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A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S. BELWARD Institute for Environment and Sustainability, EC Joint Research Centre, Italy.

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Presentation on theme: "A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S. BELWARD Institute for Environment and Sustainability, EC Joint Research Centre, Italy."— Presentation transcript:

1 A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S. BELWARD Institute for Environment and Sustainability, EC Joint Research Centre, Italy D. ERCHOV Center of Forest Ecology and Productivity, Russia Global Land Cover 2000

2 SPOT 4 - VEGETATION Data Type of data product Standard S10 products including:  Spectral channels  NDVI  Angular information  Status map Geographic window : 42 0 N N and 5 0 E E Time window : 3 d decade of March 1999 – 1 st decade of November 1999 (23 of S10 products)

3 STEP 1: Image pre-processing and generation of advanced data products STEP2: Image classification SPOT4- VGT S10 data Contaminated pixels and snow cover detection Generated masks Generation of the advanced data products Seasonal mosaics Wetness Index Anisotropy Index Wave- Likeness Index Snow Cover ISODATA clustering of seasonal mosaics Initial labelling of clusters Spectral- temporal clusters map Semantic clusters map Decomposing of ambiguous semantic clusters Mono- semantic clusters map Land Cover Map Merging of semantic clusters into thematic classes Land Cover Mapping Method GIS Database (topographic and thematic maps, DEM, forest inventory statistics and etc) Derived Auxiliary Products

4 From Hall et al., 1998: "Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Ice- and Sea Ice-Mapping Algorithms. Version 4.0" Normalised Difference Snow Index

5 Detection of the contaminated pixels Step 1: Detection of the pixels utterly contaminated by snow and clouds with pre-specified thresholds pixel with co-ordinates at fixed decade of observation t* set of pixels with presence of snow or/and ice set of pixels with presence of clouds set of pixels without presence of snow/ice or clouds

6 Detection of the contaminated pixels Steps 2  J: Detection of the defective detectors and “slightly” contaminated by snow/clouds pixels with adaptive thresholds derived from time series of data

7 Seasonal Mosaics spring summer autumn

8 Wave-Likeness Index (WLI) t1t2t3t4t5t6t7t8t9……………tn-1tn time of observation NDVI (NDVI e, t e ) (NDVI max, t max ) (NDVI b, t b ) a b -1 d d a where Cropland

9 Bi-spectral Gradient Wetness Index (BGWI) SWIR NIR BGWI Wetland BGWI-NDVI- BGWI Summer Mosaic NIR-MIR-RED Pure Water Analysing pixel

10 Surface Anisotropy Relative Linear Indexes (SARLI) RED-NIR: Slope - Slope - Interception NIR-MIR: Slope - Slope - Interception SARLI is derived based on the linearised RPV Bi-directional Reflectance Distribution Function model to characterise a surface anisotropy properties

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13 The SibTREES Land Cover Classes Floristic differences Removed for GLC 2000 Global legend. New class = Evergreen needle leaf

14 Available Country-wide Forest Inventory Data to compare with GLC2000 Map Forest inventory database contains for each forest management unit the data on forest area, tree species composition, volume, area of non-forested land categories and some other information

15 Forest Management Units selected for comparison with GLC 2000 map 679 forest management units

16 GLC200 Forest Cover in comparison to Forest Inventory Data

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18 GLC 2000 Map in comparison to SPOT-HRV image SPOT-HRV Image Simplified Forest Map Simplified GLC 2000 Map SPOT-VGT Image

19 Ongoing development to improve the Northern Eurasia’s GLC2000 Product  splitting some of the forest classes according to trees cover density  reducing of ambiguity between “cropland” and “grassland” classes  introducing to the map legend the mosaic classes such as “cropland/natural vegetation” and “forest/other vegetation”  comparison (pre-validation checking) with available forest inventory and other available land cover data to expose main divergences


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