****UPDATED**** This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_85MIR50_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_85MIR50_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_85MIR50_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: MIROC5 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5.

The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models.

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org Lineage: Predictive models of vegetation classes were derived using the two-step process originally developed for individual species distribution modelling with GDM (described in Elith et al. 2006). The first step uses a Generalised Dissimilarity Model (GDM) of vascular plants (VAS_V5_R11) to derive a set of scaled environmental variables for current (e.g. 1990 baseline) and future climates (e.g. 2050). The second step applies this data in a kernel regression to predict each vegetation class using training data derived from the pre-clearing mapping of 77 Major Vegetation Sub-groups. The training data comprised c.155,000 locations defined by randomly sampling within each vegetation class, proportional to their observed areal extent. These locations were then attributed with the baseline values of the GDM-scaled environmental variables. Separate kernel regressions were then run for the baseline and future climate scenarios using the baseline training data. In this way, the future distribution of each vegetation class was projected based on its affinity with present-day ecological environments.

At any location (grid cell), the kernel regression considers the surrounding relative density of training sites of the target vegetation class as a proportion of other types and generates a predicted probability for that class for the focal grid cell. A probability surface for the predicted proportions, varying from 0 to 1, is generated for each of the 77 mapped Major Vegetation Sub-groups. This method is infrequently used in ecology because of the need first to scale and reduce the dimensionality of the predictor variables (Lowe 1995). The GDM step reduces dimensionality (by choosing the variables to use) and scales the predictor variables using similarity-decay functions which equate to the multivariate distances expected by kernel regression. The kernel regression thus incorporates interactions by modelling ecological distances and vegetation class densities within a truly multivariate predictor space, with no assumption of additivity.

Kernel regression aims to optimise model performance in terms of the accuracy of predictions at any single location according to the area predicted for each class. The predicted proportions of common vegetation types are typically greater than for rarer vegetation types. Therefore, cell by cell, the class with the maximum probability selected to represent spatially varying vegetation class mosaics on a single map (essentially one dimension) will often be the common type, at the expense of locally rare and nationally rare types. Therefore, the best way to view the results, and to inform planning, is the individual probability surfaces. These properly reveal where the rarer vegetation types have a likelihood of persistence. Higher probabilities associated with other vegetation types at the same location can be viewed as a measure of the extent to which those other vegetation types may compete. However the outcome, at least in the medium term, may be more driven by the extant occurrence of ecosystems and their ability to persist under marginal conditions.

Generalised maps assembled from individual projected vegetation class probabilities indicate which of the baseline vegetation classes may be most suited to the environment of a particular location in the future. However, the suitability of that vegetation class to the future environment may still be relatively low and a number of other vegetation classes may be almost equally suited. A more conservative view can be obtained from maps of the projected probabilities for individual vegetation classes (see related materials for the individual probability datasets).

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

Two different maps of the Köppen-Geiger climate classification: World map of the Köppen-Geiger climate classification observed using CRU TS 2.1 temperature and GPCC Full v4 precipitation data, period 1976-2000. World map of the Köppen-Geiger climate classification projected using IPCC A1FI Tyndall SC 2.03 temperature and precipitation scenarios, period 2076-2100.

Under various scenarios, land use changes in Belgium are simulated at 10-meter resolution. Three SSP-RCP scenarios were used to model the land use trends in the present (2020) and the year 2050 at the national level in Belgium. Key inputs to the model include regional land use demand, quantification of the suitability of grid cells for different land use types, and a reference land cover map. The 10 meter-resolution baseline land use map of Belgium was sourced from the European Space Agency (ESA) WorldCover for the reference year 2020. The classification systems ESA is different from LUH2. To make these datasets comparable for land use simulations, we performed reclassification based on the guidelines provided by Pérez-Hoyos et al. (2012); Dong et al. (2018); Liao et al. (2020) to unify the land use classes, except water, into six general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) bare/sparse vegetation, and 6) undefined.

Geoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and …Show full descriptionGeoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and external organisations. Since seabed sediment data are collected at sparsely and unevenly distributed locations, spatial interpolation methods become essential tools for generating spatially continuous information. Previous studies have examined a number of factors that affect the performance of spatial interpolation methods. These factors include sample density, data variation, sampling design, spatial distribution of samples, data quality, correlation of primary and secondary variables, and interaction among some of these factors. Apart from these factors, a spatial reference system used to define sample locations is potentially another factor and is worth investigating. In this study, we aim to examine the degree to which spatial reference systems can affect the predictive accuracy of spatial interpolation methods in predicting marine environmental variables in the continental AEEZ. Firstly, we reviewed spatial reference systems including geographic coordinate systems and projected coordinate systems/map projections, with particular attention paid to map projection classification, distortion and selection schemes; secondly, we selected eight systems that are suitable for the spatial prediction of marine environmental data in the continental AEEZ. These systems include two geographic coordinate systems (WGS84 and GDA94) and six map projections (Lambert Equal-area Azimuthal, Equidistant Azimuthal, Stereographic Conformal Azimuthal, Albers Equal-Area Conic, Equidistant Conic and Lambert Conformal Conic); thirdly, we applied two most commonly used spatial interpolation methods, i.e. inverse distance squared (IDS) and ordinary kriging (OK) to a marine dataset projected using the eight systems. The accuracy of the methods was assessed using leave-one-out cross validation in terms of their predictive errors and, visualization of prediction maps. The difference in the predictive errors between WGS84 and the map projections were compared using paired Mann-Whitney test for both IDW and OK. The data manipulation and modelling work were implemented in ArcGIS and R. The result from this study confirms that the little shift caused by the tectonic movement between WGS84 and GDA94 does not affect the accuracy of the spatial interpolation methods examined (IDS and OK). With respect to whether the unit difference in geographical coordinates or distortions introduced by map projections has more effect on the performance of the spatial interpolation methods, the result shows that the accuracies of the spatial interpolation methods in predicting seabed sediment data in the SW region of AEEZ are similar and the differences are considered negligible, both in terms of predictive errors and prediction map visualisations. Among the six map projections, the slightly better prediction performance from Lambert Equal-Area Azimuthal and Equidistant Azimuthal projections for both IDS and OK indicates that Equal-Area and Equidistant projections with Azimuthal surfaces are more suitable than other projections for spatial predictions of seabed sediment data in the SW region of AEEZ. The outcomes of this study have significant implications for spatial predictions in environmental science. Future spatial prediction work using a data density greater than that in this study may use data based on WGS84 directly and may not have to project the data using certain spatial reference systems. The findings are applicable to spatial predictions of both marine and terrestrial environmental variables. You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

This layer is a subset of Global Landcover 1992- 2020 Layer. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

GCOM-C/SGLI L3 Map Classified cloud fraction (CFR3) (1-Day,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. This dataset is daily map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-3 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The geometric transformation of remotely sensed imagery from one map projection to another necessitates a data resampling operation which alters the recorded values. The global Landsat archive is made available in the Universal Transverse Mercator (UTM) projection system which preserves geographic shape across small area but introduces small errors in distance and area. As remote sensing-based studies develop from local scales to regional and global, they need to adopt more appropriate map projections from which accurate area measurements can be made. While effects of resampling on recorded values have been studied in the past, the impacts on higher-level results such as land cover have not been widely reported. This study investigates an approach for monitoring land cover and land change using two input datasets derived from identical source Landsat data, where one input dataset is transformed to an equal-area map projection and thereby resampled. Recorded surface reflectance values are changed through the reprojection/resampling process, and our study highlights observed differences in derived land cover from these two different input datasets throughout the various stages of deriving land cover and related characteristics. Our findings suggest that large-scale analyses of land cover will not be substantially impacted by reprojection of input data, but small-scale analyses should exercise caution when interpreting timing and magnitude of pixel-level change and classification dynamics.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc

New global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present day (1980–2016) and for projected future conditions (2071–2100) under climate change. The maps are stored in GeoTIFF format as unsigned 8-bit integers. We also provide a legend file (legend.txt) linking the numeric values in the maps to the Köppen-Geiger climate symbols.Please cite the following paper when using the maps in any publication: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data 5:180214, doi:10.1038/sdata.2018.214 (2018).

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Large scale final map products were created within ArcMap and designed to show both the orthophoto coverage and the vegetation maps. For the vegetation maps, colors were assigned and the polygons labeled with the dominant vegetation and modifier and, where present, the second vegetation and modifier. For the orthophoto maps, the photos were simply plotted at the same scale and area coverage as the vegetation maps. Additional planimetric map data included roads, trails, hydrology, boundaries and a UTM coordinate grid. Legends are designed to provide full definitions of the vegetation and buffer classes and modifiers, as well as information about the park, map projection, data sources and authorship. All maps are projected to the Universal Transverse Mercator Coordinate System, North American Datum of 1984, in the local zone for the specific park

Map information- Veg Classes: 35 Polygons: 7,907 Avg Polygon size(ha) 2.58 Map Scale: 1:26,000

A Web Map to provide convenient visualisation of the NZ Marine Environment Classification in Mercator 41 projection on a NIWA basemap:The Marine Environment Classification (MEC), a GIS-based environmental classification of the marine environment of the New Zealand region, is an ecosystem-based spatial framework designed for marine management purposes. Developed by NIWA with support from the Ministry for the Environment (MfE), Department of Conservation and Ministry of Fisheries, and with contributions from several other stakeholders, the MEC provides a spatial framework for inventories of marine resources, environmental effects assessments, policy development and design of protected area networks. Two levels of spatial resolution are available within the MEC. A broad scale classification covers the entire EEZ at a nominal spatial resolution of 1 km, whereas the finer scale classification of the Hauraki Gulf region has a nominal spatial resolution of 200 m. Several spatially-explicit data layers describing the physical environment define the MEC. A physically-based classification was chosen because data on these physical variables were available or could be modelled, and because the pattern of the physical environment is a reasonable surrogate for biological pattern, particularly at larger spatial scales. Classes within the classification were defined using multivariate clustering methods. These produce hierarchal classifications that enable the user to delineate environmental variation at different levels of detail and associated spatial scales. Large biological datasets were used to tune the classification, so that the physically-based classes maximised discrimination of variation in biological composition at various levels of classification detail. Thus, the MEC provides a general classification that is relevant to most groups of marine organisms (fishes, invertebrates and chlorophyll) and to ecologically important abiotic variables (e.g., temperature, nutrients).An overview report describing the MEC is available as a PDF file (External Link). The overview report covers the conceptual basis for the MEC and results of testing the classification: MEC Overview (PDF 2.7 MB)_Item Page Created: 2019-07-24 04:00 Item Page Last Modified: 2025-04-05 18:57Owner: steinmetzt_NIWANew Zealand Marine Environment Classification - Exclusive Economic Zone (EEZ)Item id: 9104c9b367f14e76ac48af3725d68dacNew Zealand Marine Environment Classification - MEC EEZ 40 classItem id: 9104c9b367f14e76ac48af3725d68dacNew Zealand Marine Environment Classification - MEC EEZ 20 classItem id: 9104c9b367f14e76ac48af3725d68dacNew Zealand Marine Environment Classification - MEC EEZ 10 classItem id: 9104c9b367f14e76ac48af3725d68dacNew Zealand Marine Environment Classification - MEC EEZ 05 classItem id: 9104c9b367f14e76ac48af3725d68dacNew Zealand Marine Environment Classification - CoastlineItem id: 9104c9b367f14e76ac48af3725d68dac

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes:

Percent of neighborhood over 8% of slope

Slope Classes

0 - 20%

400

21% -50%

300

51% - 80%

200

81%

100

Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:

Change in elevation

Relief Class ID

0 – 30 meters

10

31 meter – 90 meters

20

91 meter – 150 meters

30

151 meter – 300 meters

40

301 meter – 900 meters

50

900 meters

60

The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:

Percent of neighborhood over 8% slope in upland or lowland areas

Profile Class

Less than 50% gentle slope is in upland or lowland

0

More than 75% of gentle slope is in lowland

1

50%-75% of gentle slope is in lowland

2

50-75% of gentle slope is in upland

3

More than 75% of gentle slope is in upland

4

Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. 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.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Köppen-Geiger climate classification divides climates into 5 main climate groups and 30 different classes based on patterns of seasonal precipitation and temperature, and has been used as a way to understand how climate can influence the structure of ecosystems and other natural systems. This multidimensional raster layer provide access to historical and projected Köppen-Geiger classes for the planet using the latest CMIP6 climate models that were downscaled and ensembled at 1-km resolution. Dataset SummaryPhenomenon Mapped: Köppen-Geiger climate classificationCell Size: 1-kilometerPixel Type: 8 bit unsignedCoordinate System: WGS1984Extent: WorldVisible Scale: All scales are visibleSource: gloh2o.org/koppen/Publication Date: March 2024Using the layerThis layer supports visualization and analysis. Using the multidimensional capabilities of ArcGIS Online and ArcGIS Pro, you can access 4 different emissions scenarios, called Shared Socioeconomic Pathways, along with time. Emissions ScenariosSSP1-2.6: One of the most optimistic scenarios, global CO2 emissions are cut severely, but not as fast, reaching net-zero after 2050. Temperatures stabilize around 1.8 degC higher by the end of the century. (Reuters 2021)SSP2-4.5: This is a “middle of the road” scenario. CO2 emissions hover around current levels before starting to fall mid-century, but do not reach net-zero by 2100. Socioeconomic factors follow their historic trends, with no notable shifts. Progress toward sustainability is slow, with development and income growing unevenly. In this scenario, temperatures rise 2.7 degC by the end of the century. (Reuters 2021)SSP3-7.0: On this path, emissions and temperatures rise steadily and CO2 emissions roughly double from current levels by 2100. Countries become more competitive with one another, shifting toward national security and ensuring their own food supplies. By the end of the century, average temperatures have risen by 3.6 degC. (Reuters 2021)SSP5-8.5: This is a future to avoid at all costs. Current CO2 emissions levels roughly double by 2050. The global economy grows quickly, but this growth is fueled by exploiting fossil fuels and energy-intensive lifestyles. By 2100, the average global temperature is a scorching 4.4 degC higher. (Reuters 2021)Time ExtentsSix 30-year average periods were calculated. Each is referred to by the mid-year in the range:1901–1930 (1915)1931–1960 (1945)1961–1990 (1975)1991–2020 (2005)2041–2070 (2055)2071–2099 (2085)Accessing the DimensionsIn ArcGIS Pro, the Multidimension ribbon will activate when the layer is selected. From there you can select the SSP dimension or the Standard Time. In ArcGIS Online, access the Multidimensional information from the righthand menu. Select the SSP and time. Note: To access the pop-up when using Map Viewer, please deactivate the time slider. Otherwise there is a conflict between the time selection in the time slider and the time selection in the Multidimensional information. Source InformationData were download from https://www.gloh2o.org/koppen/The full details are described by Beck et al 2023. Beck, H. E., T. R. McVicar, N. Vergopolan, A. Berg, N. J. Lutsko, A. Dufour, Z. Zeng, X. Jiang, A. I. J. M. van Dijk, and D. G. Miralles. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Scientific Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6

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

The map title is Rock Types. Map scale. North arrow pointing to the north. Map projection is Hammer-Aitoff. Border of Canada. Great Lakes Border for each theme category within Canada. Neat line around the map. Each theme category is identified by a number that corresponds to the legend. Legend is divided into three categories: Metamorphic rocks, Deformed Sedimentary and Igneous rocks, Flat Lying Sedimentary rocks. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

GCOM-C/SGLI L3 Map Classified cloud fraction (CFR7) (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 8 days map-projected statistics product. This dataset includes number of cloud pixels identified as ISCCP Class-7 (1:Cirrus, 2:Cirro-stratus, 3:Deep convection, 4:Altocumulus,5:Alto-stratus, 6:Nimbo-stratus, 7:Cumulus, 8:Strato-cumulus, 9:Stratus) The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

The sediment map of the Continental Margin Mapping Program (CONMAP) series is a compilation of grain-size data produced by the U.S. Geological Survey (USGS) and includes both published and unpublished studies. Sediment was classified using the 1929 Wentworth grain-size scale and the 1954 Shepard scheme of sediment classification. Certain grain-size categories are combined because of the paucity of some sediment textures. True boundaries between sediment types are highly irregular or gradational. This is due to textural variability not characterized at this scale, and because the accuracy of the navigational systems used during the earlier studies is limited. Sediment classification reflects the dominant surficial sediment type for that area and does not infer that other sediment types are not present. Blank parts of the maps indicate areas where data are insufficient to infer sediment type. This data layer is supplied primarily as a gross overview and to show general textural trends.

© U.S. Geological Survey This layer is a component of Physical Oceanographic and Marine Habitat.

MarineCadastre.gov themed service for public consumption featuring layers related to the Physical and Oceanographic and Marine Habitat themes. This map service presents spatial information about MarineCadastre.gov services across the United States and Territories in the Web Mercator projection. The service was developed by the National Oceanic and Atmospheric Administration (NOAA), but may contain data and information from a variety of data sources, including non-NOAA data. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. The NOAA Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Data Services Newsletter (http://coast.noaa.gov/digitalcoast/publications/subscribe). For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© MarineCadastre.gov

The compressed folder contains the raw LCZ maps generated directly from the Random Forest model without the spatial and temporal post-classification processing. The aim of this folder is to provide data users the flexibility to employ different post-classification techniques as needed. The format of the raw maps are the same as the final product, i.e., each Geo Tiff file represents one year of map from 1986 to 2020. All LCZ maps have a spatial resolution at 100m and projection of USA Contiguous Albers Equal Area Conic (EPSG=5070). The LCZ classes are indicated by numbers 1-17.