Land_Cover/Wetlands_2024 (ImageServer)

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Service Description: This layer is a high-resolution wetlands layer for Virginia Beach, Virginia. Three wetlands classes were mapped: (1) emergent wetlands, (2) shrub/scrub wetlands, and (3) forest wetlands at 1 Foot resolution. The primary sources used to derive this wetlands layer were 2024 LiDAR data and 2023 NAIP imagery. Wetlands were classed based on the average height of vegetation in the 2024 LiDAR. The emergent class represents wetlands where the average vegetation height ranged from 0-6ft. The shrub/scrub class represents wetlands where the average vegetation height ranged from 6ft - 20ft. The forest class represents wetlands where the average vegetation height is above 20ft. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. The dataset was created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). 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. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.

Name: Land_Cover/Wetlands_2024

Description: This layer is a high-resolution wetlands layer for Virginia Beach, Virginia. Three wetlands classes were mapped: (1) emergent wetlands, (2) shrub/scrub wetlands, and (3) forest wetlands at 1 Foot resolution. The primary sources used to derive this wetlands layer were 2024 LiDAR data and 2023 NAIP imagery. Wetlands were classed based on the average height of vegetation in the 2024 LiDAR. The emergent class represents wetlands where the average vegetation height ranged from 0-6ft. The shrub/scrub class represents wetlands where the average vegetation height ranged from 6ft - 20ft. The forest class represents wetlands where the average vegetation height is above 20ft. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. The dataset was created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). 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. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.

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: U8

RasterFunction Infos: {"rasterFunctionInfos": [ { "name": "VBWetlands_2024", "description": "Raster Fucntion tempalte for 2024 wetlands layer", "help": "" }, { "name": "VBWetlands_2018", "description": "Raster Function Template for 2018 Wetlands.", "help": "" }, { "name": "None", "description": "", "help": "" } ]}

Mensuration Capabilities: Basic

Inspection Capabilities:

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 Virginia Beach.

Service Data Type: esriImageServiceDataTypeGeneric

Min Values: 0

Max Values: 3

Mean Values: 0.22248082534654

Standard Deviation Values: 0.6998145488445

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