Addressing the global food demand is an urgent priority for governments worldwide. Efficient and effective methods for gauging crop production are crucial. Relying solely on ground-based measurements proves inefficient and expensive, prompting exploration of remote sensing using vegetation indices as a viable alternative. This study sought to achieve three objectives: estimating chlorophyll content in paddy fields, evaluating leaf nitrogen content, and predicting yields. The investigation utilized Sentinel-2A satellite imagery, Soil Plant Analysis Development (SPAD) for chlorophyll measurement, and employed statistical and accuracy analyses. Findings revealed an increase in chlorophyll and leaf nitrogen content from the vegetative to maturity phases, followed by a decline at maturity. NDVI and GNDVI emerged as superior to SAVI and VARI for chlorophyll estimation, attributed to their spectral sensitivity. Likewise, nitrogen prediction showed similar trends, with NDVI and GNDVI exhibiting better RMSE values compared to SAVI and VARI, albeit marginally. However, yield prediction accuracy varied, with NDVI proving most accurate, followed by SAVI, VARI, and GNDVI, indicating the latter's reduced predictive precision due to nitrogen sensitivity. In scenarios where nitrogen is not the predominant yield-limiting factor, NDVI could outperform GNDVI in forecasting yield. Received: 2023-07-22 Revised: 2024-04-18 Accepted: 2024-08-24 Published: 2024-10-10

The current activities in photogrammetry technology such as the permission to apply non-metric cameras, development of Structure from Motion (SfM), and potential usage of videogrammetry are part of the answers to the need for low-cost camera-based mapping. Therefore, this study aimed to test and compare the accuracy of photogrammetry and videogrammetry methods for three-dimensional (3D) modeling obtained using a non-metric camera with SfM processing. Terrestrial Laser Scanner (TLS) was used to obtain comparative data and the results showed a degradation of photo resolution in videogrammetry method, causing a reduction in the number of point clouds produced compared to photogrammetry. Moreover, the point cloud test showed that the surface variation results for both methods were identical to 3D modeling with a higher point density recorded in photogrammetry and the relative distance was different by 0.125 meters. The average difference in point cloud between photogrammetry and TLS was 0.062 meters while videogrammetry and TLS had 0.106 meters. The absolute test produced an RMSE value of 0.022 meters for photogrammetry and 0.032 meters for videogrammetry at a 95% confidence interval, indicating the two methods produced similar data quality. The results led to the conclusion that videogrammetry had satisfactory values and could be used as an alternative in 3D modeling but was not considered better than photogrammetry. Received: 2023-08-13 Revised: 2024-05-27 Accepted: 2024-09-20 Published: 2024-10-10

Recent peri-urbanization, primarily characterized by declining agricultural land and a growing population, is a primary driver of peri-urban dynamics. As urban-centric activities and demands rise, unmanaged urban sprawl causes socio-cultural disruptions, pollution, and economic instability. The intricate processes dependable for these negative impacts are primarily associated with population dynamics and land use changes. This study investigates the trends of demographic and land-use changes in the peri-urban area and evaluates the interrelationships between these two factors. We utilize the multi-decade population and land-use transformations from 1990 to 2020 in the peri-urban area of Denpasar City – one of the most metropolitan areas in Indonesia. The results reveal that the peri-urban areas encountered unprecedented population growth and urban sprawl. The results indicate that the inner peri-urban area has encountered more substantial changes in population and land use than the outer area. Nevertheless, specific demographic trends have a positive influence on peri-urban land-use transformation. This elucidates why land use is progressively diverging from household demographic dynamics, specifically in population growth, population density, and the proportion of males and individuals in non-productive age groups. The interlinked relationship between these variables suggests land use conversion in peri-urban areas. The results emphasize the need for governmental planners to carefully examine demographic and land-use trends when formulating regional cross-border plans to promote sustainable peri-urban development.

Semarang City is facing significant environmental challenges, with land subsidence being a critical issue that intensifies flood inundation and worsening flood damage. As urban areas expand and climate change impacts become more pronounced, understanding and mitigating flood risks are crucial for sustainable urban development and disaster management. Therefore, this study aimed to assess flood risk induced by land subsidence using machine learning to improve flood management. Five different machine learning models (MLMs) were used to assess flood risk, which included Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Additionally, fourteen different indices and 2884 sample points were used to train and test the models, with hyperparameter optimization ensuring fairness in comparisons. To address uncertainty in the sample dataset, flood hot spots were used to validate the rationality of flood risk zoning maps. The study investigated driving factors of different flood risk levels, focusing on flood areas to determine flood risk mechanisms in the highest-risk areas. The results showed that KNN performed the best and provided the most reasonable flood risk value among the models. Meanwhile, curve number (CN), distance to the river (DTRiver), and Building Density (BD) were identified as the top three significant factors of flood risk, ranked using the average score decrease in KNN model. Finally, this study expanded the application of machine learning for flood risk assessment and also deepened understanding of the potential mechanisms of flood risk, and provided perceptions about better flood risk management.

This study examines the severe dust storm from May 12-16/2022,culminating on May 15, with a comprehensive analysis and explanation. The weather maps from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) weather maps were used to identify systems and patterns that contributed to the storm's activity, continuity, and tracking as well as maps of pressure compounds and wind vectors in levels 1000 and 850 mbar that appear with the dust state for the same days accompanied by tracking patterns in the middle of the turbosphere 500 mbar to give a comprehensive analytical view of climate conditions at each level of pressure and higher systems supportive of their persistence on the surface. The northwesterly winds are the main factor that carries dust over long distances. The eastern desert in Syria, the Empty Quarter in the Kingdom of Saudi Arabia, the desert of western Iran, and the desert region of western Iraq are among the main sources of dust In its atmosphere. Based on weather maps of the surface and upper levels of storm days, the concentration of dust reached very high levels in Iraq's airspace and surrounding countries, including Egypt, Jordan, Lebanon, Palestine, and Syria. The intensity of the dust gradually decreased as the area was affected by wet westerly winds with relatively low temperatures and a relative increase in wind speeds due to the impact of the study area on the atmospheric decline centered around the Turkish island of Cyprus.

Indonesia has the largest mangrove forest in the world, around 3.3 million hectares or 19.5% of the entire mangrove’s world population. Mangroves have many ecological and economic benefits and are also threatened by several conditions, such as a decrease in area, land, degradation, and the health of mangrove vegetation. One of the methods in maintaining the sustainability of mangrove ecosystems is mapping the biophysical aspects of vegetation, namely mapping the percentage of mangrove canopy cover using field measurements or remote sensing. This study aims to compare the accuracy of Light Detection and Ranging (LiDAR) data based on All Return Cover Index (ARCI) and First Return Cover Index (FRCI) algorithms in mapping the percentage of mangrove canopy cover and analyzing its spatial distribution. The study area is a mangrove forest in Ratai Bay Pesawaran Lampung. This forest is dominated by a dense and evenly distributed canopy cover class with an average value of 78.24% which was acquired using the hemispherical photography method. ARCI and FRCI methods are dominated by the dense and evenly distributed cover class with an average percent cover value of 85.39% and 89.78%, respectively. The accuracy of mapping the percentage of mangrove canopy cover using FRCI is higher than ARCI, with a maximum accuracy value of 93.08% and a standard error of 5.95%. That value shows that using LiDAR data with the FRCI method for mapping the percentage of mangrove canopy cover produces a high accuracy value.

Settlement as a place to live is one of the main needs that need to be met for the survival of human life. The need for settlement will increase along with the increase in population. The development of settlements in Sewon District. Bantul, Indonesia which is very dynamic requires monitoring to ensure that the distribution of settlements is in accordance with the spatial pattern plan that has been determined by the local government. Imagery as a remote sensing product which is then processed with the help of GIS [HV1] can be used in monitoring the distribution of settlements because it can provide more detailed information regarding land use, including settlements. This research aims to evaluate the distribution of settlements against the detailed spatial plan (RDTR) for the Sewon Urban Area (BWP) of Bantul Regency in 2018-2038. This study uses visual interpretation techniques of SPOT-7 PMS imagery in mapping existing settlement land, field surveys, and GIS processing. The research results show that mapping settlement land using imagery produces an overall accuracy of 95.20%. The settlement evaluation shows that there is a suitability of settlement land with the detailed spatial plan reaching 579.88 hectares or 87.26%, while the settlement land that is not in accordance with the detailed spatial plan is 9.62 hectares or 1.45%, and the settlement land that is temporarily not in accordance with the detailed spatial plan is 75.05 hectares or 11.29% of the total settlement area in Sewon District. Local governments must pay more attention to existing settlements with regular monitoring so that the existing settlements that are not in accordance with the detailed spatial plan will not expand.

This research highlights the lifestyle of digital nomads, which is becoming a more common lifestyle in today's digital age. Bali is one of the most popular destinations for nomads. This research aims to explore the motivations of digital nomads in Bali. The research uses the case study research method by mapping several informants as research subjects. This article discusses the experiences of foreign tourists in Bali, particularly digital nomads, and the migratory lifestyle. It explores conversations and perspectives on Bali's attractiveness, working life, cultural interaction, and modern mobility. The findings show that the concept of 'nomad' is associated not only with high levels of mobility, but also with individuals who have chosen to settle in Bali due to finding the culture and lifestyle appealing. Modern mobility reflects the search for meaning in one's life experiences, which may include a search for stability and a depth of experience in one place. Furthermore, the discussion examines that the emergence of Bali's digital nomad community brings both economic benefits and challenges of overtourism on local communities.

The Kumitir site, associated with the Majapahit Empire, is a significant archeological discovery. Archeologists from the East Java Cultural Heritage Preservation Center (BPCB), uncovered a structure at this site, buried beneath boulder-sized rocks. According to historical literature, the collapse of Majapahit was caused by volcanic eruptions from the Anjasmoro, Arjuno, or Welirang complexes. Therefore, this study aimed to recreate the gravity-driven mass flow covering the Kumitir Site. Geological surveys, including sediment structure analysis and grain orientation measurements, were conducted to provide new information on paleocurrent and ancient sedimentary processes at the site. Digital Elevation Map (DEM) and the Laharz simulation tool facilitated the creation of reconstructed lahar flow maps using open-source DEM data with an eight-meter resolution. The results of the boulder analysis showed that a paleochannel played a significant role in the burial site, with two sources identified, namely Mount Welirang (Welirang alluvial fan) and the Anjasmoro complex (Old Jatirejo alluvial fan). Meanwhile, the combination of methods applied signified the direction of the Welirang alluvial fan (ESE-NNW) and the Jatirejo Tua alluvial fan (SSW-NNE). Volumes of 9 million m3 and 65 million m3 were the most relevant parameters for estimating the lahar flows of the western and eastern craters, respectively.

The rapid population growth and rising per capita incomes have caused the city to generate massive amounts of municipal waste, indicating a serious environmental threat. Solid waste disposal has become a crucial problem for several municipalities. The selection of suitable waste disposal sites is a crucial problem in the urban areas of developing countries like Pakistan due to unsatisfactory urban planning and management to decrease human being’s health risks. Particularly, Dera Ghazi (D.G.) Khan district is facing the issue of identifying suitable sites for solid waste disposal. This research aims to select appropriate potential sites suitable for solid waste disposal purposes in the D.G. Khan. Primary datasets used for this study are Landsat-8 satellite imagery, digital elevation model (DEM) with 30-meter resolution for slope extraction. Other criteria included roads, railroads, and rivers digitized using the topographical map of the study area. The maps are prepared to incorporate overlay and suitability analysis using Geographic Information System (GIS), Remote Sensing techniques, and Analytical Hierarchy Process (AHP), a multi-criteria analysis. The final suitability map of study area is prepared using a GIS software suite and categorized as highly, moderately, least, and unsuitable regions, of which 3% is entirely unsuitable area, 70% less suitable, 26.16% moderately suitable, and 0.84% highly suitable area. The suggested disposal sites have been carefully selected to assist policymakers in determining the most sensitive areas and resolving waste management issues with the slightest contamination of water bodies and the environment.