LandLandcov_Wetlands2022.tif

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Metadata:

Identification_Information
Data_Quality_Information
Spatial_Data_Organization_Information
Entity_and_Attribute_Information
Distribution_Information
Metadata_Reference_Information

Identification_Information:

Citation:

Citation_Information:

Originator: University of Vermont Spatial Analysis Laboratory
Publication_Date: 20240320
Title: LandLandcov_Wetlands2022.tif
Geospatial_Data_Presentation_Form: remote-sensing image

Description:

Abstract:: This dataset depicts wetlands in Vermont. This dataset is an update to a previously made landcover wetlands dataset in 2016. This update was part of a larger project to update several statewide landcover datasets and uses imagery from both 2021 and 2022 for this updated layer. The landcover wetlands dataset in 2016 was created with an automated feature-extraction process that relied on high-resolution LiDAR and multispectral imagery. In particular, a Compound Topographic Index (CTI) layer derived from LiDAR was used to identify landscape features that have suitable topography and flow potential for wetlands. Moderate-scale (10 m) statistical models developed by Patrick Raney of Ducks Unlimited were also used during classification in a data-fusion approach that maximized the value of individual inputs. After initial identification, mapped features were assigned to one of three primary wetlands classes: Emergent, Scrub\Shrub, and Forested. The class assignments were based primarily on vegetation height (as estimated from LiDAR) and spectral characteristics (e.g., features with short vegetation that appeared very bright in leaf-off multispectral imagery were assigned to the Emergent category). The initial map was then generalized to eliminate unnecessary detail using a minimum mapping unit of 0.1 acres. In a final step, the automated output was manually reviewed against multispectral imagery and obvious errors of commission and omission were corrected. More than 57,000 manual corrections were incorporated into the final layer. Overall, the combination of automated feature extraction and manual corrections was biased toward over-prediction, focusing on capture of borderline features whose functional status cannot be definitvely established with remote-sensing data alone (i.e., it is generally easier to discount false wetland features than it is to locate omitted ones). Known areas of overestimation include managed forestlands with extensively-modified drainage patterns and wide river and stream channels.
Purpose:: This dataset was developed as part of the Updated High-Resolution Land Cover Mapping for Vermont. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area.
Supplemental_Information:: This assessment was completed by the University of Vermont's Spatial Analysis Laboratory with funding from the State of Vermont. The methods used were developed by the University of Vermont Spatial Analysis Laboratory in partnership with the USDA Forest Service. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing tree canopy goals.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Currentness_Reference: State of Vermont; 2021/2022

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -73.454160
East_Bounding_Coordinate: -71.465281
North_Bounding_Coordinate: 45.018369
South_Bounding_Coordinate: 42.722638

Keywords:

Theme:

Theme_Keyword_Thesaurus: None
Theme_Keyword: tree canopy
Theme_Keyword: Urban
Theme_Keyword: UTC
Theme_Keyword: Land cover

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Categories
Theme_Keyword: imageryBaseMapsEarthCover

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: Vermont
Place_Keyword: Vermont
Place_Keyword: Vermont

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2021/2022

Access_Constraints: None

Use_Constraints: None

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: University of Vermont Spatial Analysis Laboratory
Contact_Person: Jarlath O'Neil-Dunne

Contact_Position: Director

Contact_Address:

Address_Type: mailing and physical
Address: Aiken Room 205
Address: 81 Carrigan Drive
City: Burlington
State_or_Province: VT
Postal_Code: 05405
Country: US

Contact_Voice_Telephone: 802-656-3324

Contact_Facsimile_Telephone: 802-656-8683

Contact_Electronic_Mail_Address: joneildu@uvm.edu

Data_Set_Credit:

University of Vermont Spatial Analysis Laboratory in collaboration with Vermont Center for Geographic Information.

Native_Data_Set_Environment: Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.0.10450

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:: No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control (QC). Accuracy assessments from previous UTC land cover datasets indicate that the use OBIA techniques in combination with a detailed QC yield an overall accuracy of 95% or better.

Logical_Consistency_Report: Valid raster dataset.

Completeness_Report:

Complete coverage for the study area using the most recent boundary data.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Title: Planimetric data

Type_of_Source_Media:

Source_Citation_Abbreviation: Planimetric data

Source_Contribution: Vector

Source_Information:

Source_Citation:

Citation_Information:

Title: National Agricultural Imagery Program 4-band data

Source_Scale_Denominator: 12000

Type_of_Source_Media:

Source_Citation_Abbreviation: NAIP

Source_Contribution: 4-band, leaf-on aerial imagery

Source_Information:

Source_Citation:

Citation_Information:

Title: LiDAR

Type_of_Source_Media:

Source_Citation_Abbreviation: LiDAR

Source_Contribution: LiDAR point clouds

Process_Step:

Process_Description:: Object based image analysis (OBIA) using a rule-based expert system
Process_Date: 20240308

Process_Step:

Process_Description: Manual corrections to land cover classification
Process_Date: 20240313

Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster

Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 509176
Column_Count: 313546

Entity_and_Attribute_Information:

Detailed_Description:

Entity_Type:

Entity_Type_Label: LandLandcov_Wetlands2022.tif
Entity_Type_Definition: UTC
Entity_Type_Definition_Source: UVM Spatial Analysis Laboratory

Attribute:

Attribute_Label: OID

Attribute_Definition: Internal feature number.

Attribute_Definition_Source: ESRI