Important Note: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020.By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter.1. Click the filter button.2. Next, click add expression.3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button.5. Under unique values click style options.6. Click the symbol next to No Change at the bottom of the legend.7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro.1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties.2. In the dialogue that comes up, choose the tab that says processing templates.3. On the right where it says processing template, choose the pair of years you would like to display.The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer:Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe.Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes.Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

The ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.

The NVIS vegetation attributes contain information on vegetation structure (growth form, height and cover) and floristics (genus and species) as documented in the Australian Vegetation Attribute Manual Version 7.0 (NVIS Technical Working Group, 2017).The NVIS detailed Level 1-6 vegetation descriptions make up theNVIS Information Hierarchy andare used to assign the Major Vegetation Groups and Major Vegetation Subgroups classifications. The hierarchy is based on structural and floristic information including dominant genus, growth form, height and cover and are preferably collected at the Level 6 Sub-Association (sub-stratum) level. For many reasons including different scales and classification methods, not all data is collected at this level of detail. Currently there are over 19,500 distinct NVIS vegetation descriptions in the NVIS database. For more information refer to the Australian Vegetation Attribute Manual.These detailed vector data products may be used at a regional scale and allow for more complex analyses when joined with the associated Lookup Table of Flat File. They are available in Present (Extant) and Estimated Pre-1750 (pre-European - where available) themes. Data is available under CC BY. It is recommended the datasets be used alongside the Key Layers to better understand the source data attributes such as differing scales, age of data etc.For this update, Version 7.0, the extant datasets for Queensland, Australian Capital Territory, New South Wales and Tasmania have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for Australian Capital Territory, New South Wales and Tasmania. Conversely, Queensland directly provided the MVG/MVS assignments for the state.This dataset is not comparable with earlier versions of NVIS.Reference: NVIS Technical Working Group (2017) Australian Vegetation Attribute Manual: National Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., de Lacey, C. and Bossard, K.B. (Eds)USE INSTRUCTIONS----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Datasets:the File Geodatabase contains the following information:ItemExplanationNVIS7_0_AUST_EXT_{State}This dataset is a vector layer delineating the extant ('present') native vegetation types across Australia as a feature class for each State or TerritoryNVIS7_0_LUT_AUST_DETAILThis table: is a lookup table containing NVIS Version 7.0 vegetation descriptions. The table contains a total of 19,519 NVIS vegetation types.NVIS7_0_LUT_AUST_FLATThis table is a lookup table containing NVIS Version 7.0 vegetation descriptions in a simpler, deconstructed table, allowing for improved analyses and use of the NVIS detailed vegetation descriptions. The table contains a total of 19,519 NVIS vegetation types.Table Joins:NVIS7_0_LUT_AUST_DETAILThis table joins to the NVIS 7.0 spatial data for all states and territories (NVIS_ID in this table to NVISDSC1 in NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE] ). For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC2-6. It is recommended that users refer to the Australian Vegetation Attribute Manual V7.0 for understanding of the NVIS hierarchy (Level 1-6 descriptions) -https://www.dcceew.gov.au/environment/land/publications/australian-vegetation-attribute-manual-version-7.Once this table has been joined, a simple display option is to use the field "NVIS7_0_LUT_AUST_DETAIL.MVG_NAME" (or MVS_NAME if preferred) which includes the names of the NVIS Major Vegetation Groups (MVGs).A legend or 'shadeset' for the MVGs and MVSs can be found packaged with the detailed vector data: NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE].Use the field "MVG_NUMBER" or "MVS_NUMBER" for the symbology.NVIS7_0_LUT_AUST_FLATFor complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC1-6. This LUT is a deconstruction of the Level 5 string within the NVIS detailed data (for NVIS Level 1-6 strings use NVIS7_0_LUT_AUST_DETAIL) where provided by the state/territory (not all veg descriptions have Level 5/6). It is recommended that users refer to the Australian Vegetation Attribute Manual V7.0 for understanding of the NVIS hierarchy (Level 1-6 descriptions) and structural information -https://www.dcceew.gov.au/environment/land/publications/australian-vegetation-attribute-manual-version-7. A legend or 'shadeset' for the MVGs and MVSs can be found packaged with the detailed vector data: NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE]. This table joins to the NVIS 7.0 spatial data for all states and territories (NVIS_ID in this table to NVISDSC1 in NVIS7_0_EXT_[STATE] and NVIS7_0_PRE_[STATE] ). For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC2-6. Once this table has been joined, a simple display option is to use the field "NVIS7_0_LUT_AUST_FLAT.MVG_NAME" (or MVS_NAME if preferred) which includes the names of the NVIS Major Vegetation Groups (MVGs).Retrieving data by state or territory:the first number of theNVIS_ID corresponds to a specific state or territory and can be used to subset the larger datasetCodeExplanation1Australian Capital Territory2New South Wales3Northern Territory4Queensland5South Australia6Tasmania7Victoria8Western AustraliaSymbology:To enable full Major Vegetation Group descriptions to appear in the legend in an ArcGIS Desktop map or ArcGIS Pro project, the following layer files will need to be imported and the symbology set using the relevant attribute field. Layer files are within the zipped package.

The NVIS vegetation attributes contain information on vegetation structure (growth form, height and cover) and floristics (genus and species) as documented in the Australian Vegetation Attribute Manual Version 7.0 (NVIS Technical Working Group, 2017).The NVIS detailed Level 1-6 vegetation descriptions make up the NVIS Information Hierarchy and are used to assign the Major Vegetation Groups and Major Vegetation Subgroups classifications. The hierarchy is based on structural and floristic information including dominant genus, growth form, height and cover and are preferably collected at the Level 6 Sub-Association (sub-stratum) level. For many reasons including different scales and classification methods, not all data is collected at this level of detail. Currently there are over 19,300 distinct NVIS vegetation descriptions in the NVIS database. For more information refer to the Australian Vegetation Attribute Manual V7.0.These detailed vector data products may be used at a regional scale and allow for more complex analyses when joined with the associated Lookup Table of Flat File. They are available in Present (Extant) and Estimated Pre-1750 (pre-European - where available) themes. Data is available under CC BY. It is recommended the datasets be used alongside the Key Layers to better understand the source data attributes such as differing scales, age of data etc.For this update, Version 7.0, the extant datasets for Queensland, Australian Capital Territory, New South Wales and Tasmania have been updated. An automated, data-driven procedure, followed by thorough manual checks, was undertaken to make any necessary updates to MVG/MVS assignments for Australian Capital Territory, New South Wales and Tasmania. Conversely, Queensland directly provided the MVG/MVS assignments for the state.This dataset is not comparable with earlier versions of NVIS.Reference: NVIS Technical Working Group (2017) Australian Vegetation Attribute Manual: National Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., de Lacey, C. and Bossard, K.B. (Eds)USE INSTRUCTIONS----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Datasets: the File Geodatabase contains the following information: ItemExplanation NVIS6_0_AUST_PREThis dataset is a vector layer delineating the estimated pre-1750 native vegetation types across Australia NVIS6_0_LUT_AUST_DETAIL This table: is a lookup table containing NVIS Version 7.0 vegetation descriptions. The table contains a total of 19,519 NVIS vegetation types. NVIS6_0_LUT_AUST_FLATThis table is a lookup table containing NVIS Version 7.0 vegetation descriptions in a simpler, deconstructed table, allowing for improved analyses and use of the NVIS detailed vegetation descriptions. The table contains a total of 19,519 NVIS vegetation types.Table Joins:NVIS7_0_LUT_AUST_DETAIL This table joins to the NVIS 7.0 spatial data for all states and territories (NVIS_ID in this table to NVISDSC1 in NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE] ). For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC2-6. It is recommended that users refer to the Australian Vegetation Attribute Manual V7.0 for understanding of the NVIS hierarchy (Level 1-6 descriptions) - https://www.dcceew.gov.au/environment/land/publications/australian-vegetation-attribute-manual-version-7. Once this table has been joined, a simple display option is to use the field "NVIS7_0_LUT_AUST_DETAIL.MVG_NAME" (or MVS_NAME if preferred) which includes the names of the NVIS Major Vegetation Groups (MVGs). A legend or 'shadeset' for the MVGs and MVSs can be found packaged with the detailed vector data: NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE].Use the field "MVG_NUMBER" or "MVS_NUMBER" for the symbology.NVIS7_0_LUT_AUST_FLAT For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC1-6. This LUT is a deconstruction of the Level 5 string within the NVIS detailed data (for NVIS Level 1-6 strings use NVIS7_0_LUT_AUST_DETAIL) where provided by the state/territory (not all veg descriptions have Level 5/6). It is recommended that users refer to the Australian Vegetation Attribute Manual V7.0 for understanding of the NVIS hierarchy (Level 1-6 descriptions) and structural information - https://www.dcceew.gov.au/environment/land/publications/australian-vegetation-attribute-manual-version-7. A legend or 'shadeset' for the MVGs and MVSs can be found packaged with the detailed vector data: NVIS7_0_AUST_EXT_[STATE] and NVIS7_0_AUST_PRE_[STATE]. This table joins to the NVIS 7.0 spatial data for all states and territories (NVIS_ID in this table to NVISDSC1 in NVIS7_0_EXT_[STATE] and NVIS7_0_PRE_[STATE] ). For complex analyses and to extract maximum information from NVIS spatial data, this LUT can also be linked to NVISDSC2-6. Once this table has been joined, a simple display option is to use the field "NVIS7_0_LUT_AUST_FLAT.MVG_NAME" (or MVS_NAME if preferred) which includes the names of the NVIS Major Vegetation Groups (MVGs).Retrieving data by state or territory: the first number of the NVIS_ID corresponds to a specific state or territory and can be used to subset the larger datasetCodeExplanation 1 Australian Capital Territory 2 New South Wales 3 Northern Territory 4 Queensland 5 South Australia 6 Tasmania 7 Victoria 8 Western AustraliaSymbology: To enable full Major Vegetation Group descriptions to appear in the legend in an ArcGIS Desktop map or ArcGIS Pro project, the following layer files will need to be imported and the symbology set using the relevant attribute field. Layer files are within the zipped package.