Urban Tree Canopy Assessment. This was created using the Urban Tree Canopy Syracuse 2010 (All Layers) file HERE.The data for this map was created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Because the full map is too large to be viewed in ArcGIS Online, this has been reduced to a vector tile layer to allow it to be viewed online. To download and view the shapefiles and all of the layers, you can download the data HERE and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

This layer depicts areas of tree canopy within urban Invercargill, New Zealand. Tree canopy has been identified through deep learning processes - specifically object detection with Text SAM, a deep learning package made by Esri Analytics and available in ArcGIS Living Atlas. Text SAM is a visual language model used to identify objects in imagery through the input of text prompts. Tree canopy has been identified through using 'tree' as the text prompt. The deep learning package has been applied to the Invercargill 0.1m Urban Aerial Photos (2022) provided by Land Information New Zealand (LINZ), and the resulting layer has been clipped to the boundary of urban Invercargill provided by Stats NZ. All processing to create this feature layer have been carried out on ArcGIS Pro 3.4. The confidence values for the polygons within the layer average at approximately 0.25, meaning that the Text SAM deep learning model has 25% confidence that an object is tree canopy.

Urban Tree Canopy Assessment. Created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Includes all layers, but is too large to be viewed in ArcGIS Online. To view all of the layers, you can download the data and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

Coastal grid of 6-acre cells containing 2010 and 2019 canopy estimates, change values, and site visit observations for incorporated areas in Chatham, Glynn, Bryan, Liberty, McIntosh, and Camden Counties, Georgia. Grid created using the Fishnet tool in ArcGIS Pro. 2010 and 2019 canopy data was aggregated to each grid cell to determine change. Cells with significant change +- 3 acres of loss or gain were visually inspected to determine areas for site visits.

Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management. Methods Priority survey site selection: We selected sites for UAV emergent kelp canopy surveys using a prioritization framework for kelp recovery efforts based on data from OAV surveys, subtidal surveys, areas of cultural significance, areas of economic significance, accessibility, and proximity to marine protected areas (MPAs) (1). A total of 37 sites were identified in Mendocino and Sonoma Counties (i.e., the “North Coast”), hereafter referred to as ‘priority sites’. Ten of the sites are in actively managed state MPAs and 27 are in the Greater Farallones National Marine Sanctuary (NMS). Thirty-six of the 37 sites were surveyed with UAVs between 2019 and 2020, with 21 sites surveyed in both 2019 and 2020. The average priority site area was 1 km2 (range 0.2-1.7 km2).
UAV flights, timing and environmental sources of variation and error Due to the 90 km stretch of coastline within which the noncontiguous priority sites are located, numerous pilots participated in data collection and we developed a repeatable workflow building upon the efforts of Katherine C. Cavanaugh et al. 2021 to ensure data consistency. We obtained state and federal permits to allow UAV use in restricted areas and we established criteria for UAV launch sites (i.e., public coastal access, no large obstacles, flat area with minimal ecological impact potential, and located mid-way in the survey area to maintain telemetry link between the UAV and controller). We used small UAV platforms from the same manufacturer and each pilot selected their own flight software. Pilots flew at an altitude of 120 m above mean sea level with a minimum front and side overlap of 75%, nadir angle of the sensor, auto white balance, and UAV speeds between 10 to 12 m/s. The image processing softwares used included Agisoft Metashape, DroneDeploy, and Pix4D; all orthomosaics were reviewed by expert annotators and when output orthomosaics were incomplete or contained significant defects, the imagery was reprocessed using at least one of the two other software options.

All UAV pilots acquired imagery using the built-in Red-Green-Blue (RGB) sensor. We coordinated flights to coincide with the annual peak biomass of bull kelp, which typically occurs in late summer/early fall on the North Coast. Our team surveyed during the lowest tide series of the month and aimed to survey at the lowest tide of the day, as tidal height and surface currents have been shown to impact the amount of kelp canopy exposed on the water surface (2) and these impacts can vary regionally (3). Because sun angle, wind, and weather conditions varied significantly throughout the data collection process, surveys were not restricted to a specific daily tidal height or current speed; data were collected when field conditions allowed for stable UAV launch and landing and this structure resulted in random sampling throughout the tidal range within and between years, addressing sampling bias in our data. Kelp Detection, Classification, and Quantification We identified kelp pixels in each UAV image using a band combination between the red and blue bands (Red - Blue), which has been shown to best distinguish kelp from water in RGB-UAV imagery relative to other RGB vegetation indices (3). Before applying a threshold to our image, we manually masked all terrestrial objects (e.g., land and intertidal rocks). Due to radiometric and spectral variability present in the imagery, we manually selected thresholds to distinguish kelp from seawater. For individual sites with high levels of spectral variability due to turbidity, sun glint, or other artifacts, a single threshold could not be used for kelp identification because the threshold varied throughout the image within a site (3). For these sites, we gridded images into subsets (ranging from 1000 x 1000 m areas to 5000 x 5000 m areas, depending on the level of variability), and each grid was assigned a unique threshold. As a result, multiple thresholds were used for classification for these sites. We mosaicked the classified grids back to their original extent and manually reviewed all classified mosaics for quality assurance. We used binary classification values (i.e., “Kelp” or “Not Kelp”) except for mixed-species marine algal beds and the occasionally blurred image, which were assigned “No Data” values. We worked in a GIS environment to determine the area of kelp at a given site by multiplying the number of kelp pixels by the area of the pixels (ArcGIS Pro 2.7).

Comparison to multi-decadal Landsat data:

To give multi-decadal temporal context to the UAV surveys, we examined long-term trends in kelp canopy dynamics along the North Coast using Landsat satellite imagery. (n=36). To control for differences in available reef habitat between priority sites, we selected the maximum area of kelp canopy (m2) that occurred within a site in each year and normalized that amount by the historical maximum extent of emergent kelp canopy (i.e., the cumulative area within a site where kelp was ever observed between 1984-2020) to produce a time series of annual, proportional coverage values. We also used Landsat emergent kelp canopy data to produce maps of canopy persistence at our case-study sites, where relative persistence was defined as the number of years from 1984-2020 in which a pixel contained kelp canopy (4). Maps of emergent kelp canopy for case-study sites during a given year used the maximum canopy area observed. (1) Hohman, R., Hutto, S., Catton, C. and F. Koe. 2019. Sonoma-Mendocino Bull Kelp Recovery Plan. Plan for the Greater Farallones National Marine Sanctuary and the California Department of Fish and Wildlife. Greater Farallones Association. San Francisco, CA. 166 pp. https://farallones.org/wp-content/uploads/2019/06/Bull-Kelp-Recovery-Plan-2019.pdf. (2) Britton-Simmons, K., Eckman, J. E. & Duggins, D. O. Effect of tidal currents and tidal stage on estimates of bed size in the kelp Nereocystis luetkeana. Marine Ecology Progress Series vol. 355 95–105 (2008). (3) Cavanaugh, K. C., Cavanaugh, K. C., Bell, T. W. & Hockridge, E. G. An Automated Method for Mapping Giant Kelp Canopy Dynamics from UAV. Front. Environ. Sci. Eng. China 0, (2021). (4) Bell, T. W., Allen, J. G., Cavanaugh, K. C., & Siegel, D. A. (2020). Three decades of variability in California’s giant kelp forests from the Landsat satellites. In Remote Sensing of Environment (Vol. 238, p. 110811). https://doi.org/10.1016/j.rse.2018.06.039

This map features Africa Land Cover at 30m resolution from MDAUS BaseVue 2013, referencing the World Land Cover 30m BaseVue 2013 layer.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.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.MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.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.

Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. A replacement for this item is available for your use. Esri recommends updating your maps and apps to use the new version. Forests are provided here as an excerpted subset of the World Land Cover 30m BaseVue 2013 layer. Separating forests is useful as a cartographic layer on environmentally oriented maps and analytically as a basis for ecosystem and habitat definition.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 4 classes of forest are featured 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).Woody 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.MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.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.

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.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.MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.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.