Wood River Valley Forest Enhancement GIS Assessment Blaine County, Cities of Sun Valley & Ketchum

Prepared by:

Ecosystem Sciences Foundation The Keystone Concept www.ecosystemsciences.com www.thekeystoneconcept.com

July 2020 Ecosystem Sciences Foundation and The Keystone Concept

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Wood River Valley (WRV) Forest Enhancement GIS Assessment Ecosystem Sciences Foundation and The Keystone Concept

Table of Contents

Introduction ...... 1 Deliverables ...... 2 Project Area ...... 2 Methods ...... 4 Land Cover ...... 4 Urban Tree Canopy (UTC) ...... 6 Exclusion Layer ...... 6 Reclassify Land Cover and apply Exclusion ...... 6 Priority Planting Areas (PPAs) ...... 7 Results ...... 8 Project Area ...... 8 Land Cover...... 8 Urban Tree Canopy ...... 9 Priority Planting Areas (PPAs) ...... 10 Blaine County ...... 11 Land Cover...... 11 Urban Tree Canopy ...... 12 Priority Planting Areas (PPAs) ...... 14 Ketchum ...... 16 Land Cover...... 16 Urban Tree Canopy ...... 18 Priority Planting Areas (PPAs) ...... 20 Sun Valley ...... 22 Land Cover...... 22 Urban Tree Canopy ...... 24 Priority Planting Areas (PPAs) ...... 26 County and City Specific Geodatabases ...... 28 Summary and Conclusions ...... 30 Literature Cited ...... 32

Wood River Valley (WRV) Forest Enhancement GIS Assessment Ecosystem Sciences Foundation and The Keystone Concept

Introduction The Wood River Valley (WRV) Forest Enhancement Geographic Information System (GIS) Assessment provides high-resolution data (land cover, urban tree canopy and priority tree planting locations) that assists Blaine County and the cities of Ketchum and Sun Valley in the identification and prioritization of land management and land use planning decisions (Figure 1). The data created through this project provides information related to emergency preparedness and disaster planning, forest treatments in the Wildland Urban Interface (WUI), river/stream restoration, stormwater management, street tree planting and management (to enhance various aspects of communities including economic development), and forest health treatments. Although the focus of the project relates to forests (within the cities and county lands), non-forested (streets, buildings, bare ground etc.) areas are mapped as well. Similar data has been created for cities and counties throughout Idaho (Plan-it Geo 2013, Plan-it Geo and Ecosystem Sciences 2018). Such data has been integral to informed and effective decision making for municipalities within these regions.

This GIS Assessment is the foundational piece of a larger project called the Wood River Valley (WRV) Collaborative Forest Enhancement Project. The WRV Collaborative includes Blaine County and the area’s four cities (Ketchum, Sun Valley, Hailey and Bellevue), several public, state and federal agencies (e.g. BLM and USFS, IDL), as well as numerous non-profit partners (TU, WRLT, SVI, etc.). The WRV Collaborative developed a $300,000 grant funding proposal for a community forest enhancement project sponsored by the Idaho Department of Lands (IDL) for the USDA Forest Service (USFS) Landscape Scale Restoration (LSR) Program. The WRV specific data derived through this GIS assessment provides the foundation for the LSR project. The GIS data informs the drafting of Urban Forest Management Plans for the 5 entities (county and 4 cities) and the prioritization of on-the-ground actions (e.g. forest health, tree planting, fuels reduction etc.). Funding through the LSR grant is also available to the County and cities for on-the- ground management activities.

As mentioned above, the GIS data created through this project provides local scale data that informs active forest management, conservation, and promotion (growth) of tree canopy throughout the WRV. The primary focus of the project is the populated areas of the WRV, including lands within municipal boundaries and County lands within the WUI. Forested lands within the WUI often abut federal land, enabling the data to be used to inform joint management (e.g. forest health or fuel reduction projects occurring on federal land expanded to adjacent private or state land). The data produced by this GIS Assessment includes 2-meter resolution land cover and urban tree canopy data (an improvement from the currently available 30-meter land cover data [National Land Cover Data or NLCD]), as well as prioritized planting locations. The fine scale data will assist the county and cities in managing its natural resources and infrastructure in many ways, including:

• Identify specific locations for the management and mitigation of wildland fire risk • Improve forest health through strategic forest management activities (forest health treatments, riparian and forest restoration)

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• Improve water quality through strategic planting and identification of urban infrastructure enhancements (suspended pavement systems aka silva-cells) that intercept and clean stormwater flowing to the Big Wood River • Reduce flood danger through riparian plantings – improve floodplain condition through plantings • Community infrastructure enhancements (tree planting to improve public safety, economic development, walk-ability and bike-ability) • Improvements to address environmental justice issues through strategic tree planting

Deliverables Several deliverables are provided through this project including data and a report that details the methods and results of the GIS Assessment. The GIS data is fully documented, which means that metadata is provided. The primary deliverable is a File Geodatabase containing three GIS datasets:

• Land Cover data (Raster and Feature Class): This layer breaks the project area into 13 classes and serves as the baseline input for UTC layer classification and the identification of priority planting areas (PPAs). Land cover classes (e.g. tree canopy, irrigated vegetation, road etc.) represent the vegetative, soil, or built environment that covers the project area when viewed from an aerial perspective. • Urban Tree Canopy (UTC) data (Raster and Feature Class): Land cover classes are grouped into eight UTC classes. UTC classes categorize the landscape to support urban forest management through the identification of Tree Canopy and Potential Planting Areas (vegetative and impervious). • Priority Planting Areas (PPAs) (Point Feature Class): PPAs are sites where trees can be planted and correspond with the UTC layer’s “PPA Vegetation” (Possible Planting Area – Vegetation) and “PPA Impervious” (Possible Planting Area - Impervious). PPAs are prioritized for several ecosystem services (e.g. Air Quality, Water Quality, Riparian Restoration and Wildlife habitat).

Project Area The project area for the WRV Forest Enhancement GIS Assessment encompasses over 15,000 acres (Table 1) and includes portions of Blaine County and the cities within the Wood River Valley (Figure 1) (Hailey and Bellevue are not included in this assessment). For this GIS assessment the project area is 11,882 acres (Table 1).

Table 1. Acres per entity in the project area. Name Acres Percent Blaine County 3,669 24% Ketchum 2,089 14% Sun Valley 6,126 40% Total 11,882 100%

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Figure 1. WRV Forest Enhancement Project Area (Bellevue and Hailey are not included at this time)

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Methods This section describes the methods used to derive this urban tree canopy assessment GIS data. The process begins with mapping land cover, which is then used to generate metrics to determine UTC classes and identify priority planting areas.

Land Cover The most fundamental component of an urban tree canopy assessment is the creation of an initial land cover dataset. Remote sensing was used to determine the land cover classes. Remote sensing and file geodatabase creation occurred in ESRI’s ArcGIS Pro. The remote sensing medium (imagery and data) used to create the land cover data was 2019 high- resolution (4-band, 1-meter) aerial imagery from the USDA’s National Agricultural Imagery Program (NAIP) and 2015 Lidar data of the Wood River Valley (Quantum Spatial 2016). A mixed Remote Sensing methodology relying on image segmentation and object- oriented techniques was employed to derive the land cover classes.

Image segmentation breaks an image, or dataset, into segments (classes) allowing for specific analysis within those “segments”. Segmenting an image removes much of the noise (variations in image brightness values) during the classification process. For example, the Blaine County “all roads” layer was employed to derive a right-of-way (ROW) layer, which was used to analyze the spectral (brightness) characteristics of the NAIP imagery specifically within the ROW. Several image segmentation layers were created during the remote sensing process: 1. Normalized Difference Vegetation Index (NDVI) (2019 NAIP), 2. Slope Raster (2015 Lidar), 3. Height Raster (2015 Lidar), and 4. the aforementioned ROW layer (Blaine County’s all roads feature class).

Object oriented remote sensing allows for the software to define objects, in this case tree canopy and buildings. Spectral characteristics from the 2019 NAIP imagery were combined with height information from the 2015 lidar to determine “objects” (tree canopy and buildings) in ESRI’s ArcGIS Pro.

The image segmentation and object-oriented remote sensing technique resulted in 32 initial land cover classes. A Majority Filter was applied to the initial land cover layer (32). The majority filter tool replaces cells in a raster based on contiguous neighboring cells (ESRI 2019). Following the majority filter application, an “Eliminate tool” was used to remove polygons under 4m2 (43ft2). The resultant layer required some minor editing and merging of the initial 32 land cover classes to get to the desired 12 land cover classes.

Tree canopy was one of the resultant 12 land cover classes. Tree canopy was stratified into “deciduous” and “coniferous” based on the spectral signatures of the 2019 NAIP imagery. This step entailed traditional remote sensing where only the spectral signatures of the imagery’s 4 bands (R, G, B and NIR) are used to determine classes. Only the “tree canopy” area, identified through the image segmentation/object oriented remote sensing, was analyzed to determine deciduous v. coniferous canopy. This final step resulted in the 13 land cover classes described in Table 2.

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Table 2. Land cover classes, descriptions, codes, and inclusions Land Cover Class Code Description Inclusions Irrigated or water-dependent non-canopy vegetation, generally less than 0.2m tall (7 inches) but may include vegetation up to 2m. In urban areas this cover class relates to lawn/parks/ball fields. Non-urban areas this cover class 102 Irrigated is water-dependent (riparian, within drainage areas, or related to 201 Vegetation 101 seeps/springs). 202 Green non-canopy vegetation that may or may not depend on irrigation or water. Landscaped areas are common in this class. Vegetation in this cover class is less than 2m (6.6ft) and generally greater than 0.2m. In urban areas, 101 Non- this cover class includes fringe green vegetation adjacent to roads, lawns, 103 Irrigated and tree canopy. In non-urban areas this class includes green shrubs and 201 Vegetation 102 small trees that are not connected to water sources. 202

Non-canopy vegetation dominated by scrub and shrub vegetation types (Sagebrush, Bitterbrush etc.). This cover class is naturally occurring in the 102 Scrub/Shrub 103 uplands area and can be found within the urban areas as well. 301 101 Tree Canopy vegetation that is primarily broadleaf deciduous. In urban areas this 102 Canopy – class can includes native and non-native hardwoods. In a natural setting this 103 Deciduous 201 class includes cottonwood, alder, willow, maple etc. 202 Canopy vegetation that is primarily coniferous. In urban areas this class 101 Tree includes planted coniferous trees such as Blue Spruce (amongst other 102 Canopy – species). In a non-urban setting this class includes Douglas fir, Lodgepole 103 Coniferous 202 pine Ponderosa pine etc. 201 502 This cover class includes bare ground and soil that may or may not include 503 Soil and Dry dry vegetation. If vegetation is present within this class, it is generally less 504 Vegetation 301 than 0.2m (7 inches). 505 This non-canopy vegetation cover class focuses primarily on row crops. Some pasture may be included, but generally this cover class includes 101 Agriculture 401 planted agriculture. 102

This impervious cover class includes elements of the built environment 503 greater than 2m (6.6ft); structures, residences, apartments, commercial and 504 Building 501 industrial buildings, outbuildings and in some cases fences. 505 503 This impervious cover class includes pavement within the right-of-way 504 Road 502 (ROW). 505 This impervious cover class includes pavement areas adjacent to commercial areas. Blaine County did not have a parking lot layer, so this 502 class underestimates the extent and number of parking lots within the project 504 Parking Lot 503 area. 505 502 This impervious cover class delineates sidewalks within the project area. 503 Sidewalk 504 Sidewalk is limited to the extent "Bike Trail" layer provided by Blaine County. 505 This impervious cover class includes non-vegetative impervious areas 501 throughout the project area. This class includes patios and concrete areas 502 Other adjacent to buildings and non-classified sidewalks, parking lots, and road. 503 Impervious 505 This is a very inclusive cover class. 504 This cover class includes lakes, rivers, creek, canals and ponds. This class does not include pools. This class represents water on the 2019 NAIP Water 901 imagery on the date it was acquired.

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Table 2 above describes the land cover classes found in the file geodatabase (land cover dataset). The land cover classes must be understood within the context of the greater landscape. For example, irrigated vegetation in the urban environment is easy to understand as it is most likely lawn or some other “landscaped vegetation” that is dependent on water. Users will note that this class occurs in the uplands or “non-urban” areas. In this landscape context “irrigated vegetation” must be viewed as a land-cover class that is dependent on a natural source of water; a nearby spring or being in an ephemeral or seasonal waterway. Similarly, soil and dry vegetation occurs within the channel of the Big Wood River. In this context soil and dry vegetation may be referring to cobble or gravel bars, essentially a “bare ground” cover type within the channel.

Table 2 includes a column named “Inclusions.” Inclusions are land cover types that are often included within another land cover type. For example, a portion of “soil and dry vegetation” may be classified as “road” or “other impervious.” This occurs when the spectral characteristics are similar, or the area was “included” with a neighboring larger polygon because of its size (less than 4m2). Inclusions are a common occurrence in “high- resolution” remote sensing applications (Robinson et. al), as high-resolution images vary based on atmospheric conditions, ground conditions, time of day or season the image was acquired, and local variation of building materials (e.g. roofs material and pavement types). The initial land cover dataset included over 700,000 pixels, and therefore hand-editing inclusions is not practical. Therefore, understanding the inclusions per land cover class is important to using and understanding the data.

Urban Tree Canopy (UTC) The Urban Tree Canopy (UTC) raster and feature class layers were derived by reclassifying the land cover data and then applying an exclusion layer. The exclusion layer identifies areas that are not suitable for planting trees, primarily for public safety and tree survival.

Exclusion Layer The exclusion layer identifies areas where planting trees is not desirable. Exclusion areas limit the potential planting area to reduce the problems trees cause in terms of public safety (e.g. finding fire hydrants or obscuring driver viewshed at corners) while promoting tree survival (to allow for canopy to grow unobscured). The exclusion area was created by buffering several datasets and then merging them into one layer. The exclusion parameters are: • Buildings (501) = 4ft buffer (tree survival) • Fire Hydrants (BC) = 8ft buffer (public safety) • Street Intersections (BC) = 55ft buffer (public safety) • Tree Canopy [601] = 10ft buffer (tree survival)

Reclassify Land Cover and apply Exclusion The initial UTC layer is the land cover layer reclassified to the UTC classes (Table 3). For example, the UTC class “Tree Canopy” equates to the land cover classes “Tree Canopy - Deciduous” and “Tree Canopy – Coniferous” (Table 3). Several UTC classes are a one to one reclassification from the land cover (e.g. Unsuitable Agriculture, Water, Unsuitable

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Soil) (Table 3). Once the exclusion layer is applied to the UTC layer, portions of PPA Vegetation and PPA Impervious are changed to Unsuitable Vegetation and Unsuitable Impervious respectively (Table 3). Essentially, the exclusion layer changes potentially “suitable” vegetative and impervious planting areas to “unsuitable.” The final UTC layer provides the potential planting area within vegetative and impervious classes that will enable the identification of Priority Planting Areas (points where it is possible to plant trees).

Table 3. UTC classes, reclassified land cover included in UTC and if exclusion layer applied (Y/N) UTC UTC Layer Code Land Cover Class Included Exclusion Tree Canopy 601 Tree Canopy - Deciduous and Coniferous N PPA - Vegetation 611 Irrigated and non-irrigated vegetation Y PPA - Impervious 612 Parking Lot Y Unsuitable Vegetation 621 Scrub/Shrub + excluded PPA (Veg) Y Unsuitable Impervious 622 Impervious classes + excluded PPA (Imp) Y Unsuitable Agriculture 623 Agriculture N Unsuitable Soil 624 Soil and Dry Vegetation N Water 900 Water N

Priority Planting Areas (PPAs) Priority planting areas (PPAs) represent places where it is feasible to plant trees and the requisite resources (space for canopy to form, not under the canopy of other trees, access to water etc.) are available to promote survival. PPAs were derived by creating a 15m x 15m fishnet (50ft x 50ft) that covers the project area (Plan-it Geo and Ecosystem Sciences 2018). The fishnet is then intersected with the UTC classes PPA Vegetation and PPA Impervious. This intersection breaks the potential planting areas (vegetative and impervious) into 50ft grid cells. PPAs represent the center point of the 50ft grid cells, ensuring that any planting site is a minimum of 50ft from another planting site.

PPAs were then prioritized for Air Quality, Water Quality, Wildlife Habitat and Riparian Restoration. Prioritization occurred in ArcGIS Pro by intersecting the PPAs with the following feature classes: • Air Quality (AQ) = PPAs adjacent to streets (ROW) and within parking lots • Water Quality (WQ) = PPAs Within 100ft of surface water. • Wildlife Habitat Connectivity (WL) = PPAs within 100ft of large canopy areas (>5acres) and within 100ft of surface water • Riparian Restoration (RR) = within the National Flood Hazard Layer (surrogate for floodplain)

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Results Results presented herein include the entire project area (Blaine County area, and the Cities of Ketchum and Sun Valley) and individually for the Blaine County area, the City of Ketchum and the City of Sun Valley. Results are summarized for land cover (acreage and percent of area), UTC (acreage and percent of area), and PPAs (total and sum for each priority [AQ, WQ, RR, WL]).

Project Area The project area encompasses nearly 12,000 acres of Blaine County, Idaho and includes portions of the County as well the Cities of Ketchum and Sun Valley (Table 4). Maps for the project area are not included as the fine scale of the data does not display well at the broad scale of the project area.

Land Cover Scrub/Shrub (4,230 acres) is the most abundant land cover type (Table 4), accounting for 36% of the project area (Figure 2). Forest area (2,162 acres) encompasses 19% of the project area, with most of that area being Deciduous (15%) (Table 4) (Figure 2). Impervious land cover classes (Building, Road, Parking Lot, Sidewalk and Other Impervious) account for 13% of the project area (Table 4, Figure 2). Parking lot and sidewalk are both less than 1% of the project area (Figure 2), and these classes are underrepresented in this dataset.

Table 4. Project Area (PA) Land Cover Results PA Blaine Total Land Cover Class County Ketchum Sun Valley Acres PA % Irrigated Vegetation 763 283 640 1,686 14% Non-Irrigated Vegetation 81 45 280 405 3% Scrub/Shrub 589 392 3,248 4,230 36% Tree Canopy – Deciduous 846 420 475 1,742 15% Tree Canopy – Coniferous 84 84 252 420 4% Soil and Dry Vegetation 700 215 750 1,664 14% Agriculture 106 57 15 178 1% Building 91 215 158 463 4% Road 189 201 166 556 5% Parking Lot 0 24 12 36 0% Sidewalk 4 7 11 22 0% Other Impervious 118 104 98 320 3% Water 99 38 22 159 1% Total 3,669 2,087 6,126 11,882 100%

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Figure 2. Project Area Land Cover

Urban Tree Canopy Tree Canopy (2,172 acres) covers 19% of the project area. Less than 7% of the project area is suitable for planting trees (PPA classes = 669 acres) (Table 5, Figure 3). Impervious area unsuitable for planting trees covers 1,365 acres, which is only 11% of the project area. Conversely, vegetative areas that are unsuitable for planting trees encompass nearly 50% of the project area (5,656 acres).

Table 5. Project Area (PA) UTC Results (Acres)

PA Total UTC Class Blaine County Ketchum Sun Valley Acres PA % Tree Canopy 933 507 733 2,172 19% PPA - Vegetation 379 88 192 659 6% PPA - Impervious 0 20 10 30 0% Unsuitable Vegetation 1,052 631 3,972 5,656 48% Unsuitable Impervious 401 531 433 1,365 11% Unsuitable Agriculture 106 57 15 178 1% Unsuitable Soil 700 215 749 1,664 14% Water 99 38 22 159 1% Total 3,669 2,087 6,126 11,882 100%

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Figure 3. Project Area UTC

Priority Planting Areas (PPAs) Nearly 18,000 priority planting areas (PPAs) were identified within the project area (Table 6, Figure 4). Blaine County contains the most PPAs with over 9,600 sites (Table 6). PPAs can be symbolized, in the GIS data, to show any number of priorities, such as Air Quality (AirQ), Water Quality (WaterQ), Wildlife Habitat Connectivity (Wild Hab) and Riparian Restoration (Riparian) (Figure 4). Wildlife habitat connectivity is the most abundant prioritized PPA category with over 4,500 sites identified in the project area (Table 6). The other three PPA categories are well represented in the GIS data with over 2,000 sites for Air Quality, Water Quality and Riparian Restoration (Table 6, Figure 4).

Table 6. Project Area PPA Results and Per Prioritization Categories

Name Total PPAs AQ WQ Wildlife Riparian Blaine County 9,624 824 1,553 2,838 1,797 Ketchum 2,894 828 426 897 402 Sun Valley 5,379 917 487 770 185 Project Area 17,897 2,569 2,466 4,505 2,384

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20,000 17,897 18,000

16,000

14,000

12,000

10,000

8,000

6,000 4,505 4,000 2,569 2,466 2,384 2,000

0 Total AirQ WaterQ Wild Hab Riparian

Figure 4. Project Area PPAs Total and Per Prioritization Category

Blaine County Blaine County’s portions of the project area includes acreage along the East Fork of the Big Wood River and on the north side of Hailey adjacent to the City. Blaine County’s two areas include 3,669 acres (Table 4).

Land Cover Tree Canopy (4,230 acres) is the most abundant land cover type (Table 4) within the Blaine County area, accounting for 25% (Figure 5, Figure 6). The majority of the tree canopy in the Blaine County area is deciduous (23%) (Figure 5, Figure 6). Impervious land cover classes (Building, Road, Parking Lot, Sidewalk and Other Impervious) account for 10% of the Blaine County area (Table 4, Figure 5). Most of Blaine County’s impervious area is roads (Figure 5). Water is 3% of the Blaine County mapped Area (Figure 5), which is the most per entity (i.e. Blaine County, Ketchum, Sun Valley) (Table 4).

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900 23% 800 21% 19% 700 16% 600 500 400 300 5% 200 3% 2% 2% 3% 2% 3% 100 0% 0% 0

Irrigated Vegetation Non-Irrigated Vegetation Scrub/Shrub Tree Canopy – Deciduous Tree Canopy – Coniferous Soil and Dry Vegetation Agriculture Building Road Parking Lot Sidewalk Other Impervious Water

Figure 5. Blaine County Land Cover Results

Urban Tree Canopy Tree Canopy (933 acres) covers 25% of the Blaine County area (Table 5, Figures 7 & 8). Roughly 10% of the Blaine County area is suitable for planting trees (PPA classes = 379 acres) (Table 5, Figures 7 & 8). Impervious area unsuitable for planting trees covers 401 acres, which is only 11% of the Blaine County area. Conversely, vegetative areas that are unsuitable for planting trees encompass nearly 29% of the Blaine County area (1,052 acres) (Figure 7).

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Figure 6. Blaine County Land Cover Example (legend in inset previous page)

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1,200 29% 1,000 25%

800 19%

600 Acres 10% 11% 400

200 3% 3% 0% 0

Tree Canopy PPA - Vegetation PPA - Impervious Unsuitable Vegetation Unsuitable Impervious Unsuitable Agriculture Unsuitable Soil Water

Figure 7. Blaine County UTC Results

Priority Planting Areas (PPAs) Over 9,600 priority planting areas (PPAs) were identified within the Blaine County area (Table 6, Figure 9). As mentioned earlier, Blaine County contains the most PPAs compared to Ketchum and Sun Valley (Table 6). Wildlife habitat connectivity is the most abundant prioritized PPA category with over 2,800 sites identified in the Blaine County Area (Table 6). Over 1,500 Water Quality and Riparian Restoration priority sites occur in the Blaine County dataset, which is not surprising considering the amount of water in the area (Table 6, Figure 9 & 10).

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Figure 8. Blaine County UTC Example Map (legend in inset previous page)

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12,000

10,000 9,624

8,000

6,000

4,000 2,838 1,797 2,000 1,553 824

0 Total AirQ WaterQ Wild Hab Riparian

Figure 9. Blaine County PPAs Total and Per Prioritization Category

Ketchum The City of Ketchum encompasses 2,089 acres. Ketchum’s urban characteristics, including a centralized downtown with dense buildings and roadways, are represented in the Land Cover and UTC results.

Land Cover Ketchum has the second highest Tree Canopy of the 3 mapped areas within the project area. With over 500 acres within the city limits, tree canopy is the most abundant land cover type, accounting for 24% of the area (Deciduous = 20%, Coniferous = 4%) (Table 4, Figure 11). Ketchum also has the highest acreage of impervious area amongst the 3 areas. Impervious land cover classes (Building, Road, Parking Lot, Sidewalk and Other Impervious) account for 26% of Ketchum (Table 4, Figure 11). Roads and Buildings account for the majority of impervious area within Ketchum (Figure 11 & 12). Scrub/Shrub covers 19% of Ketchum (Tale 4), (Figures 11 & 12).

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Figure 10. Blaine County PPAs Example Map (Water Quality category highlighted in Red)

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450.0 20% 19% 400.0 350.0 300.0 14% 250.0 10% 10% 10%

Acres 200.0

150.0 5% 100.0 4% 2% 3% 1% 2% 50.0 0% 0.0

Figure 11. City of Ketchum Land Cover Results

Urban Tree Canopy Tree Canopy (933 acres) covers 24% of Ketchum (Table 5, Figures 12 & 13). Only 5% of the Ketchum is suitable for planting trees (PPA classes = 108 acres) (Table 5, Figures 13 & 14). Impervious area unsuitable for planting trees covers 531 acres, which accounts for 25% of Ketchum. Conversely, vegetative areas that are unsuitable for planting trees encompass nearly 30% of the Blaine County area (631 acres) (Figures 13 and 14).

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Figure 12. Ketchum Land Cover Example Map (legend inset on previous page)

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700 30% 600 25% 24% 500

400

Acres 300 10% 200 4% 2% 100 3% 1% 0

Tree Canopy PPA - Vegetation PPA - Impervious Unsuitable Vegetation Unsuitable Impervious Unsuitable Agriculture Unsuitable Soil Water

Figure 13. Ketchum UTC Results

Priority Planting Areas (PPAs) Nearly 2,900 priority planting areas (PPAs) were identified in Ketchum (Table 6, Figure 15). Ketchum’s urban nature (dense building and roadways) limit the PPA area, and therefore has the least amount of PPAs within the project area (Table 6). Ketchum has many Air Quality PPAs, which is expected as these PPAs are associated with roads. Over 400 Water Quality and Riparian Restoration priority sites occur in Ketchum, which is expected as the Big Wood River and Trail Creek flow through the city (Table 6, Figure 15 & 16).

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Figure 14. Ketchum UTC Example Map (legend inset on previous page)

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3,500

3,000 2,894

2,500

2,000

1,500

1,000 828 897

426 500 402

0 Total AirQ WaterQ Wild Hab Riparian

Figure 15. Ketchum PPAs Total and Per Prioritization Category

Sun Valley The City of Sun Valley encompasses 6,126 acres. Sun Valley is a disjointed urban area with significant wildland intermixed with homes and commercial areas. Dollar Mountain, for example, located in the middle Sun Valley’s city boundary, is primarily Shrub/Scrub and Soil and Dry Vegetation; both land cover types are not suitable for tree planting. These city characteristics, including significant “wildland areas”, are represented in Sun Valley’s and Land Cover and UTC results.

Land Cover Sun Valley has lowest percent tree canopy (12%) of the 3 areas mapped within the project area (Figure 17). Yet, Sun Valley is home to over 700 acres of tree canopy (Deciduous = 475 acres, Coniferous = 252 acres) (Figure 17). Sun Valley has the highest acreage of Scrub/Shrub in the project area (3,248 acres), which accounts for roughly 53% of the City (Table 4, Figures 17 & 18). Impervious land cover classes (Building, Road, Parking Lot, Sidewalk and Other Impervious) account for only 7% of Sun Valley (Table 4, Figure 17). Roads and Buildings account for most of the impervious area within Sun Valley, although it is a small percentage (6%) of the overall City (Figure 17 & 18).

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Figure 16. Ketchum PPAs Example Map (Riparian Restoration category highlighted in Red)

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3,500 53%

3,000

2,500

2,000

Acres 1,500

1,000 10% 12% 8% 500 5% 4% 3% 3% 0% 0% 0% 2% 0% 0

Figure 17. Sun Valley Land Cover Results

Urban Tree Canopy Tree Canopy covers 12% of Sun Valley (Table 5, Figures 19 & 20). Only 3% of the Sun Valley is suitable for planting trees (PPA classes = 202 acres) (Table 5, Figures 19 & 20). The Impervious area unsuitable for planting trees covers 433 acres, which accounts for 7% of the City. Conversely, vegetative areas that are unsuitable for planting trees encompass nearly 65% of the Sun Valley area (Figures 19 and 20). The tremendous amount of unsuitable vegetation within Sun Valley is a result of expansive areas of Scrub/Shrub found within the city limits. Scrub/Shrub is unsuitable for tree planting.

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Figure 18. Sun Valley Land Cover Example Map (legend inset on previous page)

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4,500 65% 4,000 3,500 3,000 2,500

Acres 2,000 1,500 1,000 12% 12% 7% 500 3% 0% 0% 0% 0

Tree Canopy PPA - Vegetation PPA - Impervious Unsuitable Vegetation Unsuitable Impervious Unsuitable Agriculture Unsuitable Soil Water

Figure 19. Sun Valley UTC Results

Priority Planting Areas (PPAs) Over 5,300 priority planting areas (PPAs) were identified within Sun Valley (Table 6, Figure 21). Sun Valley’s expansive Scrub/Shrub area (e.g. Unsuitable Vegetation) limits the PPA area, and therefore Sun Valley has less PPAs than Blaine County despite encompassing nearly 2,500 more acres (Table 6). Sun Valley has the most Air Quality PPAs of the three areas mapped, which is expected as these PPAs are associated with roads and the city is home to over 166 acres of road (Figures 21 & 22). With the great extent of “wildland” in Sun Valley, it is not surprising that 770 Wildlife Habitat Connectivity PPAs were identified within the city limits (Figure 21).

Wood River Valley (WRV) Forest Enhancement GIS Assessment 26 Ecosystem Sciences Foundation and The Keystone Concept

Figure 20. Sun Valley UTC Example Map (legend inset on previous page)

Wood River Valley (WRV) Forest Enhancement GIS Assessment 27 Ecosystem Sciences Foundation and The Keystone Concept

6,000 5,379

5,000

4,000

3,000

2,000

917 1,000 770 487 185 0 Total AirQ WaterQ Wild Hab Riparian

Figure 21. Sun Valley PPAs Total and Per Prioritization Category

County and City Specific Geodatabases GIS data per entity (Blaine County, City of Ketchum, and City of Sun Valley) is provided in a File Geodatabase. Each Geodatabase will contain the same datasets (Sun Valley Example below). Land Cover data is provided as a raster (Raster_Sun_Valley_Land_Cover) and a polygon feature class (Sun_Valley_Land Cover). Similarly, UTC data is provided as a raster (Raster_Sun_Valley_UTC) and a polygon feature class (Sun_Valley_UTC). PPAs are included in the File Geodatabase as a point Feature Class. City and the County mapped area boundaries are also included in Geodatabase (Sun_Valley_Boundary).

Sun Valley Example File Geodatabase

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Figure 22. Sun Valley PPAs Example Map (Air Quality category highlighted in Red)

Wood River Valley (WRV) Forest Enhancement GIS Assessment 29 Ecosystem Sciences Foundation and The Keystone Concept

Summary and Conclusions The Wood River Valley (WRV) Forest Enhancement GIS Assessment provides high- resolution data (land cover, urban tree canopy and priority planting locations) that assists Blaine County, Ketchum and Sun Valley in the identification and prioritization of land management and land use planning decisions. The GIS Assessment is a portion of a larger project called the Wood River Valley (WRV) Collaborative Forest Enhancement Project. The data created though this GIS Assessment will be used in the greater project to assist in the drafting of Community Forest Management plans for County and the cities, as well as in the identification and prioritization of on-the-ground actions (e.g. tree planting, forest health initiatives, fuel reduction activities, or overall planning) that grant funds will support.

The high resolution (2m) data (land cover, UTC and PPAs) supports local scale management activities. The primary function of the GIS data is to inform active forest management decisions such as where canopy reduction (public safety, infrastructure or viewshed concerns) is needed or where canopy expansion (growth or tree plantings, aesthetics [street trees]) can occur in the WRV.

The primary focus of the project is forested areas within and around municipalities and the WUI within the County, however non-forested areas are included. Since the project area encompasses non-forested areas, land cover results must be viewed in context in which they reside. For example, “irrigated vegetation” within a community most likely equates to lawn. The same land cover class outside of a community (e.g. hillslopes or non- developed area) includes short green vegetation (grasses and forbs) that is dependent on water. Most likely, “irrigated vegetation” in this landscape context is associated with a spring/seep or located in a seasonal/ephemeral channel.

The project area includes over 11,880 acres encompassing the cities of Ketchum (2,87 acres) and Sun Valley (6,126 acres), as well as 3,669 acres of Blaine County (Figure 1). Blaine county’s portion of the project area includes two large areas’ one adjacent to the north side of the City of Hailey, and the other extending from the Big Wood River northeast along the East Fork of the Big Wood River (Figure 1).

Tree Canopy encompasses 2,160 acres, equating to 19% of the project area. Fifteen percent of the forested area is deciduous, leaving the remaining 4% as coniferous. The most abundant land cover class in the dataset is Scrub/Shrub, which covers over 4,000 acres, 36% of the project area. The hillslopes adjacent to cities, and within the cities, are dominated by shrubs, therefore it is not surprising that this cover class is so expansive.

Scrub/Shrub is considered a non-suitable area for planting trees, as these areas generally need additional resources to support survival (i.e. irrigation). While trees may be planted anywhere additional resources need to be devoted to ensuring their survival, and the focus of potential planting areas (UTC dataset) is to identify areas where resources exist that promote survivorship. This also to reduces the costs of tree planting while also ensuring survival, as cost are less if irrigation is readily available. Parking lots are deemed potential planting areas in Urban Tree Canopy studies, as it is assumed that the areas adjacent to

Wood River Valley (WRV) Forest Enhancement GIS Assessment 30 Ecosystem Sciences Foundation and The Keystone Concept them have the resources needed to promote survival once the parking lot is surface is removed.

Only 6% percent of the project area is deemed suitable for tree planting (PPA-Vegetation). Forty-eight percent of the total mapped area is Unsuitable Vegetation (see Scrub/Shrub discussion above), while 11% is Unsuitable Impervious (roads, building etc).

Blaine County’s two parcels include the most Tree Canopy of the three mapped areas, with over 930 acres. Sun Valley has the second most with 733 acres. Impervious area is abundant in the project. Twenty-five percent of Ketchum is Unsuitable Impervious. This is not surprising since Ketchum has the densest downtown (urban characteristics) within the project area.

To assist the cities and county, NGOs and citizens to determine their planting needs, nearly 18,000 priority planting areas (PPAs) were identified (Table 6). The number of PPAs indicates that there is ample opportunity to increase tree canopy in the project area. The planting sites are attributed allowing GIS users to strategically pick planting sites that serve a distinct purpose. For example, users of the planting site data within a city can select trees that will improve air quality, water quality, wildlife habitat connectivity or riparian restoration.

Overall, the data presented herein assists in management decisions at the local scale (city- wide scale to a city block). The data can be used to support many types of projects including mitigating wildland fire risk, improving forest health, improving water quality, reducing flood danger, and enhancing community infrastructure (tree planting to improve public safety, economic development, walk-ability and bike-ability).

Similar data has been created for cities and counties throughout Idaho (Plan-it Geo 2013, Plan-it Geo and Ecosystem Sciences 2018). The WRV-specific data created through this project, can be added to the greater statewide database. Such data has been integral to informed and effective decision making for counties and municipalities in these regions.

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Literature Cited

Plan-it Geo. 2013. Treasure Valley Urban Tree Canopy Assessment. Prepared for the Idaho Department of Lands.

Plant-it Geo and Ecosystem Sciences. 2018. Idaho Urban Tree Canopy Assessment (Pocatello area, Idaho falls area, Moscow-Lewiston, Coeur D’Alene area, Sandpoint). Prepared for the Idaho Department of Lands.

Robinson, C., Le, H., Malkin, K. Soobitsky, R., Czawlytko, J., Dilkina, B., and Jojic, N. 2019. Large scale high resolution land cover mapping with multi-resolution data. IEEE Xplore.

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