The Land Cover Map of Northern Eurasia method, product and initial users' feedback Global Land Cover 2000 S. Bartalev, A. Belward EC JRC, Italy   D. Erchov and A. Isaev CFEP RAS, Russia

SPOT 4 - VEGETATION Data Type of satellite data : S10 product, including Sun -Earth - Sensor angular data Geographic window : 420N - 750N and 50E -1800E Time windows : two time-windows were considered: Data from March to November of year 1999 were used to produce the land cover classification Data from June to August of year 2000 were employed for the burned area class updating

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

Normalised Difference Snow Index Snow/Ice Clouds Water Vegetation Red channel NDSI

Contaminated pixels detection Step 1: Detection of the snow related pixels Step 2: Detection of the pixels contaminated by clouds Step 3: Detection of the pixels contaminated by defective SWIR detectors

Snow-free duration within observation period Observation period considered is 21.03.99 - 11.11.99 of year 1999

Seasonal Mosaics spring summer autumn

The seasonal dynamic of the land cover types’ spectral signatures RED Bright soil Dark soil Water Spruce forest Larch forest Pine forest Broadleaf forest Grassland NIR Soil line Max LAI line Spring Summer Autumn

Wave-Likeness Index (WLI) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 t1 t2 t3 t4 t5 t6 t7 t8 t9 … tn-1 tn time of observation NDVI (NDVI e, t e) (NDVI max, t max) (NDVI b, t b) a b -1 d Cropland where

Comparison WLI and Land-Use Map for Northern Kazakhstan Wave-Likeness Index Land Use Map

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

Thematic interpretation of clusters Both spectral properties and environmental criteria were involved into experts' analysis to assign thematic labels for the clusters: (i) relations between spectral properties and land cover characteristics (green biomass, water contents and etc.). (ii)  geographical distribution of natural and anthropogenic phenomena, environmental relation and processes, and etc. Spectral properties Semantic hypothesis Spectral clusters Thematic classes Environmental criteria Thematic interpretation process

Use of the advanced data products for land cover mapping Seasonal dynamic of the clusters' spectral signatures with respect to soil-line was essential to assign main vegetation and land cover types BGWI was employed to assign clusters to either wetland or dryland cover types WLI separated cropland from other vegetation types Snow cover duration was used to localise classes belonging to the tundra Anisotropy indexes were used to separate deciduous forest and humid grassland, and also dark evergreen needle-leaf forest and Palsa-bogs classes

Environmental criteria for the thematic interpretation of clusters Geographical location Physiographic factors (climate, altitude above the see level and etc.) Spatial pattern (compactness, dispersivity, and etc.) Spatial context Known facts regarding natural and anthropogenic disturbances in the ecosystems Natural ecosystem processes (successions, phenology)

The land cover map updating Summer of 1999 Summer of 2000 Forest burns occurred during fire season of year 2000 were detected to update the land cover map

Land Cover Map legend OTHER VEGETATION TYPES AND COMPLEXES TUNDRA WETLANDS FORESTS SHRUBLANDS GRASSLANDS NON-VEGETATED LAND COVER TYPES

6 7 1 2 5 8 4 3

Land Cover Map : 1. Moscow region

Land Cover Map : 2. Northern part of East Siberia (Evenkiya)

Land Cover Map : 3. Altai region

Land Cover Map : 4. Baykal lake region

Land Cover Map : 5. Central part of Yakutia region

Land Cover Map : 6. Magadan region

Land Cover Map : 7. Kamchatka peninsula

Land Cover Map : 8. Sakhalin island

The Land Cover Map validation Qualitative validation Elimination of macroscopic errors in the land cover map Evaluation of map acceptance by potential regional users Quantitative validation Cross-comparison with existing data on the land cover (national statistic, maps and etc.)

Qualitative Validation Approach Validation is based on 20 х 20 regular grid cells Systematic evaluation by group of experts for different ecosystem types (forest, tundra, cropland and etc.) using available reference data and experts’ knowledge Final revision of the land cover map based on the validation database records to eliminate macroscopic errors

Qualitative Validation Data Base

Sources of reference data for Qualitative Validation

Quantitative Validation Approach Use of the national forest statistic and land-use map for the comparison with GLC 2000 land cover map Estimation of the land cover classes' area at the level of administrative regions of Russia Statistical cross- comparison of the land cover classes' proportion

Administrative regions of Russia

Forested area from GLC 2000 map against forest statistics Total forest cover of Russia, thousand ha   Total official forest statistic of Russia, 1998 774250.9 Russian Forest Service Data, 1998 643048.3 Forest map of former USSR (ed. A.S. Isaev, 1990) 791049.9 Northern Eurasia map, GLC 2000 796047.3 Percent of forest cover by administrative regions of Russia, % National forest statistic, 1998 Northern Eurasia map, GLC 2000 Forest Service statistics, 1998 Northern Eurasia map, GLC 2000

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

GLC 2000 map in comparison to forest map of Russia Non-changed forest classes Deciduous Broadleaf forest is replaced by Evergreen Needleleaf Evergreen Needleleaf forest is replaced by Deciduous Broadleaf Forests (mainly Deciduous Broadleaf) is not included into National forests statistics

Cropland area from GLC-2000 map against IIASA's Land Use map Cropland area is derived from both maps at the level of administrative regions of Russia.

Use of GLC 2000 Map for forest fire monitoring in Russia GLC 2000 map is employed by Moscow Regional Environment Department for forest fire monitoring. Active fires (on the map in the red colour) are detected from NOAA-AVHRR satellite data.

CONCLUSIONS A new Northern Eurasia land cover map has been created as a part of Global Land Cover 2000 project The EC Joint Research Center and Russian Academy of Science have established this map to support forest and land management throughout Northern Eurasia The map is made up of a series of advanced products derived from the S10 VEGETATION-SPOT4 data, including seasonal mosaics, snow duration, directional properties describing anisotropy, wetness index, phonological descriptors

CONCLUSIONS The land cover map has been qualitatively validated by group of regional experts and compared with national forest statistics and land-use data. Validation has shown satisfactory accuracy and reliability of the map Comparison of the land cover map with existing forest map indicated areas of the forest type changes as result of logging, fires and forest successions and demonstrated limitation of Russian forest inventory system The land cover map is already employed by forest fire monitoring system of Moscow region and number of other potential users is foreseeing