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331	Topic E: Sustainable Development Goals	ABS-301
Evaluating CHIRPS Satellite-Based Rainfall Data for Hydrologic Modeling and Climate Impact Assessment in the Abra River Basin, Philippines Nathaniel R. Alibuyog(1), Shivherly Benedict Feland T. Dolores(2), and Rodel T. Utrera(1) (1)Coastal Engineering Research and Management Center, Mariano Marcos State University, nralibuyog[at]mmsu.edu.ph (2)College of Computing and Information Sciences, Mariano Marcos State University, stdolores[at]mmsu.edu.ph (3)Research Directorate, Mariano Marcos State University, rtutrera[at]mmsu.edu.ph Abstract Reliable rainfall data are essential for hydrologic modeling and water resource planning, particularly in data-scarce and topographically complex regions like the Abra River Basin (ARB) in Northern Luzon, Philippines. This study evaluates the performance of CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), a satellite-based rainfall product, against ground-observed data from the PAGASA Vigan station for the period 1990-2020. Comparative statistical analysis revealed that CHIRPS captured higher mean annual rainfall (2,571.8 mm) with lower variability (CV = 14.53%) than PAGASA (2,190.4 mm- CV = 22.23%). CHIRPS also demonstrated improved spatial representation, particularly in higher elevation areas of the basin and across climatic transitions, as reflected in a lower seasonality index (0.76 vs. 0.99). Hydrologic simulations using the Soil and Water Assessment Tool (SWAT) showed that CHIRPS-based inputs produced more consistent baseflow and total water yield estimates, validating its reliability for modeling ungauged basins. Furthermore, future climate scenarios (SSP5-8.5 for 2050 and 2070) based on CHIRPS input projected increases in rainfall (up to 15%), surface runoff (42%), groundwater recharge (9%), and total water yield (18%), alongside elevated evapotranspiration rates due to warming temperatures. These results emphasize the utility of CHIRPS as a robust alternative to sparse ground observations for hydrologic modeling, climate change impact assessments, and sustainable water resource management. The study highlights how remote sensing technologies contribute to SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action) through data-driven planning in vulnerable watersheds. Keywords: Satellite-based rainfall estimation, Abra River Basin, Remote sensing for water resources, Sustainable Development Goals (SDGs), CHIRPS precipitation data Share Link | Plain Format | Corresponding Author (Rodel Tolosa Utrera)

332	Topic E: Sustainable Development Goals	ABS-306
Integrating Multi-Temporal Remote Sensing for Monitoring Seaweed Aquaculture in South Sulawesi, Indonesia M. Akbar AS (a,d*), Nurjannah Nurdin (b,c,d), Aswar Anas (c) (a) The Environmental Science Study Program, Doctoral Program, Graduate School, Hasanuddin University, Makassar, 90245. Indonesia *akbarmuhammad.1818[at]yahoo.com (b) Department of Marine Science, Marine Science and Fisheries Faculty, Hasanuddin University, Makassar, 90245. Indonesia (c) Department of Remote Sensing and Geographic Information System, Vocational Faculty, Hasanuddin University, Makassar 90245. Indonesia (d) Research and Development Center for Marine, Coast, and Small Islands, Hasanuddin University, Makassar 90245. Indonesia Abstract South Sulawesi is the leading seaweed-producing province in Indonesia, significantly contributing to the national and global supply chains of tropical seaweed, particularly Kappaphycus alvarezii. Despite its importance, the spatial and seasonal dynamics of seaweed aquaculture in this region have been poorly documented. This study aimed to assess the spatiotemporal dynamics of seaweed cultivation patterns across the four leading seaweed-producing regencies in South Sulawesi, Indonesia, using multitemporal high-resolution satellite imagery spanning the rainy, first transitional, dry, and second transitional seasons. PlanetScope satellite imagery with a spatial resolution of 3 m was utilized to capture seaweed farm dynamics across four seasonal periods commonly recognized in the Indonesian monsoon cycle: rainy season, first transitional season, dry season, and second transitional season. Through image preprocessing, classification, and time-series comparison, this study identified variations in seaweed cultivation areas and temporal planting patterns across the study sites. The results revealed that the planting intensity and spatial coverage of seaweed aquaculture varied significantly by season. The highest cultivation extent typically occurs during the dry season, whereas the lowest is observed during the peak of the rainy season, likely due to unfavorable environmental conditions such as high wave energy and reduced water clarity. Each regency exhibits distinct cultivation rhythms influenced by local environmental conditions and farming practices. This study demonstrates the potential of multitemporal remote sensing as an effective and scalable method for monitoring seaweed farming activities. These findings provide essential insights for regional aquaculture planning, seasonal risk management, and optimizing planting cycles to increase productivity. Moreover, this approach offers a scientific basis for policy interventions aimed at enhancing sustainable seaweed aquaculture practices in Indonesia^s coastal zone Keywords: Seaweed aquaculture- Multi-Temporal- Remote Sensing- South Sulawesi Share Link | Plain Format | Corresponding Author (M. Akbar AS)

333	Topic E: Sustainable Development Goals	ABS-52
Remote Sensing Assessment of Ocean Acidification Stressors on Mesoamerican Reef Coral Ecosystems Naydeline Teresita Smith(a*), Po-Chun Hsu(b) a)Center for Space and Remote Sensing Research, National Central University, Taiwan Abstract Anthropogenic CO&#8322- uptake is driving ocean acidification, with profound implications for the carbonate chemistry of coral reefs and their ecosystem resilience. This study leverages Copernicus Level 4 satellite datasets of carbon flux variables, including fugacity of CO&#8322- (fgCO&#8322-), partial pressure of CO&#8322- (spCO&#8322-), surface pH, and total alkalinity, to systematically assess the spatial and seasonal variability of acidification stress across the Mesoamerican Reef region (89.5-85.5&#8239-W, 15.5-22&#8239-N). Our decadal analysis reveals distinct seasonal acidification peaks, characterized by elevated fgCO&#8322- and spCO&#8322-, along with reduced pH and alkalinity, which are concentrated in southern reefs (16-17N) from February to June and shift northward (17-22&#8239-N) from July to September. The period from October to January represents the least acidified season. These acidification hotspots coincide with intensified circulation zones, suggesting enhanced biogeochemical cycling directly mediated by physical transport processes. The findings highlight well-defined spatiotemporal windows of carbonate chemistry stress that are critical for anticipating coral bleaching and reduced calcification. By isolating acidification dynamics from temperature-driven influences, this study contributes to a targeted framework for remote sensing of coral reefs. Aligned with SDG 14 (Life Below Water), these insights support ecosystem-based management and climate-adaptive strategies aimed at preserving reef biodiversity and sustaining the livelihoods of coastal communities. Keywords: : Ocean Acidification, Carbon Flux, Coral Reef Resilience, Mesoamerican Reef Share Link | Plain Format | Corresponding Author (Naydeline Teresita Smith)

334	Topic E: Sustainable Development Goals	ABS-55
A Scoping Review of Geospatial Strategies for Resilience in Rural Heritage Communities Muhammad Adib Akmal Azzihan and Norzailawati Mohd Noor Department of Urban and Regional Planning, Kulliyyah of Architecture and Built Environment, International Islamic University Malaysia, 53100 Kuala Lumpur, Malaysia Abstract Rural communities are increasingly exposed to social, economic, and climatic pressures that threaten their cultural heritage. Although geospatial technology is increasingly used in community planning and cultural heritage documentation, it has the potential in strengthening rural community resilience which remains underexplored. Therefore, this interdisciplinary approach is important for understanding how the integration of technology, cultural heritage, and community can contribute to long-term resilience. While geospatial and cultural heritage research are increasing, the integration of cultural heritage digitisation remains rarely systematically investigated. This gap hinders the understanding of technology^s application in holistic community development. Therefore, this study aims to identify global research trends related to strengthening rural cultural heritage resilience through geospatial data, to organise methodological and theoretical approaches of existing studies, and to identify gaps based on the analysis results. The research method used is based on a search of the Scopus database and subsequent analysis using thematic scoping review. The findings identify three main themes: the use of mapping technology in heritage conservation, community-based approaches to developing resilience, and the gap between technological innovation and social empowerment. This study thus contributes to an interdisciplinary understanding and highlights the need for a new framework that connects technology and cultural heritage in a community-centred manner. The findings suggest that future research offers great opportunities to develop more sustainable rural development policies and strategies through community-based geospatial approaches. Keywords: Geospatial, Rural, Heritage, Resilience, and Rural Planning. Share Link | Plain Format | Corresponding Author (Muhammad Adib Akmal Azzihan)

335	Topic E: Sustainable Development Goals	ABS-317
Urban Lakeshore Microclimate Regulation in Jakarta: Land Surface Temperature Insights from Landsat 8 Prita Ayu Permatasari, Gatot Prayoga, and Luisa Febrina Amalo IPB University Abstract Air temperature in urban areas tends to be higher compared to suburban areas. To address this issue, local governments utilize blue open spaces, which can increase humidity and lower air temperature. In addition, the availability of green open space along lakeshores also offers potential for reducing air temperature. This study aims to measure the microclimate regulation services of three lakeshores in Jakarta (Setu Babakan, Srengseng Urban Lake Forest, and South Sunter Lake) using Landsat 8 imagery. The results show that Srengseng Urban Lake Forest has the lowest mean land surface temperature (LST) at 18.81 C, followed by South Sunter (19.67 C) and Setu Babakan (20.07 C). These findings indicate that LST can be influenced by various factors beyond land cover, such as slope and terrain gradient. These factors can serve as important considerations for stakeholders in designing thermally comfortable public open spaces for visitors. Keywords: blue open space, lake, temperature, urban Share Link | Plain Format | Corresponding Author (Prita Ayu Permatasari)

336	Topic E: Sustainable Development Goals	ABS-63
Detection of Potential Fishing Zones in Aceh Waters Using Satellite-Derived Sea Surface Temperature and Chlorophyll Fronts Faqih Musyaffa (a*), Po-Chun Hsu (a) a) Center for Space and Remote Sensing Research, National Central University, Taoyuan City 32001, Taiwan (R.O.C.) *faqih.musyaffa[at]gmail.com Abstract Detecting potential fishing zones (PFZs) is crucial for enhancing sustainable fisheries and mitigating environmental variability in Aceh waters, situated in the eastern tropical Indian Ocean. This study investigates the dynamic oceanographic conditions of the region to identify PFZs for tuna (Thunnus sp.), supporting sustainable fisheries management. Long-term multi-source satellite and reanalysis datasets from 1998 to 2024, including sea surface temperature (SST) and chlorophyll-a (CHL) from NOAA Coral Reef Watch and Copernicus Marine Environment Monitoring Service, were analyzed alongside ocean current and wind data. These oceanographic variables were compared with annual tuna catch data from 2018 to 2023 provided by Statistics Indonesia, Aceh Province. Monthly composite datasets were used to derive SST gradient magnitudes and CHL front zones, delineating thermal and biological boundaries. Results reveal pronounced seasonal variability, with significant SST and CHL fronts particularly evident during the December-February monsoon season. Ocean currents and wind fields exhibit clear seasonal shifts aligned with monsoonal changes. PFZs were identified by overlaying SST-CHL front intersections with wind-current convergence zones and validated using tuna catch records. The findings indicate a positive association between tuna distributions and the presence of thermal and CHL fronts. This approach demonstrates the effectiveness of remote sensing in identifying PFZs. It supports the development of early warning systems and climate-resilient, ecosystem-based fishery management strategies for Aceh and similar regions. Keywords: Sea Surface Temperature- Chlorophyll- Oceanic Front- Potential Fishing Zones Share Link | Plain Format | Corresponding Author (Faqih Musyaffa)

337	Topic E: Sustainable Development Goals	ABS-325
Spatial Assessment of Superblock and Miniblock Implementation Potential in Ulaanbaatar Using GIS and Open Geospatial Data Azzaya Byambajav, Enkhjargal Natsagdorj, Sainnbuyan Bayarsaikhan School of Natural Sciences, School of Sciences, National University of Mongolia, Ulaanbaatar, Mongolia Abstract Rapid urbanization and car-dependent growth in Ulaanbaatar have intensified traffic congestion, reduced pedestrian accessibility, and diminished public green spaces. Expanding road networks alone cannot sustainably address these challenges. Globally, human-centered urban design models such as superblocks and miniblocks have proven effective in reorganizing street networks, restricting through-traffic, and creating multifunctional public spaces. This study aims to assess the spatial feasibility of implementing the superblock model in Ulaanbaatar to enhance mobility, environmental quality, and public space accessibility. Building on the methodology by Eggimann (2022), we integrated OpenStreetMap road and building footprints, khoroo-level population data, land ownership structure, and green space information into ArcGIS Pro for analysis. The methodology applied four core criteria: geometric parameters (area and perimeter), building coverage ratio, weighted population allocation, and population density. The results identified 37 spatially suitable miniblocks characterized by high residential density, compact built form, and strategic opportunities for increased pedestrianization and green infrastructure. Implementing superblocks in these areas could reduce internal vehicle dominance, improve walkability, and expand multifunctional open spaces. This GIS-based framework provides a transferable approach for identifying superblock opportunities in other Asian cities with similar urban challenges. Keywords: superblock, miniblock, GIS, urban planning, building coverage, population density, green space, Ulaanbaatar Share Link | Plain Format | Corresponding Author (Azzaya Byambajav)

338	Topic E: Sustainable Development Goals	ABS-328
Survey and field interview analysis regarding the rejection by the Balai Adat Manggajaya community of the agreement on administrative boundaries between Hulu Sungai Tengah Regency and Kotabaru Regency Sadianoor(a*), Samsul R.(b), Gusti R. E.(c), Yazid F. A.S.(d), Madalia I.(e), Deden R.P.(f), Andi A.P.(g), and Syamsul M.(h) a)Government Officer, Department of Public Housing and Settlement Areas, Hulu Sungai Tengah Regency Government, Indonesia *sadianoor[at]umbjm.ac.id b)Regent, Government of Hulu Sungai Tengah Regency, Indonesia c)Vice Regent, Government of Hulu Sungai Tengah Regency, Indonesia d)Chairman of Commission I, Regional House of Representatives of Hulu Sungai Tengah Regency, Indonesia e)Government Officer, Department of Public Housing and Settlement Areas, Hulu Sungai Tengah Regency Government, Indonesia f)Government Officer, Department of Public Housing and Settlement Areas, Hulu Sungai Tengah Regency Government, Indonesia g)Lecturer, Study Program of Urban and Regional Planning, Faculty of Engineering, Universitas Muhammadiyah Banjarmasin, Indonesia h)Professor, Doctoral Program, Indonesia Defense University, Indonesia Abstract This study investigates the reasons behind the rejection by the Balai Adat Manggajaya community of the administrative boundary agreement between Hulu Sungai Tengah Regency and Kotabaru Regency, signed in 2021. The research addresses issues caused by boundary delineation that did not integrate terrestrial and photogrammetric approaches, resulting in inaccurate administrative lines. Such inaccuracies have disrupted the indigenous land rights of the Manggajaya community, highlighting the conflict between these administrative decisions and the goals of sustainable development, which should be sensitive to local geographic and community contexts. The study utilizes a mixed-method approach, incorporating field surveys and direct interviews, supported by spatial analysis to assess the delineation accuracy. The results reveal significant discrepancies between the agreed boundary and the local indigenous land claims, underlining the necessity for boundary policies that are adaptive and inclusive of social and spatial realities. These findings emphasize the importance of participatory approaches in boundary-setting and contribute to a better understanding of socio-spatial dynamics in administrative boundary decisions. Keywords: boundary disputes, indigenous land rights, survey, spatial analysis, participatory planning Share Link | Plain Format | Corresponding Author (sadianoor sadianoor)

339	Topic E: Sustainable Development Goals	ABS-333
Flood Assessment of Catchment Areas and Drainage Networks with Aggregate Quarrying in Angono, Rizal using GIS and Remote Sensing John Mclien Venadas University of the Philippines Diliman Abstract Rizal Province consists of high-elevated and low-lying areas, with the latter being critically affected by floods during typhoons including the municipality of Angono. With an average of 20 typhoons annually, floods are widely experienced due to multiple contributing factors including the amount of rainfall, topographic changes, drainage network blockages and disruptive human activities. At least two quarrying activities in Rizal Province, primarily for construction materials, have been ongoing for decades. The study evaluated the periods in 2000 and 2024 to assess the overall transformation brought by these activities and its effects on the changing environment. Quarrying sites delineation and catchment modeling were done to determine the flow direction and discharge patterns of the surface runoff using RS and GIS techniques. Then, two flood simulations were done using the LISFLOOD-FP model and Typhoon Enteng rainfall to determine the difference between the flood behavior of the two test periods. Elevation data, manning coefficient and boundary conditions were the main distinguishing parameters in the flood simulations. The results showed significant flooding at the sub-catchments due to the topographic patterns that served as temporary basin causing water accumulation, and at the low-lying areas that serve as the outlet points of the discharged and overflowed floodwaters of the high-elevated catchments after reaching their maximum capacities. Another finding suggests an inverse relationship between drainage network flood capacity and flooding in lowlands. This means that greater floodwater volume in drainage channels can correspond to smaller flooded areas with reduced depths at the outlet points. This occurs because the drainages also serve as sub-catchment areas, lowering flood depths in low-lying areas and slowing the flood depth increase. However, in drainage networks with very steep slopes, water flows more quickly, leading to greater accumulation in receiving areas and potentially higher flood depths. Keywords: Flood Mapping- LISFLOOD-FP Model- Quarrying Share Link | Plain Format | Corresponding Author (John Mclien Venadas )

340	Topic E: Sustainable Development Goals	ABS-338
Community-Based Climate Change Adaptation and Mitigation Strategies in Banggae Timur Subdistrict, Majene Regency Ameliya Magfirah (a*), Budimawan (b), Muhammad Farid Samawi (b) a) Department of Regional Planning and Development, Postgraduate School, Hasanuddin University *ameliy.aamagfirah[at]gmail.com b) Department of Marine Science, Fisheries and Marine Faculty, Hasanuddin University Abstract Climate change has become a global challenge with significant impacts on coastal areas, including Banggae Timur Subdistrict, Majene Regency. Phenomena such as coastal abrasion, tidal flooding, infrastructure damage, and declining economic productivity threaten the sustainability of local livelihoods. This study aims to formulate community-based climate change adaptation and mitigation strategies tailored to local conditions, while supporting the achievement of the Sustainable Development Goals (SDGs), particularly SDG 13 (Climate Action), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life Below Water), and SDG 17 (Partnerships for the Goals). A mixed-method approach was employed, combining vulnerability analysis using the IPCC AR4 framework with strategy formulation through the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). Data were collected through questionnaires, in-depth interviews, field observations, and document review. The results indicate that the communitys vulnerability level in Banggae Timur is medium to high, influenced by coastal hazard exposure, economic dependence on marine resources, and limited institutional adaptive capacity. AHP-PROMETHEE analysis prioritized adaptation strategies such as strengthening local institutional capacity and community participation, developing climate-resilient coastal infrastructure, and diversifying livelihoods based on local resources. Mitigation strategies include coastal ecosystem rehabilitation, improving energy efficiency, and integrating climate policies into local development planning. This study provides practical contributions for local governments, academics, and communities in formulating inclusive adaptation and mitigation policies. The implementation of these strategies can accelerate SDG achievement through the synergy of disaster risk reduction, ecosystem protection, and the strengthening of socio-economic resilience in coastal communities. Keywords: Climate Change, Adaptation, Mitigation, AHP, Promethee Share Link | Plain Format | Corresponding Author (Ameliya Magfirah)

341	Topic E: Sustainable Development Goals	ABS-340
Mapping Cropping Intensity Transitions in the Philippines from 2001 to 2024 Using Harmonic Analysis of MODIS NDVI Time Series Arlo Jayson Sabuito 1,3, Ainalyn Nerves 1,2, Keith Ann Cabello 1, Ariel Blanco 1,3 1 Philippine Space Agency (PhilSA) 2 Marine Science Institute, College of Science, University of the Philippines 3 Department of Geodetic Engineering, College of Engineering, University of the Philippines Abstract This study aims to track and analyze changes in cropping intensity across the Philippines to understand spatial and temporal patterns of agricultural practice. This analysis uses MODIS Terra surface reflectance data from 2001 to 2024 to derive a nationwide NDVI time series. The data were grouped into 3-year periods to model dominant crop cycle patterns using Harmonic analysis, and a moving maximum filter was applied to enhance the detection of vegetation peaks. The first derivative of the filtered temporal signal was computed, and sequences of zero slope segments were extracted to represent valid crop cycles. Cropping Intensity (CI) was calculated averaging the detected crop cycles per year, resulting in values of 1, 2, or 3 cycles per year. Transition analysis across CI maps revealed that 2 cycles/year was the most stable class, consistently accounting for 45-54% of valid pixels. Frequent transitions occurred between CI=2 and CI=3, and between CI=1 and CI=2. However, the portion of pixels retained in CI=1 declined over time: from 6.5% in 2001-2004 to 2.6% in 2020-2022. This suggests a reduction in areas with single cropping cycles and a general shift toward more intensive cropping. Latitudinal and longitudinal averages of cropping intensity throughout the years were also computed to assess general regional patterns. The temporal profiles for both latitudes and longitudes aggregates show increasing trend in cropping intensity across most regions over the study period. The results support the use of remote sensing derived cropping intensity monitoring as a tool for assessing agricultural dynamics in the country and its potential in guiding sustainable agricultural planning. As a next step, the approach can be extended to support land use change analyses and broader assessments relating to the overall agricultural shifts in practices and trends in the Philippines. Keywords: Cropping Intensity, Agriculture, Food Security, Harmonic Analysis Share Link | Plain Format | Corresponding Author (Arlo Jayson Sabuito)

342	Topic E: Sustainable Development Goals	ABS-346
Integrating Professional Geospatial Certification with National Geospatial Information Action Plan 2025-2029: Challenges and Strategic Opportunities in the Era of Emerging Technologies Agus S.B, Yanuarsyah I, Hidayat J, Setiawan I IPB University, Ibn Khaldun University of Bogor, Pakuan University, LSP MAPIN Abstract This paper explores the strategic integration of Indonesia^s Professional Geospatial Certification Institute (LSP MAPIN) with the National Geospatial Information (NGI) Action Plan 2025-2029, focusing on challenges and opportunities in the era of emerging technologies. The rapid advancement of Artificial Intelligence (AI), unmanned aerial vehicles (UAVs), cloud computing, and big data analytics has transformed geospatial data acquisition, processing, and dissemination. The National GI Action Plan positions geospatial information as a critical infrastructure for achieving national development targets under the RPJMN 2025-2029, emphasizing multi-source data integration, National Geospatial Data Infrastructure (NGDI) development, and thematic GI services. However, aligning professional certification with these technological demands requires addressing skill gaps, updating competency standards, and expanding assessor capabilities. This paper presents a strategic roadmap for synchronizing LSP MAPIN^s certification programs with the Action Plan, including short-, medium-, and long-term milestones, and highlights opportunities for regional collaboration and adoption of digital certification systems. By benchmarking international best practices, the study aims to strengthen Indonesia^s geospatial human resources to support sustainable development in the digital era. Keywords: Certification, emerging technologies, skill gaps, Action Plan, human resources Share Link | Plain Format | Corresponding Author (Iksal Yanuarsyah)

343	Topic E: Sustainable Development Goals	ABS-352
Integrated Planning of River Transport and Ecotourism Development Along the Tallo River Corridor, Makassar, Indonesia Mohammad Azhar Shauqy, Arif Fuddin Usman, Henny Haerany a. Marine Transportation, AMI Makassar Maritime Polytechnic, South Sulawesi, Indonesia b. Urban & Regional Planning, Alauddin State Islamic University, South Sulawesi, Indonesia Abstract Abstract The rapid urban expansion of Makassar has led to significant transportation challenges, particularly in accommodating the increasing demand for mobility while maintaining environmental sustainability. This study explores the feasibility of integrating river transport and ecotourism along the Tallo River as a strategic response to urban congestion, social inclusion, and environmental degradation. Drawing on a multidisciplinary approach, the research combines hydrological surveys, land-use analysis, stakeholder interviews, and spatial planning assessments to develop a comprehensive framework for water-based transportation and ecotourism development. The Tallo River, which traverses multiple subdistricts and remains underutilized, possesses physical and ecological attributes suitable for revitalization. The study identifies potential docking points, evaluates water depth, flow velocity, and sediment conditions, and assesses community readiness for engagement. Findings reveal strong alignment between local needs, ecological conservation, and urban mobility, demonstrating that river transport can complement existing land-based infrastructure. Simultaneously, the river^s surrounding natural and cultural assets-such as mangrove ecosystems, bird habitats, and community-based tourism-offer high potential for sustainable ecotourism. This initiative aligns with Indonesia^s regulatory framework, particularly Law No. 17/2008 on Shipping, as reinforced by Ministerial Regulation No. 59/2021 on Inland Water Transport, and the strategic direction of Law No. 10/2009 on Tourism, which is currently under policy refinement to strengthen sustainable and community-based tourism., further supports implementation. The study concludes that integrated river-based transport and ecotourism development can serve as a transformative strategy for mid-sized cities in the Global South, particularly where rivers remain underutilized for urban services. Recommendations include initiating pilot projects, enhancing institutional collaboration, and ensuring inclusive community participation to support long-term sustainability. This research contributes to the growing discourse on nature-based solutions and urban resilience, offering insights that are applicable beyond Makassar. The proposed model serves as a replicable framework for other tropical cities seeking to balance mobility, tourism, and ecological preservation. Keywords: Tallo River- River transport- Ecotourism- Sustainable urban planning- Makassar Share Link | Plain Format | Corresponding Author (Henny Haerany)

344	Topic E: Sustainable Development Goals	ABS-99
Comparative Analysis of Statistical Thresholding Techniques in Nighttime Light Segmentation Nurin Izzati Azmi and Norzailawati Mohd Noor* Department of Urban and Regional Planning, Kulliyyah of Architecture and Built Environment, International Islamic University Malaysia, 53100 Kuala Lumpur, Malaysia *norzailawati[at]iium.edu.my Abstract Nighttime light (NTL) data has become an effective instrument for tracking urban growth, providing vital information on the temporal and spatial development patterns. Nighttime light accurately and systematically depicts human activities and affected areas. This study analyses differences in statistical threshold methods in defining nighttime light patterns. The threshold methods^- Otsu, Tsallis and Kapur, were employed to extract built-up areas from nighttime light data, allowing for the identification of key urban expansion corridors relative to the city centre. Assessing the temporal dynamics of urbanisation is critical for understanding urban growth, infrastructure demands, and planning needs. By comparing the outputs of these thresholding methods, this study highlights how the choice of statistical technique can influence the accuracy and interpretation of built-up areas using high-resolution nighttime light data. The Chinese Academy of Sciences engineered a satellite in 2021 that offered higher 10-m spatial resolution nighttime light data from the Sustainable Development Science Satellite-1 (SDGSAT-1), providing the opportunity for more in-depth analysis. With the latest and higher-resolution nighttime light imagery, the study also evaluates the effectiveness of multiple thresholding methods in detecting dynamic urban development patterns in Selangor, with a particular focus on rural areas. Selangor has increasingly focused on expanding suburban districts to promote economic equality and reduce dependency on the city centre. This growth reflects policy-driven efforts to decentralise economic opportunities. Understanding these dynamics is essential for aligning urban expansion with the SDGs, particularly Goal 11- Sustainable Cities and Communities, which highlights inclusive, safe, resilient, and sustainable urbanisation. The study highlights how remote sensing techniques, particularly nighttime light analysis, can serve as a cost-effective and scalable method for monitoring urban sprawl, informing policymakers in their efforts to enhance infrastructure planning, optimise resource allocation, and promote sustainable land use practices. Keywords: SDGSAT-1- Nighttime Light Data- Remote Sensing- Urban Development- Image Segmentation Share Link | Plain Format | Corresponding Author (Nurin Izzati Azmi)

345	Topic E: Sustainable Development Goals	ABS-100
Estimation of Rice Productivity in South Sulawesi, Indonesia Using Meteorological Data Nur Azizah (a), Masayuki Matsuoka (a*) a) Department of Information Engineering, Mie University 1577, Kurimamachiya, Tsu, 514-8507, Japan *matsuoka[at]info.mie-u.ac.jp Abstract Accurate estimation of rice productivity is critical for ensuring food security in South Sulawesi, Indonesia, where rising consumption and declining yields have increased reliance on imports. This study aims to develop a predictive model of rice productivity in South Sulawesi by analyzing the relationship between meteorological factors and reported yields. We hypothesize that key climatic variables precipitation, temperature, solar radiation, and related parameters may have significant explanatory power for estimating annual rice productivity at the district level. The research focuses on six districts with varying production levels. Meteorological data from January 2022 to December 2024 are being sourced entirely from global reanalysis and satellite-based datasets (ERA5, CHIRPS, etc.) to ensure comprehensive spatial and temporal coverage. Production reports from official agricultural statistics will serve as the dependent variable. Data preprocessing and exploratory analysis are underway to identify seasonal patterns and anomalies. The next phase is to apply machine learning models, including Random Forest and Gradient Boosting, to evaluate the predictive contribution of each climatic factor. While the analysis is ongoing, the expected outcomes include insights into the relative influence of meteorological variables on rice productivity and a validated, scalable prediction model. These findings aim to inform adaptive planning strategies and improve agricultural sustainability in Indonesia. Keywords: Rice, Productivity, Estimation, Meteorological, Models Share Link | Plain Format | Corresponding Author (Nur Azizah)

346	Topic E: Sustainable Development Goals	ABS-367
Development of a Smart Agriculture Village Spatial Database through Remote Sensing-Based Geographic Voluntary Activity Wikan Jaya Prihantarto1*, Muhammad Ismail1, Randi Proska Sandra2, Yusra Aprilia Putri1, Ridho Al Farezi1, Alan Kurniawan1, Gatot Supangkat Samidjo3 1Remote Sensing and Geographic Information System, Vocational School, Universitas Negeri Padang, Padang, Indonesia 2Electronic Engineering Department, Universitas Negeri Padang, Padang, Indonesia 3Department of Agrotechnology, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia Abstract Food sovereignty and security are targets of the Government of Indonesia as outlined in the second point of the Asta Cita Vision, which is also relevant to the Sustainable Development Goals (SDGs), especially zero hunger. This vision must be supported by good management of agricultural resources and based on the community^s ability and knowledge. As a follow-up, Smart Agriculture Village is one of the potential programs to be initiated through increasing the knowledge of independent agricultural management at the village level. The support of spatial database of agricultural resources is important for that program. This study discusses the development of spatial databases through collaboration between educational institutions and communities through Voluntary Geographic Activity (VGA) as a first step in the initiation of Smart Agriculture Village. This program focused on Air Manggis Selatan Village, Lubuk Sikaping Sub-district, Pasaman Regency, West Sumatra Province. PlanetScope imagery was used as the main data that was interpreted in a participatory manner by members of the Guguak Farmers Group accompanied by experts from academia. The farmer group was involved in validating the interpreted data and maps through surveys using geotagged forms on mobile devices. The result of this work was a spatial database of food agricultural resources covering 25.86 ha managed by 62 farmers. In addition, information on varieties, productivity, and land ownership was also obtained, which is then presented spatially in an interactive WebMap. This study became the first step in the development of spatial database and information related to the condition of food agriculture in the study area. The presentation of the database in WebMap is useful as a publication medium and a tool for monitoring agricultural advancement that is sourced from actual local knowledge. In addition, this study helped to increase community knowledge in utilizing spatial information technology that supports agricultural data management. Keywords: agriculuture, village, remote sensing, spatial database, geographic voluntary activity Share Link | Plain Format | Corresponding Author (Wikan Jaya Prihantarto)

347	Topic E: Sustainable Development Goals	ABS-124
Comparative Estimation of Groundwater Recharge Using the Thornthwaite-Mather Method in Vegetable Farming Areas: A Case Study of Sarangan and Pujon, Indonesia Aprizal Verdyansyah (a,b*), Tang-Huang Lin (a) a) Center for Space and Remote Sensing Research (CSRSR), National Central University, Taiwan b) The Indonesian Agency for Meteorology, Climatology, and Geophysics *verdyansyahaprizal[at]gmail.com Abstract Highland vegetable farming areas in Sarangan and Pujon, Indonesia, face increasing water stress from intensive agricultural practices, threatening water resource sustainability. This study aims to estimate and compare monthly groundwater recharge patterns in both locations from 2021 to 2023 to support sustainable water management. By applying the Thornthwaite Mather water balance method and utilizing remote sensing data for precipitation (CHIRPS) and evapotranspiration (MODIS), this study analyzes the hydrological dynamics in both regions. The results indicate that groundwater recharge is highly seasonal, occurring exclusively during the rainy season (November to April) when precipitation exceeds the rate of evapotranspiration. During the study period, Sarangan showed a higher average annual recharge (2201 mm/yr) compared to Pujon (1883 mm/yr). Notably, the 2023 El Nino phenomenon caused a significant rainfall deficit, leading to a drastic decrease in recharge rates in both locations. This impact was more severe in Pujon, where soil moisture failed to reach field capacity, resulting in very low recharge. This study successfully demonstrates the effectiveness of the Thornthwaite Mather method for recharge estimation in highlands and underscores the vulnerability of groundwater resources to extreme climate variability. These findings emphasize the urgent need for adaptive, climate informed water resource planning to ensure the future sustainability of the agricultural sector. Keywords: Sarangan, Pujon, groundwater, CHIRPS, MODIS, Thornthwaite-Mather, El Nino Share Link | Plain Format | Corresponding Author (Aprizal Verdyansyah)

348	Topic E: Sustainable Development Goals	ABS-191
DEVELOPMENT OF CITIZEN SCIENCE MOBILE APP FOR BIODIVERSITY INVENTORY WITH GOOGLE VISION AI Sivaraman, H. and Azmy, S.N. Universiti Teknologi Malaysia Abstract This research presents the development of a mobile application for biodiversity inventory using a citizen science approach. The app, named BioMap, is designed to support biodiversity documentation and conservation through active public participation. It features automated identification of biodiversity elements and image-based geolocation extraction and mapping. The primary objective is to address critical challenges in biodiversity data collection through crowdsourcing, particularly the issues of inaccurate geotagging and species misidentification found in existing citizen science platforms. BioMap not only leverages citizen science to improve the accuracy of biodiversity inventories but also addresses the limited availability of public-involved validation methods. The app is developed using Flutter, primarily for Android devices, and integrates real-time data synchronization via Firebase Firestore. It also includes an interactive map powered by the Google Maps API and a dual-validation flagging system, allowing users to mark uploaded data as either correct or incorrect to inform others about its reliability. Field testing conducted at Hutan Simpan Kekal Panti demonstrated the effectiveness of the application across all core functionalities. Notably, all uploaded records (n=10) were accurately georeferenced and mapped, indicating a high level of spatial precision and usability based on user-reported outcomes. Keywords: mobile apps, GIS, automated mapping, citizen science, biodiversity Share Link | Plain Format | Corresponding Author (Suzanna Noor Azmy)

349	Topic E: Sustainable Development Goals	ABS-202
Quantifying urban cooling benefits with SDGSAT-1 nighttime light and thermal infrared data Long Ye(a,b), Tengfei Long(a,b)*, Weili Jiao(a,b),Elhadi Adam(c) a)Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing, China b)University of Chinese Academy of Sciences, Beijing , China * longtf[at]aircas.ac.cn c)University of the Witwatersrand, Johannesburg 2050, South Africa Abstract Accelerating global urbanization and expanding impervious surfaces have intensified the Urban Heat Island (UHI) effect, posing significant challenges to urban livability and resident well-being. Leveraging the unique capability of China^s first Sustainable Development Science Satellite (SDGSAT-1) to acquire concurrent high-quality night-time Light (NTL) and thermal infrared (TIR) data, this study develops an innovative methodology for quantifying urban cooling benefits. We first construct a predictive model for Land Surface Temperature (LST) using a Random Forest (RF) model, with SDGSAT-1 NTL data (indicating human activity intensity) and Digital Elevation Model (DEM) data as key input features. Building upon this, we introduce the novel Cooling Benefit Index (CBI), defined as the difference between the RF-predicted LST and the actual LST derived from TIR data (CBI = Predicted LST - Actual LST). This index precisely quantifies the localized cooling capacity within urban areas. Empirical analyses across multiple representative cities demonstrate the method^s effectiveness in identifying and quantifying cooling benefits. Results show that maximum CBI values exceeding 2.2 Kelvin (K) are common, validating the index^s utility. Areas exhibiting high CBI values, indicating significant cooling benefits, are predominantly located at urban peripheries or in regions with substantial vegetation cover, effectively mitigating local UHI. Conversely, urban cores consistently display lower CBI values, reflecting pronounced UHI intensity and limited cooling potential. The spatially detailed CBI distribution maps accurately reveal the heterogeneous pattern of cooling benefits within cities. This research not only highlights the substantial potential of synergistic SDGSAT-1 NTL and TIR data for generating fine-scale urban thermal comfort maps and quantifying cooling efficiency, but also provides a robust scientific foundation and decision-making support for UHI mitigation strategies, Keywords: Night-time light, Land surface temperature, Cooling benefit index (CBI), Sustainable Development Goal , Urban heat island Share Link | Plain Format | Corresponding Author (Long Ye)

350	Topic E: Sustainable Development Goals	ABS-231
Urban Poverty Risk Map Using Night-Time Light (NTL) Data: A Geospatial Analysis of Colombo District, Sri Lanka. 1. Madhumali.U.H.G.H. , 2. Rajanayake R.M.A.B. GeoEDGE (Pvt) Limited Abstract Urban poverty is an acute issue in the fast-growing cities of the Global South. Slums and economic inequalities are often unequally distributed. Traditional methods of measuring poverty in Sri Lanka, and particularly the Colombo District, rely on outdated and seldom conducted household surveys. This study investigates whether data from satellite imagery based on Night-Time Light (NTL) can be an inexpensive yet timely means to identify urban poverty risk at high spatial resolution. We utilized data from VIIRS-DNB and DMSP-OLS sensors to examine the correlation between socio-economic conditions like income, population density, and accessibility to infrastructure and nighttime light intensity in the GNDs of Colombo. We used machine learning models to classify and map the risk of urban poverty by combining NTL data and other data, such as census data and land use. The resulting maps show considerable variation throughout the city, highlighting underserved regions with low light intensity and little infrastructure. According to these findings, the study highlights the practical relevance of integrating satellite-based Night-Time Light data with geospatial analysis to support data-informed decision-making in urban development. In cities like Colombo, where ground-level socioeconomic data may not be up to date or even nonexistent, NTL data provide a cost-effective and current solution for spatial imbalance in development identification. By identifying economically disadvantaged areas frequently characterized by low-light zones, this approach makes possible more precise targeting of high-risk areas.This information is important for policymakers, planners, and humanitarian agencies in cities to prepare inclusive urban development strategies, disburse infrastructure investment priorities, and monitor success toward achieving sustainable urban development. Keywords: Night-Time Satellite Imagery, Urban Poverty, Sustainable Development Goals (SDGs), Spatial Analysis, Remote Sensing Share Link | Plain Format | Corresponding Author (Harshani Madhumali)

351	Topic E: Sustainable Development Goals	ABS-248
Seagrass Percent Cover Mapping Around Teluk Terima Using Machine Learning for Blue Carbon Stock Estimation Alfian1, M. Yozar Amrozi1, Puji Rahmaini1, and Pramaditya Wicaksono.2 1Departement of Geographic Information Science: Postgraduate Student, Faculty of Geography, Universitas Gadjah Mada, Indonesia 2Departement of Geographic Information Science: Lecturer, Faculty of Geography, Universitas Gadjah Mada, Indonesia Abstract Seagrass ecosystems are important for absorbing blue carbon and providing ecosystem services in coastal areas. But, there is not a lot of spatial data on seagrass distribution and above-ground carbon (AGC) estimates in Indonesia especially Teluk Terima. This study aims to map seagrass coverage percentages and estimate AGC using PlanetScope SuperDove imagery with a Random Forest (RF) machine learning approach. Field data was collected using the photo transect method to generate training and validation data. Image processing included sunglint correction and pixel-based classification using the RF algorithm. Classification results showed an overall accuracy of 59.1% with a seagrass class user accuracy of 59.4%. Seagrass cover distribution was identified as dominant in the Tanjung Kotal and Labuan Lalang areas with density variations ranging from 0% to 82.6%. The estimation results indicate that AGC values vary from low to high levels, with the model demonstrating a moderate correlation between seagrass cover percentage and AGC. These findings indicate that RF is capable of identifying the spatial distribution of seagrass and predicting carbon stocks with sufficient accuracy, supported by representative field data. The results of this study are expected to serve as a reference for spatial data-based conservation management and climate change mitigation planning through the preservation of seagrass ecosystems in coastal areas. Keywords: Blue Carbon, Climate Change Mitigation, Machine Learning, Seagrass Share Link | Plain Format | Corresponding Author (Alfian Alfian)

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