The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.

The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.

Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (https://vegetationmap4africa.org/species.html)

The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (https://vegetationmap4africa.org/vegetation_map.html) and for PDF versions of the map, see the documentation (https://vegetationmap4africa.org/documentation.html). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (https://vegetationmap4africa.org/docs/versionhistory/). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.

When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <https://doi.org/10.5281/zenodo.11125645>.

The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen

If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <https://doi.org/10.5281/zenodo.11125645>

A simplified version of the map can be found on Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.

Purpose: The dataset is the one used the manuscript "An open source GIS-based decision support system for forest accessibility mapping" (Journal of Maps, T&F) refers to. Specific content: Vector and raster input dataset include administrative boundaries, land uses, forest types, road network, forest management plan and morphological data computed using a digital elevation model, necessary to run the GIS-based DSS model SOFIA described in the manuscript. The dataset including filename, description and data source is detailed in the Readme.txt file.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing true Irrigable area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, young perennials and wetland. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.5% using the Land IQ legend and 98.3% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). DWR added Adjusted Day Of Year (ADOY) for peak NDVI date corresponding to CROPTYP category. The date received by Land IQ was delivered in a Julian date format (YYYYDDD) and was converted into the ADOY by DWR for statistical purposes. Land use boundaries delineated by Land IQ were not revised by DWR.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

This data set consists of GIS layers that describe the soils of the BOREAS SSA. The original data were submitted as vector layers that were gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection. These data layers include the soil code (which relates to the soil name), modifier (which also relates to the soil name), and extent (indicating the extent that this soil exists within the polygon). There are three sets of these layers representing the primary, secondary, and tertiary soil characteristics. Thus, there is a total of nine layers in this data set along with supporting files. The data are stored in binary, image format files.

These data (vector and raster) were compiled for spatial modeling of salinity yield sources in the Upper Colorado River Basin (UCRB) and describe different scales of watersheds in the Upper Colorado River Basin (UCRB) for use in salinity yield modeling. Salinity yield refers to how much dissolved salts are picked up in surface waters that could be expected to be measured at the watershed outlet point annually. The vector polygons are small catchments developed originally for use in SPARROW modeling that break up the UCRB into 10,789 catchments linked together through a synthetic stream network. The catchments were used for a machine learning based salinity model and attributed with the new results in these vector GIS datasets. Although all of these feature classes include the same polygons, the attribute tables for each include differing outputs from new salinity models and a comparison with SPARROW model results from previous research. The new model presented in these datasets utilizes new predictive soil maps and a more flexible random forest function to improve on previous UCRB salinity spatial models. The raster data layers represent aspects of soils, topography, climate, and runoff characteristics that have hypothesized influences on salinity yields.

CORINE Land Cover 2000 kuvaa koko Suomen maankäyttöä ja maanpeitettä vuonna 2000. Aineisto koostuu rasterimuotoisesta paikkatietokannasta (erotuskyky 25 * 25 m) ja vektorimuotoisesta paikkatietokannasta, jossa pienin maastossa erottuva alue on vähintään 25 ha ja kapeimmillaan 100 metriä. Aineisto on tuotettu Sykessä olemassa oleviin paikkatietoaineistoihin sekä satelliittikuvatulkintaan perustuen. Vektoriaineisto tuotettiin yleistämällä rasteriaineistoa eurooppalaisen CLC2000 -hankkeen sääntöjen mukaan.

Vektoriaineistoissa maankäyttöä/maanpeitettä kuvataan kolmitasoisella hierarkisella luokittelulla. Viisi pääluokkaa ( rakennetut alueet; maatalousalueet; metsät sekä avoimet kankaat ja kalliomaat; kosteikot ja avoimet suot sekä vesialueet) jaetaan toisella tasolla yhteensä 15 alaluokkaan. Kolmannella luokittelutasolla pääluokat jaetaan yhteensä 44 alaluokkaan. Rasteriaineistossa on joidenkin luokkien kohdalla vielä neljännen tason kansallisia luokkia.

Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0). Aineistosta on julkaistu INSPIRE-tietotuote.

Käyttötarkoitus: Vektoriaineisto, jossa minimikuviokoko on 25 ha, on tuotettu Euroopan ympäristövirastolle osana Euroopan laajuista CORINE-hanketta. Tarkempi 25 m resoluutiolla oleva rasteriaineisto on tarkoitettu kansalliseen käyttöön kuvaamaan maanpeitettä/maankäyttöä. Aineistoja voidaan käyttää paikkatietoanalyysien lisäksi myös taustakarttoina.

Lisätietoja: https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/CorineLandCover2000.pdf https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/clc2000_luokat.xls

http://www.syke.fi/fi-FI/Tutkimus_kehittaminen/Tutkimus_ja_kehittamishankkeet/Hankkeet/Maankaytto_ja_maanpeiteaineistojen_tuottaminen_CORINE_Land_Cover_2000_hankkeessa/Maankaytto_ja_maanpeiteaineistojen_tuott%289788%29 https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/clc2000_luokat.pdf

CORINE Land Cover 2000 dataset provides information on Finnish land cover and land use on 2000. The data was produced as a part of the European CLC 2000 project.

Dataset includes several spatial layers: • CLC raster (resolution of 25x25 m) • CLC vector (minimum mapping unit 25 hectares and minimum width 100 m) • Source raster (resolution of 25x25 m) on the source data used in the interpretation • Age raster (resolution of 25x25 m) on the year of the soure information

The dataset has been produced in the Finnish Environment Institute (Syke), based on automated interpretation of satellite images and data integration with existing digital map data. The vector dataset was produced from raster data by generalization according to the CORINE 2000 project class definitions.

The nomenclature of the vector data has 3 hierarchy levels. The first level classes are: artificial surfaces, agricultural areas, forests and seminatural areas, wetlands and open bogs, water and marshes. Second level has 15 classes and third level 44 sub-classes. The raster dataset has an additional fourth, national class in some of the sub-classes.

The vector land cover dataset (25 ha) was produced for the European Environment Agency as a part of the European CORINE-project for harmonized land cover map and statistics in Europe. The more specific raster dataset (25 m x 25 m) was produced for national use to provide information on Finnish land cover and land use. The datasets are can be used in analyses and as background maps.

Information on the source material and age of the source element can be used to validate the results of analyses. The source material is generally from year 2000 (+/- 1 year).

CLC2000 perustuu vuosina 1999 - 2002 otettuihin LANDSAT 7 ETM satelliittikuviin, joista tuotettiin IMAGE2000 -satelliittikuvamosaiikki ja maanpeitetietoa. Maanpeitetieto saatiin analysoimalla satelliittikuvilta mm. puuston pituutta ja peitteisyyttä, puulajisuhteita, kasvillisuustyyppiä ja –peittoa kuvaavia jatkuvia muuttujia. CLC2000- luokat on saatu kynnystämällä ja yhdistämällä maanpeitetietoja luokkamääritelmien mukaisesti. Satelliittikuvien kalibroinnissa ja tulkinnassa on käytetty VTT:n kehittämiä työkaluja, joita on edelleen kehitetty projektin aikana. Tulkinnassa tarvittavina maastotietoina on käytetty Metsähallituksen ja UPM:n metsä- ja biotooppikartoitusta. Lähtötietoina olevien paikkatietoaineiston päivitykseen (rakennetut alueet) sekä rantakosteikoiden luokitteluun käytettiin puoliautomaattista satelliittikuvien tulkintaa. Maanpeitetietojen yhdistäminen paikkatietoaineistojen kanssa: Satelliittikuvatulkinnan avulla saatava maanpeitteisyyttä kuvaava tieto yhdistettiin paikkatietoaineistojen sisältämän maankäyttö- ja maaperätiedon kanssa. Tärkeimmät lähdeaineistona käytetyt paikkatietoaineistot olivat SLICES-maankäyttöaineisto, Maastotietokannan maaperätiedot ja Digi- ja väestötietoviraston Väestötietojärjestelmä (Rakennus- ja huoneistorekisteri). Suomessa tuotettiin rasterimuotoinen paikkatietoaineisto, jossa pienin kartoitettava yksikkö vastaa maastossa 25 x 25 metrin alaa. CORINE- määritysten mukainen vektoriaineisto tuotettiin yleistämällä kansallista rasteriaineistoa tätä tarkoitusta varten kehitetyllä automaattisella yleistysprosessilla. Yleistetyn aineiston pienin maastossa erottuva alue on vähintään 25 ha ja kapeimmillaan 100 metriä.

Satellite image interpretation IMAGE2000 satellite image mosaic, which the CLC2000 is based on, consists of LANDSAT 7 ETM satellite images taken during 1999-2000. The selected satellite images were geometrically corrected by Metria Sweden. Radiometric preprocessing included atmospheric correction and topographic correction in Northern Finland. The mosaicking of individual satellite images was carried out in order to get stratumwise and nationwide mosaics for interpretation and visualization purposes. In forests and semi-natural areas as well as in wetlands following land cover variables were estimated from the satellite images: tree height (m), tree crown cover (%), volume of broadleaved trees (m3/ha) and total volume (m3/ha). Additionally in northern Finland ground vegetation type was estimated for non-forested areas. Continuous land cover variables were transformed into discrete CORINE classes by thresholding the variables according to the class descriptions in CORINE nomenclature. Syke and the Technical Research Centre of Finland (VTT) developed a production line based on calibration of satellite measurements and automated satellite image interpretation. Metsähallitus and UPM provided field datasets. Finnish Forest Research Institute validated the forest and semi-natural areas using National Forest Inventory Data. Data integration with GIS data Satellite image derived land cover data were combined with existing digital land use and soil information. The principal geographic data sources were SLICES- land use database, soil information from the Topographic Database of Finland produced by the National Land Survey of Finland and the Population Information System produced by Population Register Centre. Detailed class definitions and production methods can be found in annex documents.

The resulting national raster database (25m x 25 m) was generalized according to the CORINE 2000 project class definitions.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. See additional information also. Purpose Indicates the current primary use or management objective of the land. Dataset History Source DataQueensland Government - Land use mapping (1999); Landsat TM and ETM imagery; Spot5 imagery; High resolution ortho photography through the Spatial Imagery Subscription Plan (SISP); Queensland Digital Cadastral Database (DCDB) (2009), Queensland Valuation and Sales Database (QVAS) (2009); Queensland Nature Refuges (2009); Queensland Estates (2009); Queensland Herbarium's Regional Ecosystem, Water Body and Wetlands datasets (2009); Statewide Landcover & Trees Study (SLATS) Queensland Dams and Waterbodies (2009) and land cover change data; scanned aerial photography (1999-2009).Additional verbal & written information on land uses & their locations was obtained from regional Queensland Government officers, Local Government Authorities, land owners & managers, private industry as well as from field observations & checking.Data captureA range of existing digital datasets containing land use information was collated from the Queensland Government spatial data inventory and prepared for use in a GIS using ArcGIS and ERDAS Imagine software.Processing steps To compile the 1999 baseline mapping, datasets containing baseline land cover (supplied by SLATS), Protected Areas, State Forest and Timber Reserves, plantations, coastal wetlands, reserves (from DCDB) and logged forests were interpreted in a spatial model to produce a preliminary land use raster image.The model incorporated a decision matrix which assigned each pixel a specific land use class according to a set of pre-determined rules.Individual catchments were clipped from the model output and enhanced with additional land use information interpreted primarily from Landsat TM and ETM imagery as well as scanned and hardcopy aerial photography (where available). The DCDB and other datasets containing land use information were used to help identify property and land use type boundaries. This process produced a draft land use raster.Verification of the draft land use dataset, particularly those with significant areas of intensive land uses, was undertaken by comparing mapped land use classes with observed land use classes in the field where possible. The final raster image was converted to a vector coverage in ARC/Info and GIS editing performed.The existing 1999 baseline (or later where available) land use dataset (vector) formed the basis for the 2006 and 2009 land use mapping. The 2006 & 2009 datasets were then updated primarily by interpretation of SPOT5 imagery, high-res orthophotography, scanned aerial photography and inclusion of expert local knowledge. This was performed in an ESRI ArcSDE geodatabase replication infrastructure, across some nine regional offices. The DCDB, QVAS, Estates, Queensland Herbarium wetlands and SLATS land cover change and waterbody datasets were used to assist in identification and delineation of property and land use type boundaries. Digitised areas of uniform land use type were assigned to land use classes according to ALUMC Version 7 (May 2010).This "current" land use mapping product presents a complete state-wide land use map of Queensland, after collating the most current land use datasets within a single mapping layer.An independent validation was undertaken to assess thematic (attribute) accuracy under the ALUM classification. Please refer to the orignal source data for the validation results. Dataset Citation Queensland Department of Science, Information Technology, Innovation and the Arts (2013) Bioregional_Assessment_Programme_Land use mapping - Queensland current. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/740d257f-b622-49c2-9745-be283239add3.

Download: HereThe Saskatchewan Ministry of Environment, Forest Service Branch, has developed a forest resource inventory (FRI) which meets a variety of strategic and operational planning information needs for the boreal plains. Such needs include information on the general land cover, terrain, and growing stock (height, diameter, basal area, timber volume and stem density) within the provincial forest and adjacent forest fringe. This inventory provides spatially explicit information as 10 m or 20 m raster grids and as vectors polygons for relatively homogeneous forest stands or naturally non-forested areas with a 0.5 ha minimum area and a 2.0 ha median area. Digital terrain model (DTMRAW) is an expression of the bare earth orthometric elevation (m). DTMRAW is available here as a color-mapped 16-bit unsigned integer raster grid in GeoTIFF format with a 5 m pixel resolution. An ArcGIS Pro layer file (*.lyrx) is supplied for viewing DTMRAW data in the following 50 m elevation intervals.Domain: [NULL, 200…1500].RANGELABELREDGREENBLUE200 <= DTMRAW < 225200279432225 <= DTMRAW < 2752505111351275 <= DTMRAW < 3253007613170325 <= DTMRAW < 37535010015089375 <= DTMRAW < 425400124169108425 <= DTMRAW < 475450148188127475 <= DTMRAW < 525500173206146525 <= DTMRAW < 575550197225165575 <= DTMRAW < 625600226232127625 <= DTMRAW < 67565025523888675 <= DTMRAW < 72570025520363725 <= DTMRAW < 77575025516738775 <= DTMRAW < 82580018812655825 <= DTMRAW < 8758501218572For more information, see the Forest Inventory Standard of the Saskatchewan Environmental Code, Forest Inventory Chapter.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This web map service (WMS) is the 25m raster version of the Land Cover Map 2015 (LCM2015) for Great Britain and Northern Ireland. It shows the target habitat class with the highest percentage cover in each 25m x 25m pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats.The 25m raster web map service is the most detailed of the LCM2015 raster products, both thematically and spatially, and it is derived from the LCM2015 vector product. For LCM2015 per-pixel classifications were conducted, using a random forest classification algorithm. The resultant classifications were then mosaicked together, with the best classifications taking priority. This produced a per-pixel classification of the UK, which was then 'imported' into the spatial framework, recording a number of attributes, including the majority class per polygon which is the Land Cover class for each polygon.Find out more about Land Cover Map 2015 at ceh.ac.uk.LCM2015 is available for download to Catchment Based Approach (CaBA) Partnerships in the desktop GIS data package. Please contact your CaBA catchment host for further information.

CORINE Land Cover 2006 kuvaa koko Suomen maankäyttöä ja maanpeitettä vuonna 2006. Eurooppalaisessa CLC2006 -projektissa tuotettiin Suomen alueelta vuoden 2006 maanpeiteaineistot sekä laadittiin maanpeitteen muutoksia välillä 2000-2006 kuvaavat aineistot. Aineistot luotiin kahdella tarkkuustasolla: EU vaatimusten mukaisesti ja kansalliseen käyttöön. Aineisto koostuu rasterimuotoisesta paikkatietokannasta (erotuskyky 25 * 25 m) ja vektorimuotoisesta paikkatietokannasta, jossa pienin maastossa erottuva alue on vähintään 25 ha ja kapeimmillaan 100 metriä. Kansallisen muutosaineiston pienin kuvio on 1 ha ja eurooppalaisen 5 ha.

Aineisto on tuotettu Sykessä olemassa oleviin paikkatietoaineistoihin sekä satelliittikuvatulkintaan perustuen. Vektoriaineisto tuotettiin yleistämällä rasteriaineistoa eurooppalaisen CORINE2006-hankkeen sääntöjen mukaan.

Vektoriaineistoissa maankäyttöä/maanpeitettä kuvataan kolmitasoisella hierarkisella luokittelulla. Viisi pääluokkaa ( rakennetut alueet; maatalousalueet; metsät sekä avoimet kankaat ja kalliomaat; kosteikot ja avoimet suot sekä vesialueet) jaetaan toisella tasolla yhteensä 15 alaluokkaan.. Kolmannella luokittelutasolla pääluokat jaetaan yhteensä 44 alaluokkaan. Rasteriaineistossa on joidenkin luokkien kohdalla vielä neljännen tason kansallisia luokkia.

Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0). Aineistosta on julkaistu INSPIRE-tietotuote.

Käyttötarkoitus: Vektoriaineisto, jossa minimikuviokoko on 25 ha/muutos 5 ha, on tuotettu Euroopan ympäristövirastolle osana Euroopan laajuista CORINE-hanketta. Tarkempi 25 m resoluutiolla oleva rasteriaineisto ja 1 ha muutosaineisto on tarkoitettu kansalliseen käyttöön kuvaamaan maanpeitettä/maankäyttöä. Aineistoja voidaan käyttää paikkatietoanalyysien lisäksi myös taustakarttoina.

Lisätietoja: https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/CorineLandCover2006.pdf https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/clc2006_luokat.xls

http://www.syke.fi/fi-FI/Tutkimus_kehittaminen/Tutkimus_ja_kehittamishankkeet/Hankkeet/Maankaytto_ja_maanpeiteaineistojen_tuottaminen_CORINE_Land_Cover_2006_hankkeessa/Maankaytto_ja_maanpeiteaineistojen_tuott%289088%29 https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/clc2006_luokat.pdf

CORINE Land Cover 2006 dataset provides information on Finnish land cover and land use on 2006, and it's changes from 2000 to 2006. The data was produced as a part of the European CLC 2006 project.

Dataset includes several spatial layers: • CLC raster (resolution of 25x25 m) and one in • CLC vector (minimum mapping unit 25 hectares and minimum width 100 m). • Source raster (resolution of 25x25 m) on the source data used in the interpretation • Age raster (resolution of 25x25 m) on the year of the soure information • Change vector (minimum mapping 5 hectares) • Change raster (minimum mapping unit 1 hectares)

The dataset has been produced in the Finnish Environment Institute (Syke), based on automated interpretation of satellite images and data integration with existing digital map data. The vector dataset was produced from raster data by generalization according to the CORINE 2006 project class definitions.

The nomenclature of the vector data has 3 hierarchy levels. The first level classes are: artificial surfaces, agricultural areas, forests and seminatural areas, wetlands and open bogs, water and marshes. Second level has 15 classes and third level 44 sub-classes. The raster dataset has an additional fourth, national class in some of the sub-classes.

The vector land cover dataset (25 ha) and the change dataset (5 ha) were produced for the European Environment Agency as a part of the European CORINE-project for harmonized land cover maps and statistics in Europe. The more specific raster dataset (25 m x 25 m) and the change (1 ha) was produced for national use to provide information on Finnish land cover and land use. The datasets are can be used in analyses and as background maps.

Information on the source material and age of the source element can be used to validate the results of analyses. The source material is generally from year 2006 (+/- 1 year).

CLC2006-aineiston tuottamisessa käytetty IMAGE2006 satelliittikuvamosaiikki koostuu IRS P6 - ja SPOT 4/5 satelliittikuvista. Kuvat on vastaanotettu vuosina 2005 ja 2006. Satelliittikuvilta estimoitiin puuston pituus ja puuston kokonaislatvuspeitteisyys ja lehtipuuston latvuspeitteisyys. Puustoa kuvaavat muuttujat tuotettiin Metsäntutkimuslaitoksessa käyttäen hyväksi valtakunnan metsien inventoinnin maastoahavaintoaineistoa ja satelliittikuvien tulkintamenetelmää (k-nn). Prosessin osana myös metsäaineistojen laatu on varmistettu. Automaattista satelliittikuvatulkintaa käytettiin myös Ylä-Lapissa puurajan yläpuolisilla alueilla CLC-maanpeiteluokan määrittämisessä. Tulkinnassa hyödynnettiin lisäksi mm. Metsähallituksen biotooppiaineistoa, digitaalista korkeusmallia ja maaperää kuvaavia teemoja Maastotietokannasta. Lisäksi muutamia luokkia tulkittiin satelliittikuvilta manuaalisesti digitoimalla, kuten golfkentät. CLC2006-luokkien muodostamiseen on käytetty useita eri paikkatietoaineistoja, tärkeimpinä Maastotietokanta, Digiroad, Väestötietojärjestelmä (Rakennus- ja huoneistorekisteri), SLICES ja CLC2000. Paikkatietoaineistoista saatu maankäyttö ja maaperätieto yhdistettiin satelliittikuvilta tulkittuihin maanpeitetietoihin. Lopputuloksena saatiin rasterimuotoinen paikkatietoaineisto 25 metrin pikselikoolla.

Satellite image interpretation IMAGE2006 satellite image mosaic, which the CLC2006 is based on, consists 47 IRS P6 images and 36 SPOT 4/5 images from the years 2005 and 2006.

The satellite images were geometrically corrected by Metria Sweden. Atmospheric correction and in Northern Finland also topographic correction was executed in Syke as well as manual cloud masking. After these corrections mosaicing of individual satellite images were carried out.

From the satellite images it was possible to estimate tree heights and both overall crown coverage and crown coverage of broad-leaved trees. These were produced in Finnish Forest Research Institute and the National Forest Inventory Data was used. During this process, the quality of the data was also verified. Automatic satellite imagery interpretation was also used to determine the land cover classes above the tree line in Northern Lapland. Some classes were also manually interpreted from the satellite images (eg. golfcourses).

Data integration with GIS data Many geographic data sources were used in CLC2006 creation, most significantly Topographic Database of Finland, Digiroad (digital road database of Finland), SLICES land use database and CLC2000 database. Soil information and land use data from the geographic sources were integrated with data interpreted from satellite images. Detailed class definitions and production methods can be found in annex documents.

The resulting national raster database (25m x 25 m) was generalized according to the CORINE 2006 project class definitions and land cover change rules.