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通过整合卫星和空中遥感数据来绘制土地利用和土地覆盖变化的地图

Meng-Hsuan Lin^1,2, Ying-Tong Lin³, Min-Lin Tsai¹

¹Research Center for Environmental Changes, Academia Sinica, 128, Academia Rd., Sec.2, Nankang, 11529, Taipei, Taiwan.

Environmental monitoring and assessment

|February 8, 2024

概括

一个新的基于现象学的分类模型 (PCM) 使用遥感数据准确地绘制了年度土地利用和土地覆盖变化 (LULCC) 的地图. 这种方法增强了环境监测,并减少了对不同土地类型的手动分类工作.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
地理信息系统 (GIS) 是指地理信息系统.

背景情况:

土地利用和土地覆盖变化 (LULCC) 动态对于环境监测,管理和政策制定至关重要.
评估LULCC的准确和有效方法对于了解环境变化至关重要.

研究的目的:

开发和评估一种基于现象学的分类模型 (PCM),用于绘制年度LULCC的地图.
整合光谱,时间和高度数据,以提高LULCC分类准确度.
分析特定研究领域的LULCC动态和相关性.

主要方法:

利用来自SPOT图像的月度规范差异植被指数 (NDVI) 和近红外 (NIR) 来测定时间地形特征.
整合规范差异构建指数 (NDBI) 从Landsat和空中LiDAR天花板高度到PCM.
将PCM性能与随机森林和主要组件分析进行比较.

主要成果:

PCM的整体性能为0.85,超过了其他方法.
在森林,建筑用地,内水,农业和草原/灌木中确定了年度LULCC动态.
揭示了土地类型之间的显著相关性,例如草原/灌木和农田之间的负相关性 (r = -0.79).

结论:

PCM提供了一种高效准确的方法,用于年度LULCC映射,减少手工工作.
在陶高原地区的分析显示了土地类型的波动和相互依赖.
台北市的分析表明,城市化和城市绿化之间存在平衡,局部绿化事件普遍存在.

关键词:

空载激光雷达的天花板高度.土地覆盖面/土地利用变化土地卫星图像土地卫星图像现象学现象学是指现象学.这是SPOT图像.

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