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Forum Geografi
Published by Universitas Muhammadiyah Surakarta
ISSN : 08520682     EISSN : 24603945     DOI : -
Core Subject : Science,
Earth & Planetary Sciences
Aim Forum Geografi is an open-access, peer-reviewed journal dedicated to advancing the field of geography by publishing original scientific articles on spatial issues. The journal prioritizes studies that employ rigorous spatial analysis and geospatial technologies—such as GIS and remote sensing—to deepen theoretical and empirical understanding of spatial patterns, processes, and interactions in both physical and human geography. Focus The primary focus of Forum Geografi is on studies that apply Geographic Information Systems (GIS) and/or remote sensing for spatial and regional analysis of physical and human geographic phenomena. Scope - Human and environment interaction - Geomorphology: process and modelling - River basin: process and modelling - GIS and/or Remote sensing-based disaster-related study - Urban, rural, and regional study
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Geologi
Articles 97 Documents
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Assessing Household Climate Resilience Index to Climate Change Impacts in DKI Jakarta: A Robust Geographically Weighted PCA Approach Marta Sundari; Kusman Sadik; Aji Hamim Wigena; Anwar Fitrianto; Rizaldi Boer; Zulkarnain Zulkarnain
Forum Geografi Vol. 40 No. 3 (2026): December 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.15979

Abstract

Household climate resilience is spatially heterogeneous because households experience different combinations of exposure, sensitivity, and adaptive capacity across urban settings. This study developeds a local Household Climate Resilience Index (HCRI) for DKI Jakarta using Robust Geographically Weighted Principal Component Analysis based on the Minimum Covariance Determinant estimator (Robust GWPCA-MCD). The analysis used 221 mainland household observations from 17 urban villages in five municipalities of DKI Jakarta, based on the 2022 household resilience survey conducted by CCROM-SEAP IPB University and DLH DKI Jakarta. The final index was constructed using 30 indicators representing exposure, sensitivity, incremental adaptation, and transformational adaptation. Classical GWPCA and Robust GWPCA-MCD were compared to evaluate whether robust local covariance estimation improved the local information structure of the index. The results showed that the Robust GWPCA-MCD produced a substantially stronger local PC1 structure than the classical GWPCA. The classical GWPCA generated a median local PC1 variance of only 14.81%, whereas the Robust GWPCA-MCD increased the median local PC1 variance to 83.66%, with values ranging from 63.42% to 99.999%. The selected model used an adaptive bisquare kernel with 96 nearest neighbors, MCD α = 0.75, and an approximate local h-subset of 72 observations. The HCRI classification identified 43 households (19.46%) as extremely low resilience, 12 households (5.43%) as very low, 52 households (23.53%) as low, 58 households (26.24%) as high, and 56 households (25.34%) as very high, with no household classified as extremely high. Spatial mapping showed that the lower and higher resilience categories were unevenly distributed across sampled urban villages. Local loading analysis further revealed that exposure indicators, particularly distance to flood sources, and sensitivity indicators, including housing characteristics, land area, medication expenditure, and post-drought health burden, dominated different local HCRI structures. These findings demonstrate that the Robust GWPCA-MCD provides a statistically robust and spatially interpretable framework for identifying local household resilience profiles and supporting targeted urban climate adaptation planning.
Linking Weather Variability and Wildfires Across Tropical Ecoregions: Evidence from Sumatra, Indonesia Asshaffa Naim; Andung Bayu Sekaranom; Sudrajat Sudrajat; Fitria Nucifera
Forum Geografi Vol. 40 No. 3 (2026): December 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.15980

Abstract

Wildfires in tropical regions exhibit strong spatial variability that reflects differences in ecoregion characteristics and weather sensitivity. This study aims to examine the relationship between weather dynamics and wildfire distribution across six different ecoregion characteristics in Sumatra. MODIS Fire hotspot data were used to analyze wildfire distribution from 2016–2023, combined with ERA5 and SMAP soil moisture data. Negative binomial regression and incidence rate ratio analysis were performed to obtain a statistical measure of the influence of weather. The results show that wildfire occurrence was highly concentrated in coastal and swamp ecoregions, with peat swamp forests (PSFs) accounting for the highest hotspot density. Pre-fire weather analyses showed that swamp and mangrove ecoregions exhibited higher sensitivity to relatively small rainfall deficits and temperature increases compared to upland forests. The negative binomial regression indicated that the strongest weather–wildfire associations were in freshwater swamp forest (FSFs), where weather variables yielded the highest pseudo-R² (0.226). The distinct weather sensitivity of each ecoregion was further explained by the IRR values, which showed strong temperature sensitivity in the Sunda Shelf mangrove (SSM) region and a prominent rainfall-related suppression of wildfire activity in FSFs. The findings demonstrate ecoregion-specific wildfire sensitivity and the need for targeted wildfire management strategies, particularly in peatland and coastal ecosystems in Sumatra.
An Evaluation of Phosphorus Control Interventions through a Multi-Scenario Approach to Controlling Eutrophication in Lake Rawapening Indonesia Iwan Rhosadi; Djati Mardiatno; Muhammad Anggri Setiawan; Dimas Maula Hayat
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16151

Abstract

Eutrophication triggered by excessive nutrient loading has become a major driver of lake degradation in many parts of the world. Lake Rawapening is one of Indonesia’s national priority lakes for environmental restoration and has experienced eutrophic conditions progressing toward a hypertrophic state due to high phosphorus loads (P-load) and total phosphorus concentrations (TP-C). Despite the implementation of lake rehabilitation programs, their effectiveness in improving trophic status has not been quantitatively evaluated, limiting stakeholders’ understanding of the outcomes. This study evaluates the effectiveness of rehabilitation interventions in controlling the trophic state of Lake Rawapening using a calibrated SWAT hydrological model. Seven scenarios were developed by combining land use changes from seasonal agriculture to agroforestry; fertilizer management in paddy fields; and lake deepening. Model simulations compared TP-C responses across scenarios. The results indicate that conversion of seasonal agricultural land to agroforestry is the most effective intervention,  reducing TP-C by 33% (from 0.029 mg/L to 0.020 mg/L) and improving the trophic state from eutrophic to mesotrophic. Conversely, fertilizer management scenarios, even when applied according to recommended dosages, consistently increased TP-C by 14%. Lake deepening scenarios also tended to elevate TP-C by 20.9% due to increased water volume of 90% and phosphorus residence time (P-RS) of 92.4%. The study provides a measurable and replicable approach for simulating spatial dynamics and trophic responses to lake rehabilitation interventions. The findings support the development of multi-scenario management strategies for Lake Rawapening and other lakes in Indonesia.
Annual Spatiotemporal Dynamics of Surface Urban Heat Island Intensity in Makassar City, Indonesia (2013-2024): An Integrative Local Climate Zone Framework Mirnayani Mirnayani; Purnama Budi Santosa; Ailian Chen
Forum Geografi Vol. 40 No. 3 (2026): December 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16328

Abstract

Urbanization is altering the local climate, but 2D models still fall short in representing the drivers of urban heat distribution because they don’t account for 3D factors. To address this limitation, this study utilizes a Local Climate Zone (LCZ) framework as a representative model of 3D urban morphology. A three-stage integration methodology that includes Fuzzy Logic, GIS-based rules, and spatial aggregation is utilized to classify the LCZ. By linking the urban transformation, as represented by LCZ, with Land Surface Temperature (LST), this research examines the spatiotemporal trends of Surface Urban Heat Island Intensity (SUHII) in Makassar City, Indonesia (2013-2024). SUHII is calculated by subtracting Land Surface Temperatures (LST) in each LCZ class, with LST obtained from LST Landsat OLI/TIRS imagery. SUHII spatial patterns are analyzed using the Global Moran’s Index and the Getis-Ord Gi*. The results showed that converting more than 1,650 hectares of natural landscape (LCZ D and G) into open-to-densely built-up zones (LCZ 6 and LCZ 3) significantly alters the surface energy balance. SUHII peaked at 8.51°C in 2017, but the mean intensity sharply decreased to 0.01°C in 2023, a decrease attributed to El Niño. This structural clustering is confirmed by the high Moran’s Index values (0.70–0.79), a p-value of 0.00, and z-scores consistently above 65, indicating a shift toward thermal homogenization and a progressive reduction in distinct cooling zones. The findings indicate the implementation of a two-track strategy: structural improvement in the highly heat-exposed urban center districts (Rappocini and Mamajang) and the preservation of natural infrastructure within the rapidly developing peripheral districts (Biringkanaya and Tamalanrea) to prevent further increases and expansion of hotspots.
Assessing the NDDI Drought Index Using the Google Earth Engine for Peat Fire Risk Monitoring in Siak, Indonesia Sigit Sutikno; Novreta Darfia; Ilham Hanafiah Wy; Randhi Saily; Sinta Haryati Silviana; Koichi Yamamoto
Forum Geografi Vol. 41 No. 1 (2027): April 2027
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16518

Abstract

Peatland ecosystems across tropical regions are prone to severe fires during drought periods, making timely and accurate early drought monitoring vital for fire risk mitigation. This study assesses drought conditions in Siak Regency, Indonesia, employing the Normalized Difference Drought Index (NDDI) derived from Sentinel-2 satellite imagery using the Google Earth Engine (GEE) platform. NDDI, which combines indices of vegetation greenness (NDVI) and moisture (NDWI), was used to map peatland dryness. The research analysed NDDI patterns for each month from 2019 to 2024 and was tested against MODIS and VIIRS fire hotspots both spatially, by overlaying hotspots on classified drought maps, and temporally, using Spearman correlation. The results show that the two approaches produced an informative contrast. Spatially, fire was concentrated strongly in the driest classes: on average 91% of hotspots (85% pooled across all 507 detections made) fell within the severe and extreme drought classes (NDDI > 0.25). A chi-square test confirmed that this distribution was clearly not a question of chance (chi-square = 96.1, p < 0.001). Temporally, however, the month-to-month correlation was weak (mean rho = 0.24), so the surface signal alone does not set fire timing within a particular year. An independent CHIRPS rainfall record confirmed the wet- and dry-year ranking and linked annual rainfall to burnt areas (rho = -0.94, p = 0.005). NDDI is therefore a reliable indicator of areas where peatland is fire-prone, and is best used as a spatial fire-susceptibility layer within a multi-factor early-warning system, being transferable to other tropical peatlands.
Multi-decadal Shoreline Change and Salt Production Exposure in Cirebon, Indonesia: A Cloud Geospatial and Transect-Based Analysis (1995–2025) Anang Widhi Nirwansyah; Dimara Kusuma Hakim; Ana Andriani; Ismail Demirdag; Shreema Rana
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16748

Abstract

Coastal salt farming in the Global South is mainly situated in in low-lying intertidal plains, where shoreline migration and morphodynamic variability can quickly manifest as a loss of productive space and disruption of salt-pan infrastructure. In this study, we quantify multi-decadal shoreline change along the Indonesian Cirebon coast during the period 1995–2025 and delineate critical exposure corri-dors where either shoreline retreat or progradation is most likely to cross-cut salt-pan systems, marking areas of intense shoreline mobility and geomorphologic instability. These represent zones of increased rather than direct inundation risk, where shoreline retreat (or progradation) is most likely to cross-cut salt-pan systems. Repeatable geospatial workflow integrated cloud-based multi-temporal optical satel-lite processing was employed, together with transect-based shoreline-change statistics, in order to de-rive the End Point Rate, Linear Regression Rate, Net Shoreline Movement and Shoreline Change Enve-lope within an uncertainty-aware stability framework. Transect classifications are based more on net accretion than net erosion by count, but show a very skewed magnitude distribution, such that local-ized retreat displays larger absolute extremes compared with progradation, yielding slightly negative mean values despite positive medians. Alongshore stratification (west–central–east) indicates that ex-treme values of cumulative displacement and rate are concentrated in the eastern sector, whereas smaller magnitudes and more stable central tendencies are present along the western and central sec-tors. The shoreline changes scale is strongly correlated with the total displacement magnitude, suggest-ing that segments with maximum mobility also show broad positional ranges, as predicted by clustered landscape hotspots. Category-based interpretation adopts these metric signatures and converts them in-to classes of salt-farming exposure, allowing for a spatially explicit basis for prioritization of parcel overlays, field verification and adaptation planning throughout high-change corridors. The findings advocate hotspot-oriented coastal risk management and the use of geospatial decision support tools for livelihood-sensitive coastal production landscapes.
Rainfall Prediction Using Machine Learning in Nineveh Governorate, Iraq Khaled Nafea; Ali Mahdi Jawad Al-Dujaili
Forum Geografi Vol. 40 No. 3 (2026): December 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16759

Abstract

Accurate rainfall forecasting is essential in arid and semi-arid regions such as Iraq due to increasing climate variability, water scarcity, and the growing need for sustainable water resource management. This study proposes an integrated spatiotemporal framework for rainfall forecasting in Nineveh Governorate, Iraq, combining non-parametric trend detection, multivariate correlation analysis, and ensemble machine learning. Monthly climate data from seven stations (1994-2024) were analysed using the Mann-Kendall test and Sen’s slope estimator to assess long-term rainfall dynamics. Pearson and Spearman coefficients were applied to examine interrelationships among climatic variables and to mitigate multicollinearity prior to model construction. Four predictive models (XGBoost, CatBoost, Random Forest, and LSTM) were combined through a weighted stacking ensemble to improve robustness and reduce forecasting uncertainty. Results indicate no statistically significant long-term monotonic trend in annual rainfall; however, clear spatial heterogeneity and seasonal variability were observed. The stacking model outperformed the individual models, achieving an R² value of 0.981 (98.1%), indicating high predictive performance., with reduced RMSE and MAE values. Findings highlight the dominance role of temperature-humidity-pressure interactions in shaping rainfall behaviour in semi-arid environments. The proposed framework enhances both predictive reliability and structural interpretation of rainfall variability under regional climate stress conditions.
Estimation of Rice Chlorophyll Content in Salt-Affected Soils Using UAV-Based Multispectral Sensing Juhadi; Fajar Dwi Handoko; Eva Banowati; Tjaturahono Budi Sanjoto; Vina Nurul Husna
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16868

Abstract

The decline in rice productivity in coastal areas is often associated with agricultural land salinization. Chlorophyll, as an indicator of plant health, can be monitored using remote sensing technology. This study aims to evaluate the capability of multispectral UAV imagery to estimate rice chlorophyll content and compare the effectiveness of several vegetation indices in saline coastal paddy fields. Data were collected over ±30 hectares of rice fields in Kendal Regency using a DJI Phantom 4 Multispectral UAV during the vegetative stage (30–45 days after planting). Field measurements included chlorophyll content (SPAD) and soil electrical conductivity (EC). The results showed a weak and statistically insignificant correlation between soil salinity and rice chlorophyll content (r = 0.119; p = 0.571). These findings suggest that under the specific conditions of this study, characterized by moderate salinity levels (±4.47 dS/m) and the use of rice varieties with varying degrees of tolerance (Ciherang as tolerant and IR32 as moderately tolerant), the spatial variability of chlorophyll was more strongly influenced by phenological stages and micro-environmental factors than by salinity stress. Among the evaluated indices, red-edge-based indices showed the best performance, with CIre (R² = 0.831) and NDRE (R² = 0.822) yielding the lowest estimation errors, while NDVI (R²= 0.297) was limited by spectral saturation and CIg (R²=0.415) was affected by its sensitivity to plant canopy structure. These results indicate that red-edge-based indices are highly effective for mapping chlorophyll variability. The limited impact of salinity observed in this study is likely due to the tolerance of the rice varieties and to salinity levels remaining below critical thresholds. Thus, CIre and NDRE are recommended as the most effective indices for estimating rice chlorophyll in coastal paddy fields using multispectral UAV imagery.
Assessing the Association between ENSO, IOD, and Streamflow Variability in the Lower Batanghari River Basin, Sumatra, Indonesia Linda Handayani; Munajat Nursaputra; Rustan; Nanang T. Puspito; Rusmawan Suwarman
Forum Geografi Vol. 41 No. 1 (2027): April 2027
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.16926

Abstract

This study examines the relationship between the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and seasonal streamflow variability. Streamflow was simulated using the Soil and Water Assessment Tool Plus (SWAT+) during two specific land-use periods (2000 and 2023), integrating topographic, soil, land-use and climate data, and evaluated at three lower stations: Muara Kilis, Sungai Duren and Simpang Berbak. Pearson and partial correlation analyses were used to assess antecedent June-July-August (JJA) and concurrent September-October-November (SON) climate conditions against September-November (SON) streamflow. Both ENSO and IOD showed consistently negative relationships with SON streamflow. The results reveal distinct seasonal patterns: JJA conditions showed clearer ENSO-streamflow relationships, whereas during SON, the IOD-streamflow relationships became more pronounced, with correlation reaching -0.64 at Muara Kilis. Partial correlation showed that the associations remained significant after accounting for the other climate index, although their magnitudes varied in the JJA-SON and SON-SON relationships. These findings demonstrate seasonal and spatial variability in climate-streamflow relationships across the lower Batanghari River Basin.
Global Synthesis of Socio-Ecological System Research in Watersheds: A Systematic Review and Dynamic Framework Proposal Mochammad Syaifulloh; Yohanes M F Seko Raja Mijang; Baso Samsu Rijal; Rabiyathul Adawiyah Abbas; Dina Aprilia; Enjang Asri; Nurlaila Sahara Worabay
Forum Geografi Vol. 41 No. 2 (2027): August 2027
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.17065

Abstract

Watersheds are increasingly understood as socio-ecological systems (SES), reflecting the close interaction between ecological processes and human activities. However, research trends, thematic developments, and knowledge gaps in watershed-based SES studies have yet to be comprehensively synthesized. This study aims to examine the evolution of SES research in watershed contexts through a systematic review of the scientific literature published between 1999 and 2025. Following the PRISMA approach, records were retrieved from Scopus and Web of Science. A total of 689 documents were initially identified; 455 remained after duplicate removal, and 96 articles met the final eligibility criteria for analysis. The review reveals a substantial increase in watershed-SES publications after 2018. Earlier studies primarily focused on conceptual development, whereas more recent research has expanded toward governance, climate adaptation, resilience, land-use pressures, and agricultural–water interactions. Geographic patterns indicate that studies conducted in the Global North tend to emphasize institutional and modeling approaches, while research in the Global South more frequently addresses socio-ecological pressures and local environmental challenges. The synthesis also highlights the growing use of spatial analytical tools, particularly Geographic Information Systems (GIS) and remote sensing, to investigate watershed dynamics and support management decisions. Based on the findings, this study proposes a conceptual Dynamic SES–Watershed Framework that links ecological thresholds, governance processes, adaptive capacity, resilience, and land-use pressures within watershed systems. However, as a review-based study, the framework is intended as a conceptual foundation for future empirical testing rather than a validated model. The findings contribute to a broader understanding of watershed sustainability and provide directions for future SES research and management.

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