Spatial Data Analysis for Environmental and Climate Monitoring Using Google Earth Engine and Random Forest Classification
DOI:
https://doi.org/10.64803/ikosstemi.v2.152Kata Kunci:
Google Earth Engine, Random Forest, land-cover classification, Sentinel-2, climate monitoringAbstrak
Land-cover change is a primary driver of environmental degradation and local climate alteration, yet up-to-date, spatially explicit information remains scarce in rapidly developing regions such as North Sumatra, Indonesia. This study aims to develop an accurate, reproducible workflow for environmental and climate monitoring by integrating cloud-based spatial data processing with machine-learning classification. Multitemporal Sentinel-2 surface-reflectance imagery (2019 and 2024) and Landsat-derived land-surface temperature were processed on the Google Earth Engine (GEE) platform. Spectral bands were combined with vegetation, water, and built-up indices (NDVI, NDWI, NDBI, EVI, SAVI, BSI) and topographic data, after which a Random Forest (RF) classifier distinguished six land-cover classes from stratified training samples. The RF model achieved an overall accuracy of 92.4% and a kappa coefficient of 0.906, outperforming Classification and Regression Tree (86.1%) and Support Vector Machine (88.7%) baselines. NDVI, the near-infrared band, and NDWI were the most influential predictors. Between 2019 and 2024 the built-up area expanded by 18.7% while forest and cropland contracted, and these changes coincided with a measurable rise in land-surface temperature, confirming a strong inverse relationship between vegetation cover and surface heating. The results demonstrate that the GEE–RF approach provides a low-cost, scalable, and replicable basis for routine environmental and climate monitoring to support evidence-based regional planning.
Referensi
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052
Belgiu, M., & Dragut, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Habibie, M. I. (2022). The application of machine learning using Google Earth Engine for remote sensing analysis. Jurnal Teknoinfo, 16(2), 233. https://doi.org/10.33365/jti.v16i2.1872
Ouchra, H., Belangour, A., & Erraissi, A. (2024). Supervised machine learning algorithms for land cover classification in Casablanca, Morocco. Ingenierie des Systemes d Information, 29(1), 377–387. https://doi.org/10.18280/isi.290137
Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222. https://doi.org/10.1080/01431160412331269698
Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sensing, 12(15), 2411. https://doi.org/10.3390/rs12152411
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
Supiyandi and R. b. Mailok, "Urban Vegetation Cover Prediction Model Using Google Earth Engine Based on NDVI Sentinel-2 in Batu Bara Regency, Indonesia," 2025 International Conference on Computing and Applied Informatics (ICCAI), Medan, Indonesia, 2025, pp. 1-7, https://doi.org/10.1109/ICCAI65301.2025.11278944
Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
Tassi, A., & Vizzari, M. (2020). Object-oriented LULC classification in Google Earth Engine combining SNIC, GLCM, and machine learning algorithms. Remote Sensing, 12(22), 3776. https://doi.org/10.3390/rs12223776
Thanh Noi, P., & Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18(1), 18. https://doi.org/10.3390/s18010018
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Hak Cipta (c) 2026 Supiyandi Supiyandi, Ramlah binti Mailok (Author)
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