Mapping the human footprint from satellite measurements in Japan
Mapping the human footprint from satellite measurements in Japan
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DOI：
10.1016/j.isprsjprs.2013.11.020
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发表时间：
2014-02
影响因子：
12.7
通讯作者：
Fan Yang;B. Matsushita;Wei Yang;T. Fukushima
中科院分区：
文献类型：
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作者：
Fan Yang;B. Matsushita;Wei Yang;T. Fukushima
随着全球城市化进程的加快和气候变化的加剧，“人类足迹”的量化已成为生物多样性保护和区域环境管理领域的迫切目标。人类足迹的定义是特定人类活动对地球表面的影响，主要表现为不透水表面（与工业和城市化有关）和耕地（与农业有关）。在这里，我们提出了一种方法，称为排序的时间混合分析与后分类（STMAP）映射不透水表面和农田同时在子像素级，以满足需求，精确的人类足迹信息在全国范围内。STMAP方法应用四端元排序的时间混合分析，提供万年青林，落叶林，农田，不透水的表面作为第一步的初始分数。端元选择从排序的时间配置文件的MODIS标准化差异植被指数（NDVI），作为指导的主成分分析。年最高地表温度和平均稳定夜间光照进行统计分析，以提供阈值后分类，以进一步区分农田和落叶林和裸露的土地不透水表面。作为STMAP的四个输出，森林、农田、不透水面和裸地的分数被导出。我们使用的参考地图的不透水面和农田获得的Landsat/TM和ALOS精确的土地利用/土地覆盖图在亚像素级的性能进行评估，所提出的方法。高空间分辨率的历史卫星图像被用于进一步评估STMAP方法得出的农田结果。结果表明，STMAP方法在估算日本不透水面和农田方面具有很好的精度。与STMAP方法得到的均方根误差为6.3%的估计不透水面和9.8%的估计耕地。本研究结果可为遥感技术在生态学研究和环境管理中的应用提供新的思路和方法。
Due to increasing global urbanization and climate change, the quantification of “human footprints” has become an urgent goal in the fields of biodiversity conservation and regional environment management. A human footprint is defined as the impact of a particular human activity on the Earth’s surface, which can be represented mainly by impervious surfaces (related to industry and urbanization) and cropland (related to agriculture). Here we present a method called sorted temporal mixture analysis with post-classification (STMAP) for mapping impervious surfaces and cropland simultaneously at the subpixel level to fill the demand for precise human footprint information on a national scale. The STMAP method applies a four-endmember sorted temporal mixture analysis to provide the initial fractions of evergreen forests, deciduous forests, cropland, and impervious surfaces as a first step. Endmembers are selected from the sorted temporal profiles of the MODIS-normalized difference vegetation index (NDVI), as guided by a principal component analysis. The yearly maximum land surface temperatures and averaged stable nighttime light are then statistically analyzed to provide the thresholds for post-classification to further separate cropland from deciduous forest and bare land from impervious surface. As the four outputs of STMAP, the fractions of forest, cropland, impervious surfaces and bare land are derived. We used the reference maps of impervious surfaces and cropland obtained from the Landsat/TM and ALOS precise land-use/land-cover map at the subpixel level to evaluate the performance of the proposed method, respectively. Historical satellite images with high spatial resolution were used to further evaluate the cropland results derived with the STMAP method. The results showed that the STMAP method has promising accuracy for estimating impervious surfaces and cropland in Japan. The root mean square errors obtained with the STMAP method were 6.3% for the estimation of impervious surfaces and 9.8% for the estimation of cropland. Our findings can extend the applications of remote sensing technologies in ecological research and environment management on a large scale.