Land_Cover/Land_Cover_2018 (ImageServer)

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Service Description: High resolution land cover dataset for Virginia Beach, Virginia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2018 LiDAR data and 2018 NAIP imagery. Ancillary data sources included GIS data provided by Virginia Beach, Virginia Beach, Virginia or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3500 and all observable errors were corrected.

Name: Land_Cover/Land_Cover_2018

Description: High resolution land cover dataset for Virginia Beach, Virginia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2018 LiDAR data and 2018 NAIP imagery. Ancillary data sources included GIS data provided by Virginia Beach, Virginia Beach, Virginia or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3500 and all observable errors were corrected.

Single Fused Map Cache: false

Extent:

Initial Extent:

Full Extent:

Pixel Size X: 1.0

Pixel Size Y: 1.0

Band Count: 1

Pixel Type: U4

RasterFunction Infos: {"rasterFunctionInfos": [ { "name": "Colormap_LC_2018", "description": "A raster function template.", "help": "" }, { "name": "None", "description": "Make a Raster or Raster Dataset into a Function Raster Dataset.", "help": "" } ]}

Mensuration Capabilities: Basic

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

Rendering Rule:

Min Scale: 0

Max Scale: 0

Copyright Text: University of Vermont Spatial Analysis Laboratory in collaboration with Tree Canopy Change Assessment Virginia Beach 2012-2018.

Service Data Type: esriImageServiceDataTypeGeneric

Min Values: 1

Max Values: 7

Mean Values: 2.6547818894936

Standard Deviation Values: 1.7445818350972

Object ID Field:

Fields: None

Default Mosaic Method: Center

Allowed Mosaic Methods:

SortField:

SortValue: null

Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Bilinear

Max Record Count: null

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: null

Max Mosaic Image Count: null

Allow Raster Function: true

Allow Copy: true

Allow Analysis: true

Allow Compute TiePoints: false

Supports Statistics: false

Supports Advanced Queries: false

Use StandardizedQueries: true

Raster Type Infos:

Has Raster Attribute Table: true

Edit Fields Info: null

Ownership Based AccessControl For Rasters: null

Child Resources: Info Raster Attribute Table Histograms Statistics Key Properties Legend Raster Function Infos

Supported Operations: Export Image Identify Measure Compute Histograms Compute Statistics Histograms Get Samples Compute Class Statistics Query Boundary Compute Pixel Location Compute Angles Validate Project