SSRS Journal A: Agro-Environmental Research

Cloud Computing and Machine Learning Approach for Mangrove Mapping in Marine Protected Area (MPAs) of Banggai, Sulawesi Island Archipelago, Indonesia

Thu, 25 Dec 2025 00:00:00 +0000

Mangroves are coastal forest ecosystems that serve multiple functions for coastal communities. Their ecological role as essential habitats for coastal and estuarine fauna, combined with their economic function as areas for fish production, makes mangrove ecosystems crucial for coastal and marine management. Unfortunately, within the context of sustainable fisheries, mangrove areas in Sulawesi remain underexplored, especially using satellite-based approaches. Therefore, this study focuses on quantifying the extent of mangroves within the Marine Protected Areas (MPAs) of Banggai – Banggai Kepulauan – Baggai Laut, Sulawesi Seascape. Utilizing a combination of Landsat-8 and Landsat-9 satellites through the Google Earth Engine (GEE) cloud computing platform, the mangrove mapping process incorporated machine learning techniques. In the MPAs of Banggai-Bangkep-Balut, mangroves were mapped covering an area of 5,323 hectares, approximately 0.62% of the total designated protected area. Mangrove detection using the two Landsat satellites achieved high accuracy levels, with an overall accuracy of 0.91 and a kappa statistic of 0.83. The error matrix indicated 6 misclassifications out of 73 validation points. These findings confirm the reliability of mangrove data within the Banggai MPAs management framework, supporting its use as blue carbon data to strengthen national emission reduction policies. In addition, cloud computing has proven to be excellent in extracting mangrove data in MPA areas and is highly recommended in future monitoring to create sustainable MPA governance.

Mapping Ecotourism Suitability Zones and Potential: A Case Study Using Remote Sensing Geospatial Information in Bogor Regency

Thu, 25 Dec 2025 00:00:00 +0000

Over the past few decades, the trend toward sustainable development globally has increased, and there are many cases of development that has a negative impact on the environment. The ecotourism scheme is one of the answers that can be used to reconcile economic development with aspects of environmental sustainability. Along with the increasing use of ecotourism schemes, of course, the provision of adequate infrastructure is fundamental to running the scheme. This research aims to map ecotourism suitability zones in Bogor Regency based on a geoecotourism model obtained through the integration of various remote sensing geospatial information. The model can produce an integrated landscape component analysis consisting of distance from water bodies, land use, distance from roads, distance from settlements, aspect, slope, and elevation. The results of the analysis are grouped into four classification zones according to their ecotourism potential, namely low, medium, high, and very high ecotourism potential zones. Based on the classification of the ecotourism potential zone, the direction of infrastructure development in each zone is determined to maximize the ecotourism scheme that is carried out. The results of this study are expected to provide recommendations to the Bogor Regency Tourism Office regarding the provision of appropriate infrastructure in various ecotourism zones in Bogor Regency and implement the Society 5.0 concept in Indonesia.

Spatiotemporal Platform for Assessing the Quality of Irrigated Paddy Soils in Cidadap Village, Subang Regency

Thu, 25 Dec 2025 00:00:00 +0000

Indonesia is one of the world’s largest rice producers, with national rice production reaching 34.1 million tons. In order to maintain food security and support the national economy, soil quality plays a crucial role in sustaining agricultural productivity. Soil salinity is an important indicator affecting nutrient availability and plant growth. Increased salt content in paddy soils can inhibit water and nutrient uptake, leading to reduced rice yields, particularly in intensively managed irrigated paddy fields. Cidadap Village, Subang Regency, as an intensively cultivated agricultural area, experiences interannual fluctuations in soil salinity levels. The methods used in this study include irrigated paddy field detection through supervised classification using Landsat 8 OLI/TIRS imagery and estimation of electrical conductivity (EC) as a salinity indicator based on regression analysis with the Vegetation Soil Salinity Index (VSSI). All analyses were conducted on the Google Earth Engine (GEE) platform to support efficient and large-scale spatial data processing. Monitoring results indicate that salinity levels in irrigated paddy fields in Cidadap Village fluctuated during the 2020–2024 period, with EC values predominantly falling within the non-saline to low-salinity categories (1.8–3.6 dS/m), although several areas exhibited moderate salinity increases (>4 dS/m). These variations are influenced by hydrological conditions, rainfall, and irrigation water distribution. All spatial visualizations are presented through the Data Indo InaPad platform based on Earth Engine Apps as a practical and easily accessible tool for monitoring soil salinity conditions.

Assessment of Bioluminescent Fungi and Their Ecological Relationship in Mount Halimun Salak National Park, West Java Province

Thu, 25 Dec 2025 00:00:00 +0000

Bioluminescent fungi are fungi that possess unique structures and are capable of emitting light (bioluminescence). Gunung Halimun Salak National Park (GHSNP) is one of the habitats of bioluminescent fungi, with its ecosystem consisting of tropical rainforest and high rainfall. The research conducted aimed to determine the bioecology of the habitat, including air temperature, air humidity, substrate pH, shading vegetation, macromorphology, and population density of bioluminescent fungi. Data collection was carried out during night and day using the purposive random sampling method in two observation plots at the Cikaniki Research Station, GHSNP. The ecological data collected included air temperature and humidity, which were gathered as time series data, and canopy cover, analyzed using hemispherical photography. Results showed that bioluminescent fungi inhabit moist environments with substrates consisting of dead wood or litter layers that have an acidic pH. Based on time series collection, bioluminescent fungi experience daily air temperatures ranging from 20–24 °C and air humidity levels between 84–98%. Two species of bioluminescent fungi were found in both observation plots, namely Mycena sp., characterized by white fruiting bodies measuring 1.2 cm, and Species X, which remains unidentified. Population density in Plot 2 was higher compared to Plot 1, reaching 9 individuals per plot. The type of vegetation in the habitat of bioluminescent fungi in Plot 2 was also more diverse and accompanied by understory plants such as begonias, palms, and ferns, resulting in a more humid habitat and a greater distribution pattern in Plot 2 compared to Plot 1. Differences in population density between plots of bioluminescent fungi in each plot indicate that habitat and environmental factors influence the distribution pattern and growth of bioluminescent fungi.

Understanding of Pest Species Distribution on Land Use, Geographic Location, and Climate Factor using Species Distribution Modelling: Case Study in Java Island, Indonesia

Thu, 25 Dec 2025 00:00:00 +0000

Pest distribution has a broad impact across sectors, including agriculture, plantations, and forestry. The impacts are predicted to continue to increase in the future, so understanding pest distribution patterns is important as a basis for more effective pest management decision-making. This study aims to analyze the distribution of pests in Java Island based on land use, geographical location, and climate factors. A total of 2,777 individuals from 14 pest families in GBIF citizen science data were analyzed using a machine-learning-based Species Distribution Modeling (SDM) approach to map habitat suitability. Modeling was conducted using 12 WorldClim bioclimate variables: temperature (bio1, bio2, bio4-bio11), thermal (bio3), and rainfall (bio12). Model accuracy was evaluated using two metrics: Area Under the Curve (AUC) and Kappa. The results showed that most pest families were distributed across three land-use types, with a dominance on agricultural land and at elevations below 500 meters above sea level. Land Surface Temperature (LST) data shows that pests are generally found at temperatures between 15°C and 35°C. Among all bioclimate variables analyzed, annual rainfall (bio12) has the highest influence on habitat suitability. The model performs well, with an average AUC of 0.88 and a Kappa of 0.64, indicating that the predicted pest distribution is accurate enough for pest management planning in the study area. Information on the spatial distribution and potential habitats is needed to target natural treatments.