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

Crop types classification using MODIS

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

17-Apr 7-May 27-May 16-Jun 6-Jul 26-Jul 15-Aug 4-Sep 24-Sep

PVI

Peas

Melilot

Potato

Alfalfa

Perennials

Spring crops

Fallow

Rape

Winter rye

Barley+Peas mixture

Ground-truthOmission

(%)1 2 3 4 5 6 7 8 9 10

Classificatio

n

1 58 0 0 0 0 11 0 0 0 0 5,9

2 0 127 0 0 3 34 0 0 3 1 24,4

3 0 0 101 0 0 10 7 1 0 0 15,1

4 0 0 0 26 0 0 0 0 0 0 0,0

5 0 0 1 1 797 116 17 8 1 0 15,3

6 2 3 3 0 2 5822 9 6 4 4 0,6

7 0 1 7 0 2 49 574 4 0 0 9,9

8 0 0 0 0 0 127 0 175 1 0 42,2

9 0 0 0 0 0 14 7 0 72 0 22,6

10 0 0 0 0 0 42 0 0 0 46 47,7

Commission (%)

3,3 3,1 9,8 3,7 0,9 6,5 4,5 9,8 11,1 9,8 93

Burnt area mapping using MODIS

Detection of SWVI statistical anomalies

Thermal anomaliesBurnt area maps

Combined SWVI and thermal anomalies

SWVI time-series

Multi-annual MODIS data

-100

0

100

200

300

400

500

30 60 90 120 150 180 210 240 270 300

day

SW

VI (x

10

00

)

mean mean-2*sdev mean+2*sdev current year

fire

Contaminated pixels detection

SWVI time-series restoration

Burnt area for year 2009

Fires in Central European Russia in 2010

The considered period:

July 1 – August 21, 2010

The fires have been

mapped using MODIS data

and MOD14 thermal

anomalies detection

algorithm implemented

within the Satellite

Monitoring Information

System of Russian Forest

Service

Ground-truth collection using HR data samples Burns severity assessment using MODIS data

Forest burns severity assessment

Field measurements HR Satellite images Burns severity assessment

Land cover

Data analysis

Statistical relationships

Burn severity assessment

Burns severity by forest types

Lost forest areaassessment

Burnt area mapping

-0,5 0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,00

20

40

60

80

100

120

140

160

180

Чи

сл

о н

аб

л.

Усыхающие насаждения

10 20 30 40 50 60 70 80 90 100

Степень повреждения по данным MODIS

0

2

4

6

8

10

12

Число н

абл.

Statistics on forest mortality

Landsat-TM 07.09.2009; RGB:NIR-SWIR-Red

Burns limits from MODIS

Forestry districts

< 10%

11-40%

41 - 80%

>80%

Trees mortality

Post-fire assessment of trees mortality

Response of forests to drought 2010

Vyksunskyy administrative district

Semenovskyy administrative district

25.07.2010

07.08.2010

TerraNorte: Data Products on-line

http://terranorte.iki.rssi.ru/