Cropland change detection with SPOT-4 VEGETATION imagery in Inner Mongolia, China

The policy of ecological return of cultivated land has been carried out for several years in China and the cultivated land is decreasing. The objective of this study is to explore the potential and the methodology for the cropland change detection with Discrete Fourier Transform (DFT) approach using high temporal resolution imagery and some ancillary data. The data used in this study are 10-day composite SPOT-4 VEGETATION (VGT) Normalized Difference Vegetation Index (NDVI) over the period from April to November in 1998 and 2002 respectively, and the ancillary data include the existing land cover dataset derived from TM images and agricultural phonological calendar. The DFT method was applied to the NDVI data set on a per pixel basis. The magnitude of the difference of amplitudes in the first three harmonics was used to identify the areas where changes might occur, and then the unsupervised classification was used to determine the types of change. The methodology used in this study can minimize the influence of noise and phenology variance to the change detection. The result showed that the significant change of cropland and other land cover can be detected with this method.

[1]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

[2]  Aaron Moody,et al.  Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform , 2001 .

[3]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.

[4]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[5]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[6]  C. Justice,et al.  A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data , 1994 .

[7]  Jiyuan Liu,et al.  Study on spatial pattern of land-use change in China during 1995–2000 , 2003, Science in China Series D Earth Sciences.

[8]  Massimo Menenti,et al.  Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data , 2000 .

[9]  D. Legates,et al.  Crop identification using harmonic analysis of time-series AVHRR NDVI data , 2002 .

[10]  Jiyuan Liu,et al.  Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .

[11]  G. Perry,et al.  Monthly burned area and forest fire carbon emission estimates for the Russian Federation from SPOT VGT , 2003 .

[12]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[13]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[14]  L. Eklundh,et al.  Fourier series for analysis of temporal sequences of satellite sensor imagery , 1994 .

[15]  Alan H. Strahler,et al.  Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change pro , 1994 .

[16]  C. Justice,et al.  Characterization and classification of South American land cover types using satellite data , 1987 .

[17]  Stephen S. Young,et al.  Land-cover change analysis of China using global-scale Pathfinder AVHRR Landcover (PAL) data, 1982?92 , 2001 .

[18]  Wout Verhoef,et al.  Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images , 1993 .

[19]  A. Strahler,et al.  The use of temporal metrics for land cover change detection at coarse spatial scales , 2000 .

[20]  William Salas,et al.  Fourier analysis of multi-temporal AVHRR data applied to a land cover classification , 1994 .

[21]  J. Pereira,et al.  Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia , 2002 .