Forest cover classification using Landsat ETM plus data and time series MODIS NDVI data
作者:Jia, K (Jia, Kun)[ 1,2 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3 ] ; Zhang, L (Zhang, Lei)[ 4 ] ; Wei, XQ (Wei, Xiangqin)[ 4 ] ; Yao, YJ (Yao, Yunjun)[ 1,2 ] ; Xie, XH (Xie, Xianhong)[ 1,2 ]
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
卷: 33  页: 32-38
DOI: 10.1016/j.jag.2014.04.015
出版年: DEC 2014

摘要
Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data. (C) 2014 Elsevier B.V. All rights reserved.
通讯作者地址: Zhang, L (通讯作者)
 Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.
地址:
 [ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
 [ 2 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
 [ 3 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
 [ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China