Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.

Floods in Pekalongan, Indonesia, often occur due to river water overflowing during heavy monsoon rain. Simultaneously, the northern coastal area of Pekalongan, located adjacent to the Java Sea, has been affected by coastal floods due to sea level rise. The flood conditions in this area were exacerbated by land subsidence, leading to coastal inundation. Monitoring land subsidence in Pekalongan has become essential in predicting other possible land subsidence occurrence areas and mitigating the possible hazards caused by land subsidence. The analysis of land subsidence has been much easier since the introduction of radar satellites. In this study, 124 synthetic aperture radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2022 in descending tracks were used. The data were processed through a time-series interferometry SAR (InSAR) method based on the improved combined scatterers interferometry with optimized point scatterers (ICOPS) algorithm to provide accurate measurements over large areas by improving the selection of measurement points from persistent scatterer (PS) and distributed scatterer (DS) points using a deep learning algorithm based on a convolutional neural network (CNN), and the resulting optimized measurement points were then spatially clustered using optimized hot spot analysis (OHSA) to estimate significant points statistically and define them as hot spot points. The results of time-series deformation in Pekalongan were compared with the GPS station measurements. From the comparison, a good correlation in terms of deformation patterns between time-series InSAR and GPS measurements was observed. Our study revealed that land subsidence in Pekalongan has occurred mostly in settlement areas under the young alluvium soil, which cannot support many buildings’ maximum compression. Another cause of land subsidence in Pekalongan is excessive groundwater extraction in settlement areas. Thus, compaction in the aquifer areas may occur as a result of the reduced effective stress of the pore pressure. Further analysis of this study would involve monitoring groundwater activity using data from the GRACE satellite and comparing them with weather station data. The analysis of the two datasets aims to understand the relationships between groundwater storage data and the monthly precipitation in Pekalongan. Finally, the potential outcomes of land subsidence in Pekalongan will be assessed using the geographic information system (GIS) method based on susceptibility mapping.

Kwangjae Lee. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.2

The Interferometric Synthetic Aperture Radar (InSAR) has significantly advanced in its usage for analyzing surface information such as displacement or elevation. In this study, we evaluated a digital elevation model (DEM) constructed using X-band KOMPSAT-5 interferometric datasets provided by the Korea Aerospace Research Institute (KARI). The 28-day revisit cycle of KOMPSAT-5 poses challenges in maintaining interferometric correlation. To address this, four KOMPSAT-5 images were employed in a multi-baseline interferometric approach to mitigate temporal decorrelation effects. Despite the slightly longer temporal baselines, the analysis revealed sufficient coherence (>0.8) in three interferograms. The height of ambiguity ranged from 59 to 74 m, which is a moderate height of sensitivity to extract topography over the study area of San Francisco in the USA. Unfortunately, only ascending acquisition mode datasets were available for this study. The derived DEM was validated against three reference datasets: Copernicus GLO-30 DEM, ICESat-2, and GEDI altimetry. A high coefficient of determination (R² > 0.9) demonstrates the feasibility of the interferometric application of KOMPSAT-5.