LaHMa: a landscape heterogeneity mapping method using hyper-temporal datasets
LaHMa: a landscape heterogeneity mapping method using hyper-temporal datasets
复制标题
LaHMa：一种使用超时态数据集的景观异质性绘图方法
DOI：
10.1080/13658816.2012.712126
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发表时间：
2012
影响因子：
5.7
通讯作者：
A. Skidmore
中科院分区：
文献类型：
--
作者：
C. Bie;T. Nguyen;Amjad Ali;R. Scarrott;A. Skidmore
一种新的定量方法提取景观异质性地图（LaHMa）从超时相遥感数据。特征提取方法是数据驱动的，无偏的，并建立在常用的数据减少技术的迭代自组织数据分析（ISODATA）聚类的支持下，发散分离指数。首先，通过ISODATA聚类对归一化植被指数（NDVI）的时空变化进行分类。其次，一系列准备好的聚类图被覆盖，以检查和检测聚类之间的边界在同一位置出现的频率。这一步确定聚类之间的边界强度，并检测聚类内部的空间异质性。该方法的结果进行了探索，典型的农业定义的景观湄公河三角洲，越南，使用NDVI图像时间序列从SPOT植被和MODIS-Terra。该方法提取有用的景观异质性特征，可以支持土地覆盖制图，需要破碎和土地覆盖梯度的信息。
A new quantitative method extracts a landscape heterogeneity map (LaHMa) from hyper-temporal remote-sensing data. The feature extraction method is data-driven, unbiased, and builds on the commonly used data reduction technique of Iterative Self-Organizing Data Analysis (ISODATA) clustering with the support of divergence separability indices. First, the relevant spatial–temporal variation in normalized difference vegetation index (NDVI) is classified through ISODATA clustering. Second, a series of prepared cluster maps are overlaid to examine and detect the frequency with which boundaries between clusters occur at the same location. This step identifies the boundary strength between clusters and detects spatial heterogeneity within them. Results of the method are explored for the typical agriculture-defined landscape of the Mekong delta, Vietnam, using NDVI-imagery time-series from SPOT-Vegetation and MODIS-Terra. The method extracts useful landscape heterogeneity features and can support land-cover mapping requiring information on fragmentation and land-cover gradients.