Deriving Landcover Information over Siberia using MERIS and MODIS data

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1 Deriving Landcover Information over Siberia using MERIS and data Dr Laine Skinner (1) and Dr Adrian Luckman (1) (1) University of Wales Swansea, Singleton Park, SA2 8PP Swansea, UK. ABSTRACT Estimating the extent of various land cover types at regional and global scales is an important source of information required by a variety of applications. Although techniques for classifying remotely sensed data have been presented and discussed for many years, it is only recently that classification analysis over such large areas has been possible. The generation of the kilometer resolution IGBP and UMD global land-cover maps (based upon AVHRR data) indicates an early advance in this area. We may now be approaching the next evolutionary step in large-scale land cover mapping. The availability of globally acquired data from satellite-borne medium resolution optical sensors has increased dramatically with the recent launch of the Terra, Aqua, ENVISAT and SPOT5 platforms. However, a critical factor is that the spatial, spectral and radiometric resolution of these 'next generation' sensors are, in many geographic areas, far more suited to identifying certain land cover classes when compared to previous sensors. In particular, this is true over the Siberia region were many land features are sub-kilometer in size with similar spectral properties. Previous land cover results for this area, for example the IGBP and Global Land Cover 2000 maps, have been limited in their accuracy because of this. The main focus of this paper is upon deriving land cover information over the Siberia region using data from the MERIS sensor. Classification of the data at the sensors full resolution (300m) allowed many more land cover features to be identified compared to a classification based upon the reduced resolution mode (1km). However, the limited wavelength range of the MERIS spectral bands hindered the ability to discriminate some of the vegetation classes. The accuracy of the thematic result was reduced in comparison to a classification derived using data for the same area (spatial resolution of 500m). The implications of these results are discussed further in the paper and suggestions for future use of these data sources are also made. 1 INTRODUCTION The work presented in this paper has been completed as part of the SIBERIA-II project funded under the EU 5 th Framework program (EVG ). The overall objective of SIBERIA-II is to demonstrate the viability of full carbon accounting (including GHGs: CO2, CO, CH4, N2O, NOx) on a regional basis using the environmental tools and systems available to us today and in the near future. A key factor to the success of the project relies upon the accurate identification of the different land-cover types within the region. The recent publication of the Global Landcover Classification 2000 (GLC2000) signifies the current state of the art in earth observing classification techniques at the global scale [1]. The kilometer resolution GLC2000 map identifies various course classes over the entire Earth s surface with further sub-classes over specific regions. In particular, over Northern Eurasia, some 29 classes are identified within the general forest, shrubland, grassland, wetland, agricultural and non-vegetated land-cover types. These significant results were achieved using data acquired in 2000 by the SPOT VGT sensor. This raises the question, what results may be achieved using the and MERIS sensors which acquire data in more spectral bands and at a higher spatial resolution? This paper presents some initial results towards this goal. 2 CLASSIFIER METHODOLOGY The methodology used to obtain a land-cover information within the SIBERIA-II project was based upon a supervised technique with the aim of producing a hard classification result. The required land-cover classes were originally identified by examining the needs of the end user for this product. That is, as input into multi-layered GIS landscape model (developed by IIASA, Vienna [10]) and two DGVM s (SDGVM and LPJ [9]). Consequently, a 3-level hierarchical class definition system was defined to include, at the highest level, identification of single forest species and a variety of shrubland and grassland classes. Proc. MERIS User Workshop, Frascati, Italy, November 2003 (ESA SP-549, May 2004)

2 The initial results presented in this paper are focused upon the level 2 classes (see Figures 3 and 4). These classes do not vary greatly from those already identified in the GLC2000 map [1] and IGBP map [2]. Although the differences are such, that a direct comparison between either of these previous results is not possible. However, we may still expect to see similar patterns and macro-features, particularly when comparing with the more recent GLC2000 map. 2.1 Reference Data (Training and Testing Data) In supervised classification procedures, the selection of the reference data used to train the classifier and test the results is of critical importance to both the final analysis and the conclusions which may then be drawn. Typically, a random set of sample points over the entire region are selected to represent the full variability in all land-cover types of interest [3]. Unfortunately, in many cases, particularly regional to global scale projects, it is often not possible to identify the land-cover type for a large number of randomly selected sites. Obtaining a representative sample then becomes a subjective approach where the sample selected is a model for the population. Bias within such a model must be, at a minimum, qualified before meaningful conclusions may be drawn over the entire region. Within the SIBERIA-II project, the selection of reference polygons was limited to those areas covered by the available reference/ground data. The two primary sources of reference data were provided by: i. 1:50000 Forest Inventory test sites containing information on the species, stocking density and age of forest stands. ii. Expert interpretation of Landsat imagery Reference polygons were selected using both data sources to identify areas were confident predictions of the land-cover could be made. The selection criteria also required that each polygon be a minimum 2Km 2 in area and compact in shape. The reference polygons created are shown in Figure 1. The selection was heavily biased within managed forest areas (for the forest classes) and urban/agricultural regions for the remaining classes. The effect of this method for selection of the training data on the final result has yet to be determined (i.e. how representative is this sample over the entire Siberia region?). 2.2 EO Data The two remote sensing data sets used to derive land-cover information were from the [4] and MERIS sensors [5]. In each case a temporal archive of data throughout the spring and summer of 2003 was acquired. A description of each data set is given in Table 1. Fig. 1: The 4 main administrative regions in Siberia are outlined in red. The available Landsat scenes are shown in blue along with the selection of ~1000 training polygons in purple. The spatial distribution of these polygons is heavily biased towards the southern regions. Table 1: The EO data acquired for classification. Sensor Date Description (MOD09) May Oct Day composites, 7 bands (visible, NIR and SWIR) 500m pixel size MERIS (FR_L2) 10 days each in June + July Single scenes, 15 bands (visible and NIR), 300m pixel size

3 The surface reflectance data is a highly processed product providing a great deal of value added information. In particular, the high quality of the spatial and temporal compositing means that no further processing is required by the user prior to image classification. In contrast, the MERIS data product is aimed more towards research activities. As a result, the level 2 product requires further temporal, spatial and quality assurance preprocessing. The main advantage associated with the MERIS data is the increased spatial resolution (300m as opposed to the resolution at 500m). This higher resolution could be critical to the accurate identification of land-cover types in the Siberia region. Quality Flags Date 1 Date 2 Date N 8-day comp. MERIS or MERIS DEM 8-day comp. MERIS or MERIS DEM ASAR ASAR ASAR Masked Input Training data from input sets Create decision tree using c5.0 Reference Polygons 8-day comp. MERIS or MERIS DEM Extract training: a) Random, b) Expert c) Geo-stratified 2.3 Classification process The c5.0 decision tree classifier [6] was selected following the methodology implemented by the land-cover team at Boston [7]. The non-parametric nature of decision tree classifiers place fewer constraints on the data (and expectations on the class signatures) whilst providing a robust method for class prediction. The addition of boosting to these classifiers allow conditional probabilities to be derived for each class, thus providing a method to combine a priori or ancillary data with the classification results [8]. Class Map 1 Classify Input Class Map 2 Temporal and spatial filtering FINAL Classified Image Class Map N Ancillary Data Fig. 2: A flow chart showing the processing steps in the classification procedure. A flow chart showing the classification steps is shown in Figure 2. Class signatures are derived for the areas defined by the reference polygons from a data stack for a specified date (typically a data stack containing all data acquired in an 8 day period) 1. A decision tree is then constructed based on these signatures using the c5.0 classifier. Predictions for the land cover type for all pixels are made by applying the decision tree to the remote sensing data stack. Probability of class membership for all classes is then calculated for each pixel. This process is repeated for each 8 day data stack to produce a number of independent classification results. 1 Currently ASAR data is not used in the classification process.

4 Currently, the various classification results are combined using a temporal mode filter. That is, for each pixel, selecting the class that occurs most often in all classification layers. A drawback to this method is that all pixels in each classification layer are treated equally, allowing no ability to take into account the confusion that may exist between spectrally similar classes. In addition, phonological and local disturbance/change effects are also ignored. A more advanced method has been suggested by [8] using Bayes rule to calculate the overall probability of class membership for each pixel based upon the conditional probability of each classification result. Essentially, this method allows each pixel to be weighted based upon information provided by the classifier (which in turn is derived from the spectral properties of the remote sensing data) or alternatively using a pre-defined value derived from other ancillary information (such as phonological data, coarse vegetation zone maps, etc.). This technique will be implemented in the next phase of the methodological development. 4 RESULTS Initial classification results over the entire Siberia region based upon the data are promising. Qualitative comparisons with the GLC2000 and IGBP land-cover maps show similar macro-features identified in each case. In contrast, data distribution problems concerning the MERIS imagery resulted in only limited success when classifying this data. A sub-area around Lake Baikal, towards the south of the region, has been processed and classification results were derived. A comparison of the and MERIS class maps is not possible at this early stage of development. However, some interesting observations have been made on the type of results which have already been obtained. 4.1 Scaling Issues of Land Cover Classes Previous global land cover maps, such as IGBP and GLC2000, were created using kilometer resolution remote sensing data, a mapping scale of around 1:3,000,000. However, this scale is, to some degree, inappropriate for mapping the land cover classes defined in each map. Many of the classes simply do not exist as homogeneous areas of land at this scale. As a result, many of the pixels in the remote sensing data will contain a mixture of classes. Therefore, it is difficult to justify the use of a hard classification output where each pixel can only belong to one class. This is also the case over the Siberia region. Further analysis of the Landsat imagery revealed that many areas over Siberia are only identifiable as homogeneous land covers at a scale of 1:1,000,000 (i.e. 300m size pixels). In particular, managed forest stands, areas near to rivers, urban centres and mountainous regions contain land-cover units between 300m to 1km in size. Comparison of Results Figure 3 shows the classification results from the GLC2000 map, the 500m classification and also the MERIS 300m classification for an agricultural area towards the south of the region 2. The increase in spatial resolution of the and MERIS data allows finer features of the landscape to be identified. The main river system is identified in all three results. However, tributaries to this system are only visible in the MERIS classification. The increase in spatial resolution allows a significant increase in the detail provided by the thematic result. A further example is also shown in Figure 4. In this case, the size of the river and its tributaries is not large enough to be identified from either the 1km or 500m data. The river system is only identified when the pixel size of the remote sensing data is at 300m, as in the case of the MERIS data. This highlights a critical advantage in using finer scale information - a completely different set of conclusions on the landscape would be drawn when examining either the 1km or 500m classification in Figure 4. The appearance of the river system in the 300m MERIS data significantly alters our understanding of this area. These results are typical over the southern region of Siberia. Heterogeneity in the land-cover types tend to occur within the kilometer size cells previously used to categorize the area (e.g. GLC2000). 5 SUMMARY A robust and fully automated classification of the Siberia region has been presented identifying 17 land-cover classes. A key factor to the success of this process has been the high quality of the surface reflectance composites. However, the increased spatial resolution of the sensor (500m) and MERIS sensor (300m) have provided the 2 The classes in the GLC2000 map are different from those in the and MERIS classifications. However, the general class types are similar (i.e. deciduous forest types, needle-leaf forest types, grassland, etc.). The colours selected for each class in the three maps represent similar land-cover types.

5 a) GLC2000 1Km b) 500m c) MERIS 300m Fig. 3: Showing an agricultural region in the south west of the Siberia region. Classification results using 3 different sensors are shown. At 1km resolution, some features in the GLC2000 map are lost when compared with the results derived using 500m and MERIS 300m data. a) GLC2000 1Km b) 500m c) MERIS 300m Fig. 4: Showing a river basin in the south of the Siberia region. Classification results using 3 different sensors are shown. The river system identified in the MERIS 300m map is completely missed by both the 500m map and GLC2000 1km map. As a result, a landscape analysis of this area based upon the MERIS results would be completely different if based upon the other two maps. GLC2000 Legend Evergreen Needle-leaf Forest Deciduous Broadleaf Forest Needle-leaf/Broadleaf Forest Mixed Forest Broadleaf/Needle-leaf Forest Deciduous Needle-leaf Forest Broadleaf/Deciduous Shrubs Needle-leaf Evergreen Shrubs Humid Grasslands Steppe Lichen-Moss Tundra Heath Tundra Swampy Tundra Bogs and Marsh Palsa Bogs Croplands Forest/Other Vegetation Complex Forest/Cropland Complex Cropland/Other Vegetation Complex Recent Burns Riparian Vegetation Bare Soils and Rock Water Bodies Permanent Snow/Ice Urban Salt Pans Burns of year 2000 /MERIS Legend Water Barren Urban Cropland Cropland/Forest Complex Light Coniferous Forest Dark Coniferous Forest Soft Deciduous Forest Grassland Wetland Tundra (Lichen-Moss) Steppe Tundra (Heath) Unproductive Needle-leaf Forest Unproductive Broadleaf Forest

6 most striking results when compared with previous classification maps. A number of examples have been presented where the interpretation of the thematic result fundamentally changes when using the higher resolution data. Kilometer resolution imagery is simply not an appropriate scale to identify the land-cover classes of interest in many areas of Siberia using a hard classification approach. Further research is now underway to process the remaining MERIS imagery and derive a Siberia-wide classification based upon this data. A regional comparison with the classification and other land-cover maps, such as the GLC2000, will then be possible. 6 ACKNOWLEDGEMENTS This research was supported as part of the SIBERIA-II project funded under the EU 5 th Framework protocol (EVG ). The authors would also like to thank Dr. Mark Friedl and the Land-cover team at Boston, USA for discussions on the use of the c4.5 and c5.0 decision tree classifiers. REFERENCES 1. Bartholome, E., Belward, A. S., Achard, F., Bartalev, S., Carmona Morena, C., Eva, H., Fritz, S., Gregoire, J.-M., Mayaux, P., St ibig, H.-J., Global land cover mapping for the year 2000 Project status November 2002 (ref EUR 20524), Report to European Union, REF , Belward, A., S., Estes, J., E., Kline, K., D., The IGBP-DIS global 1-km land-cover data set DIScover: A project overview, Photogrammetric Eng and Rem. Sens., 65(9), pp , Stehman, S.V, Practical implications of design-based inference for thematic map accuracy assessment, Rem Sens Environ, 72, pp35-45, Strahler, A., Muller, J. P., Lucht, W., Schaaf, C. B., Tsang, T., Gao, F., Li, X., Lewis, P., Barnsley, M. J., BRDF/Albedo product: Algorithm Theoretical Basis Document, Version 5.0, Technical document published by Boston Universit, MERIS Team, MERIS Algorithm Theoretical Basis Document, Doc. No. PO-TN-MEL-GS-0005 European Space Agency, Quinlan, J.R., 1993, C4.5: Programs for Machine Learning, San Mateo, Calif. Morgan-Kaufman 7. Stahler, A., Muchoney, D., Borak, J., Friedl, M., Gopal, S., Lambin, E., Moody, A., Land cover product Algorithm Theoretical Basis Document, Version 5.0, Technical document published by Boston University, McKiver, D.K. and Friedl, M.A., Using prior probabilities in decision-tree classification of remotely sensed data, Rem. Sens. Environ., 81, pp , Cramer W. et al., Global response of terrestrial ecosystem structure and function to CO 2 and climate change: results from six dynamic global vegetation models, Global Change Biology, 7, , Nilsson S, A Shvidenko, V Stolbovoi, M Gluck, M Jonas, M Obersteiner (2000) Full carbon account for Russia, IR , IIASA, Vienna, 180 pp