中国1：25万遥感物候模式分区（2021年）

植被物候变化是造成自然场景土地覆被类内光谱特征异质的主要原因，本数据集利用遥感观测的反映地表植被绿度状态的植被指数和反映地表植被生长发育节律的关键物候期构建分区指标体系，以反映微地貌形态、坡面属性及地表物质组成的地貌小区为分区单元，采用空间约束层次聚类算法，构建了面向自然场景土地覆被分类的中国遥感物候模式分区，可为全国尺度土地覆被典型样本库构建、土地覆被分区分类策略实施等提供数据支持。Vegetation phenological changes represent the primary cause of intra-class spectral characteristic heterogeneity of land covers in natural scenes. This dataset constructs a zoning indicator system by utilizing vegetation indices (reflecting the greenness status of surface vegetation) and key phenological periods (capturing the growth and developmental rhythms of surface vegetation) derived from remote sensing observations. Taking geomorphic units that characterize micro-topographic morphology, slope attributes and surface material composition as zoning units, this work developed a remote sensing phenological pattern regionalization of China for natural scene land cover classification through spatially constrained hierarchical clustering algorithms. It can provide data support for the construction of national-scale typical land cover sample databases, the implementation of land cover zoning and classification strategies, and other relevant research and application scenarios.

搜集汇总

背景与挑战

背景概述

该数据集是基于2021年遥感观测构建的中国1:25万遥感物候模式分区，旨在通过植被指数和关键物候期指标，以地貌小区为单元，采用空间约束层次聚类算法，反映植被物候变化对土地覆被光谱异质性的影响。它主要用于支持全国尺度土地覆被典型样本库构建和分区分类策略，为土地覆被研究提供数据基础。