The potential contribution of SPOT 4/VEGETATION data for mapping Siberian forest cover at continental scale

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1 The potential contribution of SPOT 4/VEGETATION data for mapping Siberian forest cover at continental scale S. Bartalev (1&2), F. Achard (1), D. Erchov (2) and V. Gond (1) (1) Joint Research Centre of the European Commission I Ispra (VA), Italy TP , fax , (2) International Forest Institute , 69 Novocheriomushkinskaya str., Moscow, Russia , fax , Introduction The increasing concentration of atmospheric CO 2 and associated climate change require the detailed study of carbon balance/flux in forest ecosystems. The boreal forest areas, which most part is in Russian Siberia, occupy about 17 % of the earth s land surface and its role in the global climate change processes is very important but still not well understood. The state and evolution of the Russian forests is thus of major national, European and global significance. Russia s forests represent: about 22 % of the forested area and 21 % of volume of the wood resources of the planet (World Bank, 1997); about 15 % of the above-ground carbon stocks and 75 % of the total carbon stocks of the boreal forest zone (World Bank, 1997); significant contribution to the global atmospheric carbon fluxes (Isaev et al., 1995, Nillson et al., 2000). The current methods for carbon pool and fluxes assessment of Russian forest at national and subnational levels are based on a database including forested and non-forested land categories, forest species and age structure, and dynamic processes. But this countrywide forest information is only available at the level of the forestry management units and is only revised every five-years. In some regions this forest inventory information was derived from aerial visual surveys conducted in the 1950 s. In particular the Northern Siberian and Far Eastern regions are characterised by coarse geographical and very old descriptions of the forest resources. Natural and anthropogenic fires, clear-cutting, insect damage and industrial pollution are some of the main factors causing change in the Siberian forest ecosystems. Accounting is needed to assess the boreal forest contribution to the global carbon balance and for characterising these disturbances. In addition to disturbances, reforestation and natural growing processes should also be well characterised. The ongoing forest mapping exercise at country level from SPOT4-VEGETATION data is a one main researches activity the Sib-TREES project (Bartalev et al., 1999). This project is a joint initiative from the Joint Research Centre of the European Commission (Ispra, Italy) and the International Forest Institute (Moscow, Russia). The main objective of the project is to develop a remote sensing based system for Siberian forest monitoring. Such system will allow to improve the understanding of current conditions and the evolution of the Siberian forest in the context of International Conventions, in particularly the Kyoto Protocol to the UN framework convention on climate change. Rationale for Russia s forest mapping exercise

2 Presently a few maps are existing at national or global scale, which are describing the spatial distribution of Russia s forests or vegetation. Among them, the most well known examples are the Forest Map of USSR (Isaev et al, 1990) and the IGB-DIS Global Land Cover Map (Belward et al., 1999). When evaluating such maps it has to be taken into account: (i) the map legend and the criteria used to define of the classes, (ii) their thematic and spatial accuracy. In general the users requirements are well defined for large-scale maps, as they are used for local forest management and protection plans. The present absence of well-defined legend criteria for small-scale thematic maps (from a user s point of view) makes a quantitative accuracy assessment difficult. A few Forest Maps of USSR, at the 1:2,500,000 scale have been elaborated from the mid 50 s (Vorobiev et al., 1985) until the end of the 80 s (Isaev et al., 1990). The map legend of the latest contains about 40 different classes, including forested areas and non-forested areas. The forested areas are classified according to dominant tree species and tree density. For tree density, a threshold of 0.3 is used to distinguish two classes: sparse and dense forest. This map was produced from the compilation of more detailed information (inventories or maps) from various sources at different dates and with heterogeneous accuracy. In spite of the fact that this map is most widely used source of forest cover information at national level, the map has important shortcomings: The definition of the map legend is based not only on ecosystem criteria but also in significant part on economical criteria. In particular, economically important tree species are mentioned as dominant forest species as soon as they represent more than 30% of total volume of wood (in particular for oak and Siberian pine); The forest inventory data, which were used as source of information, are significantly heterogeneous as regards the methods and accuracy of stands evaluation, and data collection dates; The limited accuracy of the map in the areas inventoried partly by visual observation from airplanes in the 1950 s and partly by the so-called photo-statistical method with more recent satellite photographs and selective ground surveys. These areas are located in Siberia and Far East regions and represent a total of 37.5 % of Russia s forested areas; The changes of forest cover in a quite significant number of regions, which happened after the data collection used for the map preparation. The IGBP global land cover map at a 1 km spatial resolution was developed using NOAA-AVHRR data acquired from April 1992 to March 1993 (Loveland et al., 2000). The reduced legend of this map include 17 classes of land cover, but for the Eurasian boreal zone the vegetation is characterised mainly by 7 different classes: Evergreen Needleleaf Forest, Deciduous Needleleaf Forest, Deciduous Broadleaf Forest, Mixed Forest, Open Shrublands, Cropland/Natural Vegetation Mosaic and Croplands. A map validation exercise was carried out using high-resolution satellite data, Landsat-TM and SPOT (Loveland et al., 1999). The accuracy of the map even with such a limited number of classes varies significantly for the different classes: from 57.14% for the Deciduous Needleleaf Forest class to 100.0% for the Open Shrublands class. The average accuracy of the map over the Eurasian continent is 68.8%. That figure can obviously not be considered as satisfying for most of the potential users at national level. Furthermore the comparison between both above-mentioned vegetation maps over Russia demonstrates significant disagreements. The Sakhalin island is given as example (fig. 1) where large divergences between the two maps can not be explained by forest inventory data inaccuracies or forest cover change, because this region has been inventoried at the end of the 1980 s with low-altitude air photographs and intensive ground surveys. In summary, the presently existing maps of Russia s forests have significant limitations due to their inaccuracies or simple legends. One promising way to develop an advanced forest map of Russia is to 2

3 use satellite data from recent advanced sensors, in particular data from the VEGETATION (VGT) instrument on board the SPOT4 satellite. Our first scientific task was to investigate the potential capabilities of these data for this goal. Assessment of capabilities of SPOT4/VGT data for boreal forest mapping Description of the data used in the study SPOT4-VEGETATION data This feasibility study for boreal forest mapping over Russia has been carried out using a time series of S- 10 standard products from the VGT sensor during the vegetative growing season: from end of March 1999 until beginning of November The selected window is covering the full Eurasian boreal zone from 42 0 N to 75 0 N and from 5 0 E to E (fig. 2). The S-10 products are generated from a ten-day observations period with a selection rule based on the maximum value of NDVI in order to reduce the impact of clouds. Due to the applied atmospheric correction procedure, the values of pixels are corresponding to Top of Atmosphere reflectances. Moreover the S-10 products contains information of acquisition date and geometrical conditions of sun illumination and satellite observation for each pixel. An initial quick assessment of S-10 products has brought into light some particularities that has been taken into consideration for the forest mapping exercise: In spite of the maximum NDVI selection procedure the impact of clouds is still present in the S-10 products, sometimes in strong proportions, especially over Northern regions; Due to the variability of geometrical conditions of illumination/observation and the non-lambertian characteristics of the vegetation cover, and in particular of the forest, the S-10 imagery is affected by strong impact of BRDF effect. Other ancillary data The following data sets were collected in a data base to be used as ancillary information for the exercise: NOAA-AVHRR satellite data (whole Russia coverage during vegetation season of ); Selective satellite imagery at medium (RESURS-O1/MSU-SK) or high (SPOT/HRV) resolution; Forest map of USSR at scale 1:2,500,000; Forest growing conditions zoning map of USSR at scale 1:16,000,000 (Kurnaev, 1973); Forest inventory data at sub-national and local levels (maps, forest statistics and dendrological description) for a few test zones. Assessment of spectral, temporal and angular properties of forest types with SPOT4-VEGETATION data Design of experimental investigations Classification of satellite imagery for forest mapping can be made using different features extracted from the data. The technical characteristics of the VEGETATION instrument allowed to assume that the spectral properties of a single product and as well spectral-temporal and spectral-angular properties for the different land cover types could be used as main features for class separation. The experimental investigations aimed first to verify and to evaluate the usefulness and capabilities of these features. 3

4 Spectral properties of single S10 products A first assessment of the spectral reflectance properties from SPOT4-VGT instrument was carried out by comparison with spectral reflectance properties from NOAA-AVHRR instrument. The investigations were conducted on a test zone in Krasnoyarsk region where the following land cover were identified from existing large-scale maps: closed forest, clear cuts, burned scars, forest damaged by insects, hayfields, pastures, urban territories, forest clearings, bogs and water. Among the various forest types a few sites were selected from local inventory data with the following dominant tree species: pine (Pinus sylvestris), fir (Abies sibirica), birch (Betula spp.), and aspen (Populus tremula). One VGT S-10 product and one NOAA-AVHRR images were taken in the month of August 1999 to be compared (fig. 3). The spectral signatures of the different land cover and forest types have been produced from a representative pixel sampling for all VGT and AVHRR channels. To minimise atmospheric effects and to reduce the influence of directional effects, only pixels having similar conditions (acquisition dates and Sun/View azimuth and zenith angles) on both images were retained. The divergence criterion characterises the level of separability of the different land cover classes in a multi-dimensional space of spectral features: tables 1 for the general land cover types and table 2 for the forest species. Figures 4 compares the separability of selected land categories and forest types between S-10 product and NOAA-AVHRR data (using the bi-dimensional reflectance scattering plots). The analysis of separability of the main land cover classes and forest types with various dominant species leads to the following results: in most of the cases, SPOT4-VGT data, when compared to NOAA-AVHRR data, allow a better separability between classes; the middle infrared channel of the VGT instrument gives significant improvement for discrimination between land cover categories, in particular between burned area, forest damaged by insects and water bodies. On another hand some uncertainties have to be mentioned: there is a significant confusion between forests with pine and forests with deciduous species. That could be explained by imprecise identification from satellite images of the areas occupied by these classes and requires further investigations; due to the absence on the test site of representative areas of middle closed and sparse forest stands for several species including larch and spruce, no assessment was made for these classes. Temporal properties of the Normalised Difference Vegetation Index A crucial natural phenomena, which have to be taken into consideration for large scale mapping of boreal forest from satellite data, is the phenological dynamic of the vegetation. The large diversity of bio-geographical growing conditions of Siberian boreal forests, which are extending from 45 0 N latitude until 70 0 N latitude, are resulting in a variety of vegetation parameters. In particular the dates of beginning or end of the vegetative period and of its phenological phases (such as leaves shedding, full foliation, defoliation, etc ) (Elagin, 1994) and the intensity of biophysical processes such as photosynthetic activity will be directly expressed in the temporal variations of the spectral reflectances. Moreover, the periods of the beginning or end of phenological phases can be different for various species and tree ages in similar geographical conditions (Vorobiev et al., 1985, Elagin, 1994). These characteristics can be used as a feature to classify remote sensing data for forest cover mapping. In this 4

5 respect the frequent repetitiveness of VGT system acquisitions (daily) can be considered as an important advantage for boreal forest mapping. First the seasonal dynamic of the Normalised Difference Vegetation Index (NDVI) was assessed for different Siberian forest ecosystems types using digital map of forest growing conditions zoning for stratification (Kurnaev, 1973). The temporal profiles of averaged values of NDVI have been derived for the following ecosystem types: arctic desert, tundra, grassland, coniferous forest, deciduous forest, steppe and semi-desert (fig. 5). The analysis of these temporal profiles leads to some conclusions regarding the capabilities of a multi-temporal VGT dataset for forest cover classification, in particular: the average NDVI values of almost all investigated ecosystem types (excluding arctic desert and semi-desert areas) is characterised by an increase during Spring, reaching a maximum during Summer and a decrease during Autumn; during almost the whole vegetative period the average NDVI values for the coniferous and deciduous forest types are significantly different with permanently higher value for deciduous forest types; during Spring (until second decade of May) and Autumn (from third decade of September) the average NDVI values of deciduous forests, steppe and semi-desert area are very similar. That can be explained by the contribution of the grass layer of deciduous forest stands which is predominant without tree leaves; the average NDVI values of tundra and grassland are significantly different during Spring (from third decade of April until end of May) and Summer (July), and quite different over whole vegetative season compared to all other types of ecosystems; the arctic desert area is characterised by very low average NDVI value (with small peak during first decade of August) that is significantly different from all other types of ecosystems. Use of directional properties The dependence of spectral reflective properties of different land cover types from the directions of illumination and observation can be described with Bidirectional Reflectance Distribution Function (BRDF). The BRDF of most natural land cover types, including vegetation, are far from isotropic. As results the remote sensing reflectances are strongly influenced by the relative position of the three elements of the system Sun-Earth-sensor. Moreover as with optical satellite instruments the range of the illumination/observation geometrical conditions is increasing from equator to poles, the need to take into consideration the BRDF effect is reinforced for boreal zones compared to other regions. The usual approach to take into account the BRDF effects is to normalise the values of the spectral reflectances using parametrical models (Dymond et al., 1999, Duchemin, 1998, Roujean et al., 19992). These models are correcting the reflectances to match a given set of viewing conditions by using parameters which are related to the different observed land cover surfaces. Such BRDF normalisation enables to classify the multi-spectral imagery in an easier and more efficient way. But it is worth noting that the angular data that are used in such models to normalise the reflectances could also be used directly in the algorithms of analysis to extract additional thematic information. One effective approach to classify remote sensing satellite data, keeping the spectral angular information, could be based on the use of BRDF model parameters as input in the classification process. These parameters are features that can describe integrally and uniquely the reflective characteristics of a surface (for a given model). The above mentioned parameters can be estimated by inversion of a BRDF model using a set of spectral reflectances under various directions of illumination and observation but with constant surface and atmosphere conditions. The last condition can only be satisfied with the VGT instrument with a certain degree of assumption. This condition is directly related to the frequency of observation, which is different for the three standard products of the VGT instrument, decreasing from P 5

6 to S-1 and S-10 products. In fact two or three daily observations of the same area are available with P product at 65 0 N latitude, but the S-1 or S-10 products provide only one observation on a daily or 10- days period basis respectively. Based on these facts the use of multi-temporal S-10 products will probably not allow to reach a satisfying accuracy for the estimation of BRDF model parameters. An assessment of such approach has to be carried out with P and S-1 products. Another approach to extract thematic information, by combining spectral angular properties of land cover and being more suitable for analysis of S-10 products, can be deducted from the following sequence of remarks: the NDVI which maximum value criteria is used to create the 10-days composites keeps a dependence on directions of illumination and observation (Duchemin, 1998); the mentioned directional dependence of NDVI and associated channels can differ for the different land cover types; as result, the S-10 products might be used together with the angular data describing relative position of the Sun-Earth-sensor elements to discriminate the land cover types and to increase the classification efficiency; the correlation between angular data and land cover types can be improved by extending the number of observations and by looking for averaged parameters more independent from the remaining noise. To test and verify these assumptions time series of SPOT4-VGT S-10 products were used (including azimuth and zenith angles of Sun and view directions as relative azimuth and phase angles). The calculation of simple statistics (mean and median) aimed at reducing effect of remaining noise. The best result (evaluated by visual qualitative correlation with forest map) was obtained with the means of view zenith angle (VZA), Sun zenith angle (SZA) and relative azimuth angle (RAA) calculated for the full Summer period (June-August) when the phenological conditions of the boreal forest are quite stable (fig. 6). It seems that these angular parameters averaged over time are correlated not only with the presence/absence of forest but also with other structural forest parameters (such as species composition and tree cover density ). It is necessary to note that this analysis is only qualitative. Its aim was only to make an initial evaluation of the hypothesis. The proposed approach to map boreal forests by combining land cover spectral temporal properties with angular properties has to be further investigated. Forest mapping exercise: preliminary results Preliminary results of forest cover classification of VGT S-10 products over Central Siberia have been obtained. In this preliminary exercise, the method for analysis of the satellite imagery was developed from the temporal profiles of land cover reflectances without taking into account the angular information. The procedure was included the three main following steps: Detection of cloudy and snowy pixels; Production of seasonally optimised mosaics for three seasons: spring, summer and autumn. The duration of snow-free period and the NDVI temporal behaviour are used to define the transitions between periods; Unsupervised classification of the [3 mosaics x 4 channels] and labelling into main forest ecosystems and land cover categories. 6

7 As the snowy period in the boreal zone is strongly correlated with the duration of the vegetative season, the detection of snow was considered as an initial step before splitting the whole time series of satellite observations into three seasonally optimised sub-sets. To detect snow cover, we used its spectral properties in the VGT visible and SWIR channels: snow cover has strong visible reflectance and strong short-wave absorption characteristics. A normalised different snow indexes NDSI (Hall et al., 1998) was used. NDSI has been calculated as follows from VGT blue and SWIR channels (1 and 4): NDSI = (CH1-CH4) / (CH1+CH4). To distinguish snow and clouds on satellite imagery we used the following criterias: Snow/Ice: CH1 >= 0.1 AND NDSI >= 0.1 Clouds: CH1 >= 0.1 AND < NDSI < 0.1 Land/Water: CH1 < 0.1 OR NDSI <= -0.1 The threshold values have been adjusted to best fit with results of visual discrimination of snow and clouds. The definition of time transitions between spring, summer and autumn seasons is then based on the analysis of temporal behaviour of NDVI with snow-free pixels. The dates of seasonal transitions (between spring-summer and between summer-autumn) have been defined from piecewise-linear approximation of NDVI as function of time. The spring season was considered as the period of NDVI increase and the autumn season as the period of NDVI decrease. These date limits were used to produce seasonally optimised multichannels mosaics. The spring and autumn mosaics were produced from the selection of the median seasonal pixel values (excluding cloudy pixels). The summer mosaic was produced by selection of the pixels corresponding to maximum NDVI during whole vegetative season. An unsupervised classification of these three seasonally optimised mosaics (including all 4 channels) was then performed with the Erdas-ISODATA algorithm. The thematic labelling of the resulting clusters was carried out using the Russian forest map as reference. The following reduced thematic classes were used: light coniferous forest (with two sub types: pine or larch), dark coniferous forest, deciduous broadleave forest, bogs and wet lands, sparse forest, scars, stones, tundra and other non forested lands. The labelled classification was first compared to the Russian forest map (Fig. 7). Then a validation of the classification results has been performed using a limited independent data set of forest inventory maps available at local level (Table 3). The results of this accuracy assessment show clearly the need to improve the classification methods (accuracy varies from 56% to 77% for the forest classes). It is planned to test in a next step new methods by taking into account the directional properties of reflectances and by introducing into the classification procedure an auxiliary stratification layer related to the main natural ecoregions. Conclusion Russia s boreal forests have extremely important environmental and economical significance at global level. The development of an accurate and up-dated forest map at continental level is still an issue today. This study aimed at assessing the potential of data from the SPOT4-VEGETATION instrument for boreal forest mapping. The results presented here are preliminary, but nevertheless allow to make some concluding remarks: 7

8 The spectral properties, as well as spectral-temporal and spectral-directional properties of reflectances of VGT data could be combined as classification features to improve boreal forest cover mapping; The spectral separability of main forest classes is significantly higher from a single VGT S-10 product than from a single date AVHRR scene. The presence of a SWIR channel gives an advantage for the discrimination of burned area and forests damaged by insects; The temporal profiles of NDVI from VGT data are useful to improve the separation between coniferous and deciduous forest, and to separate deciduous forests from other land categories during the periods with presence of grass cover; To use the bidirectional reflectance properties of the forest cover types to improve classification accuracy from VGT data, it is necessary to consider the following points: - retrieving the parameters of a BRDF model and using them as features for the classification step can be appropriate with P and S-1 VGT products; - with S-10 VGT products it seems more appropriate to average values of illumination and observation angles over main vegetative seasons (spring, summer, autumn) as features for the classification; The proposed approach to map forest cover from S-10 VGT data is based on the creation of optimised seasonal mosaics taking into consideration phenological dynamic. Such optimised seasonal mosaics are generated after snow cover detection and from the analysis of NDVI profiles; A low accuracy was obtained for the preliminary results of forest cover classification (between 54% and 77%). To improve the classification results there is a need to test approaches taking into account the bidirectional reflectance properties. References Bartalev S.A., Achard F., Isaev A.S. and J.P. Malingreau, 1999, Siberian Forest Resources and Environment Monitoring by Satellites: Presentation of the SibTREES Project Concept and its Initial Steps. Proceedings of the Workshop Assessment Methods of Forest Ecosystem Status and Sustainability, V.N. Sukachev Institute of Forest, Russian Academy of Science, Krasnoyarsk, pp Belward A.S., Estes J.E., Kline K.D., 1999, The IGBP-DIS Global 1-km Land-Cover Data Set DISCover: A Project Overview, Photogrammetric Engineering and Remote Sensing, 65, pp. Duchemin B., NOAA/AVHRR Bidirectional Reflectance: Modelling and Application for the Monitoring of a Temperate Forest, Remote Sensing of Environment 67 (1) (1998) pp Dymond J.R., Shepherd J.D., Qi J. A simple Physical Model of Vegetation Canopy Reflectance // Submitted to Journal of Geophysical Research, 1999 Elagin I.N. 1994, Seasons year in Russian forests, Novosibirsk, All-Russian Inc. Nauka p. [In Russian] Engelsen O., Pinty B., and M.M. Verstraete, 1996, Parametric Bidirectional Reflectance Factor Models: Evaluation, Improvements and Application, Joint Research Centre of European Commission, Research Report EUR 16426, 120 pp. 8

9 Hall D.K., Tait A.B., Riggs G.A., Salomonson V.V., with contributions from Chien J.Y.L., Klein A.G., October 7, 1998: "Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Iceand Sea Ice-Mapping Algorithms. Version 4.0" Isaev A.S. et al., 1990, Forest map of USSR. Isaev A.S. et al., 1995, Environmental problems of carbon gas absorption through reforestation and forest plantation in Russia (Analytic review), Moscow: The Center of Environmental Policy of Russia.156 p. [In Russian] Kurnaev S.F., 1973, Forests growing conditions zoning of USSR, Moscow, [In Russian] Loveland T.R. et al., 1999, An Analysis of the IGBP Global Land-Cover Characterisation Process, Photogrammetric Engineering and Remote Sensing, 65, pp. Loveland T.R., Reed B.C., Brown J.F., Ohlen D.O., Zhu Z., Yang L. and J. W. Merchant, 2000, Development of a global land cover characteristics database and IGBP DISCover from 1km AVHRR data, International Journal of Remote Sensing, 21 (6&7): Nilsson S. et al. 2000, Full Carbon account for Russia. IIASA, Interim Report IR , 180 pp Roujean J.P., Leroy M., Deschamps P.Y., 1992, A bidirectional reflectance model of the earth s surface for the correction of remote sensing data, Journal of Geophysical Research, 97: Vorobiev G. et al. Eds, 1985, Forest encyclopaedia: 1-st volume, Moscow, Sov. Encyclopaedia. 563 p. [In Russian] World bank, 1997, Russia: forest policy during transition. A World Bank country study. 9

10 Figure 1: Comparison between the forest map of Russia and the IGBP global land cover map over the Sakhalin island. 10

11 Figure 2: S-10 product over the Eurasian boreal zone (42 0 N to 75 0 N from 5 0 E to E). SCAR SCAR FIR / SPRUCE FIR / SPRUCE CLEARCUTS CLEARCUTS WATER WATE DEAD FIR DEAD FIR GLADES GLADE PINE BIRCH / ASPEN PINE BIRCH / ASPEN BOGS BOGS SETTLEMENTS SETTLEMENTS Figure 3: Comparison between a VGT S-10 product (11-20 August 1999) and an AVHRR image (11 August 1999) 11

12 GLADES NIR HAYFIELDS CLOSED STANDS CUTTINGS BOGS NIR SCARS SETTLMENTS HAYFIELDS BOGS GLADES PASTURE SETTLMENTS SCARS CUTTINGS WATER PASTURE WATER CLOSED STANDS S-10 VGT RED S-10 VGT SWIR NIR GLADES HAYFIELDS SETTLMENTS SCARS PASTURE Figure 4: Comparison the separability of selected land categories between a VGT S-10 product (11-20 August 1999) and an AVHRR image (11 August 1999) with bidimensional reflectance scattering plot CLOSED STANDS BOGS WATER CUTTINGS NOAA-AVHRR RED

13 0,90 Arctic desert 0,80 0,70 0,60 Tundra Grassland Coniferous forest Deciduous forest Steppe Semi-desert 0,50 0,40 0,30 0,20 0,10 0,00-0,10 MAR3 APR1 APR2 APR3 MAY1 MAY2 MAY3 JUN1 JUN2 JUN3 JUL1 JUL2 JUL3 AUG1 AUG2 AUG3 SEP1 SEP2 SEP3 OCT1 OCT2 OCT3 NDVI NOV1 Decades Figure 5: Temporal profiles of averaged values of NDVI for selected ecosystem types Color composite of mean values of VZA and SZA and RAA Forest map Figure 6: Means of view zenith angle (VZA), Sun zenith angle (SZA) and relative azimuth angle (RAA) calculated for the full summer period (June-August) 13

14 Classification of S-10 VGT data Forest map of Russia (scale 1:2,500,000) Figure 7: Comparison between the classification result from VGT S-10 data and the Russian forest map 14

15 Table 1 Comparison of separability of land categories using SPOT4-VGT S-10 (channels: 2,3&4) and NOAA-AVHRR (channels: 1&2) data Land category Closed forest / 0.0 Clear cuts / / 0.0 Burned areas / / / 0.0 Hayfields / / / / 0.0 Pastures / / / / / 0.0 Urban territories / / / / / / 0.0 Forest clearings / / / / / / / 0.0 Marshlands / / / / / / / /0.0 Water / / / / / / / / 0.0 Table 2 Comparison of separability of dominant forest species using SPOT4- VGT S-10 (channels: 2,3&4) and NOAA-AVHRR (channels: 1&2) data Forest Specie Birch Aspen Pine Fir Damaged fir Birch 0.00 / 0.00 Aspen 0.03 / / 0.00 Pine 0.39 / / / 0.00 Fir 1.00 / / / / 0.00 Damaged fir 1.00 / / / / /

16 Table 3 Confusion matrix between result of the VGT classification and local forest inventory maps on selected test sites in Krasnoyarsk region Number of pixels of the different classes in the map derived with SPOT4-VEGETATION data Name of Class Total pixels number % of omissions LCF - Pine LCF - Larch DCF Spruce/Fir/Cedar DF Birch/Aspen Bogs / Wet Land Sparse forest Scars Stones Tundra Other NFL Total pixels number % of commission LCF - Light coniferous forest / DCF - Dark coniferous forest / DF Deciduous forest / NFL non forested lands 16