EUROPEAN COMMISSION Eurostat. Directorate E: Sectoral and regional statistics Unit E-4: Regional statistics and Geographical Information

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1 .no EUROPEAN COMMISSION Eurostat Directorate E: Sectoral and regional statistics Unit E-4: Regional statistics and Geographical Information GRANT Pilot studies on the provision of harmonized land use/land cover statistics (Synergies between LUCAS and the national systems) (LUCAS Grant, Part A) Norway Interim report 1 EUROSTAT/Contract No.: Doc. No.: 1 Issue/Rev.: 2 Date:

2 Metadata This document outlines the project activities during the to period of the implementation. Affiliation/Function Name Date Prepared Approved Authorized Statistics Norway, Division for Natural Resources and Environmental Statistics/Statistics Adviser Statistics Norway, Division for Natural Resources and Environmental Statistics/Head of Division Erik Engelien Jørn Kristian Undelstvedt YYYY.MM.DD Accepted EUROSTAT/ Project Manager YYYY.MM.DD Signatures Name Date Signature Signature of authorisation and overall approval Signature of acceptance by EUROSTAT YYYY.MM.DD YYYY.MM.DD Distribution Affiliation Name Address Copies EUROSTAT Contractor 5, rue Alphonse Weicker L-2721 Luxembourg electronic copy electronic copy

3 Document Status Sheet Issue Date Details First Document Issue Revised report included material from Task force presentation. Document Change Record # Date Request Location Details

4 Table of contents: METADATA INTRODUCTION EXISTING DATA SOURCES FOR LAND COVER AND LAND USE PILOT DATA COLLECTION SUMMARY OF ACCESS CONDITIONS TO MICRO-DATA COMPLIANCE AND PRECISION FOR EACH DATA SET METHODOLOGY AND TECHNICAL SOLUTIONS RESULTS LAND COVER QUALITY ASSESSMENT FOR LAND COVER LAND USE QUALITY ASSESSMENT FOR LAND USE FEASIBILITY AND SUSTAINABILITY OF POSSIBLE SUBSEQUENT DATA DELIVERIES (UPDATE CYCLE) ACTION LIST OF FUTURE ACTIVITIES... 17

5 1 Introduction Pilot studies on the provision of harmonized land use/land cover statistics (Synergies between LUCAS and the national systems) - Part A - refers to the delivery of harmonised quality-assured land cover/use statistics according to a predefined classification and a given precision. In the following chapters the progress achieved and the activities planned for the next reporting period should be described for each topic. A brief timetable for Part A as presented in the grant application: January April: Tasks May June: Interim report August December: Tasks Action list for remaining activities - Part A of the pilot study: Description of task Task 3 (and partly task 2): Implement production line and produce results. Estimated time of completion (month) August 2013 Task 4 Quality assessment. September 2013 Task 5 Final report with assessment of feasibility and sustainability of possible subsequent data deliveries, in addition to results and description of data sources and methodology. November 2013

6 2 Existing data sources for land cover and land use The most important data source for land use and land cover In Norway is the land use and land resource map which has been produced by Statistics Norway for the first time in Several data sources are used as input. Both map databases and registers are used as input. In order to make an adaptation to the LUCAS survey we have to go one or two steps back and process the data in new ways. We also have to supplement with other data sources. So far we have identified the adequate supplementary data sources and also got hold of most of them. In the final report we will describe the data sources appropriately. ID Theme Title Produc er CC_ 001 Land use / land cover Nation al land use and land cover polyon data set Statistic s Norway Abstract Se chapter 2.1. Data type Map data set/ statisti cs Quality and validity Spatial resolution (scale, distance/unit, NUTS level ) 1: to 1 : INSPIRE Conformance (which Regulation, Decision) Last Update 1.Jan 2011 Frequency of update Yearly Link to further inform ation See referen ce chapter 10. Access conditions (for micro-data) The Statistics Act Merknad [BE1]: LU or LC Merknad [BE2]: (statistical data, in-situ data, mapping based GIS-data, register data, other administrative data)

7 2.1 Land use and land resource map In 2012, Statistics Norway published new statistics on land use and land resources in Norway (Statistics Norway 2012). The statistics were produced by combining a broad spectrum of digital cartographic data. The description in this chapter is extracted from Steinnes (2013). This is the most important data source for land use and land cover statistics and it is based on several map data bases and registers. Because of the importance the methodology and data is described in some detail: Classification of land In order to produce cost-effective land use statistics covering all municipalities in Norway, existing cartographic databases and registers, including the classifications/nomenclature these are based on, must be used. Statistics Norway has created a hierarchical classification system, Standard for classification of land use and land cover, which is mainly based on existing standards and nomenclatures. The main categories in the standard used in the land use statistics are given in Table 2. The complete standard can be viewed at ssb.no/stabas/ (Statistics Norway, 2012a). Table 2: The main categories in the classification system for land use statistics Land use category Residential areas Recreational facilities Built-up areas for agriculture and fishing Industrial commercial and service areas Education and day care facilities Health and social welfare institutions Cultural and religious activities Transport and telecommunications Technical infrastructure Emergency services Defence Green areas Sports facilities Unclassified built-up areas and related land

8 Fully cultivated land Not fully cultivated land Forest Open firm ground Wetland Bare rock, gravel and block fields Permanent snow and glaciers Inland waters Marine waters Unclassified undeveloped areas Main features of the methodology The method applied is based on utilising the highest quality data sources available, but where no optimal data source exists, the next best quality data sources are used. In practical terms, the method is an automatic geographic information system (GIS) that defines, classifies and assembles the data into a hierarchy. The final hierarchy is made up of data sets from various data sources that are processed and made compatible before being assembled. The area resource map AR-STAT is used to calculate land resources in the undeveloped areas and also partly for the delimitation of built-up areas, but this is overridden if other map data indicates that areas are developed. Since several of the data sources overlap general criteria are needed for determining when a certain type of data is favoured, since the method is automatic and no manual assessments of individual objects are made. In order to ensure that such criteria work well in all municipalities the criteria are based on defined core data sets. Criteria on how to form polygons from the simplest objects (points and lines) are based on information from adapted core data sets. Some of the main processes in the method are described below Adapting buildings The population of buildings and associated data on building number and type is taken from Statistics Norway s copy of the national Cadastre ( Matrikkelen ) on 1 January for the relevant year. The building-part of the cadastre is a point data set. Where possible, the buildings from the Cadastre are linked to the building outline using a scale of 1:5000. Over 90 per cent of the buildings are linked to building outlines. Linking the building data and building outline enables a more accurate calculation of the base area of the buildings. Additionally, a building that spans multiple properties affects the land use

9 classification of all the properties. Where we only use the building point from the Cadastre the land use is only affected for the property where the point is located Delimitation of built-up areas In the statistics on land use and land resources all areas with buildings are classified as built-up. In addition, the building types within a built-up area determine the classification of that area. The builtup area can be made up of one ground property, part of a property or the area immediately surrounding the building. A property can be made up of several separate built-up areas that are classified individually. In order to produce the data set where buildings determine the land use, a number of data sets need to be used: land resource map, properties, roads, water and coastlines, in addition to buildings from both the Cadastre and as building outlines. The methodology used to adapt built-up areas and calculate the utilisation rate is shown in Figure 1. Figure 1. Method for defining built-up areas and calculating utilisation rate The land resource map AR-STAT, in which land is divided into polygons based on land type, is used to define built-up areas. When defining built-up land, the land types built-up and open firm ground (firm ground that is not agricultural land, forest, built-up or traffic areas) are the primary land types since both of these can define the built-up part of a property. The land resource map is simplified by merging undeveloped land categories, and is assembled using a digital property map. Road areas are removed from the data source. Some properties only contain one land type and are treated as a whole property, while properties with more than one land type are broken down into parts of properties that are treated individually in the onward process.

10 Buildings are included and the utilisation rate is calculated for each property or part of property. The utilisation rate is the base area of the buildings as a ratio of the built-up area. Where a property consists of both built-up and other land, an utilisation rate is calculated for both the built-up area and for the remainder of the property. Properties and parts of properties are classed as built-up if the utilisation rate is high enough. In general, the utilisation rate must be at least 4 per cent. The shaded areas are classified as built-up in this housing estate Utilisation rate It is natural in many cases for properties with buildings to be classified as built-up, and for the buildings to determine the more detailed land use category. However, the use of a whole property is not necessarily determined by the buildings. For instance, a park is still a park, and not a retail area, despite the presence of a newsstand in the park. In such cases, only a small area surrounding the building is normally classified according to the building, while the rest of the property is allocated a land category from other sources. This is done by placing a buffer (an area of specified distance around a map item) around the building. The park is classified as a park, while a small area surrounding the newsstand is classified as a retail area. It can generally be assumed that the smaller the part of the area that is taken up by the buildings the weaker the correlation between the use of a property and the building types. We have therefore decided to set a threshold based on the utilisation rate of the property, and this is defined as the ratio between the area of the building s base and the area of the property. Based on an analysis of the utilisation rate in areas we already know are built-up, we have chosen to set the threshold for the utilisation rate at 4 per cent. Where the utilisation rate is higher than this, the entire area is regarded as built-up and classified according to the building types found therein. This rule is applicable regardless of how the area is classified in the land resource map. This means that the statistics are less dependent on when the land resource map is updated. If a housing estate

11 has been built in a forest area the threshold for when these properties should be included is the same, 4 per cent, whether the land resource map is updated to show the properties as built-up or they continue to be classified as forest. The larger the properties become, the higher the probability that only parts of the property are affected by the buildings within the properties. In order to limit errors, we are setting more stringent requirements for the utilisation rate of the largest properties. Additionally, some building types, such as works buildings associated with agriculture and fishing will require less space, and an exception from the 4 per cent threshold is also made for these. The exceptions are shown in Table 3. Examples of how classifications based on utilisation rate and size work in practice are presented in Figure 1. Table 3. Thresholds for when an area is to be regarded as built-up Area Land types in AR-STAT Building types Threshold for utilisation rate, per cent 0 10 decare All land types All 4 Over 10 decare Built-up All 4 Over 10 decare All excl. built-up All 10 All sizes Forest, farmland and pastures Works buildings in agriculture 50 All sizes All land types Boathouses Classification of built-up areas The classification of a built-up area is determined by the buildings located in the area. This applies regardless of whether the area s definition is based on the land resource map and property boundaries, or whether the area is defined according to the buffer method. Where a built-up area contains buildings that belong to different land use categories, the said area is defined as mixed on the map, but in the statistics the area is broken down into the different classifications, based on the base area of the buildings. The principle is illustrated in Figure 3. Figure 3. Breakdown of area from a mixed built-up area

12 The built-up area in the example contains buildings that belong to different land use categories and is presented as mixed on the map. In the statistics, the area will be broken down into the different land use categories of the various buildings, based on the base areas of the buildings. In this example, 40 per cent of the base area is workshops. Forty per cent of the area of the property is thus classified as workshops and 60 per cent as residential. Where the buildings in the built-up area are points from the Cadastre, it is the base area stipulated in the Cadastre that is used to determine how much of the area is to be allocated to the different categories. Other definitions Built-up areas with buildings make up a large part of the built-up area, and we consider the result to be of good quality. Some of the other data sources we use are more uncertain. The complete methodology is described in Steinnes, Composition of hierarchy The adapted data sets are assembled into one hierarchy according to the principle shown in Figure 4. Roads are at the top of the hierarchy and AR-STAT is at the bottom.

13 Although each area has several data sets, the hierarchy provides a unique land classification for all areas. For example, a road crossing a farmyard is classified as a road, a football pitch on school grounds is school area and a parking space at a shopping centre is classified as shopping centre. Figure 4. Adapted data is assembled into a hierarchy. Principle drawing Calculating the statistics After combining the different data sources, the finished map is projected from WGS84 UTM 33 to Lambert s azimuthal equal-area projection (ETRS89-LAEA). The area is then calculated for the individual areas and the statistics are assembled. Where built-up areas were defined as mixed in the map, the area is broken down into the land use categories defined by the buildings in the area Quality of the statistics The methodology focuses on using the most detailed map sources in order to produce the best possible figures for status. The data sources are likely to become more populated and of a higher quality in the coming years and this will affect the statistics on changes. Consequently, the calculation method codes the data sources for the land so that an overview can be retained of changes in the use of data sources. 2.2 Register of applications for production subsidies The statistical land use/ land resource map is only delimiting crops in to three classes: fully cultivated, infield pastures and surface cultivated meadow to fodder and grazing. In order to differentiate further we use the register of applications to production subsidies. The administrative record of everyone who applies for an agricultural production subsidy from the Norwegian Agricultural Authority is the compilation basis of the statistics. The register contains information on area use and the number of livestock on holdings.

14 The register is collected, prepared and quality assured in conjunction with the regular statistics on crops by Statistics Norway. ( The statistics is published every year. Final figures are normally ready for publication in November the year after. Figures at the level of detail required by LUCAS are normally not published, but are prepared at county level as part of the yearly production routine. 2.3 The national forest survey 2.4 AR18x18 (The national adaptation to LUCAS 2003) 2.5 SAT-Skog (satellite based map of forests) 2.6 Map of protected areas 3 Pilot data collection No separate basic data collection has been carried out as part of this project, however supplementary statistics and map databases has been collected for this study. 4 Summary of access conditions to micro-data Our access to map databases and registers is regulated by The Statistics Act. According to this act Statistics Norway has free access to all relevant registers and map databases for producing official statistics. In addition Statistics Norway participates in Norway Digital which facilitates easy access to a wide range of national digital data. Norway Digital is the Norwegian government's initiative to build the national geographical infrastructure. Norway Digital is since 2005 a working co-operation and infrastructure with reference data and thematic data available, more than 100 operational web map services, geoportal and other services. Thus Norway Digital is an existing implementation of the infrastructure described by the European Inspire-directive. 5 Compliance and precision for each data set We have compared the classes in Statistics Norway s land use and land cover classification (and statistics/ map database) and identified the main challenges for adaptation to LUCAS. We have furthermore identified possible solutions for adaptation regarding these challenges. This includes collection of supplementary data sources and reprocessing the existing data. We have no LUCAS survey in Norway and it is difficult to obtain exact precision for each class. However, we will make assessments regarding the precision. We have made a mapping of the land use/ land cover classes in the national data set to the LUCAS classes. Many of the classes are easily converted to LUCAS land use and land cover classes, but there are some challenges. In the following we give a short description of the difficult classes starting with the main levels and ending with the most detailed classes:

15 Land cover main classes: Grassland/ bare land/ shrub land within built up areas is not differentiated in the national map or statistics We will use proportional distribution by land use class Woodland Temporarily unstocked areas within forest are included in our map We use statistics on tree age by NUTS 2/3 for allocation to F bare land Land use classes (2-digit level): U330 Under construction is not part of our classification Exists map data, but probably not very good, but will be explored further Building date can be used to identify recent building activity U340 Commerce, finance and business Public administration is in part included land use is based on building function and in some cases (especially land use 3-digit level) not 100 per cent compatibility between national classes and LUCAS Land cover 2-digit level: D10 Shrubland with sparse treecover (included in D20) E10 Grassland with sparse treecover (included in E20) F20 Sand (included in F10) B40 Dry pulses, vegetables and flowers Includes rutabaga and carrots which should have been B20 Flowers is included in B80 because we can not separate in data B80 Other permanent crops Not able to separate forest tree nurseries and flowers, which are included C10 Broadleaved woodland, C20 Coniferous woodland, C30 Mixed woodland Same classes in national map database but somewhat different limits of criteria H20 Coastal wetlands Not on map or statistics Land cover 3-digit level: More discrepancies arise for this detailed level and need for more supplementary data e.g.: Number of floors Further division of woodland Land use 3-digit level: Several of the classes would need reprocessing Difficult to cover: U220 Industry and manufacturing (may use business register to obtain NACE, but geocoding is not optimal. However, we will try to pursue a similar approach as done by The Netherlands presented at the task force meeting in June.) U410 Abandoned areas

16 6 Methodology and technical solutions The main source for the statistics is the land use and land cover database. This will be used as the basis on which adaptation to LUCAS will be done. Data for many of the classes in the LUCAS-nomenclature are easily converted from the national nomenclature, but others are more difficult to convert. For some classes we will reprocess the existing data sources which are included in the land use and land cover database. For other classes, especially crops, we will depend on administrative register data. Some classes must be adapted based on the national sampling scheme for land use and land resources. So far we have decided on the major features of the methodology but the implementation remains. We will however reuse and adapt existing production lines from the national land use statistics as far as possible. Figure 5 shows a schematic view of the methodology. The main task is to disentangle the combined land use and land cover map into pure land cover and land use classes. For example the land use class forestry is obtained by combining land cover forest and map of protected areas. Figure 5. Principle drawing of the methodology. As the methodology for the national land use/ land cover map is based on automatic geoprocessing of the data sources, we will implement the LUCAS adaptation by separate production lines which is dependant on the national routines and which can be run in conjunction with the national statistical work on land use/ land cover. 7 Results The compilation of the results remains to be done. 7.1 Land Cover

17 7.2 Quality assessment for Land Cover 7.3 Land use 7.4 Quality Assessment for Land Use 8 Feasibility and sustainability of possible subsequent data deliveries (update cycle). Assessments regarding these issues remain to be done. 9 Action list of future activities Remains to be done. 10 References Aune-Lundberg, L. and Strand, G.H. (2011): Land resource classification in mountain areas. Report from Norwegian Forest and Landscape Institute 01/ in_areas.pdf.pdf Bjørdal, I. and Bjørkelo, K. (2006): AR5 klassifikasjonssystem. Manual from Norwegian Forest and Landscape Institute 01/ Gundersen, G.I. (2012): Landbruket eier tre fjerdedeler av Norge. (Agriculture owns three quarters of Norway Norwegian only) Statistics Norway (2012a): Standard for classification of land use and land cover. Statistics Norway (2012b): Land use and land cover, Statistics Norway (2012c): Land use in urban settlements, Steinnes, M. (2013): Arealbruk og arealressurser. Dokumentasjon av metode. (Land use and land resources. Documentation of method. Norwegian only). Documents 12/2013. Statistics Norway.

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