| ArcGIS REST Services Directory |
| Home > services > USFS_roadside_targets_and_priorities_map_WFL1 (FeatureServer) > All Layers and Tables | | API Reference |
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.
The following tasks were performed to create the final spatial layer:
Task 1 – Data Collection and Field Collection
This task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals.
Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP).
The following aerial imagery was used for this project:
NAIP 2020 4-band 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer
NAIP 2022 CIR 60cm resolution aerial imagery for California - https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer
LA County Aerial Imagery 2017 – 0.7 inch resolution - https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer
2016 Aerial Imagery Monterey – 4-inch resolution - https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer
Riverside_County_2020_State - 4-inch resolution - https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer
2022 San Bernardino County Imagery- 4-inch resolution - https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer
San Diego 2020 – 9-inch resolution - https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer
San Diego County Imagery_2019 – 9-inch resolution - https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer
Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer
ESRI Imagery – 50cm resolution - https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer
Task 2 – Roadway Edge Digitizing and Buffering
Heads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge.
In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified.
Task 3 – Digitizing and Attributing Roadside Vegetation Polygons
The roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:
Ground Surface
1 Native
2 Cut Slope
3 Fill Slope
4 Urban
Cover Type
1 Conifer Forest
2 Oak Woodland
3 Mixed Conifer/Hardwood
4 Riparian
5 Shrubland
6 Grassland
7 Urban
8 Rock/Barren
Non-Native Cover
1 <10%
2 10-50%
3 50-75%
4 75-100%
5 Urban
Heads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.
Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.
Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.
Task 4 – Ground Truthing 5%
DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing.
Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.