Version 1
: Received: 2 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (12:59:35 CEST)
How to cite:
Junior Fernandes, P.; Nagai, M. Land Cover Change Detection with Optical and SAR Satellite Data after Tropical Cyclone Seroja - Case Study in Dili, Timor Leste. Preprints2024, 2024100171. https://doi.org/10.20944/preprints202410.0171.v1
Junior Fernandes, P.; Nagai, M. Land Cover Change Detection with Optical and SAR Satellite Data after Tropical Cyclone Seroja - Case Study in Dili, Timor Leste. Preprints 2024, 2024100171. https://doi.org/10.20944/preprints202410.0171.v1
Junior Fernandes, P.; Nagai, M. Land Cover Change Detection with Optical and SAR Satellite Data after Tropical Cyclone Seroja - Case Study in Dili, Timor Leste. Preprints2024, 2024100171. https://doi.org/10.20944/preprints202410.0171.v1
APA Style
Junior Fernandes, P., & Nagai, M. (2024). Land Cover Change Detection with Optical and SAR Satellite Data after Tropical Cyclone Seroja - Case Study in Dili, Timor Leste. Preprints. https://doi.org/10.20944/preprints202410.0171.v1
Chicago/Turabian Style
Junior Fernandes, P. and Masahiko Nagai. 2024 "Land Cover Change Detection with Optical and SAR Satellite Data after Tropical Cyclone Seroja - Case Study in Dili, Timor Leste" Preprints. https://doi.org/10.20944/preprints202410.0171.v1
Abstract
This study utilizes a combination of optical PlanetScope imagery and Synthetic Aperture Radar (SAR) data from Sentinel-1 to investigate land cover changes in Dili, Timor-Leste, following Tropical Cyclone Seroja. The primary objective is to evaluate how integrating these datasets enhances disaster monitoring and response in flood-affected areas. The Random Forest classifier, known for its robustness in handling high-dimensional and noisy data, was applied to classify land cover into six distinct classes: vegetation, water, built-up areas, bare soil, clouds, and shadows. The classification was performed across three phases—pre-disaster, post-disaster, and recovery—using Google Earth Engine (GEE). Binary segmentation using Otsu thresholding was applied to the SAR images to refine the classification by delineating water bodies from non-water areas. The study achieved high classification accuracy, with overall accuracy ranging from 97.2% to 98.7%, and the Kappa index, which measures agreement between classified and reference data, remained consistently strong, indicating reliable model performance. Key results include a significant increase in water bodies and extensive damage to vegetation and built-up areas during the post-disaster phase, followed by partial recovery in the subsequent period. Despite the high accuracy, urban areas posed classification challenges due to misclassification between built-up and bare soil categories. This research offers a methodological framework for integrating optical and SAR data with machine learning for land cover change detection in post-disaster scenarios. The societal benefits of this study include improved disaster preparedness, enhanced recovery planning, and valuable insights into flood impact mitigation in regions like Timor-Leste, where technical and data limitations persist.
Keywords
land cover change; change detection; disaster monitoring; Random Forest classifier; Otsu Thresholding; SAR data; optical imagery; Timor-Leste
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
