Pilot studies on the provision of harmonized land use/land cover statistics: Synergies between LUCAS and the national systems

Transcription

1 1 Pilot studies on the provision of harmonized land use/land cover statistics: Synergies between LUCAS and the national systems Norway Erik Engelien Division for Natural resources and Environmental Statistics, Statistics Norway 1

2 Outline Part A Synergies between LUCAS and the national systems Part B Integration of in-situ surveys with LUCAS 2

3 Data sources AR-STAT- land cover Norwegian Forest and Landscape Institute (NFLI) Based on: AR5, AR50 and AR-mountain 3

4 Delimitation of built-up land Built-up Transport Woodland Open firm ground Chosen land plots N Utilisation rate on property is too low. Buffer represents land use. 4

5 Data sources - Statistics Norway s land use/ land cover map Standard classification Ground property map Building- and address register Land cover map (AR- STAT) National map series (1: : ) 5

6 Methodology LUCAS adaptation The national land use/ cover map as basis in to which other data must conform Convert to LUCAS classes Supplementary data and reprocessing the map data set where necessary Separate certain classes and features which is not separated in national data set Combine supplementary data with land use/ cover data set Aggregate statistics with land use/ cover map as sum total Adjust and combine supplementary statistics with the aggregated statistics 6

7 Land cover classes some notes Discerning land cover from land use Artificial category (A), where attached grassland and bare land should have been excluded from the SN classes Adapting statistics on crops to the map The crops statistics from the register of applications for production subsidies are distributed on the map area for fully cultivated land. The distribution is done proportionally by county Identifying clear cuts within woodland Forest age as proxy (NUTS 2) -> E20 grassland Identifying shrub land, grassland and bare land Some mountain areas only classified by satellite imagery «Open firm ground» ->Special routines for allocation (mostly shrub land) The statistics is confined to land areas 7

8 Land use classes some notes Manufacturing Implemented similar routine as presented by The Netherlands last TF meeting: All manufacturing land use areas closer than 100 metres from manufacturing businesses get the industry-code (NACE) from The Central Register of Establishments and Enterprises. If it is more than one business which corresponds geographically with the land use area, the LU code representing most businesses is chosen. Transitional use not part of the national land use classification extracted all buildings where building work started the last 6 month of the previous year from the Cadastre map feature called Construction site which includes major construction activity. Forestry Woodland excluding protected areas 8

9 Technical implementation ArcGIS models, python scripts and SAS programs 9

10 Pilot study results Land cover in Norway Per cent 1 % 7 % 5 % 3 % A00 Artificial B00 Cropland C00 Woodland 19 % 37 % D00 Shrubland E00 Grassland 2 % F00 Bare land and lichens/ moss G00 Water areas H00 Wetlands 26 % 10

11 Land cover in Norway. County Per cent 20 Finnmark Finnmarku 19 Troms Romsa 18 Nordland 17 Nord-Trøndelag 16 Sør-Trøndelag 15 Møre og Romsdal 14 Sogn og Fjordane 12 Hordaland 11 Rogaland 10 Vest-Agder 09 Aust-Agder 08 Telemark 07 Vestfold 06 Buskerud 05 Oppland 04 Hedmark 03 Oslo 02 Akershus 01 Østfold The whole country A00 Artificial B00 Cropland C00 Woodland D00 Shrubland E00 Grassland F00 Bare land and lichens/ moss G00 Water areas H00 Wetlands 0 % 20 % 40 % 60 % 80 % 100 % 11

12 Land use in Norway Per cent 0 % 0 % 1 % 4 % 31 % U612 Natural areas not in other economic use U120 Forestry U110 Agriculture U410 Transport networks U510 Permanent residential use 64 % Other land use 12

13 Built up land use in Norway Per cent 5 % 3 % 4 % 5 % U410 Transport networks U510 Permanent residential use 8 % U520 Temporary residential use 49 % U200 Secondary production, except energy production U340 Cultural entertainment and recreational services U330 Community services Other built up land use 26 % 13

14 Quality assessment, extract Land use: Manufacturing Statistics on more detailed level requires the use of the Register of Enterprises and Businesses which has some limitations regarding land use statistics: Not all areas for manufacturing coincide with manufacturing businesses due to: a) missing geo-coding, b) inaccurate geo-coding and c) differing populations (business register vs. manufacturing sites in the maps/ Cadastre 14

15 Quality assessment, continued Comparing the land cover statistics with AR18x18 The biggest differences occur for grassland, shrub land and lichens. The land cover classes in the statistics based on maps (AR Mountain), do not fit well with the LUCAS classes as extracted from AR18x18 and this explains the discrepancy. Another major discrepancy arises for wetlands. The adaptation based on maps is underestimating the wetlands (which in Norway is mainly peat land, mires and fens above the tree line). Difficult to identify these kinds of wetland in Norway on aerial photography. Broadleaved, coniferous and mixed woodland The map statistics include more coniferous woodland than is actually present 15

16 Feasibility and sustainability of possible subsequent data deliveries (update cycle). 16

17 Action list of future activities Land cover One alternative approach to the compilation of statistics from maps used in this study, is applying a two step approach: The areas falling under each of the woodland classes in the map can be examined using the National Forest Inventory. The result is a description of the actual composition of forest inside each of the map categories with the same names. This can be used to compile more correct statistics for the LUCAS classes. The subalpine and alpine categories can be handled using a similar approach. It is possible to use the AR18X18 survey to examine the classes in the map and describe the actual composition of each map class in terms of LUCAS classes. The result can be used to compile more correct statistics for the LUCAS classes; shrub land, grassland, lichen, rocks and wetland. 17

18 Action list of future activities Land use Elaborate more on the issues regarding secondary production There are also some quality issues regarding tertiary production Consider preparing the national land use map data base to a more detailed level for parts of the classification. This would make it easier and more flexible to aggregate statistics for different purposes (different European classifications for instance) 18

19 Part B: Integration of in-situ surveys with LUCAS By National Forest and Landscape Institute Public agency under Ministry of Agriculture and Food 19

20 Norwegian in situ survey: Based on LUCAS x 18 km grid = 1100 sample points 20

21 Norwegian in situ survey: 10 SSUs (as LUCAS 2003) Differences: Focus: Outfields Wall-to-wall field survey of the entire PSU (0,9 km 2 ) New LUCAS attributes not included Implementation period: 10 years The Norwegian in situ survey is based on LUCAS 2003 methodology, but the Norwegian focus on the country s outfield resources in areas where access is difficult (and costly) has resulted in a different development of the survey 21

22 Quality exercise as compared to other sources The Norwegian survey data are not suitable for estimation of small phenomena. In Norway, built-up land is scattered and many occurrences are thin lines (roads etc) or small areas (isolated settlements). Far better sources exists for measurement of such classes The categories D, E and F (Shrubland, Grassland and Open natural land) is a challenge, because it is not clear how they correspond with the actual vegetation in the sub-alpine region. A professional effort is needed in order to set a standard for the conversion of vegetation classes in these areas to LUCAS classes. 22

23 Integration with LUCAS or LUCAS derived data Sampling strategy The national sampling follows LUCAS 2003 and can not be adjusted to LUCAS as of today Nomenclature Norwegian nomenclature can with some effort be converted to LUCAS In situ data collection Lack of full compatibility Some of the gaps can be filled in from existing measurements Other data can only be obtained by additional field work Additional field work is mainly for artificial and agricultural areas (4 %) Schedule The Norwegian in situ survey is implemented over a ten year period Once the first iteration has been completed new iterations can be based on Orthophoto in the outfields (continuous 5-year schedule) Field survey for points on agriculture and artificial land (syncronized with LUCAS) 23

24 Proposal on how to implement the next national in-situ data collection integrated with the LUCAS survey requirements Agriculture or artificial land SSUs (AR18x18 plots) Process A: Data conversion Process B: Supplementary surveys (optional) Process C: Photointerpretation Process D: Supplementary field survey 24

25 Future activities Classification: Norwegian and Eurostat experts should review the LUCAS land cover classes together in order to determine the appropriate translation of Norwegian land cover classes to LUCAS classes. Making national statistics LUCAS compliant using national sampling data: Implement a variant of the production system for national statistics (report Part A) using the national area frame survey to determine the distribution of LUCAS classes within the Norwegian statistical classes (based on methodology in Strand and Aune-Lundberg 2012). Test the adaption of the national in situ survey: Test the adaption of the national in situ survey to LUCAS in a smaller region. 25