Vol. 19 No. 2 (2025), published on December 2025, comprises 5 research articles unified by an environmental-hazard and resource-management theme. The issue opens with a systematic literature review: Sampelan, Pratiwi, Baihaqi, and Agustiarini (Climatological Station of West Nusa Tenggara, BMKG) conduct a PRISMA-compliant survey of spatial air temperature modeling from remote sensing data using machine learning approaches, drawing on 1,487 screened articles from Google Scholar and Scopus published between 2016 and 2025, and analyze trends in algorithm preference, predictor variables, and validation metrics across the reviewed corpus (doi: 10.12962/inderaja.v19i2.8450, pp. 62–74). The following three articles are empirical case studies covering distinct hazard domains: Suharyanto and Bioresita (Department of Geomatics Engineering ITS) apply Landsat-9 data combined with Multi-Criteria Decision Analysis (MCDA) to delineate flood vulnerability zones in Bekasi Regency, identifying 86,454.87 ha (68.5%) as Medium risk and 21,831.52 ha as High risk (doi: 10.12962/inderaja.v19i2.7758, pp. 82–89); Wardhana, Bioresita, and Hayati (Department of Geomatics Engineering ITS) develop an adaptive threshold method utilizing active remote sensing data (SAR) to detect and analyze oil spill distribution in the western Java Sea, an area of intensive oil industry and maritime activity (doi: 10.12962/inderaja.v19i2.7799, pp. 90–100); Iqbal, Yanuarsyah, and Hermawan (Department of Informatics Engineering, Ibn Khaldun University of Bogor) employ the Relative Difference Normalized Difference Vegetation Index (rdNDVI) on the Google Earth Engine platform to detect landslide potential in Leuwiliang District, Bogor Regency (doi: 10.12962/inderaja.v19i2.8801, pp. 105–113). The closing article addresses renewable energy: Hafizh and Prarikeslan (Department of Geography, Universitas Negeri Padang) carry out a high-resolution rooftop solar photovoltaic potential assessment for Padang Utara Subdistrict using entirely open-source remote sensing data and cloud-computing workflows (doi: 10.12962/inderaja.v19i2.8800, pp. 101–104). The methodological landscape of this issue reflects a pronounced shift toward computational platforms — MCDA, GEE, and adaptive threshold algorithms — and machine learning as a recurring analytical paradigm, while the satellite data portfolio centers on Landsat-9 (flood), SAR/oil spill, and open-source optical data (solar PV); institutional authorship is heavily concentrated at the Department of Geomatics Engineering ITS (appearing in three articles via Bioresita), followed by BMKG (one SLR article) and Ibn Khaldun University of Bogor (one landslide article), indicating a coherent research cluster anchored at ITS.