New logic for large-scale land cover classification based on remote sensing

Quanfang Wang, Haiwen Zhang, Hangzhou Sun, Jiayong Li
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引用次数: 2

Abstract

Nowadays it's still very difficult to find accurate information on land-cover areas and types, which mainly results from the confusion between land use types and land cover types (e.g., many researchers equated land cover with land use and land use types were often employed for the replacements of land cover types) and the absence of a standard land cover classification system with an unambiguous, repeatable definition of land cover and quantificational classification criteria so as to the classification result comparable. In this study, a new logic for land cover classification at regional scale has been introduced. The critical features of this classification are that: it's indeed distinguished from land use classification system and driven by remote sensing so that repeatable and efficient re-classifications of existing land cover will be possible; spectrum and primary attributes of plant-canopy structure (i.e. permanence of aboveground live biomass, leaf longevity and leaf type) are adopted as the primary criterions of land cover classification; based on the phonological difference among broadly defined vegetation, some typical land cover is easily distinguished by using the characteristics of seasonal dynamic; mixed land cover is differentiated by its constituent characteristics and influence on land surface processes. Taking the areas between Yangtze River Basin and Weihe River Basin in China as a case and using time-series MODIS 250 m data (i.e. NDVI and reflectance), a two-level hierarchical land cover classification scheme was produced for the areas. At the initial stage, the entire study area was mapped into seven classes, i.e. evergreen cover (woody), seasonal green cover (woody), seasonal green cover (herbaceous), seasonal green cover (crops), seasonal green cover (mixed), grey cover (non-vegetated and terrestrial) and blue cover (aquatic or regularly flooded). The sub-classes includes Coniferous evergreen forest, Broadleaf deciduous forest, Single cropping in one year, Continuous double cropping in one year, grassland, Wetland, Urban or Built-up land, Barren or Sparsely land, River, Lake, Mixed Cover of Crop and tree, etc.
基于遥感的大尺度土地覆盖分类新逻辑
目前,关于土地覆盖面积和类型的准确信息仍然很难找到,这主要是由于土地利用类型和土地覆盖类型之间的混淆(例如,许多研究人员将土地覆盖等同于土地利用,土地利用类型经常被用来代替土地覆盖类型)以及缺乏一个标准的土地覆盖分类体系。可重复定义土地覆被和定量化分类标准,使分类结果具有可比性。本文提出了一种新的区域尺度土地覆盖分类逻辑。这种分类的关键特点是:它确实区别于土地利用分类系统,并由遥感驱动,以便对现有土地覆盖进行可重复和有效的重新分类;采用植物冠层结构的光谱和主要属性(地上生物量持久性、叶片寿命和叶片类型)作为土地覆被分类的主要依据;基于广义植被之间的音系差异,利用季节动态特征可以很容易地区分出一些典型的土地覆盖;混合土地覆被是根据其组成特征和对地表过程的影响来区分的。以中国长江-渭河流域为例,利用MODIS 250 m时序数据(即NDVI和反射率),提出了该地区的两级分层土地覆盖分类方案。在初始阶段,将整个研究区域划分为7类,即常绿植被(木质)、季节性植被(木质)、季节性植被(草本)、季节性植被(作物)、季节性植被(混合)、灰色植被(无植被和陆生)和蓝色植被(水生或定期淹水)。亚类包括:常绿针叶林、阔叶落叶林、一年一熟、一年连作、草地、湿地、城市或建成地、荒地或疏地、河流、湖泊、作物与树木混合覆盖等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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