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Projected
GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
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NAD_1983_StatePlane_California_II_FIPS_0402_Feet
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Shelby Huth
Davey Resource Group
GIS Analyst
295 S. Water Street Suite 300
Kent
OH
44240
shelby.huth@davey.com
US
330-673-5685 ext. 8877
Shelby Huth
True
Shelby Huth
File Geodatabase Feature Class
external
37809efbadb20f616cbae487845d732b
Ali Koenig
Parks and Recreation Services, City of Napa
Parks Project Manager
1500 Jefferson Street, Napa, CA 94559
akoenig@cityofnapa.org
(707) 257-9955
True
Ali Koenig
Tina McKeand
Davey Resource Group
Urban & Community Forestry Specialist
6005 Capistrano Ave, Suite A
Atascadero
Califonia
93422
tina.mckeand@davey.com
US
928-246-7048
8:00 am - 5:00pm PST
Tina McKeand
Tina McKeand
BlockGroups
The purpose of this project was to conduct a top down canopy assessment approach. Utilizing the most current 2018 National Agricultural Imagery Program (NAIP) 60cm imagery and advance remote sensing technology, land cover features were identified by using an object-based image analysis (OBIA) methodology to process and analyze high resolution imagery. This technique allows a more accurate and cost-effective automated feature extraction of land cover classes. The final GIS land cover layer allows the city of Napa, California to conduct additional spatial analyses necessary to identify and map the existing land cover layer for future.
City of NapaCaliforniaNapa CountyUrban tree canopy (UTC)Tree CanopyPervious surfaceOpen waterImpervious surfaceBare soilLand coverCensus BlocksBlock Group
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-size:10pt">With the size of the study area measured at approximately 18.38 square miles, a cost-effective and accurate strategy for assessing the urban forest is the use of remotely sensed and semi-automated classification methods to inventory the current canopy cover and to analyze data for future planting goals.</SPAN></P></DIV></DIV></DIV>
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-size:10pt">As determined by Napa, California</SPAN></P></DIV></DIV></DIV>
As determined by Napa, California
Tina McKeand
Davey Resource Group
Urban & Community Forestry Specialist
6005 Capistrano Ave, Suite A
Atascadero
Califonia
93422
tina.mckeand@davey.com
US
928-246-7048
8:00 am - 5:00pm PST
Tina McKeand
Tina McKeand
Version 6.2 (Build 9200) ; Esri ArcGIS 10.9.1.28388
1
-122.359002
-122.222165
38.351145
38.208437
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-size:10pt">As determined by Napa, California</SPAN></P></DIV></DIV></DIV>
As determined by Napa, California
These data are believed to be logically consistent, though no formal test were performed. Structural integrity is consistent within the parameters for ESRI ArcGIS 10 Geodatabase format.
Land cover acreage are calculated using the ArcGIS field calculator and percentage of coverage per land cover class are determined by dividing the current acreage for each class by the total area of the City.
Using advanced GIS and remote sensing software, Davey's GIS Analyst utilized a top-down, automated feature extraction approach to process and identify an initial 3-class land cover layer of canopy, impervious surfaces, and all other landcover. ArcGIS 10 and Overwatch's Feature Analyst were used to complete the initial automated feature extraction. Ancillary GIS data, provided by the City, was also utilized to aid in the initial extraction and the quality control process. Bare soils were digitized by a GIS Techinican so they are not confused with impervious surfaces. The open water layer was created by combining hydrologic datasets from the city as well as manually panning through the city limits to identify any new water features they may have not been included in the original data. After extraction and editing, the initial 4-class land cover layer included: Tree/shrubs, Impervious surfaces, bare soils and open water. These features were imported into a custom model to generate the fifth land cover class which included pervious surfaces (grass and meadow areas).
Feature extraction provides a much more cost effective way of creating land cover layers than manually digitizing images
The main QC process was completed at a scale of 1:2000 by GIS Technicians and Specialists in order to ensure that the land cover classification layer was 90% to 95% accurate. During this process, editing was completed using ArcGIS® to identify, clean, and correct any misclassification or topology errors in the final land cover dataset. The use of secondary sources like the Map2Google tool in ArcGIS® and Microsoft's Birds Eye View was also be used to aid in the quality check process. Once the initial intensive QC process was completed, each of the layers were examined by the GIS Analyst to correct any missed misclassifications and also to add any additional features, if necessary.
Multiple QC procedures ensure that Davey will provide the best possible land cover results.
Metadata created.
Random plot locations were generated and verified to ensure that the data met client standards and assured that automated mapping and data analysis performed by GIS specialists accurately reflected the true nature and extent of the land cover. A total of 1,000 points were generated for the city of Napa, Califonia. Each point was examined and a confusion matrix was created to calculate overall accuracy, Kappa coefficient, and a user's and producer's accuracy.
Davey statistically tests all land cover data to ensure the urban tree canopy assessment yields statistically significant results.
After completing the accuracy assessments, the final comprehensive land cover dataset was processed in ArcGIS® using geoprocessing, analysis, and data management tools to measure the existing land cover layer. Additional GIS analysis was completed using current data sources for further investigation and in support of the urban canopy assessment process. Area and percentages of land cover classes were calculated for each type and metadata was created.
To analyze land cover metrics and deliver all data in an organized fashion.
BlockGroups
Feature Class
69
OBJECTID
FID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
GISJOIN
GISJOIN
String
15
0
0
GIS identifying number
STATEFP
STATEFP
String
2
0
0
COUNTYFP
COUNTYFP
String
3
0
0
TRACTCE
TRACTCE
String
6
0
0
BLKGRPCE
BLKGRPCE
String
1
0
0
GEOID
GEOID
String
12
0
0
UniqID
UniqID
Integer
4
0
0
Unique identification number
CANOPY_ACRES
CANOPY_ACRES
Double
8
0
0
Amount of tree canopy acreage in a given feature boundary.
CANOPY_PERCENT
CANOPY_PERCENT
Double
8
0
0
Tree canopy percentage as a part of the total feature geometry.
IMPERVIOUS_ACRES
IMPERVIOUS_ACRES
Double
8
0
0
Amount of impervious surface acreage in a given feature boundary.
IMPERVIOUS_PERCENT
IMPERVIOUS_PERCENT
Double
8
0
0
Impervious surface percentage as a part of the total feature geometry.
PERVIOUS_ACRES
PERVIOUS_ACRES
Double
8
0
0
Amount of pervious surface acreage in a given feature boundary.
PERVIOUS_PERCENT
PERVIOUS_PERCENT
Double
8
0
0
Pervious surface percentage as a part of the total feature geometry.
BARE_SOIL_ACRES
BARE_SOIL_ACRES
Double
8
0
0
Amount of bare soil acreage in a given feature boundary.
BARE_SOIL_PERCENT
BARE_SOIL_PERCENT
Double
8
0
0
Bare soil percentage as a part of the total feature geometry.
WATER_ACRES
WATER_ACRES
Double
8
0
0
Amount of open water acreage in a given feature boundary.
WATER_PERCENT
WATER_PERCENT
Double
8
0
0
Open water percentage as a part of the total feature geometry.
ACRES
ACRES
Double
8
0
0
Area expressed in acres
CANOPY_ACRES_2012
CANOPY_ACRES_2012
Double
8
0
0
Canopy area expressed in acres for 2012
CANOPY_PERCENT_2012
CANOPY_PERCENT_2012
Double
8
0
0
Percent of canopy in 2014
DIFFERENCE_ACRES_12_22
DIFFERENCE_ACRES_12_22
Double
8
0
0
The difference in acres between 2012 and 2022
PERCENT_CHANGE_12_22
PERCENT_CHANGE_12_22
Double
8
0
0
The percent change from 2012 to 2022
ABSOLUTE_CHANGE
ABSOLUTE_CHANGE
Double
8
0
0
The absolute change of the canopy from 2012 to 2022.
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
PREFERRABLE_ACRES
PREFERRABLE_ACRES
Double
8
0
0
Potential additional tree canopy acreage in a given feature boundary.
PREFERRABLE_PERCENT
PREFERRABLE_PERCENT
Double
8
0
0
Potential additional tree canopy percentage as a part of the total feature geometry.
MAXIMUM_UTC_PERCENT
MAXIMUM_UTC_PERCENT
Double
8
0
0
Represents the maximum amount of tree canopy possible by adding current tree canopy percentage and the preferrable percentage together. Value is represented as a percentage.
dataset
EPSG
5.3(9.0.0)
69
Simple
FALSE
69
TRUE
FALSE
20240509
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