DocumentCode
512996
Title
Land use/land cover (LULC) characterizaitoin with MODIS time series data in the Amu River Basin
Author
Song, Kaishan ; Wang, Zongming ; Liu, Qqingfeng ; Lu, Dongmei ; Yang, Guang ; Zeng, Lihong ; Liu, Dianwei ; Zhang, Bai ; Du, Jia
Author_Institution
Northeast Inst. of Geogr. & Agric. Ecology, CAS, Changchun, China
Volume
4
fYear
2009
fDate
12-17 July 2009
Abstract
Improved and up-to-date land use/land cover (LULC) data sets are needed over intensively land use/cover change area in the Amur River Basin (ARB) to support science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the Amur River Basin using MODIS 250 m Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) time series data in 2001 and 2007. A combination of unsupervised ISODATA and hierarchical decision tree classification were performed on 12-month time-series of MODIS NDVI data over the study region. The MODIS land cover result of Northeast China was evaluated using existing land use/cover data, and the rest part was evaluated by LULC information derived from LANDSAT-TM. MODIS 250m NDVI, LSWI and reflectance datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multitemporal signatures of the major land cover types over the region. The overall classification accuracy was 0.81 and the kappa coefficient is 0.64. In conclusion, this method has been used successively for LULC change monitoring in the year 2001 and 2007. The result indicate that MODIS 250 NDVI time series data can derive relatively accurate LULC information for hydrological and climate modeling.
Keywords
decision trees; environmental factors; environmental science computing; geophysical signal processing; hydrological techniques; pattern classification; rivers; time series; vegetation mapping; AD 2001; AD 2007; Amu river basin; LANDSAT-TM; MODIS LSWI time series; MODIS NDVI time series data; MODIS time series data; climate modeling; environmental change issues; hierarchical decision tree classification; hydrological modeling; land surface water index; land use-land cover characterization; normalized difference vegetation index; northeast China; unsupervised ISODATA; Classification tree analysis; Decision trees; Land surface; MODIS; Reflectivity; Remote sensing; Rivers; Satellites; Spatial resolution; Vegetation mapping; Amur River Basin; LULC; NDVI; S-G filters;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location
Cape Town
Print_ISBN
978-1-4244-3394-0
Electronic_ISBN
978-1-4244-3395-7
Type
conf
DOI
10.1109/IGARSS.2009.5417375
Filename
5417375
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