Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent cloud cover in tropical regions. A Synthetic Aperture Radar (SAR) offers a cloud-independent alternative for burned area mapping. This study investigates the performance of a Stacking Ensemble Neural Network (SENN) model using polarimetric features derived from both C-band (Sentinel 1) and L-band (Advanced Land Observing Satellite—Phased Array L-band Synthetic Aperture Radar (ALOS-2/PALSAR-2)) data. The analysis covers three representative sites in Indonesia: peatland areas in (1) Rokan Hilir, (2) Merauke, and non-peatland areas in (3) Bima and Dompu. Validation is conducted using high-resolution PlanetScope imagery(Planet Labs PBC—San Francisco, California, United States). The results show that the SENN model consistently outperforms conventional artificial neural network (ANN) approaches across most evaluation metrics. L-band SAR data yields a superior performance to the C-band, particularly in peatland areas, with overall accuracy reaching 93–96% and precision between 92 and 100%. The method achieves 76% accuracy and 89% recall in non-peatland regions. Performance is lower in dry, hilly savanna landscapes. These findings demonstrate the effectiveness of the SENN, especially with L-band SAR, in improving burned area detection across diverse land types, supporting more reliable fire monitoring efforts in Indonesia.

The Trans-Sulawesi railway construction runs for 144 km from South to North of the South Sulawesi's east coast. The railway line mostly was built on an embankment over agricultural flat, while on the hill, the railway line built in an incised slope. These actions essentially alter the surrounding landform. The changing landform could have its effect on how the surface runoff flow during the rainy season. In this study, we explore the potential of satellite-derived digital elevation models (DEMs) on detecting the changed landform over the Takkalasi Watershed. We employed the SPOT-7's tri-stereo acquisition mode, acquired on 17th April 2020. In these preliminary results, we compare the DEM results from each pair combination as well as the combination of all three pairs. The use of different tie-points (TP) and ground control points (GCP) were also investigated. Fast Fourier Phase Transformation algorithm were employed for the TP extraction, while the semi-global matching algorithm were employed for the epipolar DEM extractions. The produced DEMs then validated using the reference data taken from dGPS, UAV DEM, as well as the ICESat-2 ATL08 data. Our initial results shows that the best DEM generated is produced by the forward and backward stereo pair using only nine (9) GCP with the average vertical Root Mean Square Error (RMSE) of 4.4 meter. At 3 m ground sampling distance, the incised hill is easier to detect on SPOT-7 DEM compared to the embankment.