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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Management Research)
Alyavara Mayang Ferynandari
Brawijaya University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Remote Sensing for Sustainable Development: Multi-Temporal Landsat Analysis of Land-Use Change and Urbanization in the Rejoso Watershed (2005–2024) Alyavara Mayang Ferynandari; Ery Suhartanto; Linda Prasetyorini
Jurnal Penelitian Pendidikan IPA Vol 12 No 1 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i1.13639

Abstract

Rapid urbanization and shifting agricultural practices are reshaping watershed sustainability in Indonesia, yet their spatial and hydrological implications in the Rejoso Watershed (East Java) remain insufficiently quantified. This study evaluates land-use/land-cover (LULC) dynamics over 2005–2024 using multi-temporal Landsat imagery from five observation years (2005, 2011, 2015, 2020, and 2024). A hybrid classification (ISODATA clustering combined with visual interpretation) was validated using 250 ground points and confusion matrix metrics (overall accuracy and Kappa). Vegetation declined from 54.72% (197.11 km²) in 2005 to its minimum in 2020 at 38.06% (137.09 km²), then recovered to 41.28% (148.70 km²) in 2024. Agricultural land expanded from 32.14% (115.77 km²) to 52.28% (188.32 km²) in 2020 before contracting to 46.96% (169.14 km²) in 2024, indicating a notable post-2020 trend reversal with vegetation regrowth and reduced cropland extent. Built-up areas increased steadily (4.14% to 7.54%), while open land fluctuated and water bodies remained <1% with a slight decline. The 2020 map achieved the highest accuracy (95.83%; κ=0.96). These findings highlight upstream LULC reconfiguration and continued downstream urbanization, supporting integrated watershed management, upland rehabilitation, and stricter spatial planning.
Evaluasi Kinerja Teknik Koreksi Bias untuk Meningkatkan Akurasi Global Precipitation Measurement (GPM) Di Daerah Aliran Sungai Rejoso, Indonesia (Performance Evaluation of Bias Correction Techniques for Enhancing Global Precipitation Measurement (GPM) Accuracy in the Rejoso Watershed, Indonesia) Alyavara Mayang Ferynandari; Ery Suhartanto; Linda Prasetyorini
Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Management Research) Vol 9 No 2 (2025): Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Manageme
Publisher : Asosiasi Peneliti dan Teknisi Kehutanan dan Lingkungan Hidup Indonesia (APTKLHI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59465/jppdas.2025.9.2.165-180

Abstract

Enhancing the accuracy of satellite rainfall data from the Global Precipitation Measurement (GPM) satellite is essential to support its vital role in hydrological applications and disaster management. This study aims to identify biases and inaccuracies in GPM data, particularly in regions characterized by complex terrain and extreme rainfall, which significantly impact the reliability. To address this, various bias-correction techniques, including Linear Scaling, Regression Analysis, and Correction Factor Methods, were applied and evaluated using metrics such as the Nash-Sutcliffe Efficiency (NSE) and correlation coefficients. The results indicate that the Linear Scaling method outperforms the others, achieving the greatest NSE of 0.906 and a correlation coefficient of 0.904. Simultaneously, the Regression and Correction Factor methods exhibited robust performance with NSE values ranging from 0.79 to 0.80. The application of the correction arises from the merging of the Linear Scaling method with the IDW-based spatial interpolation technique, facilitating the validation of the spatial distribution of adjusted rainfall data. This hybrid methodology enhances comprehension of spatial-temporal variability and extreme phenomena. This study enhances the precision of satellite rainfall data and lays a foundation for hydrological modeling and disaster risk mitigation.
Co-Authors Ery Suhartanto Ery Suhartanto Linda Prasetyorini Linda Prasetyorini