Developing indices of temporal dispersion and continuity to map natural vegetation |
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| Institution: | 1. National Engineering Research Centre of Geospatial Information Technology, Key Laboratory of Spatial Data Mining & Information Sharing of the Ministry of Education, Fuzhou University, Fuzhou 350002, Fujian, China;2. Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68558, USA;1. MaIAGE, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France;2. INRA, UMR1347 Agroécologie, 21065 Dijon, France;1. Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China;2. School of Architecture and Materials, Chongqing College of Electronic Engineering, Chongqing 401331, China;1. Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA;2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;1. School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China;2. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | An accurate and updated natural vegetation map is imperative for sustainable environmental management. This paper proposed a novel natural vegetation mapping algorithm based on time series images. Several indices of temporal dispersion and continuity were established for this purpose: low density (LD), medium density (MD), high density (HD) and medium continuity (MC). These indices were developed based on the particular percentiles-determined section of the EVI2 temporal profiles obtained through continuous wavelet transform. The natural vegetation was generally characterized as with lower temporal dispersion and greater temporal continuity compared with agricultural crops. The proposed methodology incorporated the indices of temporal dispersion and continuity and was applied to 13 provinces in central East China based on 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index with two bands (EVI2) in 2013. An overall accuracy of 92.97% was obtained when compared with 2715 ground truth sites. There was also a good agreement (kappa index = 0.8049) on the distribution and areas of different vegetation types between the MODIS-estimated image and the Landsat 8 OLI interpreted data on two test regions. This study demonstrated the efficiency of the transform and metric integrated time series classification approaches in the fields of land and vegetation cover mapping. |
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| Keywords: | Vegetation mapping Temporal dispersion Temporal continuity Time series classification Continuous wavelet transform |
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