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31	Topic A: General Remote Sensing	ABS-379
Multi-Condition Evaluation of Sentinel-1 for Rubber Canopy: Normal, Leaf-Fall, and Pestalotiopsis Outbreak Ariq Anggaraksa Riesnandar (a), Masita Dwi Mandini Manessa (a), Milla Putri Melina (a), Farida Ayu (b) (a) Department Geography, Faculty Mathematic and Science, Universitas Indonesia, Indonesia (b) Graduate School of Sustainability Studies, University of Toyama, Japan. Abstract We assess rubber (Hevea brasiliensis) canopy monitoring at the Sembawa Rubber Plantation, South Sumatra, Indonesia, using Sentinel-1 C-band Synthetic Aperture Radar (SAR) dual polarization (vertical-vertical, VV- vertical-horizontal, VH). The target variable is percent Green Canopy Cover (GCC%), derived from expert interpretation of multispectral Uncrewed Aerial Vehicle (UAV) orthomosaics. After radiometric calibration, speckle filtering, and terrain correction, we built Ascending (ASC) and Descending (DSC) time series and computed six dual-polarization indices: Normalized Difference SAR Index (NDSI), Radar Forest Degradation Index (RFDI), Radar Vegetation Index (RVI), volume-to-surface scattering ratio (VR), cross-polarization ratio (CR), and the VV-to-VH ratio. Window-specific Pearson correlations between GCC% and radar variables were statistically significant but low. The strongest signals occurred in March 2022 (normal, post outbreak): VV-to-VH ratio (ASC) r = 0.318, VR (ASC) r = 0.292, NDSI and RFDI (ASC) r = 0.273, RVI (ASC) r = &#8722-0.273, CR (ASC) r = &#8722-0.254. During July 2024 leaf fall, ASC features weakened (absolute r &#8776- 0.14-0.15). The February 2023 outbreak showed only modest links (absolute r &#8804- 0.14). November 2024 was largely not significant. June 2022 had very weak ASC links and no DSC availability. A complementary global analysis yielded absolute r &#8776- 0.13-0.29 with all p < 0.001. These significant but low linear associations indicate non-linear scattering-canopy relations and possible geometry effects. The identified feature set supports development of linear mixed-effects models and machine-learning approaches for plantation-scale canopy estimation and early detection of change under seasonal defoliation and disease stress. Keywords: Sentinel-1- Synthetic Aperture Radar (SAR)- dual polarization (VV, VH)- VV/VH ratio- Green Canopy Cover (GCC%) Share Link | Plain Format | Corresponding Author (Masita Dwi Mandini Manessa)

32	Topic A: General Remote Sensing	ABS-380
Analysis of The Nutrient Transport Process in The Southern Java Sea And Their Interplay With Oceanic Eddy Occurrences Kamasan M.W.1*, Osawa. T.1, Imaoka K.1 The Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Japan Abstract Keywords: nutrient transport- coastal upwelling- wind-driven transport- monsoon variability, climatological phenomena Share Link | Plain Format | Corresponding Author (Takahiro Osawa)

33	Topic A: General Remote Sensing	ABS-132
Precise Building Boundary Extraction Using Deep Learning Method Lin Y. H.1, Tsai F.2* 1Student, Department of Civil Engineering, National Central University, Taiwan 2*Professor, Center for Space and Remote Sensing Research, National Central University, Taiwan 0123kan[at]gmail.com, *ftsai[at]csrsr.ncu.edu.tw Abstract Buildings stand out as the most important elements in urban areas, and their morphology and spatial characteristics are distinctly captured in high-resolution satellite imagery. Although LiDAR point clouds enable more precise 3D reconstructions of urban structures, their high cost and lack of real-time availability impose significant constraints. In contrast, satellite images offer shorter revisit intervals and more cost-effective coverage. Deep learning methods introduce an innovative, automated pipeline for interpreting remote sensing data. Among these approaches, Mask R-CNN has proven effective at extracting building boundaries from high-resolution satellite imagery. Recent studies have emphasized producing smoother, more consistent boundary geometries and strengthening the robustness of network across diverse urban scenes. In this study, we used Mask R-CNN as the base method for feature extraction. We experimented with different datasets offering varied spatial characteristics, aiming to fine-tune the model for high-resolution satellite imagery in Taiwan and integrate a boundary-regularization module to produce cleaner, sharper building boundaries. Future work will focus on developing a fully automated framework tailored for Taiwan, aiming to achieve higher accuracy in building footprint extraction and Level-of-Detail (LoD)-2 building modeling. Keywords: Building boundary extraction, Deep learning, Mask R-CNN Share Link | Plain Format | Corresponding Author (Yun-Hao Lin)

34	Topic A: General Remote Sensing	ABS-138
Estimation of Terrestrial Albedo from Multiple Reflectance Observations in a Single Day for SGLI Albedo Products Susaki, J.(a*), Ishii, Y.(b), Kuriki, A.(c), Kimura, Y.(d) and Ono, A.(e) (a) Professor, Graduate School of Engineering, Kyoto University, Japan (b) Assistant Professor, Graduate School of Engineering, Kyoto University, Japan (c) Technical Staff, Graduate School of Engineering, Kyoto University, Japan (d) Student, Graduate School of Engineering, Kyoto University, Japan (e) Associate Professor, Faculty of Humanities, Tenri University, Japan Abstract This paper examines algorithms for estimating terrestrial albedo for the products of the Global Change Observation Mission - Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency (JAXA). We focused on an approach using parameters derived from a bidirectional reflectance distribution function (BRDF) model. In operational products of the Moderate Resolution Imaging Spectroradiometer (MODIS), kernel-driven BRDF model parameters are estimated from multiple sets of reflectance and they are applied to estimate the land surface albedo. However, one of the challenges in this algorithm is to require multiple reflectance over several or dozens of days in order to achieve robust parapmeter estimation. Instead, another approach has been proposed that estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. Our preliminary experiments using in situ multi-temporal data demonstrated that this algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. Therefore, in this research, we propose an approach that estimates the land surface albedo from multiple reflectance observed within several hours in a single day. We conducted field measurement in barren land, Tottori Sand Dunes, Japan. We calculated the land surface albedos from those data sets, and compared the albedos with the actual albedos and those obtained using the traditional approach. We found that the error of the albedos estimated by the proposed approach is acceptable. In near future, we^ll examine the performance of the proposed method by using actual satellite images. Keywords: Terrestrial albedo, BRDF, In-situ measurement, narrowband-to-broadband (NTB) conversion Share Link | Plain Format | Corresponding Author (Junichi Susaki)

35	Topic A: General Remote Sensing	ABS-143
Accuracy Assessment of Andaliman (Zanthoxylum acanthopodium) Growth Suitability Using Google Earth Engine and Presence Data^ Lasriama Siahaan, Decky Indrawan Juneidi, Alan Wahsalam 1. Center for Environmental Research, IPB University 2. Research Center for Ecology and Ethnobiology, National Research and Innovation Agency (BRIN) 3. Department of Forest Resources Conservation and Ecotourism, IPB University Abstract The propagation and regeneration of Andaliman (Zanthoxylum acanthopodium DC.) within its natural habitat are characteristically slow and challenging. Ecologically, this species is an endemic plant located in the highland regions surrounding Lake Toba. Despite its limited distribution, Andaliman possesses significant economic and pharmaceutical potential. However, there is a paucity of spatial and temporal data regarding its habitat suitability and growth potential. This study aims to investigate the spatial and temporal dynamics of Andaliman habitat suitability using the Google Earth Engine (GEE) platform, which enables the efficient processing of multitemporal satellite imagery. The research utilizes remote sensing techniques to analyze the dynamics of land suitability. Field surveys provided Andaliman presence point data, which, in conjunction with Sentinel-2A imagery, aimed to enhance accuracy. The data were processed using the Google Earth Engine platform, resulting in a habitat suitability model with an accuracy exceeding 75% and providing valuable insights into temporal vegetation changes. These findings can inform planning efforts by identifying restoration areas and promoting sustainable Andaliman cultivation, thereby supporting conservation initiatives based on Nature-Based Solutions in the Lake Toba region. Keywords: endemic, GEE, North Sumatera, sentinel-2A, Zanthoxylum acanthopodium Share Link | Plain Format | Corresponding Author (Lasriama Siahaan)

36	Topic A: General Remote Sensing	ABS-167
Evaluating the Reliability of Super-Resolution Satellite Imagery for Infrastructure Mapping: A Comparative Study of AI-Enhanced Sentinel-2 and High-Resolution Drone Imagery Amri Rasyidi (a*), Yanuar Adji Nugroho (a) a) ESRI Indonesia, Jalan Jend. Gatot Subroto Kav 18. Jakarta Selatan 12710, Indonesia *amri.geodesy[at]gmail.com Abstract Artificial intelligence-driven super-resolution techniques generate visually convincing high-resolution satellite imagery from coarser inputs, yet their reliability for operational GeoAI applications remains scientifically unvalidated. While super-resolution methods can enhance 10.0 m Sentinel-2 L2A imagery to appear equivalent to 1.0 m resolution, the accuracy of AI-generated pixels for downstream semantic segmentation tasks lacks empirical assessment. This study quantifies performance boundaries between super-resolved Sentinel-2 using enhancement algorithms and native 1.0 m resolution drone imagery for infrastructure mapping applications, particularly road extraction. Three datasets covering identical geographic areas with temporally synchronized acquisition dates are compared using CNN-based and Transformer-based models architectures. Performance evaluation employs mean Intersection over Union (mIoU) and boundary accuracy metrics to assess both segmentation quality and edge preservation critical for infrastructure applications. Road extraction serves as a key test case, as typical road widths (&#8805-5.0 m) provide optimal conditions for evaluating super-resolution effectiveness against native 10.0 m Sentinel-2 limitations. Results demonstrate measurable performance difference in super-resolved imagery compared to native high-resolution data, with accuracy losses varying by model architecture. This research establishes evidence-based decision criteria for practitioners choosing between cost-effective super-resolution enhancement and native high-resolution acquisition, contributing to responsible deployment of AI-enhanced satellite imagery in operational remote sensing workflows. Keywords: super-resolution, semantic segmentation, Sentinel-2, drone imagery, reliability assessment Share Link | Plain Format | Corresponding Author (Amri Rasyidi)

37	Topic A: General Remote Sensing	ABS-176
Hybrid Random Forest and Support Vector Machine Classification for Benthic Habitat Mapping using Sentinel-2 Imagery Huwaida Nur Salsabila (a*), Setiawan Djody Harahap (b), Abhista Fawwaz Sahitya (c) (a) Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar, South Sulawesi 90224, Indonesia (b) Master in Remote Sensing, Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Kab. Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia (c) Blue Carbon Research Group, Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Kab. Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia Abstract Accurate benthic habitat mapping is essential for coastal management and ecosystem monitoring. However, remote sensing-based classification faces challenges due to the small size of benthic objects and their submerged nature, which increases the risk of misclassification. Machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been widely used to address these limitations, yet each has its drawbacks, RF may overfit. At the same time, SVM can misclassify and is sensitive to complex samples. This study proposes a hybrid classification method to overcome these limitations by fusing RF and SVM outputs within Google Earth Engine. The study area is located along the coast of Bontang City, East Kalimantan, Indonesia. Sentinel 2 imagery was classified using RF (ntree=50), SVM (gamma=10, cost=10), and a hybrid approach. The hybrid fusion rule applies RF and SVM agreement where available and resolves disagreement using a neighbourhood majority vote. Three benthic classes were mapped: coral/algae, seagrass, and bare substrate. RF yielded an overall accuracy of 0.785, while SVM reached 0.819. The hybrid method achieved the highest overall accuracy of 0.822, with producers and users accuracy outperforming both individual classifiers in most classes. Additional tests with varied RF and SVM parameters confirmed the robustness of the hybrid approach. Spatially, the hybrid classification reduced salt and pepper noise and improved coherence across benthic zones. These results demonstrate that the hybrid fusion method enhances benthic habitat mapping accuracy and offers a reliable solution for coastal monitoring applications. Keywords: Benthic habitat- Remote sensing- Random forest- Support vector machine- Hybrid classification Share Link | Plain Format | Corresponding Author (Huwaida Nur Salsabila)

38	Topic A: General Remote Sensing	ABS-181
RS-GIS based Rice Residue Energy Potential Assessment: A Case Study in Ubay, Bohol Galang W.N. (a*), Abucejo M.I.(b), Cinco F.R.(b), Daquio R.(a), Japos A.M.(a), Madanguit V.(a) and Mende, G.(a) (a) College of Engineering and Computer Studies, Engineering Department, Holy Name University, Philippines *wgalang[at]hnu.edu.ph (b)College of Engineering and Computer Studies, Computer Studies Department, Holy Name University, Philippines Abstract Rice residue management is a critical aspect of sustainable agriculture and renewable energy development. This study explores the application of geospatial technology for assessing the energy potential of rice residues in Ubay, Bohol. Utilizing remote sensing (RS) and geographic information system (GIS), spatial distribution and biomass availability were analyzed to estimate the recoverable energy from rice husks and straw. The study highlighted the use of data collected from SENTINEL-2 satellite images that was customized for environmental monitoring depicting the province of Bohol vegetation index for the past five years. The satellite images was complemented by Unmanned Aerial Systems (UAS) equipped with multi-spectral sensors enabling the identification of vegetation and agricultural residues. As a result, it was found that the municipality of Ubay rice crop residue in the form of rice husk and rice straw theoretical potential in mass amounted to 2,584,433 metric tons and 3,022,729 metric tons annually. The majority of Ubay^s rice plantations are concentrated in the northwest and central parts of the municipality, which aligns with the clustering of villages that ranked highest in bioenergy potential. The bioenergy potential estimates can reach up to 5,851,350 GJ for rice husk and 6,236,230 GJ for rice straw. Findings emphasized the potential of geospatial tools in identifying high-yield areas, optimizing collection strategies, and promoting sustainable energy solutions.&#8232- Keywords: remote sensing, GIS, biomass, waste-to-energy, geospatial technology Share Link | Plain Format | Corresponding Author (Wenyville Nabor Galang)

39	Topic A: General Remote Sensing	ABS-193
On the influence of initial guess DEM for gcp-less digital elevation model extraction using photogrammetry on SPOT-6/7 tristereo imagery - A case study of South Sulawesi Zylshal Zylshal *1, Parwati Sofan 1, Indri Pratiwi J 2, Athar Abdurrahman Bayanuddin 34 1 Research Center for Geoinformatics, National Research and Innovation Agency (BRIN), Bandung, Indonesia 2 Bureau for Public Communication, General Affairs, and Secretariat, National Research and Innovation Agency (BRIN), Jakarta, Indonesia 3 Directorate of Laboratory Management, Research Facilities, and Science and Technology Park, National Research and Innovation Agency (BRIN), Jakarta, Indonesia 4 Remote Sensing Graduate Study Program, Universitas Gadjah Mada, Indonesia Abstract Digital Elevation Model (DEM) generation from satellite photogrammetry is a well-established method for extracting topographic information from optical stereo imagery. With the increasing agility of modern satellites, tri-stereo acquisitions (SPOT-6/7, Worldview-3) are now commonly available. Although the use of Ground Control Points (GCPs) is standard for ensuring absolute accuracy, their collection is costly, time-consuming, and labor-intensive. To address these limitations, GCP-less DEM extraction approaches have been developed, relying on Rational Polynomial Coefficients (RPCs) and an initial seed DEM to constrain the object-space geometry. This study evaluates the influence of three different seed DEMs-ALOS World 3D (AW3D30), FABDEM, and the Indonesia-specific DEMNAS-on GCP-less DEM extraction using an iterative bundle adjustment approach on SPOT-6/7 panchromatic tri-stereo imagery. Three test sites in South Sulawesi were selected to represent varied landform and land cover characteristics. All DEMs were converted to an ellipsoidal vertical datum for consistency. A total of 27 DEMs were generated at a 6-meter ground sampling distance (GSD) and evaluated based on visual quality, horizontal alignment, and vertical accuracy. Vertical validation was conducted using 86 independent control points (ICPs) obtained through GNSS field surveys. Most outputs showed good visual fidelity, though some contained voids from cloud cover. Horizontal accuracy remained within one pixel across all outputs. Vertical Root Mean Square Error (RMSE) ranged from 1.18 to 4.79 meters. DEMNAS performed best as an initial seed DEM for GCP-less processing, with mean shift magnitude of 0.42 meters (x-direction) and 0.42 meters (y-direction). It also produced the lowest average vertical error, with RMSE, Mean Absolute Error (MAE), and Normalized Median Absolute Deviation (NMAD) of 4 m, 2.57 m, and 2.14 m, respectively. Given the limited availability of DEMNAS, AW3D30 presents a viable globally available alternative. Further testing with additional seed DEMs is recommended to enhance the generalizability of this GCP-less approach. Keywords: DEM, GCP-less, SPOT6, Photogrammetry, FABDEM Share Link | Plain Format | Corresponding Author (Zylshal Zylshal)

40	Topic A: General Remote Sensing	ABS-195
A Multi-Year, Multi-Location Sampling Method to Improve the Robustness of Forest Cover Classification in Google Earth Engine Shomat F, Radinal, Permana J, Pratomo T D A, Budihandoko, Y Fauna & Flora International Indonesia Programme Natural Resources Conservation Center (KSDA) of West Kalimantan Abstract Monitoring forest land cover change is essential for evaluating conservation impacts, with remote sensing providing a powerful tool for this purpose. However, conventional supervised classification methods often yield inconsistent results year-on-year, primarily because training samples are typically collected from the same year and location as the imagery under analysis, limiting their temporal transferability. This study investigates an alternative approach to enhance consistency by developing more robust and generalized spectral signatures. We tested a sampling strategy that generates training data from multiple years and diverse geographical locations. Utilizing the Google Earth Engine platform, we employed a Random Forest classifier on a multitemporal stack of Landsat 8 imagery. A comparative analysis was conducted, contrasting our proposed multi-year, multi-location sampling method with the traditional single-year, single-location approach. The performance of both methods was evaluated using Kappa Analysis. The results reveal that the multi-year, multi-location sampling strategy yielded a marginally higher Kappa coefficient compared to the conventional method. These findings suggest that developing a more generalized training dataset can improve classification accuracy and, crucially, enhance the consistency of long-term land cover monitoring efforts, leading to more reliable assessments of conservation outcomes over time Keywords: Forest Cover Monitoring, Google Earth Engine, Landsat 8, Temporal Consistency, Training Sample Generalization Share Link | Plain Format | Corresponding Author (Fazlurrahman Shomat)

41	Topic A: General Remote Sensing	ABS-201
Quantifying the Impact of Data Augmentation on Road Segmentation Performance in Deep Learning Models Julio Jeremia Sinabutar (a*), Jiann-Yeou Rau (a) a) Department of Geomatics, National Cheng Kung University No.1, Daxue Rd, East Dist, Tainan City 701, Taiwan *juliojeremia46[at]gmail.com Abstract This study investigates the impact of data augmentation on road segmentation performance using deep learning models trained on UAV imagery. The dataset consisted of 894 training images and 100 validation images, sourced from 3 geographically diverse locations, i.e., Tainan City (Taiwan), Kendari City (Indonesia), and Yogyakarta City (Indonesia). Model performance was evaluated on a high-resolution true orthophoto from Pematangsiantar City (Indonesia). A total of 40 model configurations were combined from various segmentation architectures, encoder backbones, and two probability thresholds for evaluation (0.05 and 0.5). Data augmentation covered 100% of the training data, distributed across saturation (1/3), hue (1/3), darkening (1/6), and brightening (1/6) transformations. Based on the Intersection over Union (IoU) metric, 26 out of 40 models show improved performance after augmentation, while the remaining 14 experienced a decrease. On average, data augmentation results in a 4.185% increase in IoU. A one-tailed paired t-test confirmed that this improvement was statistically significant for a 95% confidence interval (p = 0.02056), and Cohen^s d of 0.33 indicated a small effective size. These results suggest that even modest augmentation strategies can enhance overall segmentation performance in UAV-based road extraction tasks, although the degree of benefit may vary across model configurations. Keywords: Cohen^s d- Data augmentation- Deep learning- Paired t-test- Road segmentation Share Link | Plain Format | Corresponding Author (Julio Jeremia Sinabutar)

42	Topic A: General Remote Sensing	ABS-203
Restoration of Inoperative Terrain Data on Planetary Surfaces Using Deep Generative Models for DEM Reconstruction and Morphometric Analysis Kavitha A. Dr. C.Heltin Genitha St. Joseph^s college of Engineering, Chennai, TamilNadu, India Abstract The ultimate problem of data inoperative terrain ridged planetary surfaces exists in all less illuminated areas, causing combination shadows from the oblique solar angles, topographic occlusion, and objects in the form of rocks, dust clouds, or artificial noise. Such shadows corrupt the Digital Elevation Model and affect thorough surface feature identification and morphometric analysis. The application of deep generative models, with an emphasis on Generative Adversarial Network-(GAN)s and Variational Autoencoder-(VAE)s, for restoring or reconstructing elevation data most times corrupted or missing. By learning elevation propagation models, these executions enhance the quality of Digital Elevation Model-(DEM)s through the Global Accuracy Index (GAI) for surface classification. With the aid of high-resolution DEMs, point cloud data, and multi-sensor imagery, datasets from the Mars and Lunar reconnaissance orbiter missions are being worked on. Pre-processing involves damage detection using histogram analysis and edge filters such as Sobel and Laplacian. Binary masks are created to identify missing zones and serve as conditional inputs for model training. The elevation data is normalized and then co-registered with auxiliary thermal or multispectral imagery. The constraint VAE loss encodes the terrain into latent distributions for probabilistic reconstruction with cold fusion. The GAN-based U-Net branch reconstructs masked DEMs while ensuring realistic outputs through a discriminator. The entire outcome is again compiled into a single DEM through confidence-weighted fusion. Morphometric parameters such as slope, aspect, and curvature are derived from the reconstructed DEMs. The parameters are classified using Support Vector Machines (SVMs) and Deep Convolutional Neural Networks (DCNNs). Model performance evaluation is carried out using RMSE, SSIM, PSNR, and GAI. Model validation is performed against ground-truth and legacy datasets. Keywords: Planetary Surfaces, VAE, DEM, GAN, Morphometric Parameters Keywords: Planetary Surfaces, VAE, DEM, GAN, Morphometric Parameters Share Link | Plain Format | Corresponding Author (Kavitha Arunachalam)

43	Topic A: General Remote Sensing	ABS-207
Cloud-Free Sentinel-2 Mosaic Generation for the Republic of Korea Donggyu Kim1, Seungchan Lee2, Chuluong Choi3 1 Bachelors student, Division of Earth and Environmental System Science, Pukyong National University, Republic of Korea 2 Masters student, Division of Earth and Environmental System Science (Spatial Information Engineering), Pukyong National University, Republic of Korea 3 Professor, Division of Earth and Environmental System Science (Spatial Information Engineering), Pukyong National University, Republic of Korea *cuchoi[at]pknu.ac.kr Abstract In large-scale satellite imagery mosaicking, the presence of clouds obstructs analysis and necessitates cloud removal- however, uniform cloud elimination often generates missing data regions that can bias downstream applications. This study develops an automated pipeline leveraging GPU-accelerated processing to produce cloud-free mosaic images across the Republic of Korea using Sentinel-2 satellite imagery. A total of 1,352 Level-1C Sentinel-2 scenes collected from March through July 2025 covered 22 ESA-standard tiles of the Korean Peninsula. First, datastrip-derived scene fragments were spatially coregistered and merged into unified GeoTIFFs. Path-dependent variations in spatial resolution and coordinate reference systems were reconciled via nearest-neighbor resampling to ensure consistent pixel alignment. In the primary cloud-screening stage, initial binary masks were generated using cloud probability thresholds based on pixel-level cloud scores- unused spectral bands were removed and cloud pixels were masked out. For secondary cloud separation, monthly Normalized Difference Vegetation Index (NDVI) was computed for each scene to identify the clearest image as the master, with the remaining scenes designated as slaves. Slave scenes informed NDVI-based masking and only scenes achieving a minimum 45 percent no-cloud land ratio qualified for master selection. To fill missing data in the master image, pixel-wise linear regression coefficients precomputed from slave scenes were applied via a linear regression formula to estimate absent reflectance values, followed by sequential spatial interpolation. Residual gaps were then filled using a neighbor-based gap-filling algorithm that honors local spatial continuity. Finally, for months where all Sentinel-2 scenes were cloud-obscured, auxiliary imagery from alternative satellite sources was tiled and resampled into the workflow. The proposed method enhances data completeness and processing efficiency, offering a robust solut Keywords: mosaic- cloud elimination- fill missing data- spatial interpolation- gap-filling Share Link | Plain Format | Corresponding Author (donggyu kim)

44	Topic A: General Remote Sensing	ABS-210
Characteristics of texture index of damaged buildings using time-series high-resolution satellite images for the 2024 Noto Peninsula earthquake Masashi SONOBE Department of Civil Engineering College of Science and Technology, Nihon University Abstract In the Noto Peninsula earthquake that occurred in January 2024, the earthquake and tsunami caused human casualties, collapsed buildings, and caused severe damage to infrastructure. In cases where damage is severe and widespread, it is necessary to grasp the extent of the damage as quickly as possible, and collecting damage information using satellite remote sensing is effective. Furthermore, the resolution of the satellite image has been increased, and Method of object-based and pixel-based texture for extracting detailed damage information of the building and grasping the damaged area have been studied. However, there have been few time-series surveys of damage and recovery before and after a disaster and one year after the disaster using texture analysis of satellite images. In this paper, the distribution characteristics of texture indices obtained from high-resolution satellite images observed immediately after the earthquake and approximately one year after the earthquake were evaluated using detailed field survey results to investigate building damage and recovery status before and after the disaster. The target area was the Horyu-cho area of Suzu City, where buildings were damaged by the earthquake and tsunami caused by the 2024 Noto Peninsula earthquake. First, histogram matching was performed using satellite images taken three times before and after the disaster, and then texture indices were calculated using a co-occurrence matrix to evaluate the distribution. In this study, we used dissimilarity and homogeneity as a texture index based on the Gray-Level Co-occurrence Matrix(GLCM). The band used was the red band after pan-sharpening. The pixel size was resampled to 0.5m. The frequency distribution of pixel values of usage data is different. For this reason, pixel values with cumulative frequency between 2% and 98% were used to convert to 8-bit images. First, the characteristics were grasped from the obtained texture index by visual interpretation. In addition, the average value within the building polygon was calculated, and the characteristics of the texture indices of damaged and restored buildings immediately after the disaster, depending on whether or not there were flooded areas, were grasped from the results of field surveys. The results showed that flooded areas tend to be more dissimilarity and less homogeneity immediately after a disaster. These were presumably the result of buildings being washed away or collapsed in areas flooded by the tsunami. This tendency is smaller in non-flooded areas, but the dissimilarity tends to be higher immediately after a disaster. This was presumably due to debris from buildings that collapsed due to the earthquake in non-flooded areas. In addition, damage levels were divided according to whether or not there was flooding and whether or not buildings collapsed, and the results were compared with the index values, suggesting the possibility of classifying runoff and building collapse. In addition, damage levels were divided according to whether or not there was flooding and whether or not buildings collapsed, and the results were compared with the index values, suggesting the possibility of classifying runoff and building collapse. From these results, we confirmed the effectiveness of texture analysis using high-resolution satellite images in grasping the damaged buildings before and immediately after the disaster and in the restoration situation one year after the disaster. This suggests that the method can be applied to disasters that require early collection of damage information in the event of a disaster. Keywords: Disaster, Earthquake, Tsunami, Damaged buildings, Texture analysis Share Link | Plain Format | Corresponding Author (MASASHI SONOBE)

45	Topic A: General Remote Sensing	ABS-215
Natural Light Diffusion for Aerial Photography: An Optical Strategy to Improve Image Clarity in Tropical Region Dipo Andimuharrom (a*), Asep Adang Supriyadi (a), Doddy Mendro Yuwono (b), Putro Nugroho (c) (a) Department of Sensing Technology, Indonesia Defense University, West Java, Indonesia *dipo.andimuharrom[at]tp.idu.ac.id (b) Department of Remote Sensing, Indonesia Geospatial Information Authority, West Java, Indonesia (c) Faculty of Communication and Crative Design, Budi Luhur University, Jakarta, Indonesia Abstract Unmanned aerial vehicles (UAVs) have become essential tools in geovisual data acquisition for applications such as environmental mapping, spatial planning, and disaster mitigation, especially in geographically diverse and tropical regions like Indonesia. However, image quality is often degraded by harsh shadows caused by direct sunlight, limiting interpretability and reducing the accuracy of visual data. While many studies focus on post-processing corrections, this study investigates the potential of optimizing lighting conditions during image acquisition. We examine the role of cloud cover as a natural diffuser and simulate its effect using multi-layer diffusion filters in a controlled lab setting. Aerial photography simulations were conducted using a high-angle camera setup, directional lighting, and layered diffusers to replicate atmospheric light scattering. Quantitative analyses-including grayscale transformation, histogram analysis, and light scatter metrics-revealed that increasing diffuser layers led to more uniform lighting, reduced shadow intensity, and clearer surface visibility. These findings suggest that natural lighting conditions, particularly overcast skies, can be strategically utilized during UAV operations to enhance image quality without relying solely on computational corrections. This research contributes to UAV imaging methodology by providing practical insights into light-based optimizations in tropical field environments. Keywords: UAV imaging, aerial photography, lighting diffusion, shadow reduction, image quality Share Link | Plain Format | Corresponding Author (Dipo Andimuharrom)

46	Topic A: General Remote Sensing	ABS-234
Geometric Accuracy Analysis of Tie Point-Based RFM Refinement Using Bundle Adjustment Framework Seunghwan Ban, Taejung Kim Inha University Abstract Rational Function Models (RFMs) are commonly used for georeferencing satellite imagery due to their flexibility and sensor-agnostic nature. However, initial RFM parameters often contain inherent geolocation errors resulting from limited precision of attitude and orbit data used for sensor modeling. As a result, the geolocation accuracy of RFM-based products can be limited without ground control points (GCPs). This study analyzes the accuracy of our bundle adjustment framework that refines RFM parameters using only tie points extracted from overlapping satellite images. experiments were conducted by incrementally increasing the number of images from 2 to 9. We evaluated multi-view redundancy on adjustment stability during the tie point-based RFM refnment framework. In this framework, the bundle adjustment was performed in a relative model space using tie points rather than being fitted to absolute coordinate frame using GCPs. to ensure the adjustment results were quantitatively superior to the original RFM solution, GCPs were used only to evaluate the changes in absolute geolocation accuracy before and after the adjustment. Reprojection errors (RMSE) of the modeling tie points were consistently below 0.5 pixels across all cases, indicating stable adjustment performance. Independently acquired manual checkpoints from two images showed reprojection errors below 0.8 pixel. Subsequent evaluation using GCPs showed that image-space reprojection errors were reduced by approximately 1 to 5 pixels in most cases compared to the original RFM and the errors were dependent on the image configuration. Importantly, the spatial patterns of the residual errors exhibited directionally coherent trends, suggesting the possibility of systematic post-adjustment correction. These findings indicate that the proposed framework may be extended by incorporating minimal GCP input or reference image constraints. This would allow the adjusted model to be anchored to the absolute coordinate space, thereby enhancing global correction pipelines without full GCP dependence. Keywords: RFM, bundle adjustment, free adjustment, satellite imagery, geolocation accuracy Share Link | Plain Format | Corresponding Author (Seunghwan Ban)

47	Topic A: General Remote Sensing	ABS-239
Precision Geometric Correction of Very-High-Resolution Satellite Imagery Using Multi-Resolution GCP Chip Matching Hyeona Kim (a), Taejung Kim (b*) a) Program in Smart City Engineering, Inha University 100 Inha-ro, Incheon 22212, Republic of Korea b) Dept. of Geoinformatic Engineering, Inha University 100 Inha-ro, Incheon 22212, Republic of Korea *tezid[at]inha.ac.kr Abstract With advancements in satellite image processing technologies and the expansion of various remote sensing applications, the demand for very high-resolution satellite imagery is increasing. To effectively utilize satellite image products across diverse fields, it is essential to eliminate geometric distortions of raw imagery through precise geometric correction and ensure consistent positional accuracy. To this end, a Ground Control Point (GCP) chip database, which integrates accurate ground coordinates and image patches, is constructed and automatically matched with satellite images to establish a precision sensor model. The performance of the matching and the accuracy of sensor model is influenced by the resolution of both the satellite imagery and the GCP chips. This study performs geometric correction of very high-resolution satellite imagery using GCP chips of different resolutions and evaluates the performance of the resulting precision sensor model. GCP chips and satellite images are upsamped to meet their spatial resolution. The upsampled chips and images are matched against each other to generate GCPs automatically. Different upsampling ratios were applied for matching, with bicubic interpolation used during the upsampling process. The absolute positional accuracy of the final orthoimage was evaluated to confirm that it met the level required for practical applications. The experiment used WorldView-3 satellite imagery with a ground sampling distance of 30cm and GCP chips at multiple spatial resolutions. Accuracy was assessed based on model point error, checkpoint error, and mapping error. The results showed the lowest errors when high-resolution UAV chips were used. With upsampling, the checkpoint pixel error improved by approximately 62 percent, and the orthoimage mapping error decreased by about 40 percent compared to raw-resolution matching. The final checkpoint and mapping errors remained within 1.5 pixels and 0.5 meters, respectively, demonstrating that the proposed method was applicable for generating very high-precision image maps automatically. Keywords: Very-High-Resolution Satellite Image- Geometric Correction- GCP Chip Matching Share Link | Plain Format | Corresponding Author (Hyeona Kim)

48	Topic A: General Remote Sensing	ABS-241
Identification of Groundwater Potential Zones using Geo-information: A Case of Bharatpur Metropolitan, Nepal Sanjeev Kumar Raut(a*), Arjun Dulal(a), Subash Bhandari(b) a) Land Management Training Center Dhulikhel, Kavre, Nepal *sanjeevraut.4[at]gmail.com b) Kathmandu University Dhulikhel, Kavre, Nepal Abstract needs. The purpose of this research was to identify the groundwater potential zones via the use of remote sensing (RS) and Geographic information system (GIS), which is essential in evaluating, preserving, and monitoring various groundwater-related development programs. Man-made pressures include overexploitations of groundwater and climate changes have led to strain on groundwater resources. As the use for consumable water increases for human consumption, agriculture, and industrial grow, needs to evaluate the groundwater potential and aquifer productivity also increase. Groundwater inspections have been historically done by field survey method, which is inefficient or not practical in terms of time and resources. Arc GIS software is utilized to manipulate datasets. The LULC map of the study area is developed using Landsat 8 satellite data. A soil map is created using the Geo network portal for FAO, rainfall map from the Climate Research Unit, and DEM is acquired using ASTER from earth data. Six thematic maps are applied, each with an appropriate weight and rank assigned based on its characteristics and connection with groundwater. All the thematic layers are combined into a GIS domain, and weight values are put to the attribute table for each polygon. The groundwater potential zone map has been classified into four zones, i.e., very poor, poor, moderate, and good. These results will help hydrogeologists, decision-makers, planners, and local authorities formulate better groundwater resource planning in the Bharatpur Metropolitan. Keywords: Groundwater, GIS, Remote sensing, Zones, Weighted overlay analysis Share Link | Plain Format | Corresponding Author (sanjeev kumar raut)

49	Topic A: General Remote Sensing	ABS-254
Evaluation of the Usability of Geostationary Satellite Images for Gap Filling of Polar Orbiting Satellite-based NDVI Sung-Joo Yoon, Han-Sol Ryu, Jinyeong Kim, Tae-Ho Kim * Ocean Convergence Division, Underwater Survey Technology 21. Inc., Korea * thkim[at]ust21.co.kr Abstract To understand vegetation responses to climate and environmental changes, long-term vegetation index analysis based on satellite imagery is used as a crucial tool for identifying spatiotemporal variability in crop growth and its causes. Polar orbiting optical satellite imagery is widely used for normalized difference vegetation index (NDVI) analysis. However, data gaps caused by clouds often hinder vegetation monitoring. To eliminate data gaps, recent studies have attempted to generate gap-free vegetation indices using synthetic aperture radar (SAR) images or spatial statistics techniques. In this study, we propose a method that utilizes the geostationary ocean color imager (GOCI)-II, capable of producing Earth observation images hourly, to generate gap-free vegetation indices of Sentinel-2. We present a conditional generative adversarial network (cGAN) model based on the U-Net architecture to transform GOCI-II images to match the spatial and spectral features of Sentinel-2 images. Using Sentinel-2 products as ground truth, the model is trained to minimize both pixel-level reconstruction error and adversarial loss, enabling the generation of transformed GOCI-II data with a resolution similar to Sentinel-2 data. The experiment was conducted on agricultural land in South Korea. The results showed that the converted GOCI-II output achieved a significant structural similarity index measure (SSIM) and low root mean square error (RMSE) compared to actual Sentinel-2 observations. Visual inspections also confirmed that the spatial texture and radiometric consistency were maintained. These results suggest that the proposed model can be utilized as an important preprocessing component in a gap-filling framework and effectively generate Sentinel-2 compatible data even under cloudy conditions. In future studies, the converted output will be used to restore cloud-covered NDVI values, enabling continuous vegetation monitoring in frequently cloud-covered regions. Keywords: GOCI-II, Sentinel-2, gap filling data, vegetation monitoring Share Link | Plain Format | Corresponding Author (Sungjoo Yoon)

50	Topic B: Applications of Remote Sensing	ABS-256
Adaptive Sensor Fusion of LiDAR and Stereo Camera for Robust Autonomous Navigation in Outdoor Environments Kenta Ishizuka (a*), Akito Arai (a), Arata Nagasaka (a), Kazuyuki Hashimoto (b), Shotaro Kobayashi (b), Masafumi Nakagawa (a) a) Shibaura Institute of Technology, Japan *ah21014[at]shibaura-it.ac.jp b) Watanabe Engineering Co., Ltd, Japan Abstract Autonomous vehicles are widely promoted as a solution to reduce traffic accidents and improve logistics efficiency. However, many technical challenges remain before they can be put into practical use, such as improving the accuracy of self-position estimation and object recognition. Sensors installed in autonomous vehicles must be cost-effective while delivering high accuracy and real-time performance. Although previous research has achieved high-precision sensing, continuous output increases data processing demands data and power consumption. High-accuracy recognition using LiDAR and stereo cameras has been reported, but most approaches require significant computational resources, such as GPU processing, which hinders real-time performance. On the other hand, integrating data from different types of sensors is considered effective for 3D measurement and navigation under changing weather conditions. However, the performance largely depends on the integration method. This study proposes a method for dynamically optimizing the output of LiDAR and stereo cameras based on environmental conditions to improve measurement performance in both sunny and rainy weather. In addition, the proposed method was applied to LIO-SAM, which combines non-repetitive scanning LiDAR with visual simultaneous localization and mapping (Visual SLAM) using a stereo camera, and its effectiveness was evaluated. However, sufficient self-position estimation accuracy was not achieved. Frame integration, horizontal plane estimation, and mask processing using reflection intensity values were attempted, but improvements remained limited. Future work will focus on developing point cloud correction techniques specific to non-repetitive scanning LiDAR and refining frame-to-frame interpolation using velocity estimation. Keywords: sensor fusion, LiDAR, stereo camera, LIO-SAM, Visual SLAM, autonomous vehicle Share Link | Plain Format | Corresponding Author (Kenta Ishizuka)

51	Topic B: Applications of Remote Sensing	ABS-1
Carbon Stock Assessment of Pagatban Communicty Conserved Mangrove Forest in Negros Oriental, Philippines Using Planet NICFI Data Lawas, C. J. C. 1*, Carreon, J. C. M .2, Cortes A. C.3 1CVSC: University Researcher, University of the Philippines Cebu 2DBES: Alumnus, University of the Philippines Cebu 3DBES: Former Faculty, University of the Philippines Cebu Abstract Pagatban Mangrove Community Forest in Negros Occidental Philippines is a community conserved forest where no carbon assessment has been conducted to date. Quantifying the carbon stored in this conserved forest is essential to assess its potential to mitigate climate change to ensure its sustainable management and protection. Moreover, at the local government level establishing a rapid and comprehensive method of estimating carbon stocks of their mangrove forests is an important input in their data based policy decision making to reach carbon neutrality. This study implemented a non-destructive method of assessment through ground-based measurements in combination with high resolution and analysis ready remotely sensed data from Planet NICFI. Carbon estimation using allometric equations per sampling plots were calculated using the field-based DBH measurement. Remotely sensed data from Planet NICFI was analyzed to obtain the NDVI vegetation index and spatial extent of the mangrove area. The calculated field-based values were then correlated with the obtained transformed NDVI values. Results show that the NDVI values of the study area range from 0.44 - 0.87. The regression analysis between the NDVI values per sampling plot for the measured carbon stock is expressed as Y= 949.75x + 416.81 with coefficient of determination (R2) 0.99. Total carbon stocks estimated from field-based estimation using allometric equation is 19,430.4280 Ton/Ha and total carbon stocks estimated using the transformed NDVI values is 19,430.4306 Ton/Ha. The correlation between the field calculated values for the carbon stock and the estimated values using the transformed NDVI values showed a strong positive relationship with an r value of 0.938. The results indicated the potential of using field-based measurements in combination with the use of high resolution, analysis ready NICFI Planet data for local carbon assessment. Keywords: Conserved mangrove forest, Carbon stock assessment, NDVI, NICFI Planet data Share Link | Plain Format | Corresponding Author (Cora Jane Lawas)

52	Topic B: Applications of Remote Sensing	ABS-257
Study of Sediment Land Elevation Dynamics Using Airborne LiDAR in the Ajkwa Estuary, Mimika, Papua, Indonesia Pratama S.A.P. , Pramadarsah A. and Helmi M. Department of Oceanography, Faculty of Fisheries and Marine Sciences, Universitas Diponegoro, Indonesia Environmental Division, PT Freeport Indonesia, Mimika, Papua, Indonesia Abstract Estuarine environments are highly dynamic due to tidal activity and sediment input from rivers, often causing rapid elevation changes. The dynamic changes in the Ajkwa Estuary due to rapid sedimentation require a comprehensive and well-structured programme aimed at enhancing ecosystem services, which provide ecological benefits and support local livelihoods. This initiative seeks to establish a sustainable and harmonious estuarine environment through spatial planning, particularly by organising mangrove zones as integral elements of an ecologically, socially, and sustainably valuable landscape. This study examines elevation changes and the role of geo-tube and e-groin (bamboo structures) in enhancing sedimentation. Airborne LiDAR data from 2017, 2019, 2022, and 2024 were processed into digital elevation models (DEMs), and elevation change was analysed using the DEM of Difference (DoD) method within the Geomorphic Change Detection (GCD) tool. Although the DoD method has been used elsewhere with DEM data, its application in estuarine areas remains limited. This study enhances estuary monitoring by applying the method in a dynamic coastal system supported by LiDAR technology. Results show elevation loss between 2017 and 2019. From 2022 to 2024, after installing sediment management geo-tube and e-groin (bamboo structures), intervention areas recorded gains 99.49% in Area A and 93.91% in Area B, while the non-structured area showed a more stable pattern. The findings confirm that sediments transported into the ModADA area can accumulate effectively with the aid of geo-tube and bamboo structures, making them suitable for mangrove planting and contributing to sustainable estuary management. Keywords: Ajkwa estuary, geomorphic change detection (GCD), digital elevation model (DEM), LiDAR, DEM of difference (DoD) Share Link | Plain Format | Corresponding Author (Surya Adi Putra Pratama)

53	Topic B: Applications of Remote Sensing	ABS-4
Enhanced Detection of Geomorphic Changes in the Khor Al-Sabiyeh Coastal Environment Using SAR, Optical Imagery, and Machine Learning Ahmad E. Aldousari 1*, Temitope D. Timothy Oyedotun 2*, Saud Reyadh AlKhaled 3, Helene Burningham 4 1 Department of Geography, Assistant Professor, Social Sciences College, Kuwait University, Kuwait City, Kuwait 2 Department of Geography, Professor, Faculty of Earth and Environmental Sciences (FEES), University of Guyana, P O Box 10 1110, Turkeyen Campus, Guyana 3 Department of Architecture, Assistant Professor , College of Architecture, Kuwait University, Kuwait City, Kuwait 4 Coastal and Estuarine Research Unit, Professor, UCL Department of Geography, Gower Street, London, WC1E 6BT, UK * dr.dousari[at]ku.edu.kw (Corresponding author) * : temitope.oyedotun[at]uog.edu.gy (Corresponding author) Abstract Coastal wetlands are among the most ecologically significant landscapes, yet they are highly susceptible to geomorphic changes driven by both natural processes and anthropogenic pressures. This study presents an integrated remote sensing and machine learning approach to enhance the detection of geomorphic changes in Khor Al-Sabiyeh, a critical coastal wetland in Kuwait. By utilising the complementary strengths of Synthetic Aperture Radar (SAR) and optical imagery (Landsat-8 and Sentinel-2), we developed an analytical framework that overcomes the limitations of conventional monitoring methods. Using Google Earth Engine (GEE) for data preprocessing, we generated cloud-free annual composites for 2020 and computed a suite of spectral indices, including NDVI, NDMI, MNDWI, GCVI, SR, and custom band ratios, which provided detailed insights into geomorphic dynamics, water bodies, and land surface conditions. This study used supervised machine learning classifiers, particularly Random Forest, to detect and classify geomorphic transformations with high accuracy. The model was validated using cross-validation techniques and statistical metrics, such as overall accuracy and the Kappa coefficient, confirming its reliability and robustness. The results revealed distinct spatial patterns of erosion, accretion, and land cover changes, which have direct implications for environmental planning in the region. The results from this research show the potential of integrating SAR, optical datasets, and machine learning for a timely and accurate assessment of landscape changes in fragile coastal systems. The methodological framework adopted in this study is transferable and scalable, offering valuable applications for similar systems globally. This approach supports evidence-based environmental governance and enhances resilience in the face of climate change and human-induced alterations. Keywords: Coastal Wetlands- Geomorphic Change Detection- Khor Al-Sabiyeh Kuwait- Machine Learning- Optical Imagery- Remote Sensing Integration- Synthetic Aperture Radar (SAR). Share Link | Plain Format | Corresponding Author (Ahmad AlDousari)

54	Topic B: Applications of Remote Sensing	ABS-5
MAPPING FOREST IN DARKHAN-UUL PROVINCE, MONGOLIA THROUGH SENTINEL-2 SPECTRAL INDICES Bayanmunkh Norovsuren1,6, Ochirkhuyag Lkhamjav 2,3,6, Tsolmon Altanchimeg3,6, Ulziisaikhan Ganbold4,6, Bayarmaa Enkhbold4,6, Gankhuyag Purev5,6, Tserennadmid Bataa5, Battuya Sanjaakhand5,6* 1Spatial data analysis department, Center for policy research and analysis, Ulaanbaatar, Mongolia (bayanmunkh.n[at]cpra.ub.gov.mn) 2 Department of Civil Engineering, National Central University, Taoyuan 32017, Taiwan (ROC) (olkhamjav[at]g.ncu.edu.tw) 3 Institute of Geography and Geoecology, Mongolian Academy of Science, Ulaanbaatar, Mongolia (tsolmon_a[at]mas.ac.mn- ochirkhuyag_l[at]mas.ac.mn) 4 School of Geology and Mining Engineering, Mongolian University of Science and Technology (ulziis[at]must.edu.mn- ebayarmaa[at]must.edu.mn ) 5 Mongolian University of Life Science, Ulaanbaatar, Mongolia (battuya[at]muls.edu.mn) 6 Mongolian Geospatial Association, Ulaanbaatar 15141, Mongolia (info[at]geomedeelel.mn) Abstract Analyzing forest health in Mongolia through spectral indices derived from Sentinel-2 satellite data provides an innovative and effective approach for environmental monitoring and management. The study area, located in the northern region of Mongolia, is dominated by mixed forest ecosystems. This research utilizes high-resolution, multi-spectral imagery from Sentinel-2 satellites, part of the European Space Agency^s Copernicus program, to assess forest health across Darkhan-Uul Province. The primary objective is to compare changes in the main forest canopy classes within this region. We calculated key spectral indices-including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and the Spectral Forest Index (SFI)-to assess forest health, moisture content, and stress factors. The methodological framework involved acquiring and preprocessing Sentinel-2 images to correct for atmospheric disturbances and cloud cover, calculating relevant spectral indices, and analyzing these indices to derive insights into forest health status. Results demonstrate that Sentinel-2 data significantly improved forest canopy health mapping accuracy by approximately 10% compared to conventional methods. Forest area extent was determined by assessing canopy health conditions, and degraded forest areas were identified in several locations. These findings will inform forest management planning and contribute to enhanced forestry practices in Darkhan-Uul Province, Keywords: Remote sensing, forest health, forest spectral index, forest health management Share Link | Plain Format | Corresponding Author (Ochirkhuyag Lkhamjav)

55	Topic B: Applications of Remote Sensing	ABS-261
Calculating Forest Area Changes Using Different Machine Learning Methods Bolorchuluun Chogsom1*, Zandanbat Tsog-Urnukh2 1Department of Geography, National University of Mongolia, Mongolia- 2Data specialist, National Statistical Organizaton, Mongolia Abstract Forests occupy 9.1% of Mongolia^s total land area, or a very small part. In the era of advanced technology, it is necessary to process remote sensing data with machine learning methods and compare the results. The aim is to find a suitable method for calculating forest changes by processing this forest area change using machine learning methods on active sensing images and comparing it with optical sensing. To calculate forest changes, machine learning methods were compared on active sensing ^Sentinel-1^ satellite images and validated on optical sensing ^Landsat-8^ satellite images. As a result of this research, four machine learning methods used to calculate forest area were tested: support vector machine, K-nearest neighbor method, maximum similarity method, and random forest. Of these, the most similar method was feasible, while the other methods were moderately feasible. This research is innovative in that it combines active sensing with optical sensing and compares the differences between machine learning algorithms using classification methods. Keywords: Mashine Leaerning, SVM, K-NN, Maximum Likehood, Random Forest Share Link | Plain Format | Corresponding Author (Bolorchuluun Chogsom)

56	Topic B: Applications of Remote Sensing	ABS-6
SPATIAL TEMPORAL MODELLING OF POTENTIAL LIGHT POLLUTION IMPACTS IN MT. APO NATURAL PARK A. Masongsong (b), L. Lunas (b), F. Celestial (a), Jeark Principe (b) (a) Department of Geography, University of the Philippines Diliman (b) Department of Geodetic Engineering, University of the Philippines Diliman Abstract Artificial light at night (ALANs) negatively impacts biodiversity since it disrupts essential life and ecological processes. Some known effects of light pollution are disturbances to bird migration patterns, insect circadian rhythms, and plant phenology. ALANs continue to be a rising threat due to rapid urbanization and development near the proximity of protected areas. This study uses satellite imagery from the VIIRS Day-Night Band and Landscan population data from 2012 to 2022 to model light pollution^s growth and potential impact on the Mt. Apo Natural Park, an important protected area in Mindanao, Philippines. A 5-km buffer zone from the protected area was made to assess the influence of nearby metropolitan and suburban areas. By generating linear regression models using brightness and population data, projections for nighttime light expansion are produced until 2042. High-priority areas for conservation efforts and management are determined to coincide with polluted pixels and are classified as part of the Strict Protection Zone. Lights are also continuously present in restoration zones, which can hinder the progress of biodiversity rehabilitation. For future studies, the integration of field measurements to assess skyglow and its application to other protected areas (such as marine protected areas) is recommended. Keywords: Light Pollution, Multi-linear Regression, VIIRS DNB, NTL Modelling Share Link | Plain Format | Corresponding Author (Trisha Leigh Lunas)

57	Topic B: Applications of Remote Sensing	ABS-262
Estimating Ground Surface Vertical Displacement Using DInSAR Interferometry Narandulam Naranbaatar1, Bolorchuluun Chogsom2 1MonMap LLC, Mongolia 2Department of Geography, National University of Mongolia, Mongolia Abstract This study estimates vertical ground surface displacement caused by the Mw 4.4 earthquake that occurred on May 19, 2024, in Bogd soum, Ovorkhangai Province, central Mongolia. The selected area lies along active segments of the Ikh Bogd fault system, which is associated with the historic 1957 Mw 8.1 Gobi-Altay earthquake. Specifically, deformation patterns were examined along Fault 20 (north) and Fault 21 (south), as defined in the Mongolian Seismic Fault Map. The analysis utilized Level-1 Single Look Complex (SLC) data acquired in Interferometric Wide Swath (IW) mode by the Sentinel-1A satellite. Two acquisitions from May 15, 2024 (pre-event) and June 7, 2024 (post-event) were processed using the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique within the ESA SNAP software environment to estimate vertical displacement. Results from interferometric processing revealed measurable vertical surface movements, with subsidence reaching up to 7.38 cm and uplift up to 4.29 cm. These findings demonstrate the potential of DInSAR for detecting subtle surface deformations induced by moderate-magnitude seismic events. The study contributes to seismic risk assessment in tectonically active regions of Mongolia and supports the use of SAR-based time-series approaches for long-term ground deformation monitoring. Keywords: DInSAR, Vertical Displacement, Earthquake, Sentinel-1, Ground Deformation Share Link | Plain Format | Corresponding Author (Narandulam Naranbaatar)

58	Topic B: Applications of Remote Sensing	ABS-7
SAR Displacement Mapping on Myanmar Earthquake Myint.M.M Chief Scientist, Mapping and Natural Resources Informatics, Switzerland maungmoe.myint[at]mnri.ch Abstract ABSTRACT Mapping surface displacement at major cities and along the Sagaing fault is an important undertaking after 7.7 Magnitute Earthquake near Sagaing town and 6.7 Magnitude Earthquake near Mandalay town on 28 March 2025 in Myanmar. Surface displacement away from the satellite look direction causes an increase in the path (translating to phase) difference. The objective is to extract the displacement component from the different components of the observed phase using the Sentinel-1 Synthetic Aperture Radar (SAR) interferometric pair with small baseline while considering time baseline as small as possible to avoid decorrelation. Differential Interferometry DInSAR is applied to create an interferometric displacement map (expressed in meter) in line-of-sight (LOS) using a pair of Sentinel-1 SLC SAR co-polarized images of same sensor, geometry, incidence angle, data type, to show the land movement over time from the first pre-earthquake image to the second post-earthquake image. One fringe cycle in the interferogram corresponds to displacement relative to the SAR antenna only half of the wavelength. As the Sentinel-1 C band wavelength is 5.6 cm, one fringe cycle in this study corresponds to 2.8 cm of displacement. The methodology started with pre-earthquake and post-earthquake images in slant range geometry, applied precise orbit information, input the reference digital elevation model, highly accurate co-registration for correcting shift of between images up to 1000th of pixel, some range and azimuth corrections, computation and generation of raw interferogram, interferogram flattening for creating differential interferogram, interferogram filtering to reduce the phase noise for creating filtered interferogram and coherence, phase unwrapping to resolve the 2pi ambiguity to generate the absolute phase values, phase to displacement conversion for conversion of interferometric absolute phase to terrain displacement and geocoding to map projection. Each full cycle of differential phase corresponds to half of wavelength in SAR viewing direction. It is 2.5 cm in this study. Precision map and refined displacement map are also produced. This study successfully emphasises the displacement at Manadaly township where sinking is dominant, and also highlights the displacement at Sagaing township where uplift is dominant. It also signifies why the older Sagaing bridge structural damaged during the earthquake. Moreover, the study indicates the dominance of uplift of western side along the Sagaing fault. This study also answers why the Nay Pyi Taw town was damaged although it is 280 km south of earthquake epicenter. The quantitative uplift and sink values are also contributed. Keywords: SAR, interferometry, earthquake, displacement Share Link | Plain Format | Corresponding Author (Maung Moe Myint)

59	Topic B: Applications of Remote Sensing	ABS-263
Land Subsidence Assessment Using the D-InSAR Method in Siak Regency, Riau, Indonesia Santika Tristi Maryudhaningrum (a*), Giri Bayuaji (b) a) Faculty of Exploration and Production Technology, Pertamina University, Indonesia *santika.tristi[at]universitaspertamina.ac.id b) Faculty of Earth Science and Technology, Bandung Institute of Technology, Indonesia Abstract Land subsidence is a phenomenon in which the land surface slowly or rapidly decreases, often caused by natural or anthropogenic factors. The phenomenon of land subsidence has occurred in several regions in Indonesia with various causes. Siak Regency is an area characterized by extensive peatlands with an average thickness of 3-5 meters and high water content. This condition makes the soil highly susceptible to hydrocompaction processes, which occur due to excessive water loss, and can contribute to the occurrence of natural land subsidence. In this area, the phenomenon of land subsidence has had several impacts, including damage to existing infrastructure and the destruction of vegetation. This study aims to identify and map the distribution of land subsidence in Siak Regency using the D-InSAR analysis. In this study, Sentinel-1A was used to determine the rate of land subsidence. The study^s results showed that land subsidence occurred in several sub-districts, with subsidence rates exceeding 4 cm/year at certain points. These findings make a significant contribution to the field of remote sensing-based geohazard monitoring, particularly in tropical lowland regions. The spatial information generated can be the basis for efforts to mitigate the risk of land subsidence and support the formulation of more adaptive and sustainable regional development policies. Keywords: D-InSAR- hydrocompaction- land subsidence- mitigation- Siak Share Link | Plain Format | Corresponding Author (Santika Tristi Maryudhaningrum)

60	Topic B: Applications of Remote Sensing	ABS-9
Analysis of Ground Movement Event in Brebes Regency on April 17 2025, Using the DInSAR Method with SNAP Desktop M. Faturrahman Wirasakti, Irghan Santiz R.A, S.T Ministry of Public Works Abstract On April 17, 2025, Mendala Village in Brebes Regency, Indonesia, experienced significant ground deformation triggered by extreme rainfall totaling 200 mm over a two-day period. This event was exacerbated by clay-rich soil, steep slope morphology, and ongoing deforestation. Based on data from the Regional Disaster Management Agency (BPBD), the incident resulted in damage to 112 residential structures, the displacement of 383 residents, road cracking over a 5 km stretch, and maximum land subsidence of &#8722-20 cm affecting approximately 50 hectares. This study applied the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique using Sentinel-1 imagery acquired on April 8 and 20, 2025, processed through the SNAP Desktop platform to assess surface displacement. The results revealed vertical ground movement ranging from &#8722-20 cm to +22 cm, with most areas remaining relatively stable, in alignment with field observations reported by BPBD. The deformation was primarily associated with excessive hydrological loading and anthropogenic disturbances, particularly deforestation. Based on these findings, the study recommends several mitigation strategies, including population relocation, improved slope drainage, reforestation, and continuous monitoring utilizing InSAR-based remote sensing approaches. Keywords: Ground deformation, Brebes Regency, DInSAR, SNAP Desktop, Sentinel-1 Share Link | Plain Format | Corresponding Author (Muhammad Faturrahman Wirasakti)

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