Date (Creation date)

2017-12-06

Date (Publication date)

2018-01-15

Date (Revision date)

2024-09-10

Edition

2017

Citation identifier

https://www.dov.vlaanderen.be/dataset/62667b12-ae06-4c3a-b744-debc4331d398

Purpose

The Belgian Soil organic carbon (SOC) stock maps for topsoils (0-30 cm) were created answering a call from the Intergovernmental Technical Panel on Soils of the Global Soil Partnership (GSP) for regional SOC data. Based on this regional data in a unique 1km grid the Global Soil Organic Carbon Stock (GSOC) Map was compiled by the GSP.

Status

Completed

Maintenance and update frequency

As needed

GEMET - INSPIRE themes, version 1.0

• Bodem

GEMET - Concepten, versie 2.4

• ondergrond

• landbouw

• koolstof

• totaal aan organische koolstof

• grasland

• akker

• bos

High-value dataset categories

• Aardobservatie en milieu

• Bodem

Keywords

• HVD

Keywords

• DOV

• Vlaanderen

• Ondergrond

• Databank Ondergrond Vlaanderen

• Departement Omgeving

INSPIRE - Spatial scope

• Regionaal

GDI-Vlaanderen Trefwoorden

• Herbruikbaar
• Kosteloos
• Vlaamse Open data
• Toegevoegd GDI-Vl
• Lijst M&R INSPIRE
• Metadata GDI-Vl-conform
• Geografische gegevens

Access constraints

Other restrictions

Other constraints

geen beperkingen

Use constraints

Other restrictions

Other constraints

In uitvoering van het Vlaamse decreet voor hergebruik van overheidsinformatie, is het hergebruik geregeld d.m.v. de Modellicentie voor gratis hergebruik. Dit betekent dat elk commercieel of niet-commercieel hergebruik voor onbepaalde duur is toegelaten, zonder dat daar kosten aan verbonden zijn. Als enige gebruiksvoorwaarde geldt een bronvermeldingsplicht.

Other constraints

Bij het gebruik van de informatie die DOV aanbiedt, dient steeds volgende standaardreferentie gebruikt te worden: Databank Ondergrond Vlaanderen - (vermelding van de beheerder en de specifieke geraadpleegde gegevens) - Geraadpleegd op dd/mm/jjjj, op https://www.dov.vlaanderen.be

Other constraints

Volgende aansprakelijkheidsbepalingen gelden.

Other constraints

Toegang en gebruik voor het uitvoeren van taken van algemeen belang, op niveau van Vlaamse overheidsinstanties is geregeld door het Besluit van de Vlaamse Regering met de regels voor toegang en gebruik van geografische gegevensbronnen toegevoegd aan de GDI, door deelnemers GDI-Vlaanderen. Dit gebruik is kosteloos.

Other constraints

Toegang en gebruik voor het uitvoeren van taken van algemeen belang door niet-Vlaamse overheidsinstanties is geregeld door het Besluit van de Vlaamse Regering met de regels voor toegang en gebruik van geografische gegevensbronnen toegevoegd aan de GDI, door overheidsdiensten die geen deelnemer zijn aan GDI-Vlaanderen. Dit gebruik is kosteloos.

Other constraints

De toepassing of interpretatie van de koolstofstockkaarten moet met de nodige deskundigheid en voorzichtigheid gebeuren. De gegevens worden enkel meegedeeld ter informatie. De Vlaamse overheid biedt geen absolute zekerheid betreffende de koolstofstocks.

Spatial representation type

Grid

Distance

40.00  meter

Language

Nederlands; Vlaams

Topic category

• Geoscientific information

Begin date

1997-01-01

End date

2014-12-31

Supplemental Information

Separately published data Wallonia:

- Project : http://geoportail.wallonie.be/catalogue/47e4ea34-fe00-4712-b795-4a85fdab7dd7.html

- Stocks : http://geoportail.wallonie.be/catalogue/530b194d-5f66-482f-b536-22db24249f91.html

- Uncertainties: http://geoportail.wallonie.be/catalogue/c7caec6d-9393-414b-a5c7-362521720b08.html

Unique resource identifier

EPSG:31370

Distribution format

Name	Version
GeoTIFF	1.0

OnLine resource

carbon_sd_40 ( OGC:WMS-1.3.0-http-get-capabilities )

WMS-capabilities Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

carbon_sd_40 ( GLG:KML-2.0-http-get-map )

KML Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

carbon_sd_40 ( OGC:WMS-1.3.0-http-get-map )

WMS-map Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

carbon_sd_40 ( OGC:WMS )

WMS-endpoint Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

organische_koolstof:carbon_sd_40 ( OGC:WCS-2.0.1-http-get-capabilities )

WCS-capabilities Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

carbon_sd_40 ( OGC:WCS-2.0.1-http-get-coverage )

WCS-coverage Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

carbon_sd_40 ( OGC:WCS )

WCS-endpoint Soil Organic Carbon Stock Maps for Belgium: standard deviation (40 m grid)

OnLine resource

DOV bodemverkenner applicatie ( WWW:LINK-1.0-http--related )

Link naar DOV bodemverkenner applicatie

Hierarchy level

Dataset

Statement

Type of Sampling

- Agricultural land (Flanders): Auger (composite samples of 5 points sampled by auger and located within a 4m radius circle centered on known coordinates);

- Agricultural land (Wallonia): Auger (composite samples of 5 points sampled by auger and located within a 4m radius circle centered on known coordinates;

- Forest (Flanders): Edelman auger (Eijkelkamp, the Netherlands);

- Forest (Wallonia): Auger (composite samples based on 21 soil cores regularly distributed on each plot, i.e. a 18m radius circle);

Programme for data collection

- Agricultural land (Flanders): SOC stocks monitoring – CARBIOSOL soil monitoring network (resampling of 592 sites from the National Soil Survey of 1949-1972) for points in Wallonia. No specific programme for points in Flanders;

- Agricultural land (Wallonia): SOC stocks monitoring – CARBIOSOL soil monitoring network (resampling of 592 sites from the National Soil Survey of 1949-1972);

- Forest (Flanders): -;

- Forest (Wallonia): Soil monitoring (RFIW: Regional Forest Inventory of Wallonia, Southern Belgium);

Sampling Period

- Agricultural land (Flanders): 2004 - 2008;

- Agricultural land (Wallonia): 2005 - 2014;

- Forest (Flanders): 1997 - 2002;

- Forest (Wallonia): 2004 - 2014;

Total number of soil profiles

- Agricultural land (Flanders): 352;

- Agricultural land (Wallonia): 592;

- Forest (Flanders): 740 profiles in 306 forest plots;

- Forest (Wallonia): 773;

Georeferencing

- Agricultural land (Flanders): Coordinates in WGS84 handheld GPS;

- Agricultural land (Wallonia): Coordinates in LAMBERT 72;

- Forest (Flanders): Coordinates in WGS84 handheld GPS;

- Forest (Wallonia): LAMBERT 72;

Sampling depth

- Agricultural land (Flanders and Wallonia): sampling by horizons to a minimum depth of 30cm;

- Forest (Flanders): Full profile sampling by horizons, commonly to 1.2 m. An equal-area quadratic smoothing spline is fitted to the layer carbon density (CD) data (t C/ha). The SOC stock was computed by integrating the CD spline from 0 to 30 cm. For spline method see De Vos et al. 2015;

- Forest (Wallonia): 0-20cm (a predictive function to estimate 0-30 cm Forest Soil Organic Carbon stocks from 0-20 cm SOC stocks in fine earth has been fitted based on ForSite database (Flanders, Northern Belgium) and applied in the procedure. Then, the estimated 0-30 cm SOC stocks were corrected from rock fragments content, i.e. % volume)

Sampling Design: random, systematic (e.g. transect,catena, toposequence), land use etc.

- Agricultural land (Flanders): In Wallonia: sampling by landscape unit (=unit of semi-homogeneous soil type, land use and agricultural region). Resampling of historical sampling locations of the Aardewerk database (Van Orshoven et al. 1988) that remained under the same land use (cropland or grassland) since the original profile sampling (Goidts et al. 2009). The total number of profiles during this sampling campaign was 427. This sampling effort was complemented by supplementary sampling at 202 locations in Flanders and Wallonia for landscape units that lacked data (Meersmans, 2011). (See map of sampling locations);

- Agricultural land (Wallonia): Convenience sampling (resampling of 592 sites from the National Soil Survey of 1949-1972; see map of sampling locations);

- Forest (Flanders): The ForSite sampling scheme was the standard scheme during the regional forest inventory (Waterinckx & Roelandt, 2001) as described in Dutch by De Vos (1998). At each forest inventory plot, 3 locations were sampled according to following rules: (i) first location was set as close as possible to the circular plot center, (ii) the other 2 locations were chosen close to the plot circumference (i.e. circa 18 m from the center) and such that they comprise maximum within plot variation (e.g. highest and lowest point on a slope, wettest and driest spot, locations with and without ground vegetation) and (iii) are representative for the plot and the surrounding forest stand. (See map of sampling locations);

- Forest (Wallonia): The RFWI sampling network is a 1,000 m (east–west) x 500 m (north–south) grid. At each node, a 18m radius circle is investigated. Only 10% of the nodes are sampled for SOC analysis (see map of sampling locations).

Soil Organic Carbon: Measured

SOC analysis method *

- Agricultural land (Flanders): bulk soil samples were air-dried, sieved (2 mm), and analysed according to the Walkley & Black method (Walkley and Black 1934), a correction factor of 1.33 was applied

- Agricultural land (Wallonia): i/ 434 sites were analysed by dichromate oxydation (Walkley & Black Method; Walkley and Black, 1934). Samples were air-dried, gently grounded, and sieved over 2 mm. 100 of these samples were also analyzed for SOC content by dry combustion method (see ii/ below) and used for fitting a linear regression between the results of both methods (Walkley & Black and dry combustion - corrected from inorganic carbon content). This linear regression was used to correct results of Walkley & black method from incomplete oxydation and making them comparable to the results obtained by dry combustion method corrected from inorganic carbon content (Chartin et al., 2017). ii/ 158 sites were analyzed by dry combustion (Variomax CN, Elementar GmbH, Germany), and then corrected from inorganic carbon content. Samples were air-dried, gently grounded, and sieved over 2 mm

- Forest (Flanders): Mineral soil samples were oven-dried at 40 °C to avoid chemical decomposition at higher temperatures. A subsample was dried at 105°C to determine the moisture correction factor needed for recalculating the analysis results on dry mass basis. Oven-dried mineral soil was crushed and sieved to pass a 2-mm mesh screen using an SK100 mill (Eijkelkamp, Giesbeek, the Netherlands). Carbon analysis was performed using various methods: 88% of the samples were analysed by Loss-on-ignition (LOI), 35% by total analyser (TOC) and 6% by unmodified Walkley & Black method (WBC). A quarter of all samples were assessed both by TOC and LOI, to calibrate regression functions as described in De Vos et al. (2005). LOI was determined on a 3 g oven-dry soil sample, dried at 105°C, weighed in porcelain crucibles and ignited in a muffle-furnace (Nabertherm L9-C6, Lilienthal, Germany) at 550°C for 3 h. Prior to ignition of the sample, the porcelain crucible was preheated at 550°C for 30 min and cooled down to room temperature in an exsiccator. Subsequently the crucible was weighed, sample was added and weighed and gradually heated till 550°C. Ignition temperature remained constant for 3 h. Ignited samples were still hot when placed in the exsiccator and the percentage of weight loss was determined with a precision of 10-4 g when the crucibles were at room temperature. TOC was analysed according to ISO10694 with a Shimadzu TC analyser. The applied Walkley an Black method is described in De Vos et al. 2007

- Forest (Wallonia): All the samples were analyzed by dichromate oxydation (Modified Springer & Klee Method; Springer & Klee , 1954).

Bulk Density (if Measured)

- Agricultural land (Wallonia): 3 intact cores of 100cm³ (d = 53 mm) were taken on the middle of each horizon within the 4m radius circles investigated. Measurements were corrected from stone contents in order to obtain bulk density of fine earth only (< 2mm);

- Forest (Flanders): bulk density was sampled together with mineral soil sampling. A Riverside auger (Eijkelkamp, the Netherlands) was used in combination with a ring holder (Eijkelkamp, the Netherlands) for collecting undisturbed sample cores for bulk density determination. Standard sharpened steel cylinders (type Kopecky) of 100 cc volume (d = 53 mm, h = 50 mm) were used. Bulk density was measured after determination of the soil moisture retention curve at 8 matric potentials (determination of soil water retention curves). The samples were oven dried (105°C) till constant weight (> 24 h). Method described in De Vos et al. (2005b).

Bulk Density (if Estimated)

Pedotransfer functions, default values, citations

- Agricultural land (Flanders): BD = 1000 * (1.66 – 0.318*sqrt(%SOC)) (Manrique & Jones, 1991);

- Forest (Wallonia): Estimated using the Pedotransfer function developed by Manrique & Jones (1991)

Coarse Fragments (if Measured)

Wallonia: % volume (based on mean values measured on the 0-30cm depth for each soil types in Wallonia by the CARBIOSOL soil monitoring network in grasslands)

Coarse Fragments (if Estimated)

Flanders: rock fragments in Flemish soils are assumed to be absent, as they mostly are

Organic layers

Not included in the present soil map.

Peat: Sampling and description method

Forest (Flanders): Peat soils were sampled under forest (N=4), the average value of these 4 plots was used for all peat soils under forest (2437 ha).

Metadata Digital Soil Mapping

- Agricultural land (Flanders):

i) The following empirical regression model was derived based on a dataset of 352 profiles.

%SOC = LandUse + a.clay + b.H2Omin + c.LandUse.Clay + d.LandUse.H2Omin

Based on the Belgian soil map and the VITO land use map (Poelmans, 2014), this regression equation was applied to the entire territory of Flanders. Total carbon stock (Cs, in t C/ha) was computed as: Cs = %SOC * BD * Depth (Where BD was estimated as specified above; Depth = 0.3 m)

ii) The uncertainty reported is the model uncertainty on point estimates for each data point, in which the estimated model parameters are simulated 1000 times, under the assumption that they are independent and normally distributed variables, using their model estimation and standard error as distribution parameters. (see Goidts, 2009 and Meersmans, 2011 for further details);

- Agricultural land (Wallonia):

i) A Generalized Additive Model (GAM; Wood, 2001) was fitted on 2/3 of the dataset. Spatialized environmental covariates (40m x 40m) were used as inputs on the model to map SOC stocks over croplands and grasslands in Wallonia (Southern Belgium). The covariates identified during the model calibration procedure as having the most significant influences on SOC stock spatial variability were (by decreasing order of importance): the land use, the clay and fine silt content, the geographical coordinates, the slope gradient and length, the maximum groundwater table depth and the temperature. The external validation (on the remaining 1/3 of the dataset) gave a R² of 0.64 and a RMSE of 16 Mg C / ha.

ii) The computation of the prediction uncertainty accounts for the errors associated to both the estimations of a) SOC stocks and b) parameters of the spatial model (GAM). Hence, two consecutive stochastic simulations (Monte-Carlo method) were used to produce 10,000 (i.e., 100 x 100) independent spatialized datasets. Based on these 10,000 individuals, mean SOC stocks and standard deviation (SD) were computed for each pixel. (see Chartin et al., 2017 for further details);

- Forest (Flanders):

i)The average soil carbon stock in the upper 30 cm (Cs, in t C/ha) is computed per texture-drainage class of the Belgian soil map. This value is pasted into the 10x10 m² grid of the land use map.

ii) The uncertainty of the mean (precision) is based on the margin of error (ME) derived from half the 95% confidence interval (CI95%). CI95% are estimated based on bias corrected and accelerated (BCa) percentiles at 2.5 and 97.5% determined by bootstrapping (B = 5000 resamples).

- Forest (Wallonia):

i) A Generalized Additive Model (GAM; Wood, 2001) was fitted on 2/3 of the dataset. Spatialized environmental covariates (40m x 40m) were used as inputs on the model to map SOC stocks over forest in Wallonia (Southern Belgium). The covariates identified during the model calibration procedure as having the most significant influence on SOC stock spatial variability in forest were: the clay and fine silt content, the geographical coordinates, the elevation, the slope gradient, the Topographical Position Index, the climate data (mean annual rain and temperature) and the minimum groundwater table depth. The external validation (on the remaining 1/3 of the dataset) gave a R² of 0.41, a mean error of 0.3 Mg C /ha, a MAE of 16 Mg C /ha and a RMSE of 18.2 Mg C / ha.

- ii) The computation of the prediction uncertainty (standard deviation, SD) accounts only for the errors associated to the estimation of the parameters of the spatial model (GAM). The mgcv package in R provides a Bayesian approach to compute standard errors for the predictions (Wood, 2001).

Quality Measures (Digital Soil Mapping): Please provide Mean error (ME), Mean absolute error (MAE) and root mean squared error (RMSE), R2

- Agricultural land (Flanders): see Goidts, 2009 and Meersmans, 2011 for further details;

- Agricultural land (Wallonia): The external validation (on the remaining 1/3 of the dataset) gave a R² of 0.64 and a RMSE of 16 Mg C / ha. see Chartin et al., 2017 for further details;

- Forest (Flanders): The uncertainty of the mean (precision) is based on the margin of error (ME) derived from half the 95% confidence interval (CI95%). CI95% are estimated based on bias corrected and accelerated (BCa) percentiles at 2.5 and 97.5% determined by bootstrapping (B = 5000 resamples).

- Forest (Wallonia): The external validation (on the remaining 1/3 of the dataset) gave a R² of 0.41, a mean error of 0.3 Mg C /ha, a MAE of 16 Mg C /ha and a RMSE of 18.2 Mg C / ha.

Resampling Method (if used)

1. Flanders 10 to 40m: i) Block Statistics: Neighbourhood: 4*4 Cell, Statistics type: Mean, Ignore NoData. ii) Resample: Cell size: 40*40, Resampling Technique: Nearest, Environments > Processing Extent > Snap: Wallonie;

2. Combine Flanders and Wallonia: Mosaic To New Raster: Pixel Type: 64bit, Number of Bands: 1, Mosaic Operator: Mean;

3. Map Belgium 40m: Belge Lambert72 to WGS84: Project Raster: Geographic Transformation: 3;

4. Map Belgium 40m to 1000m: i) Block Statistics: Neighbourhoud: Map Height&Width: properties Grid_FAO.tif, Statistics type: Mean, Ignore NoData; ii) Resample: Cell size: properties Grid_FAO.tif, Resampling Technique: Nearest, Environments > Processing Extent > Snap: Grid_FAO.tif, iii) Project Raster: WGS84, Cell Size: properties Grid_FAO.tif

Data Holders:

Vlaamse overheid, Vlaams Planbureau voor Omgeving (contact person: Katrien Oorts) and Service Public de Wallonie (contact person: Patrick Engels)

Data Handlers:

Caroline Chartin1, Suzanna Lettens2, Pieter Verschelde2, Sabine Buyle3, Katrien Oorts3*, Patrick Engels4, Esther Goidts4, Martien Swerts3, Bruno De Vos2, Bas van Wesemael1, * Corresponding author

1) Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium 2) Environment and Climate unit, Research Institute for Nature and Forest, 1070 Brussels, Belgium 3) Vlaams Planbureau voor Omgeving, Departement Omgeving, Vlaamse overheid, 1000 Brussel, Belgium 4) Service Public de Wallonie, Direction Générale de l'Agriculture, des Ressources Naturelles et de l'Environnement (DGO3), 5100 Namur, Belgium

Comments, Remarks

The use or interpretation of the GSOC map has to be done with the needed expertise and caution. The map is the result of computer modeling. The data are offered for informational purpose only. The Flemish and Walloon Government offers no certainty about the data. The Flemish and Walloon Government and the authors cannot be hold responsible for any damage or costs, resulting from the use of the information offered, neither for possible inaccurate information or assessments.

References Agricultural land (Flanders)

- Goidts, E., et al. (2009). Magnitude and sources of uncertainties in soil organic carbon (SOC) stock assessments at various scales. European Journal of Soil Science 60(5): 723-739.

- Manrique LA, Jones CA. Bulk density of soils in relation to soil physical and chemical properties. Soil Sci Soc Am J. 1991;55:476-81.

- Meersmans, J., et al. (2011). Spatial analysis of soil organic carbon evolution in Belgian croplands and grasslands, 1960-2006. Global Change Biology 17(1): 466-479.

- Van Orshoven, J., et al. (1988). A structured database of Belgian soil profile data. Pédologie 38: 191-206.

- Poelmans, L., et al. (2014). Landgebruikskaart NARA-T 2014. VITO in opdracht van het Instituut voor Natuur en Bosonderzoek.

References Agricultural land (Flanders)

- Chartin C., Stevens A., Goidts E., Krüger I., Carnol M., van Wesemael B., 2017. Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia). Geoderma Regional 9, 73-86.

- Sherrod L.A., Dunn G., Peterson G.A., Kolberg R.L., 2002. Inorganic Carbon Analysis by Modified Pressure-Calcimeter Method. Soil Sci. Soc. Am. J. 66, 299–305.

- Walkley A., Black I.A., 1934. An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–37.

- Simon N. Wood. mgcv: GAMs and generalized ridge regression for r. R news, 1(2):20-25, 2001.

References Forest (Flanders)

- De Vos, B. 2009. Uncertainties of forest soil carbon stock assessments in Flanders, Doctoral dissertation no. 865 In., Faculty of Bioscience Engineering, K.U.Leuven, Leuven, Belgium.

- De Vos, B. 1998b. Praktische methodiek voor bodemstaalname in het kader van de bosinventarisatie. Versie 1. , Instituut voor Bosbouw & Wildbeheer, Ministerie van de Vlaamse Gemeenschap, Geraardsbergen, pp. 18.

- De Vos, B., Vandecasteele, B., Deckers, J. & Muys, B. 2005. Capability of loss-on-ignition as a predictor of total organic carbon in non-calcareous forest soils. Commun. in Soil Sci Plant Anal., 36, 2899-2921.

- De Vos, B., Van Meirvenne, M., Quataert, P., Deckers, J. & Muys, B. 2005. Predictive quality of Pedotransfer functions for estimating bulk density of forest soils. Soil Sci Soc. Am. J., 69, 500-510.

- De Vos, B., Lettens, S., Muys, B. & Deckers, J. A. 2007. Walkley–Black analysis of forest soil organic carbon: recovery, limitations and uncertainty. Soil Use and Management, 23, 221-229.

- De Vos, B., Cools, N., Ilvesniemi, H., Vesterdal, L., Vanguelova, E. & Carnicelli, S. 2015. Benchmark values for forest soil carbon stocks in Europe: Results from a large scale forest soil survey. Geoderma, 251-252, 33-46.

- Waterinckx, M. & Roelandt, B. 2001. Methodiek voor de bosinventarisatie van het Vlaamse Gewest. Deel I. [Methods]. In: De bosinventarisatie van het Vlaamse Gewest [Forest Inventory of the Flemish Community]. Ministerie van de Vlaamse Gemeenschap, Afdeling Bos en Groen, Brussel, pp. 9-49.

References Forest (Wallonia)

- Manrique L.A., Jones C.A., 1991. Bulk-density of soils in relation to soil physical and chemical-propoerties. Soil Sci. Soc. Am. J. 55, 476-481.

- Springer U., Klee J, 1954. Pru¨fung der Leistungsfa¨higkeit von einigen wichtigeren Verfahren zur Bestimmung des Kohlenstoffs mittels Chromschwefelsa¨ure sowie Vorschlag einer neuen Schnellmethode. J Plant Nutr Soil Sci 64:1–26

- Simon N. Wood. mgcv: GAMs and generalized ridge regression for r. R news, 1(2):20-25, 2001.

Meta-metadata

File identifier

c41ae3d2-7975-457f-94d7-4ab83d659432 XML

Metadata language

Nederlands; Vlaams

Character set

UTF8

Parent identifier

Soil Organic Carbon Stock Maps for Belgium

037427b6-d9ad-43ec-9c1e-b423396266d6

Hierarchy level

Dataset

Hierarchy level name

dataset

Date stamp

2024-11-09

Metadata standard name

ISO 19115/2003/Cor.1:2006/INSPIRE-TG2.0

Metadata standard version

GDI-Vlaanderen Best Practices - versie 2.0