Overview of new MODIS and Landsat data derived products to characterise land cover and

change over Russia

Sergey BARTALEV

Russian Academy of Sciences Space Research Institute (IKI)

15 Aprile 2013, GOFC-GOLD Symposium, Wageningen

LAGMA : Locally Adaptive Global Mapping Algorithm

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

2005 2010

Automated technology for annual land cover mapping based on MODIS data

2000

2005

2012

The homogenous time-series of land cover maps of Russia has been

developed for the period of years 2000-2012.

Forest change detection using the land cover maps time-series

2000 2001 2002

2005 2006 2008

Forest area dynamics for Russia over period of years 2000-2012

The forest area estimated using MODIS data derived land cover maps.

The annual forest area change (%) is as compared to year 2000.

-1,8

-1,6

-1,4

-1,2

-1,0

-0,8

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

годы

%

Dynamics of coniferous species relative area (%) in forest cover of Russia

75,6

75,7

75,8

75,9

76,0

76,1

76,2

76,3

76,4

76,5

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

годы

%

The estimates is based on the MODIS data derived time-series of land

cover maps for years 2000-2012.

The estimation is based on MODIS data derived land cover maps for 2000-2012. The

relative (%) tree species area changes estimated as compared to year 2000.

-10,0

-8,0

-6,0

-4,0

-2,0

0,0

2,0

4,0

6,0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

годы

%

Темнохвойные Лиственница Сосна

Relative area (%) dynamics of different coniferous species in forest of Russia

0,25

0,3

0,35

0,019 0,024 0,029 0,034 0,039 0,044 0,049

reflectance in RED (620—670 nm) band

reflecta

nce in N

IR (

841—

876 n

m)

band

oak

birch

maple

aspen

linden

Forest species classification using MODIS time-series

Deciduous Broadleaf Forest in TerraNorte RLC map

Forest Map of USSR (1990, 1:2,5 mln) Forest species mapping using MODIS time-series

The forest cover is classified considering dominant tree

species using seasonal time-series of MODIS data

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

30,0%

35,0%

40,0%

45,0%

Spruce Fir Siberian

Pine

Pine Larch Oak Erman's

Birch

Birch Aspen

official state statistics

map based estimates

0,00%

0,10%

0,20%

0,30%

0,40%

0,50%

0,60%

Beech Linden Maple

Forest species area % : MODIS derived estimates vs. official statistics

2010

Method of forest GSV retrieval based on ASAR and MODIS data products synergy

Forest GSV Map

Locally-adaptive regression fitting

Local classes’ signatures of GSV

(BIOMASAR) and Surface Reflectance

(MODIS Snow Composite)

TerraNorte Land Cover BIOMASAR MODIS

LAGMA

Enhanced forest GSV retrieval is based on Envisat-ASAR derived BIOMASSAR product

and MODIS data snow composite synergy (250 m, year 2010)..

Enhanced forest GSV map vs. BIOMASAR

Forest GSV map BIOMASAR map

Enhanced forest GSV map vs. BIOMASAR: Pareto Boundary evaluation approach

0

3

6

9

12

15

18

21

24

0 3 6 9 12 15 18 21

Co

mm

iss

ion

err

or,

%

Omission error, %

MODIS

BIOMASAR2

BIOMASAR Enhanced GSV map

GSV Threshold

Forest cover

TerraNorte RLC Forest cover

100

150

200

250

300

350

100 150 200 250 300 350

Sto

ck

vo

lum

e,

m3

/ha

Stock volume, m3/ha

MODIS

BIOMASAR

GSV error in comparison to ground truth data at 1 km pixel:

MODIS - 14% ; BIOMASAR - 26%

y = 1,12x - 35,44 R² = 0,99

y = 1,03x - 28,15 R² = 0,99

0

2500

5000

7500

10000

12500

15000

0 2,500 5,000 7,500 10,000 12,500 15,000

To

tal s

toc

k v

olu

me

, m

illi

on

m3

Total stock volume according official statistics, million m3

BIOMASAR

MODIS

Linear(BIOMASAR)

Linear(MODIS)

Comparison with official statistics for administrative units of Russia (2010 )

Enhanced forest GSV map vs. BIOMASAR: comparison to ground truth data and official statistics

Landsat-TM/ETM+ clouds/shadow masking

Landsat-TM Clouds detection (in red) Geometric shadow belt (in black)

Detected clouds (red) and shadows (green)

1. Clouds detection

2. Geometric modelling of shadows belts

3. Filtering of the geometric shadows

4. Spatial filtering of clouds and shadows

Landsat-TM/ETM+ composite for year 2011

The automatic data processing chain allows yearly production of the Landsat-TM/ETM seasonal composites

Burnt area mapping technology using Landsat-TM data is in operation (> 3500 burs were mapped in Russia for 2011 fire season)

Combined SRBA&HRBA burnt area map:

Russian Federation, fire season of year 2011

The map includes burnt area polygons mapped with both MODIS (SRBA

product) and Landsat-TM/ETM+ (HRBA product) data

Forest burn severity map of Russia for year 2012

Forest burn severity assessment: Russia, fire season 2012

Totally 7012 th ha or 64% of forest area is seriously damaged (high severity and lost forest) in Russia during fire season 2012

Web-based Vegetation Monitoring Service VEGA: vega.smislab.ru

VEGA (in operation since May 2011) is developed by

the Space Research Institute of the Russian Academy

of Sciences to provide vegetation status analysis tools

based on multi-annual and near-real-time EO data