Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product  

作　　者：Ram C. Sharma Keitarou Hara Hidetake Hirayama Ippei Harada Daisuke Hasegawa Mizuki Tomita Jong Geol Park Ichio Asanuma Kevin M. Short Masatoshi Hara Yoshihiko Hirabuki Michiro Fujihara Ryutaro Tateishi 

机构地区：[1]Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan [2]Center for Environmental Remote Sensing, Chiba University, Chiba, Japan [3]Graduate School of Informatics, Tokyo University of Information Sciences, Chiba, Japan [4]Foundation of River and Basin Integrated Communications, Tokyo, Japan [5]Natural History Museum and Institute, Chiba, Japan [6]Department of Regional Design, Tohoku Gakuin University, Sendai, Japan [7]Awaji Landscape Planning and Horticulture Academy, University of Hyogo, Hyogo, Japan

出　　处：《Advances in Remote Sensing》2017年第1期54-65,共12页遥感技术进展（英文）

摘　　要：Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.

关 键 词：VEGETATION PHYSIOGNOMY MULTI-SPECTRAL TOPOGRAPHY MODIS Mapping Machine Learning Japan MCD12Q1 

分 类 号：R73[医药卫生—肿瘤]