Analysing spatio-temporal characteristics of surface parameters from NOAA AVHRR data

Knowledge on the spatio-temporal variability of surface parameters such as surface temperature, albedo or percentage vegetation cover is an important prerequisite for environmental modeling on regional to global scales. Data of this kind are generally lacking. However, they may be derived from remote sensing data with appropriate spatial and temporal resolutions. Given the regional to global extension of model calculations and considering the surface parameters of interest, the AVHRR on board the NOAA satellites currently is the only sensor capable of providing the required information. The paper describes the use of geostatistical methods for the analysis of multitemporal sets of surface parameters as derived from AVHRR data. Through the retrieval of semi-variograms for varying grid-cell sizes and directions, the spatial variability of the given surface parameters is described. By fitting models to the experimental semi-variograms, parameters such as the range and the sill may be retrieved in an consistent, reliable and automatic way. While the systematic analysis of images allows for the description of the spatial distribution of these parameters, the analysis of multitemporal data sets results in the description of their temporal evolution. The paper presents the methodology and results obtained for a multi-year set of HRPT data. It shows the difference in scale, strength, and cyclic behaviour of the spatial variability to be expected from different surface parameters.<<ETX>>

[1]  Ann Henderson-Sellers,et al.  A Climate Modelling Primer , 1987 .

[2]  I. Gibson.  Statistics and Data Analysis in Geology , 1976, Mineralogical Magazine.

[3]  C. Woodcock,et al.  The use of variograms in remote sensing: I , 1988 .

[4]  G. Henebry.  Detecting change in grasslands using measures of spatial dependence with landsat TM data , 1993 .

[5]  Pietro Alessandro Brivio,et al.  Aspects of spatial autocorrelation of landsat TM data for the inventory of waste-disposal sites in rural environments , 1993 .

[6]  Kenneth C. McGwire,et al.  Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland , 1993 .

[7]  David J. Diner,et al.  Retrieval of aerosol optical properties from multi-angle satellite imagery , 1992, IEEE Trans. Geosci. Remote. Sens..

[8]  M. A. Oliver,et al.  Semi‐variograms for modelling the spatial pattern of landform and soil properties , 1986 .

[9]  Covariance of Greenness and Terrain Variables over the Konza Prairie , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[10]  C. Woodcock,et al.  The use of variograms in remote sensing. I - Scene models and simulated images. II - Real digital images , 1988 .

[11]  S. Rasool,et al.  ISLSCP - International Satellite Land-Surface Climatology Project , 1984 .

[12]  P. Curran.  The semivariogram in remote sensing: An introduction , 1988 .

[13]  J. C. Price,et al.  Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer , 1984 .