Digital Classification and Mapping of Urban Tree Cover: City of Woodbury Metadata

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Digital Classification and Mapping of Urban Tree Cover: City of Woodbury



This page last updated: 09/28/2014Metadata created using Minnesota Geographic Metadata Guidelines


Metadata Summary

Originator Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota
Abstract Quickbrd multispectral imagery was used to classify the city of Woodbury land into land cover
Browse Graphic View a sample of the data
Time Period of Content Date 2009
Currentness Reference QuickBird satellite imagery acquired on June 9, 2009
(The image was clear and cloud-free except for a small cloud and shadow in the lower center of the image.)

Access Constraints The Remote Sensing and Geospatial and Analysis Laboratory, University of Minnesota, has attempted to produce accurate maps, statistics and information of land cover and impervious surface area. However, it makes no representation or warranties, either expressed or implied, for the data accuracy, currency, suitability or reliability for any particular purpose. Although every effort has been made to ensure the accuracy of information, errors and conditions originating from the source data and processing may be present in the data supplied. Users are reminded that all geospatial maps and data are subject to errors in positional and thematic accuracy. The user accepts the data “as is” and assumes all risks associated with its use. The University of Minnesota assumes no responsibility for actual or consequential damage incurred as a result of any user's reliance on the data. The data are the intellectual property of the University of Minnesota.The Remote Sensing and Geospatial and Analysis Laboratory, University of Minnesota, has attempted to produce accurate maps, statistics and information of land cover and impervious surface area. However, it makes no representation or warranties, either expressed or implied, for the data accuracy, currency, suitability or reliability for any particular purpose. Although every effort has been made to ensure the accuracy of information, errors and conditions originating from the source data and processing may be present in the data supplied. Users are reminded that all geospatial maps and data are subject to errors in positional and thematic accuracy. The user accepts the data “as is” and assumes all risks associated with its use. The University of Minnesota assumes no responsibility for actual or consequential damage incurred as a result of any user's reliance on the data. The data are the intellectual property of the University of Minnesota.
Use Constraints This data may be used for educational and non-commercial purposes, provided proper attribution is given. Secondary distribution of the data is permitted, but not supported by the University of Minnesota. By accepting the data, the user agrees not to transmit this data or provide access to it or any part of it to another party unless the user includes with the data a copy of this disclaimer.This data may be used for educational and non-commercial purposes, provided proper attribution is given. Secondary distribution of the data is permitted, but not supported by the University of Minnesota. By accepting the data, the user agrees not to transmit this data or provide access to it or any part of it to another party unless the user includes with the data a copy of this disclaimer.
Distributor Organization Remote Sensing and Geospatial Analysis Lab, Univeristy of Minnesota
Ordering Instructions see website or contact infosee website or contact info
Online Linkage Click here to download data. (See Ordering Instructions above for details.) By clicking here, you agree to the notice in "Distribution Liability" in Section 6 of this metadata.


Full metadata for Digital Classification and Mapping of Urban Tree Cover: City of Woodbury


Go to Section:
1. Identification_Information
2. Data_Quality_Information
3. Spatial_Data_Organization_Information
4. Spatial_Reference_Information
5. Entity_and_Attribute_Information
6. Distribution_Information
7. Metadata_Reference_Information


Section 1 Identification Information Top of page
Originator Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota
Title Digital Classification and Mapping of Urban Tree Cover: City of Woodbury
Abstract The project objective was to generate a digital land cover classification of the City of Woodbury in GIS-compatible format, with emphasis on mapping the tree cover that can be used by the City to evaluate existing tree cover and potential for additional plantings. Tree cover is defined as the leaves, branches and stems cover the ground when viewed from above.The project objective was to generate a digital land cover classification of the City of Woodbury in GIS-compatible format, with emphasis on mapping the tree cover that can be used by the City to evaluate existing tree cover and potential for additional plantings. Tree cover is defined as the leaves, branches and stems cover the ground when viewed from above.
Purpose The project objective was to generate a digital land cover classification of the City of Woodbury in GIS-compatible format, with emphasis on mapping the tree cover that can be used by the City to evaluate existing tree cover and potential for additional plantings. Tree cover is defined as the leaves, branches and stems covering the ground when viewed from above.
Time Period of Content Date 2009
Currentness Reference QuickBird satellite imagery acquired on June 25, 2009

Progress Complete
Maintenance and Update Frequency None planned
Spatial Extent of Data Woodbury, Minnesota, USA
Bounding Coordinates -92.9848130
-92.8618562
44.9493863
44.8617595
Place Keywords Woodbury, Minnesota, USA
Theme Keywords Urban Tree Cover, Impervious surface, QuickBird, remote sensing
Theme Keyword Thesaurus
Access Constraints The Remote Sensing and Geospatial and Analysis Laboratory, University of Minnesota, has attempted to produce accurate maps, statistics and information of urban tree cover, land cover, and impervious surface area. However, it makes no representation or warranties, either expressed or implied, for the data accuracy, currency, suitability or reliability for any particular purpose. Although every effort has been made to ensure the accuracy of information, errors and conditions originating from the source data and processing may be present in the data supplied. Users are reminded that all geospatial maps and data are subject to errors in positional and thematic accuracy. The user accepts the data “as is” and assumes all risks associated with its use. The University of Minnesota and project affiliates assume no responsibility for actual or consequential damage incurred as a result of any user's reliance on the data. The data are the intellectual property of the University of Minnesota.The Remote Sensing and Geospatial and Analysis Laboratory, University of Minnesota, has attempted to produce accurate maps, statistics and information of urban tree cover, land cover, and impervious surface area. However, it makes no representation or warranties, either expressed or implied, for the data accuracy, currency, suitability or reliability for any particular purpose. Although every effort has been made to ensure the accuracy of information, errors and conditions originating from the source data and processing may be present in the data supplied. Users are reminded that all geospatial maps and data are subject to errors in positional and thematic accuracy. The user accepts the data “as is” and assumes all risks associated with its use. The University of Minnesota and project affiliates assume no responsibility for actual or consequential damage incurred as a result of any user's reliance on the data. The data are the intellectual property of the University of Minnesota.
Use Constraints This data may be used for educational and non-commercial purposes, provided proper attribution is given. Secondary distribution of the data is permitted, but not supported by the University of Minnesota. By accepting the data, the user agrees not to transmit this data or provide access to it or any part of it to another party unless the user includes with the data a copy of this disclaimer.This data may be used for educational and non-commercial purposes, provided proper attribution is given. Secondary distribution of the data is permitted, but not supported by the University of Minnesota. By accepting the data, the user agrees not to transmit this data or provide access to it or any part of it to another party unless the user includes with the data a copy of this disclaimer.
Contact Person Information Marvin Bauer, Professor
Remote Sensing and Geospatial Analysis Lab, Univeristy of Minnesota
1530 Cleveland Avenue North
St. Paul , MN 55108
Phone: (612)624-3703
Fax: (612)625-5212
Email : mbauer@umn.edu
Browse Graphic View a sample of the data
Browse Graphic File Description
Associated Data Sets St. Paul and Minneapolis datasets also availableSt. Paul and Minneapolis datasets also available


Section 2 Data Quality Information Top of full metadata Top of page
Attribute Accuracy Accuracies for the state are reported in the supplemental map file titled: Woodbury Tree Canopy Mapping - Final Report.pdf
Logical Consistency
Completeness Data provides complete coverage of Woodbury, Minnesota, USA.Data provides complete coverage of Woodbury, Minnesota, USA.
Horizontal Positional Accuracy
Vertical Positional Accuracy
Lineage
The primary land classifications were produced using object based image analysis (OBIA) techniques available in eCognition Developer version 8.0. Ancillary software utilized in the project included ArcGIS version 9.3 and ERDAS Imagine version 2010. Additional customized routines were written in Python version 2.5 scripting language to support processing as required. Shapefile information was provided by the City of Woodbury to help identify streets, roads and highways and water features.
The following principle steps were followed to implement the project:
  • QuickBird Imagery was pan sharpened using subtractive resolution in ERDAS Imagine.
  • QuickBird Imagery was georeferenced utilizing the available RPC files and a 30 meter DEM layer.
  • A customized Python script was used to divide the georeferenced imagery into 750 x 1000 meter tiles with 10% overlap for further processing. This step created 180 individual tiles.
  • The planimetrics layer containing road information was processed using ET GeoWizards to close polylines and create polygons for subsequent use in eCognition.
  • A rule set was developed in eCognition for a representative tile:
    • Supportive image layers were created such as Normalized Difference Vegetation Index (NDVI) and Lee’s Sigma Edge Extraction to aid classification efficacy.
    • Image objects were generated representing roads and water features from shapefiles and classified as such.
    • Remaining portions of the image were classified utilizing algorithms available in eCognition taking advantage of spectral information as well as other elements of image interpretation such as context, shape, size, site, association, pattern, shadows and texture.
    • Classification was exported from eCognition into a TIF raster file.
  • The rule set was fine tuned and tested on an additional 15 random tiles spread throughout Woodbury.
  • The final rule set was used to classify all 180 tiles comprising Woodbury using eCognition Server.
  • Individual classified tiles were joined into a single mosaic using geometric seam lines in ERDAS Imagine Mosaic Pro.
  • The resulting mosaic was used to create an accuracy assessment in ERDAS Imagine using 1,217 stratified random points.
  • The classification mosaic was manually edited to create features for areas within the clouds and their shadows.
  • The classification mosaic was then manually examined and edited to eliminate errors.
  • Error corrections were re-run in eCognition Server to incorporate corrections.
  • The final land cover mosaic was manipulated by ERDAS Imagine and ArcGIS into the output geodatabase utilizing both raster and vector forms of the data.

Accuracy assessment of the final classification, including evaluation of a stratified random sample of 1,143 points and comparison to Woodbury tree inventory data. An object-oriented image analysis approach in which the imagery was first segmented into objects with similar pixels based on the spatial, 4
as well as the spectral-radiometric (color) attributes was used for the image classification. Research has shown that it is the best approach for classification of high resolution imagery (Blaschke, 2010; Platt and Rapoza, 2008). Objects include more information than individual pixels, enabling the ability to take advantage of all the elements of image interpretation, particularly spatial information, including shape, size, pattern, texture, and context. Context is especially useful. Humans intuitively integrate “pixels” into objects and use contextual relationships to interpret images and draw intelligent inferences from them. Ancillary data such as GIS layers of, for example, streets and water bodies, could also be incorporated into the decision rules.

The object-oriented image analysis process in eCognition can broadly be split into two components, segmentation and classification. Segmentation primarily uses spectral information about individual pixels in the imagery to combine them into larger image objects or segments. As an example, individual pixels which comprise the roof of, for example, a building are combined where the brightness, NDVI and color information are similar to form an image object that represents the building. Other scaling information can be specified to regulate the size range of the desired objects. Once these image objects are created, they can be classified using a multitude of decision rules which utilize not only their spectral characteristics but also spatial information such as shape, size, proximity to other object types, texture, etc. The overall process is very dependent on the quality of the initial segmentation into image objects.

Accuracy assessments were performed on the results both before and after the tiles were edited for corrections. The accuracy assessment was executed by generating stratified random points across the image and comparing the classified results to reference imagery (color ortho photos provided by the City and imagery from ArcGIS online). Stratified random point selection assures each class will be weighted proportionately to the total number of features in that class across the image. There were 1,217 points in the before correction sample and 1,105 in the after correction sample. The difference is that the sample points that were initially on the image but just outside the administrative boundary of Woodbury were removed from consideration in the after correction sample.

In addition, the tree inventory locations provided by the City were overlaid on the final classification map for comparison.

Tabulation of the percent area of each of the six land cover classes.
Class
Percent
Tree Cover
21.5
Grass/Shrub
48.4
Impervious
13.8
Buildings
5.7
Water
7.0
Bare Soil
3.6
Total
100.0

Source Scale Denominator


Section 3 Spatial Data Organization Information Top of full metadata Top of page
Native Data Set Environment eCognition Developer version 8.0, ArcGIS version 9.3.1, and ERDAS Imagine version 2010
Geographic Reference for Tabular Data
Spatial Object Type Raster
Vendor Specific Object Types
Tiling Scheme


Section 4 Spatial Reference Information Top of full metadata Top of page
Horizontal Coordinate Scheme Universal Transverse Mercator
Ellipsoid Geodetic Reference System 80
Horizontal Datum NAD83
Horizontal Units Meters
Distance Resolution 30
Altitude Datum Not applicable
Depth Datum Not applicable
Cell Width
Cell Height
UTM Zone Number 15N


Section 5 Entity and Attribute Information Top of full metadata Top of page
Entity and Attribute Overview
Entity and Attribute Detailed Citation


Section 6 Distribution Information Top of full metadata Top of page
Publisher Remote Sensing and Geospatial Analysis Lab, Univeristy of Minnesota
Publication Date 04/12/2011
Contact Person Information Marvin Bauer, Professor
Remote Sensing and Geospatial Analysis Lab, Univeristy of Minnesota
1530 Cleveland Avenue North
St. Paul , MN 55108
Phone: (612)624-3703
Fax: (612)625-5212
Email: mbauer@umn.edu
Distributor's Data Set Identifier wood_final_classification_x.img
Distribution Liability This data may be used for educational and non-commercial purposes, provided proper attribution is given. Secondary distribution of the data is permitted, but not supported by the University of Minnesota. By accepting the data, the user agrees not to transmit this data or provide access to it or any part of it to another party unless the user includes with the data a copy of this disclaimer.This data may be used for educational and non-commercial purposes, provided proper attribution is given. Secondary distribution of the data is permitted, but not supported by the University of Minnesota. By accepting the data, the user agrees not to transmit this data or provide access to it or any part of it to another party unless the user includes with the data a copy of this disclaimer.
Transfer Format Name HFA/Erdas Imagine Images (.img)
Transfer Format Version Number
Transfer Size
Ordering Instructions see website or contact infosee website or contact info
Online Linkage Click here to download data. (See Ordering Instructions above for details.) By clicking here, you agree to the notice in "Distribution Liability" in Section 6 of this metadata.


Section 7 Metadata Reference Information Top of full metadata Top of page
Metadata Date 12/05/2006
Contact Person Information Marvin Bauer, Professor
Remote Sensing and Geospatial Analysis Lab, Univeristy of Minnesota
1530 Cleveland Avenue North
St. Paul , MN 55108
Phone: (612)624-3703
Fax: (612)625-5212
Email: mbauer@umn.edu
Metadata Standard Name Minnesota Geographic Metadata Guidelines
Metadata Standard Version 1.2
Metadata Standard Online Linkage http://www.gis.state.mn.us/stds/metadata.htm


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