• land values for the past five years (where available)

• the valuing year used to calculate council rates

  </font></li><li><font size='4'>the
  valuation basis 
  
  </font></li><li><font size='4'>the
  property number, address, and zoning information 
  
  </font></li><li><font size='4'>the area
  and boundaries of non strata properties 
  
  </font></li><li><font size='4'>notice of
  any concessions or allowances that apply to the land value. 
  

The map does not show land values for individual strata properties.

• property sales information for individual properties from 2001

  </font></li><li><font size='4'>property
  sales information at a street and suburb level for the last five
  years (where available
  </font></li><li><font size='4'>area for
  non strata properties 
  
  </font></li><li><font size='4'>the
  dealing number and sale date (or contract date) 
  
  </font></li><li><font size='4'>the date
  the property sales information was last updated 
  
  </font></li><li><font size='4'>whether
  the property is strata or non strata, or if the sale is part of a
  multi property sale. 
  

Contact us

Phone : 1800 110 038

Mon-Fri, 8:30am – 5:00pm

Please call TIS National on 131 450 and ask them to call Valuation Services on 1800 110 038.

Metadata

All datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update …Show full descriptionAll datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update frequency for each dataset varies depending on the dataset. All these datasets are used in the land values and property sales map web map application. Please see individual metadata for each dataset below. Land value and property sales map can be found HERE.For more information regarding the Land valuation and Property Sales information data please contact : valuationenquiry@property.nsw.gov.auFor all other datasets, please contact ss-sds@customerservice.nsw.gov.au

The Biodiversity Values Map (BV Map) identifies land with high biodiversity value that is particularly sensitive to impacts from development and clearing. The BV Map is one of the triggers for determining whether the Biodiversity Offset Scheme (BOS) applies to a clearing or development proposal.

The BV Map has been prepared by the Department of Planning and Environment (DPE) under Part 7 of the Biodiversity Conservation Act 2016 (BC Act). A range of mapping layers are included in the BV map. These mapping layers have been developed and are maintained by a range of agencies and councils. The inclusion of these layers on the BV map requires the approval of the Environment Agency Head or delegate.

The BV Map shows areas that have been added in the last 90 days as the BOS does not apply to development proposals lodged within this time period. Areas that no longer meet one of the criteria for being included on the BV map will also be removed in map updates.

It is planned to update the BV Map quarterly, however users of the BV Map are strongly encouraged to visit the BMAT website and BMAT Tool viewer regularly to be up to date with the latest version and other related information. The spatial data for this version is available from the Web Service (see link below).

The latest version of the BV map can be viewed in the Biodiversity Values Map and Threshold (BMAT) Tool (see URL link below).

More information on the BV map is available at - https://www.environment.nsw.gov.au/topics/animals-and-plants/biodiversity-offsets-scheme/clear-and-develop-land/biodiversity-values-map-and-threshold-tool

The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I develop the first high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimated conservation costs, especially at the expensive tail of the distribution. This might have led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges, and can assist with the targeting of public ecosystem service investments. Methods See Methods & Materials in Nolte (2020) PNAS

The Biodiversity Values Map (BV Map) identifies land with high biodiversity value that is particularly sensitive to impacts from development and clearing. The BV Map is one of the triggers for determining whether the Biodiversity Offset Scheme (BOS) applies to a clearing or development proposal. The BV Map has been prepared by the Department of Planning and Environment (DPE) under Part 7 of the Biodiversity Conservation Act 2016 (BC Act). A range of mapping layers are included in the BV map. These mapping layers have been developed and are maintained by a range of agencies and councils. The inclusion of these layers on the BV map requires the approval of the Environment Agency Head or delegate. The BV Map shows areas that have been added in the last 90 days as the BOS does not apply to development proposals lodged within this time period. Areas that no longer meet one of the criteria for being included on the BV map will also be removed in map updates. It is planned to update the BV Map quarterly, however users of the BV Map are strongly encouraged to visit the BMAT website and BMAT Tool viewer regularly to be up to date with the latest version and other related information. The spatial data for this version is available from the Web Service (see link below). The latest version of the BV map can be viewed in the Biodiversity Values Map and Threshold (BMAT) Tool (see URL link below).

NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.Known Issues Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs.* The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

• land \tvalues for the past five years (where available) \t

  \t
• the \tvaluing year used to calculate council rates \t \t
• the \tvaluation basis \t \t
• the \tproperty number, address, and zoning information \t \t
• the area \tand boundaries of non strata properties \t \t
• notice of \tany concessions or allowances that apply to the land value. \t

The map does not show land values for individual strata properties.

• property \tsales information for individual properties from 2001 \t \t
• property \tsales information at a street and suburb level for the last five \tyears (where available \t
• area for \tnon strata properties \t \t
• the \tdealing number and sale date (or contract date) \t \t
• the date \tthe property sales information was last updated \t \t
• whether \tthe property is strata or non strata, or if the sale is part of a \tmulti property sale. \t

Contact us

Phone : 1800 110 038

Mon-Fri, 8:30am – 5:00pm

Please call TIS National on 131 450 and ask them to call Valuation Services on 1800 110 038.

Metadata

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This dataset includes raster-based nominal land value data of Istanbul City. It is created using open-source QGIS software with several spatial analyses, such as proximity, terrain, and visibility. The dataset has 10 metres spatial resolution.

Map of the standard land values ​​determined by the expert committee for property values ​​in Berlin on January 1st, 2023.

This map contains a hexbin layer that provides users with information on the mean values for city property tax, total property values, improvement values and land values within each hexbin.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

The article analyses the relation of market prices in the agricultural land market and selected pedological characteristics of traded lands. During the period of 2009–2018 in 12 districts of Slovakia more than 153,000 plots with different pedo-ecological and geographic conditions have been analysed. Based on soil types, texture composition, steepness, gravel content, and depth, corresponding price levels were derived, and soil price maps were developed. The highest valued soils are of chernozem type (EUR 1.64 m−2), loamy soils (EUR 0.86 m−2), soils on flat land (EUR 1.09 m−2), slightly gravelly soils (EUR 1.02 m−2), and deep soils (EUR 1.10 m−2). The land price is evidently highly correlated with its qualitative parameters. Using GIS technologies, the entire territory of Slovakia has been categorized by this means and a so-called basic map of agricultural soil market prices in Slovakia has been created.

DescriptionThe dataset consists of 5 land cover maps rasters of Mont Avic watershed (5500 ha) for the past period and of 2 land cover simulations for the future. The 7 time steps are: 1965, 1975, 1988, 2006, 2017 (past); 2050, 2100 (future).Simulations were obtained through an integration of Markov Chain and Multi-Layer Perceptron models by comparing 1965 and 2017 maps.Mont Avic Park represents a subalpine forest landscape in Northwestern Italy with an intense history of forest exploitation for mining activities.MetadataCell size (X,Y): 10, 10 metersCoordinate System: WGS84 UTM Zone 32N (EPSG: 32632)NoData Value: 255Data type: 8 bit unsigned integerFormat: GeoTIFFCompression: LZWLand cover classification: object-oriented segmentation and video photointerpretation (Garbarino et al. 2013)Land class values: 1 = dense forest; 2 = sparse forest and shrubs; 3 = grassland; 4 = human; 5 = unvegetated (Future projections lack in human land cover class)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS. This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This map features land cover data represent a descriptive thematic surface for characteristics of the land's surface such as densities or types of developed areas, agricultural lands, and natural vegetation regimes. Land cover data are the result of a model, so a good way to think of the values in each cell are as the predominating value rather than the only characteristic in that cell.Land use and land cover data are critical and fundamental for environmental monitoring, planning, and assessment.This web map uses Terrain with Labels vector layers as its basemap.Dataset SummaryBaseVue 2013 is a commercial global, land use / land cover (LULC) product developed by MDA. BaseVue covers the Earth’s entire land area, excluding Antarctica. BaseVue is independently derived from roughly 9,200 Landsat 8 images and is the highest spatial resolution (30m), most current LULC product available. The capture dates for the Landsat 8 imagery range from April 11, 2013 to June 29, 2014. The following 16 classes of land use / land cover are listed by their cell value in this layer: Deciduous Forest: Trees > 3 meters in height, canopy closure >35% (<25% inter-mixture with evergreen species) that seasonally lose their leaves, except Larch.Evergreen Forest: Trees >3 meters in height, canopy closure >35% (<25% inter-mixture with deciduous species), of species that do not lose leaves. (will include coniferous Larch regardless of deciduous nature).Shrub/Scrub: Woody vegetation <3 meters in height, > 10% ground cover. Only collect >30% ground cover.Grassland: Herbaceous grasses, > 10% cover, including pasture lands. Only collect >30% cover.Barren or Minimal Vegetation: Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water.Not Used (in other MDA products 6 represents urban areas or built up areas, which have been split here in into values 20 and 21).Agriculture, General: Cultivated crop landsAgriculture, Paddy: Crop lands characterized by inundation for a substantial portion of the growing seasonWetland: Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species)Mangrove: Coastal (tropical wetlands) dominated by Mangrove speciesWater: All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streamsIce / Snow: Land areas covered permanently or nearly permanent with ice or snowClouds: Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunctionWoody Wetlands: Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with, or covered by water. Only used within the continental U.S.Mixed Forest: Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S.Not UsedNot UsedNot UsedNot UsedHigh Density Urban: Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation where constructed materials account for >60%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.Medium-Low Density Urban: Areas with 30%-70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to an 16,000 x 16,000 pixel limit, which represents an area of nearly 300 miles on a side. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.\For more information, see the Landscape Layers group on ArcGIS Online.

A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356. The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv et al. (2023). Data description Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes. To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover. Land surface category (LSC) These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories. Discrete LSC classification legend: Map code Land cover class 11 Tree (leaf-on) 12 Shrubland (leaf-on) 13 Grassland 14 Woody vegetation (leaf-off) 15 Wilted herbaceous vegetation 21 Bare/sparse vegetation 22 Water 24 Built-up In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype. Land use land cover After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system. The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions. Discrete LC classification legend: Map code Land cover class 10 Tree cover 20 Shrubland 30 Grassland 40 Cropland 50 Built-up 60 Bare/sparse vegetation 80 Permanent water bodies 90 Herbaceous wetland Land use land cover change Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change. Files The zip files contain the following data: lsc.zip: land surface category maps over the three AOI’s lc.zip: LULC maps over the three AOI’s change.zip: change maps over the three AOI’s These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files

The land use and cover category contains the "geostat25" and "wslhabmap" datasets.

The geostat25 dataset describes the land use and cover of Switzerland. After resampling the “Downscaled Land Use/Land Cover of Switzerland” source data (Giuliani et al., 2022) to the SWECO25 grid, we generated individual layers for the 65 land use and cover classes and the 3 time periods (1992-1997, 2004-2009, and 2013-2018) that were available. For each class and period, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 2,730 layers. Final values were rounded and multiplied by 100.

The wslhabmap dataset (land use and cover category) describes the natural habitats of Switzerland. After rasterizing and resampling the “Habitat Map of Switzerland v1” source data (Price et al., 2021) to the SWECO25 grid, we generated individual layers for 41 categories (32 classes and 9 groups). The groups correspond to the first level of the TypoCH classification and the classes to the second level. For details on the TypoCH classification see Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Guide des milieux naturels de Suisse : Écologie, menaces, espèces caractéristiques. Rossolis. For each of the 41 categories, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given category in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 574 layers. Final values were rounded and multiplied by 100.

The detailed list of layers available is provided in SWECO25_datalayers_details_lulc.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path.

References:

G. Giuliani, D. Rodila, N. Külling, R. Maggini, A. Lehmann, Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. Land 11, 615 (2022).

B. Price, Huber, N., Ginzler, C., Pazúr, R., Rüetschi, M., "The Habitat Map of Switzerland v1," (Birmensdorf, Switzerland, 2021)

Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P.-L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: A cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1), Article 1. https://doi.org/10.1038/s41597-023-02899-1

V2: metadata update

Kriging interpolation results from 2014–2017. (ZIP)

Global maps of agricultural expansion potential at a 300 m resolution

This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.

Abstract:

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.

Data description:

We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.

Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.

Repository content:

Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository.

Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).

File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”

• "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.

• "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.

• "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.

Raster metadata:

Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs

Notes on use:

Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.