. This study showed that the C-band dual-polarization SAR data have potential for classifying peat depth classes, particularly on oil palm plantations, and might serve as an efficient tool in peat depth classification used for sustainable management of tropical peatlands.
From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).
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