Mapping the human footprint from satellite measurements in Japan |
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| Institution: | 1. Graduate School of Life and Environmental Science, University of Tsukuba, 1-1-1, Tennoudai, Tsukuba, Ibaraki 305-8572, Japan;2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100857, China;1. Faculty of Geography, Philipps-Universität Marburg, Deutschhausstrasse 10, D-35032 Marburg, Germany;2. Institute of Geography, University of Hamburg, Bundesstrasse 55, D-20146 Hamburg, Germany;3. Deutsches GeoForschungsZentrum (GFZ), Telegrafenberg, D-14473 Potsdam, Germany;4. Institute of Geobotany, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, D-06108 Halle/Saale, Germany;5. TARL, The University of Texas at Austin, 1 University Station R7500, Austin, TX 78712, USA;6. State Key Laboratory of Grassland Agri-ecosystem, College of Life Science, Lanzhou University, Lanzhou, Gansu 730000, China;7. Faculty of Biology, Department of Ecology, Philipps-Universität Marburg, Karl-von-Frisch Str. 8, D-35043 Marburg, Germany;1. Graduate School of Life and Environmental Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, Ibaraki 305-8572, Japan;2. Faculty of Life and Environmental Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, Ibaraki 305-8572, Japan;3. Kasumigaura Environmental Science Center, Okijuku-machi 1853, Tsuchiura, Ibaraki 300-0023, Japan;1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation & Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong 510275, China;2. Guangdong Climate Center, Guangzhou, Guangdong 510640, China |
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| Abstract: | 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. |
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| Keywords: | Human footprint Impervious surface Cropland Temporal mixture analysis MODIS NDVI time series |
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