Name
Title | ||
|---|---|---|
Land use/cover characterizaitoin with MODIS time series data with hybrid classification mothed over Australia for 2001 and 2003 |
Abstract | ||
|---|---|---|
Improved and up-to-date land use/land cover (LULC) data sets are needed over the whole country of Australia to support science and policy applications focused on understanding the role and response of the LULC to environmental change. The main goal of this study was to map LULC in Australia using MODIS 250 m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI) and reflectance time series data of 2000 and 2003. NDVI time-series were filtered by the Savitzky-Golay algorithm in the present study to smooth out noise. A combination of unsupervised ISODATA and a hierarchical decision tree classification were performed on 2 years 12-month time-series MODIS data. Also, Australian Vegetation Map and other land use/land cover data set were used as labeling reference during the classification process. The MODIS land cover products were evaluated using existing land use/cover data derived from Landsat TM as reference data (AUS-2000), also LULC information derived from 11 scenes of Landsat-5 TM data were used as validation data source. The overall classification accuracy was 76.4%. It turned out that our result is acceptable because the relative high resolution of MODIS data and more prior knowledge was applied. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5417761 | IGARSS |
Keywords | Field | DocType |
environmental change,radiometry,lswi,lulc data sets,data smoothing,smoothing methods,normalized difference vegetation index,savitzky-golay algorithm,ndvi,pattern classification,modis time series data,land surface vegetation index,unsupervised isodata,s-g filter,australia,reflectance time series data,geophysical signal processing,lulc,environmental factors,spectroscopy,ad 2003,hierarchical decision tree classification,vegetation mapping,ad 2001,land use-land cover characterization,australian vegetation map,hybrid classification method,landsat-5 tm data,landsat tm data,decision trees,time series,high resolution,labeling,remote sensing,time series data,reflectivity,decision tree,reference data,satellites | Reference data (financial markets),Time series,Data set,Vegetation,Computer science,Remote sensing,Normalized Difference Vegetation Index,Multispectral pattern recognition,Land cover,Land use | Conference |
Volume | ISSN | ISBN |
3 | 2153-6996 | 978-1-4244-3395-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaishan Song | 1 | 66 | 17.79 |
| Mingming Jia | 2 | 366 | 41.64 |
| Mohsin Hafeez | 3 | 2 | 1.75 |
| Zongming Wang | 4 | 72 | 19.71 |
| Dongmei Lu | 5 | 8 | 2.46 |
| Lihong Zeng | 6 | 0 | 1.69 |
| Dianwei Liu | 7 | 7 | 3.32 |
| Bai Zhang | 8 | 20 | 8.49 |
| Jia Du | 9 | 12 | 4.51 |
| Qingfeng Liu | 10 | 0 | 0.68 |