Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments

The need for utilizing long, consistent time series of NOAA-AVHRR NDVI data is particularly pressing in semi-arid countries where, however, the scarce availability of high resolution Local Area Coverage (LAC) data often leads to their substitution with Global Area Coverage (GAC) images, with consequent loss of spatial detail. The efficient integration of the few available LAC images with the more abundant GAC data is therefore a topic of high practical importance. On the basis of previous investigations about the integration of data with different spatial and temporal properties, a new approach is currently proposed to generate a NDVI data set with enhanced features. The approach merges the temporally stable, high spatial resolution land cover variations derived from LAC images with the temporally variable NDVI information brought by GAC data. After its presentation, the approach is applied to a case study using LAC and GAC images taken over two semi-arid Mediterranean African countries, Algeria and Tunisia. The result is an integrated data set with the pixel size of the LAC images (about 1 km) and the temporal coverage of the GAC data. The evaluation of this product by comparison to independent LAC images indicates its good quality in terms of both radiometric and geometric features.

[1]  Effects of environmental spatial variability on the differences between NOAA-AVHRR LAC and GAC NDVI data , 2001 .

[2]  Fabio Maselli,et al.  Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments , 1998 .

[3]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[4]  R. H. Evans,et al.  Nonlinearity corrections for the thermal infrared channels of the advanced very high resolution radiometer: assessment and recommendations , 1993 .

[5]  John R. G. Townshend,et al.  Global data sets for land applications from the Advanced Very High Resolution Radiometer: an introduction , 1994 .

[6]  C. Justice,et al.  An evaluation of the global 1-km AVHRR land dataset , 2000 .

[7]  S. Prince A model of regional primary production for use with coarse resolution satellite data , 1991 .

[8]  Fabio Maselli,et al.  Definition of Spatially Variable Spectral Endmembers by Locally Calibrated Multivariate Regression Analyses , 2001 .

[9]  C. Rao,et al.  Degradation of the visible and near-infrared channels of the advanced very high resolution radiometer on the NOAA-9 spacecraft : assessment and recommendations for corrections , 1993 .

[10]  S. Los Linkages Between Global Vegetation and Climate: An Analysis Based on NOAA Advanced Very High Resolution Radiometer Data. Degree awarded by Vrije Universiteit, Amsterdam, Netherlands , 1998 .

[11]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[12]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

[13]  J. Snyder,et al.  Map projections for global and continental data sets and an analysis of pixel distortion caused by reprojection , 1995 .

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

[15]  H. Kerdiles,et al.  NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa , 1995 .

[16]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[17]  K. Edwards,et al.  The use of intensity-hue-saturation transformation for producing color shaded-relief images , 1994 .

[18]  W. J. Carper,et al.  The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data , 1990 .

[19]  Fangju Wang,et al.  Fuzzy supervised classification of remote sensing images , 1990 .

[20]  J. W. Brown,et al.  Exact Rayleigh scattering calculations for use with the Nimbus-7 Coastal Zone Color Scanner. , 1988, Applied optics.