Paper Info
Title
Land use/land cover (LULC) characterizaitoin with MODIS time series data in the Amu River Basin
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.
Year
DOI
Venue
2009
10.1109/IGARSS.2009.5417375
IGARSS
Keywords
Field
DocType
amu river basin,ad 2007,normalized difference vegetation index,ndvi,modis ndvi time series data,pattern classification,rivers,modis time series data,unsupervised isodata,climate modeling,hydrological techniques,environmental science computing,hydrological modeling,environmental change issues,s - g filters,landsat-tm,geophysical signal processing,lulc,amur river basin,environmental factors,hierarchical decision tree classification,vegetation mapping,s-g filters,ad 2001,land use-land cover characterization,land surface water index,decision trees,time series,modis lswi time series,northeast china,reflectivity,river basin,remote sensing,satellites,decision tree,spatial resolution,time series data,environmental change,temporal resolution,climate model
Time series,Climate model,Drainage basin,Computer science,Remote sensing,Normalized Difference Vegetation Index,Environmental change,Multispectral pattern recognition,Land cover,Land use
Conference
Volume
ISSN
ISBN
4
2153-6996
978-1-4244-3395-7
Citations 
PageRank 
References 
0
0.34
0
Authors
9
Name
Order
Citations
PageRank
Kaishan Song16617.79
Zongming Wang27219.71
Qingfeng Liu300.68
Dongmei Lu482.46
Guang Yang511.10
Lihong Zeng601.69
Dianwei Liu773.32
Bai Zhang8208.49
Jia Du9124.51