用卫星数据对比阿曼各省份2016-2021年土地利用变化
Comparative Analysis of the Land Use and Land Cover Changes in Different Governorates of Oman using Spatiotemporal Multi-spectral Satellite Data
- 基于哨兵2号多光谱数据，用机器学习分类土地类型
- 发现城市扩张和耕地变化在不同地区差异显著
- 适合关注中东地区可持续发展与环境监测的研究者
土地覆盖与土地利用（LULC）变化是卫星影像的重要应用，在资源管理、城市化、土壤与环境保护及可持续发展方面具有关键作用。现有研究广泛采用多光谱时空卫星数据结合先进机器学习算法来监测和预测LULC变化。本研究利用开源哨兵2号（Sentinel-2）卫星数据，分析并比较2016至2021年间阿曼苏丹国各行政区（省）的年度土地利用变化。采用监督学习算法对水体、农作物、城市用地等土地覆盖类型进行训练与分类，构建模型后应用于研究区域，实现了该时段内不同行政区间的有效对比评估。
原文摘要 · Abstract (English)
Land cover and land use (LULC) changes are key applications of satellite imagery, and they have critical roles in resource management, urbanization, protection of soils and the environment, and enhancing sustainable development. The literature has heavily utilized multispectral spatiotemporal satellite data alongside advanced machine learning algorithms to monitor and predict LULC changes. This study analyzes and compares LULC changes across various governorates (provinces) of the Sultanate of Oman from 2016 to 2021 using annual time steps. For the chosen region, multispectral spatiotemporal data were acquired from the open-source Sentinel-2 satellite dataset. Supervised machine learning algorithms were used to train and classify different land covers, such as water bodies, crops, urban, etc. The constructed model was subsequently applied within the study region, allowing for an effective comparative evaluation of LULC changes within the given timeframe.
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