Overview on Land Cover and Land Use Monitoring in Russia Russian Academy of Sciences Space Research Institute Sergey Bartalev Joint NASA LCLUC Science Team Meeting and GOFC-GOLD/NERIN, NEESPI Workshop Monitoring land cover and land use in boreal and temperate Europe August 25-28, 2010, Tartu, Estonia
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Overview on Land Cover and Land Use Monitoring in Russia · Arable lands map based on MODIS. MODIS derived arable lands map vs. HR imagery based fields’ limits. Crop types classification
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Overview on Land Cover and Land Use
Monitoring in Russia
Russian Academy of Sciences
Space Research Institute
Sergey Bartalev
Joint NASA LCLUC Science Team Meeting and GOFC-GOLD/NERIN, NEESPI Workshop
Monitoring land cover and land use in boreal and temperate Europe
August 25-28, 2010, Tartu, Estonia
Russian Academy of Sciences’ activity related to LCLU satellite monitoring
– land cover mapping using MODIS data and LAGMA
method;
– agricultural monitoring with focus on arable land and
crops mapping;
– burnt area mapping and severity assessment using
MODIS and high-resolution optical data;
– TerraNorte Information System
Some features of R&D at IKI
Focus is on national (entire Russia) and sub-continental (Northern Eurasia) monitoring
Primary sources of EO data are moderate resolution satellite instruments (mainly MODIS and SPOT-VGT), while the role of high-res. (e.g. Landsat-TM, SPOT-HRV/HRVIR, RapidEye) data for national monitoring is rapidly increasing
Focus on long-term time-series data analysis for land cover mapping and monitoring
Development of automatic satellite data processing chains to perform monitoring in the routine and repeatable manner
OTHER VEGETATION TYPES AND COMPLEXES
TUNDRA
WETLANDSFORESTS
SHRUBLANDS
GRASSLANDS
NON-VEGETATED LAND
COVER TYPES
GLC2000 legend for Northern Eurasia
Main features of GLC2000 Northern
Eurasia land cover map1-km resolution SPOT-Vegetation data for year 2000
Mapping method involves:
i. set of advanced spectral-temporal and spectral-angular indexes to distinguish various land cover types
ii. clustering and significant human input for labelling and decomposing of ambiguous semantic clusters
Advantages:
– large number of mapped land cover types
– high level mapping accuracy
Disadvantages:
– limited repeatability
Towards better land cover mapping:
main directions of consideration
- spatial resolution of mapping according to satellite
sensors ability (1 km => 250 m)
- mapping accuracy
- mapping repeatability (annual as the target)
- possibility to modify mapping legend (e.g. to increase
number of thematic classes)
Cloud-free summer MODIS composite
Cloud-free winter MODIS composite
Classification based on LAGMA method
Satellite
data
Training
samples
Training samples
spatial regularization
(gridding)
Classes’
signatures for
cell-grid nodes
Contextual Maximum
Likelihood classification
New land
cover map
TerraNorte RLC mapping method
GLC 2000 Forest map
Auxiliary thematic products
Peatlands map
Spectral mixture
modeling
Expert
evaluation and
correction
Thematic source data
Training data preparation
Histogram
filtration
GIS
analysis
Manual
selection
GIS analysis
Burnt area Croplands Water mask Urban mask
Contextual Maximum Likelihood Classification
Local spectral-temporal signatures of classes Spectral-temporal MODIS data composites
Maximum likelihood classifier
Covariation of
metrics
Average of
metrics
Number of
samples
Metrics for the pixel
Probabilities for classes
The land cover map for Russia based on MODIS 250 m
TerraNorte RLC Map for 2005
The Legend of TerraNorte RLC Map
Boschetti et al. Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary // Remote Sensing of Environment 91 (2004) 280–292
The Pareto Boundary method to estimate accuracy of the land cover map
Pareto optimum: for 250 m resolution
for 1000 m resolution
Site 2: Komi RepublicSite 1: Karelia Republic
1
2
TerraNorte RLC accuracy assessment for two test sites
PVI time-series analysis
0
0,1
0,2
0,3
0,4
2002 2003 2004 2005 2006 2007
PVI
arable lands
natural vegetation
NIR
RED
Soil line
PVI
A
PVI= - 0.83*RED+0.56*NIR-0.005
PVI=Distance (A, Soil line)
Inter-annual PVI dynamic similarity analysis and multi-annual phenological features retrieval
The features for arable lands mapping with MODIS multi-annual data time-series
Features Description Formula Feature Image Histograms
Index of shortest vegetation period
1/ 21..
( )j j
L Fj N
L min t t,
( ) ( )2
maxL F
PVIPVI t PVI t ,
,L max F maxt t t t
Index of vegetation spring development 1..
ijj N
i spw
MSI min PVI
Index of seasonal biomass decrease
min
1
1
Nsw
j
j
N
i
j i sw
PVI
NSMI const
PVI
Arable lands map based on MODIS
MODIS derived arable lands map vs. HR imagery based fields’ limits