This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs.

This dataset includes a series of R scripts required to carry out some of the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.

The scripts have been designed within the context of the R Processing Provider, a plugin that integrates the R processing environment into QGIS. For all the information about how to use these scripts in QGIS, please refer to Chapter 1 of the book referred to above.

The dataset includes 15 different scripts, which can implement the calculation of different metrics in QGIS:

Change statistics such as absolute change, relative change and annual rate of change (Change_Statistics.rsx)

Areal and spatial agreement metrics, either overall (Overall Areal Inconsistency.rsx, Overall Spatial Agreement.rsx, Overall Spatial Inconsistency.rsx) or per category (Individual Areal Inconsistency.rsx, Individual Spatial Agreement.rsx)

The four components of change (gross gains, gross losses, net change and swap) proposed by Pontius Jr. (2004) (LUCCBudget.rsx)

The intensity analysis proposed by Aldwaik and Pontius (2012) (Intensity_analysis.rsx)

The Flow matrix proposed by Runfola and Pontius (2013) (Stable_change_flow_matrix.rsx, Flow_matrix_graf.rsx)

Pearson and Spearman correlations (Correlation.rsx)

The Receiver Operating Characteristic (ROC) (ROCAnalysis.rsx)

The Goodness of Fit (GOF) calculated using the MapCurves method proposed by Hargrove et al. (2006) (MapCurves_raster.rsx, MapCurves_vector.rsx)

The spatial distribution of overall, user and producer’s accuracies, obtained through Geographical Weighted Regression methods (Local accuracy assessment statistics.rsx).

Descriptions of all these methods can be found in different chapters of the aforementioned book.

The dataset also includes a readme file listing all the scripts provided, detailing their authors and the references on which their methods are based.

This dataset comprises land use maps of Maputo city, with exception of the KaTembe urban district, for the years 1964, 1973, 1982, 1991 and 2001. It is the digital version of the land use maps published by Henriques [1] and revised under the LUCO research project.

The land use of Maputo city was identified from: i) aerial photographs (1964, 1982, 1991), orthophoto maps (1973) and IKONOS images (2001); ii) documentary sources, such as the Urbanization Master Plan (1969) and the Maputo City Addressing (1997); iii) the recognition made during several field survey campaigns. The methodology is described in Henriques [1].

Land use was classified into three levels, resulting from a hierarchical classification system, including descriptive and parametric classes. Levels I and II are available in this repository.

Level I, composed by 10 classes, contains the main forms of occupation: built-up areas (residential, economic activity, equipment, and infrastructure) and non-built-up areas (vacant or "natural"). It is geared towards analyses that serve policymaking and resource management at the regional or national scale [1].

Level II, composed by 31 classes, discriminates the higher hierarchical level according to its functional land use to become useful for municipal planning and management in municipal master plans, for example [1].

Maps are available in shapefile format and include predefined symbology-legend files, for QGIS and ArcGIS (v.10.7 or higher). The urban land use classes are described in Portuguese and English, and their meaning is provided as an accompanying document (ULU_Maputo_Nomenclatura_PT.pdf / ULU_Maputo_Nomenclature_EN.pdf).

Data format: vector (shapefile, polygon)

Reference system: WGS84, UTM 36S (EPSG:32736)

Original minimum mapping unit: 25 m2

Urban Land Use dataset attributes:

[N_I_C] – code of level I

[N_I_D_PT] – name of level I, in Portuguese

[N_I_D_EN] - name of level I, in English

[N_II_C] – code of level II

[N_II_D_PT] - name of level II, in Portuguese

[N_II_D_EN] - name of level II, in English

Funding: this research was supported by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P. Project number: FCT AGA-KHAN/ 541731809 / 2019

[1] Henriques, C.D. (2008). Maputo. Cinco décadas de mudança territorial. O uso do solo observado por tecnologias de informação geográfica [Maputo. Five decades of territorial transformation. Land use assessed by geographical information technologies]. Lisboa, Instituto Português de Apoio ao Desenvolvimento (ISBN: 978-972-8975-22-7).

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

The Niassa Selous land cover and change dataset covers an area of 139 163km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3943 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]).

The Salonga land cover and change dataset covers an area of 66 625km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2016, LCC:2019). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3069 verified land cover points based on the 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]). Data and Resources This is an imported dataset. For details on files and resources visit the source: https://doi.org/10.1594/PANGAEA.931984 Key Landscape for C... Land cover Salonga validation data Cite this as Szantoi, Zoltan, Brink, Andreas, Lupi, Andrea (2021). Dataset: Salonga Key Landscape for Conservation Land Cover and Validation Datasets (2016-2019). https://doi.org/10.1594/PANGAEA.931984 DOI retrieved: 2021

The Garamba land cover and change dataset covers an area of 265 976km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000, 2019). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 7168 (2000-2017) and 4647 (2017-2019) verified land cover points based on the [up to] 32 modular (2000-2017) and [up to] 14 aggregated (2017-2019) level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]).

The Caribbean land cover and change dataset covers an area of 89 883km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 4029 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2017), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2017), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

The Wapok land cover and change dataset covers an area of 57 776km^2^ and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3522 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]).

The Greater Virunga land cover dataset covers an area of 39062km2 and mapped with both dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover map was derived (2015). The map was fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 4630 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Z., Brink, A., Lupi, A., Mannone, C., and Jaffrain, G.: Key Landscapes for Conservation Land Cover and Change Monitoring Thematic and Validation Datasets for Sub-Saharan Africa, Earth Syst. Sci. Data, 12-3001-2020, https://doi.org/10.5194/essd-12-3001-2020.

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. 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

The Madagascar land cover and change dataset covers an area of 124 012km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3995 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]).

The Upemba land cover change dataset covers an area of 47 318km2 and mapped with both dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover change map was derived (year 2019). The map was fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3228 verified land cover points based on the [up to] 14 aggregated land cover change classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Related dataset: https://doi.pangaea.de/10.1594/PANGAEA.920847

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2015), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2015), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022
Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee Easement
Puerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 
2020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 census
OpenStreetMap data “multipolygons” layer, accessed 3-14-2023

A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

TNC Lands - Public Layer, accessed 3-8-2023
U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)

Mapping Steps

Most mapping steps were completed using QGIS (v 3.22) Graphical Modeler.
Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.
Merge the terrestrial PR and VI PAD-US layers.
Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature.
Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.
Fix geometry errors in the resulting merged layer using Fix Geometry.
Intersect the resulting fixed file with the Caribbean Blueprint subregion.
Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.
Clip the Census urban area to the Caribbean Blueprint subregion.
Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.
Dissolve all the park polygons that were selected in the previous step.
Process all multipart polygons to single parts (“explode”) again.
Add a unique ID to the selected parks. This value will be used to join the parks to their buffers.
Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 mi buffer is created for each park.
Assess the amount of overlap between the buffered park and the Census urban area using overlap analysis. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix. This step creates a table that is joined back to the park polygons using the UniqueID.
Remove parks that had ≤2% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.
Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.
Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. 
Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.
Reclassify the polygons into 7 classes, seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).
Export the final vector file to a shapefile and import to ArcGIS Pro.
Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = 75+ acre urban park 5 = >50 to <75 acre urban park 4 = 30 to <50 acre urban park 3 = 10 to <30 acre urban park 2 = 5 to <10 acre urban park 1 = <5 acre urban park 0 = Not identified as an urban park Known Issues

This indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. 
This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.
This indicator includes parks and beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.

Other Things to Keep in Mind

This indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. 
The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast

The Caribbean land cover and change dataset covers an area of 89 883km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 4029 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]).

This dataset provides hand-labeled points for four different land cover classes in coastal areas (sand, seawater, grass, trees).

It was created based on photointerpretation of high-resolution imagery in Google Earth Pro and QGIS, referring to the year 2019.

This dataset was used for the random forest classification of satellite imagery in the following manuscript:

"Satellite image processing for the coarse-scale investigation of sandy coastal areas".

If you use any part of this dataset, please cite as follows:

The Yangambi land cover change dataset covers an area of 7276km2 and mapped with both dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover change map was derived (2019). The map was fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 2421 verified land cover points based on the [up to] 14 aggregated land cover change classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, [Earth System Science Data|https://www.earth-system-science-data.net/]).

This archive contains:

├── classification_dpt64_16_classes.tif : the 16 classes land cover map

├── classification_dpt64_16_classes_confusion_matrix.png : the confusion matrix. Have a look at it, it is performed on a different dataset than the one used for training the classifier.

├── classification_dpt64_21_classes.tif : the 21 classes land cover map including post-treatments (https://framagit.org/Schwaab/projet_predateurs64/-/blob/main/scripts/ClassificationPostProcess.py)

├── colorFile.txt : color file for symbology

├── configfile_iota2.cfg : iota2 configuration file (in case you are already using iota2. If not, what are you waiting for ?)

├── document_methodologique.pdf : technical report (french) for the classification

├── nomenclature.txt : nomenclature file

├── reference_data_2018.shp : the training and validation data set in its 2018 version (for crops)

├── reference_data_2019.shp : the training and validation data set in its 2019 version (for crops)

├── reference_photo_interpretation.shp : the part of the training and validation data set that has been photo interpreted with a field giving the potential species or combinations of associated vegetation

├── reference_tree_nomenclature.png : a visual about the reference data

├── stratification_3_zones.shp : the stratification layer that has helped improve classification results. It is based on landscape entities (https://data.le64.fr/explore/dataset/entite-paysagere/)

├── style_16_classes.qml : the Qgis style layer 16 classes

└── style_21_classes.qml : the Qgis style layer 21 classes

Description:

The land cover map of the French department Pyrénées-Atlantiques (64) is based on Sentinel-2 (L2A level) satellite images performed with Iota² chain (https://framagit.org/iota2-project/iota2/). The algorithm used is Random Forest. The time series used ranges from 2017 to 2018.

During the development phase of this classification, the collection of additional training data on the photo-interpreted classes 'landes basses' (low heath shrublands), 'landes hautes' (high heath shrublands) and 'landes hautes avec arbres' (high heath shrublands with young-growth forest) has led to a remarkable increase of the number of pixels of these classes and with it the visual quality of the map. However, this increase has been linked with only minor to almost no significant improvement of the F-scores on these classes. Some are still massively confused with other land covers like grasslands and broadleaf mature forests. Especially the mixed class 'landes hautes avec arbres' (high heath shrublands with young-growth forest).

We take it as a limit of the reference data that is built from divers data sources and would always beneficiate from more training samples of shrubby classes and a better precision of the class 'forêt de feuillus' (broadleaf mature forests). But this could also show the limit of pixel-oriented classifications for mixed/textured classes (classes with high intra-class heterogeneity). Experimentations using a contextual method – the Auto-context method now being included in Iota2 thanks to Dawa Derksen and Iota2 developers (http://lannister.ups-tlse.fr/oso/donneeswww_TheiaOSO/iota2_documentation/develop/autoContext.html) – has unfortunately not been conclusive on that matter yet.

The study area corresponds to the Peyne watershed, which covers approximately 76 km2 and is located in Languedoc-Roussillon in southern France (43° 35′N, 3° 19′E). The smaller enclosed watershed is called the Bourdic subwatershed and covers 7 km2. The area of the Peyne watershed is mostly covered by perennial crops (mainly vineyards), and five towns are present in the zone. In 2012, the IGN released a large, open-access database of aerial black-and-white photographs from 1937 until the present. We selected a sample of images from the IGN database covering the Peyne watershed from 1962 to 2003 with a time interval between 4 and 5 years. We completed the series by taking orthophotos from 2005 to 2012 processed by the IGN from colour photographs. The aerial photographs were orthorectified using structure-from-motion approach and corrected from vignetting effects to get orthophotos defined at pixel resolution of less than 1 m. The satellite images were also sampled at the same resolution. The raster database was then transformed into polygons with a minimal area of approximately 200 m2 using manual digitizing and classification procedures to separate field entities and their associated land use categories using QGIS software. Land uses of each homogeneous polygon were classified according to the Corine Land Cover nomenclature expanded to the fourth level of detail for vineyards to distinguish goblet vines from trellised vines, with a special case noted for undefined vines when the difference in land management was difficult to detect. The distinction between goblet and trellised vineyards was based on the presence of a clear row orientation in the pictures.

Data on the dynamics of Land Cover at different scales: Kef-Siliana region and sub spatial units of Agroecological Living Lab Landscape in Tunisia (2017-2022). The dataset comprises information regarding land cover change (LCC) analyzed through QGIS 3.28.15 using Semi-Automatic Classification Plugin (SCP) and ecological degradation and/or improvement induced by the past land cover change were assessed using the UNCCD Good Practice Guidance. This analysis is conducted within the Kef-Siliana region and sub-spatial units of the Agroecological Living Lab Landscape in Tunisia.