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181	Topic B: Applications of Remote Sensing	ABS-139
Multi-Mission Satellite Remote Sensing for Spatiotemporal Retrieval of Riverine Water Turbidity in the Chao Phraya River, Thailand Teerawat Suwanlertcharoen*, Siam Lawawirojwong and Kampanat Deeudomchan Geo-Informatics and Space Technology Development Agency (Public Organization), Thailand *teerawat[at]gistda.or.th Abstract Riverine water turbidity is a key indicator of water quality, influencing light penetration, primary productivity, and the transport of sediments and pollutants, factors that directly affect aquatic ecosystems, public health, and water resource management. Given the dynamic nature of river systems, there is a growing need for accurate and timely methods to monitor turbidity over broad spatial and temporal scales. This study explores the potential of multi-mission satellite remote sensing for spatiotemporal retrieval of turbidity in the Chao Phraya River, a major waterway in Thailand that flows into the Gulf of Thailand. The research integrates multispectral data from Sentinel-2 (A and B), Landsat-8 and Landsat-9, and hyperspectral imagery from the EMIT mission by NASA. In-situ turbidity data from the Metropolitan Waterworks Authority (MWA) were used for algorithm development and validation. Atmospheric correction and sunglint removal were performed using the Dark Spectrum Fitting (DSF) algorithm in the ACOLITE software, chosen for its proven accuracy over inland and turbid waters. Surface reflectance in the red band was analyzed using a piecewise semi-empirical model employing polynomial and linear regression techniques. Validation against in-situ data showed strong agreement (R2 = 0.901, RMSE = 5.75 NTU, MAE = 4.68 NTU), demonstrating the effectiveness of the retrieval approach. The results reveal distinct spatiotemporal turbidity patterns along the river throughout 2024. This study confirms the robustness of multi-mission satellite remote sensing for turbidity monitoring and offers valuable insights for water quality management. The findings support informed decision-making for environmental protection and sustainable development, particularly given the influence of the river on the coastal ecosystems of the Gulf of Thailand. Keywords: Riverine Water Turbidity, ACOLITE, Satellite Remote Sensing, Chao Phraya River, Piecewise Regression Share Link | Plain Format | Corresponding Author (Teerawat Suwanlertcharoen)

182	Topic B: Applications of Remote Sensing	ABS-142
C-Band SAR-Based Backscatter Modeling for Monitoring Oil Palm Age Rika Hernawati (a,b)*, Soni Darmawan (b), Josaphat Tetuko Sri Sumantyo (a,c) a). Department of Environmental Remote Sensing, Chiba University, Japan b). Department of Geodetic Engineering, Institut Teknologi Nasional Bandung, Indonesia *rikah[at]itenas.ac.id c). Department of Electrical Engineering, Universitas Sebelas Maret, Indonesia Abstract Oil palm age information is a crucial indicator for estimating plantation productivity. Understanding age distribution for all plantation can improve management practices, including harvest estimation, fresh fruit bunch yield prediction, taxation, replanting planning, fertilization scheduling, and early detection of diseases. This study proposes remote sensing-based approach to modeling oil palm age using Synthetic Aperture Radar (SAR), specifically C-band data with dual polarization. The objective is to examine the relationship between SAR backscatter characteristics and oil palm age. Sentinel-1A SAR imagery acquired on November 14, 2022, was used in related with plantation block data showing planting years from 2000 to 2022. The methodology used included preprocessing, radiometric calibration, speckle filtering, terrain correction, extraction of scattering values, and building a scattering model. The results show strong correlation between backscatter values and oil palm age, achieving a classification accuracy of 78% for VV polarization and 71% for VH polarization. Future research can utilize SAR data to capture age-related structural changes as well as field-based validation involving biophysical parameters and yield data will strengthen the reliability of remote sensing-based age estimates. Keywords: Oil Palm Age, SAR, C-band, Backscatter Share Link | Plain Format | Corresponding Author (Rika Hernawati)

183	Topic B: Applications of Remote Sensing	ABS-144
Integrated PS-InSAR and SBAS InSAR for Lava Dome Collapse Detection: Case Studies from Mt. Merapi Zahratunnisa (1*), Asep Saepuloh (2), I G. B. Eddy Sucipta (3) (1) Student, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Indonesia. (1) Lecture, Geology Engineering Study of Earth Science Engineering Department, Universitas Syiah Kuala, Indonesia. (2) Professor, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Indonesia. (3) Professor, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Indonesia. *zahratunnisa[at]usk.ac.id Abstract Lava domes at stratovolcanoes can evolve through stable and unstable structural phases, with transitions that are often gradual but can lead to sudden hazardous events. One of the most dangerous consequences of dome instability is collapse, which may trigger pyroclastic flows, among the deadliest volcanic hazards. This study focuses on understanding lava dome deformation as an early indicator of potential collapse by integrating Permanent Scatterers InSAR (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques. Using temporal Sentinel-1 SAR imagery, we investigated ground deformation at Mount Merapi, an active stratovolcano in Indonesia. At Mt.Merapi, a new lava dome began forming within the summit amphitheatre left by the 2010 climactic eruption, and continued to grow under the persistent influence of pyroclastic density current (PDC) activity since 2018. The integrated InSAR approach addresses the limitations of the individual techniques: PSInSAR exhibits ~12.5% higher coherence but lower point density, whereas SBAS offers denser spatial coverage but is prone to temporal error accumulation. Combining both methods enables high-resolution spatiotemporal monitoring of surface deformation and enhances our ability to detect precursory signals to dome collapse.This research demonstrates the value of satellite-based remote sensing in characterizing dome instability and supports improved hazard assessments for strato-volcanic systems. Keywords: lava dome- deformation- SBAS-InSAR- PS-InSAR- Mt. Merapi Share Link | Plain Format | Corresponding Author (Zahratunnisa zahratunnisa)

184	Topic B: Applications of Remote Sensing	ABS-145
ANALYSIS AND MAPPING OF MANDOTI GLUTINOUS RICE GROWTH IN SALUKANAN VILLAGE, BARAKA DISTRICT, ENREKANG REGENCY USING GOOGLE EARTH ENGINE (GEE) Trisnawaty AR1,2*, Rinaldi Sjahril3, Muh. Riadi3, Nurlina Kasim3, Rafiuddin3, Aswar Anas4 1Study Program of Agricultural Science, Graduate School, Hasanuddin University, Makassar-90245, Indonesia 2Department of Agrotechnology, Faculty of Science and Technology, Universitas Muhammadiyah Sidenreng Rappang, Sidrap-91651, Indonesia. 3Department of Agrotechnology, Faculty of Agriculture, Universitas Hasanuddin, Makassar-90245, Indonesia. 4Research and Development Center for Marine, Coastal and Small Island, Hasanuddin University, Makassar-90245, Indonesia Abstract Mandoti glutinous rice is a local variety with high economic value that is widely cultivated in Enrekang Regency. This study aims to analyze and map the growth of Mandoti rice in Salukanan Village, Baraka District, using the Google Earth Engine (GEE) platform. The data used included Sentinel-2A imagery (time series) to monitor vegetation growth dynamics using the Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI), and Enhanced Vegetation Index (EVI) indices during the rice growth phase. The analysis was conducted to identify the distribution patterns and growth rates of Mandoti rice plants at various growth phases. The results showed that the NDVI and EVI index values showed similar patterns in monitoring rice growth. The peak vegetative growth was detected in June-August with NDVI values of 0.634-0.636 and EVI of 1.906-2.438, respectively. A decline in values occurred in September-October with NDVI values of 0.473-0.519 and EVI of 1.204-1.278, indicating that the plants had entered the generative and harvest phase. Therefore, these two indices are significant in monitoring the growth of Mandoti rice. This information is expected to support decision-making in cultivation management and increase Mandoti rice productivity in Salukanan Village. Keywords: EVI, Google Earth Engine, LSWI, Mandoti, NDVI, Sentinel-2A. Share Link | Plain Format | Corresponding Author (Trisnawaty AR)

185	Topic B: Applications of Remote Sensing	ABS-148
Landslide Displacement Observation in A Mountainous Area using Temporal Geo-spatial Data Acquired from Drone Mapping Kim S.S., Jung Y.H.,Koo S., and Park J.W. Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea. Abstract The objective of this study is to propose a methodology for assessing the potential occurrence of additional landslides over time in mountainous areas, utilizing state-of-the-art drone photogrammetry techniques and time-series mapping analysis. The experimental equipment employed in this study includes the DJI Matrice 350 RTK UAV, mounting on the high-resolution optical sensor Zenmuse P1. A total of approximately 2,000 UAV-derived aerial photos of the landslide-affected areas of Mt. Toham were captured and processed into true ortho-image maps and point cloud data to generate 3D terrain models. These imagery maps were produced between September, October, December 2024, and March 2025, and the generated geospatial products served as experimental data for this study. Both oblique and nadir photos were simultaneously captured to produce true ortho-imagery maps, with approximately 2 cm GSD. The study area focuses on regions suspected of ground displacement due to landslides in the Mt. Toham. The total landslide area was estimated approximately 1.8 hectares, with a collapse volume of 21,244 m3. In the time-series landslide observation method of this study, the first step was to select and extract fixed reference points on the ground which were clearly identifiable in the imagery, considering the challenging accessibility of the mountainous terrain. The subsequent step involved utilizing the orthophoto maps and 3D terrain cross-section data generated through drone-based mapping to monitor centimeter-level positional changes over successive observation periods. This approach enabled the analysis of the potential for further landslide occurrences based on temporal changes in the terrain morphology. Keywords: UAVs, Drone photogrammetry, Temporal mapping analysis, Landslide, Change detection Share Link | Plain Format | Corresponding Author (Seongsam Kim)

186	Topic B: Applications of Remote Sensing	ABS-149
Characterizing Land Cover Change in Degraded Urban Watersheds through Temporal Dynamics Pattern Analysis Yudi Setiawan (a*), Kustiyo (b), Liyantono (a), Rizqi Fahma Sidik (a), Rizki Moch Rijaldi (a)) a) Center for Environmental Research, International Research Institute for Environment and Climate Change, IPB University, Darmaga Campus, Bogor 16680, Indonesia *setiawan.yudi[at]apps.ipb.ac.id b) Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN), Bandung 40135, Indonesia Abstract Urban watersheds in Indonesia face increasing environmental degradation due to unregulated land use change and urban expansion. This study focuses on three critical catchments-Ciliwung, Kali Bekasi, and Citarum-which are frequently exposed to flooding, ecological stress, and land instability. We analyzed land cover change from 1990 to 2024 using multi-temporal Landsat imagery and a temporal dynamics pattern approach previously developed to classify land cover transitions based on change trajectories. This method captures distinct patterns such as gradual vegetation decline, abrupt urban expansion, and cyclic disturbances. Building on earlier results, we applied trajectory grouping and temporal segmentation to improve the detection of persistent and reversible changes. The findings reveal consistent trends of vegetation loss and increasing built-up areas, particularly in riparian and floodplain zones. In Ciliwung and Kali Bekasi, urban expansion often follows short phases of regrowth, while in Citarum, conversion is largely irreversible. These dynamics indicate unstable land management and limited effectiveness of spatial planning. This study demonstrates how temporal-based classification improves understanding of long-term land cover dynamics in complex urban environments. The approach supports targeted restoration and planning strategies for more sustainable watershed management. Keywords: Temporal dynamics- Landscape degradation- Spatio-temporal analysis Share Link | Plain Format | Corresponding Author (Yudi Setiawan)

187	Topic B: Applications of Remote Sensing	ABS-152
A Comparative Evaluation of Point Clouds Data Acquired Using Drone LiDAR in Various Terrains Koo S.1, Jung Y.H.2, Park J.W.1, and Kim S.S.3* 1Researcher, Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea 2Senior Researcher, Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea 3Senior Researcher Officer, Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea *sskim73[at]korea.kr Abstract LiDAR technology collects precise 3D spatial information by integrating lasers with high-precision IMUs and GNSS. Advancements in sensor technology have enabled the use of LiDAR on drones for the efficient acquisition of high-resolution point cloud data. Specifically, LiDAR^s multi-return signal processing technology records multiple reflections from a single laser pulse, allowing the sensor to penetrate obstacles like dense foliage and effectively acquire point data from the ground surface. In this study, data was acquired and performance was compared in a forested area, flat terrain using a DJI Matrice 350 RTK drone equipped with Zenmuse L1 and L2 scanners. The scanning altitude was varied from 50 to 150 meters. The study found that the L2 demonstrated higher point density and superior vertical (Z-axis) precision compared to the L1. Notably, it effectively acquired ground point cloud data even within complex, densely vegetated forest environments. Additionally, it was confirmed that while multi-return signals can be useful in complex terrains like forests, they may not always provide reliable point cloud data depending on the specific scanning environment. For simple flat terrain or sparsely forested areas, the study found that using only the first, second, and third returns is sufficient to acquire reliable data. In conclusion, the L2 strength in collecting reliable point cloud data and performing accurate 3D terrain modeling in complex terrain makes it a suitable instrument for disaster cause investigation and precision terrain analysis. Keywords: Drone LiDAR, Point Cloud, Multiple return, Point density, Ground point Share Link | Plain Format | Corresponding Author (SEUL KOO)

188	Topic B: Applications of Remote Sensing	ABS-153
Multidimensional UAV-Based Assessment of Rice Bacterial Leaf Blight: Integrating Spectral, Textural, Thermal, and Spatial Features with Machine Learning Arif K Wijayanto (a*)(b)(c), Lilik B Prasetyo (b), Chiharu Hongo (d) a) Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan b) Department of Forest Resource Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor 16680, Indonesia c) Environmental Research Center, IPB University, Bogor 16680, Indonesia d) Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba 263-8522, Japan Abstract Bacterial Leaf Blight (BLB) disease poses a significant threat to rice yields, potentially leading to losses of up to 50%. To offset these losses, the government of Indonesia through the Ministry of Agriculture has implemented agricultural insurance schemes that depend on precise damage assessments to ensure equitable compensation. However, the program relies heavily on traditional manual assessments which often lack of consistency and accuracy. This research introduces an integrated model that combines textural, thermal, and patch fragmentation metrics, all derived from UAV-based multispectral and thermal imagery, to improve the detection of BLB damage. The study was carried out in the Cihea irrigation area in Cianjur, Indonesia, utilizing multisensory UAV data from specified irrigation blocks. The model^s performance was assessed using machine learning approaches and compared with evaluations from pest observers. Findings reveal that integrating multiple features significantly enhances the accuracy of disease classification, achieving an overall accuracy of 0.998. Keywords: drone- fragmented patches- plant disease- rice paddy- textural analysis Share Link | Plain Format | Corresponding Author (Arif Kurnia Wijayanto)

189	Topic B: Applications of Remote Sensing	ABS-154
Optimal multi-scale segmentation for residential pattern detection lei zhang Aerospace Information Research Institute, Chinese Academy of Sciences, China Abstract In using object-based classifier for land cover classification, analysts typically encounter serious issues of producing segmented objects to fit the boundary of real land cover types, they usually occupy different scale levels. This study concentrates on the multi-scale segmentation to reduce errors for object identification. The result reveals that geographic object-based image analysis (GEOBIA) for residential dwellings identification outperforms pixel-based approach, and multi-scale GEOBIA improves recognition accuracy than single-scale GEOBIA. The multi-scale approach produced a significantly higher overall accuracy of 91%, whereas single-scale GEOBIA produced 84% and pixel-based classifier produced 73%. The traditional per-pixel approach is not very efficient in identifying residential dwellings- the dark roofs of residential dwellings have extremely spectral similarity with ploughed but bare cropland, mining mound and brick sites. The segmentation experiment demonstrated that the ratio of shape to spectrum 0.3:0.7 is optimal parameter setting, the weighted adjustment of smoothness and compactness presented no an obvious difference in effect, weight of compactness 0.6 and smoothness 0.4 indicated acceptable selection that regularly shaped segments better match the general form of houses. The shape features including object size, ratio of length to width, and compactness effectively improved spectral identification. The confused types of residential dwellings, industrial zone, and fallow field have best boundary fit at scale level of 20, 30, and 100, respectively. The industrial zone classified at scale level 30 and fallow field identified at scale level of 100 successfully removed the misclassified candidate residential dwellings at scale level of 20. Multi-scale GEOBIA approach has less spectral and shape mixed effects than approach employing single-scale GEOBIA and pixel-based classifier. Keywords: Object-based, Residential dwellings, Multi-scale segmentation Share Link | Plain Format | Corresponding Author (lei zhang)

190	Topic B: Applications of Remote Sensing	ABS-155
IMPACT OF LAND USE LAND COVER CHANGES ON WILDLIFE PRESENCE IN ENDAU ROMPIN JOHOR (PETA) NATIONAL PARK MAT KHIR, N. F. A., HASSAN, N. AZMY, S.N., and TARMIDI M.Z. UNIVERSITI TEKNOLOGI MALAYSIA Abstract This study was carried out at Endau-Rompin National Park, Johor, Malaysia to determine the impact of Land Use and Land Cover (LULC) changes on animal presence between 2016 and 2020 along non paved road . The main problem addressed is how human activities like as logging and agricultural development trigger habitat deterioration which can affects wildlife presence .Continuous loss of forest cover caused by mankind^s activities has the potential to disturb ecological balance and affect the existence and spatial distribution of species .The study is focused by its two main objectives: (1) to analyse LULC changes between 2016 and 2020, and (2) to determine the impact of animal presence in relation to LULC changes along non- paved logging paths. Satellite imagery classification using Geographic Information System (GIS) and remote sensing techniques are used to determine LULC changes. Camera trap data were acquired from four different sites (Cameras G, H, I, and J) along logging roads. The significance of differences in animal presence across the research years was determined using Analysis of Variance (ANOVA). The data show that forest areas have been decreasing while agricultural land and bare land areas are increasing. Wildlife presence peaked in 2016 and then dropped in 2019 and 2020. Furthermore, several species^ migratory habits changed, with some becoming more nocturnal possibly as a response to human disturbance. The ANOVA results indicates that LULC changes have a significant long-term impact on animal presence and habit. The relevance of this study derives from its contribution to biodiversity conservation and ecological planning in tropical forest environments. The results can be particularly beneficial to policymakers, conservation-focused NGOs, rural area planners, and scientific groups exploring to balance development and long-term protection of animals. Keywords: Land use land cover change, wildlife presence, tropical rainforest, machine learning, remote sensing approach Share Link | Plain Format | Corresponding Author (HASSAN NOORDYANA)

191	Topic B: Applications of Remote Sensing	ABS-156
The Applications of Remote Sensing and GIS for Utilities Sector in Indonesia: Challenges and Opportunities Rifqi Oktavianto (a*), Very Fernando (b) a) Solution Engineering Department, Esri Indonesia, Indonesia *roktavianto[at]esriindonesia.co.id b) Information System and Technology Division, PT PLN (Persero), Indonesia Abstract The utilities sector, encompassing critical infrastructure such as electricity, water, gas, and telecommunications, is fundamental to socio-economic development- however, in a geographically diverse and rapidly developing nation like Indonesia, managing and optimising these networks presents significant challenges, including vast service areas, complex topographies, and vulnerability to natural hazards. This paper provides a comprehensive overview of how Remote Sensing (RS) and Geographic Information Systems (GIS) offer powerful tools for data acquisition, analysis, and visualisation, presenting key opportunities for utility providers to make informed decisions through applications such as precise infrastructure mapping, accurate asset management, optimising route selection, monitoring land use changes, identifying environmental risks, supporting disaster preparedness and response, and facilitating predictive maintenance to ensure operational continuity. Ultimately, the strategic integration of RS and GIS empowers Indonesian utility companies to overcome these challenges, achieving greater operational efficiency, reducing costs, improving service delivery, and enhancing network resilience against environmental and anthropogenic threats, highlighting successful implementations and identifying future avenues for leveraging these geospatial technologies to meet the growing demands of Indonesia^s utilities landscape and contribute to national development goals. Keywords: Remote Sensing, GIS, Utilities Sector, Indonesia, Infrastructure Share Link | Plain Format | Corresponding Author (Rifqi Oktavianto)

192	Topic B: Applications of Remote Sensing	ABS-159
Satellite-Based Monitoring of Land Subsidence in the Twin Cities of Pakistan Using Sentinel-1A SAR Data Muhammad Abid and Muhammad Farooq Iqbal Applied Geo-Informatics Research Lab, Department of Meteorology, COMSATS University Islamabad (CUI), Islamabad, Pakistan Abstract Ground deformation and land subsidence have emerged as significant geo-environmental hazards, leading to substantial economic losses in many major cities worldwide. Conventional ground-based approaches such as Global Positioning System (GPS), leveling surveys, and other in-situ measurements are reliable for detecting land subsidence. However, these methods are often labor-intensive, time-consuming, and costly. To address these challenges, the use of satellite-based techniques, particularly Synthetic Aperture Radar (SAR), has proven effective for monitoring land subsidence over large areas. This study focuses on detecting and monitoring ground deformation and subsidence in the twin cities of Rawalpindi and Islamabad, Pakistan, from 2018 to 2022, using Sentinel-1A SAR data. Rawalpindi, the fourth-largest city in Pakistan, and its adjacent city Islamabad are experiencing rapid urban expansion. Both the cities heavily depend on groundwater extraction through tube wells as a primary source of water. The overexploitation of groundwater by residents, industries, and municipal authorities has significantly impacted subsurface stability. The study utilized Differential Interferometric SAR (DInSAR) techniques, employing Interferometric Wide (IW) Single Look Complex (SLC) images from Sentinel-1A^s C-band sensor. Successive image pairings were generated between January 2018 to January 2022. The analysis focused on determining displacement in both the Line of Sight (LOS) and vertical direction. Findings revealed that average LOS displacement ranged from -97 mm to +76 mm, while average vertical displacement ranged from +102.39 mm to -80.37 mm during the study period. Significant land subsidence was observed in the southern section of Rawalpindi, particularly in the Old Rawalpindi area, which is characterized by high population density and extensive groundwater usage. Conversely, parts of Islamabad showed signs of uplift, possibly due to variations in underground aquifer recharge and geological factors. The study suggests that excessive groundwater extraction, urbanization, and underlying geological and lithological conditions are the primary drivers of the observed surface deformations. Keywords: Synthetic Aperture Radar (SAR), Sentinel-1, Differential Interferometric SAR (DlnSAR), Land Deformation, Subsidence Share Link | Plain Format | Corresponding Author (Muhammad Farooq Iqbal)

193	Topic B: Applications of Remote Sensing	ABS-160
Changes in Satoyama Landscapes in Sumedang, West Java, Indonesia Anwar Nasrudin (1)(2)*, and Akemi Itaya (2) 1) Sustainability Science Master^s Program, Graduate School, Padjadjaran University, Indonesia 2) Forest Engineering Laboratory, Graduate School of Bioresources, Mie University, Japan Abstract The purpose of this study was to investigate the impacts of recent urbanization and the change of Satoyama in Sumedang, West Java, Indonesia, based on land cover and use changes using satellite images in order to discuss a strategy for the conservation of Satoyama landscapes. Significant improvements in classification accuracy were achieved, with overall accuracy rising from 73% to 85% and Kappa accuracy from 66% to 79%. The analysis revealed a decrease in dryland agriculture and shrub-mixed dryland farms, alongside an increase in forested areas and settlements. These changes have important implications for the sustainability of Satoyama landscapes, as increased forest areas support biodiversity. The Satoyama Index, reflecting the health of socio-ecological landscapes, improved from 2003 to 2013 but slightly declined by 2023 due to urbanization pressures. Cluster analysis identified three watershed groups with distinct characteristics: Cluster 1, dominated by dryland agriculture and rice fields with a low Satoyama Index- Cluster 2, characterized by shrub-mixed dryland farms and the highest Satoyama Index- and Cluster 3, with diverse land uses and a moderate Satoyama Index. Each cluster requires tailored land use management strategies to balance development and conservation. The findings emphasize the need for integrated land management to preserve satoyama landscapes and promote biodiversity. Effective strategies include enhancing agroforestry, supporting sustainable agriculture, and implementing comprehensive urban planning. This research highlights the critical role of adaptive management in maintaining ecological resilience and supporting sustainable development in the face of ongoing land use changes. Keywords: Cluster analysis- Land cover and use change- Satoyama index- Satoyama landscape Share Link | Plain Format | Corresponding Author (Anwar Nasrudin)

194	Topic B: Applications of Remote Sensing	ABS-164
Mapping Cloud Seeding Potential Areas from MODIS Cloud Top Pressure Siti Nabila Shamsul Anuar (a), Rohayu Haron Narashid (a*), Tajul Rosli Razak (b) a) Faculty of Built Environment, Universiti Teknologi MARA, Perlis Branch, Arau Campus, 02600 Arau, Perlis, Malaysia b) School of Computing Science, College of Computing, Informatics, and Mathematics, Universiti Teknologi Mara, 40450 Shah Alam, Selangor, Malaysia Abstract Cloud seeding is a weather modification technique that involves materials such as silver iodide or hygroscopic salts being introduced into clouds to stimulate rainfall. However, the variability of weather patterns and the unsuitability of clouds often limit its effectiveness. Low clouds at elevations of 0 to 2 km are favourable for cloud seeding operations (CSOs). Nowadays, the location of low clouds can be detected using satellite remote sensing. Therefore, this study aims to identify potential areas for CSOs by analysing remotely sensed low cloud distribution during the inter-monsoon season. The potential areas were retrieved from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Cloud Top Pressure (CTP) data using an International Satellite Cloud Climatology Project (ISCCP) threshold method and mapped with Geographic Information System (GIS) overlay analysis. The results show a strong spatial relationship between the identified areas and actual CSO locations conducted by the Malaysian Meteorological Department (MET) in 2019 and 2023, with low cloud thresholds between 680 and 1000 hectopascals (hPa). Thus, potential location areas based on low clouds can be detected and mapped using remote sensing satellite images. While some dams consistently met the criteria for seeding, others exhibited interannual variability due to complex atmospheric factors. This emphasizes the importance of real-time weather monitoring and adaptive strategies to improve the effectiveness of cloud seeding operations Keywords: Cloud seeding, Cloud classification, Low-level cloud, Cloud Top Pressure Share Link | Plain Format | Corresponding Author (SITI NABILA SHAMSUL ANUAR)

195	Topic B: Applications of Remote Sensing	ABS-166
Mapping Landslide Susceptibility Using the Random Forest and Land Use Correlation in Northern Bandung Denny Lumban Raja (a*), Sukristiyanti (b), Yukni Arifianti (c), Fitriani Agustin(d), Roni Marudut Situmorang (e) a) Study Program of Geological Technology, Bandung Polytechnic of Energy and Mining, Indonesia b) Research Center for Geological Disaster, National Research and Innovation Agency (BRIN), Indonesia c) Geological Agency of Indonesia, Indonesia Abstract Northern Bandung has a very high potential for landslides, triggered by its steep slopes and high elevations. Identifying landslides area is essential for evaluating and mitigating risks. This study conducted a classification of landslide susceptibility mapping in Northern Bandung using a machine learning technique called random forest. Machine learning methods are being used more frequently to solve various scientific and engineering problems. A total of 3056 landslide points were collected through field surveys, stakeholders from the Center for Volcanology and Geological Hazard Mitigation and Google Earth time series image interpretation. Furthermore, 14 factors potentially influencing landslides were considered, including slope gradient, elevation, aspect, profile curvature, flow direction, TRI, TWI, lithology, land use, road density, river density, lineament density, rainfall, and (Normalized Difference Vegetation Index (NDVI). This dataset was used to develop a geospatial database, with the landslide inventory subsequently split into 70% for training and 30% for testing the models. This approach validates the effectiveness of the Random Forest algorithm in accurately mapping landslide susceptibility, as demonstrated by an accuracy value of 0.98. The results indicate that areas most susceptible to landslides are correlated with dryland agriculture and plantations. These findings can support the development of effective strategies for landslide hazard mitigation Keywords: Landslide- Machine learning- Random forest- Northern Bandung Share Link | Plain Format | Corresponding Author (Denny Lumban Raja)

196	Topic B: Applications of Remote Sensing	ABS-168
Contrasting Ecosystem Quality Outcomes: An Analysis Spanning Three Decades Across the Protected, Ancestral, and Reforestation Zones of the Magbando Watershed, Occidental Mindoro, Philippines Kyle Pierre R. Israel, Kristina S.A. Cordero-Bailey Department of Community and Environmental Resource Planning, College of Human Ecology, University of the Philippines Los Banos Abstract Effective management of watersheds with overlapping conservation and ancestral land designations requires robust monitoring. This study assesses more than 35 years of spatiotemporal ecosystem quality dynamics in the Magbando watershed, Mindoro Island, which contains the Mts. Iglit-Baco Natural Park (an ASEAN Heritage Park) and the Buhid-Bangon ancestral domain. Using cloud-based remote sensing, the research employed the Remote Sensing Ecological Index (RSEI), which integrates greenness (NDVI), dryness (NDBSI), wetness (LSM), and heat (LST) via Principal Component Analysis to provide a holistic ecological assessment. The results reveal that zones under indigenous and formal protection have the highest ecological quality. Ancestral domains ranked highest (peak RSEI 0.717: good), followed by overlapping protected/ancestral areas (0.656: good) and protected areas alone (0.624: good). Conversely, ecological health declined significantly in other zones, with lands under the National Greening Program (NGP) registering a lower ^moderate^ quality (0.597) and unprotected areas showing the lowest values (0.519). These findings strongly suggest that traditional indigenous land management practices within the Buhid-Bangon domain are more effective at maintaining long-term ecosystem integrity than the studied government-led reforestation program. The results challenge conventional top-down restoration approaches and highlight the vital role of Indigenous Peoples as effective environmental stewards. Further field validation is needed to investigate the specific land-use practices driving these outcomes and to better integrate indigenous knowledge into national conservation and restoration strategies. Keywords: ancestral domain, protected areas, National Greening Program, ecological quality, Google Earth Engine Share Link | Plain Format | Corresponding Author (Kyle Pierre Rodriguez Israel)

197	Topic B: Applications of Remote Sensing	ABS-169
Analysis of Focal Length Variations by UAV Fligth methods and Oblique Imagery Effects Seungchan Lim. 1, Dongyu Kim.2, and Chuluong Choi.3* 1) Division of Earth and Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, Republic of Korea 2) Division of Earth and Environmental System Science, Pukyong National University, Republic of Korea 3) Division of Earth and Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, Republic of Korea *cuchoi[at]pknu.ac.kr Abstract Focal length (FL) varies depending on the type of UAV (Unmanned Aerial Vehicle) used for aerial photography. The FL, defined as the distance from the lens center to the image sensor, includes not only the original focal length but also the calibrated focal length (CFL), which is utilized during the photogrammetry process. The CFL can vary depending on distortion correction and flight conditions. The Mavic 3 Enterprise (M3E) automatically captures oblique images in the final phase of Grid missions using the Pilot 2 app to perform altitude optimization. This study aims to analyze changes in FL under various flight methods and processing conditions, and to examine the impact of including oblique images on mapping accuracy. The experiment was conducted using the M3E at Pukyong National University (Yongdang Campus), South Korea, on June 23, 2025. A total of three flights were conducted using Grid, 8-directional, and Dome methods. The maximum flight altitude was set to 35 meters, with altitude variations applied in the 8-directional and Dome methods. Five flight methods were compared: Grid (with oblique images), NOgrid (without oblique images), 8-directional, Dome, and ALL. Each method was analyzed under four processing conditions: Original, GCP, Cut, and GCP+Cut. FL analysis was performed using Agisoft^s Metashape. The results showed that the NOgrid, which excluded oblique images, showed significant fluctuations and instability in FL values compared to the Grid. In particular, the F_error for Grid remained stable between 0.062 and 0.065 under all conditions, whereas NOgrid showed a sharp increase up to 3.019 before GCP correction, which was reduced to 0.501 after GCP application. These findings indicate that using GCPs is effective in reducing focal length correction errors, and their role becomes even more critical in the absence of oblique imagery. Keywords: Focal Length (FL)- Unmanned Aerial Vehicle (UAV)- Calibrated Focal Length (CFL)- Ground Control Point (GCP) Share Link | Plain Format | Corresponding Author (SeungChan Lim)

198	Topic B: Applications of Remote Sensing	ABS-170
Evaluation of Volcanic Cloud Top Height Retrievals Using Geostationary Satellite and Inversion Algorithm Tjandra, K.*, Mangla R. and Salinas, S.V. Centre for Remote Imaging, Sensing and Processing (CRISP), National University of Singapore (NUS), Singapore Abstract Volcanic cloud top height is an important parameter for ash dispersion models to enable a first order initialization of volcanic eruption parameters so that an accurate volcanic ash cloud dispersion pattern and eruption mass rate can be modeled. In this study, we evaluate the possible retrieval of volcanic cloud top height by exploiting the availability of satellite thermal band observations (brightness temperature, Tb) and radiative transfer simulations. Two different methods are evaluated, the first method uses only observed Tb from Geostationary satellites (Geo-Kompsat 2A and Himawari-9), and the second method is based on the Inversion algorithm that uses the satellite Tb and a radiative transfer model. A case study of the recent Lewotobi eruption on 17th June 2025 is selected for the evaluation of these two methods. The time series of estimated height from these two methods are compared with the VAAC official forecast, and the assumption of each method is discussed. This study showcases the potential of Geo-Kompsat-2A and Himawari satellite that can be used complementarily in future studies. Keywords: remote sensing application, natural disaster management, volcanic eruptions, Lewotobi Share Link | Plain Format | Corresponding Author (Kurniawan Tjandra)

199	Topic B: Applications of Remote Sensing	ABS-171
Landslides Controlled Structural Geomorphological Activity on the Lembang Fault, West Java, Indonesia Murni Sulastri 1,2*, Imam A. Sadisun3, Adang Saputra1, Asep Mohamad Ishaq Shiddiq1 and Asti Gindasari Masse 4 1Geological Technology Study Program, Politeknik Energi Pertambangan Bandung, Indonesia 2 Doctoral in Geological Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung (ITB), Indonesia 3Applied Geology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung (ITB), Indonesia 4Department of Geology Engineering, Faculty Engineering, Univeristas Hassanudin Makasar (UNHAS), Indonesia Abstract The Lembang Fault is an active fault that can trigger earthquakes and landslides in the East to North Bandung region. In addition to tectonic influences, the activity of the Lembang fault is also thought to be influenced by volcanism which can affect the mechanism of fault movement. Tectonic processes play an important role in the formation of the morphology of a region. The existence of this fault has shaped the morphology of the Lembang plain in the north and formed a west-east trending hilly belt. Tectonic geomorphology is the main factor controlling the development of landforms in active tectonic areas and has a significant influence on the natural appearance of mountains and watersheds located to the east of the Lembang, Cikapundung, analysis of northwest-southeast trending geomorphological indications is used to study tectonic activity in the research area in the form of river shapes, the results of field observations in the form of elongated geomorphology, the presence of triangular pairs, lithology, joints, fault mirrors and landforms and geology of the research area support these results. Keywords: Lembang Fault, Lineaments analysis, geomorphology, Geological Structure, landforms Share Link | Plain Format | Corresponding Author (Murni Sulastri)

200	Topic B: Applications of Remote Sensing	ABS-172
A Physics-Based Band-Ratio Algorithm for Methane Detection with PRISMA Satellite Data Liew S.C., Tan L., Salinas S.V. Centre for Remote Imaging, Sensing and Processing (CRISP), National University of Singapore Abstract Greenhouse gases, particularly methane, are primary drivers of climate change, with methane possessing a global warming potential 83 times higher than carbon dioxide over two decades. Consequently, monitoring the spatial and temporal distribution of methane emission from both natural and human sources is crucial for climate modeling, prediction, and validating carbon reduction targets. Satellite sensors offer significant advantages over ground-based methods for this purpose, providing rapid, wide-area coverage, access to remote locations, global monitoring capabilities, high-resolution localized detection, and the ability to track emission trends over time. Satellites detect methane through two primary modes: measuring the absorption of sunlight by methane molecules using hyperspectral imaging in the short-wave infrared (SWIR) bands, or by analyzing natural thermal emission from Earth surface in the mid-wave infrared (MWIR) band. While instruments like TROPOMI on Sentinel-5P offer global observations of methane emission, their spatial resolution is insufficient for localized sources. In a previous work presented in IGARSS 2024, we introduced a physics-based inverse modeling algorithm using the linear matrix inversion technique for retrieving methane concentration of a near-ground methane cloud. In this paper we show that the physics-based algorithm may be simplified to a simple band-ratio method using appropriately selected SWIR spectral bands, for detecting and quantifying methane emissions from hyperspectral PRISMA satellite data. This method was validated using a synthetic dataset and successfully applied to a super-emitter site in Turkmenistan to map methane columnar density. This work demonstrates the importance of physics-based modeling in providing insights for retrieving quantitative information from a simple band-ratio algorithm. Keywords: Methane detection, hyperspectral data, PRISMA, physics-based modeling, band-ratio Share Link | Plain Format | Corresponding Author (Soo Chin Liew)

201	Topic B: Applications of Remote Sensing	ABS-177
Accuracy Assessment of Sentinel-1 SAR-Derived DEMs: Comparative Analysis with SRTM and ALOS References Ochirkhuyag Lkhamjav1,3,4, Fuan Tsai 1, 2* 1 Department of Civil Engineering, National Central University, Taoyuan City, Taiwan 320317 2 Center for Space and Remote Sensing Research, National Central University, Taoyuan City, Taiwan 320317 3 Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, Mongolia 4 Mongolian Geospatial Association, Ulaanbaatar 15141, Mongolia *olkhamjav[at]g.ncu.edu.tw- ftsai[at]csrsr.ncu.edu.tw Abstract Accurate topographic mapping is vital for scientific, environmental, and engineering applications. Digital Elevation Model (DEM), including Digital Terrain Model (DTM) and Digital Surface Model (DSM), are essential for these purposes. Synthetic Aperture Radar (SAR) interferometry, particularly from Sentinel-1, offers significant potential for generating and updating DEMs due to its all-weather, high-resolution capabilities. This study conducts a comprehensive validation of Sentinel-1 SAR-derived DEMs by comparing them with established reference datasets, the Shuttle Radar Topography Mission (SRTM) and Advanced Land Observing Satellite (ALOS) DEMs, across diverse terrains (600-1,500 m elevation). Using statistical metrics such as Pearson correlation, Root Mean Square Error (RMSE), and descriptive statistics, the research evaluates geometric fidelity and absolute elevation accuracy. Results indicate exceptional relative accuracy, with correlation coefficients (r > 0.999) for both reference datasets, confirming Sentinel-1^s ability to preserve topographic structure. However, a systematic elevation bias of 38-39 m was observed, with RMSE values of approximately 38-39 m and low standard deviations (2.94-3.24 m), indicating high precision despite absolute offsets. These findings suggest that while Sentinel-1 interferometry excels in relative elevation mapping, calibration is critical for absolute accuracy. The study highlights Sentinel-1^s potential for supplementing global DEMs, particularly in low-vegetation environments. Terrain-specific bias corrections, advanced processing techniques like Persistent Scattered Interferometry, and sensor fusion with laser altimeter or multi-frequency SAR are recommended for enhanced accuracy. These insights provide a robust framework for operational topographic mapping, supporting applications in geosciences and environmental monitoring. Keywords: SAR, DEM, Sentinel-1, accuracy, topography Share Link | Plain Format | Corresponding Author (Ochirkhuyag Lkhamjav)

202	Topic B: Applications of Remote Sensing	ABS-179
Scalable Deep Learning Framework for Non-Urban Landcover Mapping in Tropical Regions Using IFSAR Data Yanuar A.N. (a*), Muhammad Nizar Y.P. (b) a) Product and Technology Lead, Solution Engineering, Esri Indonesia, Jalan Gatot Subroto 6, Kota Jakarta Selatan 12710, Indonesia *ynugroho[at]esriindonesia.co.id b) Data Scientist, Professional Services, Esri Indonesia Abstract Indonesia has 1,705,029 km square of non-urban territory, which faces significant landcover mapping challenges due to persistent cloud coverage limiting optical remote sensing applications. We propose a scalable GeoAI framework leveraging cloud-penetrating Interferometric Synthetic Aperture Radar (IFSAR) for comprehensive tropical landcover assessment. Our methodology integrates high-resolution X-band airborne SAR Ortho-rectified Radar Imagery (X-ORI) and complementary RGB aerial photographs. To overcome single-polarization grayscale SAR limitations in automated classification, we develop a flexible two-stage deep learning pipeline with alternative enhancement approaches. The first stage employs either: (1) CycleGAN for unsupervised image-to-image translation, converting grayscale SAR into synthetic RGB imagery, or (2) advanced pansharpening techniques, including Brovey Transform, Gram-Schmidt, and wavelet-based fusion to enhance spatial and spectral resolution. Both approaches improve semantic interpretability for subsequent processing. The second stage utilizes U-Net architecture for semantic segmentation, generating detailed landcover maps across diverse tropical landscapes. A comparative analysis demonstrates the effectiveness of both enhancement methods in various scenarios. Results yield three key outcomes: (1) high-resolution, cloud-free enhanced imagery for tropical environments, (2) development of flexible, reusable GeoAI models with multiple enhancement options, and (3) an efficient, scalable solution for national-level landcover monitoring. This framework provides a transformative approach for land management, deforestation monitoring, and sustainable development planning in tropical, cloud-dense regions. Keywords: IFSAR, Landcover Mapping, CycleGAN, Pansharpening, Deep Learning Share Link | Plain Format | Corresponding Author (Yanuar Adji Nugroho)

203	Topic B: Applications of Remote Sensing	ABS-182
Assessment Characteristic of Precipitable Water Vapor Using GNSS Satellite and Model ERA-5 in Surabaya, East Java, Indonesia Prasetyo Umar Firdianto (a*), Bangun Muljo Sukojo (b) a) b) Geospatial Laboratory, Department of Geomatics Engineering, Sepuluh Nopember Institute of Technology, Kampus ITS Sukolilo, Surabaya 60111, Indonesia *prasetyo.firdianto[at]bmkg.go.id a) Maritime Meteorological Station of Tanjung Perak, Indonesia Agency of Meteorology Climatology and Geophysics (BMKG), Kalimas Baru Street 97b, Surabaya 60165, Indonesia Abstract Precipitable Water Vapor (PWV) is crucial parameter in weather analysis and forecasting because it determines total water vapor content in troposphere. Radiosonde (RA) as insitu observation has limitations in spatia and temporal resolution. GNSS Satellites based on zenith tropospheric delay and ERA-5 Model based on specific humidity can be used to monitoring spatiotemporal PWV. The Data used are Radiosonde observation, rinex of Cors Station-CSBY, hourly data on pressure level of ERA-5 Model, rainfall observation of Meteorological Station and GSMaP Satellite, and Water Vapor channel Himawari Satellite. The methods used are correlation analysis, regression, and based on extreme weather cases in 2022-2023. The results are GNSS-PWV and ERA5-PWV can represent PWV observation. Correlation are 0.90 and 0.86, RMSE 2.51 and 3.16. In addition, the ERA5 Model can describe PWV spatially and consistent to upper-level moisture observation of Himawari Satellite and rainfall of GSMaP Satellite in case of extreme weather event. Climatology of PWV is generally higher during Asian monsoon (DJF period, December-January-February) than in Australian monsoon (JJA period, June-July-August). According the result, GNSS-PWV can be used with high temporal resolution and ERA5-PWV spatially to build of weather and climate supplementary information. Keywords: PWV- GNSS- ERA-5- Spatiotemporal- Surabaya Share Link | Plain Format | Corresponding Author (Prasetyo Umar Firdianto)

204	Topic B: Applications of Remote Sensing	ABS-183
Assessment of Geostationary Environment Monitoring Spectrometer (GEMS) Tropospheric NO2 Measurements Using Ground-Based Pandora Instrument in Quezon City Jayra Emeryl Blanche (a*), Ellison Castro (a), Ma. Angelica De Hitta (a), James Cesar Refran (a), Jeniffer De Maligaya (a) a) Philippine Space Agency (PhilSA) *jayra.blanche[at]philsa.gov.ph Abstract Through the PAPGAPI-PAN PH project of the PhilSA, four Pandora spectrometers were installed across the Philippines in 2024 to support national air quality monitoring. On April 25-30, 2025, an AQI of 90 was reported in Quezon City, classified as moderate and approaching unhealthy levels. This enabled an initial assessment of how Geostationary Environment Monitoring Spectrometer (GEMS) aligns with ground-based measurements from Pandora at the Manila Observatory. This study compared the tropospheric nitrogen dioxide (NO2) column data retrieved from the two instruments. GEMS retrievals were filtered under different cloud conditions using cloud fraction (CF) thresholds (<0.3, <0.5, <0.7), while Pandora data were averaged 10 minutes from each GEMS observation, then corrected using a distance-weighted correction to account for the slant path, especially at high SZA. GEMS and Pandora NO2 columns exhibited good correlation (R = 0.736, RMSE = 3.62 x 10^15 molec/cm^2) under low-cloud conditions, while slightly lower agreement was found in cloudier conditions. After applying distance-weighted correction, R reached 0.752 with an RMSE of 3.88 x 10^15 molec/cm^2 for CF < 0.3. This suggests that while correlation slightly improved, overall error did not decrease. Hence, applying the correction on a limited dataset had minimal impact, indicating that this alone may not fully increase the correlation of GEMS and Pandora measurements at higher SZAs. Overall, the two instruments showed good agreement, supporting the need for continued evaluation across broader conditions and timeframes. By expanding the temporal range, future assessments can capture NO2 variability as well as long-term patterns, providing a more comprehensive performance evaluation of GEMS and other satellite-based measurements. Further refinement of correction approaches may also be needed to improve the comparisons and support their potential use in developing air quality forecasting tools and predictive models. Keywords: GEMS, Pandora, Tropospheric NO2, Remote Sensing, Air Quality Share Link | Plain Format | Corresponding Author (Jayra Emeryl Blanche)

205	Topic B: Applications of Remote Sensing	ABS-187
Integrating Meteorological and Remote Sensing Geospatial Data for Forest Fire Risk Modelling in East Kalimantan Akhmad Haris Karsena (a,*), Tang-Huang Lin (a) a) Center for Space and Remote Sensing Research, National Central University No. 300, Zhongda Rd., Zhongli District, Taoyuan City 320, Taiwan *hariskarsena[at]gmail.com Abstract Forest and land fires pose a recurrent dry season threat in East Kalimantan, Indonesia, undermining peatland ecosystems, public health, and economic security. This study evaluates whether an integrated remote sensing and meteorological geospatial framework that merges MODIS FIRMS hotspot density with meteorological, biophysical, and anthropogenic variables can deliver a more accurate and timely fire risk map for 2000 to 2020 than hotspot counts alone. A Multi Criteria Evaluation combined with a Weighted Linear Combination assimilates CHIRPS precipitation, ERA5 air temperature, relative humidity, 10 m wind speed, MODIS land surface temperature and NDVI, ESA WorldCover land cover classes, 30 m digital elevation metrics, and distances to roads and settlements, after each layer is fuzzy normalised and weighted via the Analytic Hierarchy Process. Model performance is evaluated against independent hotspot subsets using receiver operating characteristic analysis, area under the curve (AUC), and success rate metrics. Expected results point to clusters of high and very high risk in coastal peatlands and degraded agricultural fronts where August-October rainfall deficits, elevated temperatures, and low humidity coincide with intensive human activity, the composite model is anticipated to achieve an AUC above 0.80, indicating robust predictive skill. The aim of this research is to explore the spatial link between environmental conditions and forest fire activity, with the expectation that the results can support improved fire risk planning. Keywords: Remote sensing, Meteorological data, Forest fire risk modelling, Fuzzy logic normalisation, Multi Criteria Evaluation (MCE), East Kalimantan Share Link | Plain Format | Corresponding Author (Akhmad Haris Karsena)

206	Topic B: Applications of Remote Sensing	ABS-188
The Research on Landcover Change in the Qinghai-Tibet Plateau in China Liang Zhu, Bingfang Wu State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences Abstract The Qinghai-Tibet Plateau is the largest plateau in China, and the highest plateau in the world, which is known as ^roof of the world^. Climate change and human activities are having a significant impact on the ecosystems of the Qinghai-Tibet Plateau. Land cover data of the Qinghai-Tibet Plateau from 1970s to 2020 was obtained based on object-oriented classification method using Landsat and Sentinel2 images. The study shows that the largest area of the Tibetan Plateau ecosystem is the grassland ecosystem, which accounts for 55% of the total area of the Qinghai-Tibet Plateau in 2020. The changed landcover area between 1970s and 2020 accounts for 1.4% of the total area of the Qinghai-Tibet Plateau. Keywords: Land cover- Remote Sensing- GIS Share Link | Plain Format | Corresponding Author (LIANG ZHU)

207	Topic B: Applications of Remote Sensing	ABS-189
Empirical Application of Polarimetric Synthetic Aperture Radar for Rice Phenology Monitoring in Irrigated and Favorable Rainfed Ecosystems Jean Rochielle F. Mirandilla1 3*, Megumi Yamashita1, Mitsunori Yoshimura2 1 Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan- s235342z[at]st.go.tuat.ac.jp (J.R.F.M)- meguyama[at]cc.tuat.ac.jp (M.Y.) 2 College of Bioresource Sciences, Nihon University- yoshimura.mitsunori[at]nihon-u.ac.jp 3Philippine Rice Research Institute- jrfmirandilla[at]exchange.philrice.gov.ph (J.R.F.M) *s235342z[at]st.go.tuat.ac.jp Abstract Rice in the Philippines is cultivated under two primary ecosystems, irrigated and rainfed. Rainfed areas can be further categorized as favorable or unfavorable based on rainfall distribution. Management and challenges in production in these ecosystems are observed to be different. This study focused on monitoring rice phenology in irrigated and favorable rainfed ecosystems in Iloilo Province, Philippines, using multi temporal polarimetric Synthetic Aperture Radar data. Multitemporal SAR datasets were used to capture the temporal dynamics of rice growth across two cropping seasons, 2019 semester 2 and 2020 semester 1. A total of 37 dual polarization SAR images were acquired from Sentinel 1B, C band, VV and VH polarizations, and ALOS PALSAR 2, L band, HH and HV polarizations. All images underwent preprocessing, followed by the generation of 2 x 2 covariance matrices, which were analyzed using H A alpha decomposition to extract polarimetric parameters. Six key rice growth stages were identified and used to compare the polarimetric bands such entropy, alpha and anisotropy, land preparation, seedling, tillering, reproductive, ripening, and harvested. Statistical analysis such as segmented regression were performed to identify growth stage specific changes. The approach enabled the characterization of key phenological stages and the comparison of crop development patterns across the two rice ecosystems. Moreover, notable differences were observed between the two SAR sensors, particularly in relation to water presence in rice paddies. This study demonstrates the potential of dual-polarization SAR for operational rice monitoring and for distinguishing phenological behavior under different water management regimes in rice ecosystems. Keywords: Irrigated Rice Ecosystem, Favorable Rainfed Rice Ecosystems, Polarimetric SAR Share Link | Plain Format | Corresponding Author (Jean Rochielle Flores Mirandilla)

208	Topic B: Applications of Remote Sensing	ABS-194
Evaluating the Effectiveness of Planted Mangrove Forests Using Vegetation Indices and Land Surface Temperature: A Remote Sensing Approach in Bagan Nakhoda Omar, Malaysia Mohd Fairuz Bin Fuazi (a*), Wan Shafrina Binti Wan Mohd Jaafar (a) a) Institute of Climate Change, The National University of Malaysia *p145711[at]siswa.ukm.edu.my Abstract Mangrove restoration has become a critical strategy in mitigating coastal degradation and enhancing ecosystem resilience. This study aims to assess the effectiveness of planted mangrove forests in Bagan Nakhoda Omar, Selangor, using a multi-temporal remote sensing approach. The analysis integrates three vegetation indices-Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Green NDVI (GNDVI)-derived from SPOT 5 and SPOT 6 imagery, alongside Land Surface Temperature (LST) extracted from Landsat 5 and Landsat 8 data. Five temporal datasets spanning from 2006 to 2024 were analyzed to monitor the growth and health of mangrove plantations initiated in 2008 and 2009. The effectiveness of the restoration efforts was evaluated through Pearson correlation analysis between the vegetation indices and LST. Results indicate a consistent increase in VI values and a corresponding decrease in LST over time, suggesting improved canopy cover and microclimatic regulation. Strong negative correlations (r > -0.70) between VIs and LST were observed in later years, particularly in 2019 and 2024, reflecting the success of long-term planting initiatives. The integration of multiple VIs provided a more robust assessment of vegetation dynamics, while LST served as a complementary indicator of ecosystem recovery. This study demonstrates the utility of combining spectral and thermal remote sensing metrics for evaluating mangrove restoration outcomes. The findings offer valuable insights for coastal ecosystem monitoring and support evidence-based decision-making in future rehabilitation programs. Keywords: Planted Mangroves, Vegetation Indices, Land Surface Temperature, Remote Sensing, Temporal Monitoring Share Link | Plain Format | Corresponding Author (MOHD FAIRUZ BIN FUAZI)

209	Topic B: Applications of Remote Sensing	ABS-196
Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) shortwave cloud radiative forcing from Himawari-8 and FY-4A Husi Letu Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences. Beijing 100101, China Abstract Cloud shortwave radiation forcing (SWRF) with high spatial-temporal resolutions and precision are essential for Earth^s land surface or top-of-atmosphere (TOA) radiation budget calculations. The cloud optical thickness (COT) and cloud effective radius (CER) are critical input parameters for accurately estimating SWRF. In this study, we proposed a COT and CER algorithm that suitable for water and ice clouds from the new-generation geostationary satellite Himawari-8 and FY-4A measurements, based on the radiative transfer theory and optimal method. Based on the retrieved COT and CER, a method for estimation of cloud SWRF at the surface and TOA was proposed based on the look-up-table (calculated by the radiative transfer model - RSTAT) method. Validation of SWRF with the CERES level-3 product, estimated SWRF at the surface and the TOA by this study show very good agreement, with R values of 0.97 and 0.98, root-mean-square-error values of 15.0 Wm-2 and 16.6 Wm-2. While, mean-bias-error values of -5.6 Wm-2 and -8.5 Wm-2 indict that our SWRF results have a slightly under-estimation. This research can provide important reference for the subsequent full radiation budget (shortwave plus longwave) estimation. Detailed products please see our homepage (http://www.slrss.cn/care_zh/). Keywords: Cloud shortwave radiation forcing, cloud remote sensing, CARE Share Link | Plain Format | Corresponding Author (Husi Letu)

210	Topic B: Applications of Remote Sensing	ABS-198
Tracking rapid mariculture expansion in Xuan Dai Bay, Viet Nam (2015 - 2024) with Sentinel-1 SAR time-series imagery Xuan Truong Trinh (a*), Wataru Takeuchi (b), Masahiko Nagai (c) a) Faculty of Engineering, Yamaguchi University, Japan *trinh[at]yamaguchi-u.ac.jp b) Institute of Industrial Sciences, The University of Tokyo, Japan c) Yamaguchi University Center for Research and Application of Remote Sensing, Yamaguchi University, Japan Abstract Mariculture increasingly underpins global food security, yet its accelerated growth-especially in developing nations-often outpaces regulatory capacity and threatens coastal ecosystems. This study offers the first decadal, object-based time-series assessment of floating mariculture infrastructure in Xuan Dai Bay, central Viet Nam, derived from Sentinel-1 C-band synthetic-aperture radar (SAR) imagery acquired between 2015 and 2024. Multi-temporal Lee filtering and annual median compositing suppressed speckle and wave-induced noise, enhancing the backscatter signal of cage arrays. Image objects were generated with the Simple Non-Iterative Clustering (SNIC) algorithm and classified using a Random Forest model that fused backscatter statistics with geometric metrics. The approach achieved an overall accuracy of 70 %, with empty water surfaces mapped with 100 % producer accuracy, reflecting their consistently low backscatter. Commission errors were concentrated among cages, ponds and near-shore structures, indicating the value of auxiliary optical data or refined geometric descriptors. Time-series analysis reveals a 3.4-fold increase in cage area and a marked intensification between 2018 and 2022. Such expansion risks exacerbating eutrophication, habitat degradation and biodiversity loss. Our findings demonstrate the utility of freely available SAR archives for routine, large-scale surveillance of offshore aquaculture and highlight the pressing need for stronger management frameworks in Viet Nam. Future work should integrate higher-resolution SAR and multispectral sensors to resolve small cages and quantify water-quality impacts in situ, enabling more precise environmental assessments and evidence-based policy making. Keywords: coastal management, environment quality, SAR, object-based image analysis, machine learning Share Link | Plain Format | Corresponding Author (Xuan Truong Trinh)

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