1. Kaisu Harju Finnish Environment Institute Eurostat March 13th 2018
Provision of Harmonised Land Use Information – LUCAS and National Systems EUROSTAT Grants 2015
Kaisu Harju
Finnish Environment Institute
Eurostat March 13th 2018

2. Grants 2015 Project: Provision of Harmonised Land Use Information – LUCAS and National Systems
Goal: To study how to get LUCAS compatible statistical data using national data sources
Focus on land use, continuation of the work started in Eurostat Grants 2014 (land cover)
Project duration: –
Consortium
Finnish Environment Institute SYKE (coordination) and Natural Resources Institute Finland LUKE
Kaisu harju, SYKE

3. Tasks WP1: Inventory of existing national data sources on land use
Comparability of national datasets and Lucas LU classification
Specific LUCAS LU classification for grant 2015 –projects was used
WP2: Methodology development and calculation of results
A method for data integration and statistical data production was developed and tested using a pilot areas
Statistical tables for year 2012 were produced for 3 selected NUTS3 areas
WP3: Quality assessment and feasibility study
The results were evaluated using Lucas 2012 field observations and selected NFI 2016 survey results
The feasibility of future updates using proposed and tested approach was evaluated including access and use conditions of the national datasets

4. Inventory of national data sources and their comparability to LUCAS land use classification
Good availability of the national registers, spatial datasets and in-situ surveys
Most datasets have full national coverage and they also cover all LUCAS LU classes in main level
Some data gaps: detailed levels of secondary and tertiary production classes and abandoned areas
Buildings and other topographic map layers
Road and Rail network
Forests,
Fields
Protected areas, national parks and willdernes reserves
Land cover data
Lakes and rivers 4
Etc.

5. LUCAS Land Use Classification for Grants 2015
Primary production
U110
Agriculture
U120
Forestry
U130
MiningAndQuarrying
U140
AquacultureAndFishing
U150
OtherPrimaryProduction
U200
SecondaryProduction
U210
RawIndustry
U220
HeavyEndProductIndustry
U230
LightEndProductIndustry
U240
EnergyProduction
U250
OtherIndustry
U300
TertiaryProduction
U310
CommercialServices
U320
FinancialProfessionalAndInformationServices
U330
CommunityServices
U340
CulturalEntertainmentAndRecreationalServices
U400
TransportNetworksLogisticsAndUtilities
U410
TransportNetworks
U420
LogisticalAndStorageServices
U430
Utilities
U500
ResidentialUse (includes other compatible use)
U600
OtherUses
U610
Transitional use
U620
Abandoned areas
U630
Natural areas not in other economic use
U6xx
Other
U111
CommercialAgriculturalProduction
U112
FarmingInfrastructure
U113
AgriculturalProductionForOwnConsumption
U241
Nuclear based energy production
U242
Fossil fuel based energy production
U243
Biomass based energy production
U244
Renewable energy production
U411
RoadTransport
U412
RailwayTransport
U413
AirTransport
U414
WaterTransport
U415
OtherTransportNetwork
U431
ElectricityGasAndThermalPowerDistributionServices
U432
WaterAndSewageInfrastructure
U621
Abandoned industrial**
U622
Abandoned commercial**
U623
Abandoned residential**
U624
Other abandoned
 HILUCS definitions of LU classes were followed

6. Methodology development and calculation of results
The methodology for producing LUCAS compatible land use data was based on combining different spatial data layers into one spatial land use data layer
High resolution (5 m * 5m) spatial data about land use  each pixel representing only one land use class
LUCAS LU statistic calculated based on this data

8. Results of the Pilot Areas
Results were calculated for three NUTS3-areas representing various conditions in Finland
FI1C1 Southest Finland: urban center, agricultural areas
FI197 Pirkanmaa: forests, agricultural areas, large lakes and also urban center
FI1D7 Lappi: sparsely populated, protected areas and wilderness reserves, large national parks, specific feature: reindeer husbandry

9. Spatial Data and Analysis in Producing Statistics
Processing of high resolution spatial data is time consuming
Spatial data enables easy calculation of areas for each land use class at different levels (regional/national/etc…)
Spatial data enables further spatial analysis and joint use with other data sources
Spatial data/map gives a visual interpretation the LU classification  how well it describes the real world
Example:
Both protected areas and inland water areas classified as ”natural areas not in other economic use”
 Class for inland waters would be needed

10. Some Considerations about the classification
Different land use functions are mixed, especially all tertiary sector classes
It is challenging to define detailed tertiary sector land use information as areas. Different tertiary sector functions are mixed and they also overlap with both industrial and residential areas.
In Finland the proportion of built-up area is small: especially challenging to produce reliable statistics about secondary and tertiary sectors at very detailed level
Use of forest areas in Finland is versatile and defining one land use value is difficult: Forestry, recreation, berry picking, hunting etc. are overlapping uses of forests.
Finland has very large inland water areas and the main use of water areas is difficult to define as they are used for fishing, recreation, water transport etc. at the same time. It would be useful to have a specific class for water areas in LU classification.
The versatile use of forests and water areas is based on “everyman rights”, which refer to the right of everyone in Finland to enjoy outdoor pursuits regardless of who owns or occupies an area.

11. Conclusions and Some Feedback
LUCAS land use classification had some problems from national point of view
LUCAS and Copernicus programmes could support each other more
E.g. LUCAS in situ data could not be used in validating Copernicus products, because data content was not sufficien for that purpose
LUCAS grants was a good opportunity to study the usage of spatial data in producing statistics
Open data policy enables the flexible usage of national datasets
Using spatial data for producing statistics is a very interesting option.

12. Thank you!
The name of the presenter, SYKE