BALANS Land Cover and Land Use Classification Methodology

Transcription

1 BALANS Planning and management in the Baltic Sea Region with land information from EO Environment and Climate Programme Area 3.3: Centre for Earth Observation Contract Nº ENV4-CT BALANS Land Cover and Land Use Classification Methodology Prepared by: Ulrihca Malmberg Metria Miljöanalys Balans-utv June 2001 Metria Miljöanalys, FEI, GRID-Arendal, GRID-Warsaw, Novosat Oy, SMHI

2 METRIA LANTMÄTERIET Project Document name Document ID Dnr BALANS BALANS Land Cover and Land Use BALANS-utv-24 Classification Methodology Issued by Approved by Issue date Status Version Ulrihca Malmberg Birgitta Olsson Approved 2.0 BALANS Land Cover and Land Use Classification Methodology Metria Miljöanalys, P.O. Box 355 SE STOCKHOLM Visit address: Vasagatan 11, 5 tr Tel: Fax:

3 Page 3 Updates Date Version Changed by Changes Ulrihca Malmberg Ulrihca Malmberg Original report. WP4000: Prototype Database Specification and Implementation. Uppdated version for public distribution together with BALANS Landcover and Landuse dataset. Changes in agricultural reference datasets used for classification. Abstract: Keywords: Land Cover classification, Methodology, Implementation and Operationalisation recommendations BALANS, Land Cover, Classification, WiFS, Medium resolution satellite data Document: balans_landcover_classification_methodology_ver2..doc

4 Page 4 Table of Contents SUMMARY INTRODUCTION THE BALANS PROJECT PROTOTYPE DATABASE SPECIFICATION AND IMPLEMENTATION INPUT DATA PROJECTION SATELLITE DATA Selection criteria for satellite data Initial stage database Final satellite dataset Terra (EOS AM-1) MODIS data REFERENCE DATA Finnish National Land Use and Forest Classification Swedish Terrain Type Classification CORINE LC CORINE LC - Poland IAKS 99 Sweden Areal Informations Systemet Denmark FIRS Regions and Stratas Baltic Sea Region GIS, Maps and Statistical Database Baltic Sea drainage basin Watershed areas Agricultural areas Heightrelated reference data Candidate reference data not used in classification work World Atlas of Agriculture World Vector Shoreline Baltic Sea Region GIS, Maps and Statistical Database...34 Population density...34 Coastline...34 Landcover...35 Wetlands MapBSR Digital Chart of the World IGBP PELCOM LAND COVER AND LAND USE CLASSES LAND COVER AND LAND USE LEGEND CLASS DESCRIPTION...37 ARTIFICIAL SURFACES CLASSIFICATION BACKGROUND REGIONS TO CLASSIFY LAND COVER REFERENCE DATA FOR THE PROJECT AREA AGRICULTURAL REFERENCE DATA FOR THE PROJECT AREA SCENE SELECTION CRITERIA PER REGION LAND COVER CLASSES USED IN THE CLASSIFICATION WORK CLASSIFICATION PRINCIPLES PER LAND COVER CLASS Artifical areas Open land Agricultural land Seminatural areas Coniferous forest...49

5 Page Deciduous forest Wetland Water bodies Bare rock Glaciers and perpetual snow Clouds No data CLASSIFICATION PROCESS REFERENCE DATA PREPARATION Recoding, resampling and reprojection Creating agricultural mask for areas with agricultural reference data Creating tree limit image Creating cloud masks per satellite scene PROCEDURES PERFORMED PER CLASSIFICATION AREA Subsets of input images Additional geometric correction of reference data Generating height related statistics Creation of cloud and no data mask Creation of water mask Removing clouds and water from satellite data Preparation of reference data for unsupervised classification Unsupervised classification First iteration Creating majority classes per cluster First iteration Defining correct classified pixels First iteration Unsupervised classification Second iteration Creating majority classes per cluster Second iteration Defining correct classified pixels Second iteration Creating a majority image Creation of image with classified water candidates Creating Artificial mask Expansion of existing Artificial mask Generating Artificial surfaces where no reference data exists Creating Wetland mask Splitting Forest class in areas with Mixed forest as a land cover class in reference data Handling mixed clusters Mixed clusters in areas with high resolution reference data Mixed clusters in areas with CORINE LC as reference data Creation of snow mask Combining all images into a single land cover database Areas with Swedish Terrain Type Classification as reference data Areas with CORINE LC as reference data Removing forest pixels above tree limit Separating Open land into either Agricultural land or Seminatural areas Creating metadata image with scenes used per pixel BALANS LAND COVER AND LAND USE DATABASE BALANS LAND COVER AND LAND USE DATABASE FULL RESOLUTION BALANS LAND COVER AND LAND USE DATABASE GENERALISED PRODUCT SCENES PER PIXEL FOR BALANS DATABASE METADATA INFORMATION OPERATIONALISATION (WP4500) BACKGROUND AND OBJECTIVES PERFORMED WORK Use of satellite data Classification and interpretation of clouds in the satellite image Evaluation of Expert Classifier, ERDAS Imagine Implementation of Models for Classification FUTURE IMPROVEMENTS...77

6 Page 6 8 FIGURES IN REPORT TABLES IN REPORT REFERENCES LINKS OF INTEREST...84

7 Page 7 SUMMARY The purpose of this report is to describe the classification efforts performed in Work package 4000, Method Development, within the BALANS project. This includes selection of methods for classification as well as production of a land cover and land use database with a resolution of 150 metres, covering the entire Baltic Sea drainage basin. Satellite Data When selecting satellite data, the main objective was to have a basic coverage of satellite data for the entire Baltic Sea drainage basin with every part of the area covered by cloud free data. This basic coverage was not to take into account the need for applications like change detection and multi-temporal data from the same year. Medium resolution satellite data was considered the most suitable satellite type for producing the land cover database, taking into account the price and time limits for the production of the database. After initial studies of RESURS MSU-SK data and IRS-1C/D WiFS data, it was decided to use data from the WiFS sensor. Reference data Different types of reference dataset have been used in the classification process. Some reference datasets cover the entire Baltic Sea drainage basin, while others cover only parts of the region, primarily countrywise. If the original resolution of the reference dataset is better than 150 metres (the resolution of the satellite data), the dataset will considered to be ground truth in the classification process. Reference data with a resolution lower than 150 metres, will be considered as a valuable input, but will not function as the complete truth, instead taking advantage of the better resolution of the satellite data. By using this approach, the reference data is handled different in the classification procedure depending on its resolution, hence having less or more influence on the final land cover database. All reference datasets have been reprojected to the BALANS projection, and in cases where the original resolution is better than 150 metres, the reference data has been resampled to 150 metres, with pixels perfectly aligned geographically to the satellite images. Some land cover classes in the reference datasets have been recoded to correspond to the BALANS Land Cover classes, while others are separated into different subclasses of a single BALANS class if they are expected to have spectral dissimilarities in the satellite data. The reference datasets used in the classification process are (giving the original resolution): Finnish National Land Use and Forest Classification (150 m, generalised from original dataset of 25 m) Swedish Terrain Type Classification (25 m) CORINE LC (100 m, minimum mapping unit 25 ha)

8 Page 8 Agricultural data from IAKS 99 in Sweden (25 m) Agricultural data from Areal Informations Systemet in Denmark (originally vector data, based on data in scale range 1: :50 000) Agricultural data from Poland national CORINE dataset (25 m) FIRS regions and stratas (map scale 1: ) Agricultural areas from Baltic Sea Region GIS, Maps and Statistical Database (10 km) Baltic Sea Drainage Basin Watershed Areas from Baltic Sea Region GIS, Maps and Statistical Database (map scale 1: ) Height related reference data Digital Elevation Model, Slope information and Wetness index (50 and 100 m) Land Cover Classes The basic principle when defining the BALANS Land Cover class legend, was to make the dataset directly comparable to CORINE level 1, hence using the legend and class descriptions of the CORINE database as a model for the BALANS Land Cover legend. Whenever possible, the level 1 classes have been further divided into two or more subclasses, still comparable to the CORINE class legend. The classes listed below are the land cover and land use classes for BALANS; Code Class 1 Artificial surfaces 2 Open land 21 Agricultural land 22 Seminatural areas 3 Forest 31 Coniferous forest 32 Deciduous forest 4 Wetlands 5 Water bodies 6 Glaciers and perpetual snow 7 Bare rock 99 Clouds 100 No agricultural information available (separate dataset) 254 No data

9 Page 9 Classification Methodology The classification work has been performed by Metria Miljöanalys (former Satellus AB) and the Finnish Environmental Institute. When deciding upon what classification methodology to use, several criterias had to be considered: Obtaining a product of high quality Create a methodology that is repeatable (for updating purposes) Time and cost efficiency in the production work. Independent of operator performing the classification work These criterias indicates the need for an automised procedure, class labeling each pixel by integrating different types of geographical datasets and satellite data based upon their characteristics with logical, predefined rules. In order to achieve the best quality - within the limitations set by the spatial and spectral resolution of the satellite data, the nature of the reference data and the time and cost limits for the project - the basic concept for obtaining the final BALANS Land Cover product has been to take as much advantage of the different input data as possible, aiming to use them in the best manner. This means that the final product not simply is based on the results from the classification of the satellite data, but also from integrating land cover information from reference data into the final land cover database whenever suitable. The type of integration varies depending on the nature of each reference dataset. Hence, the final product can t simply been seen as a classification, but more as a GIS generated product. It is also important to clarify that the intention has been to obtain the best possible quality areawise (i.e. FIRS region), which means that the quality level of the total dataset is not homogenous. This was considered more valuable than obtaining a homogenous product with a lower quality (equaling the quality level of areas with the lowest accuracy). Primarily it is the nature and quality of the reference datasets per area that sets the quality standard for the final output. This requires the use of metadata information describing input satellite and reference data used to classify each pixel. More land cover classes were used in the classification work than was included in the final land cover database. This primarily because one single land cover class in the final output can include several sub-classes that are spectrally different from each other in the satellite data. Therefore, these classes were handled separately whenever possible and then combined in the final steps of the classification process. The main land cover reference datasets used in this project were the Swedish Terrain Type Classification, the Finnish National Land Use and Forest Classification and CORINE LC. However, these datasets combined don t cover the entire project area, but are in those cases used to label pixels in areas with no reference data, based upon the statistics per classification cluster. The ideal situation when classifying a pixel is to use cloudfree multitemporal satellite data, registered at different times during the vegetation season, in order to collect the variations for each land cover class during the season. In practice this has not been

10 Page 10 possible for this project. Primarily the data should be as cloudfree as possible, and the number of scenes have been limited to get an almost cloud free coverage over the Baltic Sea drainage basin. It hasn t been possible to collect optimal images for the vegetation season in all cases. For the scenes used in the classification work, a maximal number of three scenes per classification area (i.e. region or sub-region) have been used. If all scenes available for a pixel should have been used, the classification model would have been very complex and time consuming. The scenes used per classification area have been selected based on their registration date, coverage of the classification area and cloud coverage. During the classification process, a pixel can match the criterias for labeling a pixel within several of the classification steps, hence being assigned to different land cover classes. However, all information produced in the different steps are merged together into a single land cover dataset in a predefined priority order, hence assigning the pixel the land cover class it has the highest priority of belonging to. The different BALANS Land Cover datasets per classification area were finally combined into a single BALANS Land Cover database for the entire Baltic Sea drainage basin. The order in which the datasets were combined was that classification areas with better reference data had higher priority. Metadata information Since the quality of each classified area might differ (depending on the nature of the reference data and satellite data used in the classification process) it is of highest importance to have good metadata information for each pixel - describing the different input data used when labeling the pixel. An image has been produced, covering the entire Baltic Sea drainage basin, giving per pixel all scenes used (in priority order) in the classification process. If a scene contains clouds for the given pixel, it is not included in this image, since it hasn t contributed to the class labeling of the pixel. One image, listing the reference datasets used in the classification process for each pixel, has also been created. Another metadata information source is the result from the validation work, that has been produced by Novosat Oy. A short version of the results from this work is described in the README.DOC file that is distributed together with the BALANS land cover and land use dataset. Operationalisation The programs and modules created for the classification work have been semioperational. Manual editing, updating input and output images as well as some variables, had to be changed manually in the models for each classification area. Initally, the ERDAS Imagine Expert Classifier module was used to merge the different input land cover datasets, including datasets produced by the operator, into the final BALANS Land Cover dataset for each classification area. An error in the module was

11 Page 11 however discovered and the use of the Expert Classifier in an operational mode is not recommended until the error has been corrected. In order to achive a more operational process, the models should be transfered into a program environment, with a graphical user interface (GUI) that allows the operator to select input satellite data and reference datasets to use in the classification process, and then automatically generate the outputs required by assigning them rule based names. Using this approach also means that several steps that currently are run one at a time, having the operator manually updating the model for each step, can be run as one single process, not requiring any additional work by the operator but selecting the input data. Future updating of the BALANS Land Cover database also need to be considered. Ideally satellite data from the same sensor and registration date or season should be used in the updating work. However, this is probably not possible for such a large area to map and therefore a method not so dependent on the nature of the satellite data should be required. The suggested improvements will not be done within the current running project, but the performed work will act as solid base for future projects.

12 Page 12 1 Introduction 1.1 The BALANS project The BALANS project is part of the European Commissions 4 th framework programme. The project consortium consists of Metria Miljöanalys (former Satellus AB) who is the project co-ordinator, Finnish Environment Institute (FEI), GRID-Arendal, GRID-Warsaw, Novosat Oy and Swedish Meteorological and Hydrological Institute (SMHI). Other organisations will also be active in the user requirement work and evaluation of the products. The project was scheduled for 30 months and lasted from January 1999 until June The aim of the BALANS project is to carry out a pre-operational demonstration of the use of information from Earth Observation relating to land management by organisations with responsibilities relating to environmental protection and management in the Baltic Sea Region. The project will produce and test the use of land cover information from medium resolution satellite data for the Baltic Sea Region. The products must have a relatively high spatial and thematic information content, while the approach adopted must make it technically and economically possible to establish a sustainable information service in the longer term that regularly provides such products for the whole region. 1.2 Prototype Database Specification and Implementation The purpose of this report is to describe the classification efforts performed in Work package 4000, Method Development and WP4500, within the BALANS project. This includes selection of methods for classification as well as production of a initial stage land cover database with a resolution of 150 metres, covering the entire Baltic Sea drainage basin. Related reports within the BALANS project are Data Correction of Medium Resolution Satellite Data (Malmberg et.al. 2000), Updating and Change Detection (Törmä, Markus, 2001) and Technical Report on Database Data Model and Innovative Data Access (Syrén, Per. 2001). Medium resolution satellite data was considered to be the most suitable satellite type to perform the work, taking into account the price and time limits, and after initial studies of RESURS MSU-SK data and IRS-1C/D WiFS data, it was decided to use data from the WiFS sensor. When deciding upon what classification methodology to use, several criterias had to be considered: Obtaining a land cover database of high quality Creating a methodology that is repeatable (for updating purposes) Time and cost efficiency in the production work. Independent of operator performing the classification work

13 Page 13 These criterias indicates the need for an automised procedure, class labeling each pixel by integrating different types of geographical datasets and satellite data based upon their characteristics with logical, predefined rules. In order to obtain as good quality as possible - within the limitations set by the spatial and spectral resolution of the satellite data, the nature of the reference data and the time and cost limits for the project - the basic concept for obtaining the final BALANS Land Cover product, has been to take as much advantage of the different input data as possible, aiming to use them in the best manner. This means that the final product not simply is based on the results from the classification of the satellite data, but also from integrating reference data into the final land cover database whenever suitable. The type of integration of the reference data differs depending on the nature of each reference dataset. Hence, the final product can t simply been seen as a classification, but more as a GIS generated product. The classification work has been performed by Metria Miljöanalys (former Satellus AB) and Finnish Environmental Institute. Since the quality of each classified area might differ (depending on the nature of the reference data and satellite data used in the classification process) it is of highest importance to have good metadata information for each pixel - describing the different input data used when labeling the pixel. An image should be produced, covering the entire Baltic Sea drainage basin, giving per pixel all scenes used (in priority order) in the classification process. If a scene contains clouds for the given pixel, it is not included in this image, since it hasn t contributed to the class labeling of the pixel.

14 Page 14 2 Input Data This chapter describes the different input datasets used to produce the BALANS Land Cover and Land Use database, including satellite data and reference data. Some information have been used as an aid to delineate land cover classes, while others also have been used directly to label pixels in the final land cover database. 2.1 Projection For the BALANS project, in order to achieve the best area adapted projection model, it was decided to define a unique set of projection parameters. The parameters defined are almost the same as for UTM34 with the exception of the scale factor for the central meridian that has been chosen to fit the Baltic Sea drainage basin area as good as possible. The BALANS projection parameters are: Projection: Transverse Mercator Ellipsoid (spheroid): WGS84 Datum: WGS84 Units: Metres Scale factor of central meridian: Longitude of central meridian: Latitude of origin: 0 False easting: (to avoid negative coordinates) False northing: Satellite data Selection criteria for satellite data When selecting satellite data, the main objective was to have a basic coverage of satellite data for the entire Baltic Sea drainage basin (Figure 1) with every part of the area covered by cloud free data. This basic coverage was not to take into account the need for applications like change detection and multi-temporal data from the same year. Medium resolution satellite data was considered the most suitable satellite type for producing the land cover database, taking into account the price and time limits for the production. After initial studies of RESURS MSU-SK data and IRS-1C/D WiFS data, it was decided to use data from the WiFS sensor. These initial studies with conclusions are described more in detail in the Balans report Data Correction of Medium Resolution Satellite Data (Malmberg et.al., 2000).

15 Page 15 Figure 1 : Baltic Sea drainage basin. The following selection criterias were defined: Satellites: IRS 1-C and IRS 1-D. Sensor: WIFS. Geographical area: The Baltic Sea drainage basin (see Figure 1). Year: 1999 is used in first hand. For the basic coverage the year is not the most important factor, but new data should be selected if possible. Season: The basic coverage of data has to be from a season when the broad-leaf forest has leaves, which gives the following dates: - Poland and Baltic States May 15 - Stockholm-Helsinki June 15 - North Finland-Russia June 25 - Swedish mountains June 25 If multiple scenes of cloud free data exist from the best year, the scene registered as few days as possible after June 25 should be selected.

16 Page 16 Cloud and Haze: Cloud and haze have to be accepted in small amounts. Select the image with the smallest amount of cloud and haze (and the right season). Radiometric defects: Radiometric defects in the data can be hard to identify in quicklooks and can therefore not be taken into account. Since there might be cases where the above mentioned criterias can t be fulfilled, additional steps for satellite scene selection had to be added. For each area the following steps were taken: 1. Select a scene that is as cloud free as possible (small amount of cumulus are accepted) and registered the right time of the year. 2. If the criteria described above can t be fulfilled, select data that is free from snow and before the trees are leafing. 3. As a third choice, data from September is selected. 4. For larger areas covered with clouds in selection [1] repeat 1-3 to select a second scene Initial stage database Initially, geometric correction of the IRS WiFS data, using simple polynomial warping of system corrected scenes was considered. Table 1 is listing the scenes corrected with this method. Table 1: Satellite scenes corrected with polynomial warping Satellite Path Row Date IRS-1D IRS-1D IRS-1C IRS-1C IRS-1C IRS-1C IRS-1C IRS-1C After control of the corrected satellite images, it was however discovered that the geometric accuracy for the scenes was too poor and that the approach with simple polynomial warping was found to be insufficient for three major reasons: 1) The spectral bands were not perfectly co-registered in the system corrected product, especially for IRS-1C. Misregistrations up to one pixel were found. This type of error is difficult to handle in an efficient way in polynomial methods. Also, the possibility of misalignments between the CCD arrays in the two separate cameras for the east and west sides are difficult to handle.

17 Page 17 2) The wide angle sensor will introduce significant terrain displacements in the east and west sides of the scene. The errors can be several pixels large, which calls for a true orthorectification approach. 3) The polynomial method would have to use high order polynoms (at least 3 rd degree) to model the differences between the system corrected and precision corrected geoometries. This would necessitate a large amount of control point measurements to be possible, hence the production time would be excessive and the stability of the correction would be low. Hence, it was decided to implement the WiFS sensor into Satellus production system for satellite image orthorectification. This work is further described in the Balans report Data Correction of Medium Resolution Satellite Data (Malmberg et.al. 2000) Final satellite dataset There is a lack of satellite data in the most northern part of the region, since no registration of WiFS data exists in this area. The figures below display the final satellite selection (precision corrected). Each IRS- 1C/D WiFS scene is described with satellite type, path/row and registration data. For scene 036/022, the attitude data was corrupted and this scene had to be corrected with higher polynomial. Figure 2 : IRS-1D 021/ Euromap Figure 3 : IRS-1D 022/ Euromap

18 Page 18 Figure 4 : IRS-1D 023/ Euromap Figure 5 : IRS-1D 026/ Euromap Figure 6 : IRS-1D 026/ Euromap Figure 7 : IRS-1C 030/ Euromap

19 Page 19 Figure 8 : IRS-1D 030/ Euromap Figure 9: IRS-1C 032/ Euromap Figure 10: IRS-1C 033/ Euromap Figure 11 : IRS-1C 036/ Bad attitude data. Corrected with higher polynomial. Euromap

20 Page 20 Figure 12 : IRS-1C 036/ Euromap Figure 13 : IRS-1C 038/ Euromap Figure 14: IRS-1D 038/ Euromap Figure 15 : IRS-1D 039/ Euromap

21 Page 21 Figure 16 : IRS-1C 044/ Euromap Figure 17: IRS-1C 045/ Euromap Terra (EOS AM-1) MODIS data MODIS (or Moderate Resolution Imaging Spectroradiometre) is the key instrument aboard the Terra (EOS AM-1) satellite. Terra MODIS is viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths. The spatial resolution of the data varies between 250 m (bands 1-2), 500 m (bands 3-7) and 1000 m (bands 8-36). Currently it is very difficult to handle MODIS data and therefore we don t know yet if this data source can be useful for land cover classification. Therefore the approach was to use WiFS data for the land cover classification work. It will however be of interest to try MODIS data and/or data from other medium resolution satellites to updating purposes of the BALANS Land Cover database. In december 2000 Laboratory of Space technology in Helsinki University of Technology implemented a data extraction and rectification tool for MODIS eos-hdf products. This enabled the usage of TERRA MODIS 250 m data in the classification work at the Finnish Environment Institute. MODIS data was downloaded as calibrated and geolocated at-aperture radiances for bands 1 ( nm) and 2 ( nm) (level 1B; see: This product includes latitude and longitude information corresponding to 1km by 1km grid. Geolocation information is interpolated linearly into 250 by 250 meter grid within each scanned data set. These geolocation data is retrieved from orbital parameters of TERRA satellite. It was proved that the accuracy of the geolocation was sufficient i.e. in most cases better than one pixel. Extracted MODIS data was converted into at aperture radiances (1000* W/m 2 /µm/sr) by using conversion parameters included in the MODIS datasets.

22 Page 22 However, the spatial resolution of MODIS data is at best 250 meters in nadir, which is significally different compared with the resolution (188 meters) of the WiFS data. Since IRS WiFS data was the main source of information in BALANS land cover classification, MODIS images were used with the lowest priority order in the classification, mainly to add information where WiFS data was missing. The images below are rectified subsets of the entire data granule, which are extremely large in area extent. Figure 18: MODIS data Figure 19: MODIS data

23 Page 23 Figure 20: MODIS data Reference data Different types of reference datasets have been used in the classification process. Some reference datasets cover the entire Baltic Sea drainage basin, while other covers only parts of the region, primarily countrywise. If the original resolution of the reference dataset is better than 150 metres (the resolution of the satellite data), the dataset will considered to be ground truth in the classification process. Reference data with a resolution lower than 150 metres, will be considered as a valuable input, but will not function as the complete truth, instead taking advantage of the better resolution of the satellite data. By using this approach, the reference data is handled different in the classification procedure depending on its resolution, hence having less or more influence on the final land cover database. All reference datasets have been reprojected to the BALANS projection, and in cases where the original resolution is better than 150 metres, the reference data has been resampled to 150 metres, with pixels perfectly aligned geographically to the satellite images. Some land cover classes in the reference datasets have been recoded to correspond to the BALANS Land Cover classes, while others are separated into different subclasses of a single BALANS class, if they are expected to have spectral dissimilarities in the satellite images.

24 Page Finnish National Land Use and Forest Classification The original resolution of the dataset is 25 metres and the data is based on: LANDSAT TM and SPOT XS images received between Digital map data (peatland, agricultural areas, settlements, etc) Forest data is based on satellite image interpretation completed in Finnish Forest Research Institute (National forest inventory, version VIII). Version three of this dataset has been used in the BALANS classification work over the Finnish and Russian territories. The reference dataset has been converted from the generalised product (resolution 200 metres) of the national land cover database. The reference data has following classes: Settlements Agricultural areas Open bogs (wetlands) Water Sparce coniferous forest (< 50 m 3 /ha) Other coniferous forest (> 50 m 3 /ha) Deciduous forest Open spaces with little or no vegetation Figure 21: Finnish National Land Use and Forest Classification. This dataset has been used to delineate the classes Artificial surfaces, Agricultural areas, Wetlands, Water bodies, Coniferous forest, Deciduous forest and Seminatural areas for Finland and Russian Karelia. Since the original product is based on data with higher resolution than the BALANS satellite scenes (25 metres compared to 150 metres), and the generalised reference dataset used for the BALANS work has a resolution of 150 m which is the same as the satellite data, this reference dataset is considered to be ground truth.

25 Page Swedish Terrain Type Classification The Swedish Terrain Type Classification is a land cover database based upon satellite data, covering all of Sweden. A nationwide coverage was produced with satellite data from and the whole area, excluding the mountainous region, was updated with data from The input data were: Digital satellite data (Landsat TM and SPOT XS, geocoded to 25 metre pixel resolution) Digital map data (National Survey s maps in scale 1: and 1:50 000, with digital thematic data for Wetlands and Settlement areas) Aerial photos were used as reference material for the classification and the classification was performed with the Maximum likelihood algorithm. Figure 22 : Swedish Terrain type classification This dataset has been used to delineate the classes Artificial surfaces, Wetlands, Water bodies, Coniferous forest, Deciduous forest, Bare rock, Glaciers and perpetual snow and Open land. Since the original product is based on data with higher resolution than used within the BALANS project (25 metres compared to 150 metres), this reference dataset is considered to be ground truth. Table 2 is listing the different land cover classes in the Swedish Terrain Type Classification and their corresponding BALANS Land Cover class (including subclasses, later to be merged into the final BALANS Land Cover classes) to which they have been recoded.

26 Page 26 Table 2: Land cover classes for Swedish Terrain Type Classification with corresponding BALANS Land Cover classes Swedish Terrain type class Water, sweet Water, salt %, I Water, salt %, II Water, salt %, III Water, salt %, IV Water, salt %, V Water, salt %, VI Water, salt %, VII Water, salt %, VIII Wetland - Wet type Wetland dry type Wetland - With water Dense, full-grown coniferous forest Sparse, full-grown coniferous forest Low or young coniferous forest Dense, high or full-grown deciduous forest Sparse, low or young deciduous forest Clear-cut General built-up area Dense built-up area with homogenous blocks of multi-storey buildings Built-up area with multi-storey buildings Built-up area mainly with single storey, detached or chain houses Industrial areas Built-up area with summer houses, mainly single storey Public building or other large building e.g. hospitals Rock or boulder Glaciers and perpetual snow Open land (e.g. agriculture, heath) Corresponding BALANS class Water Water Water Water Water Water Water Water Water Wetland Wetland Wetland Coniferous forest Coniferous forest Coniferous forest Deciduous forest Deciduous forest Seminatural areas Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Bare rock Glaciers and perpetual snow Agricultural areas or Seminatural areas

27 Page CORINE LC The CORINE programme (Coordination of Information on the Environment) was initiated 1985 within EU, with the aim of constructing environmental databases to obtain a general and between countries comparable picture of the state and changes of the environment. The database consists of 44 vegetation and land use classes with a minimum mapping unit of 25 hectares. The classes are obtained from satellite data in combination with other information, such as map data. The CORINE database have not yet been produced for all EU countries, but for a large part of the Baltic Sea drainage basin, CORINE LC data exists. Figure 23 : Corine LC 100 m dataset for the BALANS project area For the BALANS project, the 100 metre resolution dataset has been used as reference data. Although the dataset has a higher resolution than the satellite data, the product is not considered as ground truth, since the product have a minimum mapping unit that is larger than one 150 metres pixel. The original dataset consists of a total number of 44 classes that have been recoded into the following classes for the BALANS classification work: Artificial surfaces Coniferous forest Deciduous forest Mixed forest Wetlands Water Bare rock Glaciers and perpetual snow Seminatural areas

28 Page 28 This dataset has been used to delineate the classes Artificial surfaces, Wetlands, Water, Coniferous forest, Deciduous forest, Bare rock, Glaciers and perpetual snow and Seminatural areas CORINE LC - Poland In Poland, the Polish version of CORINE LC has been used to delineate the Agricultural land, since this dataset has a better original resolution (25 m) than the European scale CORINE LC. The agricultural areas have been extracted from the original dataset and resampled from 25 metres to 150 metres IAKS 99 Sweden This dataset is derived from the Swedish Board of Agriculture and shows the Agricultural land in Sweden. The original dataset used for the BALANS project had a resolution of 25 metres Areal Informations Systemet Denmark Agricultural land in Denmark was derived from the AIS project (Areal Informations Systemet). The dataset is originally vector data, based on data in scale range 1: : For further information of this dataset, please visit their homepage at FIRS Regions and Stratas The regions and stratas have been produced within the FIRS project. The task of stratification was to subdivide the European Forest ecosystem regions into more or less homogeneous forest ecosystem strata and to draw the boundaries on a map in scale of 1: which indicates the resolution of the dataset. The stratas have been defined based upon combining necessary input information (i.e. homogeneous map sets and criteria for the strata definition) with regional expertise. The smallest stratum allowed was km2. The production order was as follows: 1. The Regionalisation of Europe into major forest ecosystem regions. 2. The Stratification within the defined forest ecosystem regions into homogeneous forest strata.

29 Page 29 Figure 24 : Regions for the Baltic Sea drainage basin Figure 25 : Stratas for the Baltic Sea drainage basin The regionalisation efforts were focused not only on forest ecosystem, but also on general ecological aspects in order to make the data applicable for general ecological purposes. The variables are: Major species groups Stand density Canopy layer (vertical structure) Tree volume (as biomass indicator) Stand height Forest health condition A second group of variables were used for the additional description of the already delineated stratum: Forest management intensity Different management practices (selective logging, clear cutting) Forest functions: wood production (timber, firewood, pulpwood, Christmas trees), recreation, hunting. Property (private, public owned) Importance of forest fires And additionally:

30 Page 30 Soils (from FAO world soil map) Topography The BALANS classification work has been performed areawise, with regions delineating the classification areas. If a region is considered to be too large, it has been further divided into two or more stratas. The stratified classification is intended to provide a more reliable analysis within each stratified sample than treating the whole dataset at once Baltic Sea Region GIS, Maps and Statistical Database The Baltic Sea Region GIS, Maps and Statistical Database is a result of the Baltic Drainage basin Project (BDBP). The BDBP was a multi-disciplinary research project under the EU Environment Research Programme. The GIS database, mainly focusing upon land cover/land use and population, was developed as a joint effort between the Beijer Institute in Stockholm, the Department of Systems Ecology at Stockholm University and UNEP/GRID-Arendal. The database was used for analytical purposes during the BDBP. Following this, the GIS database was further refined and prepared for public dissemination. Concurrently, a number of maps in conventional graphical formats were prepared and included. The GIS and Maps database was first released in August Later, a number of statistical tables, also derived from the GIS database, were included Baltic Sea drainage basin Watershed areas The Sub-watershed drainage basins dataset was generated from digitising three paper maps, each with an approximate scale of 1: There are a total of 81 sub-basins in total forming the seven major watersheds that define the Baltic Sea drainage area: Bothnian Bay, Bothnian Sea, Gulf of Finland, Gulf of Riga, Baltic Proper, Danish Straits, and the Kattegat. Also associated with each sub-basin are N and P loading estimates, percent land cover values, as well as urban, rural, and total population statistics. This vector information has been used to delineate the extent of the BALANS area, using the boundary for the entire Baltic Sea drainage basin Agricultural areas Agricultural information is divided into two categories: Arable lands and Pasture lands. Each category exists as an independent data layer. The minimum resolution is a 10 km x 10 km square unit. The values associated with each unit are either percent total arable land or percent total pasture land within each pixel. For the BALANS project, the two classes has been combined into a single agricultural class. This dataset has only been used as a separate agricultural image layer to give the user an indication of the extent of agricultural data due to its low resolution, hence has not been integrated in the final land cover and land use database. Due too the low resolution of this dataset, it can only be used as an indication of where to find larger agricultural areas, and not to be seen as the truth on a pixel level. The reason for distributing this dataset together with the BALANS Land Cover and Land Use

31 Page 31 dataset, is to have some agricultural information available for areas where no other agricultural reference data of relevance existed. Figure 26 : Percentages of Agricultural areas from The Baltic Sea Region GIS, Maps and Statistical Database. The percentage level of Agricultural areas per pixel goes from red (highest) through yellow, green and grey (lowest) Heightrelated reference data Height related information has been used to recode classified pixels if they don t match predefined height related criterias. One example is to recode pixels classified as Water on steep slopes (due to shadow effect in satellite data) into Seminatural areas. For Sweden, a Digital Elevation Model with a resolution of 50 metres has been used. The dataset has been obtained from the GSD Height databank at the Swedish National Land Survey. The dataset has a resolution of 50 metres and the intention is that the height mean error should be less than 2.5 metres. For Finland and Russia, no DEM was used since there are no shadowed areas in these regions due to the flat topography.

32 Page 32 Over remaining areas, a DEM with a 100 metres resolution has been used. All areas have been combined and projected into a single 150 metres resolution dataset. Figure 27: Digital elevation model for the project area. From the digital elevation data for the project area, a slope and a wetness image have been created. Both have a resolution of 150 metres. The wetness index image have been produced using the formula (after Beven & Kirkby); Ln(a/tanb)

33 Page 33 Figure 28: Slope information for the Baltic Sea drainage basin. Bright colours indicate higher slope values. Figure 29: Wetness index information for the Baltic Sea drainage basin. Bright colours indicate higher wetness values Candidate reference data not used in classification work Other reference data sources have also been collected and controlled, but have been excluded from the classification process, either because of poor quality or because they are not considered to improve the classification quality World Atlas of Agriculture The World Atlas of Agriculture is a large Atlas showing the agricultural areas of the entire world. The Atlas was published in 1969, and the principle source for the maps is the Times Atlas of the World, defining the geographic area. Each map shows the principal categories of land use and only those categories which occupy an area on the map larger than 2 mm 2 (whatever the scale) are located in this way, smaller areas of any one category are grouped together and are shown by a separate symbol. The maps were scanned, classified (separating agricultural areas from non agricultural areas) and geometrically corrected by using rubber sheeting. However, the quality of this dataset was too poor, so it was decided not to use this information at all in the final BALANS product.

34 Page World Vector Shoreline The World Vector Shoreline is a dataset separating water from land and also showing countries. It has a world-wide coverage and is suitable for scales close to 1: The original source of data is the former Defence Mapping Agency (DMA), now renamed to National Imagery and Mapping Agency (NIMA). The accuracy requirement for the data is that 90% of all identifiable shoreline features should be located within 500 meters (2.0mm at 1: ) cirular error of their true geographic positions with respect to the preferred datum (WGS 84). The main source material for the WVS was the DMA's Digital Landmass Blanking (DLMB) data which was derived primarily from the Joint Operations Graphics and coastal nautical charts produced by DMA. The DLMB data consists of a land/water flag file on a 3 by 3 arc-second interval grid. This raster dataset was converted into vector form to create the WVS. Data can be freely downloaded from the Coastline extractor site at but that dataset does not include all the features (such as lakes, rivers,bradwaters,glaciers, international boundaries, country names, etc.) that DMA has in their dataset. Within some parts of the BALANS project area, the geometry of the dataset was very good, while it in other parts had an error of more than 300 metres. Due to this inconsistency, it was decided not to use this dataset at all Baltic Sea Region GIS, Maps and Statistical Database The Baltic Sea Region GIS, Maps and Statistical Database has already been described in section for the Baltic Sea drainage basin watershed and Agricultural areas. Population density The Population Density dataset was derived by combining urban and rural population statistics with the Administrative Units and Land Cover IDRISI datasets, generated for the Baltic Sea Drainage basin project. Population statistics were collected at the smallest adminstrative unit possible: municipality. The final resolution provided is a 5 km x 5 km unit. Final statistics represent the number of inhabitants per square kilometer. Due to the low resolution of the dataset and also because of its nature (not directly related to the signatures for the pixels in the satellite data) it was decided not to use this dataset. Coastline The Land and Ocean (Coastline) dataset was generated simply to provide users with an additional database layer that will enable them to extract additional information when used in conjunction with other datasets. The Land and Ocean (Coastline) dataset is a subset of the PONET, Political and Oceans, data layer produced by ESRI and is part of the DCW database. Users can obtain detailed (1:1,000,000) coastline data for the Baltic Sea region.

35 Page 35 The scale of the dataset and the geometric accuracy is not sufficient for the BALANS project. Landcover The Land Cover data layer consists of six classes: Forest, Open Land, Open Water, Urban Land, Glacier, and Unknown Land, which is either Forest or Open Land. The Land Cover dataset was generated from the Digital Chart of the World (DCW) with a scale of 1:1,000,000 and from the European Space Agency Remote Sensing Forest Map of Europe with a 1 km resolution. The resolution is a 1 km x 1 km unit. The scale of the dataset and the geometric accuracy is not sufficient for the BALANS project. Wetlands Wetland Distribution for the Baltic Sea region was derived from the association of regional wetland area statistics with locations represented in the DCW Land Cover data layer. Due to the quality of input statistics, it could only be provided a final Wetland Distribution data layer with a resolution of 50 km by 50 km units. The values associated with each unit are percent total wetland area within unit boundaries (i.e., a raster cell or a vector polygon). Due to the low resolution of the dataset and also because of its nature (not directly related to the signatures for the pixels in the satellite data) it was decided not to use this dataset MapBSR The purpose of the MapBSR project is to provide basic map datasets for the Baltic Sea region in the nominal scale of 1: The elements included in the database are boundaries, hydrography, transport, settlements, geographical names, elevation and national parks. The database will form a base map for geographic information systems (GIS), in which any kind of data item can be located and represented, as long its coordinates are known. Different kinds of thematic information can therefore be added to the database, such as statistics on population density or data on water quality. All datasets have not been produced yet, but some data can be ordered. However, the resolution of the dataset and the classes included, showed that the dataset was not suitable to function as reference data for the BALANS project Digital Chart of the World The Digital Chart of the World (DCW) is a 1: scale geographic database. It contains geographic features, attribute data, descriptive text, and metadata that can be used in conjunction with Arc/Info and ArcView GIS software. It was originally produced by ESRI for the U.S. Defense Mapping Agency.

36 Page 36 Due to the resolution of the dataset as well as its low geometric accuracy, it was decided not to use this dataset within the BALANS project IGBP The IGBP Land Cover database is a global land cover characteristics database that was developed on a continent-by-continent basis. All continents in the global data base share the same map projections (Interrupted Goode Homolosine and Lambert Azimuthal Equal Area), have 1-km nominal spatial resolution, and are based on 1-km AVHRR data spanning April 1992 through March Each continental database has unique elements that are based on the salient geographic aspects of the specific continent. In addition, a core set of derived thematic maps produced through the aggregation of seasonal land cover regions are included in each continental data base. For Europe the database embraces 17 classes. Because of the resolution of the dataset and the quality inconcistency, the dataset was not suitable for the BALANS project work PELCOM This dataset consists of a 1 km pan-european land cover database. The database was based on the integrative use of multispectral and multitemporal 1-km resolution NOAA- AVHRR satellite data and ancillary data. The dataset was produced during the period between 1996 to Because of the resolution of the dataset and the quality inconcistency, the dataset was not suitable for the BALANS project work.

37 Page 37 3 Land Cover and Land Use Classes The basic principle when defining the BALANS Land Cover class legend, was to make the dataset directly comparable to CORINE level 1, hence using the legend and class descripition of the CORINE database as a model for the BALANS Land Cover legend. Whenever possible, the level 1 classes have been further divided into two or more subclasses, still comparable to the CORINE class legend. More land cover classes were used in the classification work than was to be included in the final land cover database. This primarily because one single land cover class in the final output can include several sub-classes that are spectrally different from each other in the satellite data. Therefore, these classes were handled separately and then combined as a final step before obtaining the final output. 3.1 Land Cover and Land Use legend The classes listed below are the land cover and land use classes finally set for BALANS; Code Class 1 Artificial surfaces 2 Open land 21 Agricultural land 22 Seminatural areas 3 Forest 31 Coniferous forest 32 Deciduous forest 4 Wetlands 5 Water bodies 6 Glaciers and perpetual snow 7 Bare rock 99 Clouds 100 No agricultural information available (separate dataset) 254 No data 3.2 Class Description ARTIFICIAL SURFACES Consists of:

38 Page 38 Urban fabric; Areas mainly occupied by dwelling and buildings used by administrative/public utilities or collectives, including their connected areas (associated lands, approach road network, parking lots). Industrial, commercial and transport units, public services and military installations; Areas mainly occupied by industrial activities of transformation and manufacturing, trade, financial activities and services, transport infrastructures for road traffic and rail network, airport installations, river and sea port. Includes industrial livestock rearing facilities. Mine, dump and construction sites; Artificialised areas manily occupied by extractive activities, construction sites, manmade waster dump sites and their associated lands. Artificial non-agricultural vegetated areas; Areas voluntary created for recreation use. Includes green or recreational and leisure urban parks, sport and leisure facilities. OPEN LAND This class is further divided into Agricultural land and Seminatural areas. AGRICULTURAL AREAS Consists of: Arable land; Lands under a rotation system used for annually harvested plants and fallow lands which are permanently or not irrigated. Includes flooded crops such as rice fields and other inundated croplands. Permanent crops; All surfaces occupied by permanent crops, not under a rotation system. Includes ligneous crops of standards cultures for fruit production such as extensive fruit orchards, olive groves, chestnut grives, walnut groves, shrub orchards such as vineyards and some specific low-system orchard plantation, espcaliers and climbers. Pastures; Lands which are permanently used (at least 5 years) for fodder production. Includes natural or own herbaceous species, unimproved or lightly improved meadows, grazed or mechanically harvested meadows. Heterogeneous agricultural areas; Areas of annual crops associated with permanent crops on the same parcel, annual crops cultivated under forest trees, areas of annual crops, meadows and/or permanent crops which are juxtaposed, landscapes in which crops and pastures are intimately mixed with natural vegetation or natural areas.

39 Page 39 SEMINATURAL AREAS Shrub and/or herbaceous vegetation association; Bushes, scrubs, grasalnads and tall herb communities, deciduous forest recolonization, hedgerows, dwarf conifers. Mediterranean and sub-mediterranean evergreen sclerophyllous bush and scrub, recolonization and degradation stages of broad-leaved evergreen forest. Open spaces with little or no vegetation; Natural areas covered with little or no vegetation (bare rock excluded), including open, thermophile formations of sandy grounds distributed on calcareous or siliceous soils frequently disturbed by erosion, eir tracks, land sand-dunes, coastal sand-dunes and burnt areas. FOREST This class has been further divided into Coniferous and Deciduous forest. Forest is defined as >30% crown cover and the tree height shall be >7 m. Seedlings under forest are also included in this class. Areas occupied by forest and woodlands with a vegetation pattern composed of native or exotic coniferous trees, and which can be used for the production of timber or other forest products. The forest trees are under normal climatic conditions higher than 5 m with a canopy closure of 30% at least. CONIFEROUS FOREST The coniferous crown cover is larger than 50% of the total forest crown cover. DECIDUOUS FOREST The deciduous crown cover is larger or equal to 50% of the total forest crown cover. Mountain birch in the Nordic countries (less than 7 m in height) are also included in this class. WETLANDS Inland wetlands; Areas flooded or floodable during a grand part of the year by fresh, brackish or standing water with a specific vegetation coverage made of low shrub, semi-ligneous or herbaceous species. Includes water-fringe vegetation of lakes, rivers, and brooks and of fens and eutrophic mashes, vegetation of transition mires and quaking bogs and springs, highly oligotrophic and strongly acidic communities composed mainly

40 Page 40 of sphagnum growing on peat and deriving moistures of raised bogs and blanket bogs. Coastal wetlands; Areas which are submerged by high tides at some stage of the annual tidal cycle. Includes salt meadows, facies of saltmarsh grass meadows, transitional or not to other communities, vegetation occupying zones of varying salinity and humidity, sands and muds submerged for part of every tide devoid of vascular plants, active or recently abandoned salt-extraction evaporation basins. WATER BODIES Inland waters; Lakes, ponds and pools of natural origin containing fresh (i.e. non-saline) water and running waters made of all rivers and streams. Man-made fresh water bodies including reservoirs and canals. Marine waters; Oceanic and continental shelf waters, bays and narrow channels including sea lochs or loughs, fjords or fiards, ryas straits and estuaries. Saline or brackish coastal water, often formed from sea inlets by sitting and cut-off from the sea by sand or mud banks. BARE ROCK Natural area covered with bare rock. GLACIERS AND PERPETUAL SNOW Permanent snow and ice in high mountains. CLOUDS Areas consisting of clouds in satellite data. NO DATA No satellite data.

41 Page 41 4 Classification Background The classification work has been performed by Metria Miljöanalys (former Satellus AB) and Finnish Environmental Institute. When deciding what classification methodology to use, several criterias had to be considered: Obtaining a product of high quality, Create a methodology that is repeatable (for updating purposes), Time and cost efficiency in the production work, Independent of operator performing the classification work. These criterias indicates the need for an automised procedure, class labeling each pixel by integrating different types of geographical datasets and satellite data based upon their characteristics with logical, predefined rules. In order to achieve the best quality - within the limitations set by the spatial and spectral resolution of the satellite data, the nature of the reference data and the time and cost limits for the project - the basic concept for obtaining the final BALANS Land Cover product, has been to take as much advantage of the different input data as possible, aiming to use them in the best manner. This means that the final product not simply is based on the results from the classification of the satellite data, but also from integrating land cover information from reference data into the final land cover database whenever suitable. The type of integretion varies depending on the nature of each reference dataset. Hence, the final product can t simply been seen as a classification, but more as a GIS generated product. It is also important to clarify that the intention has been to obtain the best possible quality areawise (i.e. region), which means that the quality of the total dataset is not homogenous. This was considered more valuable than obtaining a homogenous product with a lower quality (equaling the quality level of areas with the lowest accuracy). Primarily it is the nature and quality of the reference datasets per area that sets the quality standard for the final output. This requires the use of metadata information describing input satellite and reference data used to classify each pixel. More land cover classes were used in the classfication work than was included in the final BALANS Land Cover database. This primarily because one single land cover class in the final output can include several sub-classes that are spectrally different from each other in the satellite data. Therefore, these classes were handled separately whenever possible and then combined in the final steps of the classification process. 4.1 Regions to classify Figure 30 shows the different classification areas (i.e. regions or sub-regions) used in the classification work. All classification steps were produced per classification area, generating a land cover dataset, and finally all datasets were merged into a single BALANS Land Cover database.

42 Page 42 Table 3 is listing the stratas included per classification area (i.e. region or sub-region) and a brief description of the area. Figure 30: Classification areas (i.e. regions) for the BALANS classification work.

43 Page 43 Table 3: BALANS classification areas Class. Area Region Region description Stratas included Comments 1 VI (+ VI-VIII) Moderate temperate (and Transition Moderate temperate to Boreal) 2 VI (+ X) Moderate temperate (and Orobioms*) 3 VI-VIII Transition Moderate temperate to Boreal 4 VI-VIII Transition Moderate temperate to Boreal 5 VI-VIII (+ VI- VII) Transition Moderate temperate to Boreal (and Transition Moderate temperate to Arid moderate temperate) 6 VIII-VI Transition Boreal to Moderate Temperate 7 VIII-VI Transition Boreal to Moderate Temperate 42,46,53,54, (68) Denmark, Germany. Bornholm island (68) included although normally not part of region VI. 38,51, (57,108,109) Poland. Smaller parts of stratas 57, 108 and 109 included altough normally not part of region VI. 68 Sweden 65,66,68 Baltic countries 63, 67 (51,65,66) Ukraine, Belorussia. Very small part of strata 63 (region VI-VII) included although normally not part of region VI-VIII. Part of stratas 51, 65 and 66 were also included although they are also included in other classification areas. This because hardly no land cover reference data existed for stratas 63 and 67, and therefore the classification area was extended to include areas with existing land cover reference data. This overlapping area was excluding from this classification area when merging the different land cover databases into a single BALANS Land Cover database. 90,91 Sweden, Norway 94,95,96 (66,67,68) Russia Part of stratas 66, 67 and 68 were also included although they are also included in other classification areas. This because hardly no land cover reference data existed for stratas and therefore the classification area was extended to include

44 Page 44 8 VIII Boreal 74,75,76 Sweden 9 VIII Boreal 48,76 Sweden areas with existing land cover reference data. This overlapping area was excluding from this classification area when merging the different land cover databases into a single BALANS Land Cover database. 10 VIII (+VIII-VI) Boreal (and Transition Boreal to Moderate Temperate) 77,98, (90) Finland incl. Åland Strata 90 over Finland included although normally not part of the region. 11 VIII Boreal 78,79,80,81 Russia. 12 VIII (+VIII-IX and X) Boreal (and Transition Boreal to Polar, and Orobioms) 97,117,118, (110,119) Finland Small parts of stratas 110 and 119 included although normally not part of the region. 13 X Orobioms* 92,93,110 Sweden, Norway. Orobioms bear the general characteristics of their surrounding climate region. Nevertheless the climate conditions differ in some essentioal aspects: temperatures are lower and precipitation higher than the surrounding zone (region). A very important ecological factor is the increasing appearance of frost at higher altitudes. 4.2 Land cover reference data for the project area The primary land cover reference datasets used in this project are the Swedish Terrain Type Classification, the Finnish National Land Use and Forest Classification and CORINE LC. However, these datasets combined don t cover the entire project area, but are in those cases used to label pixels in areas with no reference data, based upon the statistics per classification cluster. More detailed information about this process is described in section 5.2. Figure 31 shows the available land cover reference datasets and their extent for the Baltic Sea drainage basin.

45 Page 45 Figure 31: Land cover reference data per area. 1: Finnish National Land Use and Forest Classification, 2: Swedish Terrain Type Classification, 3: CORINE LandCover, 4: No land use reference data available of relevant scale. 4.3 Agricultural reference data for the project area The primary agricultural reference datasets used in this project are the Swedish IAKS 99, the Finnish National Land Use and Forest Classification and Polish CORINE LC. However, these datasets combined don t cover the entire project area, so remaining areas with no agricultural information will have the code for Open land, indicating that a separation of this class into either Agricultural land or Seminatural land in not possible. A separate image layer is also created, based on the agricultural percentage values, derived from the Baltic Sea Region GIS, Maps and Statistical Database (see section ). Figure 32 shows the available agricultural reference datasets and their extent for the Baltic Sea drainage basin. Figure 32: Agricultural reference data per area. 1: Swedish IAKS 99, 2: Areal Informations Systemet, Denmark, 3: the Finnish National Land Use and Forest Classification, 4: Polish CORINE LC and 5: No agricultural reference data available of relevant scale.

46 Page Scene selection criteria per region The ideal situation when classifying a pixel is to use cloudfree multitemporal satellite data, registered at different times during the vegetation season, in order to collect the variations for each land cover class during the season. In practice this has not been possible for this project. Primarily the data should be as cloudfree as possible, and the number of scenes have been limited to get an almost cloud free coverage over the Baltic Sea drainage basin. It hasn t been possible to collect optimal images for the vegetation season in all cases. For the scenes used in the classification work, a maximum of three scenes per classification area (i.e. region or sub-regions) have been used. If all scenes available for a pixel should have been used, the classification model would have been very complex and time consuming. The scenes used per classification area have been selected based on their registration date, coverage of the classification area and their cloud coverage. The scenes have a priority order, with the highest priority for the scene covering the largest part of the area (excluding clouds) and with the most optimal registration date for the area. 4.5 Land Cover classes used in the classification work Table 4 lists the different land cover classes that have been included in the classification process:

47 Page 47 Table 4: Land Cover classes used in the classification work and their corresponding class in the final output database. Land Cover Class Artificial surfaces Urban areas Artificial non-agricultural vegetated areas Agricultural areas Arable land Pasture Seminatural areas Shrub and/or herbaceous vegetation Open spaces with little or no vegetation Open land Bare rock Snow/ice Forest Coniferous forest Sparse coniferous forest Medium coniferous forest Dense coniferous forest Deciduous forest Mixed forest Clouds No data Corresponding BALANS Land Cover class in final output Artificial surfaces Artificial surfaces Artificial surfaces Agricultural areas Agricultural areas Agricultural areas Seminatural areas Seminatural areas Seminatural areas Seminatural areas or Agricultural areas Bare rock Glaciers and perpetual snow Coniferous or Deciduous forest Coniferous forest Coniferous forest Coniferous forest Coniferous forest Deciduous forest Coniferous or Deciduous forest Clouds No data 4.6 Classification principles per land cover class This section gives a brief overview of how each land cover class have been obtained in the classification procedure. More detailed information concerning each step in the classification procedure is described in section Artifical areas This is a spectrally inhomogenous class that can t simply be derived by classifying WiFS satellite data. Therefore it was decided to use the artificial pixels in the land cover reference datasets, i.e.swedish Terrain Type Classification (section 2.3.2), Finnish National Land Use and Forest Classification (section 2.3.1) and Corine LC (section 2.3.3),

48 Page 48 as a mask. However, the mask can be expanded with pixels that are in direct conjunction with an artificial surface and have been classified as candidates for Artificial surfaces (according to predefined rules). For areas where no such land cover reference data exists, statistics are created for all Artificial surfaces within the classification area that have reference data, and pixels in areas with no reference data that have similar spectral characteristics to those areas are candidates for the class Artificial surfaces. The pixel will finally be labeled as Artificial surfaces if it is part of a coherent group of pixels covering a minimum area, all candidates for Artificial surfaces, and the operator considers the candidate area to be an Artificial surface by visual interpretation Open land This class will be created by classification. In the final BALANS dataset, this class will only exists for areas were no agricultural reference data exists. Remaining areas classified as Open land will be split into either Agricultural land (section ) or Seminatural areas (section ) Agricultural land This class will be created by classifying a class labeled Open land that includes both Seminatural areas and Agricultural land and then use a separate agricultural mask (based on the agricultural reference data with a pixel size of 150 metres) to separate the pixels classified as Open land into either Seminatural areas or Agricultural land. For areas (countries) with no agricultural data, the agricultural mask will have a special code for No agriultural information available, and these remain coded as Open land (top class for Seminatural areas and Agricultural land). However, for the entire drainage basin, the agricultural reference data Baltic Sea Region GIS, Maps and Statistical Database Agricultural dataset (section ) is available as a separate image layer. Due to its resolution and nature of the dataset, it can only function as an indication in the final BALANS Land Cover dataset for where to find larger agricultural areas. The quality per pixel is very poor. Figure 32 shows the reference datasets used for delineating Agricultural land Seminatural areas This class will be created by classifying a class labeled Open land that includes both Seminatural areas and Agricultural land and then use a separate agricultural mask (based on the agricultural reference data with a pixel size of 150 metres) to separate the pixels classified as Open land into either Seminatural areas or Agricultural land. For areas (countries) with no agricultural data, the agricultural mask will have a special code for No agriultural information available, and these remain coded as Open land (top class for Seminatural areas and Agricultural land).

49 Page Coniferous forest This class is primarily obtained from the unsupervised classification of the satellite data (section 5.2.8). If the pixel have high enough probability (defined by specified rules) of belonging to the Coniferous forest class, it will be labeled as such. For dubious pixels, such as mixed pixels or pixels that are spectrally different from other Coniferous forest pixels (using reference data as statistics), further processing is needed. If the pixel is spectrally a mixture of Coniferous forest, Deciduous forest or Open land, it will be labeled to any of these classes according to specific predefined criterias (see section for further details). If a pixel has been classified as Coniferous forest, but is located at a higher altitude than the tree limit, it will be recoded to the class Seminatural areas. For CORINE LC data, the class Mixed forest is part of the dataset. For those pixels, spectral characteristics in the satellite data are compared to the characteristics of Coniferous and Deciduous forest, and if the pixel is spectrally most similar to Coniferous forest, it will be classified as that, as long as it s considered to be forest (section ). Pixels that can be either Water or Coniferous forest will be coded as Coniferous forest if they are not part of the water mask (section ) Deciduous forest This class is primarily obtained from the unsupervised classification of the satellite data. If the pixel have high enough probability (defined by specified rules) of belonging to the Deciduous forest class, it will be labeled as such. For dubious pixels, such as mixed pixels or pixels that are spectrally different from other Deciduous forest pixels (using reference data as statistics), further processing is needed. If the pixel is spectrally a mixture of Coniferous forest, Deciduous forest or Open land, it will be labeled to any of these classes according to specific predefined criterias (see section for further details). If a pixel has been classified as Deciduous forest, but is located at a higher altitude than the tree limit, it will be recoded to the class Seminatural areas. For CORINE LC data, the class Mixed forest is part of the dataset. For those pixels, spectral characteristics in the satellite data are compared to the characteristics of Coniferous and Deciduous forest, and if the pixel is spectrally most similar to Deciduous forest, it will be classified as that, as long as it s considered to be forest (section ) Wetland For areas where no reference data exists, statistics are created for all wetland surfaces within the classification area that have reference data, and pixels for which no reference data exist can then be candidates for the class Wetland. This will be decided if the pixel is part of a neighbouring group of pixels covering a minimum area, all candidates for Wetland, and the operator consider the candidate area to be Wetland by visual interpretation. This is a spectrally inhomogenous class that can t simply be derived by classifying WiFS satellite data. Therefore it was decided to use the Wetland pixels in the land cover reference datasets, i.e.swedish Terrain Type Classification (section 2.3.2),

50 Page 50 Finnish National Land Use and Forest Classification (section 2.3.1) and Corine LC (section 2.3.3), as a mask. For areas where no such land cover reference data exists, statistics are created for all Wetland within the classification area that have reference data, and pixels in areas with no reference data that have similar spectral characteristics to those areas are candidates for the class Wetland. The pixel will finally be labeled as Wetland if it is part of a coherent group of pixels covering a minimum area, all candidates for Wetland, and the operator considers the candidate area to be an Wetland by visual interpretation. However, if a pixel has been classified as Wetland according to this criteria, but the pixel is also classified as any of the forest classes by other criterias, it will classified as the forest class in the final output Water bodies The Water class is created by thresholding the NIR band. In some cases, additional threshold in the R band might be necessary to remove pixels from the water mask, for instance very dense urban areas that can be spectrally similar to water in the NIR band. Pixels in the water mask that have a slope value higher than a specific value and a wetness index value lower than a specific value (section 5.2.5) are removed from the water mask. Additional pixels can also be classified as Water if they are; Spectrally similar to water (comparing the unsupervised classification with the reference data), Labeled Water in the land cover reference data and have a slope value higher than a specific value and a wetness index value lower than a specific value (section 5.2.5) Bare rock This class is primarily obtained from the unsupervised classification of the satellite data. If the pixel have high enough probability (defined by specified rules) of belonging to the Bare rock class, it will be labeled as such Glaciers and perpetual snow This class is primarily obtained from the unsupervised classification of the satellite data. If the pixel have high enough probability (defined by specified rules) of belonging to the Glaciers and perpetual snow class, it will be labeled as such Clouds If no cloud free satellite data is available for a pixel, it will be labeled Clouds No data If no satellite data exists for a pixel, it will be labeled No data.

51 Page 51 5 Classification Process This chapter describes step by step the different procedures performed to obtain the BALANS Land Cover dataset for each classification area. Some steps are unique for a specific reference dataset, while others are performed for all classification areas, independent of the input reference datasets. Appendix A shows a flow chart of the classification process with the different datasets used in each processing step. During the classification process, a pixel can match the criterias for labeling a pixel within several of the classification steps, hence being assigned to different land cover classes. However, all information produced in the different steps are merged together into a single land cover dataset in a predefined priority order, hence assigning the pixel the land cover class it has the highest priority of belonging to. 5.1 Reference data preparation The reference data used in the process needed some modifications before being used in the classification process Recoding, resampling and reprojection All reference data containing land cover information was recoded to correspond to any of the land cover classes described in Table 4. Raster data was then resampled and reprojected into 150 metres resolution, perfectly aligned with the satellite data, in the BALANS projection (section 2.1). Vector data was reprojected in Arc/Info while the raster data was reprojected in Erdas Imagine, using rigorous transformation Creating agricultural mask for areas with agricultural reference data The agricultural reference datasets were used to create an agricultural mask, showing Agricultural land, No agricultural land and No agricultural information available. Creating agricultural class for Baltic Sea Region GIS, Maps and Statistical Database The agricultural information from the Baltic Sea Region GIS, Maps and Statistical Database consists of two separate files, showing the percentage of Pasture and Arable land. These two images were combined into a single image by adding the two input images, thus obtaining a percentage value for total Agricultural areas for each individual pixel. This dataset has not been used in the final BALANS Land Cover and Land Use dataset, but is available as a separate layer (band 2) in the agricultural mask Creating tree limit image This image indicates areas where no forest exist, because of altitude. The altitude value that sets the tree limit varies, primarily related to latitude but also on the climatic conditions and other ecological and biological factors. The image has been created by combining the DEM with the forest pixels in the land cover reference datasets, taking the

52 Page 52 maximum elevation value for a 3x3 pixel area for which a forest pixel exists. Each row in the output tree limit image is then assigned the maximum elevation value for that particular row. The output image consists of a forest mask, indicating if a pixel can be assigned to a forest class or not. During the time of writing this report, the image was under production and will be used on the final BALANS Land Cover database, covering the entire Baltic Sea Drainage Basin area Creating cloud masks per satellite scene Metria Miljöanalys (former Satellus AB) has developed a Cloud mask module in ERDAS Imagine development environment. This module is a combination of automatic generation of cloud mask by thresholding and the possibility to manually exclude and/or include areas from the cloud mask. Figure 33 shows the start window for the cloud mask module. 5.2 Procedures performed per classification area Subsets of input images A 150 m resolution raster mask is created from the FIRS strata vector dataset (section 2.3.7), using the stratas included for the particular classification area. The mask is then expanded with 3000 metres (20 pixels) to create overlap areas between the different classification areas. Areas outside of the Baltic Sea drainage basin are removed. This region mask is then used during the classification process to delineate the area to perform analysis within for each classification area.

53 Page 53 Figure 33: Start window for the cloud mask module. Subsets are made for all satellite scenes and their cloud masks, as well as for some of the reference data to be included in the classification process, using the region raster mask as area delineator. The land cover reference datasets for which subsets were produced are to be edited at different stages during the classification process, hence reducing the processing time by using the subsets instead of the entire dataset.

54 Page 54 Figure 34: Satellite scene 26/28, (2,1,1) for classification area 8t6:1 (region VIII-VI), covering southern Sweden and minor part of Norway. Note that this particular scene does not cover the entire classification area. Euromap. Figure 35: Reference dataset Swedish Terrain type classification for the corresponding area. The reference datasets for which such subsets are made are the Swedish Terrain Type Classification, Finnish National Land Use and Forest Classification and CORINE LC Additional geometric correction of reference data The geometric accuracy for the CORINE LC data is in many cases poor, and therefore additional geometric correction is needed. A subset, a bit larger than the classification area is created for each reference dataset, and ground control points are selected by comparing the reference data with the satellite images. For CORINE LC, a 2 nd grade polynomial transformation has been used. After geometric correction, a subset corresponding to the classification area is made of each reference dataset, using the region mask as delineator Generating height related statistics For every land cover class in each of the land cover reference datasets - Swedish Terrain type Classification, Finnish Land Use and Forest Classification and CORINE LC - height, slope and wetness statistics are calculated, using the height related reference data (section 2.3.9) as input for the statistics. These statistics (min value, max value, mean and standard deviation) are then used in later steps of the classification procedure to delineate specific land cover classes.

55 Page Creation of cloud and no data mask The satellite scenes and their corresponding cloud masks are combined into a single one band image, showing pixels with no data in all three satellite images as well as pixels with cloud in all three images. Pixels that have clouds in one or two of the satellite scenes will also be coded as cloud if the remaining satellite scenes contain no data for that given pixel. This image will then be used as input in the creation of the final land cover dataset Creation of water mask The water mask is created for each scene for the classification area by thresholding the NIR channel (band 2). The reason for not producing the water mask for the entire satellite scene, is because of radiometric variations in the scene, primarily in an east- and western direction, meaning that one threshold value isn t applicable for the entire scene. Areas with clouds (derived from the cloud mask) are not included in the water mask. In some cases, additional thresholding in the red channel (band 1) might be necessary to remove pixels from the water mask that spectrally are very similar to water in the NIR channel, for instance very dense urban areas. The principle has been to set the threshold at a level to ensure that no pixels in forest areas (primarily dense coniferous forest) will be classified as water. Also an attempt has been made to persist the shape of areas where water meets land. Using this approach has the disadvantage that shallow and/or narrow lakes normally won t be included in the water mask. However, these pixels might be classified as water in a later step (section ). At Metria Miljöanalys (former Satellus AB) has developed a Water thresholding module in ERDAS Imagine development environment. This module facilitates the thresholding work, having two image viewers open, one for thresholding (showing the extent of the water mask on top of the satellite scene) and the second for visual control by displaying the satellite data for the same area without the water mask. Figure 36 shows the start window for the watermask application.

56 Page 56 Figure 36: Start window for the Water threshold application at Metria Miljöanalys. The water masks from all satellite images are then combined in scene priority order into a single water mask for the entire classification area. The water class is primarily derived from the scene with the highest priority and additional water pixels will only be added in areas where the top priority scene lacks satellite information or consists of clouds. Pixels in the combined water mask that have a slope value higher than the maximum slope value for the water class in reference data (section 5.2.3) will be removed from the water mask as well as pixels with a wetness index value lower than the minimum value for the water class in the reference dataset. This water mask will then be used as input in several of the forthcoming steps as well as in the creation of the final land cover dataset Removing clouds and water from satellite data For each satellite image, pixels covered by clouds (using the cloud mask as input) and pixels marked as water in the watermask, are removed from the satellite image by replacing them with value zero. This is done to reduce the amount and spectral variations of pixels in the classification process, thus optimising the satellite dataset to only include pixels of interest for obtaining the additional land cover classes.

57 Page Preparation of reference data for unsupervised classification A new version of the land cover reference dataset is created by replacing Wetland pixels with the value of zero. This class will be derived directly from the land cover reference data and will not be used in the analysing process of the clusters derived from section This analysing process is described in section 5.2.9, where the clusters created by unsupervised classification are compared with the land cover reference dataset. For CORINE LC data, the agricultural classes are combined with the seminatural classes into a single Open land class and all forest classes are combined into a single Forest class. This to make the analysing process described in section correct. Separation of these classes in the final output will be performed in later stages of the classification process Unsupervised classification First iteration Each satellite image (with clouds and water pixels removed) is classified individually using unsupervised classification with 40 clusters. The scaling range is set to one standard deviation and the convergence threshold to Pixels with zero values (no data, clouds or water) are excluded from the classification process. The reason for not classifying a multitemporal satellite image, is because it would take a large amount of clusters and a lot of time consuming analysis to cover all possible combinations, since there are large cloud areas in almost all satellite scenes and also since the classification area is of such size that it normally can t be completely covered by one satellite scene. However, in later steps, the results from the different unsupervised classifications with be compared to each other as well as to land cover reference data, and each output pixel will be labeled based upon the best result from the clustering process Creating majority classes per cluster First iteration Based upon each clustered image, a majority image and a percentage image is created. The majority image is a three band image, showing the largest, second and third largest land cover class layerwise for each cluster. This is done by comparing each cluster from the unsupervised classification with the land cover reference data and finding the three largest land cover classes from the reference data for each cluster. Analysis are made only for those pixels that have corresponding land cover information in the reference data, but the entire cluster will be coded to these classes, meaning that even pixels in areas with no reference data will be coded. The reference data for this process is the data obtained from section 5.2.7, where the reference data has been recoded into fewer land cover classes. For example, the Wetland pixels in the reference data were recoded to value zero, and are therefore not included in the calculation of the three largest classes. However, the pixels in the clustered images will still be coded to any of the three largest classes, even if corresponds to Wetland in the original reference dataset. The percentage image is produced in the same manner as the majority image, but the pixel values in this file indicates the percentage value of each of the three largest classes per cluster.

58 Page Defining correct classified pixels First iteration The next step in the process is to extract pixels that have been correctly classified. A pixel is defined as being correctly classified according if it matches any of the following criterias: For clusters where % of the pixels are belonging to the majority class, all pixels in the cluster are coded to the corresponding majority class and set to be correct. For clusters where 70-89% of the pixels are belonging to the majority class, and land cover reference data exists (class value > 0) for parts or the entire classification area, only those pixels within the cluster that correspond to the majority class and are located in areas with reference data existing, will be coded to the majority class and set to be correct. Remaining pixels will be left unclassified at this step. For clusters where 70-89% of the pixels are belonging to the majority class, and land cover reference data does not exist for parts of the area (class value = 0), all pixels in the cluster that are located in areas with no reference data are coded to be the majority class and set to be correct Unsupervised classification Second iteration The pixels set to be correctly classified are removed from each of the satellite scenes (given value zero) and a second unsupervised classification is performed with the same number of clusters and parameters as described in section Since the number of pixels in the satellite images, used as input for the clustering, have been largely reduced when removing the correct classified pixels from the satellite images, the new clusters generated are more trimmed and there are less spectral variations within each cluster. This means that hopefully the new clusters will be better in separating different land cover classes than the unsupervised classifications obtained from the first iteration Creating majority classes per cluster Second iteration This procedure is the same as described in section 5.2.9, except that the input images now are the clustered images from the second iteration Defining correct classified pixels Second iteration This procedure is the same as described in section , except that the input images now are the images generated from the second iteration. The images with correctly classified pixels, generated from the two iterations, are combined into a single image consisting of correctly classified pixels for the classification area. Remaining pixels are marked as unclassified. This image will then be used as input in the creation of the final land cover dataset.

59 Page 59 Figure 37: Image showing correct classified pixels for a smaller area. Dark green is Coniferous forest, red is Artificial surfaces and black are pixels left unclassified Creating a majority image Based upon the majority images described in section , a single one band majority image is created for the entire classification area. The image is created by combining the majority images (corresponding to the satellite scenes used for the classification area) into a single, one band majority image. The labeling of each pixel is done in the following order: 1. If the pixel value in any of the majority images has the same land cover value as the land cover reference data, it will be coded to that class. 2. If neither of the majority images have a class value corresponding to the land cover reference dataset, the pixel is labeled based upon the value of the scene with the highest priority (section 4.4). However, if this value corresponds to any of the classes Water, Wetland or Artificial surfaces, the value is taken from the scene with the second highest priority. If this value is the same as for any of the above mentioned classes, the value from the scene with the lowest priority value is used. The classes Water, Wetland and Artificial surface will be derived from other steps in the process and should not be an option as a majority class. This majority image will then be used as input in the creation of the final land cover dataset.

60 Page 60 Figure 38: This majority image is the same area as in figure 32, now showing the majority class for all pixels, except for pixels part of the water mask or cloud covered. Dark green corresponds to Coniferous forest, light green equals Deciduous forest and beige is Open land. When combining this information with the image showing correctly classified pixels, the later image will have the highest priority Creation of image with classified water candidates Even though a large amount of the water pixels are part of the water mask and therefore not included in the classification process, there is still a possibility to obtain more water pixels in the final land cover dataset. This is done by extracting water pixels from clustered data where water representation is larger or equal to 25% per cluster. These pixels are then compared with the land cover reference dataset, and those pixels who match all of the following three criterias will be classified as Water: Land cover reference data is Water for the corresponding pixel, Statistics for the water class derived from the process in section match both of the formulas Slope <= mean+std Wetness index >= mean-std This water image will then be used as input in the creation of the final land cover dataset Creating Artificial mask The labeling of the Artificial surfaces class consists of two different steps. The first step is the expansion of existing Artificial mask (obtained from the land cover reference data) in areas where land cover reference data exists, while the second step is the classification of Artificial surfaces in areas where no land cover reference data is available. The Artificial mask will then be used as input in the creation of the final land cover dataset.

61 Page Expansion of existing Artificial mask For clusters that have an Artificial representation of larger than 30%, all pixels are extracted as candidates for Artificial surfaces. These pixels are spectrally similar to Artificial surfaces in the reference data. Pixels in conjunction are then grouped together and groups smaller than 100 pixels are removed. This is done for time and cost reasons, the time to control all areas would be too extensive for the scope of the project. Areas that are not directly neighbouring any Artificial pixel in the land cover reference dataset are then removed. Finally, visual interpretation is done for all remaining areas by comparing them with the satellite data for the classification area, to control if additional removal of areas are needed. Such misclassifications are for instance sandy areas (large beaches) and/or larger Agricultural areas with similar spectral characteristics as Artificial surfaces. Remaining pixels are then combined with the Artificial pixels from the land cover reference dataset into an Artificial mask. Figure 39, Figure 40 and Figure 41 gives an example of updating of existing Artificial mask in an area north of Stockholm, Sweden.

62 Page 62 Figure 39: Reference data Swedish Terrain Type Classification for an area north of Stockholm. This reference data set only contains the subclass Urban areas (red colour). Other artificial surfaces are included in the class Open land. This means that for instance highways and airports are not included in the Artificial class. Figure 40: WiFS scene 32/28, , for the corresponding area. Note that the airport and the highway are clearly visible. Euromap. Figure 41: The resulting BALANS Land Cover database(wetlands not included yet) for the area. The highway and airport have now been included in the Artificial class Generating Artificial surfaces where no reference data exists Pixels outside of the land cover reference dataset will not be classified as Artificial surfaces according to the above mentioned criterias, since they are not directly neighbouring Artificial pixels in the reference data. Therefore a special image is created that produce an

63 Page 63 Artificial mask for areas outside of reference data. The process is very similar to the one described in section For clusters that have an Artificial representation of larger than 30%, all pixels are extracted as candidates for Artificial surfaces. Pixels in conjunction are then grouped together and groups smaller than 100 pixels are removed. Finally, visual interpretation is done for all remaining areas by comparing them with the satellite data for the classification area, to control if additional removal of areas are needed. Figure 42, Figure 43 and Figure 44 shows the classification of Kaliningrad in Russia for which no land cover reference data existed Creating Wetland mask The class Wetland will be derived directly from the Wetland pixels in the land cover reference datasets. However, pixels outside of reference areas will not be classified as Wetland according to this criteria. Therefore a special image is created to produce a Wetland mask for areas outside of reference data. For clusters that have an Wetland representation of larger than 30%, all pixels are extracted as candidates for Wetland. Pixels in conjunction are then grouped together and groups smaller than 25 pixels are removed, both to reduce the production time for control of areas, but also to avoid scattered Wetland pixels. Finally, visual interpretation is done for all remaining areas by comparing them with the satellite data for the classification area, to control if additional removal of areas are needed.

64 Page 64 Figure 42: Kaliningrad in Russia. The white area is larger than 100 pixels and with > 30% Artificial surface within the cluster/clusters. Figure 43: WiFS scene 32/28, , for corresponding area. Euromap. Figure 44: The resulting BALANS Land Cover database with Artificial surfaces in red Splitting Forest class in areas with Mixed forest as a land cover class in reference data Since some land cover reference data consists of Mixed forest, which is a class that isn t to be included in the final output, pixels classified as Forest in the BALANS Land Cover database, that corresponds to this class in the reference dataset, should be labeled either as Coniferous forest or Deciduous forest. For each cluster, the majority class of either Coniferous or Deciduous forest (in reference data) is retrieved. Pixels in the cluster that correspond to the minority forest class in reference data will be coded to the minority forest class. Remaining pixels in the cluster (whether or not they are actual forest pixels) will be coded to majority forest class. This means that all Mixed forest within each cluster will be coded as the majority forest class

65 Page 65 (most similar spectrally). This ensures that a pixel that is classified as forest in the BALANS Land Cover database, but is not forest in the reference data, still we be labeled to either Coniferous or Deciduous forest. This image will then function as a lookup table for pixels classified as forest. Figure 45: WiFS scene 32/28, over Kaliningrad, Russia. Euromap. Figure 46: The forest "lookup" image for the corresponding area. Note that all pixels have a candidate forest class. Dark green is Coniferous forest and light green corresponds to Deciduous forest Handling mixed clusters Even after the second classification iteration, it will still be clusters consisting of mixed land cover classes of similar percentage amount. This not only because of spectral similarities for some classes, but also because the pixel size of 150 metres creates a large amount of pixels with mixed content in land cover distribution. For these cases, only using the classification approach won t be sufficient for separating the classes, instead the land cover reference data can be used as an input to delineate the different classes. Depending on the nature of the land cover reference datasets, different approaches are needed. Sections and describes the approaches for the reference datasets Finnish National Land Use and Forest Classification, Swedish Terrain Type Classification and CORINE LC respectively. During the classification process, a pixel can match the criterias for labeling a pixel within several of the classification steps, hence being assigned to different land cover classes. However, all information produced in the different steps are merged together into a single land cover dataset in a predefined priority order, hence assigning the pixel the land cover class it has the highest priority of belonging to Mixed clusters in areas with high resolution reference data The Finnish National Land Use and Forest Classification and the Swedish Terrain Type Classification have a higher original resolution than the WiFS satellite scene (25 metres compared to 150 metres), and the product is not generalised. Therefore it is considered to

66 Page 66 function as ground truth and can then be used to delineate land cover classes that are difficult to separate in the satellite images. Several types of mixtures can occur, but the most common and largest in m 2 are; Coniferous forest (primarily dense) and Water, primarily due to similarities in spectral characteristics for dense coniferous forest and shallow water or pixels consisting of a mixture of water and land. Coniferous and Deciduous forest. This primarily for pixels corresponding to different levels of mixed forest on the ground. Deciduous forest and Open land (primarily Agricultural areas). In Sweden, very few large homogenous deciduous forest areas exist, therefore it is difficult to find pure deciduous pixels in the satellite data. Also, deciduous forest can have similar characteristics to some Agricultural areas during certain parts of the season. In order to handle the problem with these mixed classes, the following criterias were used in priority order; 1. For clusters where Coniferous forest and Water representation combined is equal to or larger than 70% for a cluster, all pixels in the cluster will be coded as Coniferous forest. 2. For clusters where Coniferous forest representation is equal to or larger than 60% and Deciduous forest representation is equal to or larger than 20% for a cluster, the pixels that correspond to Deciduous forest in the reference data will be coded as Deciduous forest while the pixels that correspond to Coniferous forest in the reference data will be coded as Coniferous forest. Remaining pixels will be left unclassified. 3. For clusters where Coniferous and Deciduous forest representation combined is equal to or larger than 70% for a cluster, the pixels that correspond to Deciduous forest in the reference data will be coded as Deciduous forest while remaining pixels will be coded as Coniferous forest. 4. For clusters where Deciduous forest representation is equal to or larger than 20% and Open land (combination of Seminatural areas and Agricultural areas) is equal to or larger than 30% for a cluster, the pixels that correspond to Deciduous forest in the reference data will be coded as Deciduous forest while the pixels that correspond to Open land in the reference data will be coded as Open land. Remaining pixels will be left unclassified. The resulting images will be used in the final merging of the different images created to obtain the final land cover dataset for the classification area Mixed clusters in areas with CORINE LC as reference data The CORINE LC data has a higher original resolution than the WiFS satellite scene (100 metres compared to 150 metres), but the product is generalised and have a minimum mapping unit of 25 hectares. Therefore it is not considered to function as ground truth, and the unsupervised classification of the satellite scenes will be considered more reliable in separating mixed pixels. One criteria is however used to separate forest (both Coniferous and Deciduous forest) from Water;

67 Page 67 For clusters where Forest and Water representation combined is equal to or larger than 75% for a cluster, all pixels in the cluster will be coded as Forest Creation of snow mask For a smaller part of the mountainous area in Sweden and Norway, Glaciers and perpetual snow exist. For this area a snow mask was generated, simply by labeling all pixels as Glaciers and perpetual snow within the clusters, derived from the unsupervised classification at the second iteration (section ), that correspond to Glaciers and perpetual snow. This is decided by visual comparison of the clusters and the satellite data Combining all images into a single land cover database As previously mentioned, even if a pixel is assigned to a land cover class in any of the sections described in this chapter, it s not definite that it will belong to that class in the final BALANS Land Cover database. A pixel can be assigned to several land cover classes (one in each step), but will be labeled to only one of these classes in the final land cover dataset for the classification area. All information produced in the different steps described in this chapter will be merged together into a single land cover dataset for the classification area. in a predefined priority order, hence assigning the pixel the land cover code it has the highest priority of belong to. This section gives the priority order for each of the input images. If a pixel is assigned to a land cover class value, it can t be changed by any condition with lower priority, even though it can belong to that class as well. The criteria listed first has the highest priority. Only those pixels that have been labeled unclassified at a priority level will be analysed in the next priority step. Two different land cover datasets are produced for each classification area, one including Wetland and a second version without Wetland. The creation of the dataset not including Wetland is the same as the one including Wetland, except that the steps adding Wetland pixels are excluded and those pixels will instead be assigned to any of the classes Coniferous forest, Deciduous forest, Seminatural areas or Water Areas with Swedish Terrain Type Classification as reference data In Sweden, it was decided to replace cloudy areas or areas with no data with information from the Swedish Terrain Type Classification. The priority order is; 1. Pixels classified as Water in the water mask (section 5.2.5) will be coded as Water. 2. Pixels correctly classified as Water (section ) will be coded as Water. 3. Pixels classified as Water (section ) will be coded as Water. 4. Artificial surfaces in the reference data will be coded as Artificial surfaces. 5. Pixels classified as Artificial surfaces in areas with no reference data (section ) will be coded as Artificial surfaces. 6. Pixels correctly classified as any other class than Water (section ), will be coded to their corresponding class. 7. Wetland pixels in the reference data will be coded as Wetland.

68 Page Pixels classified as Wetland in areas with no reference data (section ) will be coded as Wetland. 9. Pixels classified as expanded Artificial surfaces (section ) and is not Bare rock in the reference data, will be coded as Artificial surfaces. 10. Pixels classified as Glaciers and perpetual snow in the snow mask (section ), will be coded as Glaciers and perpetual snow. 11. Pixels classified when separating mixed pixels (section ), will be coded to their corresponding class. 12. Pixels classified in the majority image (section ), will be coded to their corresponding class. 13. Pixels that are marked as Clouds (section 5.2.4) will be coded to their corresponding land cover class. 14. Pixels that are marked as Clouds (section 5.2.4) and where no land cover reference data exists, will be coded as Cloud. 15. Pixels that are marked as No data (section 5.2.4) will be coded to their corresponding land cover class. 16. Pixels that are marked as No data (section 5.2.4) and where no land cover reference data exists, will be coded as No data Areas with CORINE LC as reference data The priority order is; 1. Pixels that are marked as Clouds (section 5.2.4) will be coded as Cloud. 2. Pixels that are marked as No data (section 5.2.4) will be coded as No data. 3. Pixels classified as Water in the water mask (section 5.2.5) will be coded as Water. 4. Pixels correctly classified as Water (section ) will be coded as Water. 5. Pixels classified as Water in section will be coded as Water. 6. Artificial surfaces in the reference data will be coded as Artificial surfaces. 7. Pixels classified as Artificial surfaces in areas with no reference data (section ) will be coded as Artificial surfaces. 8. Pixels correctly classified as Forest and where the forest type is Coniferous forest (section ) will be coded as Conferous forest. 9. Pixels correctly classified as Forest and where the forest type is Deciduous forest (section ) will be coded as Deciduous forest. 10. Pixels correctly classified as any other class than Water or Forest (section ), will be coded to their corresponding land cover class. 11. Wetland pixels in the reference data will be coded as Wetland. 12. Pixels classified as Wetland in areas with no reference data (section ) will be coded as Wetland.

69 Page Pixels classified as expanded Artificial surfaces (section ) and is not Bare rock in the reference data, will be coded as Artificial surfaces. 14. Pixels classified as Glaciers and perpetual snow in the snow mask (section ), will be coded as Glaciers and perpetual snow. 15. Pixels classified as as Forest and where the forest type is Conferous forest (section ), when separating mixed pixels (section ), will be coded as Coniferous forest. 16. Pixels classified as as Forest and where the forest type is Deciduous forest (section ), when separating mixed pixels (section ), will be coded as Deciduous forest. 17. Pixels classified as Forest in the majority image and where the forest type is Coniferous forest (section ) will be coded as Conferous forest. 18. Pixels classified as Forest in the majority image and where the forest type is Deciduous forest (section ) will be coded as Deciduous forest. 19. Pixels classified as any other class than Water or Forest (section ) in the majority image, will be coded to their corresponding land cover class Removing forest pixels above tree limit Some pixels might have been classified as either Coniferous or Deciduous forest, even though they are above the tree limit. These pixels will be replaced with Seminatural areas by using the tree limit image generated in step as delineator Separating Open land into either Agricultural land or Seminatural areas The agricultural mask was merged the BALANS classification so that areas coded as Agricultural land in the mask and corresponding to Open land in the classification, was coded as Agricultural land in the final BALANS Land Cover and Land Use dataset, areas coded as No agricultural land in the mask and to Open land in the classification was coded as Seminatural areas, and areas with no agricultural information available and coded as Open land would keeps its Open land code Creating metadata image with scenes used per pixel It can be of value to have information on what satellite scenes have been used in the classification process when labeling a pixel. Therefore a three band image is created, showing in the first layer the satellite scene number for the scene with the highest priority that was used in the classification process for that particular pixel. In the second layer, the scene with the second highest priority is listed and the third layer contains the number for the scene with the lowest priority. If a scene is cloudy for a particular pixel, it is not included in the image. For example, if the scene with the highest priority consists of clouds for a specific pixel, the scene number is not registered, instead the second highest priority scene is labeled in the first layer of the image if it is cloudfree.

70 Page 70 Since the data amount would be very extensive (16-bit image) if the correct scene number is registered, iternal codes for each scene have been used instead. Table 5 is listing the codes in the metadata scene image and their corresponding satellite scene numbers. Table 5: Scene codes in the scene metadata image and their corresponding scene numbers (path/row). Number Scene 1 21/ / / / / / / / / / / / / / / /25 17 MODIS 26 July MODIS 27 August MODIS 19 September 2000

71 Page 71 6 BALANS Land Cover and Land Use Database The different BALANS Land Cover datasets per classification area were finally combined into a single BALANS Land Cover database for the entire Baltic Sea drainage basin. The order in which the datsets were combined was that classification areas with better reference data had higher priority. 6.1 BALANS Land Cover and Land Use Database full resolution The raster product or the BALANS Land Cover database is simply the output from the classification work described in chapter 5. Depending on the results from the validation work, some land cover classes might however be merged to obtain a product with higher quality per land cover class. There are some differences when comparing northern Sweden with northern Finland, especially concerning the amount of Coniferous forest and Wetland. This is mainly due to differences in the reference datasets. For Finland, the reference dataset consists mainly of Coniferous forest and only a very small amount of other land cover classes (for instance Deciduous forest). Concerning the Wetland class, only parts of Finland were covered by 1: digital map masks, causing a Wetland class to be very heterogenous, since this was mainly derived from digital map sources.

72 Page 72 Figure 47: The final BALANS Land Cover database. 6.2 BALANS Land Cover and Land Use Database Generalised product A generalised version of the full resolution BALANS dataset has also been created. The classes Coniferous forest and Deciduous forest have been merged into a single Forest class, and the classes Agricultural land and Seminatural areas have been merged into Other open land. During the generalisation, the class Bare rock also disappeared. The generalisation program used has been developed by Metria Miljöanalys for the Swedish Corine LC, and

73 Page 73 it has priority order of how to merge different land cover classes. The first step is to merge areas of the same land cover class that are at a maximum distance of 1 pixel from each other. The second step is to merge areas according to priority order for different classes. The minimum mapping unit after the generalisation is 25 hectares, except for the class Water bodies that has a minimum mapping unit of 10 hectares. The final product is in raster format and will be available free of charge. 6.3 Scenes per pixel for BALANS Database The different metadata scene images, showing the satellite scenes used when classifying each individual pixel, have also been combined into a single image for the entire Baltic Sea drainage basin. The priority order when combining the different classification areas were the same as when combining the BALANS Land Cover datasets. 6.4 Metadata Information Since the quality of each classified area might differ (depending on the nature of the reference data and satellite data used in the classification process) it is of highest importance to have good metadata information for each pixel - describing the different input data used when labeling the pixel. Except for the text information available together with the BALANS Land Cover database, two images have also been produced that can be seen as metadata images; One three layer image, giving per pixel all scenes used (in priority order) in the classification process (section 6.3). If a scene contains clouds for the given pixel, it is not included in this image, since it haven t contributed to the labeling of the pixel (Figure 48). One two layer image, describing in the first layer which land cover reference dataset that has been used per pixel, and in the second layer the agricultural reference dataset or datasets used per pixel.

74 Figure 48: Scenes used for classification of each pixel for areas classified by Metria Miljöanalys (former Satellus AB). Different colours indicate different scene combinations (three band image). Page 74

75 Page 75 7 Operationalisation (WP4500) 7.1 Background and objectives This section describes the work package Operationalisation within the BALANS project. The reporting from the work package is included here to give a more complete picture of the classification process. The objectives for WP4500 Operationalisation are: assessment of the robustness of the chosen methods (algorithms), assessment of the robustness of the operator interface in the production environment, development of robust algorithms and operator interface if necessary. 7.2 Performed work Use of satellite data After initial tests with RESURS MSU-SK and IRS-1C/D WiFS data, it was decided to use WiFS data for the classification work (see Data Correction report, Malmberg et. al. 2000). During the classification process, MODIS data was also tested over Finland by the Finnish Environmental Institute. The satellite data was used to fill in data gaps in WiFS data but also to test the potential of land cover extraction from the MODIS satellite data Classification and interpretation of clouds in the satellite image A cloud mask module has been developed in the ERDAS Imagine environment. This module is a combination of automatic generation of cloud mask by thresholding and the possibility to manually exclude and/or include areas from the cloud mask. Section describes how the cloud mask module is used within the production process. A user guide exists in Swedish Evaluation of Expert Classifier, ERDAS Imagine The work within the BALANS project has been based on the ERDAS Imagine software. In release 8.4 of the software a new function Expert Classifier was available. Expert Classifier is a hierarchy of rules, or decision tree, that describes the conditions under which a set of low level constituent information gets abstracted into a set of high level information classes. The constuent information consists of user-defined variables and includes raster imagery, vector coverages, spatial models, external programs and simple scalars. The Expert Classified is compposed of two parts: the Knowledge Engineer and the Knowledge Classifier. The Knowledge Engineer provides the interface for an expert with first-hand knowledge of data and the application to identify the variables, rules, and output classes of interest and create the hierarchical decision tree. The Knowledge Classifier provides an interface for a non-expert to apply the knowledge base and create the final output classification.

76 Page 76 When testing the Expert Classifier for the BALANS classification work, the function looked very promising at first, with a very nice user interface as well as functions for implementation of the different input dataset. Figure 49: The model for merging the different input dataset into the final land cover dataset. This is the user interface in the Knowledge Engineer module.