刘小龙等:Classification of C3 and C4 Vegetation Types Using MODIS and ETM plus Blended High Spatio-Temporal Resolution Data
被阅读 890 次
2016-01-08
Classification of C3 and C4 Vegetation Types Using MODIS and ETM plus Blended High Spatio-Temporal Resolution Data
作者:Liu, XL (Liu, Xiaolong)[ 1,2,3,4 ] ; Bo, YC (Bo, Yanchen)[ 1,2,3 ] ; Zhang, J (Zhang, Jian)[ 1,2,3 ] ; He, YQ (He, Yaqian)[ 1,2,3,5 ]
REMOTE SENSING
卷: 7  期: 11  页: 15244-15268
DOI: 10.3390/rs71115244
出版年: NOV 2015
 
摘要
The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R-2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area.
 
通讯作者地址: Bo, YC (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Res Ctr Remote Sensing & GIS, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Res Ctr Remote Sensing & GIS, Beijing 100875, Peoples R China
[ 2 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
[ 3 ] Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China
[ 4 ] Yunnan Normal Univ, Coll Tourism & Geog Sci, Kunming 650500, Peoples R China
[ 5 ] W Virginia Univ, Dept Geol & Geog, Morgantown, WV 26506 USA