The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2023. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

_

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

_

References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

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National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

[Metadata] Description: Agricultural Land Use Maps (ALUM) for islands of Kauai, Oahu, Maui, Molokai, Lanai and Hawaii as of 1978-1980.

The Agriculture Capability mapping dataset is the digitized equivalent of the legacy Agriculture Capability Scanned Maps, which date from the 1960's to the 1990s. Agriculture Capability mapping is also known as 'Soil Capability for Agriculture' and 'Agricultural Capability' mapping. Agricultural Capability is an interpreted mapping product based on soil and climate information. In general, climate determines the range of crops possible in an area and the soils determine the type and relative level of management practices required. This is legacy data and changes in climate are not reflected. For more information about the classification system see: Land Capability Classification for Agriculture. Use caution utilizing these legacy maps as the classifications were based on common land management practices and typical crops of the 1960s-1990s era, and subsequent site specific land management practices (e.g. installation of drainage) may have modified the soil conditions since the mapping was completed. This Agriculture Capability legacy mapping is included in the Soil Information Finder Tool (SIFT) mapping application. The SIFT application provides more detailed climate data (e.g. Growing Degree Days, Frost Free Period (5 C), (1960-1990 climate normals). The SIFT 'Soil query tools' may be useful for identifying areas with specific 'growing conditions' of interest based on soils present (soil name), soil texture, drainage, coarse fragment content, slope, elevation, growing degree days and frost free period. Note: This Agriculture Capability Mapping dataset is based on soil mapping at 1:100,000, 1:50,000 or 1:20,000 scale, and is more detailed than the 1:250,000 scale Canada Land Inventory (CLI) Agricultural Capability mapping (available here).

Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches.

We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model.

All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; https://force-eo.readthedocs.io/en/latest/ last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube.

For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022).

The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map).

The maps are provided as GeoTiff files together with a QGIS legend file for visualization.

Class catalogue:

10 Grassland
31 Winter wheat
32 Winter rye
33 Winter barley
34 Other winter cereal
41 Spring barley
42 Spring oat
43 Other spring cereal
50 Winter rapeseed
60 Legume
70 Sunflower
80 Sugar beet
91 Maize
92 Maize (grain)
100 Potato
110 Grapevine
120 Strawberry
130 Asparagus
140 Onion
150 Hops
160 Orchard
181 Carrot
182 Other vegetables
555 Small woody features
999 Other agricultural areas

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021).

BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

National-scale crop type maps for Germany © 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under CC BY 4.0.

Statistics Canada conducts the Census of Agriculture every five years at the same time as the Census of Population. The most recent Census of Agriculture was on May 15, 2001.The Census of Agriculture collects and disseminates a wide range of data on the agriculture industry such as number and type of farms, farm operator characteristics, business operating arrangements, land management practices, crop areas, numbers of livestock and poultry, farm capital, operating expenses and receipts, and farm machinery and equipment. These data provide a comprehensive picture of the agriculture industry across Canada every five years at the national and provincial levels as well as at lower levels of geography. The Census of Agriculture is the cornerstone of Canada's Agriculture Statistics Program. Census of Agriculture data are an indispensable public and private sector tool for analysing important changes in the agriculture and food industries;developing, implementing and evaluating agricultural policies and programs such as farm income safety nets and environmental sustainability; and making production, marketing and investment decisions. Statistics Canada uses the data as benchmarks for its regular surveys on crops, livestock and farm finances between census years. In addition, data extracted from the unique Agriculture Population Linkage Database, which links data from both the Census of Population and Census of Agriculture databases, paint a socio-economic portrait not only of farm operators but also of their families and households. This release contains all farm data and farm operations data plus selected historical files. In 2001, a census farm was defined as an agricultural operation that produces at least one of the following products intended for sale: crops (hay, field crops, tree fruits or nuts, berries or grapes, vegetables, seed); livestock (cattle, pigs, sheep, horses, game animals, other livestock); poultry (hens, chickens, turkeys, chicks, game birds, other poultry); animal products (milk or cream, eggs, wool, furs, meat); or other agricultural products (Christmas trees, greenhouse or nursery products, mushrooms, sod, honey, maple syrup products). For 2001, a new farm type classification based on the North American Industrial Classification System (NAICS) has been added to the historical classification used in previous censuses. All tabulated data are subject to confidentiality restrictions prior to release. Due to confidentiality constraints, data for those geographic areas with very few agricultural operations are not released separately, but rather merged with a geographically adjacent area.

The interoperable INSPIRE WMS is a display service that displays data in the annex schema ground (derived from the original dataset: Medium-scale agricultural site mapping Brandenburg). It gives an overview of the mapbook of medium-scale agricultural location mapping (MMK) developed in the GDR in the 1980s to describe the agricultural area on a scale of 1: 100 000. Detailed information on the creation, digitisation and application of the map book can be found at: http://www.geo.brandenburg.de/ows/htdocs/MMK_Dok_digitale_Daten_1997.pdf, as well as http://www.geo.brandenburg.de/ows/htdocs/29042020_MMK.pdf. According to the INSPIRE data specification Soil (D2.8.III.3_v3.0), the contents of the ground map are INSPIRE-compliant. The WMS contains the following layer:

- SO.SoilBody.soilBodyLabel: Includes information from the Medium Scale Agricultural Site Mapping with information on: Site regional type, lead soil shape, source rock, mesorelief.

The compliant INSPIRE-WMS Soil is a view service that delivers data in the annex schema Soil (derived from the original data set: Medium-scaled agricultural soil survey Brandenburg). It provides an overview of the map series Medium-Scaled Agricultural Site Survey which was developed in the GDR in the 1980s to describe the agricultural land on a scale of 1 : 100 000. Detailed information on the creation, digitisation and application of the maps can be found at: http://www.geo.brandenburg.de/ows/htdocs/MMK_Dok_digitale_Daten_1997.pdf, and http://www.geo.brandenburg.de/ows/htdocs/29042020_MMK.pdf. The content of the soil map is compliant to the INSPIRE data specification for the annex theme Soil (D2.8.III.3_v3.0). The WMS includes the following layer: - SO.SoilBody.soilBodyLabel: contains information of the Medium Scale Agricultural Site Mapping with details on: Location type, soil type, lithology, mesorelief.

County of Berks - Department of Agriculture - Land Preservation Map.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023).

The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.

Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

County of Berks - Department of Agriculture - Land Preservation Map.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

Version v202:
Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refinded. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

_

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

_

References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

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National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

The Land Potentially Suitable for the Agriculture Zone mapping layer was produced as part of the agricultural land mapping project commissioned by the Department of Justice, Planning Policy Unit on …Show full descriptionThe Land Potentially Suitable for the Agriculture Zone mapping layer was produced as part of the agricultural land mapping project commissioned by the Department of Justice, Planning Policy Unit on behalf of the Minister for Planning and Local Government to assist local government in applying the Agriculture Zone as part of the Local Provisions Schedules in the Tasmanian Planning Scheme. The agricultural land mapping project aims to provide support for the recalibrated rural zones in the State Planning Provisions, the Rural Zone and Agriculture Zone, specifically to provide guidance to local government in spatially applying the Agriculture Zone. This layer represents the refined mapping of land that has been subject to a series of filters to determine potential constraints as outlined in the Agricultural Land Mapping Project – Background Report: http://planningreform.tas.gov.au/_data/assets/pdf_file/0004/390874/Agricultural_Land_Mapping_Project-Background_Report-_May_2017.pdf. The filters cover issues such as: • suitability of the title to sustainably support agriculture, including potential access to irrigation water; • consideration of existing forestry land; and • potential constraints for agricultural use, including potential economic or physical barriers, and potential land use conflicts. The mapping has also been aligned to cadastre boundaries. This layer should be viewed in conjunction with the Potential Agricultural Land Initial Analysis mapping layer, which provides an additional analysis tool for determining the suitability of the land for the Agriculture Zone. The mapping should also be used in conjunction with Guideline No. 1 – Local Provisions Schedule (LPS): zone and code application: http://www.planning.tas.gov.au/news/news_items/guidance_for_drafting_lps.

This data set contains information on the agricultural land use in Germany for the year 2020.
The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in Blickensdörfer et al. 2022 and can be seen as a continuation of the dataset provided under: https://zenodo.org/record/5153047#.YWFyXn1CREZ.
The maps can be explored online in a webviewer.

Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.

Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.

New class code (V2)	Class name (V2)	Class code (V1)	Class name (V1)
1101	Winter wheat	31	Winter wheat
		34	Other winter cereals
1102	Winter barley	33	Winter barley
1103	Winter rye	32	Winter rye
1201	Spring barley	41	Spring barley
		43	Other spring cereals
1202	Oat	42	Spring oat
1300	Maize	91	Maize (silage)
		92	Maize (grain)
1401	Potatoe	100	Potatoe
1402	Sugar beet	80	Sugar beet
1501	Rapeseed	50	Winter rapeseed
1502	Sunflower	70	Sunflower
1611	Peas	60	Legume
1612	Broad beans
1613	Lupine
1614	Soy
1603	Vegetables	120	Strawberry
		130	Asparagus
		140	Onion
		181	Carrot
		182	Other leafy vegetables
1602	Cultivated grassland	10	Grassland
200	Permanent grassland
3003	Fallow land
3001	Small woody features	555	Small woody features
3002	Other areas	999	Other agricultural areas
4001	Grapevine	110	Grapevine
4002	Hops	150	Hops
4003	Orchard	160	Orchards

All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; https://force-eo.readthedocs.io/en/latest/ last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube.

The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map).

The maps are provided as GeoTiff files together with QGIS legend files for visualization.

References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

National-scale crop type maps for Germany © 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under CC BY 4.0.

The Land Potentially Suitable for the Agriculture Zone mapping layer was produced as part of the agricultural land mapping project commissioned by the Department of Justice, Planning Policy Unit on behalf of the Minister for Planning and Local Government to assist local government in applying the Agriculture Zone as part of the Local Provisions Schedules in the Tasmanian Planning Scheme. The agricultural land mapping project aims to provide support for the recalibrated rural zones in the State Planning Provisions, the Rural Zone and Agriculture Zone, specifically to provide guidance to local government in spatially applying the Agriculture Zone. This layer represents the refined mapping of land that has been subject to a series of filters to determine potential constraints as outlined in the Agricultural Land Mapping Project â Background Report: http://planningreform.tas.gov.au/_data/assets/pdf_file/0004/390874/Agricultural_Land_Mapping_Project_-Background_Report-_May_2017.pdf. The filters cover issues such as: suitability of the title to sustainably support agriculture, including potential access to irrigation water; consideration of existing forestry land; and potential constraints for agricultural use, including potential economic or physical barriers, and potential land use conflicts. The mapping has also been aligned to cadastre boundaries. This layer should be viewed in conjunction with the Potential Agricultural Land Initial Analysis mapping layer, which provides an additional analysis tool for determining the suitability of the land for the Agriculture Zone. The mapping should also be used in conjunction with Guideline No. 1 â Local Provisions Schedule (LPS): zone and code application: http://www.planning.tas.gov.au/news/news_items/guidance_for_drafting_lps.

County of Berks - Department of Agriculture - Land Preservation Map.

County of Berks - Department of Agriculture - Land Preservation Map.

County of Berks - Department of Agriculture - Land Preservation Map.

Urban agriculture offers the opportunity to provide fresh, local food to urban communities. However, urban agriculture can only be successfully embedded in urban areas if consumers perceive urban farming positively and accept urban farms in their community. Success of urban agriculture is rooted in positive perception of those living close by, and the perception strongly affects acceptance of farming within individuals' direct proximity. This research investigates perception and acceptance of urban agriculture through a qualitative, exploratory field study with N = 19 residents from a major metropolitan area in the southwest U.S. Specifically, in this exploratory research we implement the method of concept mapping testing its use in the field of Agroecology and Ecosystem Services. In the concept mapping procedure, respondents are free to write down all the associations that come to mind when presented with a stimulus, such as, “urban farming.” When applying concept mapping, participants are asked to recall associations and then directly link them to each other displaying their knowledge structure, i.e., perception. Data were analyzed using content analysis and semantic network analysis. Consumers' perception of urban farming is related to the following categories: environment, society, economy, and food and attributes. The number of positive associations is much higher than the number of negative associations signaling that consumers would be likely to accept farming close to where they live. Furthermore, our findings show that individuals' perceptions can differ greatly in terms of what they associate with urban farming and how they evaluate it. While some only think of a few things, others have well-developed knowledge structures. Overall, investigating consumers' perception helps designing strategies for the successful adoption of urban farming.

Basis of the representations of the digital ground map in the processing scale 1: 5 000 are the results of the agricultural and forestry survey of the Geological Service NRW with a dense drilling network (hole spacing 100 m). For several 100 000 hectares of land area, digital ground maps based on the German Basic Map are now available in scale 1: 5 000 (DGK 5). These large-scale floor maps are ideal floor planning documents, as they can be combined with other digital area information such as climate data, terrain models or usage plans. The maps are a prerequisite for optimised, site-adapted agricultural and forestry land use and an effective and sustainable protection of soil, groundwater and valuable biotopes. The soil maps for site exploration are not processed in the sheet section of DGK 5, but in the context of mapping processes, whose boundaries are based on specific information needs (e.g. boundaries of municipalities, districts, water protection or nature conservation areas). In the case of mapping of agricultural site sensing, all agricultural or usable areas are processed and all forest areas of the process area are processed during mapping of the forest survey. A complete record of a procedure as an extract from the information system usually includes the following contents and data formats: — digital soil map (floor types, soil types, water conditions) as PDF file — soil and evaluation maps in ALK-GIAP or ArcGIS (SHAPE) format (further on request) — Explanatory notes with textual assessment of soil conditions as print or PDF file — general information on the basics of large-scale soil mapping as PDF file and interactive MS-PowerPoint presentation — Floor and evaluation maps as color plots (on request) The data is supplied as standard without topography. It is recommended to order the topographic grid data of the district government of Cologne/Abt. 7 Geobasis NRW (www.lverma.nrw.de ) directly from the Geological Service NRW (www.lverma.nrw.de ). Thus, an optimal match between the geoscientific technical maps and the topography, which was based on the creation of the technical cards, is ensured. On the basis of the order, prior to the submission of the digital data, in addition to the copyright provisions, the purpose of use, the duration of use and the number of workplace licenses, as well as the usage and provision fee, are determined in addition to the copyright provisions. The prices quoted are for single-user licenses. The data set of the ground map information system for site exploration 1: 5 000 contains the following general evaluations on soil water, soil air and nutrient balance: — usable field capacity — air capacity — saturated water conductivity — capillary ascent of groundwater into the effective root space — cation exchange capacity — effective rooting depth — erodibility of the upper soil Specific evaluations on various topics: — Erosion hazard (wide for NRW, also possible on the basis of the soil estimation map) — Wind throw hazard — Necessity of soil protection limescales — Soil scientific foundations of forest construction planning — Appropriation of leaching water for precipitation water — Leakage rate — soil worthy of protection Information for departments of the State of NRW: The ground map information system for site exploration 1: 5 000 is available for forestry carting procedures on the intranet of the state of North Rhine-Westphalia under ForstGISonline of the Landesbetriebe Wald und Holz NRW.

The Queensland Department of Agriculture and Fisheries (DAF) completed an Agricultural Land Audit in May 2013. This was a desktop exercise using existing datasets to map the current and potential agricultural land uses across the state. This web map shows mapping from the Land Audit with supporting data sets. The data is intended for use at a regional scale and should not be used at property level. It is an information tool only and not for statutory use. Refer to https://www.daf.qld.gov.au/business-priorities/environment/ag-land-audit for further information about the Agricultural Land Audit. The Land Audit technical report https://www.daf.qld.gov.au/_data/assets/pdf_file/0003/74829/QALA-tech-report-final-13.pdf explains criteria used to map 'potential' agricultural areas, datasets used to show current agriculture and techniques used to identify 'Important Agricultural Areas'.

This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.