Bedrock composition, in particular the proportion of carbonate, can influence the dissolved inorganic carbon concentration and also the carbonate chemistry of rivers, but its effects on the nitrogen cycle in rivers are usually overlooked. Here we present geochemical composition measurements of rivers across the Pearl River Basin in China that show that dissolved organic nitrogen (DON) is positively correlated with dissolved inorganic nitrogen and follows the variation in solar radiation in the carbonate-dominated region during the wet season, but not in the non-carbonate-dominated region. In situ incubations show that organic nitrogen production is strengthened in the regions with high dissolved inorganic carbon and high temperature and solar radiation. DON zonation in the dry season is not obvious owing to the limitation of low temperature on nitrogen assimilation. We also report a similar DON contrast in carbonate-dominated and non-carbonate-dominated regions in Malang in Indonesia. Furthermore, from an analysis of global published data, we find that a modified Michaelis–Menten model incorporating carbon limitation provides a much better fit to the global latitudinal distribution of DON globally. Hence, we propose that carbonate weathering enhancement of organic nitrogen production occurs ubiquitously.

Massive land use changes in Indonesia driven by deforestation, agricultural expansion, and urbanization have significantly increased landslide susceptibility in upper watersheds. This study focuses on the Sumber Brantas and Kali Konto sub-watersheds where rapid land conversion has destabilized slopes and disrupted ecological balance. By integrating remote sensing, Cellular Automata-Markov (CA-Markov), and Random Forest (RF) models, the research aims to identify optimal land use scenarios for mitigating landslide hazards. Three scenarios were analyzed: business as usual (BAU), land capability classification (LCC), and regional spatial planning (RSP) using 400 field-validated landslide data points alongside 22 topographic, geological, environmental, and anthropogenic parameters. Land use analysis from 2017 to 2022 revealed a 1% decline in natural forest cover, which corresponded to a 1% increase in high and very high landslide hazard areas. From 2017 to 2022, landslide risk increased as the “High” category rose from 33.95% to 37.59% and “Very High” from 10.24% to 12.18%; under BAU 2025, they reached 40.89% and 12.48%, while RSP and LCC reduced the “High” category to 44.12% and 34.44%, respectively. These findings highlight the critical role of integrating geospatial analysis and machine learning in regional planning to promote sustainable land use, reduce landslide hazards, and enhance watershed resilience with high model accuracy (>81%).

The increasing population in Indonesia is challenging rice production to feed more people while rice fields are being converted to other land-use land cover (LULC). This study analyzes land use in 2015, 2017, 2019, 2021, and 2025 using an artificial neural network cellular automata (ANN-CA) and rice data from Statistics Indonesia to predict future rice status in Malang Districts, Indonesia. The primary LULC change driver was the rapid conversion of rice fields, which had their area reduced by 18% from 2019 to 2021 and 2% from 2021 to 2025. Rice fields are mainly being converted to settlements and buildings. The Kappa coefficient of simulation achieved 88%, with 91 accuracies. The model predicted a 2% lower rate of rice production but a 3% higher demand in 2025 compared to 2021. Lower rice production and higher demand are predicted to reduce the rice surplus by 57% in 2025, suggesting that the Malang district might lower its supply of rice to other areas by 2025. Our study provides a food crisis early warning system that decision makers can use to form adequate strategic plans and solutions to combat food insecurity.

The need for irrigation water is influenced by soil water content or more precisely by available water (pF 2.5 and pF 4.2). There is a need for technological breakthroughs in using Unmanned Aerial Vehicle (UAV) to identify water content quickly and broadly and accurately. The study was conducted in an area of ±18 hectares in the Sisim Sub Watershed in September 2019 at 09.00 a.m. Aerial photographs were taken at an altitude of 100 m with DJI Phantom Pro 3.0. The number of observation points was 75 points, where 15 points for validation were calculated based on the map scale. Photo processing was made using Agisoft. The Digital Elevation Model (DEMNAS) with 8.2 m resolution was used to compare the red, green and blue bands. The analysis used was Co-Kriging Geo Statistics Analysis, the compilation of algorithms based on the regression equation and ten index formulations. Validation was done by correlation continued with the regression or paired t-test if the parameter relationship was close. The available water measured in the field ranged from 5.16-48.28%. The results showed that the formulation of soil water content could be run on the Red, Green, and Blue bands, Intensity index, TGI index, ExGreen index and DEMNAS with a weak correlation (below 0.5), where TGI had the highest value (r=0.32). A test of t-pairing was not done because of a weak correlation. The highest estimation of pF 4.2 is DEMNAS (r=0.35), and pF 2.5 was on the TGI index (r=0.4).