基于遥感与机器学习的孟加拉国达卡区土地利用及植被变化分析
Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District, Bangladesh
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中文总结 AI 辅助
本研究结合遥感与机器学习，分析孟加拉国达卡区2019-2024年土地利用与植被变化，发现建成区大幅扩张、植被与水体缩减，随机森林分类精度最高，为城市可持续发展提供数据支撑。
中文摘要 AI 辅助
孟加拉国达卡区的快速城市化引发了土地利用和环境状况的重大改变，为制定合理的城市规划和实现生态可持续性，亟需开展系统性监测。本研究利用遥感数据和机器学习技术，分析2019年至2024年间土地覆盖的时空变化及植被动态。研究采用Sentinel-2 MSI和Landsat 8的高分辨率卫星影像，对土地覆盖类型进行分类，并计算归一化植被指数（NDVI）、归一化建筑指数（NDBI）和归一化水体指数（NDWI）等光谱指数。在Google Earth Engine平台内，结合带标注的地理空间训练点，应用包含决策树、K近邻（KNN）和随机森林分类器的监督式机器学习方法，通过混淆矩阵和kappa统计量开展精度评估。结果显示，五年间城市建成区面积增长了59.5%，植被面积显著下降8.46%，水体面积下降7.77%；研究还发现，从植被区和水域向城市基础设施的土地转换是主导趋势。在所有模型中，随机森林的分类精度最高。这些研究结果凸显了达卡无节制城市扩张带来的日益加剧的环境压力，同时表明遥感与机器学习工具可为制定可持续城市发展、土地利用监管及生态系统保护政策提供及时可行的数据支持。
英文摘要
Rapid urbanization in Dhaka District, Bangladesh has triggered substantial alterations in land use and environmental conditions, necessitating systematic monitoring for informed urban planning and ecological sustainability. This study employs remote sensing data and machine learning techniques to analyze spatiotemporal changes in land cover and vegetation dynamics between 2019 and 2024. High-resolution satellite imagery from Sentinel-2 MSI and Landsat 8 was utilized to classify land cover types and compute spectral indices including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). A supervised machine learning approach incorporating Decision Tree, K-Nearest Neighbors (KNN), and Random Forest classifiers was applied using labeled geospatial training points within Google Earth Engine. Accuracy assessments were conducted using confusion matrices and kappa statistics. Results indicate a 59.5% increase in urban built-up areas and a significant decline in vegetation (-8.46%) and water bodies (-7.77%) over the five-year period. Land conversion from vegetated and aquatic areas to urban infrastructure was identified as a dominant trend. Among the models, Random Forest demonstrated the highest classification accuracy. These findings underscore the growing environmental pressures driven by unregulated urban expansion in Dhaka. The study highlights the potential of remote sensing and machine learning tools in providing timely, actionable data to support sustainable urban development, land-use regulation, and ecosystem conservation policies.
