Volume IV-3/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3/W1, 57-63, 2019
https://doi.org/10.5194/isprs-annals-IV-3-W1-57-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3/W1, 57-63, 2019
https://doi.org/10.5194/isprs-annals-IV-3-W1-57-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  01 Mar 2019

01 Mar 2019

MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

N. Tsutsumida1, S. Nagai2, P. Rodríguez-Veiga1,3,4, J. Katagi5, K. Nasahara6, and T. Tadono7 N. Tsutsumida et al.
  • 1Graduate School of Global Environmental Studies, Kyoto University, Kyoto, Japan
  • 2Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Kanagawa, Japan
  • 3Centre for Landscape and Climate Research, University of Leicester, Leicester, UK
  • 4NERC National Centre for Earth Observation (NCEO), Leicester, UK
  • 5Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan
  • 6Faculty of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan
  • 7Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Ibaraki, Japan

Keywords: LULC, Geographically weighted, Accuracy assessment, Forest type classification

Abstract. Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures.