Vidya Nahdhiyatul Fikriyah

Accurate and timely assessment of soil tillage practices is crucial for monitoring sustainability in agriculture. To achieve more site-specific discrimination, it is necessary to understand the spectral and temporal properties of tillage practices across seasons. This study presents developments in tillage-practice discrimination by comparing two high-resolution remote sensing datasets, PlanetScope and Sentinel-2, to characterise and discriminate fields under intensive tillage (IT) and conservation tillage (CT) in the winter and spring seasons. A field experiment was conducted at an experimental site in the United Kingdom, collecting data on tillage practices in 2022–23 and 2023–24. We analysed the spectral and temporal characteristics of two tillage types and subsequently classified them using the random forest (RF) algorithm. Results showed reflectance differences between the two tillage treatments during the early period in both seasons. We also revealed that green, red-edge, and near-infrared wavelengths were relevant for the classification. PlanetScope showed greater potential for classifying tillage (OA = 70–80%), whereas Sentinel-2 exhibited lower performance (OA = 53–73%). Models from the winter achieved higher accuracy scores than those from the spring period, suggesting a seasonal variation in tillage discrimination. The findings highlight the utility of high-resolution satellite-based data, combined with machine learning, for mapping tillage practices and advancing precision agriculture.

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 designation of Sustainable Food Agricultural Land (LP2B) in Indonesia serves as a strategic instrument to protect productive agricultural areas from uncontrolled land-use change. However, in practice, the implementation of LP2B often encounters several challenges, particularly due to pressures from infrastructure development and non-agricultural land use interests. This study aims to map existing food agricultural land and analyse its spatial alignment with the designated LP2B areas in Kebumen Regency, Indonesia, for the year 2022. The mapping of existing food agricultural land, specifically rice fields, was conducted by updating the 2019 base map of rice fields (LBS) using Sentinel-2 satellite imagery and manual visual interpretation, followed by field surveys for verification. The spatial conformity analysis was conducted using an overlay technique to compare the existing food and agricultural land data with the LP2B data. The accuracy assessment showed an overall accuracy of 97.6% indicating that the mapping results are highly reliable. The results show that 88.87% of the existing food agricultural land (39,205.01 ha) was within designated LP2B areas. In comparison, 11.13% of the area (4,908.05 ha) was not included in LP2B zones and may be susceptible to land-use conversion. These findings suggest a need to implement stronger land protection strategies for regions outside the LP2B designation to prevent further land conversion and safeguard regional food security.

This study develops a spatial synthetic population (SSP)-based computational model to produce realis-tic, high-resolution flood-risk maps for the Upper Bengawan Solo Watershed. It combines Global Hu-man Settlement (spatial distribution) with local population statistics (attributes). The SSP is created for flood risk mapping in the Upper Bengawan Solo Watershed (BSH) using a 100 m grid from the Global Human Settlement Layer (GHSL) GHS-POP R2023A. Synthetic individuals are strategically placed around the pixel centre (radius ≤ 100 m), and each is assigned demographic attributes (age, gender, ed-ucation, occupation) validated against official county-level data. Social vulnerability is calculated through weighted aggregation (AHP) across four attributes; individual scores are combined with flood hazard intensity at each location to produce a risk index for each person. Validation shows that (i) the SSP aligns closely with reference statistics: gender and age are nearly identical (MAE ≈ 0.01–0.02%), with slight deviations in occupation (MAE 6.52%) and education (MAE 4.89%), (ii) the overall suita-bility of the SSP compared to GHS counts at pixel samples, and (iii) location plausibility testing using ESRI Sentinel‑2 Land Cover (10 m). Results indicate that (i) the SSP aligns well for gender, moderately for education and occupation, but shows significant misalignment in age, (ii) 91.96% of SSP points are in built-up land, suggesting high spatial accuracy. Medium- to high-risk patterns are mainly along the main river corridors and peri-urban areas, while rural non-built zones are mostly low- to medium-risk. These findings suggest that this methodology is scalable, reproducible, and suitable for data limited regions, enabling the production of detail risk maps that can guide mitigation and preparedness efforts.

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies.

Utilizing geographic information systems (GIS) for spatial analysis is crucial for examining, assessing, and visualizing the health status of different regions. There has been a high maternal and infant mortality rate in West Java, Indonesia, leading to a need for spatial information to support the government in planning healthcare. This study aims to examine and compare the geographic clusters between maternal mortality ratio (MMR) and infant mortality rate (IMR) utilizing tools in a GIS environment; it also aims to assess how those clusters relate to socioeconomic conditions. Data on mortalities and demography in 2020 were collected from the Department of Health Regional and Statistics Bureau. The Getis-Ord Gi* hotspot was applied for the IMR and MMR spatial clustering (low and high numbers-clusters). Further, the ordinary least square (OLS) was implemented to generate the correlation between MMR-IMR clusters and socioeconomic factors. Our results show that significantly low clusters of both MMR and IMR (with 95-99% confidence levels) were located close to urban and highly developed areas. The spatial pattern of hot and cold MMR clusters was similar to the IMR clusters (> 0.68). OLS models showed a high relationship between selected variables and IMR (R2 = 0.80), but low relationship with MMR (R2 = 0.20). A significant correlation was found between IMR and population density, income, and percentage of the population without education, while MMR was related to the number of health facilities. These findings illustrated the performed analysis capability to identify priority areas for maternal and childcare services.

Indonesia is experiencing a rise in natural disasters due to its geographical position within a tropical region, with the Upper Solo River watershed exhibiting a heightened risk of flooding. This region has already suffered numerous floods due to excessive precipitation and insufficient drainage. Susceptibility, hazard, and risk studies have been conducted to investigate this phenomenon but have been limited to specific regions within the catchment area. This study aims to construct a GIS-based flood risk model using Open-Access Spatial Data (OASD) based on diverse physical characteristics, urbanization levels, and population. We used several OASD, including SRTM, Sentinel 2 MSI, GPM v6, NASA-USDA Enhanced SMAP Global Soil Moisture Data, GHS-SMOD R2023A - Global Human Settlement Layers, and GHSL: Global Population Surfaces 1975-2030 (P2023A). The model integrates the risk parameters to identify flood risk using a weighted overlay in ArcGIS. The results demonstrate spatial heterogeneity in flood risk throughout the watershed. The result also reveals that Surakarta City, with a high proportion of its area in the 'High' (57.3%) and 'Very High' (29.54%) risk categories, is at the highest risk of flooding within the watershed. The study enhances understanding of this topic by comprehensively evaluating flood hazards, vulnerabilities, and risks. It highlights the significance of utilizing low-cost OASD to improve flood preparedness and response strategies.