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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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Bringing Geographies of Knowledge out of the Shadows? An Analysis of Geography Research Groups in Brazil Igor Martins Medeiros Robaina; Karina Eugenia Fioravante; Ítala Luzia de Andrade
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.17291

Abstract

This article presents a critical analysis of Geography research groups in Brazil, drawing on data from the CNPq Directory of Research Groups and situating these collectives within international debates on geographies of knowledge, in dialogue with postcolonial and decolonial approaches. It examines the spatial distribution, temporal evolution, thematic organization and internal structure of 616 research groups active in 2023, seeking to show how these collectives function as sites of production, contestation and legitimation of scientific knowledge. The results reveal a substantial growth of these groups over the last two decades, with particular emphasis on the centrality of Human Geography and Geography Education, as well as their concentration in institutions located in the South and Southeast regions of the country. The article concludes that these groups operate simultaneously as local, regional and national networks, while also seeking territorially embedded articulations with global logics of scientific recognition. Taken together, the analysis offers an unprecedented empirical and theoretical systematization of geographies of knowledge from the Brazilian case and opens space for dialogues on geographies of knowledge originating in the Global South.
Monitoring Lake Water Temperature and Estimating Evaporation in Western Iraq Using Ground-based Meteorological Observations and Satellite Remote Sensing Abdulqader Mahdi Salh; Bilal Bardan Ali; Ahmed M. El Kenawy; Khamis Daham Muslih; Sara Ismail Chiad
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.17296

Abstract

Monitoring and estimating water loss rates from lakes in arid regions is challenging due to the scarcity of climate data. Therefore, this study aimed to investigate the spatiotemporal variation of lake surface temperature and evaporation for Tharthar Lake (western Iraq) using ground meteorological observations and satellite remote sensing technique. Three meteorological parameters—air temperature, humidity, and wind—were used in this study to conduct temporal analysis from January–December 2023. These parameters are available in the dataset of Tikrit weather station. Land Surface Temperature (LST) was obtained by processing thermal and infrared data of Landsat 9. Further, evaporation was calculated by the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model by solving the surface energy balance and scaling the instantaneous surface fluxes to daily and seasonal totals. The METRIC model was validated by an independent machine learning-based validation. The results demonstrate that the temperature of the air in contact with the lake surface varies seasonally, ranging between 3.2 °C (January) and 47 °C (July). Additionally, relative humidity displays a seasonal pattern: declining from approximately 79% (winter) to 19% (summer). Evaporation during winter and summer was less than 1 mm /day and over 4 mm /day, respectively. The evaporation rate exhibited a clear spatial discontinuity where the evaporation loss was relatively higher along the shallow periphery and southern parts of the lake compared to the deep central areas. Evaporation dynamics and seasonal variation of the lake are primarily governed by atmospheric forces. Notably, the evident seasonal evaporation indicates that Tharthar Lake is vulnerable to considerable seasonal water loss. The approach used in this study is a flexible method for evaporation monitoring at lakes in arid and data-scarce regions and can be applied in Iraq, where similar conditions are prevalent, to support the sustainable management of water resources.
Assessment of Soil Salinity in Arid Regions Using Remote Sensing and Machine Learning Models: Evidence from Karakalpakstan Bekmurat Abdikairov; Mukhiddin Juliev; Madinabonu Kholmurodova; Zilolakhon Djanpulatova; Zuhra Khadjieva; Sojidabonu Turdalieva
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.17717

Abstract

Soil salinization is a major environmental challenge in arid and semi-arid regions, reducing agricultural productivity and threatening sustainable land management. The spatiotemporal dynamics of soil salinity in the Shimbay district (Republic of Karakalpakstan, Uzbekistan) were investigated using multi-temporal Sentinel-2 imagery and the performance of Partial Least Squares Regression (PLSR), Random Forest (RF), and Multiple Linear Regression (MLR) models was compared. Soil salinity was assessed using the Normalized Difference Salinity Index (NDSI), Normalized Difference Vegetation Index (NDVI), Soil Moisture Index, and land surface temperature, together with field-measured soil electrical conductivity. The results revealed considerable interannual variability in soil salinity, with NDSI values ranging from 0.011 to 0.058 during 2018–2025 and an increasing salinity trend in 2024–2025. Among the evaluated models, PLSR achieved the highest predictive accuracy (R2 = 0.934, root mean square error (RMSE) = 0.398, mean absolute error (MAE) = 0.329), outperforming RF (R2 = 0.810) and MLR (R2 = 0.677). These findings demonstrate that integrating Sentinel-2-derived spectral indices with advanced modeling techniques provides an effective approach for assessing regional soil salinity. The proposed framework offers valuable support for sustainable land and water management in arid agricultural regions. However, further validation using larger datasets and broader environmental conditions is recommended.
Fine-scale GIS Modelling of Peri-urban Industrial Agglomeration: Accessibility, Land-price Gradients and Growth Dynamics in Oulad Azzouz, Morocco Ayoub Zahrani; Hamza Ait Zamzami; Mohammed Elaanzouli; Jumadi Jumadi; Oumaima Boumeaza; Said Mouak; Abdelghani Ezzardi; Badreddine Ennassiri; Jamila Saidi; Taieb Boumeaza
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.18001

Abstract

Industrial expansion on the metropolitan fringe rarely follows a random geography. Firms respond to accessibility, land markets and the cumulative advantages of co-location, while inherited territorial structure constrains where new investment can be directed. Fine-scale evidence from North African peri-urban communes nevertheless remains scarce, particularly regarding emerging industrial spaces at the edge of major metropolitan economies. This study therefore develops a GIS-based analytical framework to map and explain the spatial distribution and growth dynamics of industrial establishments in the Commune of Oulad Azzouz, Casablanca-Settat, Morocco. Verification and satellite-image interpretation were integrated with transport infrastructure, administrative boundaries, and land-price information. The analysis combined temporal establishment profiling, proximity analysis, proximity-based interpretation of transport infrastructure, land-price gradient interpretation, kernel density estimation, and average nearest-neighbor statistics. Industrial growth was shown to follow a strongly clustered pattern: the observed mean nearest-neighbor distance between establishments was 289.21 m, against an expected distance of 453.07 m, yielding a nearest-neighbor ratio of 0.63 (z = −8.83, p < 0.001). Density was markedly uneven. 38% of the commune fell within high or very high-density classes, while 32% remained low or very low. Establishments concentrated along accessible road corridors and in zones where land values are moderate relative to more expensive locations near Casablanca. Once establishment counts were normalized by period length, the 2021–2023 phase indicated renewed formation intensity rather than decline. The findings deliver commune-scale geospatial evidence of industrial agglomeration on a Moroccan metropolitan fringe and support infrastructure-led, land-sensitive and environmentally cautious industrial planning.
Landscape Associations with Flood Response to Extreme Rainfall: A Remote-Sensing, Multi-Event Analysis Across Sumatra, Indonesia Kuswaji Dwi Priyono; Siti Azizah Susilawati; Dewi Novita Sari; M. Iqbal Sunariya; Yuli Priyana; Andhika Argya Pragata; Arif Rohman; Muhammad Yusuf; Mohd Hairy Ibrahim; Farha Sattar; Muhammad Nawaz
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.18079

Abstract

Tropical flooding emerges from interactions among rainfall forcing, drainage structure, land-surface condition, and evolving land cover, yet these components are often analyzed in separate remote-sensing workflows. This study develops a multi-event, sub-basin-scale association framework linking extreme-rainfall coverage, vegetation cover, built-up cover, and Sentinel-1-derived flood response across HydroBASINS Level 9 units in Sumatra, Indonesia. Nine annual area-mean daily rainfall maxima from 2017–2025 were selected using CHIRPS. Vegetation fraction was defined as the proportion of valid Sentinel-2 pixels with NDVI ≥ 0.40, built-up fraction as the proportion with NDBI ≥ 0.00, flood fraction as the proportion of Sentinel-1 pixels with Random-Forest flood probability ≥ 0.50, and extreme fraction as the proportion of CHIRPS pixels whose 7-day accumulated rainfall exceeded the local monthly 95th percentile. The full panel contained 14,409 polygon-event observations, of which 11,818 were complete across the four core parameters. Fractional logit models with event fixed effects showed a negative association between vegetation fraction and flood fraction (β = −0.073, p = 0.004) and a positive association for built-up fraction (β = 0.082, p < 0.001), whereas extreme-rainfall coverage showed no direct association after event-level heterogeneity was absorbed. The corresponding landscape effects were modest in magnitude and should be interpreted as associations rather than causal effects. Cross-scale analysis at HydroBASINS Level 8 preserved the direction of the vegetation and built-up effects. The contribution therefore lies not in a new satellite sensor or classifier, but in combining multi-sensor fractions, hydrologically aligned sub-basin units, and repeated-event statistical inference. Because the Sentinel-1 flood product is pseudo-label based and was not independently validated for all nine events, flood_fraction is treated as a SAR-derived flood indicator rather than a fully validated inundation product, and no operational warning threshold is inferred.
Can Machine Learning See Maladaptation? A Systematic Evidence Map of AI in Global South Agricultural Adaptation to Climate Change Muhammad Musiyam; Choirul Amin; Vidya Nahdhiyatul Fikriyah; Arif Rohman; Munajat Tri Nugroho; Rudiyanto Rudiyanto; Jumadi Jumadi
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.19002

Abstract

Climate change threatens agriculture and rural livelihoods across the Global South, and machine learning (ML) is increasingly deployed to support climate adaptation. Nevertheless, the literature has not systematically mapped whether ML reaches beyond upstream hazard prediction into the downstream stages where adaptation outcomes and maladaptation become visible. This article develops a PRISMA-guided systematic evidence map of AI/ML in Global South agricultural adaptation. A Scopus search using a four-block Boolean string (ML/AI, climate hazard, adaptation/livelihood, and agrarian population) returned 310 records. Records were screened through three eligibility layers (topical relevance, climate-adaptation relevance, and Global South relevance) and were coded according to the deepest stage of the adaptation cycle reached. At the current evidence-mapping stage, 241 records were retained. The studies are dominated by publications from 2024-2026 and are concentrated in India, China, Pakistan, and Ethiopia. ML is heavily concentrated upstream: hazard prediction and monitoring (151 studies; 62.7%) and adaptation decision-making (82 studies; 34.0%). Downstream stages are almost empty: only 6 studies (2.5%) address vulnerability assessment, 2 studies (0.8%) evaluate adaptation outcomes, and none (0.0%) evaluate maladaptation. Within this mapped corpus, machine learning cannot yet see maladaptation: it predicts hazards but remains largely disconnected from the livelihood consequences of adaptation responses. Future research should shift ML from predictive accuracy toward causal, longitudinal, and justice-sensitive evaluation of adaptation outcomes.
The Transformation of an Educational Hub: Modelling Urban Expansion and Its Implications for Regional Proliferation in Jatinangor, Indonesia Lili Somantri; Iqbal Eko Noviandi; Lam Kuok Choy; Nandi Nandi; Ruslan Abdul Munir
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.20221

Abstract

Rapid urbanization globally has intensified the need for sustainable administrative partitioning, yet spatial methodologies are rarely integrated into governance feasibility frameworks. While regional proliferation in Indonesia is traditionally dictated by administrative, demographic, and financial thresholds, it heavily lacks spatial-functional evaluation regarding land suitability and growth capacity. A critical gap remains in combining quantitative spatial modeling with policy-driven institutional assessments to determine the true spatial readiness of emerging autonomous regions. This study aims to evaluate the feasibility of the Jatinangor Urban Area as a candidate for a New Autonomous Region (DOB) through spatial modeling that combines urban growth data, spatial structure, development suitability, and institutional readiness. Urban change are derived from land cover data from 2017 to 2025 analyzed using Sentinel-2 imagery and the Random Forest algorithm, while projections for 2035 are analyzed using Cellular Automata–Markov. Moran’s I and LISA analyses are used to identify spatial clustering patterns and core–periphery city structures. The Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) are used to model development suitability, synthesized through Spatially-Informed SWOT. The results show rapid and uneven urban transformation spatially, with built-up areas increasing from 57.30 km² (2017) to 76.68 km² (2025), and projected to reach 83.53 km² (2035), accompanied by a decrease in agricultural land. A total of 6,104.46 hectares are highly suitable for growth center development, mainly concentrated along the Jatinangor–Cimanggung corridor and part of Pamulihan. Sukasari and similar peripheral zones still struggle with natural constraints. From an administrative view, Jatinangor actually clears the requirements for DOB establishment, but the spatial data shows a different situation. The functional readiness is highly uneven. We found a clear core-periphery gap where public facilities and economic power are centered in one place, leaving the outer areas behind in terms of access. This study recommends that the feasibility of a DOB is not only about meeting administrative requirements but must also be understood spatially to ensure functional integration, equitable service distribution, economic resilience, and environmental sustainability.

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