Overview of new MODIS and Landsat data derived products to characterise land cover and change over Russia. Sergey BARTALEV

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1 Russian Academy of Sciences Space Research Institute (IKI) Overview of new MODIS and Landsat data derived products to characterise land cover and change over Russia Sergey BARTALEV 15 Aprile 2013, GOFC-GOLD Symposium, Wageningen

2 LAGMA : Locally Adaptive Global Mapping Algorithm Local spectral-temporal signatures of classes Spectral-temporal MODIS data composites Covariation of metrics Average of metrics Number of samples Metrics for the pixel Maximum likelihood classifier Probabilities for classes

3 TerraNorte RLC Map The land cover map for Russia based on MODIS 250 m

4 Automated technology for annual land cover mapping based on MODIS data The homogenous time-series of land cover maps of Russia has been developed for the period of years

5 Forest change detection using the land cover maps time-series

6 % Forest area dynamics for Russia over period of years ,6 0,4 0,2 0,0-0,2-0,4-0,6-0,8-1,0-1,2-1,4-1,6-1, The forest area estimated using MODIS data derived land cover maps. годы The annual forest area change (%) is as compared to year 2000.

7 % Dynamics of coniferous species relative area (%) in forest cover of Russia 76,5 76,4 76,3 76,2 76,1 76,0 75,9 75,8 75,7 75, The estimates is based on the MODIS data derived time-series of land годы cover maps for years

8 Relative area (%) dynamics of different coniferous species in forest of Russia 6,0 4,0 2,0 0,0-2,0 % -4,0-6,0-8,0-10, годы Темнохвойные Лиственница Сосна The estimation is based on MODIS data derived land cover maps for The relative (%) tree species area changes estimated as compared to year 2000.

reflectance in NIR (841 876 nm) band Forest species classification using MODIS

0,044 0,049 reflectance in RED (620 670 nm) band Deciduous Broadleaf Forest in

9 reflectance in NIR ( nm) band Forest species classification using MODIS time-series 0,35 oak birch maple aspen linden 0,3 0,25 0,019 0,024 0,029 0,034 0,039 0,044 0,049 reflectance in RED ( nm) band Deciduous Broadleaf Forest in TerraNorte RLC map Forest Map of USSR (1990, 1:2,5 mln) Forest species mapping using MODIS time-series

10 The forest cover is classified considering dominant tree species using seasonal time-series of MODIS data

11 Forest species area % : MODIS derived estimates vs. official statistics 45,0% 40,0% 35,0% 30,0% 25,0% official state statistics map based estimates 0,60% 0,50% 0,40% 0,30% 0,20% 0,10% 20,0% 0,00% Beech Linden Maple 15,0% 10,0% 5,0% 0,0% Spruce Fir Siberian Pine Pine Larch Oak Erman's Birch Birch Aspen

12 Method of forest GSV retrieval based on ASAR and MODIS data products synergy TerraNorte Land Cover BIOMASAR MODIS LAGMA Local classes signatures of GSV (BIOMASAR) and Surface Reflectance (MODIS Snow Composite) Locally-adaptive regression fitting Forest GSV Map

13 Enhanced forest GSV retrieval is based on Envisat-ASAR derived BIOMASSAR product and MODIS data snow composite synergy (250 m, year 2010)..

14 Enhanced forest GSV map vs. BIOMASAR BIOMASAR map Forest GSV map

15 Enhanced forest GSV map vs. BIOMASAR: Commission error, % Pareto Boundary evaluation approach MODIS BIOMASAR2 BIOMASAR Enhanced GSV map GSV Threshold Forest cover Omission error, % TerraNorte RLC Forest cover

16 Stock volume, m3/ha Enhanced forest GSV map vs. BIOMASAR: comparison to ground truth data and official statistics Total stock volume, million m MODIS BIOMASAR BIOMASAR MODIS Linear(BIOMASAR) Linear(MODIS) y = 1,12x - 35,44 R² = 0,99 y = 1,03x - 28,15 R² = 0, Stock volume, m3/ha GSV error in comparison to ground truth data at 1 km pixel: MODIS - 14% ; BIOMASAR - 26% 0 0 2,500 5,000 7,500 10,000 12,500 15,000 Total stock volume according official statistics, million m3 Comparison with official statistics for administrative units of Russia (2010 )

17 Landsat-TM/ETM+ clouds/shadow masking Landsat-TM Clouds detection (in red) Geometric shadow belt (in black) 1. Clouds detection 2. Geometric modelling of shadows belts 3. Filtering of the geometric shadows 4. Spatial filtering of clouds and shadows Detected clouds (red) and shadows (green)

18 Landsat-TM/ETM+ composite for year 2011 The automatic data processing chain allows yearly production of the Landsat-TM/ETM seasonal composites

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

20 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

21 Forest burn severity map of Russia for year 2012

22 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

Research Institute of the Russian Academy of Sciences to provide

23 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

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