Monitoring forest dynamics with multi-scale and time series imagery

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• 作者：Chunbo Huang ; Zhixiang Zhou ; Di Wang…
• 关键词：Forest dynamics ; Remote sensing ; Multi ; scale ; Time series ; NDVI
• 刊名：Environmental Monitoring and Assessment
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：188
• 期：5
• 全文大小：1,864 KB
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• 作者单位：Chunbo Huang (1)
  Zhixiang Zhou (1)
  Di Wang (1)
  Yuanyong Dian (1)
  
  1. College of Horticultural and Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Monitoring, Environmental Analysis and Environmental Ecotoxicology
  Ecology
  Atmospheric Protection, Air Quality Control and Air Pollution
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-2959

文摘

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.