Automated Forest Loss Detection Using Machine Learning Algorithms and Sentinel-2 Imagery on Google Earth Engine: A Study in Ca Mau Province, Vietnam
DOI:
https://doi.org/10.5281/zenodo.17068824Keywords:
machine learning, random forest, sentinel-2, Ca Mau, forest loss detectionAbstract
This study presents the development and testing of machine learning models for automated forest loss detection in Ca Mau Province. The entire workflow, from preprocessing Sentinel-2 satellite imagery (Surface Reflectance - L2A) to model training and evaluation, was implemented on the Google Earth Engine (GEE) cloud computing platform. The study applied cloud filtering techniques to obtain images with cloud cover below 40%, while extracting 10 spectral bands and 8 vegetation indices, with delta indices (ΔNDVI, ΔEVI, ΔNBR, ΔSAVI) serving for model training and evaluation. Three machine learning algorithms were employed including Random Forest, Support Vector Machine (SVM), and Gradient Boosting Trees (GBT). The study utilized 2,500 sample points collected with a balanced ratio of 50% forest loss and 50% non-forest loss, and after extracting features for these sample points, the data was divided into 80% for training (train and validation) and 20% for testing, maintaining the 50:50 ratio in each subset. Evaluation results showed that the Gradient Boosting Trees model achieved the highest performance with an overall accuracy of 85.40%, recall of 72.00%, precision of 98.36%, and F1-score of 83.14%. With considerable recall and very high precision, the system demonstrates high reliability when alerting forest loss while detecting the majority of actual forest loss cases. The study successfully developed an automated forest loss detection tool on GEE, providing effective information to support local forest management efforts. However, further research on integrating Sentinel-1 radar data is needed to overcome cloud cover limitations during the rainy season and further enhance detection capabilities.
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[1] Knehtl, M., Petkovska, V., and Urbanič, G., "Is it time to eliminate field surveys from hydromorphological assessments of rivers?—Comparison between a field survey and a remote sensing approach," Ecohydrology, vol. 11, no. 2, p. e1924, 2018.
[2] Mumby, P. J., Green, E. P., Edwards, A. J., and Clark, C. D., "The cost-effectiveness of remote sensing for tropical coastal resources assessment and management," Journal of Environmental Management, vol. 55, no. 3, pp. 157-166, 1999.
[3] Asner, G., "Cloud cover in Landsat observations of the Brazilian Amazon," International Journal of Remote Sensing, vol. 22, pp. 3855-3862, 2001.
[4] Eberhardt, I. D. et al., "Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas," Remote Sensing, vol. 8, no. 3, 2016.
[5] He, J. et al., "Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests," Frontiers in Remote Sensing, Mini Review vol. Volume 5 - 2024, 2024.
[6] Thanh Son, N., Chen, C.-F., Chang, N.-B., Chen, C.-R., Chang, L.-Y., and Bui, X.-T., "Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and Object-Based Image Analysis," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp. 503-510, 2015.
[7] Chitra, N. T., Anusha, R., Kumar, S., Chandana, D. S., Harika, C., and Kumar, V. U., Satellite Imagery for Deforestation Prediction using Deep Learning. pp. 522-525, 2021
[8] Nichols, K. and Hosein, P., Estimating Deforestation using Machine Learning Algorithms. pp. 82-87, 2021
[9] Saha, S., Bhattacharjee, S., Shit, P. K., Sengupta, N., and Bera, B., "Deforestation probability assessment using integrated machine learning algorithms of Eastern Himalayan foothills (India)," Resources, Conservation & Recycling Advances, vol. 14, p. 200077, 2022.
[10] Torres, D. L. et al., "Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images," Remote Sensing, vol. 13, no. 24, 2021.
[11] Vo, Q. T., Oppelt, N., Leinenkugel, P., and Kuenzer, C., "Remote Sensing in Mapping Mangrove Ecosystems — An Object-Based Approach," Remote Sensing, vol. 5, no. 1, pp. 183-201, 2013.
[12] Ủy ban nhân dân tỉnh Cà Mau, (2025), Quyết định về việc công bố hiện trạng rừng tỉnh Cà Mau năm 2024.
[13] Drusch, M. et al., "Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services," Remote Sensing of Environment, vol. 120, pp. 25-36, 2012.
[14] Pham, T.-D., Xia, J., Baier, G., Le, N., and Yokoya, N., Mangrove Species Mapping Using Sentinel-1 and Sentinel-2 Data in North Vietnam. 2019
[15] Hoang, T. T., Truong, V. T., Hayashi, M., Tadono, T., and Nasahara, K. N., "New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring," Remote Sensing, vol. 12, no. 17, 2020.
[16] Nguyen, H.-H., Bich, N. T. N., Nghia, N. H., Lan, T. T. N., Nguyen, T. T. H., and Dang, H. V., "Estimation of changes in above-ground biomass and carbon stocks of mangrove forests using sentinel-2a in Thai Thuy district, Thai Binh province during 2015 - 2019," Vietnam Journal of Science and Technology, vol. 60, no. 1, pp. 73-91, 2022.
[17] Immitzer, M., Vuolo, F., and Atzberger, C., "First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe," Remote Sensing, vol. 8, p. 166, 2016.
[18] Hoscilo, A. and Lewandowska, A., "Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data," Remote Sensing, vol. 11, p. 929, 2019.
[19] Schuster, C., Förster, M., and Kleinschmit, B., "Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data," International Journal of Remote Sensing, vol. 33, pp. 5583-5599, 2012.
[20] Pradhan, S., Tiwari, K., and Dhar, A., "Evaluation of Sentinel 2 Red Edge Channel for Enhancing Land Use Classification," 2021, pp. 79-89.
[21] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote Sensing of Environment, vol. 202, pp. 18-27, 2017.
[22] Praticò, S., Solano, F., Di Fazio, S., and Modica, G., "Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation," Remote Sensing, vol. 13, no. 4, 2021.
[23] Chen, B. et al., "A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 131, pp. 104-120, 2017.
[24] Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., and Gascon, F., Sen2Cor for Sentinel-2. p. 3, 2017
[25] Skakun, S. et al., "Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2," Remote Sensing of Environment, vol. 274, p. 112990, 2022.
[26] European Space, A., "Sentinel-2 User Handbook," in "ESA Standard Document," European Space Agency2015.
[27] Delegido, J., Verrelst, J., Alonso, L., and Moreno, J., "Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content," Sensors, vol. 11, no. 7, pp. 7063-7081, 2011.
[28] Wang, L., Silván-Cárdenas, J., and Sousa, W., "Neural Network Classification of Mangrove Species from Multi-seasonal Ikonos Imagery," Photogrammetric Engineering & Remote Sensing, vol. 74, pp. 921-927, 2008.
[29] Rouse, J., Haas, R., Schell, J., and Deering, D., "Monitoring Vegetation Systems in the Great Plains with ERTS," NASA Special Publication, vol. 1, 1974.
[30] Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G., "Overview of the radiometric and biophysical performance of the MODIS vegetation indices," Remote Sensing of Environment, vol. 83, no. 1, pp. 195-213, 2002.
[31] Huete, A. R., "A soil-adjusted vegetation index (SAVI)," Remote Sensing of Environment, vol. 25, no. 3, pp. 295-309, 1988.
[32] Key, C. and Benson, N., "Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio," 2006, pp. LA 1-51.
[33] Lu, D., Mausel, P., Brondízio, E., and Moran, E., "Change Detection Techniques," International Journal of Remote Sensing, vol. 25, 2004.
[34] Thái, V. V., Nguyễn, H.-H. H., Lê Thị Quỳnh, Ngọc, N. M., Hiếu, T. P. H., and Liêm, N. D., "Sử dụng chỉ số viễn thám phát hiện mất rừng trên ảnh Sentinel-2 tại tỉnh Thừa Thiên-Huế," Tạp chí Khoa học và Công nghệ Lâm nghiệp, vol. 4, 2021.
[35] Miller, J. D. and Thode, A. E., "Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)," Remote Sensing of Environment, vol. 109, no. 1, pp. 66-80, 2007.
[36] Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J., and Elith, J., "A comparison of resampling methods for remote sensing classification and accuracy assessment," Remote Sensing of Environment, vol. 208, pp. 145-153, 2018.
[37] A. Ramezan, C., A. Warner, T., and E. Maxwell, A., "Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification," Remote Sensing, vol. 11, no. 2. doi: 10.3390/rs11020185
[38] Belgiu, M. and Drăguţ, L., "Random forest in remote sensing: A review of applications and future directions," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24-31, 2016.
[39] Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J. P., "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 93-104, 2012.
[40] Friedman, J., "Greedy Function Approximation: A Gradient Boosting Machine," The Annals of Statistics, vol. 29, 2000.
[41] Mountrakis, G., Im, J., and Ogole, C., "Support vector machines in remote sensing: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247-259, 2011.
[42] Pham, T. D., Yokoya, N., Bui, D. T., Yoshino, K., and Friess, D. A., "Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges," Remote Sensing, vol. 11, no. 3, 2019.
[43] Rainio, O., Teuho, J., and Klén, R., "Evaluation metrics and statistical tests for machine learning," Scientific Reports, vol. 14, 2024.
[44] Congalton, R. and Green, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Third Edition. 2019
[45] Foody, G. M., "Status of land cover classification accuracy assessment," Remote Sensing of Environment, vol. 80, no. 1, pp. 185-201, 2002.
[46] He, H. and Garcia, E. A., "Learning from Imbalanced Data," Knowledge and Data Engineering, IEEE Transactions on, vol. 21, pp. 1263-1284, 2009.
[47] Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A., "Good practices for estimating area and assessing accuracy of land change," Remote Sensing of Environment, vol. 148, pp. 42-57, 2014.
[48] Story, M. and Congalton, R. G., "Accuracy assessment: a user’s perspective," Photogrammetric Engineering, vol. 52, no. 3, pp. 397-399, 1986.
[49] Hansen, M. C. et al., "High-Resolution Global Maps of 21st-Century Forest Cover Change," Science (New York, N.Y.), vol. 342, pp. 850-853, 2013.
[50] Sokolova, M. and Lapalme, G., "A systematic analysis of performance measures for classification tasks," Information Processing & Management, vol. 45, pp. 427-437, 2009.
[51] Bullock, E. L., Woodcock, C. E., and Olofsson, P., "Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis," Remote Sensing of Environment, vol. 238, p. 110968, 2020.
[52] Pontius, R. and Millones, M., "Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment," International Journal of Remote Sensing, vol. 32, pp. 4407-4429, 2011.
[53] Dascălu, A., Catalão, J., and Navarro, A., "Detecting Deforestation Using Logistic Analysis and Sentinel-1 Multitemporal Backscatter Data," Remote Sensing, vol. 15, no. 2, 2023.
[54] Vargas, C., Itoh, T., Tsuji, S., Koide, T., Hirose, K., and Okonogi, H., "Automatic Deforestation Detection Methodology Using Sentinel-1," in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 6590-6593.
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