https://data.mfe.govt.nz/license/attribution-4-0-international/https://data.mfe.govt.nz/license/attribution-4-0-international/
This data is deprecated. Please visit https://data.mfe.govt.nz/layer/117733 for the latest version.
The LUCAS NZ Land Use Map 2016 v011 is composed of New Zealand-wide land use classifications (12) nominally at 1 January 1990, 1 January 2008, 31 December 2012 and 31 December 2016 (known as "1990", "2008", "2012" and "2016"). These date boundaries were dictated by the First and Second Commitment Periods of the Kyoto Protocol. The layer can therefore be used to create either a 1990, 2008, 2012 or 2016 land use map depending on what field is symbolised.
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The LUCAS NZ Land Use Map 2020 v005 is composed of New Zealand-wide land use classes (12) nominally at 31 December 1989, 31 December 2007, 31 December 2012, 31 December 2016, and 31 December 2020. These date boundaries are dictated by the Paris Agreement and former Kyoto Protocol. The data can therefore be used to create a map at any of the nominal mapping dates depending on what field is symbolised.
Land use areas and areas of land-use change, identified in the LUCAS NZ Land Use Map, are used to calculate greenhouse gas emissions and removals for the Land Use, Land Use Change and Forestry (LULUCF) sector of New Zealand’s annual Greenhouse Gas Inventory and the Biennial Transparency Report. These reports are submitted to meet New Zealand’s reporting and accounting obligations under the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement.
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Note: Metadata relates to LiDAR point clouds. This layer has been provided to enable users to explore coverage and capture dates of the LiDAR. To enquire about ordering the LiDAR and/or related orthophotography, please e-mail lucas[at]mfe.govt.nz.
Ministry for the Environment, Land Use Carbon Analysis System collection of swaths of LiDAR over planted forests of interest (raw and classified returns) from 2006 to 2015.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Annual land cover mapping for continental Europe based on Ensemble Machine Learning (EML), samples obtained from LUCAS (Land Use and Coverage Area frame Survey) and CLC (CORINE Land Cover) Maps, and several harmonized raster layers (e.g. GLAD Landsat ARD imagery and Continental EU DTM). The EML predicted the dominant land cover, probabilities and uncertainties for 33 classes compatible with CLC over 20 years (2000–2019), and was implemented in R and Python (eumap library).
The raster layers were mainly composed by the GLAD Landsat ARD imagery, which were downloaded for the years 1999 to 2020 considering the Continental Europe extent (land mask area and tiling system), screened to reduce cloud cover (GLAD quality assessment band), aggregated by season according with three different quantiles (i.e. 25th, 50th and 75th), and gap-filled using the Temporal Moving Window Median approach available in the eumap library. The images for each season were selected using the same calendar dates for all period:
In addition to Landsat spectral data, the EML considered night lights (VIIRS/SUOMI NPP), Global surface water frequency, Continental EU DTM, Landsat spectral indices (SAVI, NDVI, NBR, NBR2, REI and NDWI) and the max/min. monthly geometric temperature, estimated on a pixel basis and for each month.
The training data were obtained from the geographic location of LUCAS (in-situ source) and the centroid of all polygons of CORINE (supplementary source), harmonized according to the 33 CLC and organized by year, where each unique combination of longitude, latitude and year was treated as a independent sample with the following classes (the class descriptions are here):
The LUCAS points with a unique land cover class received a confidence rating of 100%, while CORINE points received 85%, values which were considered by EML as sample weight in the training phase. The points were used in a spacetime overlay approach, which considered the location and the year to retrieve the pixel values of all rasters. Some specific land cover samples (i.e. 111, 122, 131, 141, 211, 221, 222, 223, 231, 311, 312, 321, 411, 512) were screened according to convergence with pre-existing mapping products (OSM roads, OSM railways and Copernicus-OSM buildings; Copernicus high resolution layers), where, for example, “111: Urban fabric” samples located in low density building areas (> 50% according to Copernicus-OSM building layer) were removed from the final training data ( ~5.3 million samples and 178 covariates/features).
Using this training data, three ML models were trained to predict probabilities (i.e. Random Forest, XGBoost, Artificial Neural Network), which served as input to train a linear meta-model (i.e. Logistic regression classifier), responsable for predicting the final land cover probabilities of all classes. The hyperparameter optimization was conducted using a 5-fold spatial cross validation, based on a 30x30km tilling system. The uncertainties were calculated for all classes according to the standard deviation of the three predicted probabilities for each pixel, and the highest probability was selected as the dominant land cover class, resulting in 20 annual maps for continental Europe.
The training samples, covariates/features and fitted models are available through lcv_landcover.hcl_lucas.corine.eml_p_landmapper_full.lz4, a LandMapper class instance that can be loaded by eumap library (check the code demonstration). The production code used to generate the current version of the annual land cover maps is available in the spatial layer repository and considered a lighter LandMapper class instance (lcv_landcover.hcl_lucas.corine.eml_p_landmapper_light.lz4,), which not includes the training samples.
Only the dominant land cover classes are provided here. To access the probabilities and uncertainties use:
A publication describing, in detail, all processing steps, accuracy assessment and general analysis of land-cover changes in continental Europe is under preparation. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues
The Digital Geologic Map of the Lucas quadrangle, Kentucky is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcMap 9.1 layer (.LYR) files. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.4. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.1 personal geodatabase (luca_geology.mdb), as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 16N. That data is within the area of interest of Mammoth Cave National Park.
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Areas of LUCAS LUM forests that have been cleared between 2008 and 2022 inclusive. Forest clearing is detected using mosaics of satellite imagery captured over a range of dates; therefore the year of clearing provided is nominal.
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The LUCAS Chatham Islands Land Use Map 2020 v001 is composed of New Zealand-wide land use classes (12) nominally at 31 December 1989, 31 December 2007, 31 December 2012, 31 December 2016, and 31 December 2020. These date boundaries are dictated by the Paris Agreement and former Kyoto Protocol. The data can therefore be used to create a map at any of the nominal mapping dates depending on what field is symbolised.
Right of Way data from Lucas County, Ohio
Geospatial data about Lucas County, Ohio Soils (NRCS). Export to CAD, GIS, PDF, CSV and access via API.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Harmonized training samples based on LUCAS (Land Use and Coverage Area frame Survey) and CLC (CORINE Land Cover) Maps, according to the following classes:
The samples were obtained from the geographic location of LUCAS (in-situ source) and the centroid of all polygons from CLC maps, harmonized according to the above classes and organized by year, where each unique combination of longitude, latitude and year was considered as a independent sample. Some specific CORINE samples (i.e. 111, 122, 131, 141, 211, 221, 222, 223, 231, 311, 312, 321, 411, 512) were filtered according to convergence with existing mapping products (OSM roads, railways and buildings; Copernicus High-Res. Layers - HRL), where, for example, “111 - Urban fabric” samples located in low density building areas (> 50% according to Copernicus-OSM building layer) were removed. The LUCAS points with a unique land-cover class received a confidence rating of 100%, while CORINE received 85%. Using these filtered samples a spacetime overlay was performed (check the code demonstration in eumap library) considering several raster layers for Continental Europe: four season quantiles for GLAD Landsat ARD (spectral bands and indices - Potapov, 2020), DTM-based elevation and slope (Hengl, 2020), VIIRS/SUOMI NPP night lights (Hillger, 2013), Global surface water frequency (Pekel, 2016), and geometric minimum and maximum temperature derived according to Kilibarda, 2014.
The provided samples has 5,362,229 rows and 263 columns, including the geographic location, the metadata of land cover harmonization, the mapped classes according to OSM and HRL, and the result of the spacetime overlay (178 covariates).
Use the following Python/R code to open the files lcv_landcover.hcl_lucas.corine_harm.samples.overlaid.*
import joblib
samples = joblib.load('lcv_landcover.hcl_lucas.corine_harm.samples.overlaid')
readRDS('lcv_landcover.hcl_lucas.corine_harm.samples.overlaid.rds')
These samples were used to train a spatiotemporal model, which predicts the land cover for continental Europe over 20 years (2000 - 2019). To access the predictions results (dominant class, probabilities and uncertainties) use the following services:
A publication describing, in detail, all processing steps, accuracy assessment and general analysis of land-cover changes in continental Europe is under preparation. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues
Historical Lot Lines data for Lucas County, Ohio.
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This data is deprecated. Please visit https://data.mfe.govt.nz/layer/118263 for the latest version.
The Chatham Islands Land Use Map is composed of 12 land use classifications nominally at 1 January 1990, 1 January 2008, 31 December 2012 and 31 December 2016 (known as "1990", "2008", "2012" and "2016") and was created using broadly the same methodology as was used for mainland LUCAS NZ LUM 2016. These date boundaries were dictated by the First and Second Commitment Periods of the Kyoto Protocol. The layer can therefore be used to create either a 1990, 2008, 2012 or 2016 land use map depending on what field is symbolised.
Tax DuplicateItems in the Tax Duplicate are from the CADASTRE dataset and are exclusively maintained by the Lucas County Engineer’s Tax Map Department, and hosted by the Lucas County Auditor's Office GIS Department.The Cadastre dataset contains three polygon layers, one polyline layer, and sixteen annotation layers.Lucas County Engineers Tax Map Department maintains the tax maps and parcel mapping portion of the county's GIS. They review all legal descriptions before transfers are made and assign street numbers for addresses in the County outside of city limits.Cadastre Parcels are identified by Assessor number, which is a unique land identifier. Road Centerlines are provided by Lucas County EMA/911Inquiries about cadastral data should be directed to Shawn Shaffer, Engineer's Tax Map Department at 419-213-4658.Inquiries regarding the map and feature services should be directed to Jason Clay, Auditor's GIS Department at 419-213-2110.Inquiries regarding Road Centerlines should be directed to Greg Bonfiglio, Lucas County 911 Regional Council of Governments, 419-720-0275
This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.
Geospatial data about Lucas County, Ohio Census Tracts. Export to CAD, GIS, PDF, CSV and access via API.
Geospatial data about Lucas County, Ohio Waterlines. Export to CAD, GIS, PDF, CSV and access via API.
Geospatial data about Lucas County, Ohio High School Areas. Export to CAD, GIS, PDF, CSV and access via API.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
During the elaboration of EJP Soil deliverable 6.3 of Work Package 6, entitled ‘Proposal of methodological development for the LUCAS programme in accordance with national monitoring programmes’ (Bispo et al., 2021), a large majority of partners (23) suggested to launch a double sampling study by collecting national soil samples on LUCAS points to compare LUCAS and National design strategies and sampling and analytical protocols.
For Flanders region, we conducted a double-double sampling study on 166 LUCAS points that were selected as representative for cropland, grassland and woodland soils.
Each of these LUCAS points were sampled according to the LUCAS sampling scheme and the national (Cmon) scheme by our national surveyors (double sampling type 1) , and sampled by LUCAS surveyors (COBA group) for analysis by the LUCAS central lab so that also the laboratory results could be compared for the LUCAS 2022 campaign (double sampling type 2).
The LUCAS 2022 campaign selected also for Belgium 11 LUCAS Biopoints: 5 in Flanders, 1 in Brussels region and 5 in Wallonia. At the EJP National HUB we agreed to sample all 11 Belgian biopoints both for physico-chemical soil properties (this study) as for soil biodiversity assessment through eDNA metabarcoding (Lambrechts et al., 2025).
The dataset comprises 3 CSV files: (1) list of sampling points with LUCAS pointID, coordinates and metadata, (2) the physico-chemical soil data of each point according to the LUCAS and Cmon sampling scheme, (3) the soil bulk density data according to the Cmon sampling scheme.
Samples were collected between 15 June 2022 and 23 May 2023.
Variable | Description | Unit | Method |
Thickness | Layer thickness | cm | |
Depth | Depth of layer (negative for forest floor) | cm | |
Sand | Sand fraction by Laser Diffraction (LD) | vol% | ISO11277 & ISO13320 |
Silt | Silt fraction by LD | vol% | ISO11277 & ISO13320 |
Clay | Clay fraction by LD | vol% | ISO11277 & ISO13320 |
TC | Total carbon | g/kg | ISO 10694 |
TIC | Total inorganic carbon | g/kg | ISO 10694 |
TOC | Total organic carbon (TC-TIC) | g/kg | ISO 10694 |
TN | Total nitrogen | g/kg | ISO 13878 |
pH_KCl_v_v | pH KCl 1:5 v/v | - | ISO 10390 |
EC_m_v | Electric conductivity m/v | µS/cm | ISO 11265 |
EC_v_v | Electric conductivity v/v | µS/cm | ISO 11265 |
pH_H20_m_v | pH H2O 1:5 m/v | - | ISO 10390 |
pH_H20_v_v | pH H2O 1:5 v/v | - | ISO 10390 |
pH_KCl_v_v | pH KCl 1:5 v/v | - | ISO 10390 |
pH_CaCl2_m_v | pH CaCl2 1:5 m/v | - | ISO 10390 |
pH_CaCl2_v_v | pH CaCl2 1:5 v/v | - | ISO 10390 |
BD_mean | Bulk density mean of 4 rep | g/cm³ | ISO 11272 |
BD_sd | Bulk density stdev of 4 rep | g/cm³ | |
BD_CV | Bulk density CV of 4 rep | g/cm³ | |
SWC_volp_mean | Volumetric moisture content mean of 4 rep | vol% | |
SWC_volp_sd | Volumetric moisture content sd of 4 rep | vol% |
The data are evaluated in the report: De Vos, B. & Lambrechts, S. 2025. Comparing LUCAS and Cmon sampling schemes and survey data on 166 LUCAS 2022 points in Flanders, Belgium. EJP Soil Deliverable 6.3. Horizon 2020 EJP Soil grant agreement 862695. (to be published on Zenodo after Embargo period of 6 months).
Geospatial data about Lucas County, Ohio Water Course - Major ditch. Export to CAD, GIS, PDF, CSV and access via API.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Dataset organized by the Open-Earth-Monitor (OEMC) project within the context of Hackathon 2023.
The dataset (both train and test) was produced by stratified sampling of the ground-truth data provided by LUCAS Survey, funded by the European Commission. The target land cover considered level-3 classes from the harmonized legend, resulting in 72 classes distributed over 5 years (2006
, 2009
, 2012
, 2015
, 2018
):
All samples were overlaid with 416 raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.
Column names:
The columns are formed by six metadata fields separated by _
:
Column description:
All the columns can be aggregated in six thematic groups according to F1 and F2:
blue_landsat.glad.ard_{..}
: Quarterly time-series of Landsat blue band (Witjes et al., 2023)blue_mod13q1_{..}
: Monthly time-series of MOD13Q1 blue band (EarthData)evi_mod13q1.stl.trend.ols.alpha_{..}
: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)evi_mod13q1.stl.trend.ols.beta_{..}
: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)evi_mod13q1.stl.trend_{..}
: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)evi_mod13q1_{..}
: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)green_landsat.glad.ard_{..}
: Quarterly time-series of Landsat green band (Witjes et al., 2023)mir_mod13q1_{..}
: Monthly time-series of MOD13Q1 mid-infrared band (EarthData)ndvi_mod13q1_{..}
: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (EarthData)nir_landsat.glad.ard_{..}
: Quarterly time-series of Landsat near-infrared band (Witjes et al., 2023)nir_mod13q1_{..}
: Monthly time-series of MOD13Q1 near-infrared band (EarthData)red_landsat.glad.ard_{..}
: Quarterly time-series of Landsat red band (Witjes et al., 2023)red_mod13q1_{..}
: Monthly time-series of MOD13Q1 red band (EarthData)swir1_landsat.glad.ard_{..}
: Quarterly time-series of Landsat short-wave infrared-1 band (Witjes et al., 2023)swir2_landsat.glad.ard_{..}
: Quarterly time-series of Landsat short-wave infrared-1 band (Witjes et al., 2023)</li>
<li><strong>Temperature images:</strong>
<ul>
<li><code>lst_mod11a2.daytime_{..}</code>: Monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.daytime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.daytime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.daytime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.daytime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.nighttime_{..}</code>: Monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.nighttime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.nighttime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.nighttime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>lst_mod11a2.nighttime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li>
<li><code>thermal_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat thermal band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li>
</ul>
</li>
<li><strong>Climate layers:</strong>
<ul>
<li><code>accum.precipitation_chelsa.annual_{..}</code>: Accumulated precipitation over the entire year according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li>
<li><code>accum.precipitation_chelsa.annual.3years.dif_{..}</code>: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li>
<li><code>accum.precipitation_chelsa.annual.log.csum_{..}</code>: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li>
<li><code>accum.precipitation_chelsa.montlhy_{..}</code>: Accumulated precipitation for each month according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li>
<li><code>bioclim.var_chelsa.{variable_code}_{..}</code>: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For <code>variable_code</code> descriptions see <a href="https://chelsa-climate.org/bioclim/">chelsa-climate.org</a> (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al.,
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This data is deprecated. Please visit https://data.mfe.govt.nz/layer/117733 for the latest version.
The LUCAS NZ Land Use Map 2016 v011 is composed of New Zealand-wide land use classifications (12) nominally at 1 January 1990, 1 January 2008, 31 December 2012 and 31 December 2016 (known as "1990", "2008", "2012" and "2016"). These date boundaries were dictated by the First and Second Commitment Periods of the Kyoto Protocol. The layer can therefore be used to create either a 1990, 2008, 2012 or 2016 land use map depending on what field is symbolised.