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This study aims to better understand the coastal dynamics along the 6.89 km of Subang shoreline using Landsat data and GIS methods with cloud computing-based analysis. The data is processed using remote sensing techniques, image classification, and change detection algorithms. Furthermore, this research harnesses cloud computing to efficiently manipulate big data, enabling rapid and measurable analysis of coastline changes. Cloud computing-based platforms facilitate data storage, processing, and dissemination, enhancing accessibility for researchers and stakeholders. This study indicates that the area has experienced significant changes from 1990 to 2023, with the total length of the coastlines that have changed (positive stands for accretion and negative for erosion) being 8.21 km (-16,86 %) for 1990 to 2000, 6.52 km (16.21%) for 2000 to 2010, 8.14 km (6,66%) for 2010 to 2020, and 8.81 km (-19,16%) for 2020 to 2023. The results provide valuable information about erosion, accretion, and coastal morphological changes. The findings can help make informed decisions for sustainable coastal management. The methodology presented in this article demonstrates a solid approach to coastline monitoring that can be replicated in other areas for more efficient and effective coastal management and environmental preservation.

Tidal floods are one of the threats that occur in coastal areas. The impact is felt for the community, as well as other impacts felt for the surrounding ecosystem, namely the mangrove population, where tidal floods that continue to occur can have a serious impact on the community and mangrove vegetation because they can threaten the sustainability of the mangroves. This research uses GIS to map tidal flood inundation areas and determine their relationship with biophysical factors. Spearman correlation is used to determine the relationship between points in tidal flood areas and biophysical factors such as elevation, slope, distance from rivers and distance from the beach. The research results show that the four variable factors are related to the point of tidal flood inundation. Distance from the river greatly influences the tidal flood point in Medan Belawan District, with a correlation coefficient value of -0.100. This is because rivers act as a medium for the entry of water from the sea without any obstacles to entering the land, so when the river flow reaches its carrying capacity, sea water immediately overflows into residential areas.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The normalized differential open-area index (NDOAI) computed from 8-day VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, the VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, the VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 70%. [ABSTRACT FROM AUTHOR]

Privatization, often proposed as a means to regulate natural resource use, sometimes paradoxically leads to overexploitation and social exclusion. Within the unique context of Ogan Komering Ilir (OKI) Regency, Indonesia, the privatization of swamp floodplains and rivers via the "Lelang Lebak, Lebung, Sungai" (L3S) system is a testament to this dilemma. L3S grants auction winners exclusive rights to fish, thereby privatizing common-pool resources. This study delves into the intricacies of the L3S mechanism, highlighting its significance in guiding inland fisheries' management. Through stakeholder analysis, we pinpoint the crucial actors, as well as their interests, influence, and interrelationships. Our investigation revealed 20 distinct stakeholders, each playing different roles within the L3S framework. Based on their influence and vested interests, these stakeholders are categorized as key players, subjects, context setters, and crowds. This classification aids in discerning potential conflicts, cooperation, and synergies. Effective L3S execution hinges on collaboration, especially with pivotal entities such as fishery services, village and district heads, and village-owned enterprises. Insights gathered during the study indicate that while privatization has streamlined resource distribution, it intensifies overfishing and deepens socioeconomic divisions. This study calls for a harmonious blend of historical insights and modern governance, with a central focus on stakeholder collaboration and community involvement. [ABSTRACT FROM AUTHOR]

Mangrove forests serve a substantial part in coastal areas as highcarbon-storage woody vegetation ecosystems that grow on muddy and anaerobic soils. This study determined the species composition and estimated the biomass production and storage of carbon in Ambon Bay mangrove forests. Vegetation surveys for species composition were conducted using the square plot technique by making standard observation plots for density, frequency, dominance, and important value index data analysis. We used Allometric equations and the Loss-on-ignition method to calculate the biomass and soil carbon. Vegetation surveys revealed eight species of treehabitus mangroves, and Sonneratia alba was a mono-dominant species with an important value index of more than 100%. The study revealed the highest average potential of biomass and carbon in Passo Village with AGB 280.47±168.94 Mg ha-1, BGB 83.06±55.1 Mg ha-1, and sediment carbon 320.03±106.97 Mg C ha-1. The carbon stock of the mangrove forest in Ambon Bay was estimated at 400.67±166.25 Mg C ha-1. We conclude that mangrove forest carbon stores in Ambon Bay had relatively high values. Passo Village has the largest carbon store compared to other locations in the Ambon Bay mangrove forest. [ABSTRACT FROM AUTHOR]

The visual camouflage of many species living in the dense cover of the tropical rainforest become obstacles to conducting species monitoring. Unmanned aerial vehicles (drones) combined with thermal infrared imaging (TIR) can rapidly scan large areas from above and detect wildlife that has a body temperature that contrasts with its surrounding environment. This research tested the feasibility of DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to detect terrestrial and arboreal mammals in the five tree density classes in the remaining natural environment on the IPB University campus. This study demonstrated that large-size terrestrial mammal thermal signatures are visible in sparse vegetation at daytime and in the area under the canopy at night monitoring. In contrast, arboreal mammals were better detected in at early morning and night. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. The drones allow safe operation at low altitudes with low levels of disturbance to animals. Both terrestrial and arboreal mammals are well detected and easily identified when the drone is flying at an altitude < 50 m HAGL. Our preliminary results indicated that thermal surveys from drones are a promising method.

Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is

Carbon dioxide (CO2) in the atmosphere occurs as the result of various chemical, physical, and biological processes. The presence of CO2 around atmosphere greatly affects the agroforestry and agroindustry of coffee. This study aimed to describe the CO2 cycle in agroforestry and agroindustry of Gayo Arabica coffee (Coffea arabica) and net CO2 ha-1 of coffee plantation. CO2 cycle was analyzed based on the movement of CO2 around the agroforestry and agroindustry of coffee. CO2 cycle model describes net CO2, CO2 emission, CO2 reduction, and CO2 sequestration. Net CO2 ha-1 of coffee plantation was 162.75 x 10-2 t CO2 e ha-1, with CO2 emission was 203.84 x 10-2 t CO2 e ha-1, CO2 reduction was 3.10 x 10-2 t CO2 e ha-1, and CO2 sequestration was 363.49 x 10-2 t CO2 e ha-1. This research formulates the calculation of equivalent carbon emissions in the arabica coffee production system in the field and primary processing, using various methods (remote sensing analysis and calculation of direct and indirect equivalent carbon emissions). The CO2 cycle positively impacts the sustainability of agroforestry and agroindustry of Gayo Arabica coffee. [ABSTRACT FROM AUTHOR]

The Javan leopard is an apex predator whose distribution is limited to the island of Java and Nusakambangan Island, and it is classified as an endangered species. The forest habitat of the Javan leopard is subject to annual damage, which causes a decrease in its quality and suitability as a habitat for this species. This study aimed to determine the changes in the suitability of the Javan leopard habitat from 2000 to 2020 and the variables affecting it. A habitat suitability analysis was performed using the MaxEnt application. The data included coordinate points and environment variables. The variables included the distance from rivers, undisturbed tropical forests, degraded forests, permanent water, shrubs, farms, plantations, and settlements. The model produced an AUC value of 0.812, indicating the model was good. In the 2000 model year, the highly suitable habitat area of the Javan leopard was 2481.38 km2, the suitable habitat area was 2630.763 km2, and the unsuitable area was 7790.155 km2. In the 2020 model, the area of highly suitable habitat was 1429.647 km2, the area of suitable habitat was 2379.344 km2, and the area that was not suitable was 9093.081 km2. Primary forest was the variable that contributed most to the model. [ABSTRACT FROM AUTHOR]

Sugar is a significant commodity for Indonesia because the need for sugar reaches 7 million tons. Meanwhile, imports from Thailand, Australia, and Brazil were approximately 5.54 million tons in 2020. Sugarcane and sugar production in East Java province is also supported by Lamongan Regency. Therefore, this study aims to evaluate the possible sugarcane plantation expansion areas in Lamongan. The evaluation process carried out in this study was an analysis of land suitability using the analytic network process (ANP) and land availability using an overlay analysis of several policy maps. Three parameters with the highest weight of the ANP were soil drainage (0.181), cation exchange capacity and base saturation (0.134), and rainfall (0.133). The total possible area for sugarcane plantations expansion in Lamongan was 32,552.37 ha and the largest class was Possible Area 2 (65.67%). The three sub-districts with the highest possible areas include Solokuro, Ngimbang, and Mantup. We recommend that the government and stakeholders extend the area allocated to sugarcane plantations in Lamongan because the possible expansion areas are still more than 30 ha, while in the 2011–2031 spatial plan they were only 8927 ha. Expansion plans must take into consideration other uses such as residence, industry, food crops, and protected areas. [ABSTRACT FROM AUTHOR]

Paddy field conversions (PFCs) in Indonesia have become a major threat facing agricultural lands on the local, province, and national levels causing a reduction in rice production and leading to food insecurity. Hence, information on the impacts of PFCs on rice supply is crucial for future policy planning and management. This paper aimed to simulate four policy scenarios to view the impact of paddy fields' conversion on rice production to enhance the planning of future food security-related policies. Based on system dynamics modelling (SDM), the study used primary and secondary data to develop a quantitative Stock Flow Diagram from the qualitative Causal Loop Diagram. Available historical data; paddy cultivated area, population, and harvested areas have been used to test and validate the SDM. The study indicated that the overall maximum relative errors, coefficient of determination, and mean absolute error of tested variables were 1.33%, 0.760, and 1.49, respectively. Under the Business-as-usual scenario, it was found that the population of Karawang will reach 6.1 million people, and the paddy fields will be reduced to only 169 thousand ha by the end of the century. Thus, Karawang rice Availability Per Person, Surplus, and Self-sufficiency will be reduced by 66.20, 50.55, and 359%, respectively. Consequently, the obligation of 1.5 million tons a year cannot be met. Besides that, the Rice demand scenario indicated the Regency will be out of rice surplus in 2094 and 2085 when the demand for rice increases by 80% (SNO#3.3) and 100% (SNO#3.4), respectively. It's been concluded that paddy field conversion is the main threat that may undermine future rice production for Regency. The study recommended that proposed possible interventions should be considered along with formulating and implementing upcoming Regional Short, Medium, and long-term Development Plans. [ABSTRACT FROM AUTHOR]