Mapping sub-pixel forest cover in Europe using AVHRR data and national and regional statistics

There are few examples of satellite-derived land cover maps dedicated specifically to the pan-European area, and even less to Europe's forested land. In an effort to remedy this situation and produce a consistent and comparable forest database for the pan-European area, attempts are made to utilize both spatial information derived from satellite data and more traditional statistical data. This paper discusses the results of combining spectral information derived from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA AVHRR) and official statistical forest data acquired at national and regional levels. Forest probability estimates derived from an AVHRR mosaic of Europe are conjoined with official statistics in an iterative calibration procedure. The resulting database consists of maps of forest‐non-forest for the European Union (EU) and a more detailed database distinguishing the probable proportion of coniferous forest, broadleaf forest, and mixed woodland within each AVHRR pixel for France and Finland. In the latter case, regional statistics are used in the calibration procedure. An area-weighted root mean square error of 7.3% and 4.3%, respectively, was found when comparing the calibrated estimates with the Coordination of Information on the Environment (CORINE) Land Cover and the CORINE with the original AVHRR mosaic (before calibration) for 14 countries of the EU. It was found that for France, the AVHRR-derived forest database tended to underestimate the total forest area for the temperate zones, whereas Mediterranean regions (dominated by so-called other wooded land) tend to be overestimated. It appeared that overestimates of the total forest area in Finland were likely to arise from overestimates in the area of broadleaf woodland. The technique provides an innovative approach to combining statistical data with spatial information in a way so as to add value to both satellite-derived and "ground-based" statistical information.

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

[2]  Kaj Andersson,et al.  Mapping Forest in Europe by Combining Earth Observation Data and Forest Statistics , 2003 .

[3]  Eric F. Lambin,et al.  The surface temperature-vegetation index space for land cover and land-cover change analysis , 1996 .

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

[5]  Y. Yamagata,et al.  Development of WTI and turbidity estimation model using SMA — application to Kushiro Mire, eastern Hokkaido, Japan , 2001 .

[6]  Ruth S. DeFries,et al.  Global continuous fields of vegetation characteristics: A linear mixture model applied to multi-year 8 km AVHRR data , 2000 .

[7]  D. Roy,et al.  Combination of the Normalized Difference Vegetation Index and surface temperature for regional scale European Forest cover mapping using AVHRR data , 1997 .

[8]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[9]  S. Saatchi,et al.  The Global Rain Forest Mapping project - A review , 2000 .

[10]  Jesslyn F. Brown,et al.  Development of a land-cover characteristics database for the conterminous U.S. , 1991 .

[11]  L. Iverson,et al.  A technique for extrapolating and validating forest cover across large regions - Calibrating AVHRR data with TM data , 1989 .

[12]  Kaj Andersson,et al.  A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data , 1997 .

[13]  Ted S. Vinson,et al.  Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images , 1994 .

[14]  K. Kuusela.  Forest resources in Europe, 1950-1990 , 1994 .

[15]  P. Stenberg,et al.  Sub-pixel area estimation of European forests using NOAA-AVHRR data , 1999 .

[16]  Jacques Hebert,et al.  Study on European forestry information and communication system - Reports on forestry inventory and survey systems - Volume 1 - Country report for Belgium , 1997 .

[17]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[18]  Qinghan Xiao,et al.  Land cover classification with AVHRR multichannel composites in northern environments , 1996 .

[19]  R. B.,et al.  The United Nations , 1947, Nature.

[20]  J.-L. Champeaux,et al.  AVHRR-derived vegetation mapping over Western Europe for use in Numerical Weather Prediction models , 2000 .

[21]  D. Roy,et al.  Investigation of the maximum Normalized Difference Vegetation Index (NDVI) and the maximum surface temperature (Ts) AVHRR compositing procedures for the extraction of NDVI and Ts over forest , 1997 .

[22]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[23]  Frédéric Achard,et al.  Global tropical forest area measurements derived from coarse resolution satellite imagery: a comparison with other approaches , 1998, Environmental Conservation.

[24]  Eric F. Lambin,et al.  Land-cover changes in sub-saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale , 1997 .

[25]  A. S. Belward,et al.  Recent activities in the European Community for the creation and analysis of global AVHRR data sets , 1994 .

[26]  Robin L. Graham,et al.  Regional forest cover estimation via remote sensing: the calibration center concept , 1994, Landscape Ecology.

[27]  J. Eidenshink.  The 1990 conterminous U. S. AVHRR data set , 1992 .

[28]  Kungl. Skogsstyrelsen Sweden.  Statistical yearbook of forestry. 1955. , 1955 .

[29]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

[30]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .