Transcript A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S.

A Land Cover Map of Eurasia’s
Boreal Ecosystems
S. BARTALEV, A. S. BELWARD
Institute for Environment and Sustainability, EC Joint Research Centre, Italy
Global Land Cover 2000
D. ERCHOV
Center of Forest Ecology and Productivity, Russia
SPOT 4 - VEGETATION Data
Type of data product
Standard S10 products including:
 Spectral channels
 NDVI
 Angular information
 Status map
Geographic window :
420N - 750N and 50E -1800E
Time window :
3d decade of March 1999 – 1st decade
of November 1999 (23 of S10 products)
Land Cover Mapping Method
STEP 1: Image pre-processing and generation of advanced
data products
Seasonal
mosaics
SPOT4VGT S10
data
Contaminated
pixels and
snow cover
detection
STEP2: Image classification
ISODATA
clustering of
seasonal
mosaics
WaveLikeness
Index
Generation of
the advanced
data products
Spectraltemporal
clusters
map
Semantic
clusters
map
Anisotropy
Index
Wetness
Index
Generated
masks
Snow
Cover
GIS Database
(topographic and thematic maps, DEM,
forest inventory statistics and etc)
Initial
labelling of
clusters
Derived
Auxiliary
Products
Decomposing
of ambiguous
semantic
clusters
Monosemantic
clusters map
Land
Cover
Map
Merging
of semantic
clusters into
thematic
classes
Normalised Difference Snow Index
Rblu  Rswir
NDSI 
Rblu  R
From Hall et al., 1998: "Algorithm Theoretical Basis Document (ATBD) for the MODIS
Snow-, Lake Ice- and Sea Ice-Mapping Algorithms. Version 4.0"
Detection of the contaminated pixels
Step 1: Detection of the pixels utterly contaminated by snow and
clouds with pre-specified thresholds
C S  Θ if

P (Θ, t * ) 
C C  Θ if

C L  Θ if
Rblu (, t * )  0.1
AND
NDSI(Θ , t * )  0.1
Rblu (, t * )  0.1
AND
 0.1  NDSI(Θ , t * )  0.1
Rblu (, t * )  0.1
OR
NDSI(Θ , t * )  0.1
P(Θ , t * ) pixel with co-ordinates Θ at fixed decade of observation t*
C S  set of pixels with presence of snow or/and ice

CC  set of pixels with presence of clouds

C L  set of pixels without presence of snow/ice or clouds
C  C  C 
1
p
S
C
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
 
P (Θ * , t )  C pj
j
j
t Rswir (Θ * , t )  M swir
(Θ * )  2 swir
(Θ * )
C  set of contaminated pixels at step j
j
p
j
M swir
(Θ * ) the mean of R swir (Θ * , t )
j
 swir
(Θ * ) standard deviation of R swir (Θ * , t )
Θ * fixed co-ordinates
C  C  C 
j
p
j 1
p
j
p
Seasonal Mosaics
spring
summer
autumn
Wave-Likeness Index (WLI)
(NDVI max, t max)
0.8
0.7
NDVI
0.6
(NDVI e, t e)
a
0.5
0.4
b -1
0.3
a
0.2
b -1
0.1
d
d
(NDVI b, t b)
0
t1 t2 t3 t4 t5 t6 t7 t8 t9 … … … … … tn-1 tn
time of observation
Cropland
 NDVI   NDVI
WLI 
 NDVI
p
t
t
t
where
NDVI
p

 a  Sin (  (b  t  c))  d
2
Bi-spectral Gradient Wetness Index
(BGWI)
Analysing
pixel
SWIR
BGWI
NIR
Pure Water
Summer Mosaic
NIR-MIR-RED
Wetland
BGWI-NDVI- BGWI
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
The SibTREES Land Cover Classes
Forests
Evergreen Needle-leaf Dark
Evergreen Needle leaf Light
Deciduous Needle-leaf
Deciduous Broadleaf
Needle-leaf/Broadleaf
Broadleaf/Needle-leaf
Mixed Forest
Shrublands
Needle-leaf evergreen shrubs
Broadleaf deciduous shrubs
Grasslands
Humid grasslands
Steppe
Wetlands
Bogs
Marsh
Water-land mosaic
Tundra
Lichen
Moss
Swampy tundra
Heath
Tree canopy cover is >20% and height >5 metres
The Picea and/or Abies account for at least 80% of the area covered by trees
The Pinus accounts for at least 80% of the area covered by trees
The Larix accounts for at least 80% of the area covered by trees
The Betula and/or Populus are dominant, though other broadleaf trees occur in small numbers
Needle-leaf species account for 60 – 80% of the area covered by trees, broadleaf 20-40%
Broadleaf species account for 60 – 80% of the area covered by trees, Needle-leaf 20-40%
Needle-leaf and broadleaf trees present in roughly equal proportions
Floristic differences
Removed for GLC 2000
Global legend.
Shrub canopy cover is >20% and height <5 metres
class =
The species Pinus Pumila and Pinus mugaNew
are dominant
The species Betula Ermani is dominant
Evergreen
needle leaf
Tree and shrub canopy cover
<20%
Herbaceous vegetation with a growing season >5 months
Herbaceous vegetation with a growing season of <3 months
Permanent mixture of water and herbaceous or woody vegetation
Sphagnum moss and lichens are dominant
Rushes and sedges are dominant
Mixture of water bodies < 0.5 km2 and other vegetated or non-vegetated lands
Treeless, growing season of 1.5 – 2.5 months, lichens, mosses, shrubs
Dry, lichen dominated
Mosses dominant
Seasonally waterlogged
Woody vegetation, dwarfed shrubs and Ericaceae family dominant
Other vegetation types
Riparian vegetation
Recent burns
Croplands
Follows watercourses, mixture of herbaceous and woody vegetation, growing season >4 months
Burn scars <3 years old. May contain dead trees, some pioneer vegetation types
Agriculture following a bare soil, crop cover, harvest, bare soil cycle
Non-vegetated cover
Bare soil and rock
Permanent snow/ice
Water bodies
Urban
Never has vegetation cover of any kind
Snow/ice present throughout the year
Open water fresh or salt including seas, lakes, reservoirs and rivers
Buildings, roads and other man-made structures
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
Forest Management Units selected for
comparison with GLC 2000 map
679 forest management units
GLC200 Forest Cover in comparison to
Forest Inventory Data
100
y = 1.1x + 4.4
2
R = 0.82
Land Cover Map, %
80
60
40
20
0
0
20
40
60
Forest Inventory data, %
80
100
GLC200 Forest Cover in comparison to
Forest Inventory Data
Forest management units (%)
60.0
53.1
50.0
40.0
30.0
20.0
14.5
12.8
8.5
6.9
10.0
2.9
1.3
0.0
< -20%
[-10%;-20%] [ -5%;-10%]
+/-5%
[5%;10%]
Forest cover differences
[10%;20%]
> 20%
GLC 2000 Map in comparison to
SPOT-HRV image
SPOT-HRV
Image
SPOT-VGT
Image
Simplified
Forest Map
Simplified
GLC 2000
Map
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