Technical Report October 2004 Integration of Remote Sensing RSAC-65-RPT1

Humboldt-Toiyabe National Forest Intermountain Regional Office United States Department of Agriculture R5 Remote Sensing Lab Forest Service—Engineering Adaptive Management Services

Remote Sensing Applications Center Technical Report October 2004 Integration of Remote Sensing RSAC-65-RPT1

Existing Vegetation Mapping: Humboldt-Toiyabe National Forest

John Gillham, Haans Fisk, Wendy Goetz, Henry Lachowski Remote Sensing Applications Center Salt Lake City, UT

Prepared for: The Humboldt-Toiyabe National Forest USDA Forest Service The Forest Service, United States Department of Agriculture (USDA), has developed this information for the guidance of its employees, its contractors, and its cooperating Federal and State agencies and is not responsible for the interpretation or use of this information by anyone except its own employees. The use of trade, firm, or corporation names in this document is for the information and convenience of the reader. Such use does not constitute an official evaluation, conclusion, recommendation, endorsement, or approval by the Department of any product or service to the exclusion of others that may be suitable. The USDA prohibits discrimination in all its programs and activities on the basis of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or marital or family status (Not all prohibited bases apply to all programs). Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 202-720-2600 (voice and TDD). To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410 or call 202-720-5964 (voice and TDD). USDA is an equal opportunity provider and employer. For additional information, contact Henry Lachowski, Remote Sensing Applications Center, 2222 West 2300 South, Salt Lake City, UT 84119; phone: 801-975-3750; e-mail: [email protected]. This publication can be downloaded from the RSAC Web site: http://fsweb.rsac.fs.fed.us

ii Table of Contents

Executive Summary ...... iv Introduction ...... 1 Overview ...... 1 Partnership ...... 1 Study Area ...... 2 Background ...... 3 Methods ...... 5 Project Planning ...... 6 Geospatial Data Acquisition ...... 8 Image Pre-Processing ...... 9 Field Data Collection ...... 13 Office Photo-Interpreted Sites ...... 13 Draft Map Review and Revision ...... 15 Accuracy Assessment Design ...... 16 Map Products ...... 17 Existing Vegetation Map...... 17 Dominance Types ...... 18 Canopy Closure Class ...... 18 Tree Size Class ...... 18 Value-Added Products ...... 18 Accuracy Assessment Results ...... 19 User’s Class Accuracy (Errors of Commission) ...... 20 Producer’s Map Accuracy (Errors of Omission) ...... 24 Overall Map Accuracy ...... 28 Map Applications...... 29 Appropriate Uses ...... 29 Inappropriate Uses ...... 31 Conclusion ...... 32 References ...... 33 Appendices ...... 35

iii Executive Summary The Humboldt-Toiyabe National Forest required continuous vegetation information across the forest (over 7 million acres) to support their Forest Plan Revision effort. A mid-level existing vegetation map and other products were developed through a partnership comprised of the Remote Sensing Applications Center (RSAC), Region 5 Remote Sensing Lab (RSL), Adaptive Management Services (AMS), and the Humboldt- Toiyabe NF with coordination from the Regional Office. RSAC and RSL provided general project management and expert vegetation mapping support, and AMS field crews collected field samples with guidance from the Humboldt-Toiyabe NF. RSAC mapped the eastern ranger districts of Austin-Tonopah, Ely, Mountain City, Santa Rosa and Ruby Mountains-Jarbidge, while RSL was responsible for mapping the western districts of Carson and Bridgeport. Both map products were designed to approximate the Forest Service mid-level vegetation mapping standards and to be stored in the Forest GIS and National databases. This report documents the innovative techniques developed by RSAC to produce a dominance type, canopy closure, and tree size class map on the eastern districts. Existing vegetation maps provide consistent baseline information about current vegetation composition, structure and patterns. This map product can be used to assist with a variety of resource planning and monitoring activities. Some appropriate applications include: ecosystem and wildlife habitat assessments, rangeland and watershed assessments, fuel load assessments, benchmark analysis, updating range allotment management plans, threatened and endangered species modeling, and recreational activity management.

The vegetation map was prepared over a 13-month period for under 10 cents per acre. The map design was driven by the requirements of the Forest Plan Revision team, and involved reviewing known vegetation types, preparing a hierarchical classification system, and establishing a map legend. RSAC implemented a field sampling strategy and AMS field crews visited the sites and recorded ground-level information. RSAC entered the data into a database and evaluated it for consistency and accuracy in order to be used as training samples. An advanced map-making process that incorporated new data-mining technology, was used to create the existing vegetation map. This entailed processing geospatial data, segmenting imagery, producing an image cube and a data cube, generating complex decision trees, and creating and evaluating the map products. Geospatial data processing involved collecting, assembling, and deriving new geospatial data layers from 24 Landsat satellite images and nearly 300 Digital Orthophoto Quads. Image segmentation was performed on high-resolution imagery, which divided the landscape into homogeneous units. Climatic, spectral, and topographic layers were summarized for each homogeneous unit producing an image cube of 58 different layers. A data cube, which was produced by intersecting the training data with the image cube, was analyzed with data mining software to generate a series of complex decision trees. The decision trees were applied to the image cube that resulted in the existing vegetation map product. Draft maps were distributed to local field resource specialists for comment and review. Recommended changes and manual edits were incorporated into the map and a field based accuracy assessment was conducted on the final product. RSL mapped vegetation using similar techniques over the western districts (Carson and Bridgeport), which was crosswalked with RSAC’s map product and delivered to the Humboldt-Toiyabe NF as a consistent and continuous existing vegetation map product.

Existing vegetation map products were delivered to the Humboldt-Toiyabe NF in October of 2004. The map product was a digital vector layer (shapefile) compatible with Forest Service corporate software including ArcGIS, ArcView, and Erdas Imagine. Thematic vegetation categories included 7 Cover Types; 11 Sub-Cover Types; 38 Dominance Types; 3 Canopy Closures; and 3 Size Classes. Other materials prepared for the Forest include: A management summary, user guide, a project report; PowerPoint presentations; poster displays; hundreds of digital and hardcopy maps; database of field sampling information; over 5,000 digital photographs (ground & aerial); ancillary GIS and imagery layers; enhanced image products (high-resolution maps); and virtual fly-throughs for each Ranger District.

iv Introduction Overview The Remote Sensing Applications Center (RSAC) mapped existing vegetation on over 6 million acres of the eastern districts of the Humbolt-Toiyabe National Forest (HT). This mid- level map and associated map products will be used in the Forest Plan Revision. Initially, the forest completed a needs assessment and determined that vegetation and structure maps were required for conducting analysis to support the plan revision effort, but lacked the personnel resources to complete the mapping internally. Therefore, the HT formed a partnership with the Region 5 Remote Sensing Lab (RSL), the Remote Sensing Applications Center (RSAC) and Adaptive Management Services (AMS) enterprise team to produce a forest-wide vegetation layer. The mapping methods implemented for this project include using multiple sources of remote sensing imagery, training samples, and geospatial data layers with image segmentation and data-mining technologies. Vegetation map types were characterized by dominant land cover-type, canopy closure class, and tree size class. Vegetation map units were designed to meet a minimum polygon size of 5 acres. The final vegetation map product will be entered into the Forest Service corporate database called the National Resource Information System (NRIS).

Partnership The Humboldt-Toiyabe vegetation mapping team was comprised of staff from RSAC, AMS, HT regional and district personnel. RSAC provided general project management and expert vegetation mapping support in the form of geospaital data acquisition, data preparation, image processing, aerial photo-interpretation, rule-based decision-tree modeling, GIS modeling, and accuracy assessment. AMS provided RSAC with expert ground knowledge in the form of field support. The HT coordinated efforts between RSAC and AMS and provided key ancillary data, access to FIA plot data, and existing field data. In addition, the HT district personnel supplied feedback and timely review of draft and final map products. Primary team members included: Humboldt Toiyabe National Forest · Julia Richardson, Forest Vegetation Management Specialist · Joanne Baggs, Forest Botanist · Cheri Howell, Forest Ecologist · Dave McMorran, Forest GIS Coordinator · Genny Wilson, Forest Wildlife Biologist

Intermountain Regional Office · Roberta Quigley, Regional Geometronics Group Leader · Dr. Clint K. Williams, Regional Ecologist · Dave Tart, Regional Ecologist

RSAC Staff · Henry Lachowski, Integration of Remote Sensing, (IRS Program Leader) · Haans Fisk, Remote Sensing/GIS Specialist, (IRS Program Assistant) · John Gillham, Remote Sensing/GIS Specialist (Project Manager) · Wendy Goetz, Remote Sensing/GIS Specialist (Lead Analyst) · Mark Beaty, Remote Sensing/GIS Technician · Alex Hoppus, Remote Sensing Technician

Adaptive Management Services · Jo Ann Fites-Kaufman, AMS Enterprise Owner · Dave Weixelman, Ecologist/Manager Study Area The project area encompassed over 6 million acres of Forest Service, BLM, and other public and private lands, and intersected with 300 7.5- minute quadrangles. The HT requested that RSAC map vegetation on the Santa Rosa, Mountain City, Ruby Mountain/Jarbidge, Ely, and Austin/Tonopah ranger districts and include a one mile buffer beyond Forest Service administrative boundary (figure 1). The eastern ranger districts occur mainly within the Great Basin area of the western U.S. The Great Basin extends from the Sierras on the west to the Wasatch Range on the east and encompasses virtually the entire state of Nevada. Basin and range topography is characterized by isolated, long, narrow, roughly parallel mountain ranges and broad, intervening, nearly flat valleys and basins (Pirkle and others 1985). Rivers in this area flow to enclosed basins.

Climate is characterized as semi-arid to arid, and Figure 1—Location of eastern districts elevation ranges from 4,000 to over 10,000 feet. mapped by RSAC. The vegetation reflects these elevation changes, ranging from sagebrush and salt desert shrub communities in the lower elevations, to pinyon- juniper woodlands in the mid-level elevations and conifer forests occurring in the highest elevations. Whereas all the districts are dominated by sagebrush communities, pinyon-juniper woodlands occur most extensively in the southern districts.

2 Background Vegetation structure is a term used to describe certain elements of forest-dominated landscapes. Individual components of forest structure usually pertain to tree size, distribution, and spacing. In the Forest Plan Revision process, these factors are used to define current conditions, describe actions and time frames for achieving desired conditions, calculate allowable sale quantity, and assess goshawk habitat. Since vegetation structure factors are relatively fine scale, the success of most operational mapping techniques, such as Landsat, has been somewhat limited, and therefore, tend to infer this type of information through statistical analyses. In 1999, RSAC evaluated an innovative technique that integrated multispectral satellite imagery with a texture image that was derived from high spatial resolution imagery like a Digital Orthophoto Quads (Finco and others 2002). This approach was pilot tested on the Tongass National Forest and used to map standard map categories like cover type, canopy closure, and size class. An accuracy assessment was conducted on the final map products and demonstrated significant improvements as compared to traditional Landsat TM spectral classification. Another technique, called the Most Similar Neighbor (MSN), involves the use of survey- wide geospatial data sets, vegetation polygons, and a sufficient number of field samples to determine the assignment of ground-based values to all polygons throughout the study area. This method was recently tested on a portion of the Umatilla National Forest and the results of the investigation were promising (Moeur 2000). RSAC has recently tested a program called See5 to develop rule-based decision trees from geospatial data sets and site-specific attributes. RSAC worked with Forest Inventory & Analysis (FIA) to generate a variety of pixel-based map products such as National Timber Estimates of Basal Area, Tree Height, Biomass and Tree Volume (Ruefenacht 2002). This process uses every possible survey-wide geospatial data set (including texture) to determine the relationships between the geospatial data layers and a limited number of specific field- based measurements. The relationships are converted to algorithms, which are tested, ranked, and finally used to predict, or map, ground-based information for the entire study area. This data mining technique was a key method used in this feasibility study for mapping vegetation structure on the Monroe Mountain study area.

3 In the summer and fall of 2004, RSAC conducted a feasibility study for the Dixie and Fishlake National Forest to investigate and propose a reasonable solution for mapping vegetation structure and support Forest Plan Revision. Mapping procedures were tested on a four-quad study area over the Monroe Mountain area in central Utah. This study area was selected because its diversity of range and forested vegetation communities and because a wide variety of geospatial data layers were readily available. The feasibility study allowed RSAC to investigate alternative methods, develop a process, make adjustments, and automate significant portions of the data processing before implementing a efficient and operational methodology over a much larger area such as the HT. The vegetation mapping process developed in this investigation laid the foundation for mapping existing vegetation on the Humboldt-Toiyabe NF and have been presented in this paper (figure 2).

Geospatial Data Acquisition

Training Data Collection & Derivative Data Gen- Image Evaluation eration Segmentation

Data-Cube Image-Cube Construction Creation

Decision Tree Image-Cube Development Classification

Existing Vegetation Map Production

Figure 2—Flowchart of the general mapping procedure developed on the Dixie-Fishlake NF feasibility study.

4 Methods

RSAC’s mapping techniques used in this project were based partially on methods currently used by USGS EROS Data Center, Region 1 Regional Office, and Region 5 RSL (Brewer 2003; Homer 2002). This methodology involved using photo-interpreted training sites, Landsat/DOQ merged imagery, vegetation indicies and other ancillary data sets to map existing vegetation conditions. The main mapping phasess for this project included: project planning, geospatial data acquisition, image pre-processing, field data collection, segmentation and classification, draft map review and revision, and accuracy assessment (figure 3).

Figure 3—Methods flowchart for the Humboldt-Toiyabe vegetation mapping pro- 5 Project Planning In August of 2003, RSAC met with the HT, AMS, and RSL to discuss map unit design and prepare a project plan. Since the eastern and western ranger districts were being mapped independently, additional coordination was required to make certain that the spatial and thematic characteristics of the final map products would be consistent for the entire forest. Vegetation, canopy closure, and tree size classes were reviewed and a hierarchical classification system (Dominance Type) was proposed that balanced budget and time constraints (Table 1 and Table 2). The final classes approximated the mid-level mapping standards referenced in the Existing Vegetation Classification and Mapping Technical Guide (Brohman and Bryant 2004).

Table 1. Dominance Type, Sub-Cover Type and Cover Type vegetation classes. Dominance Type Sub-Cover Type Cover Type Dominance Type Sub-Cover Type Cover Type Alder Riparian Riparian Low Sage Sagebrush shrubland Shrubland Riparian Aspen Riparian Riparian Black Sage Sagebrush shrubland Shrubland Cottonwood Riparian Riparian Mtn Big Sage Sagebrush shrubland Shrubland Western Birch Riparian Riparian Wyoming Big Sage Sagebrush shrubland Shrubland Riparian Shrub Riparian Riparian Basin Big Sage Sagebrush shrubland Shrubland Riparian Grassland Riparian Riparian Mixed Sagebrush/Bitterbrush Sagebrush shrubland Shrubland Bristle Cone Pine Conifer Conifer Desert Shrub Basin shrubland Shrubland Engelmann Spruce Conifer Conifer Mixed Shrub Basin shrubland Shrubland Subalpine Fir Conifer Conifer Mountain Shrub Mountain shrubland Shrubland White Fir Conifer Conifer Alpine Community Herbacecous Herbaceous Whitebark/Limber Pine Conifer Conifer Basin Grassland Herbacecous Herbaceous Mixed Conifer Conifer Conifer Mtn Grassland Herbacecous Herbaceous Mixed Aspen/Conifer Hardwood Hardwood Barren Non-Veg Non-Veg Aspen Hardwood Hardwood Rock Non-Veg Non-Veg Curleaf Mtn Mahogany Mt.Mahogany Woodland Urban Non-Veg Non-Veg Juniper Pinyon/Juniper Woodland Mining Non-Veg Non-Veg Pinyon Pinyon/Juniper Woodland Agriculture Agriculture Non-Veg Pinyon/Juniper Pinyon/Juniper Woodland Snow Non-Veg Non-Veg Mixed Woodland Pinyon/Juniper Woodland Water Non-Veg Non-Veg

Table 2. Tree Size and Canopy Closure classes Canopy Class Canopy Closure Tree Size Class Size Class Riparian Shrub Low 10-40% Conifer Small <9" DBH High 41%+ Medium 9-21" DBH Hardwood Low 10-40% High 21'+ DBH Medium 41-70% Hardwood Small <9" DBH High 71%+ Medium 9-21" DBH Conifer Low 10-40% High 21'+ DBH Medium 41-70% High 71%+ Woodlands Low 10-20% Medium 21-40% High 41+% Shrublands Low 10-20% Meidum 21-30% High 31.+%

6 To minimize regional vegetation characteristics and to ease processing constraints the study area was divided into 6 mapping regions: Austin/Tonopah, Ely East, Ely West, Mountain City/Jarbidge, Ruby Mountains, and Santa Rosa (figure 4). For more information about the project planning, map unit design, and classification system, see Appendix A.

Mapping Regions Total Acres Austin/Tonopah 2,676,471 Ely East 776,346 Ely West 712,796 Mountain City/Jarbidge 919,259 Ruby Mountains 592,943 Santa Rosa 389,660 TOTAL ACRES 6,067,475

Figure 4—RSAC mapping regions.

7 Geospatial Data Acquisition This project involved acquiring multiple sources and scales of remotely sensed and geospatial data layers. One requirement was that any data layer used in the procedure needed to be available across the entire survey area in order to apply a consistent approach and implement the techniques efficiently. A complete set of survey-wide geospatial data layers included Landsat Enhanced Thematic Mapper (ETM) satellite imagery, Digital Orthophoto Quadrangle (DOQs), Digital Elevation Models (DEMs), Cartgraphic Feature Files, and (CFFs), State Soil Geo- graphic Database (STATSGO), Daymet climate data, and the HT administrative boundary.

Twenty-four ortho-rectified Landsat ETM Leaf- satellite imagery scenes were purchased from the EROS Data Center in Sioux Falls, South Dakota. These scenes were from the summer of 1993, 1999, 2000 and 2002 and the winter of 1999 and 2000. Landsat satellite imagery has been used in resource management for almost thirty years to map existing land-cover, conduct change detection analysis, and other resource specific assessments. This sensor records Leaf- moderate spatial resolution information (30m pixels) every 16 days. Multi-temporal Landsat images were used to capture “leaf on” (summer) and “leaf off” (fall) conditions, which when analyzed together have proven effective in classifying vegetation (figure 4)(Vanderzanden 1999, Fisk et.al. 2000). A Complete list the Landsat ETM scenes can be found in Appendix Figure 5—Multiple dates of Landsat imagery B. A total of 296 DOQs were acquired through the Geospatial Service and Technical Center (GSTC) in Salt Lake City, UT. They are computer-generated images of aerial photographs. This imagery depicts ground features in their 'true' position by having the vertical displacement removed. Most DOQs have a spatial resolution of 1 meter that provide enough detail for mapping fine-scale features at 1:24,000-scale. A complete list of the DOQs can be found in Appendix B.

Figure 6—DOQ image (1-meter).

8 DEMs (10 and 30-meter) were acquired from the Forest Service Geospatial Data Clearinghouse (GDC) website (figure 7). DEMs consist of a regular array of elevation values cast on a designated coordinate projection systems. They are used extensively in for a variety of earth science analyses such as determining slope and aspect or drainage networks. CFFs were downloaded from the Forest Service GDC website and provide information about geographic features on the earths surface, terrain, and political and administrative units (figure 7). CFF data are generally used to publish 1:24,000- scale 7.5-minute topographic quadrangle maps. They depict water bodies, wetlands, streams, transportation, constructed features and many other cartographic themes. CFFs play an important role in the process of stratifying landscapes and mapping vegetation. Color and black and white aerial photographs were provided by the forest. Aerial photographs have been the foundation of Forest Service remote sensing for almost 75 years, and continue to be a primary source of imagery today (figure 8). Interpreting aerial photography and/or high- resolution imagery are reliable alternatives to field data collection for increasing the number of training samples needed to characterize mapping concepts.

Figure 8—Natural color aerial Figure 7—CFF roads and streams overlaid on a 10-meter DEM. photography.

Image Pre-Processing The geospatial data sets were loaded on the system, assembled for the entire study area, projected to the UTM/Zone 11, Clarke 1866, NAD27 coordinate system and clipped to each of the mapping regions boundaries. All data sets were co-registered and imagery was checked for missing data, haze and clouds. If any clouds, cloud shadows or haze were found, an automated data-mining procedure was used to replace these areas (figure 9). This procedure substitutes spots of unclassifiable imagery with a set a predicted values generated using other imagery of the same area (Ruefenacht 2002). Once the geospatial data layers were co-registered and any anomalies were replaced several spectral, topographic and textural derived products were produced. (see Appendix C). All the leaf-on, leaf-off, and 1993 leaf-on Landsat ETM images were used to produce three standard

9 Eliminating Clouds and Shadows from Landsat ETM Satellite Imagery

Original Landsat ETM Image Reference Landsat ETM Image Cloud-free Landsat Image (predicted)

Figure 9—Left is the original Landsat ETM image with clouds and shadows (July 31, 2000). Center is the reference Landsat image (November 02, 1999). Right is the predicted cloud-free image. spectral indices which included the Normalized Difference Vegetation Index (NDVI), Tasseled Cap, and Principle Component Analysis (PCA) (figure 10). These types of band transformations are useful in discriminating between features in the imagery (ie. vegetated vs non-vegetated surfaces, as well as within vegetation cover-types). A resampled 3-meter DOQ was also used to compute a texture image as well as a ratio band of texture and tone, which were resampled to 10 meters to be spatially consistent with the 10-meter DEM imagery (figure 11). DEMs were used to create topographic derivatives including elevation, slope, curvature, compound topographic wetness index, potential floodplain, distance to drainage bottom, and a fully illuminated shade relief image (figure 12). Topographic models depict environmental parameters that help to better understand landscape ecology and more effectively predict land cover-types. For the mapping regions where only 30-meter DEMs were available, a resampling process was used to produce 10-meter DEMs and then the topographic derivatives were generated. Landsat and DOQ 3-meter resolution merge images were produced for the entire survey area. In creating this product, 30-meter multispectral Landsat ETM satellite imagery was pan- sharpened using the Landsat ETM 15-meter panchromatic band. Next, the 1-meter DOQ images was resampled to 3-meters and merged with the 15-meter pan-sharpened Landsat image to produce a 3-meter high-resolution merge image, (figure 13). The resulting Landsat/DOQ merge image nears photographic quality and is an excellent input for image segmentation and generating stand-level delineations. This image was the most logical source for segmenting the land into homogeneous units since it was generated from available data and provided high-resolution consistency across the survey area. For this project, the 3-meter Landsat/DOQ image was resampled to 10 meters and later used in automatically generating homogeneous, polygon segments. In addition, merged imagery can be used as a backdrop image for manually stratifying landscapes, building perspective views, creating virtual fly- throughs, and producing map compositions.

10 Landsat Enhanced Thematic Mapper Satellite Imagery Leaf-on: July 31, 2000 2000 31, July Leaf-on:

Original Landsat ETM NDVI Tasseled Cap Principle Component Analysis Leaf-off: Nov. 02, 1999 1999 Nov. 02, Leaf-off:

Original Landsat ETM NDVI Tasseled Cap Principle Component Analysis

Figure 10—Two dates of Landsat ETM are used to generate a suite of standard band

DOQ Tone and Texture Derivatives

DOQ Tone (10m) DOQ Texture (10m) DOQ Ratio Texture/Tone (10m) Image Stack of Texture and Tone (10m)

Figure 11—DOQs are used to generate texture images and ratio bands of texture and tone.

11 DEM Derived Topographic Models

Elevation

Aspect

Percent slope Shade relief Illuminated shade relief with contours

Figure 12—DEMs are used to generate a variety of topographic models and indices.

Landsat/DOQ Merge Image

DOQ (3m) Landsat Merge (15m) Zoom of the Landsat/DOQ Merge (3m)

Landsat/DOQ Merge (3m)

Figure 13—Landsat ETM was fused with a 3-meter DOQ to produce a 3-meter high-resolution merge image.

12 Field Data Collection During the fall of 2003, AMS and RSAC field crews collected information such as dominant vegetation type, canopy cover, and tree size class for the assigned field sites. The number of training samples was designed to be proportional to the total acres of each mapping region (figure 14). The process for selecting training samples was based on an unsupervised classification of the Leaf on Landsat ETM imagery. Depending on landscape complexity, 20-40 spectral classes were generated for each mapping region. Within a 1/4 mile buffer of roads, field sites were placed in homogenous zones of a spectral class with a minimum size of 90 square meters. Approximately, 8 accessible sites were located in each spectral class for each mapping region. Some of the assigned training samples did not get visited by field crews due to limited time and difficulty accessing the site usually because of steep terrain. For all of the sites visited, a local field key was used to classify dominant vegetation type and collected digital photos and GPS coordinates.

Mapping Regions Total Acres Austin/Tonopah 2,676,471 Ely East 776,346 Ely West 712,796 Mountain City/Jarbidge 919,259 Ruby Mountains 592,943 Santa Rosa 389,660

Figure 14— AMS field crews visited ground sites and recorded field information. In addition to the field sites, supplemental photo-interpreted sites were also collected while in the field. All field data was reviewed for accuracy and consistency and entered into a database to be used as training samples for developing classification models. For more information about field keys and field data collection forms, refer to Appendix D.

Office Photo-Interpreted Sites In this project, there was a lack of field-level information about life form, canopy closure, and tree size class and not enough time to wait for the data to be collected in the future. Therefore, additional training samples were produced by interpreting aerial photography and high-resolution merged imagery and recording the general land cover information. However, the use of site-specific information, gained through field observations and plot data, helped refine mapping concepts and label polygons with specific map attributes. Segmentation and Classification Image segmentation was performed on the 10-meter Landsat/DOQ-merged imagery, using eCognition software. Ecognition software is used in object-oriented image classification. It divides the landscape into spectrally and topographically homogeneous segments that are not represented in single pixels (figure 15).

13 Once segmented, 58 image data layers representing climatic, spectral, and topographic information were summarized for each of the polygon segments, yielding an image cube for each mapping region (table 3). Training sample points were then intersected with the image cube to produce a data cube. The data cube is a data file which contains all of the summarized spectral, climatic and topographic information.

Figure 15—Polygon segments developed from merged imagery.

Table 3—Data layers in Image Cube.

DEM – 10meter 21. PCA Band 2 41. Band 4 1. Elevation 22. Tassel Cap Band 1 42. Band 5 2. Slope 23. Tassel Cap Band 2 43. Band 7 3. Tri-shade Band 1 24. Tassel Cap Band 3 44. NDVI 4. Tri-shade Band 2 Landsat – Leaf Off 1999-2002 45. PCA Band 1 5. Tri-shade Band 3 25. Band 1 46. PCA Band 2 6. Curvature 26. Band 2 47. PCA Band 2 7. Wetness 27. Band 3 48. Tassel Cap Band 1 8. Distance to drain- 28. Band 4 49. Tassel Cap Band 2 age bottom 29. Band 5 50. Tassel Cap Band 3 DOQ 30. Band 7 Climate 9. Tone 31. NDVI 51. Growday 10. Texture 32. PCA Band 1 52. Precipitation 11. Ratio 33. PCA Band 2 53. Radiation Landsat – Leaf On 1999-2002 34. PCA Band 2 54. Temperature 12. Band 1 35. Tassel Cap Band 1 55. 13. Band 2 36. Tassel Cap Band 2 Soil 14. Band 3 37. Tassel Cap Band 3 55. Class 15. Band 4 Landsat – Leaf On 1993 56. Drainage 16. Band 5 38. Band 1 57. Mineral 17. Band 7 39. Band 2 58. Surface Texture 18. NDVI 40. Band 3 19. PCA Band 1 20. PCA Band 2

14 Through an iterative process, the data cube was analyzed with See5 data-mining software to generate a series of complex decision trees to model the various land cover attributes (Quinlan 1993) (figure 16). Data mining is defined as the automated extraction of hidden predictive information from databases. These customized algorithms are used to predict outcomes for future situations. Rule-based decision trees are generated from the data-mining information. Our process used geospatial data layers (i.e. Landsat ETM imagery, DEMs, soil data, daily temperature) and site-specific photo-interpreted-based measurements to generate the decision trees. See5 determines which variables are statistically significant to the classification. The relationships are converted to an algorithm (decision-tree), which is tested, ranked, and finally used to predict, or map, ground-based information across the entire study area. Each polygon is attributed with dominate species, canopy closure, and tree size class. For more information on the segmentation, image cube creation, data cube construction and classification process, refer to Appendix E.

Figure 16—Image cube combined with training data to develop decision trees and applied to create a classified map.

Draft Map Review and Revision In the spring of 2004, draft maps were distributed to local field resource specialists for comment and review. This provided an opportunity for local experts to assess the map and provide any additional input they thought was needed to improve the final maps. RSAC and district personnel spent several days in the field reviewing the maps and making corrections. The district personnel also had the opportunity, over several weeks, to further review the maps. The hardcopy maps were returned to RSAC with comments and notes. Additionally, RSAC checked the draft maps from the air using a digital sketchmapping system and a small fixed wing aircraft. Approximately 75 percent of the study area was reviewed using this process. Recommended changes and editorial comments were incorporated into the classification and the final map was generated. For a full description on the review and revision process involving refer to Appendix F.

15 Accuracy Assessment Design In the summer of 2004, a field-based assessment was performed to determine the accuracy of the final vegetation map produced. A stratified random, double-blind sampling method in which points were chosen within predetermined constraints, such as accessibility was used. All field points had to be accessible within one-quarter mile of a road and had a minimum polygon size of 90 X 90 meters. Over 1,000 point locations were spread across the study area. Field crews were instructed to implement protocols consistent with the initial field data collection effort. Field crews were provided polygon boundaries around each point, but were not given indication of the dominance type mapped. Polygons were assessed using field keys to determine the dominance type. Crews also collected canopy closure and size class information and took digital photos of the site. Out of the 1,000 assigned accuracy assessment points, 870 were visited and used in the accuracy assessment. The final vegetation map was assessed using a traditional error matrix, discussed later in this guide. For more information about how the accuracy assessment was designed and carried out, see Appendix G.

16 Map Products

A suite of map products were delivered to the HT in October 2004. The package included the existing vegetation map and numerous value-added products. RSAC also prepared technology transfer materials including a management summary, this user guide, this technical report, PowerPoint presentations, poster displays, hundreds of digital and hardcopy maps, and fly- through visualizations. For more information about the existing vegetation map product and other deliverables, refer to Appendix H.

Existing Vegetation Map The final map product provides for continuous land cover information over the eastern ranger districts of the HT: Santa Rosa; Mountain City; Jarbidge/Ruby Mountain; Ely; and Austin/Tonopah. This map shows existing vegetation types, and their structural characteristics and is formatted as a digital vector layer (shapefile) that is compatible with Forest Service corporate software such as ArcGIS, ArcView, and Erdas Imagine. The mapped area extends beyond the Forest Service administrative boundary by 1 mile encompassing a total of 6.5 million acres. Again, the reason for producing this vegetation map was to support the HT in their forest plan revision effort and for conducting various assessments and analyses. Categories mapped included Cover Types (CT), Sub-Cover Types (SCT), Dominance Types (DT), Canopy Closure (CC), and Tree Size Class (SC) (figure 17). This vegetation map is generally consistent with the mid-scale standards set forth in the Existing Vegetation Classification and Mapping Technical Guide (Brohman and Bryant 2004). In conformance with these standards, small polygons were aggregated up to five acres with the exception of riparian areas, which were only aggregated to half an acre. Map units are mutually exclusive and clearly defined by local Dominance Types.

Figure 17—Existing vegetation map and tabular attributes.

17 Dominance Types A total of 38 eight Dominance Types, which was comprised from several sources including the Society of American Forester, Society for Range Management cover types, and Alliances, were mapped. These dominance types range from specific vegetation species (i.e. Wyoming big sagebrush) to general land cover types (i.e. agriculture).

Canopy Closure Class A canopy closure map was generated by independently processing the following cover types: conifer, hardwood, riparian forest, woodland, shrubland and riparian shrubland. Once classified, each closure category was assembled into a wall-to-wall map for each mapping region. Also note that unique breaks are present between cover type categories.

Tree Size Class A tree size map was generated by independently processing conifer, hardwood and riparian forest cover types. Once classified into one of the three size classes: sapling/pole, medium and large , each size class map was assembled into a complete coverage for each mapping region.

Value-Added Products Value-added products developed as part of the vegetation mapping project were packaged and delivered to the HT. These included field-collected information, aerial photo flight line indexes, mosaics of standard geospatial data sources, as well as numerous image derivatives. Listed below are some of the products.

• Field-collected information: • 2,000 field visited samples – stored in a database and point shapefile • 5,000 digital ground photographs linked to field visited samples • 3,000 field observations – stored in a database • 4,000 aerial photo interpretations – stored in a database • Standard image product mosaics: • Digital Elevation Models (30m and 10m) • Digital Orthophoto Quads (3m) • Multiple dates of Landsat ETM imagery (30m and 15m) • Enhanced image product mosaics: • Topographic derivatives (slope, wetness, tri-shades, etc.) • Landsat derivatives (NDVI, Tassled Cap, and PCA) • High-resolution merge imagery (3m) • Additional products: • 1993 and 2003 aerial photo index - GIS coverages • 6 fly-through visualizations • 5 aerial and field photo automated slideshows • Adobe Portable Document Format (PDF) maps for each quad showing dominance type and satellite imagery.

18 Accuracy Assessment Results

The final vegetation map was assessed using field-visited site information. Traditional (deterministic) error matrices were developed for Cover Type, Sub-Cover Type, and Dominance Type levels, as well as the Canopy Closure and Tree Size Class categories. The error matrix is a square array of numbers set out in rows and columns which express the number of polygons assigned to a particular category in one classification relative to the number of pixels assigned to a particular category in another classification. For this study the columns represent the reference or field data while the rows represent the map classification. The error matrix is an effective way to represent accuracy in that the individual accuracies of each category are described along with both the errors of inclusion (Commission errors) and errors of exclusion (Omission errors) present in the classification (Congalton and Green 1999). Errors of commission are often referred to as ‘User’s Accuracy’. A commission error occurs when an area is included into a category when it doesn’t belong. It answers questions such as: “If I pick a riparian shrub polygon on the map, what are the chances it is actually going to be riparian shrub polygon on the ground?” Errors of omission are often referred to as ‘Producer’s Accuracy’. An omission error is excluding that area from the category in which it does belong. It answers questions such as: “I’m on the ground standing in a riparian shrub area, what are the chances that it is going to be correctly identified as a riparian shrub polygon on the map?”. In addition to user’s and producer’s accuracy an overall map accuracy was also calculated. The overall map accuracy answers the question: “If I throw a dart at the map, what are the chances the polygon I hit is correct compared to what is on the ground?”. All assessments of accuracy are important, but the one most commonly reported for this project is overall map accuracy. One assumption of the traditional error matrix is that an accuracy assessment sample site can have only one label. Classification scheme rules often impose discrete boundaries on continuous conditions in nature, such as vegetation communities in this case. In some situations it is difficult to place one label on a field polygon because the vegetation composition does not fit neatly into single map class. Therefore, for our study the field crews were allowed to make a second call at a site if it was appropriate. For example, a second Figure 18—Borderline woodland/shrubland site in call of shrubland might be given to a very low canopy woodland site (figure 18). Receiving only a single class label from field crews gives a bare minimum accuracy, while allowing for a first and second call where appropriate gives a maximum accuracy.

19 Error matrices are presented in the main body of this report for the Cover Type map only, with summary tables for the Sub-Cover Type, Dominance Type, Canopy Closure and Tree Size Class maps. A full error matrix for all maps can be found in Appendix I. In addition, because of time constraints, costs, and travel limitations it was not possible to get adequate field samples in some of the map classes. Only those classes which had 10 or more field sites were included in any of the accuracy assessments. User’s Class Accuracy (Errors of Commission) The user’s class accuracy was calculated by dividing the number of correctly classified sites by the total number of mapped sites in that particular class. It is relative to the mapped class area thus it reflects the likelihood of each mapped category being correctly identified. This accuracy does not take into account the total mapped area covered by a particular vegetation class only the number of field sites in that class. However, it would be useful for addressing inventory type questions.

Cover Type There were a total of 7 cover type categories that were assessed. The minimum user’s class accuracy ranged from a low of 56% in the herbaceous category to a high of 89% in the woodland category (table 4). The maximum call accuracy changed the low to 64% in the herbaceous category and the high to 92% in the woodland category (table 5).

Table 4— Cover Type Minimum User’s Class Accuracy

REFERENCE DATA Total Map User's Cover Type R HW CF NV HG WD SH Sites Accuracy R = Ri parian 60 17082007112 54% HW = Hardwood 3 44 3000555 80% CF = Co nifer 0 3 70 404990 78% NV = Non-Veg 1 0 0 6 00310 60% HG = Herbaceous 0 0 0 2 14 0925 56% WD = Woodland 1 0321231 23 261 89%

MAP DATA SH = Shrubland 7 7 1 2 13 10 277 317 87% Total Reference Sites 72 71 77 24 48 245 333 870

Table 5— Cover Type Maximum User’s Class Accuracy

REFERENCE DATA Total Map User's Cover Type R HW CF NV HG WD SH Sites Accuracy R = Ri parian 77 12 0 4 12 0 7 112 69% HW = Hardwood 2 48 2000355 87% CF = Co nifer 0 2 71 404990 79% NV = Non-Veg 1 0 0 7 00210 70% HG = Herbaceous 0 0 0 2 16 0725 64% WD = Woodland 0 0310241 16 261 92%

MAP DATA SH hrubland741087=S 290 317 91% Total Reference Sites 87 66 77 18 36 252 334 870 20 Sub-Cover Type Nine sub-cover type categories were evaluated. Although the only map categories that changed when progressing from the cover type to the sub-cover type maps were the woodland, shrubland and non-vegetated classes, the minimum and maximum accuracies percentages did expanded. The minimum user’s accuracy ranged from a low of 15% in the mountain shrubland category to a high of 85% in the pinyon/juniper category (table 6). The maximum accuracy computations increased the low to 28% in the mountain shrubland category and the high to 90% in the pinyon/juniper category.

Table 6—Sub-Cover Type Minimum and Maximum User’s

User's Accuracy Sub-Cover Type Total Sites Minimum Maximum Call Call

Riparian 112 54% 69% Hardwood 55 80% 87% Conifer 90 78% 79%

Non-Veg 8 ------Herbaceous 25 56% 64% Curlleaf Mountain Mahogany 67 60% 67% Pinyon/Juniper 194 85% 90% Mtn. Shrub 40 15% 28%

Sagebrush 227 78% 83%

21 Dominance Type A total of 36 dominance type categories were assessed. In many of the dominance type categories, such as conifer and shrubland, only a single species was present. Several classes such as Alder, Western Birch, Bristlecone Pine, Engelmann Spruce, Mixed Conifer, Aspen/Conifer Mix, Mountain Grassland, all of the non-vegetated and agriculture had less than 10 samples and therefore could not be assessed. The lowest minimum user’s accuracy occurred in the Low Sagebrush category and the highest occurred in the Pinyon/Juniper category (table 7). The percentages increased for these classes when the maximum accuracy was calculated. While all classes which had 10 or more samples sites were assessed, those class accuracies with a small number of sites must be utilized with caution.

Table 7—Dominance Type Minimum and Maximum User’s Class Accuracy User's Accuracy Dominance Type Total Sites Minimum Maximum Call Call

AL = Alder 0 ------BC = Cottonwood 18 39% 44% WB = Western Birch 2 ------RS = Riparian Shrubland 37 43% 59% RG = Riparian Grass 39 26% 49% BP = Bristlecone Pine 0 ------ES = Engelmann Spruce 0 ------SF = Subalpine Fir 29 55% 55% WF = White Fir 37 57% 65% WL = Whitebark/Limber Pine 21 24% 24% XC = Mixed Conifer 2 ------MA = Aspen/Conifer Mix 7 ------QA = Quaking Aspen 63 67% 71% CM = Curlleaf Mtn Mahogany 67 60% 67% JO = Juniper 24 33% 42% PE = Pinyon Pine 30 23% 27% PJ = Pinyon-Junier 124 70% 84% XW = Mixed Woodland 16 6% 13% AA = Low Sagebrush 37 3% 5% AV = Mtn Big Sagebrush 89 48% 58% AW = Wyoming Big Sagebrush 61 59% 64% AT = Basin Big Sagebrush 19 37% 47% DS = Desert Shrubland 12 58% 75% SB = Mixed Sage/Bitterbrush 21 24% 33% MS = Mountain Shrubland 40 15% 28% XS = Mixed Shrubland 38 8% 11% AC = Alpine Community 0 ------BG = Basin Grassland 13 38% 38% MG = Mountain Grassland 12 50% 67% BA = Barren 5 ------RO = Rock Outcrop 2 ------UB = Urban or Developed 1 ------XN = Unknown Non-Veg 0 ------AG = Agriculture 2 ------SN = Snow/Ice 0 ------WA = Water 0 ------22 Canopy Closure Twelve sub-cover type categories were evaluated. The hardwood category also included the riparian hardwood classes (Western Birch, Riparian Aspen, Cottonwood and Alder). The lowest accuracies occurred in the high canopy conifer and high canopy woodland sites while the low and medium woodland canopy classes had the highest accuracies (table 8).

Table 8— Canopy Closure Minimum and Maximum User’s Class Accuracy

User's Accuracy Canopy Closure Total Sites Minimum Maximum Call Call

Conifer 10% - 40% 10 70% 70% Conifer 41% - 70% 42 36% 57% Conifer 71%+ 17 0% 12% Hardwood & Riparian Hardwood 10% - 40% 3 ------Hardwood & Riparian Hardwood 41% - 70% 21 48% 57% Hardwood & Riparian Hardwood 71%+ 42 7% 26% Woodland 10% - 20% 78 78% 87% Woodland 21% - 40% 103 59% 83% Woodland 41%+ 50 16% 26% Shrubland 10% - 20% 135 64% 73% Shrubland 21% - 30% 80 39% 73% Shrubland 31%+ 61 25% 38% Riparian Shrubland 10% - 40% 3 ------Riparian Shrubland 41%+ 15 80% 87%

Tree Size Class There were a total of 3 tree size categories that were assessed. Again, the hardwood category included all the riparian hardwood classes (Western Birch, Riparian Aspen, Cottonwood and Alder). The minimum user’s accuracy ranged from a low of 28% in the hardwood medium size class to a high of 81% in the hardwood small size class (table 9). The high percentages did not change when the maximum accuracy was computed but the low increased to 44%.

Table 9—Tree Size Class Minimum and Maximum User’s Class Accuracy

User's Accuracy Tree Size Class Total Sites Minimum Maximum Call Call

Conifer Small (<9" DBH) 3 ------Conifer Medium (9-21" DBH) 51 67% 75% Conifer Large (>21" DBH) 8 ------Hardwood & Riparian Hardwood Small (<9" DBH) 36 81% 81% Hardwood & Riparian Hardwood Medium (9-21" DBH) 18 28% 44% Hardwood & Riparian Hardwood Large (>21" DBH) 4 ------

23 Producer’s Map Accuracy (Errors of Omission) In order to get a more representative producer’s accuracy, a second weighted error matrix was developed. This was done because there were a disproportionate number of field sites in some of the map categories. The producer’s map accuracy was calculated using a sample weighting factor based on the number of acres and the number of field sites assigned to each map category. The first step in generating this weighted accuracy was to calculate the percentage of each map class in the final maps. This was the % Area Mapped value. The Sample Weight value was derived by dividing the Area Mapped percentages by the total number of map data sites (field sites) for each category. This value was multiplied by the number of sites found in a particular cell, producing the percentages which populate the error matrix. These percentages add up to 100%. Each class producer’s accuracy was calculated by dividing the percent number of correctly classified sites by the percent number of reference (field) sites. This produced a spatially weighted producer’s map accuracy which provides a more useful tool for map assessment.

Cover Type There were a total of 7 cover type categories that were assessed. The minimum producer’s accuracy ranged from 20% in the herbaceous category to a 95% in the woodland category (table 10). The maximum call accuracy changed the low to 64% in the herbaceous category and the high to 92% in the woodland category (table 11).

Table 10—Cover Type Minimum Producer’s Map Accuracy

Reference Data % Ar ea Sample Producer's Cover Type RHWCFNVHG WD SH Mapped Weight Accuracy

R = Ri pari an 1.70% 0.0002 0.91% 0.26% 0.00% 0.12% 0.30% 0.00% 0.11% 36% HW = Hardwood 2.88% 0.0005 0.16% 2.31% 0.16% 0.00% 0.00% 0.00% 0.26% 60% CF = Co ni fer 3.71% 0.0004 0.00% 0.12% 2.88% 0.16% 0.00% 0.16% 0.37% 80% NV = Non-Veg 1.57% 0.0016 0.16% 0.00% 0.00% 0.94% 0.00% 0.00% 0.47% 49% HG = Herbaceous 1.13% 0.0005 0.00% 0.00% 0.00% 0.09% 0.63% 0.00% 0.41% 20% MAPWD DATA = Woodland 36.36% 0.0014 0.14% 0.00% 0.42% 0.28% 0.14% 32.18% 3.20% 95% SH = Shrubland 52.65% 0.0017 1.16% 1.16% 0.17% 0.33% 2.16% 1.66% 46.00% 91% Tot Ref Sites % 100% 2.53% 3.85% 3.62% 1.93% 3.24% 34.01% 50.83%

Table 11—Cover Type Maximum Producer’s Map Accuracy

Reference Data % Ar ea Sample Producer's Cover Type RHWCFNVHG WD SH Mapped Weight Accuracy

R = Ri pari an 1.70% 0.0002 1.17% 0.18% 0.00% 0.06% 0.18% 0.00% 0.11% 45% HW = Hardwood 2.88% 0.0005 0.10% 2.51% 0.10% 0.00% 0.00% 0.00% 0.16% 73% CF = Co ni fer 3.71% 0.0004 0.00% 0.08% 2.92% 0.16% 0.00% 0.16% 0.37% 81% NV = Non-Veg 1.57% 0.0016 0.16% 0.00% 0.00% 1.10% 0.00% 0.00% 0.31% 71% HG = Herbaceous 1.13% 0.0005 0.00% 0.00% 0.00% 0.09% 0.73% 0.00% 0.32% 32% MAPWD DATA = Woodland 36.36% 0.0014 0.00% 0.00% 0.42% 0.14% 0.00% 33.57% 2.23% 96% SH = Shrubland 52.65% 0.0017 1.16% 0.66% 0.17% 0.00% 1.33% 1.16% 48.16% 93% Tot Ref Sites % 100% 2.59% 3.44% 3.61% 1.56% 2.24% 34.90% 51.66% 24 Sub-Cover Type A total of 9 sub-cover type categories were evaluated. The minimum producer’s accuracy ranged from a low of 21% in the herbaceous category to a high of 91% in the pinyon/juniper woodland category (table 12). The maximum accuracy changed the herbaceous class to 32% and the pinyon/juniper to 94%.

Table 12—Sub-Cover Type Minimum and Maximum Producer’s Map Accuracy Producer's Accuracy Sub-Cover Type Total Sites Minimum Maximum Call Call

Riparian 112 35% 44% Hardwood 55 67% 75% Conifer 90 81% 83% Non-Veg 8 ------Herbaceous 25 21% 32% Curleaf Mountain Mahogany 67 61% 68% Pinyon/Juniper 194 91% 94% Mtn. Shrub 40 22% 43% Sagebrush 227 78% 82% Basin Shrub 50 38% 47% Agriculture 2 ------

25 Dominance Type Thirty-six dominance type categories were analyzed. The lowest minimum accuracies occurred in Pinyon Pine, Mixed Woodland, Mixed Shrubland and Mixed Sagebrush/Bitterbrush categories while the highest accuracies were in the Whitebark/Limber Pine and Pinyon/Juniper Woodlands (table 13). Again, because many of these classes had very few field samples and are dominated by a single species these accuracies must be used with caution.

Table 13—Dominance Type Minimum and Maximum Producer’s Map Accuracy Assessment

Producer's Accuracy Dominance Type Total Sites Minimum Maximum Call Call

AL = Alder 0 ------BC = Cottonwood 18 60% 63% WB = Western Birch 2 ------RS = Riparian Shrubland 37 13% 18% RG = Riparian Grass 39 27% 45% BP = Bristlecone Pine 0 ------ES = Engelmann Spruce 0 ------SF = Subalpine Fir 29 47% 47% WF = White Fir 37 66% 69% WL = Whitebark/Limber Pine 21 100% 100% XC = Mixed Conifer 2 ------MA = Aspen/Conifer Mix 7 ------QA = Quaking Aspen 63 69% 76% CM = Curlleaf Mtn Mahogany 67 66% 72% JO = Juniper 24 15% 58% PE = Pinyon Pine 30 1% 1% PJ = Pinyon-Junier 124 94% 96% XW = Mixed Woodland 16 0% 1% AA = Low Sagebrush 37 23% 45% AV = Mtn Big Sagebrush 89 44% 52% AW = Wyoming Big Sagebrush 61 59% 65% AT = Basin Big Sagebrush 19 10% 13% DS = Desert Shrubland 12 63% 73% SB = Mixed Sage/Bitterbrush 21 8% 14% MS = Mountain Shrubland 40 25% 47% XS = Mixed Shrubland 38 5% 7% AC = Alpine Community 0 ------BG = Basin Grassland 13 13% 14% MG = Mountain Grassland 12 17% 29% BA = Barren 5 ------RO = Rock Outcrop 2 ------UB = Urban or Developed 1 ------XN = Unknown Non-Veg 0 ------AG = Agriculture 2 ------SN = Snow/Ice 0 ------WA = Water 2 ------

26 Canopy Closure A total of 11 cover type categories were assessed. The lowest accuracy occurred in the high canopy conifer classes while the highest occurred in the high canopy hardwood/hardwood riparian and the high canopy riparian classes table 14). Some of these categories had a very limited number of field sites therefore the accuracy results may not be as dependable.

Table 14—Canopy Closure Minimum and Maximum Producer’s Map Accuracy Assessment

Producer's Accuracy Canopy Closure Total Sites Minimum Maximum Call Call

Conifer 10% - 40% 10 54% 62% Conifer 41% - 70% 42 53% 66% Conifer 71%+ 17 0% 13% Hardwood & Riparian Hardwood 10% - 40% 3 ------Hardwood & Riparian Hardwood 41% - 70% 21 33% 45% Hardwood & Riparian Hardwood 71%+ 42 73% 100% Woodland 10% - 20% 78 55% 69% Woodland 21% - 40% 103 69% 82% Woodland 41%+ 50 28% 61% Shrubland 10% - 20% 135 68% 78% Shrubland 21% - 30% 80 31% 57% Shrubland 31%+ 61 29% 47% Riparian Shrubland 10% - 40% 3 ------Riparian Shrubland 41%+ 15 86% 87%

Tree Size Class Three tree size classes were analyzed. The lowest accuracy of 25% was in the hardwood/riparian hardwood medium class while the highest accuracy occurred in the hardwood/riparian hardwood small size class (table 15).

Table 15—Tree Size Class Minimum and Maximum Producer’s Map Accuracy Assessment

Producer's Map Accuracy Tree Size Class Total Sites Minimum Maximum Call Call

Conifer Small (<9" DBH) 3 ------Conifer Medium (9-21" DBH) 51 60% 74% Conifer Large (>21" DBH) 8 ------Hardwood & Riparian Hardwood Small (<9" DBH) 36 79% 82% Hardwood & Riparian Hardwood Medium (9-21" DBH) 18 25% 45% Hardwood & Riparian Hardwood Large (>21" DBH) 4 ------

27 Overall Map Accuracy The overall map accuracy was calculated by adding up the diagonal values from the producer’s map error matrix (table 16). Again this overall map accuracy reflects the weighted accuracies for each of classes thus producing a more useful map evaluation. It answers the question: “If I throw a dart at the map, what are the chances the polygon I hit is correct compared to what is on the ground?”. The lowest accuracies occurred in the hardwood canopy closure map while the highest occurred in the cover type map. These percentages reflect the generalized nature of the cover type map and the very specific details found in the canopy closure maps.

Table 16—Overall Map Accuracy for each Vegetation Category

Overall Map Accuracy Minimum Maximum Map Type Call Call Cover Type 86% 90% Sub-Cover Type 73% 79% Dominance Type 52% 62% Conifer Canopy Closure 50% 60% Hardwood Canopy Closure 23% 39% Woodland Canopy Closure 59% 76% Shrubland Canopy Closure 51% 67% Riparian Shrubland Canopy Closure 72% 78% Conifer Size Class 48% 62% Hardwood Size Class 67% 70%

28 Map Applications

A GIS land-cover map is a useful product for addressing specific resource and land management issues. The existing vegetation maps provides consistent baseline information about current vegetation composition, structure and landscape patterns that support many Forest plan Revision needs. Vegetation maps are used to address a variety of important land management issues involving fuel loads, watersheds, rangelands, and wildlife habitat. They are also useful for modeling threatened and endangered species habitat, conducting benchmark analysis, and monitoring the sustainability of resource management practices. However, both resource specialists and land managers should acknowledge that there are appropriate and inappropriate uses for a mid-scale existing vegetation map product. Appropriate Uses Using this map product in an appropriate manner to assist with resource planning and monitoring activities will increase efficiency, accuracy, and defensibility of management practices. Appropriate uses of mid-scale existing vegetation maps include:

• Inventory assessments--summarizing acres of existing vegetation for an area of interest (e.g. a forest, district, range allotment, or watershed). • Visual quality assessments-- evaluating scenic integrity for recreational land uses. • Crosswalk development-- incorporating local resource knowledge with the vegetation map for deriving new meaningful interpretation layers (e.g.. rangeland production estimates). • Biophysical modeling-- combining other geospatial data layers with the existing vegetation map product to predict fuel loads, potential natural vegetation, and wildlife habitat. • Fragmentation analysis-- analyzing landscape patterns using a mid-scale existing vegetation map product; however, this requires much ecological knowledge and technical expertise. • Stratification—assisting with the design of multi-staged sampling to assess condition and trend for items such as aspen decline/spread.

For example, resource specialists can query land-cover information about a specific range allotment in a GIS. By overlaying range allotment boundaries on the land-cover map, total acres can be generated for individual allotments. This type of analysis was performed on the Warmsprings range allotment (Beaverhead-Deerlodge NF, Region 1) and the results of this summary are charted in figure 18. Other geospatial overlay queries help answer questions that concern intersections of interacting resources. For example, a land manager may inquire about how many miles of road are adjacent to, and are within 300 feet of riparian areas on the entire forest, a district, or a single watershed. These types of inventory assessments are relatively easy to conduct and provide valuable information about the connections between different resources and are helpful in developing sustainable land-use alternatives.

Figure 18—The total acres of each land-cover type are summarized for the Warmsprings range allotment. A crosswalk can be used to transform a general land cover map into a map that depicts minimum and maximum forage production (figure 19). The Warmsprings range allotment occupies 22,381 acres, of which 17,043 are suitable for grazing cattle. Suitable grazing land was defined by eliminating sites, residing on slopes greater than 45 percent. A crosswalk table that was based on historical data collected for the range allotment, was developed for transforming the land-cover map into a forage-production one. This was accomplished by establishing a range of values (low and high) for each cover type in the vegetation map. For example it was determined that the moderately dense sagebrush cover type produced approximately 900 lbs/acre in poor growing seasons and close to 1150 lbs/acre under optimal growing conditions. Once developed, the crosswalk was used to create two maps of forage production (low and high estimates) based on all the mapped land cover types. Given the conditions of a poor growing season, the low-forage productivity model estimated total

Figure 19—Forage production estimates can be derived from the land-cover map by developing a crosswalk table. 30 production to be approximately seven million pounds, however, the high-forage productivity model estimated total production at more than twelve million pounds. In this example extreme scenarios, rather than a seasonal averages, were developed and helped portray the upper and lower limits of forage production for the Warmsprings range allotment. Inappropriate Uses Inappropriate uses of mid-scale existing vegetation maps usually involve assessing fine-scale resource issues, such as project-level activities or features not captured in the map. Although an existing vegetation map may provide supplemental information to these activities, this map should not be used alone to determine such things as:

• Inventorying noxious weed infestations • Assessing riparian area condition • Designing timber-harvest units • Monitoring range utilization • Determining historical conditions

31 Conclusion

The Humboldt-Toiyabe National Forest received a mid-level existing vegetation map product in October 2004 to support the forest plan revision process and conduct needed assessments and analyses. It is compatible with the RSL mapping effort. Both maps can be used as one cohesive product by the Forest for analysis purposes using the crosswalk field provided. This map product provides the Forest with knowledge about current vegetation composition, structure, and patterns that will allow them to implement Forest Service policies and regulations. Over the course of this project, numerous innovative techniques were developed and refined into an operational mapping procedure. These can now be implemented on other Forests to assist with their mapping needs. For example, using 10-meter imagery significantly increased the processing efficiency of image segmentation and greatly reduced the resulting polygon file size when compared to using 3-meter imagery. In addition, generating homogeneous units reduced the spectral, topographic, and spatial variation within segments and improved the precision of assigning dominance types, canopy closure, and tree size class labels. Resource specialists need to remember that there are appropriate and inappropriate uses for this mid-level existing vegetation map product. Appropriate uses include describing vegetation diversity, assessing resource conditions, modeling species habitat, conducting benchmark analysis, designing monitoring procedures, or addressing a variety of other important land management issues. Additionally, this map may provide a preliminary cut to determine where more detailed studies are needed. Since this baseline product represents a single point in time, land managers should develop a strategy for maintaining their initial investment into the future. Scheduled updates, coordinating related work activities, and/or tracking changes over time would certainly help keep the vegetation map current and applicable to future monitoring.

32 References Brewer, C.K.; Barber, J.A.; Willhauck, G.; Benz, U.C. 2003. Multi-source and multi- classifier system for regional landcover mapping. In Proceedings of the IEEE workshop on advances in techniques for analysis of remotely sensed data. October 27th and 28th, NASA Goddard Space Flight Center, Greenbelt, MD: Institute of Electrical and Electronic Engineers; Geospatial and Remote Sensing Society.

Brohman, R.; Bryant, L. editors. 2004. Existing Vegetation Classification and Mapping Technical Guide – Review Draft, April 2003. USDA Forest Service, Washington Office, Ecosystem Management Coordination Staff.

Congalton, R.G.; Green, K. 1999. Assessing the accuracy of remotely sensed data, principles and practices. Boca Raton, FL: CRC/Lewis Publishers. eCognition Software—Definians Germany

Finco, M.; Fisk, H.; Vanderzanden, D.; Lachowski, H.; Degayner, E.; Nowaki, G.; Caouette, J. 2002 . Image texture information applied to forest structure mapping on the Tongass National Forest. Unpublished Report on file at U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center, Salt Lake City, UT. 19 p.

Homer, C.G.; Huang, C.; Yang, L.; Wylie, B. 2002. Development of Circa 2000 Landcover Database for the United States. ASPRS Proceedings, April, 2002. Washington D.C.

Moeur M. 2000—Most Similar Neighbor Technique

Pirkle, E.C.; Yoho, W.H.; Henry, J.A. 1985. Natural Landscapes of the United States Fourth Edition, Kendall/Hunt Publishing Company, Dubuque, Iowa, 417 p.

Quinlan, J.R. 1993, C4.5 programs for machine learning (SanMateo, California: Morgan Kaufmann Publishers).

Ruefenacht, B.; Fisk, H.; Lachowski, H. 2001. Using remote sensing to map sagebrush steppe ecosystems: implications for modeling sage grouse habitat for brood rearing, breeding, and nesting. Rep. No. RSAC-0033-TIP1. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center. 4 p.

Ruefenacht, B.; Hoppus, M.A.; Caylor, J.; Newak, D.; Walton, J.; Yang, L.; Homer, C.; Koeln, G. 2002. Analysis of canopy cover and impervious surface cover of zone 41. Rep. No.

RSAC-4002-RPT1. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center. 12 p.

33 Vandezanden, D.; Lachowski, H.; Jackson, B.; Clerke, W. 1999. Mapping vegetation in the Southern Appalachians with multidate satellite imagery: A wilderness case study. Rep. No. RSAC-0009-RPT1. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center. 28 p.

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34 Appendix A: Planning

Humboldt-Toiyabe National Forest United States R4 Regional Office Department of Adaptive Management Services Enterprise Agriculture

Remote Sensing Project Plan Applications Center

Remote Sensing Steering Committee Integration of Remote Sensing August 2003

Humboldt-Toiyabe Vegetation Mapping Project

Humboldt Toiyabe National Forest Remote Sensing Applications Center 1200 Franklin Way 2222 West 2300 South Sparks, NV 89431 Salt Lake City, UT 84119-2020

Julia Richardson Henry Lachowski Vegetation Management Specialist Integration of Remote Sensing - Program Leader 775-355-5342 [email protected] 801-975-3750 [email protected]

Joanne Baggs Haans Fisk Forest Ecologist RS/GIS Specialist - IRS Program Assistant 775-355-5331 [email protected] 801-975-3760 [email protected]

Dave McMorran John Gillham GIS Coordinator Remote Sensing Specialist GIS Analyst 775-355-5388 [email protected] 801-975-3827 [email protected]

Adaptive Management Services Wendy Goetz 631 Coyote St. Remote Sensing Specialist GIS Analyst Nevada City, CA 95959 801-975-3841 [email protected]

Beau Jarvis Jo Ann Fites-Kaufman Remote Sensing Specialist GIS Analyst AMS Enterprise Owner 801-975-3750 [email protected] 530-478-6151 [email protected] Intermountain Regional Office 324 25th St Dave Weixelman Ogden, UT 84401 Plant Ecologist 530-478-6843 [email protected] Roberta Quigley Regional Geometronics Leader 801-625-5188 [email protected]

Dr. Clint K. Williams Plant Ecologist 801-625-5795 [email protected]

Dave Tart Vegetation Specialist 801-625-5817 [email protected]

[Appendix A: Project Plan] 35 Overview

The Humboldt-Toiyabe (HT) National Forest has begun Forest Plan Revision (FPR) and has scheduled to complete the process within the next three years. However, the Forest currently lacks an adequate existing vegetation map product, and do not have the personnel resources to complete the mapping internally, given the short timeframes. Therefore, the HT has contracted with the Remote Sensing Appli- cations Center (RSAC) and Adaptive Management Services (AMS) enterprise team to produce a vegetation layer that will be used for Forest Plan Revision. An operational working arrangement between the HT, AMS, and RSAC has been organized for the purpose of producing standard vegetation map products during the upcoming fiscal year (2004).

Together, RSAC an d AMS are m apping cu rrent ve getation on the H-T Sa nta Rosa, Mountain Cit y/ Jarbidge, Ruby Mountain, Ely and Austin/Tonopah ranger districts plus a one mile buffer beyond Forest Service administrative boundary. The entire project area encompasses 6 m illion acres of Forest Ser- vice, other public and private lands and intersects with almost 300 7.5-minute quadrangles. Vegetation map types are to be ch aracterized by d ominant land co ver-type, canopy c losure class, and t ree size class. A map legend and classification scheme are being developed for the HT by project cooperators and will be reviewed by Region 4 vegetation specialists. Vegetation map units will be mapped to a minimum polygon size of 5 acres. Final vegetation m ap products will be loaded into the Forest Service corporate database, National Resource Information System (NRIS).

Partnership

The HT has partnered with the RSAC and AMS to generate existing vegetation map products for use in Forest Plan Revision process. This three-way partnership is arranged such that the HT is the client and RSAC and AMS are the producers of map products. The HT will serve as the central point of contact for facilitating coordi nation b etween RSAC and AMS and providing the m with k ey ancillar y data lik e th e most recent aerial photography, FIA plot data, and existing vegetation field data. In addition, the HT will provide feedback and timely review of intermediate and final map products. RSAC will provide general project management and expert vegetation mapping support in the form of geospaital data acquisition, data preparation, image processing, aerial photo-interpretation, rule-based decision-tree modeling, GIS modeling, and accuracy assessment. AMS w ill work in conjunction w ith RSAC provide expert ground knowledge in the form of classification system development, field support and also support GIS analysis, map evalu ation, a nd a ccuracy assessment pha ses of th e pr oject. AM S m ay a lso m igrate f inal m ap products into NRIS databases, but the level of completion is dependent upon how much time and money is available after completing the primary products.

Methods

The mapping procedure for this project is being modeled after methods used by Region 5 Remote Sens- ing Lab (RSL). There are two main reasons for adopting these standard vegetation mapping methods: 1) to maintain map product consistency across the HT National Forest, since the Carson and Bridgeport ranger districts are already being mapped by RSL; and 2) to ensure compatibility with the latest geospatial data dictionary standards. RSL’s proced ure encompasses seven successive mapping stages tha t include: 1) Natural Region Delineation; 2) Life Form Classification; 3) Data Collection and Descriptions; 4) Terrain Model Development; 5) Field Verification and Final Editing; 6) Forest Stand Structure Model- ing; a nd 7) Accura cy A ssessment. This m ethodology is pro ven, well established, a nd rep eatable. Therefore, it provides a solid foundation for designing a reasonable mapping procedure for this vegetation m apping project. For more in formation on RSL ’s ve getation m apping program , ple ase visit their website at: http:///www.fs.fed.us/r5/rsl/projects/mapping.

In this project, remote sensing, Geospatial Information System (GIS), an cillary information, and knowledge of the local area are core data sets which will provide essential information for producing an existing vegetation m ap. Stan dard ge ospatial data sets, su ch a s Landsat Enhanced T hematic Mapper (ETM) satellite im agery, Digital Orthophoto Quadrangles (DOQs), and Digital Elevation Models (DEMs)

36 [Appendix A: Project Plan (cont.)] provide for continuous and consistent information across very large areas. Together they will be used to facilitate image processing, classification, and environmental modeling. Survey-wide ancillary data products su ch as Ca rtographic F eature F iles ( CFFs), sections, su bsections, clim ate, g eology, a nd GAP will be used to stratify the landscape and assist with post-classification modeling and editing.

Existing site-specific information will be gathered and evaluated for this mapping effort. It will provide assistance for training, processing, and modeling spatial data sets. These data were collected and are available through several different programs including Forest Inventory and Analysis (FIA), Com- mon Stand Exam (CSE), Range Site Anal ysis (RSA), Ecological Sit e Analysis (ESA). Although ex- tremely useful, the combined total number of samples are too few for conducting the m odeling as- pects of this project. Therefore, additional information must be collected to supplement this methodology and fill in data gaps at different phases of the mapping process.

New data will be collected through a combination of conventional aerial photo-interpretation and light fieldwork to increase the total number of reference samples, while minimizing project costs. Skilled interpreters will use na tural co lor or bla ck an d white 1:12 ,000 scale aerial ph otography to asse ss cover-type, canopy closure, and size class specified locations. In addition, three to four field crews will record field observations and take ground measurements to validate photo-interpretation and add to the total number of training samples.

High-resolution digital camera imagery will also be acquired to assist with the collection of reference samples in re mote and inaccessible areas. A Kodak DCS ProB ack 6 45 digital cam era w ill b e mounted in the hull of a light aircraft and configured with a laptop computer and onboard GPS so that vertical aerial images can be captured, indexed, archived, and retrieved for analysis. This lightweight camera system has a 16MB chip with a 4027x 4027 pixel array. This technology has proven very effective for many resource management activities like mapping Burned Area Emergency Rehabilita- tion (BAER) , mapping r iparian s ystem, m onitoring rangeland resour ces, assessing t ree m ortality, and of course serving as reference data for general land cover mapping. Stereo pairs of digital camera imagery will be collected over reference samples and interpreted for cover type, canopy closure, and tree size class using ERDAS Imagine’s software package, Stereo Analyst.

Outlined below are the major tasks identified in this vegetation mapping project.

Geospatial Data Collection Assemble Satellite Imagery Landsat 7 TM 6 scenes (2 seasons summer and fall) Assemble 1-meter DOQs (if or when available) Assemble 30-meter DEMs (10-meter if available) Assemble Climate Data (Daymet or PRISM 1Km) Temperature, Precipitation, Growing Days, Solar Radiation Assemble Aerial Photography BW 1:12,000 Prints & NC Photo Copies Assemble other Geospatial Data Layers Sections, Subsections, SURGO, Riparian Veg., CFFs, etc.

Prep Geospatial Data Generate Survey Area (buffer 2 miles beyond Forest boundary) Landsat Preprocessing Terrain Correct Imagery Reproject to UTM Zone 11 Remove Clouds (Erdas/Cubist) Generate Spectral Indices Produce Pan-Sharpened Landsat Image DOQ Preprocessing

[Appendix A: Project Plan (cont.)] 37 Generate Texture and Tone Images Resample to coarser cell size (3 or 5 meters) DEM Preprocessing Create Topographic data sets (Slope, Aspect, Curvature, Hillshades, etc.)

Image Processing Stratify HT into 6 Project Areas (Stratify by Section/Subsection ) Generate Training Sample Sites Generate "Stand based" (Image Segmentation) Pixel-based classification (Life Form) Attribute segments by life form Tag segments with pixel summary statistics Label segments using rules based on summary statistic

Collect/Verify Training Data Screen Existing Data EUI plot data and convert to GIS and evaluate plots for RS samples FIA plot data request data, assign classes, obtain GIS coordinates Locate existing plots (EUI and others) on aerial photographs Collect additional field data (Ecological Gradient Models) Drive-by reconnaissance Training site plots Photo Interpretation Digital Camera Imager Acquisition and Interpretation

GIS Modeling Develop cover type classification models for subsections Analysis of field plots General reconnaissance notes Polygon/Segment attributing Develop rule-based models for labeling pixel classification within segment Generate Draft Maps of Cover Type, Canopy Closure, and Size Class

Quality Control & Editing Check samples of polygons (image segmentation process) for consistency Display polygons over DOQs for general evaluation

Accuracy Assessment Determine appropriate assessment data (FIA data points) Design assessment procedures Interpret assessment sites

GIS/Database Assessmbly Enter data into NRIS

Pilot Project (Austin/Tonopah Project Area) Process Imagery Develop Classification Methodology Assess Vegetation Map Accuracy Implement Successful Methodology

38 [Appendix A: Project Plan (cont.)]

Major Tasks FY - 2003 FY - 2004 July August September October November December January Feburary March April May June July August September

P-I Geospatial Data Collection

Landsat Satellite Imagery Digital Orthophoto Quads Digital Elevation Models Ancillary Data Layers Aerial Photos (copies)

P-II Prep Geospatial Data

Landsat Satellite Imagery Digital Orthophoto Quads Digital Elevation Models High-Resolution Merge Ancillary Data Layers

P-III Image Processing

Stratify All Data by Ecological Units Training Site Selection (PI & FW) Stand-based Segmentation Pixel-based Classification (Life Form) Attribute Segments (Life Form) Pixel-based Classification (Canopy Cover) Attribute Segments (Canopy Cover) Pixel-based Classification (Size Class) Attribute Segments (Size Class)

P-IV Collect/Verify Training Data

Screen Existing Field Data Create GIS Coverages of EUI /FIA Transfer Sites to Aerial Photos Collect Additional Training Data Analyze Field Plot Data

P-V GIS Modeling

Develop Cover-Type Models Attribute Polygon/Segments Develop Canopy Closure Models Attribute Polygon/Segments Develop Tree Size Class Models Attribute Polygon/Segments

P-VI Quality Control & Editing

Check/Edit Life Form Map Check/Edit Cover-Type Map Check/Edit Canopy Closure Map Check/Edit Tree Size Class Map

P-VII Accuracy Assessment

Design Accuracy Assessment Evaluate/Filter FIA Point Data Collect New Reference Samples Assess and Report Map Accuracies

P-VIII GIS/Database Assessmbly

Load Field Data into NRIS Load Vegetation Map Data into NRIS

PM Project Coordination & Expenses

Project Planning & Design Computer System Support Meetings & Coordination Field Work & Travel

P-IX Pilot Project (One SubPath Model)

Collect/Prepare Geospatial Data Assemble Existing Training Data

Image Processing Timeline Symbols GIS Modeling RSAC Quality Control & Editing AMS RSAC/AMS [Appendix A: Project Plan (cont.)] 39 FIELD KEY TO NEVADA VEGETATION

USED FOR CLASSIFYING FIELD AND ACCURACY ASSESSMENT PLOTS FOR THE HUM- BOLDT/TOIYABE NATIONAL FORESTS IN NEVADA (11-30-2003)

I. KEY TO VEGETATION LIFEFORMS

1A.If total plot cover > 10% in vegetation ……………………………….………………………………..2 1B.Not as above ……………………………………………………….…...…VIII Non Vegetated Types

2A.Plot is dominated by agriculture or developed landscapes………………...VIII Non Vegetated Types 2B. Not as above…………………………………………………………………………………………….3

3A.If total plot vegetation dominated by wetland or riparian vegetation. II Key to Riparian Map Types 3A. Not as above………………………………………………………………………………….…………4

4A. If total plot cover ≥10% in conifers …………………………………………………………....………5 4B. If total plot vegetation < 10% in conifers………………………………..…………………………..…6

5A. If total relative tree cover ≥ 20% in hardwoods…..………………..IV Key to Hardwood Map Types 5B. If total relative tree cover < 20% in hardwoods….. ……..…..…...... …III Key to Conifer Map Types

6A. If total plot cover ≥10% in hardwoods…………………………..…IV Key to Hardwood Map Types 6B. If total plot cover < 10% in hardwoods…………………………………………………………………7

7A. If total plot cover ≥ 10%in woodlands……………………………….V Key to Woodland Map Types 7B. If total plot cover < 10% in woodlands…………………………………………………………………8

8A. If total plot cover ≥ 10% in shrubs…………………………….……VI Key to Shrubland Map Types 8C. Not as above……..………………………………………..……...VII Key to Herbaceous Map Types

II. Key to Riparian Map Types

1A. If total plot cover ≥ 10% in hardwoods………..…………………………………………………….….2 2B. Not as above………………………………….…………………………………………………………3

2A. Western Birch has ≥ 75% (relative cover--rc) ……………………….……..…….Western Birch-WB 2B. Black (or nannorwleaf) Cottonwood has ≥ 75% rc……………………....…………... Cottonwood-BC 2C. Aspen has ≥ 75% rc….…..………………………………………………………...………...Aspen-QA 2D. Alder has > 75%rc…………………………………………………………………………....Alder-AL 2D. Not as above………………………………………………..……..…Mixed Riparian Hardwood-MH

3A. If total plot ≥ 50% shrub rc ………………..………………………………....Riparian Shrubland-RS 3B. Not as above………………………………………...………………Meadow/Riparian Herbland-RG

III. Key to Conifer Map Types

1A. One conifer species (or genus) has ≥ 50% rc……………………………………………..……………2 1B. No single conifer species (or genus) has ≥ 50% rc …………………………………………..…….….8

40 [Appendix A: Classification Scheme] 2A. Bristlecone Pine has ≥ 50% rc …………………………………………………...Bristlecone Pine-BP 2B. Not as above ………………………………………………………………………………..………….3

3A. Ponderosa Pine has ≥ 50% rc ……………………………………….……...…...…Ponderosa Pine-PP 3B. Not as above …………………………………………………………………………………..……….4

4A. Subalpine Fir has ≥ 50% rc ……………………………………………………..…Subalpine Fire-SF 4B. Not as above …………………………………………………………………………………..……….5

5A. White Fir has ≥ 50% rc …………………………………………………..……….….…White Fir-WF 5B. Not as above …………………………………………………………………………………..……….6

6A. Engleman Spruce has ≥ 50% rc ………………………………………..…..…Englemann Spruce-ES 6B. Not as above …………………………………………………………………………………..……….7

7A. Whitebark Pine or Limber Pine has ≥ 50% rc ……………..………...…Whitebark/Limber Pine-WL 7B. Not as above …………………………………………………………………………………..…..……8

8A. Engleman Spruce in combination with White Fir have ≥ 50% rc ……………..…Spruce/Fir Mix-MF 8B. Not as above …………………………………………………………………………………..….…….9

9A. Engleman Spruce in combination with any Pine species have ≥ 50% rc …….…Spruce/Pine Mix-MP 9B. Not as above …………………………………………………………………….…Mixed Conifer-XC

IV. Key to Hardwood Map Types

1A. Aspen has ≥ 50% relative tree cover ...…………………………………………..…………..Aspen-QA 1B. Aspen present with conifer (<= 50%, not dominant)…..……...……….…….Aspen/Conifer Mix-MA

V. Key to Woodland Map Types

1A. Juniper species has ≥ 75% rc …………………………………….……...……...……...….Juniper-JO 1B. Pinyon has ≥ 75% rc ………………………………………...………………………….….Pinyon-PE 1C. Juniper in combination with Pinyon Pine have ≥ 75% rc……………………....…Pinyon/Juniper-PJ 1D.Not as above……………………………………………………………………………………….……3

3A. Curlleaf Mtn Mahogany has ≥ 50% rc…………….…………...……...Curlleaf Mtn. Mahogany-CM 3B. Otherwise…………………………………………………………………...…Mixed Woodlands-XW

VI. Key to Shrubland Map Types

1A. One shrub species has ≥ 50% rc….…..…………………………………………………...………….…2 1B. Not as above…………………………………………………………………………………………….7

2A. Basin Big Sagebrush has ≥ 50% rc….…….….……………………………………...…...Basin Big-AT 2B. Not as above…………………………………………………………………………………………….3 3A. Mountain Big Sagebrush has ≥ 50% rc….…….….………………………………....Mountain Big-AV 3B. Not as above…………………………………………………………………………………………….4

[Appendix A: Classification Scheme (cont.)] 41 4A. Wyoming Big Sagebrush has ≥ 50% rc….…….….…………………….……..…...Wyoming Big-AW 4B. Not as above…………………………………………………………………………………………….5

5A. Low Sagebrush has ≥ 50% rc….…….….………………………………………....Low Sagebrush-AA 5B. Not as above…………………………………………………………………………………………….6

6A. Black Sagebrush has ≥ 50% rc….…….….……………………………………....Black Sagebrush-AN 6B. Not as above…………………………………………………………………………………………….7

7A. Sagebrush in combination with Bitterbrush has ≥ 75% rc…..…….Mixed Sagebrush/Bitterbrush-SB 7B.Not as above…………………………………………………………………………………………….8

8A. Desert shrub species (Shadescale, Saltbrush, Ephedra, Greasewood) have ≥ 50%rc...Desert Shrub-DS 8B. Not as above………………………………………………………………………………………….....9

9A. Mountain shrub species (Snowberry, Elderberry, Ribes, Rose) have ≥ 50%rc ....Mountain Shrub-MS 9B. Not as above…………………………………………………………………………………………...10

10A.Range shrub species (Rabbitbrush, etc…) have ≥ 50%rc…...……………………...... Mixed Shrub-BS

VII. Key to Herbaceous Map Types

1A. Plot dominated by grasslands………………………………..…………………………………...……..2 1B. Plot dominated by forbs…………….……………………….…………………….…...….Meadow-RG

2A. Site in Basin setting….……………………………..……………………………Basin Grassland -BG 2B. Site in Mountain setting….………………………………………………...Mountain Grassland -MG 2B. Site in Alpine setting….……………………………..……………………….Alpine Community-AC

VIII Key to Non-Vegetated Map Types

1A. Agriculture use comprise > 50% rc..……………………………………..……..……..Agriculture-AG 1B. Not as above …………………………………………………………………………………………….2

2A.Urban or developed landscapes…………………………………………………………...….Urban-UB 2B. Not as above ...…………………………………………………………………………………….…….3

3A. Snow or ice fields at the highest elevations comprise > 50% rc………………………..…Snow/Ice-SN 3B. Not as above ...……………………………………………………………………………………..…….4

4A. Open water or confined water coarse occupy > 50% rc...……………………………….….Water-WA 4B. Not as above ....………………………………………………………………………………….………5

5A. Barren landscapes (bare ground, alkali flats) > 50% rc….……...... Barren-BA 5B. Not as above ..….………………………………………………………………………………….…….6

6A. Rock Outcrops (bedrock, rock outcrop, etc) > 50% rc………………………….…..Rock Outcrop-RO 6B. Not as above ..….………………………………………………………………………………….…….7

6. Unknown non-vegetated map type (mostly mine sites/tailings)…Unknown/Mixed Non-Vegetated-XN

42 [Appendix A: Classification Scheme (cont.)]

albicaulis (Engelmann (Ponderosa (Ponderosa (bristlecone (bristlecone (subalpine fir) (subalpine

(Lodgepole pine) (Lodgepole (Douglas fir) (limber pine) pine) (limber (common juniper) juniper) (common Pinus longaeva ponderosa Engelmanii lasiocarpa contorta flexilis

(Utah juniper) juniper) (Utah communis

Pinus Pinus Pinus Pinus Abies Picea osteosperma scopulorum Mountain (Rocky juniper)

Pinus Juniperus Pinus monophylla (singleleaf pinyon) Pseudotsuga menziessi Juniperus Juniperus ns vations vations n cross section section n cross high elevations high edles > 2.5 in. long, high elevations elevations high long, in. > 2.5 edles

DRAFT simplified key to conifers of NE Nevada to Nevada conifers of NE DRAFT simplified key FF. prickleCone at scale tip, scales thickest at in. 4-sided with 1.5 longcone tip, 1 needles – FF. pine) B. Tree with short, upright trunk, often branching at base, rounded or conical open crown openroundedor crown conical branching at base, often short, trunk, upright Tree with B. BB. Tree with straight trunk, narrow pointed crown, generally not branched at base base at branched not generally crown, pointed narrow trunk, straight with Tree BB. AA. long cm less than 1 usually scale-like, Leaves AA. D. Weixelman 6-24-04 needles thanlong needle-like, 1 cm the more A. Leaves more or 2 of clusters in Needles B. C. Needles in twos twos in Needles C. (whitebark pine) (whitebark elevatio high intact, ground the on animals, by apart torn not Cone EE. ne tip, at prickle without tip, at thinnest scale Cone F. falling after intact remain which cones woody producing Plants CC. single needles thick, scales globose, Cones D. CC. Not as above, needles in groups of 3 or more more or 3 of groups in needles above, as Not CC. threes in Needles D. pine) fives in Needles DD. ele high intact, ground on not animals, by apart torn generally Cone E. clusters in grouped never single, Needles BB. falling before disintegrate cones the present, cones if or fleshy, Fruit C. berry a fruit height, m 1 than less Shrub, D. spruce) DD. Trees mainly over 5 m high high m 5 over mainly Trees DD. flat, soft, needles branches, upper on erect cones cone-like, Fruit E. delicate and thin scales oblong, Cones DD. scales between bracts notched with Cones E. i square sharp, stiff, needles scales, between bracts without Cones EE. [Appendix A: Classification Scheme] 43 MAP LEGEND Vegetation Mapping Project Humboldt-Toiyabe NF Nov. 25, 2003

COVER TYPE / LIFEFORM Conifers Hardwoods Woodlands Shrublands Herbaceous Riparian/Wetland Non-Vegetated

CONIFERS:

Bristlecone Pine Whitebark Pine/Limber Pine Subalpine Fir White Fir Engelmann Spruce Mixed Conifer

HARDWOODS:

Aspen (upland aspen) Aspen/Conifer

WOODLANDS:

Juniper Pinyon Pine Pinyon-Juniper Curlleaf Mtn Mahogany Mixed Woodlands

SHRUBLANDS:

Desert Shrubland Mixed Sagebrush/Bitterbrush Basin Big Sagebrush Mountain Big Sagebrush Wyoming Big Sagebrush Black Sagebrush Low Sagebrush Mountain Shrubland Mixed Shrubland

44 [Appendix A: Map Legend]

GRASSLANDS/HERBACEOUS:

Basin Grassland Mountain Grassland Alpine Community

RIPARIAN/WETLANDS:

Riparian Hardwood Cottonwood Western Birch Alder Riparian Aspen Riparian Shrubland Wet Meadows/Herbaceous wetlands

NON-VEGETATED:

Agriculture Snow/Ice Urban or Developed Water Barren (bare ground, alkali flats) Rock Outcrops (outcrops, scree, bedrock, etc…) Unknown non-veg (mining sites & tailings, etc…)

[Appendix A: Map Legend (cont.)] 45 CANOPY CLOSURE CLASSES

CONIFER AND HARDWOOD MAP TYPES

10%-40% 41%-70% 71%-100%

WOODLAND MAP TYPES

10% - 20% 21% - 40% 41% +

SHRUBLAND MAP TYPES

10% - 20% 21% - 30% 31% +

RIPARIAN SHRUBLAND MAP TYPES

10%-40% 41% +

TREE SIZE CLASSES

FORESTED MAP TYPES (CONIFER, HARDWOOD, & RIPARIAN FOREST MAP TYPES)

Small (< 9” DBH) Medium (9” - 21” DBH) Large (21” + DBH)

46 [Appendix A: Map Legend (cont.)] Austin/Tonopah RD's Austin/Tonopah RD's (cont) Ely RD (East Side) Ely RD (West Side) Mountain City & Jarbridge RD's Ruby Mountains RD Santa Rosa RD USGS Quad Name (7.5-Minute) USGS Quad Name (7.5-Minute) USGS Quad Name (7.5-Minute) USGS Quad Name (7.5-Minute) USGS Quad Name (7.5-Minute) USGS Quad Name (7.5-Minute) USGS Quad Name (7.5-Minute) 1 Antelope Peak 61 Kitchen Meadow 1 Arch Canyon 1 Adaven 1 Annie Creek 1 Arizonia spring 1 Andorno Ranch 2 Antelope Spring 62 Little Fish Lake 2 Baking Powder Flat 2 Badger Gulch 2 Badger Creek 2 Belmont Creek 2 Black Ridge 63 Manhattan 3 Big Springs 3 Badger Hole Spring 3 Bear Mountain 3 Big Bald Mtn 3 Buckskin Mountain 3 Arc Dome 64 McCann Canyon 4 Cave Creek 4 Big Creek Ranch 4 Bearpaw Mountain 4 Franklin Lake NE 4 Capital Peak 4 Austin 65 Mckinney Tanks 5 Cave Mtn 5 Blackjack Springs 5 Big Table 5 Franklin Lake NW 5 Coyote Moutain 5 Bakeoven Creek 66 Midas Spring 6 Cleve Creek Baldy 6 Bullwhacker Springs 6 Black Leg Creek 6 Franklin Lake SW 6 Deer Flat 6 Barley Creek 67 Millett Ranch 7 Comins Lake 7 Callaway Well 7 Buckhorn Ridge 7 Frost Creek 7 Five Fingers 7 Barrett Canyon 68 Moores Station 8 Connors Pass 8 Crows Nest 8 Bull Run Reservior 8 Gordon Creek 8 Hinkey Summit 8 Barton Spring 69 Moores Station SW 9 Duck Creek Valley 9 Currant 9 California Mountain 9 Green Moutain 9 Holloway Meadows 9 Bates Mtn. 70 Morey Peak 10 Easy Ely 10 Currant Mtn 10 Caudle Creek 10 Halleck SW 10 Hoppin Springs 10 Baxter Spring 71 Mosquito Creek 11 Ely 11 Currant Summit 11 Charleston Reservoir 11 Harrison Pass 11 Mahogany Spring 11 Baxter Spring NW 72 Mount Ardivey 12 Garrison 12 Douglas 12 Chicken Creek Summit 12 Herder Creek 12 McConnell Peak 12 Belmont East 73 Mount Jefferson 13 Giroux Wash 13 Duckwater NE 13 Coon Creek 13 Humboldt Peak 13 Mullinix Creek

13 Belmont West 74 Mud Spring 14 Hogum 14 Duckwater SE 14 Cornwall Mountain 14 Jiggs 14 Odell Mountain Appendix B:GeospatialDataAcquisition 14 Big Ten Peak East 75 Ninemile Peak 15 Kalamazoo Creek 15 Goat Ranch Srpings 15 Curtis Draw 15 Lamoille 15 Orovada 15 Big Ten Peak West 76 North Shoshone Peak 16 Kalamazoo Summit 16 Green Springs 16 Delaware Creek 16 Lee 16 Paradise Valley 16 Birch Creek Ranch 77 North Toiyabe Peak 17 Kious Spring 17 Hamilton 17 Dry Creek Reservior 17 Noon Rock 17 Paradise Well 17 Black Spring NW 78 Northumberland Pass 18 Lehman Caves 18 Heath Canyon 18 Elk Mountain 18 Pearl Peak 18 Santa Rosa Peak 18 Box Spring 79 Pablo Canyon Ranch 19 Little Horse Canyon 19 Horse Sring Hills 19 Goat Creek 19 Ruby City Creek 19 South of McDermitt 19 Brewer Canyon 80 Paradise Peak 20 Majors Place 20 Horse Track Spring 20 Gods Pocket Peak 20 Ruby Dome 20 Spring City 20 Bunker Hill 81 Park Mtn 21 Matter Creek 21 Illipah 21 Hicks Mountain 21 Ruby Lake NW 21 White Rock Canyon Digital Ortho Quadrangle 21 Burnt Cabin Summit 82 Peavine Ranch 22 Minerva 22 Inidian Garden Mtn 22 Indian Hay Meadows 22 Ruby Valley School 22 Mud Spring Canyon 22 Butler Ranch 83 Petes Summit 23 Minerva Canyon 23 Limestone Peak 23 Jacks Peak 23 Secret Valley 23 Willow Creek Ranch 23 Cape Horn 84 Pine Creek Ranch 24 Mormon Jack Pass 24 McCutchen Spring 24 Jarbidge North 24 Sherman Mountain 24 McDermit 24 Carvers 85 Potts Well 25 Mount Moriah 25 Moorman Spring NW 25 Jarbidge South 25 Soldier Peak 86 Pritchards Station 26 Needle Point Spring 26 Mount Hamilton 26 Maggie Summit 26 Station Butte 25 Carvers NE 87 Reese River Butte 27 North Schell Peak 27 Nyala 27 Mahala Creek West 27 Tent Mountain 26 Carvers NW 88 Round Mtn 28 Old Mans Cayon 28 Quinn Canyon Springs 28 Marys River Basin NE 28 Verdi Peak 27 Carvers SE 89 Saulsbury Basin 29 Preston Reservior 29 Seligman Canyon 29 Marys River Basin NW 29 Walker Canyon 28 Cloverdale Ranch 90 Savory Mtn 30 Red Ledges 30 Treasure Hill 30 Marys River Basin SE 30 Welcome 29 Corcoran Canyon 91 Secret Basin 31 Sacramento Pass 31 Troy Canyon 31 Mcafee Peak 30 Corral Wash 92 Segura Ranch 32 Schellbourne 32 Wadsworth Ranch 32 Merritt Mountain 31 Danville 93 Seyler Peak 33 Silver Canyon 33 Water Gap NW 33 Mount Ichabod 32 Dianas Punch Bowl 94 Simpson Park Canyon 34 Sixmile Canyon 34 White Pine Peak 34 Mountain City 33 Dobbin Summit 95 South Shoshone Peak 35 South Bastian Sring 35 Willow Grove 35 Owyhee 34 Downeyville 96 South Toiyabe Peak 36 South Schell Peak 36 Robinson Creek 35 Dutch Flat 97 Spencer Hot Springs 37 Spring Mtn 37 Sun Creek NW 36 Eagle Pass 98 Stargo Creek 38 Stonehouse 38 Taylor Canyon 37 East of Millett Ranch 99 Stone Cabin Ranch 39 The Cove 39 Tennessee Mountain 38 Elkhorn Canyon 100 Stone Cabin Ranch SW 40 Third Butte East 40 Three Creek 39 Ellsworth 101 Sullivan Wash 41 Ward Charcoal Ovens 41 Three Forks 40 Farrington Canyon 102 The Monitor 42 Ward Mtn 42 Triplet Butte 41 Fish Springs 103 Thunder Mtn. 43 Wheeler Peak 43 Ungina Wongo 42 Fish Springs NE 104 Tieney Creek 44 Windy Peak 44 Water Pipe Canyon 43 Fish Springs SE 105 Toms Canyon 45 Yelland Dry Lake 45 Wild Horse 44 Gabbs 106 Upper Fish Lake 46 Indian Springs Knoll 46 Winter Ridge 45 Georges Canyon Rim 107 Wallace Canyon 46 Gold Park 108 Well Canyon 47 Grantsville 109 West of Austin 48 Green Monster Canyon 110 White Sage Canyon 49 Hannapah 111 Wildcat Canyon 50 Hickison Summit 112 Wildcat Peak 51 Hobble Canyon 113 Wildcat Peak NW 52 Horse Heaven Mtn 114 Yankee Blade 53 Ione 115 Yellow Cone 54 Ione NW 55 Ione SW 56 Iron Mtn 57 Jackrabbit Spring 58 Jefferson 59 Jet Spring 60 Kingson 47 Landsat TM scenes used

Path/Row Leaf On Date Leaf Off Date Leaf On 1993 Date 39/32 Jul-99 / / 39/33 20-Jun-00 8-Oct-99 27-Jul 40/31 11-Jul-99 / 19-Aug 40/32 13-Jul-00 29-Sep-99 19-Aug 40/33 14-Aug-00 8-Oct-99 19-Aug 40/34 11-Jun-00 15-Oct-99 19-Aug 41/31 10-Jul-02 4-Sep-99 26-Aug 41/33 20-Jul-00 8-Oct-00 26-Aug 42/31 Jul-02 27-Sep-99 14-Jun

48 Appendix C: Image Pre-Processing Procedures

Landsat Imagery - Leaf On, Leaf Off and Leaf On 1993 Landsat satellite imagery has been us ed in resource management for almost thirty years. This sensor records moderate spatial resolution information (30m pixels) every 16 days. Landsat has long been used to map existing land-cover, conduct change detection analysis, and other resource specific assessments. This project specifically utilized the MS and Pan im agery from the Leaf On dates and only the MS imagery for the Leaf Off and Leaf On1993 dates.

Processing Steps: 1. Import a. All TM (30m multi-spectral and 15m pan) images are in NLAPS format b. Import TM images using ERDAS Imagine NLAPS import tool c. Import projection is Albers Conical Equal Area

2. Re-project a. TM imagery must be reprojected one image at a time – misalignment occurred when reprojection performed in batch mode b. Reproject TM images using Data Preparation -> Reproject Images in ERDAS Imag- ine c. Reproject to UTM Zone 11, NAD27, with pixel size set to 30m and 15m

3. Subset Imagery was clipped to the mapping regions boundaries using aois. For the regions which included more than one TM scene, each scene was clipped out to the region boundary and mosaiced together using the image matching option in Imagine.

Regions which contained more than 1 scene: Leaf On - Ely East, Ely West, Mtn City Leaf Off - Ely East, Ely West, Mtn City Leaf On 1993 - Austin, Ely East, Ely West, Ruby Mtns

4. Eliminate un-classifiable areas. A cloud eliminate process (for clouds, cloud shadows and haze) was used for the Mtn City and Austin regions (Leaf Off scenes).

DOQs Digital Orthophoto Quadrangles are d igital products derived from aerial photography. T his imagery depicts ground f eatures i n their 'true' position by having the vertical displa cement removed. Most DOQs have a spatial resolution of 1 meter that provide enough detail for mapping fine-scale features at 1:24,000-scale. These digital image sources are becoming more extensively used throughout the Forest Service and other land management agencies.

Processing Steps: 1. Import Downloaded quads from http://fsweb.clearinghouse.fs.fed.us/ and some were provided by GSTC. Where DOQs were not available DOQQs were used instead. 2. Reproject 3. Mosaiced DOQQs using the image matching option. 4. Resampled the 1m DOQs to 3meters using the Toolkit. Mosaiced the 3m DOQs in Imagine using the image matching option. 5. Subset the mosaiced full scene DOQs to the region boundary.

49 Image Merges All Landsat imagery (MS and Pan) was registered to the final DOQ images. A final 10-meter TM/DOQ merge image was developed for use in generating the segment polygons.

Processing Steps: 1. MS/Pan Merge – 15 meter Used the Toolkit to merge the Leaf On MS (30-meter) and Pan (15-meter) images. 2. TM/DOQ Merge – 1meter Used the “Resolution Merge” module in Imagine to merge the ms/pan 15-meter image and the DOQ image (Options used: principal component, cubic convolution, and stretched to unsigned 8-bit). 3. Resampled TM/DOQ – 10 meter Developed two models to resample the 1 meter TM/DOQs image to 10 meters.

Landsat Derivatives (NDVI, PC and Tassel Cap) Used Im agine modules “Indices, Tassel Cap, a nd Principal Com ponents” to generated NDVI, PC a nd Tassel Cap layers for the MS Leaf On, Leaf OFF and 1993 imagery. (Options used on each: stretch to unsigned 8-bit, ignore zero in stats and the mapping region aoi boundary).

DEMs Digital Elevation Models consist of a re gular array of elevation values cast on a de signated coordinate projection systems. They are used extensively in geographic information systems and to conduct a variety o f earth scie nce an alyses. For th e Sa nta R osa, Moun tain City /Jarbidge an d the Ru by Mo untain mapping regions 10-meter DEMs were available while the Austin, Ely East, and Ely West mapping regions had 30-meter DEMs.

30 meter Used the Toolkit to uncompress grid files (in both feet and meters) and mosaic into a single file for each study area. These grid files were then imported to .img files, resampled to 10 meters and subsetted to the region boundary.

Processing Steps:

1. 30m- meter DEM Generated 30m- meter image using toolkit to import & mosaic quads 2. 30m- feet DEM Generated 30m- feet image using toolkit to import & mosaic quads 3. 10m- meter DEM Resampled 30m-meter image to 10m (used Cubic Convolution) = Clipped to region boundary 4. 10m- feet DEM (Cubic convolution resampling) Resampled 30m-feet image to 10m (used Cubic Convolution) and converted to grid file. 5. 10m- feet DEM (Bilinear resampling) Resampled 30m-feet image to 10m (used Bilinear) and converted to grid file.

10 meter Used the Toolkit to uncompress grid files (in both feet and meters) and mosaic into a single file for each study area. These grid files were then imported to .img files and subsetted to the region boundary.

50 DEM Derivatives (Slope, Wetness, Tri-shade and Curvature) For the Santa Rosa, Mountain City/Jarbidge and the Ruby Mountain mapping regions 10-meter DEMs were available while the Austin, Ely East, and Ely West mapping regions had 30-meter DEMs. All the input files for these derivatives were the 10-meter DEM grid data with x, y, and z values in feet.

Slope

Processing Steps: 10m GRID files 1. Reproject GRID file into meters using ArcInfo 2. Calculate Z-values to meters using ArcInfo: GRID a. zfactor = 0.304800609601 3. Export GRID file into ERDAS Imagine *.img format 4. Run ERDAS Imagine Slope Function with the Percent Slope option selected

30m IMG files 1. Run ERDAS Imagine Slope Function with the Percent Slope option selected

Wetness Used the 10m-feet grid DEMs to derive a final wetness image (AT, EE, and EW used the 10meter DEM grid file resampled using bilinear).

Processing Steps: 1. Set 02-dem10_cti.aml program variables a. Set General Pathnames i. AML Directory - Identify the location (directory) of this AML ii. Identify the location (directory) where computations will occur - "temporder" b. Set Input Filenames and Pathnames i. Input DEM Grid ii. DEM Pathname c. Set Output Filename and Pathname i. Output CTI Grid (Grid) ii. Pathname of CTI Grid d. Output Projection Data e. Save aml file f. Run 02-dem10_cti.aml within ArcInfo g. Arc> &r 02-dem10_cti.aml h. CAUTION: Do not modify aml file while aml is running 2. Convert file that was created from the 02_dem10_cti.aml model into ERDAS Imagine IMG format. a. Open the GRID file in an ERDAS Imagine Viewer b. Select the File > Save > Top Layer As option 3. Open topo_cti_filter.gmd within ERDAS Imagine: Model Maker application a. Set the Input image within the model b. Set a path for each temp file that will be created c. Set path and name for final wetness model output

51 Curvature & Trishade

Processing Steps: 10m GRID files 1. Reproject GRID file into meters using ArcInfo 2. Calculate Z-values to meters using ArcInfo: GRID zfactor =0.304800609601 3. Export GRID file into ERDAS Imagine *.img format 4. Run Toolkit curvature and wetness function in ERDAS Imagine.

30m IMG files Run Toolkit curvature and wetness function in ERDAS Imagine.

Climate Daymet is a collection of algorithms and computer software designed to interpolate and extrapolate from daily meteorological observations to produce gridded estimates of daily weather parameters over large regions. These products were developed in part by by Peter Thornton, while the Numerical Terrady- namic Simulation Group (NTSG) at the School of Forestry, University of Montana (www.forestry.umt. edu/ntsg). There are a total of 17 climatological summary variables included in the DaymetUS database. These spatial data sets were derived from a much larger database of daily weather parameters, produced on a 1-kilometer grid over the entire conterminous United States for the period of record 1980- 1997. The daily observations used to produce the gridded surfaces came from approximately 6000 stations in the U.S. National Weather Service Co-op network and the Natural Resources Conservation Ser- vice SNOTEL network (automated stations in mountainous terrain). For detailed information regarding Daymet climatological summary products (geospatial, temporal, attributes, and complete description about the individual climatological variables) or proper citations for referencing this product, please refer to the on-line material available at www.daymet.org.

Potential Floodplain Processing Steps: 1. Stream Networks Stream networks were generated from the unclipped 10meter DEMs in feet.

2. Potential Riparian Zones (Floodplain)

a. Riparian buffers (or floodplains) were generated from the 10meter clipped grid DEMs using the floodplain.aml.

Used the following parameters:

Stream Order Maximum Maximum search elevational distance from difference in stream network meters (pixels) 1 1 3 2 3 6 3 7 9 4 10 15 5 12 21 6 14 30

52 b. Converted the grid output from the floodplain program to image files. c. Ran two models to filter out single pixels from the floodplain output. Used flood10m_filter01 & 02.gmd for the regions which used 10meter DEMs (Mtn City, Ruby Mtns and Santa Rosa) and used flood30m_filter01 & 02.gmd for the regions which used 30 meter DEMs (Austin, Ely East and Ely West). d. Converted the filtered images to grid and arc coverages.

3. Intersected the potential riparian buffer with the riparian segment

Stream Distance

Used the Erdas Module “Search”. Input the clipped stream coverages & the aoi boundary.

Used the following parameters: Vector Type: Line Use Attribute As Value: Order Output Cell Size: 10 Units: Meters Classes: 1, 2, 3, 4, 5, 6 Distance to search: 300

54 Appendix D: Field Data Collection

Field Sampling – Training Sample Design

THESE 4 STEPS WERE INPLEMENTED ON EACH MAPPING REGIONS.

A. Unsupervised classification was performed on the Landsat ETM satellite imagery (leaf-on). Bands 1-6 were used to generate the between 20 and 40 spectral classes depending on the size of the region and its diversity.

MAPPING REGION # OF SPECTAL CLUSTERS Austin 40 Ely East 30 Ely West 30 Mtn City 30 Ruby Mtns 20 Santa Rosa 20

B. Buffered the CFF road coverage by ¼ mile (402 m).

MAPPING REGION CFF ROAD ATTRIBUTES Austin 96, 100, 101, 103, 105, 106, 515 Ely East 101, 103, 105, 106, 515 Ely West 89, 96, 101, 103, 105, 106, 515 Mtn City 96, 101, 103, 105, 106, 515, 519 Ruby Mtns 101, 103, 105, 106, 515 Santa Rosa 96, 97, 101, 103, 105, 106, 515, 517, 519, 526

C. A field site shapefile was generated in ArcMap. The Landsat image, unsupervised classification, and the buffered road coverage were used to locate accessible field site locations. Training samples were chosen within the buffered zone and where a single spectral class occupied a 3 x 3 pixels cluster of pixels at a minimum. There were approximately 8 sites per spectral class.

MAPPING REGION # OF FIELD SITES Austin 317 Ely East 240 Ely West 239 Mtn City 254 Ruby Mtns 160 Santa Rosa 160 TOTAL 1,370

D. Field maps were developed at a scale of 1:35000. The Landsat/DOQ 3m merged image was used as the backdrop (band combination 4,5,3). The main travel roads, field sites, flight lines, flight points, and town and city coverages were used in each map composition. An overall reference map showing the locations of the field maps and sites was also generated.

MAPPING REGION # OF FIELD MAPS Austin 22 Ely East 8 Ely West 5 Mtn City 8 Ruby Mtns 6 Santa Rosa 4

[Appendix D: Training Sample Design] 55 Training Sample—Field Data Collection Form Page 1

56 [Appendix D: Field Data Collection Form] Training Sample—Field Data Collection Form Page 2

[Appendix D Field Data Collection Form (cont.)] 57 H-T VMP FIELD TRAINING SITE DATA FORM—DATA DICTIONARY

1) Site ID Number—Each sample site has a unique 3-digit number per study area. Append the ranger district/ study area code to it to make it unique for the entire Forest. (i.e: AT001 = Austin/Tonopah, site#1)

2) Names of collectors—Can use initials if they are unique to the entire team. Prefer names & initials listed on the first few forms done on each new study area.

3) Month/Day/Year— Date of field site sampling formatted as numbers (i.e: 09 – 23 –2003)

4) Aerial Photo#--The flightline/roll number for the color aerial photo used for that field site AND the actual photo number listed on the color aerial photo. (i.e: 2393-106)

5) Location Type—Circle either “Existing” if you sampled directly at the pre-determined sampling point given by RSAC, or “New” if you were unable to get to that point or decided to sample a completely new point some- where else (which is okay, we want representative sampling).

6) UTM E & N—UTM Easting and Northing coordinates of field site. Recorded from GPS unit every time to ensure site location is collected if it ends up being off from the pre-designated point. Ensure your GPS is set to UTM zone 11, with a spheriod and datum of Clarke 1866 and North American Datum of 1927 (NAD27) (may need to choose NAD27-CONUS on recreation type GPS units). We ask that you use either a mapping grade GPS (GeoExlporer 3, etc…) or a recreation grade GPS with WAAS (Garmin Etrex’s, etc…). If you must use something else, please record a PDOP of ‘Accuracy’ next to each UTM location recorded.

7) Azimuth—At the sampling point, take a bearing facing down slope and record the degree azimuth. If flat, note with the word “flat” instead of a bearing.

8) Slope (%)—Measure and record the percent slope of the sampling site with an inclinometer.

9) Field Photographs—Record the number of the digital photo taken at the sampling site for each N/S/E/W cardinal direction. This photo number will need to be completely unique to all photos taken by all teams for the entire study area so that when it is transferred to RSAC, it does not get confused with other team’s photos or other study area’s photos. If using a photo board, you can later go in and manually rename each photo file to the name you give it on the form. Ensure that the photo board is in the lower corner of the picture so it doesn’t obscure the rest of the landscape being captured. An example of the filename to record would be AT005-N. jpg for the Austin/Tonopah area site# 3 facing North. This method of tracking photos through unique filenames is vitally important, so please ensure it’s done carefully and recorded correctly on the field form.

10) Forest and Woodland Canopy Cover—(Estimated from a Top-down” perspective)—List out each major Confier, Hardwood, and Woodland species using its standard Plant Code. If the code is not known, its name should be written out and the code looked up and filled in later. Then estimate total canopy cover by tree size class for each species and enter it in the size class columns to the right. Determine canopy cover as if you were looking down on the stand from the air, allowing overlap to influence the counts. So smaller sized trees being overlapped by larger ones will be ignored and not counted in the canopy cover estimate. Total each size class estimate for each species in the Cover column. Size class breaks for Conifer and Hardwoods are measured by DBH and set at: 1—Seedling (<1”), 2—Sapling (1-5”), 3—Pole (5-9”), 4—Medium (9-21”), and 5— Large (21”+). Pinyon and Juniper are Woodland types and need to be measured by HEIGHT and CROWN type. The class breaks are: 1—Seed (<1’), 2—Sapling (1-3’), 3—Pole (3-9’), 4—Young Mature (>9’ with a Pyramid shaped crown), and 5—Mature (>9’ with a fuller, rounded crown).

11) Ocular Plot Composition—(Estimated from a “Top-down” perspective)--List out each major species type (tree species in one column, shrubs in the next column, etc…) using its standard plant code. If the code is not known, its name should be written out and the code looked up and filled in later. (Dead veg and bare ground should go into the Non-vegetated column.) Then estimate canopy cover for each species listed. The “Tree” section should be summarized from what was recorded in section #10 above. For the rest, again determine canopy cover as if you were looking down on the stand from the air, allowing overlap to influence the counts.

58 [Appendix D: Field Form Data Dictionary] So smaller sized trees/shrubs/etc being overlapped by larger ones will be ignored and not counted in the canopy cover estimate. Total each section to determine the dominant Lifeform for the site. Within that dominant Lifeform section, the species/community with the highest cover determines the final Veg class. These items will be listed in the Plot Summary below. EXCEPTIONS: Tree column may have conifer, hardwood, and/or woodland types listed. These 3 types should be considered separately when determining Lifeform and Veg class. Also, shrub covers should be estimated, then actually measured using transect methods (section# 12). Transects should be conducted until the technician is calibrated and estimates are close to measurements for low, medium, and high density sites.

12) Measured Shrub Cover by Line Intercept—(Only use if Lifeform is likely to be Shrubland--not for tree sites)--List the Plant Codes for each major shrub species. Measure 100’ N-S & E-W transects and list the amount of cover (number of feet worth) of each species found in the corresponding 10’ increment. Total it on the right and divide by 100 to get % cover of that species. Total all shrub species percents to get the actual shrub canopy cover for that transect. Once both transects are done, add both and divide by 2, then enter it into the “Overall CC” above. Enter that average CC % into #16. These transects should be conducted until the Ocular estimates are consistently close for low, medium, and high density sites. After that, the technician can skip the measurements (periodically they should recheck/recalibrate themselves to make sure).

13) Life Form—Decide what general lifeform/habitat type the sampling site falls under based on Ocular Plot Composition totals (#11), and record the letter-code under the “Code” column. If it is not a pure site (ie: mixed Woodland site with Shrubland underneath), please note this by filling in codes under the dominant (“Dom”) and codominant (“CoDom”) columns (lines are not in the field form for Dom/CoDom lifeform, but you can still write it in the spaces there).

Cover Type codes include: CF—Conifer HW—Hardwood WD—Woodland SH—Shrubland HG—Herbaceous/Grassland RP—Riparian/Wetland NV—Non-Vegetated

14) Veg Class—Based on the classification rules and species cover estimates (#11), determine what final vegetation class the sampling site would classify out to, then indicate its code under the “Code” column. If it is not a pure site (ie: Dominated by Subalpine Fir, but co-dominated by Bristlecone Pine) please note this by filling in codes under the dominant (“Dom”) and co-dominant (“CoDom”) columns.

Dominance Type codes include: CONIFERS BP--Bristlecone Pine PP--Ponderosa Pine WL--Whitebark Pine/Limber Pine SF--Subalpine Fir WF--White Fir ES--Engelmann Spruce MF--Spruce/Fir Mix MP--Spruce/Pine Mix XC—Mixed Conifer HARDWOODS QA--Aspen MA--Aspen/Conifer WOODLANDS JO--Juniper PE--Pinyon Pine PJ--Pinyon-Juniper CM--Curleaf Mtn Mahogany XW—Mixed Woodland

[Appendix D: Field Form Data Dictionary (cont.)] 59 SHRUBLANDS DS--Desert Shrub SB--Mixed Sagebrush/Bitterbrush AT--Basin Big Sagebrush AV--Mountain Big Sagebrush AW--Wyoming Big Sagebrush AN--Black Sagebrush AA--Low Sagebrush BS--Basin Shrub MS--Mountain Shrub XS—Mixed Shrub GRASSLANDS/HERBACEOUS BG--Basin Grassland MG--Mountain Grassland AC--Alpine Community RIPARIAN/WETLANDS BC--Black Cottonwood WB--Western Birch RS--Riparian Shrub RG—Meadows/Rip. Herbland NON-VEGETATED AG--Agriculture SN--Snow/Ice UB--Urban or Developed WA--Water BA—Barren XN—Unkown non-vegetated

15) Size Class—Based on the size class key and the data recorded in section #10, determine what overall size class for the site would be, then indicate its code under the “Code” column. If it is not a pure size-class site (i.e: dominated by Medium sized trees, but co-dominated by Saplings) please note this by filling in codes under the dominant (“Dom”) and co-dominant (“CoDom”) columns.

Tree Size Class codes: by DBH) 1—Seedlings (<1”) 2—Saplings (1-5”) 3—Poles (5-9”) 4—Medium (9-21”) 5—Large (>21”)

Pinyon/Juniper:(by height) 1—Seedlings (<1’) 2—Saplings (1-3’) 3—Pole (3-9’) 4—Young Mature (>9’ w/ pyramidal crown) 5—Mature (>9’ w/ fuller, rounded crown)

Shrubs: (by height) SS—Small shrubs (0-2’) MS—Medium shrubs(3-6’) TS—Large shrubs (6’+)

60 [Appendix D: Field Form Data Dictionary (cont.)]

16) Canopy Cover—List the measured canopy cover (%) of the dominant lifeform type. Based on either Line Intercept measurements or Ocular estimates (if transects were not done) depending on whether the dominant lifeform was a tree or shrub type. (i.e: Conifer site– list % canopy cover of all trees (even if there’s some hardwoods mixed in) in the plot.) (i.e2: Mtn Big Sage site w/ snowberry—list % cover of all shrubs-sage & snowberry)

17) Notes—Record ANY and ALL notes you think of here about the site. The more the better!

18) Cheatgrass Present—Check this box if cheatgrass (Bromus tectorum) is present in the area you’re at. You can also make notes about how bad it is at that site as well.

19) Life Form—Ocular estimate of dominant lifeform for the aerial photo polygons you delineate in the field. Same rules & description apply as #13.

20) Veg Class-- Ocular estimate of dominant veg class for the aerial photo polygons you delineate in the field. Same rules & description apply as #14.

21) Tree Size Class--Ocular estimate of size class for the aerial photo polygons you delineate in the field. Same rules & description apply as #10.

22) Canopy Closure--Ocular estimate of canopy cover for the aerial photo polygons you delineate in the field. Only estimate this if the dominant lifeform is “Shrubland”. Same rules & description apply as for shrub cover estimates described in sections #10 & 11.

23-27). Foliar Canopy Cover--Do Not fill this section in. This will be Photo-Interpreted by RSAC in the office from stereo pairs.

*--Polygon label—Fill in the site ID (#1) for each of the 5 polygons you delineate on the aerial photo in the field. Then be SURE to label each polygon A,B,C,D, or E on the photograph.

[Appendix D: Field Form Data Dictionary (cont.)] 61 Training Sample—Aerial Photo Interpretation of Field Observations

General Guidelines:

For Photo Interpretation (PI) you will need the following: 1. Gather the field forms, aerial photographs, notes from the field. 2. For the photograph of each point, find the stereo pairs. 3. Organize the photograph in order of their point numbers. 4. A light table/stereoscope 5. Aerial photo scale –protractor 1:12,500 one acre circle 6. Red photo pen, colored pen

There a re u sually (ho pefully) 5 p olygons d rawn o n the photograph per point. So metimes there are 2 points on a photograph, which can mean there ar e over 10 polygons on the photograph. The polygons are labeled in alphabetic order and/or in a number scheme (N2, N3 etc).

Within each polygon place a dot if it hasn’t already been placed, at a p lace that is m ost homogeneous and representative of the pol ygon. This is where m easurements will be m ade. Use an aerial p hoto scale-protractor 1:12,500 one acre circle with the dot in po lygon as center. M easurements are of this acre. Looking at the field form, notice the Field Ocular section there are 4 lines o f data. They are life form, veg class, tree size class, canopy closure.

Office PI work is to revaluate the field ocular measurements and make more measurements. Life Forms are m ade up o f th e following CF-Conif er, HW-Hardwood, WD-Woodland, SH-Shru bland, HG - Herbaceous/Grassland, RW-Riparian/Wetland, NV-Non-Vegetated. Determine what Life Form the polygon is. The next thing to reexamine is the Veg Class. The Veg Class is t he dominant or co dominant species or nonvegetati ve character of the poly gon. So the nu mber of possibilities is many. Single s pe- cies and m ixed species m ake innumerable possibilities. If the veg class is a tree (conif er, hardwood, woodland) then the tree size class needs to be determined. There are 5 c lass sizes, determine which one or ones that it entails. And part of that is to measure the percentage of foliar canopy cover and how that brea ks dow n a s pe rcentages. No w if its sh rubland, th en estimate canopy closure and what size class it is small, medium or large. And for herbaceous/grasslands and non-vegetated Life Forms there is nothing more needed to put down.

A few things to keep in mind: Do not make multiple A’s or B’s go A, B, C, D, and E….. Make polygons: tight exclusive homogenous. Make 5 polygons even if they are the same vegetation type. No Donuts in polygons. Use a dot in polygon; use an acre/circle easier to make more accurate estimate.

62 [Appendix D: Aerial Photo Interpretation– Field Observations] Appendix E: Segmentation & Classification

Image Segmentation – Using eCognition Generating Segments:

Image segmentation techniques were used to generate stand-level delineations that even tually formed the outer-perimeters of final vegetation polygons. A software package called eCognition was used t o create f ine-scale se gments. These sm all-sized segments form ho mogeneous a reas in the imagery minimizing spe ctral an d to pographic variation w ithin u nits and i mproves the precision of assign ing vegetation t ype, ca nopy closure, an d tre e siz e cl ass la bels. Be low a re t wo examples demonstrating image seg mentation on rangeland (lef t) an d forestland (right) vegetation. In both exa mples, d ifferent types o f inf ormation are incorporated a t increasing levels of the segm entation pro cess: Level I— Landsat/DOQ Me rge; Level II—L andsat/DOQ Mer ge a nd DOQ Te xture; an d Leve l III—Illuminated Shade Relief Image. The yellow polygons highlight the elimination of segments based on texture/tone and red polygons show how segments are returned based on topographic information

Automated Stand-Level Delineations

Rangeland Example Forested Example

Level I Segmentation: Based on Landsat/DOQ Merge Image (10m); Scale Factor 10

Average Polygon Size: 2.0 acres

Level II Segmentation: Added DOQ Texture/Tone Ratio Image (10m); Scale Factor 20

Average Polygon Size: 6.2 acres

Level III Segmentation: Refined with Illuminated Shade Relief Image (10m); Scale Factor 50

Average Polygon Size: 6.1 acres

Landsat/DOQ Merge Image is displayed as the backdrop (band combination: 4, 5, 3) [Appendix E: Image Segmentation] 63

Exporting Segments:

Once the segments/polygons have been created in eCognition, they must be exported to usable format (shapefile). First convert the segments to polygons by using the ‘Segmentation—Create Polygons’ tool. Select the correct segmentation level to con vert. (Also be sure to set: Bas e threshold 0.0 with remove slivers option checked, Shape threshold 1). Then use the ‘Export—Image Objects’ tool to actually generate ArcGIS shapefiles. (Settings: Vector File-raster, uncheck classification and class color options).

Note: During the im age se gmentation process, eCog nition segments eve rything, includ ing the b ack- ground (no data values). So when you export vector files, it includes all the erroneous background polygons, which m ust be cli pped ou t. Use ArcMap —Geoprocessing to ols a nd sele ct polygons using th e study area boundary.

64 [Appendix E: Image Segmentation (cont.)] Image Cube Creation – Custom ArcInfo AMLs

An image cube was developed that captured 58 data layers representing topographic spectral, textural climatic and other ancillary inf ormation. Th is w as acco mplished by developing a process to use th e stand-level de lineations, ge nerated ea rlier, f or summarizing th e 58 da ta la yers. For ea ch po lygon segment, a mean va lue of ea ch individual d ata l ayer, was calcula ted a nd attribu ted to th e 10 -meter pixels contained within the polygon. Af ter all th e data la yers had been summarized producing a zonal mean i mage, th e zonal m ean im ages w ere stacked into a sin gle image, which is ref erred to in this document as the image cube. Belo w is a g raphic sh owing all o f the data la yers included with th e construction of the image cube.

[Appendix E: Image Cube Creation] 65 Data Cube Construction – Using Erdas Imagine

1.) QC Rich’s rip_nonrip training points shapefile (##_riparian_points.shp) and save the new QCed file as ##_rip_nonrip_qc01.shp 2.) Adjust paths & filenames and fire off 04-riparian_points.aml in Arc. This will generate a ##_rippts coverage. Open up t his coverage in ERDAS viewer and bring up the attributes table (vector—attributes). Highlight the X-Coord, Y-Coord, Rip-Code columns (all together far right of the table) and export them (right-click—export) to the ‘data_points/riparian’ folder as ##_rippts01.dat. (Make sure Rip-Code is 1or0, not R/N) 3.) Open that .dat file and add a header line with “Rip-NonRip X-Coord Y-Coord” (1 tab between ea.) 4.) Adjust paths & flags and fire off 05-image_ripzone_mean_##.aml to generate single-band “zonal” images for each layer in the single images folder. These single-band .img’s will be built in the ‘image_cube/riparian/zones’ folder. 5.) View the outputted filenames and create a text file with the list of all these single-band images to stack (preceded by integer or float—most all will be integer), name it ##_ripzones_imagelist.txt, and save it in the ‘image_cube/riparian/zones’ folder. 6.) Open up ERDAS and use the RSAC Tools—FIA Programs—Prepare Data for Cubsit/See5 and fill in the blanks…

[imagelist from step5] [output=imagecube to ‘image_cube/riparian’] [pt coords from step2]

[set to unsigned 16bit] [unchecked] [output= info/coords to ‘data_cube/riparian’] [headers from .dat]

[output=See5 data file to ‘data_cube/riparian’]

66 [Appendix E: Data Cube Construction] Before: After:

Most others will stay continuous, except for the 4 soils layers. They have to be changed to include the following values for each layer: ##_soils_class = 0, 1, 2, 3, 4, 5, etc…..thru 79. ##_soils_drainage = 0, 1, 2, 3, 4, 5, 6, 7. ##_soils_minerals = 0, 1, 2, 3, 4, 5. ##_soils_surftexture = 0, 1, 2, 3, 4, 5, etc…thru 37.

[Appendix E: Data Cube Construction (cont.)] 67 Decision-Tree Development—Using See5

Customized algorithms were developed using a software program called See5. This program uses a data mining technique to generate rule-based decision trees. Survey-wide geospatial data layers and site-specific field-based measurements are analyzed for predictable relationships. The relationships are converted to an algorithm (regression-tree), which is tested, ranked, and finally used to predict, or map, ground-based information across the entire study area. Belo w are step- by-step instruction on ho w t o develop the decision –tree using See5.

Now resave the .names file & make a backup copy of this edited file with a .bak extension (will need later). 1.) Now you’re ready to run See5! Hooray! =) Open up the See5 program, load the ##_rip01_4see5.data file (using button), and then open the construct classifiers window . Start with the settings listed below. You may find that different areas work better with slightly different settings (ie: no fuzzy logic)

Examine the results report that pops up. At the very bottom is the “Evaluation of the TEST” data that gives a simple error matrix to show how well the model was able to fit the test data that it held back for accuracy assessment.

Get the best results possible (highest accuracy in error matrix), then re-run without using the “Use sample of” option checked in order to maximize data samples going into the model.

1.) Open up the image cube (##_ripzone_imagecube.img built from step 6) in an ERDAS viewer and open the imageinfo. Change the MapInfo to the same thing it comes up as (UTM, meters). Then change the Projection to UTM, Clarke1866, NAD1927, zone 11. You have to do this in order to reset it in ERDAS since it was originally set in ArcInfo, which ERDAS cannot recognize. After setting, remove the image from the viewer and save changes if prompted.

68 [Appendix E: Decision-Tree Development] Image Cube Classification—Using Erdas Imagine 1.) Apply the See5 results using the “RSAC Tools—FIA Programs—Apply See5 Results Spatially” button. 1--.tree file from See5 (in ‘data_cube/riparian’ folder) 2 2—image cube containing all 1 layers (in ‘image_cube/riparian’ folder) 3—Output image that will be 4 classified as riparian/non- riparian. Name it 3 ##_rip_classif01.img and save in the ‘classified_ouputs/riparian’ 5 folder 4—Set output to unsigned 8-bit thematic 5—identifies which variable is the dependant (always the 1st in Bonnie’s processes). Set to 1

2.) The output is a raster layer of 1’s with background values of 0. You need to convert this to a vector layer in order to view it against imagery in ArcMap to evaluate how well it turned out. Do this by using the ERDAS -- Vector Module—Raster to Vector tool. 3.) Open up the outputted coverage in ArcMap against 3m 4,5,3 imagery along with the ##_rip_nonrip_qc01.shp training points layer, the ##_potrippolys.shp, slope, & the floodplain coverage. Evaluate how well this first iteration classified riparian areas. Then export ##_rip_nonrip_qc01.shp to ##_rip_nonrip_qc02.shp and start editing this qc02 layer to add in more rip/non-rip points. You can flicker layers such as floodplain and slope on&off to help decide whether questionable areas are likely to be riparian or not. After thoroughly going through the image placing enough additional points to reclassify, save your edits and exit out of ArcMap. 4.) Repeat steps 2-12 using the new ##_rip_nonrip_qc02.shp and naming all new outputs 02 (for 2nd iteration). You can skip steps 4, 5, & 10 completely. On step 6, uncheck the “do you need to stack the layers?” option and enter in the ##_ripzone_imagecube.img directly instead of rebuilding it. Step 8 can be worked around by replacing the step6-generated .names file (which is very generic) with the backup file you saved in the first iteration of step8. 5.) If this new ##_rip_classif02 (2nd iteration) layer is classified accurately enough after you’ve evaluated it, then you can move on to step16, otherwise repeat the iteration process again until you have an acceptable classification. 6.) Once you have an acceptable riparian classification, you need to burn these riparian segments into the upland segments layer. Problem is that the output of step11 has no segments inside it anymore (all internal segments were dissolved). So you need to run the final ##_rip_classif0#.img through a model to force those internal segments back in. Open up the 05b-ripclassif_segs_gen.gmd model in ERDAS ModelMaker, set your “classified input” to the final ##_rip_classif0#.img, your “zonal meaned imagecube” to the ##_ripzone_imagecube.img, your “Re-segmented output” to ##_rip_classif0#_w- segs.img (under ‘classified_outputs/riparian’), and run the model. [Appendix E: Data Cube Development & See5 Classification (cont.)] 69 70 [Blank] Appendix F: Draft Map Review & Revision

[Appendix F: Draft Map Review] 71 Humboldt-Toiyabe Draft Map Review Santa Rosa District May 20th, 2004

The draft map review is an opportunity for local experts to assess the landcover classification and provide any additional input you think is needed to improve the final maps.

You have been provided with a draft maps covering your entire district. We encourage you to write notes, circle areas of concern, and document any other information on the draft maps. You have also been provided with a Draft Map Review Form. If you don’t have adequate space on the draft maps to make notes you can circle a particular area on the map, label it with a unique site #, and write your notes on the form.

Some of the classes occurred infrequently or not at all, we need to make sure that these classes have not been under-sampled.

Vegetation Classes not mapped on the district: • Forests o Western Birch o Bristlecone Pine o Engelmann Spruce o Spruce/Fir Mix o Spruce/Pine Mix o Ponderosa Pine o Subalpine Fir o White Fir o Mixed Conifer o Mixed Aspen/Conifer • Woodlands o Pinyon o Pinyon/Juniper • Shrublands o Black Sage o Basin Shrub o Desert Shrub o Mixed Shrub • Alpine Community

For other communities we had very few ground samples, specifically the grassland communities (alpine community, basin grassland and mtn grassland). We need additional data on where (or if) these classes occur. There is also some confusion within the Artemisia tridentata sagebrush subspecies (ssp. wyomingensis, vaseyana, and tridentata) and Low Sage (Artemesia arbuscula). Please review these classes carefully.

Overall we need as much information as you can provide. This includes feedback on what is correct and what is incorrect. Focus your attention on the general vegetation distribution. Our draft map revisions will be based almost solely on the information you provide to us and will directly reflect your comments.

Schedule:

You will have a total of 4 weeks to review the draft maps. Please call at the end of every week to briefly update us on your review progress and report any questions or concerns. We need to receive the draft maps & comments by June 21st.

72 [Appendix F: Map Review Solicitation Letter] Send them to: John Gillham Remote Sensing Applications Center 2222 West 2300 South Salt Lake City, UT 84119

Contact Info:

John Gillham [email protected] (801) 975-3827

Wendy Goetz [email protected] (801) 975-3841

[Appendix F: Map Review Solicitation Letter (cont.)] 73 74 [Blank] APPENDIX G: Accuracy Assessment Design

H-T VEGETATION MAPPING ACCURACY ASSESSMENT FIELD MANUAL (JUNE-JULY 2004)

[Appendix G: Accuracy Assessment Field Manual] 75 TABLE OF CONTENTS

1. Introduction ……………………………………………………………………………2 2. Tools ………………………………………………………………………………...…3

3. Field data collection procedures………………….....………………………………….4

4. Schedule. …………………………………………………………………………….…7

5. Contact Information……………...………………………..…..…………………..…...7 .

76 [Appendix G: Accuracy Assessment Field Manual (cont.)] Introduction

Over the next 2 months, AMS Enterprise Team personnel will be collecting ground information that will be used to evaluate and assess the overall accuracy of the Humboldt-Toiyabe vegetation map. The purpose of this Field Manual is to explain the procedures for collecting the ground information. The data collection process for this project is very similar to the procedures used to collect the original data for the draft maps. Only basic land cover information (species, size class, and canopy cover) will be collected at each site for this phase of the project.

[Appendix G: Accuracy Assessment Field Manual (cont.)] 77 TOOLS

You have been provided with several tools to use in gathering the field data. They are • Field Key to Nevada Vegetation • Field Maps o Overview quad index maps of each study area o High-resolution satellite imagery maps with polygons, sites, and roads o 8.5x11” close-ups of each AA site & polygon with high-res imagery • Field Forms • List of UTM northing/easting coordinates for the AA sites

The following text describes each one of these tools:

FIELD KEY TO NEVADA VEGETATION

You will use the same field key that were provided to you for the vegetation data collection last fall. The key contains the vegetation classification system for vegetation communities in the HT National Forest. Please review these classification keys before you begin any fieldwork and have them available during the data collection. The field data collection will focus on the determination of the correct vegetation class.

Field Maps

Field maps for each area were produced at a scale of ~1:50,000. Each map has a high-resolution satellite scene in the background with the field sites, roads, and hydrology identified on top. The field sites will consist of an area approximately 1 acre (150ft X 150ft) in size that will be assessed to represent the polygon on the map.

Field Equipment

1. GPS Unit – to navigate to site and verify location of site Set the projection information on your GPS unit to:

Projection: UTM (UTM UPS on etrex) Datum: NAD27 (NAD27 CONUS on etrex) Spheroid: GRS1980 (not listed on etrex) Zone: 11 (not listed on etrex) The only GPS units approved for this assessment are GeoExplorer 3’s or Garmin Etrex’s that are WAAS enabled. Any others need to be approved by RSAC prior to using. 1. Compass – navigation 2. Field Manual (details field procedures), field forms, and close-up printouts of polygons 3. Digital Camera and greaseboard – to document site 4. Flagging – to mark the edge of the sites 5. Forestry pole or similar item to help mark the center of the plot.

78 [Appendix G: Accuracy Assessment Field Manual (cont.)] Field Data Collection Procedures

The field data collection process has been simplified for the accuracy assessment. Only landcover information will be collected. The actual time needed at each field site to collect all data should be no more than 10-15 minutes.

Step 1 – Plan

Review the field maps and the geographic distribution of the field sites that have been assigned. The field sites are marked with a unique ID on the map. A quad map, the field map, or the GPS unit can be used to navigate to the site shown on the map. A directory of site numbers and UTM northing/easting position (in meters) will be provided to you.

You will probably be able to collect data on 10-15 field sites per day. Each crew will need to be averaging 11-12 sites/day. Therefore, it is important that you plan your day to be efficient. Choose sites that are grouped together to minimize travel time.

Step 2 – Access

The pre-delineated field sites have been chosen to be spectrally homogeneous. We have also attempted to locate sites so that they are close to roads. However, there is no guarantee that the sites will be accessible.

If you cannot directly access the site, but can clearly see it from some vantage point, you will still be able to fill out the field form; you will just have to note that you viewed the site from a distance. This is a last resort option though. We do not recommend viewing sites from a distance unless it is the only option. If you find that the site is completely inaccessible and cannot be viewed, note that the site is unobservable on the field form, and go to the next site.

We have also provided you with a list of the entire field sit locations (UTM coordinates). Use this information to plug into your GPS unit and locate the exact center of the field site.

Step 3 – Site Overview

Identify the center point of the site and mark it. Each polygon chosen as a field site is a minimum of 300ft by 300ft or approximately 2 acres. Walk through the site to get a sense of the vegetation. Notice the distribution of vegetation. While the vegetation on the site may be heterogeneous (e.g. trees, shrubs, grass/herbaceous), the site should be generally representative of one vegetation class. If it is not, you will need to move the site to an area within the polygon that is representative of the whole polygon. It is very important to imagine where the boundaries of the polygon are on the landscape (use the close-up printouts to help) and get a “Big Picture” for that polygon as a whole. Step 4 –Fill out field form

You are now ready to enter information into the field form. Information requested includes:

[Appendix G: Accuracy Assessment Field Manual (cont.)] 79 1. Site ID Site #: Enter the unique plot number designated on the field map. (made up of a 2-letter district code and a 3 digit number. ie: AT-001 or RM-026 or SR-132)

2. Names Enter the first and last name of observer completing the form.

3. Date Give the data the field data collection was taken.

4. Level of Observation If you are able to directly access the field site then circle “Site visited”. If you are unable to access the site, but are able to view it well enough to generally characterize the land cover and fuel model, circle “Viewed from a distance”. If the site is inaccessible and unobservable, circle “Not observable”.

5. GPS Location If you can access the site, provide the Easting and Northing in UTM coordinates at the center of the site.

6. Direction and Number of Photograph Take 4 digital photographs of the site, one in each cardinal direction. IMPORTANT: Take the first photo facing north with the greaseboard in the picture. Then go clockwise shooting east, south, then west. As long as you strictly adhere to this order, you don’t need to rewrite and include the greaseboard for the E,S,W pictures. It is best to take the photo from the center of the site. However, if you cannot access the site, but you can view it, take the picture from the edge of the site looking in, zoomed in as much as possible (then note on the form which direction you were looking from the road towards the polygon).

7. Ocular Plot Composition Determine the dominant and next most abundant (codominant) species of conifer, hardwood, woodland, and shrub (not all may be present) and write down their NRCS/SCS codes in the boxes. (Codes for most species can be found at end of packet.) Then determine and record the % total canopy cover of each of the major lifeform classes. This should be determined imagining a top-down perspective and should not exceed 100%. (ie: Conifer=20% + Shrub=40% + Grass=30% + Non-veg=10% equals 100% total top-down cover) Then look at each tree lifeform and determine size classes for each. Record the relative percent of each size class within that lifeform. (ie: Conifer—half is <9” and half is 9-21”, with %canopy cover of 20% means you’d record 10% <9”DBH and 10% 9-21”DBH to equal the total of 20% canopy cover present on the plot.) So size class boxes should add up to the % canopy cover on the right.

80 [Appendix G: Accuracy Assessment Field Manual (cont.)] Plot Summary—1st Call

8. Lifeform Based on your observations from walking through the site and the numbers recorded in section 7 above, use the Field Key to determine the Lifeform class for this polygon. Circle the appropriate class on the form.

9. Veg Class Based on your observations from walking through the site and the numbers recorded in section 7 above, use the Field Key to determine the Veg Class for this polygon. Circle the appropriate class on the form. The veg class code circled should be in the section below the lifeform you circled. Note: Be careful circling codes. Please make sure we know which code you meant to circle.

10. Size Class Based on your observations from walking through the site and the numbers recorded in section 7’s size classes above, determine the dominant size class for this polygon. Circle the appropriate class on the form. Note: This will only be done for Conifers and Hardwoods (including riparian hardwoods).

11. Canopy Cover Based on your observations from walking through the site and the numbers recorded in section 7’s canopy cover column, determine the % canopy cover of the dominant lifeform class you circled in section 8. Circle the appropriate class on the form. Note: There are different cover breaks for SH-shrubs, WD-woodlands, and C/H-conifer/hardwoods. Please make sure you review these breaks carefully and choose accordingly.

Plot Summary—2nd Call

12-14. ---(REQUIRED) This section is the same as the one above (8-11), except that you are making a second guess/call as to what the polygon should be classified as. Think about it as if you can’t call it the first choice listed above, what else would you call it? So for example, if you have a woodland site with 12% PJ and 88% low sage, according to the key, you’d call it a PJ site. But if you weren’t allowed to call it a PJ site, your next choice would be to call it a low sage site right? That’s what we want you to do in this section. Give us your next best call for the polygon. Note: You must make a second call even if the polygon is obviously your first call. We need this information consistently for all sites in order to do our assessment.

15. Notes Please give us LOTS and LOTS of notes. Use this space to enter any additional information that you think will help characterize the site. Note especially any variation on the site. (Anything that could be useful for assessing the site’s accuracy.) Do you think this site may have changed drastically since 2000? Do you notice that it’s actively managed somehow? If so, what type of management (tree harvesting, prescribed burning, grazing, etc..)? Is the site’s dominant veg class really close to something else also? If you feel it could go either way, please let us know. Also if it’s the opposite and you’ve made the required 2nd call, but know that it obviously belongs only in your first call’s class, let us know. [Appendix G: Accuracy Assessment Field Manual (cont.)] 81

Schedule

**--Someone from the team will be required to call in to Wendy at the end of each trip to discuss progress, problems, questions, plans for next period’s trip, etc… After that call, all completed field forms, maps, etc.. need to be mailed off to RSAC. Before sending any field forms to RSAC make a copy for yourself incase anything gets lost in the mail. Send all completed field forms & maps to:

Wendy Goetz Remote Sensing Applications Center 2222 West 2300 South Salt Lake City, UT 84119

1st Delivery: Send out Monday – June 21st 2nd Delivery: Send out Monday – July 5th 3rd Delivery: Send out Monday – July 19th 4th Delivery: Send out Monday – Aug 2nd

(All field data must be received by RSAC by Wednesday – August 4th )

RSAC Contact Information

Wendy Goetz [email protected] (801) 975-3841

John Gillham [email protected] (801) 975-3827 cell: 801-232-6028

82 [Appendix G: Accuracy Assessment Field Manual (cont.)] [Appendix G: Accuracy Assessment Field Manual (cont.)] 83 Field Crews were also equipped with a hard copy of the Classification Scheme (Field Key) (See Appendix A)

84 [Appendix G: Accuracy Assessment Field Manual (cont.)] [Appendix G: AA Field Plot Location Map] 85 Accuracy Assessment Sample—Field Data Collection Form Page 1

[Appendix G: AA Field Data Collection Form] 86 Appendix H: Map Products

[Appendix H: Hard Copy Existing Vegetation Map Products] 87 88 [Appendix H: Digital 7.5-minute Quad Existing Vegetation Map Products] [Appendix H: Digital 7.5-minute Quad Natural Color Map Products] 89 90 [Appendix H: Digital 7.5-minute Quad Color-Infrared Map Products] Existing Vegetation Summary Tables

[Appendix H: Tabular Summaries] 91 92 [Appendix H: Tabular Summaries (cont.)] [Appendix H: Tabular Summaries (cont.)] 93 94 [Appendix H: Map Codes & Labels [Appendix H: Vegetation Type Hierarchy ] 96 Shapefile Attribute Table Shapefile

96 [Appendix H: Attribute Table] Appendix I. Accuracy Assessment Results

Cover Type User’s Class Accuracy

S tandard Error M atrix Minimum Correct REFERENCE DATA (Min Correct - Includes 1st call only) Tot Map User's Cover Type R HW CF NV HG W D SH Sites Accuracy

R = Riparian 60 17 0 8 20 0 7 112 53.57% HW = Hardwood 3 44 30005 55 80.00% CF = Conifer 0370 4049 90 77.78% NV = Non-Veg 1006 003 10 60.00% HG = Herbaceous 000214 09 25 56.00%

MAP DATA MAP W D = W oodland 10321231 23 261 88.51% SH = Shrubland 77121310277 317 87.38% Tot Ref 7271772448245333870 Sites

S tandard Error M atrix M aximum Correct REFERENCE DATA (Max Correct - Includes 1st or 2nd call) Tot Map User's Cover Type R HW CF NV HG W D SH Sites Accuracy R = Riparian 77 12 0 4 12 0 7 112 68.75% HW = Hardwood 2 48 20003 55 87.27% CF = Conifer 0271 4049 90 78.89% NV = Non-Veg 1007 002 10 70.00% HG = Herbaceous 000216 07 25 64.00%

MAP DATA MAP W D = W oodland 00310241 16 261 92.34% SH = Shrubland 741087290 317 91.48% Tot Ref 8766771836252334870 Sites

Sub-Cover Type User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (Min Correct - Includes 1st call only) Tot Map User's Sub-Covertype R HW CF NV HG CM PJ MTS SA BSH AG Sites Accuracy R = Riparian 60 170020000618 112 53.57% HW = Hardwood 3 44 300002210 55 80.00% CF = Conifer 0370 40313600 90 77.78% NV = Non-Veg 0004 0000301 8 * 50.00% HG = Herbaceous 000114 001621 25 56.00% CM =Mahogany 0020040 171520 67 59.70% PJ = Pinyon/Juniper 101219165 01500 194 85.05%

MAP DATA MAP MTS = Mtn Shrub 06005206 18 3 0 40 15.00% SA = Sagebrush 71118268177 16 0 227 77.97% BSH =Basin Shrub 000100013117 0 50 34.00% AG = Agriculture 10000000001 2 * 50.00% Tot Ref 72 71 77 13 48 56 189 22 269 42 11 870 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (Max Correct - Includes 1st or 2nd call) Tot Map User's Sub-Covertype RHWCFNVHGCMPJMTSSABSHAGSites Accuracy R = Riparian 77 120012000614 112 68.75% HW = Hardwood 2 48 200001110 55 87.27% CF = Conifer 0271 40313600 90 78.89% NV = Non-Veg 0005 0000201 8 * 62.50% HG = Herbaceous 000116 000611 25 64.00% CM =Mahogany 0020045 131420 67 67.16% PJ = Pinyon/Juniper 001107175 01000 194 90.21%

MAP DATA MTS = Mtn Shrub 040052011 15 3 0 40 27.50% SA = Sagebrush 71105235189 14 0 227 83.26% BSH =Basin Shrub 000000012722 0 50 44.00% AG = Agriculture 10000000001 2 * 50.00% Tot Ref 87 67 77 11 38 59 192 22 266 44 7 870 Sites

[Appendix I: User’s Accuracy Results] 97 Dominance Type User’s Class Accuracy—Minimum Values 98

[Appendix I: User’s Accuracy Results] Dominance Type User’s Class Accuracy—Maximum Values 99

[Appendix I: User’s Accuracy Results] Conifer Canopy Closure User’s Class Accuracy

Standard Error M atrix Minimum Correct REFERENCE DATA (Includes 1st call only) Tot Map User's Conifer Canopy Closure CFL CFM CFH Sites Accuracy

CFL = Conifer (10%-40%) 7 21 10 70.00% CFM = Conifer (41%-70%) 26 15 1 42 35.71%

MAP DATA CFH = Conifer (71%+) 11 6 0 17 0.00% Tot Ref 44 23 2 69 Sites

Standard Error M atrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Tot Map User's Conifer Canopy Closure CFL CFM CFH Sites Accuracy

CFL = Conifer (10%-40%) 7 21 10 70.00% CFM = Conifer (41%-70%) 17 24 1 42 57.14%

MAP DATA CFH = Conifer (71%+) 11 4 2 17 11.76% Tot Ref 35 30 4 69 Sites

Hardwood (Upland and Riparian) Canopy Closure User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Tot Map User's Hardwood Canopy Closure HWL HWM HWH Sites Accuracy

HWL = Hdwd (10%-40%) 2 10 3 * 66.67% HWM = Hdwd (41%-70%) 10 10 1 21 47.62%

MAP DATA MAP HWH = Hdwd (71%+) 17 22 3 42 7.14% Tot Ref 29 33 4 66 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Tot Map User's Hardwood Canopy Closure HWL HWM HWH Sites Accuracy HWL = Hdwd (10%-40%) 3 00 3 * 100.00% HWM = Hdwd (41%-70%) 9 12 0 21 57.14%

MAP DATA MAP HWH = Hdwd (71%+) 15 16 11 42 26.19% Tot Ref 27281166 Sites

100 [Appendix I: User’s Accuracy Results] Hardwood (Upland and Riparian) Canopy Closure User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (1st call only) Tot Map User's Woodland Canopy Closure WDL WDM WDH Sites Accuracy

WDL = Woodland (10%-20%) 61 16 1 78 78.21% WDM = Woodland (21%-40%) 31 61 11 103 59.22%

MAP DATA WDH = Woodland (41%+) 19 23 8 50 16.00% Tot Ref 111 100 20 231 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (1st or 2nd call) Tot Map User's Woodland Canopy Closure WDL WDM WDH Sites Accuracy

WDL = Woodland (10%-20%) 68 91 78 87.18% WDM = Woodland (21%-40%) 14 85 4 103 82.52%

MAP DATA MAP WDH = Woodland 41%+) 19 18 13 50 26.00% Tot Ref 101 112 18 231 Sites

Shrubland (Upland) Canopy Closure User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (1st call only) Tot Map User's Shrubland Canopy Closure SHL SHM SHH Sites Accuracy

SHL = Shrubland (10%-20%) 86 37 12 135 63.70% SHM = Shrubland (21%-30%) 34 31 15 80 38.75%

MAP DATA MAP SHH = Shrubland (31%+) 25 21 15 61 24.59% Tot Ref 145 89 42 276 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (1st call only) Tot Map User's Shrubland Canopy Closure SHL SHM SHH Sites Accuracy

SHL = Shrubland (10%-20%) 99 24 12 135 73.33% SHM = Shrubland (21%-30%) 16 58 6 80 72.50%

MAP DATA MAP SHH = Shrubland (31%+) 25 13 23 61 37.70% Tot Ref 140 95 41 276 Sites

[Appendix I: User’s Accuracy Results] 101 Riparian Shrubland Canopy Closure User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (1st call only) Tot Map User's Riparian Shrub Canopy Closure RSL RSH Sites Accuracy

RSL = Riparian Shrub (10%-40%) 1 2 3 * 33.33% RSH = Riparian Shrub (41%+) 3 12 15 80.00% MAP DATA MAP Tot Ref 4 14 18 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (1st or 2nd call) Tot Map User's Riparian Shrub Canopy Closure RSL RSH Sites Accuracy

RSL = Riparian Shrub (10%-40%) 1 2 3 * 33.33% RSH = Riparian Shrub (41%+) 2 13 15 86.67% MAP DATA Tot Ref 3 15 18 Sites

102 [Appendix I: User’s Accuracy Results] Conifer Size Class User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (1st call only) Tot Map User's Conifer Tree Size Class CFS CFM CFL Sites Accuracy

CFS=Conifer--Sm all(<9"DBH) 1 20 3 * 33.33% CFM=Conifer--Med(9-21"DBH) 16 34 1 51 66.67%

MAP DATA MAP CFL=Conifer--Large(>21"DBH) 530 8 * 0.00%

Tot Ref 22 39 1 62 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (1st or 2nd call) Tot Map User's Conifer Tree Size Class CFS CFM CFL Sites Accuracy

CFS=Conifer--Sm all(<9"DBH) 2 10 3 * 66.67% CFM=Conifer--Med(9-21"DBH) 13 38 0 51 74.51%

MAP DATA MAP CFL=Conifer--Large(>21"DBH) 530 8 * 0.00% Tot Ref 20 42 0 62 Sites

Hardwood (Upland and Riparian) Size Class User’s Class Accuracy

Standard Error Matrix Minimum Correct REFERENCE DATA (1st call only) Tot Map User's Hardwood & Riparian Tree Size Class HRS HRM HRL Sites Accuracy

HRS=Hardwood & Rip--Small(<9"DBH) 29 70 36 80.56% HRM=Hardwood & Rip--Med(9-21"DBH) 13 5 0 18 27.78%

MAP DATA MAP HRL=Hardwood & Rip--Large(>21"DBH) 310 4 * 0.00%

Tot Ref 45 13 0 58 Sites

Standard Error Matrix Maximum Correct REFERENCE DATA (1st or 2nd call) Tot Map User's Hardwood & Riparian Tree Size Class HRS HRM HRL Sites Accuracy

HRS=Hardwood & Rip--Small(<9"DBH) 29 70 36 80.56% HRM=Hardwood & Rip--Med(9-21"DBH) 10 8 0 18 44.44%

MAP DATA HRL=Hardwood & Rip--Large(>21"DBH) 310 4 * 0.00%

Tot Ref 42 16 0 58 Sites

[Appendix I: User’s Accuracy Results] 103 Cover Type Producer’s Map Accuracy and Overall Map Accuracy Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Min Correct - Includes 1st call only) Producer's Cover Type % Area Sample R HW CF NV HG WD SH Tot Map Accuracy Weight Sites R = Riparian 1.70% 0.0002 0.91% 0.26% 0.00% 0.12% 0.30% 0.00% 0.11% 1.70% 35.99% HW = Hardwood 2.88% 0.0005 0.16% 2.31% 0.16% 0.00% 0.00% 0.00% 0.26% 2.88% 59.90% CF = Conifer 3.71% 0.0004 0.00% 0.12% 2.88% 0.16% 0.00% 0.16% 0.37% 3.71% 79.54% NV = Non-Veg 1.57% 0.0016 0.16% 0.00% 0.00% 0.94% 0.00% 0.00% 0.47% 1.57% 48.90% HG = Herbaceous 1.13% 0.0005 0.00% 0.00% 0.00% 0.09% 0.63% 0.00% 0.41% 1.13% 19.62%

MAP DATA WD = Woodland 36.36% 0.0014 0.14% 0.00% 0.42% 0.28% 0.14% 32.18% 3.20% 36.36% 94.63% SH = Shrubland 52.65% 0.0017 1.16% 1.16% 0.17% 0.33% 2.16% 1.66% 46.00% 52.65% 90.51%

Tot Ref 2.53% 3.85% 3.62% 1.93% 3.24% 34.01% 50.83% 1 Sites Overall Map Accuracy 85.86%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Max Correct - Includes 1st or 2nd call) Producer's Cover Type % Area Sample R HW CF NV HG WD SH Tot Map Accuracy Weight Sites R = Riparian 1.70% 0.0002 1.17% 0.18% 0.00% 0.06% 0.18% 0.00% 0.11% 1.70% 45.01% HW = Hardwood 2.88% 0.0005 0.10% 2.51% 0.10% 0.00% 0.00% 0.00% 0.16% 2.88% 73.04% CF = Conifer 3.71% 0.0004 0.00% 0.08% 2.92% 0.16% 0.00% 0.16% 0.37% 3.71% 80.93% NV = Non-Veg 1.57% 0.0016 0.16% 0.00% 0.00% 1.10% 0.00% 0.00% 0.31% 1.57% 70.77% HG = Herbaceous 1.13% 0.0005 0.00% 0.00% 0.00% 0.09% 0.73% 0.00% 0.32% 1.13% 32.45%

MAP DATA MAP WD = Woodland 36.36% 0.0014 0.00% 0.00% 0.42% 0.14% 0.00% 33.57% 2.23% 36.36% 96.20% SH = Shrubland 52.65% 0.0017 1.16% 0.66% 0.17% 0.00% 1.33% 1.16% 48.16% 52.65% 93.23%

Tot Ref 2.59% 3.44% 3.61% 1.56% 2.24% 34.90% 51.66% 1 Sites Overall Map Accuracy 90.17%

Sub-Cover Type Producer’s Map Accuracy and Overall Map Accuracy Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Min Correct - Includes 1st call only) Producer's Sub-Covertype % Area Sample R HW CF NV HG CM PJ MTS SA BSH AG Tot Map Accuracy Weight Sites R = Riparian 1.70% 0.0002 0.91% 0.26% 0.00% 0.00% 0.30% 0.00% 0.00% 0.00% 0.09% 0.02% 0.12% 1.70% 34.77% HW = Hardwood 2.88% 0.0005 0.16% 2.31% 0.16% 0.00% 0.00% 0.00% 0.00% 0.10% 0.10% 0.05% 0.00% 2.88% 67.37% CF = Conifer 3.71% 0.0004 0.00% 0.12% 2.88% 0.16% 0.00% 0.12% 0.04% 0.12% 0.25% 0.00% 0.00% 3.71% 81.13% NV = Non-Veg 1.42% 0.0018 0.00% 0.00% 0.00% 0.71% 0.00% 0.00% 0.00% 0.00% 0.53% 0.00% 0.18% 1.42% * 45.80% HG = Herbaceous 1.13% 0.0005 0.00% 0.00% 0.00% 0.05% 0.63% 0.00% 0.00% 0.05% 0.27% 0.09% 0.05% 1.13% 20.78% CM =Mahogany 5.58% 0.0008 0.00% 0.00% 0.17% 0.00% 0.00% 3.33% 1.42% 0.08% 0.42% 0.17% 0.00% 5.58% 61.25% PJ = Pinyon/Juniper 30.78% 0.0016 0.16% 0.00% 0.16% 0.32% 0.16% 1.43% 26.18% 0.00% 2.38% 0.00% 0.00% 30.78% 91.01%

MAP DATA MTS = Mtn Shrub 3.65% 0.0009 0.00% 0.55% 0.00% 0.00% 0.46% 0.18% 0.00% 0.55% 1.64% 0.27% 0.00% 3.65% 21.62% SA = Sagebrush 42.62% 0.0019 1.31% 0.19% 0.19% 0.19% 1.50% 0.38% 1.13% 1.50% 33.23% 3.00% 0.00% 42.62% 77.51% BSH =Basin Shrub 6.38% 0.0013 0.00% 0.00% 0.00% 0.13% 0.00% 0.00% 0.00% 0.13% 3.95% 2.17% 0.00% 6.38% 37.57% AG = Agriculture 0.15% 0.0008 0.08% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.08% 0.15% * 17.91% Tot Ref 2.61% 3.42% 3.55% 1.55% 3.06% 5.44% 28.76% 2.53% 42.87% 5.77% 0.42% 1 Sites Overall Map Accuracy 72.97%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Max Correct - Includes 1st or 2nd call) Producer's Sub-Covertype % Area Sample R HW CF NV HG CM PJ MTS SA BSH AG Tot Map Accuracy Weight Sites R = Riparian 1.70% 0.0002 1.17% 0.18% 0.00% 0.00% 0.18% 0.00% 0.00% 0.00% 0.09% 0.02% 0.06% 1.70% 43.84% HW = Hardwood 2.88% 0.0005 0.10% 2.51% 0.10% 0.00% 0.00% 0.00% 0.00% 0.05% 0.05% 0.05% 0.00% 2.88% 75.48% CF = Conifer 3.71% 0.0004 0.00% 0.08% 2.92% 0.16% 0.00% 0.12% 0.04% 0.12% 0.25% 0.00% 0.00% 3.71% 82.55% NV = Non-Veg 1.42% 0.0018 0.00% 0.00% 0.00% 0.89% 0.00% 0.00% 0.00% 0.00% 0.36% 0.00% 0.18% 1.42% * 70.71% HG = Herbaceous 1.13% 0.0005 0.00% 0.00% 0.00% 0.05% 0.73% 0.00% 0.00% 0.00% 0.27% 0.05% 0.05% 1.13% 31.51% CM =Mahogany 5.58% 0.0008 0.00% 0.00% 0.17% 0.00% 0.00% 3.75% 1.08% 0.08% 0.33% 0.17% 0.00% 5.58% 67.67% PJ = Pinyon/Juniper 30.78% 0.0016 0.00% 0.00% 0.16% 0.16% 0.00% 1.11% 27.76% 0.00% 1.59% 0.00% 0.00% 30.78% 94.27%

MAP DATA MTS = Mtn Shrub 3.65% 0.0009 0.00% 0.37% 0.00% 0.00% 0.46% 0.18% 0.00% 1.00% 1.37% 0.27% 0.00% 3.65% 43.11% SA = Sagebrush 42.62% 0.0019 1.31% 0.19% 0.19% 0.00% 0.94% 0.38% 0.56% 0.94% 35.48% 2.63% 0.00% 42.62% 82.07% BSH =Basin Shrub 6.38% 0.0013 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.13% 3.44% 2.81% 0.00% 6.38% 46.86% AG = Agriculture 0.15% 0.0008 0.08% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.08% 0.15% * 20.93%

Tot Ref 2.66% 3.33% 3.54% 1.26% 2.30% 5.54% 29.45% 2.33% 43.24% 5.99% 0.36% 1 Sites Overall Map Accuracy 79.10%

104 [Appendix I: Producer’s & Overall Accuracy Results] Dominance Type Producer’s Map Accuracy—Minimum Values 105

[Appendix I: Producer’s & Overall Accuracy Results] Dominance Type Producer’s Map Accuracy—Maximum Values 106

[Appendix I: Producer’s & Overall Accuracy Results] Conifer Canopy Closure Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producer's Conifer Canopy Closure % Area Sample CFL CFM CFH Tot Map Accuracy Weight Sites

CFL = Conifer (10%-40%) 51.04% 0.0510 35.72% 10.21% 5.10% 51.04% 53.92% CFM = Conifer (41%-70%) 41.14% 0.0098 25.47% 14.69% 0.98% 41.14% 53.12%

MAP DATA MAP CFH = Conifer (71%+) 7.82% 0.0046 5.06% 2.76% 0.00% 7.82% * 0.00%

Tot Ref 66.26% 27.66% 6.08% 1 Sites Overall Map Accuracy 50.42%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producers Conifer Canopy Closure % Area Sample CFL CFM CFH Tot Map Accuracy Weight Sites

CFL = Conifer (10%-40%) 51.04% 0.0510 35.72% 10.21% 5.10% 51.04% 62.20%

CFM = Conifer (41%-70%) 41.14% 0.0098 16.65% 23.51% 0.98% 41.14% 66.12%

MAP DATA CFH = Conifer (71%+) 7.82% 0.0046 5.06% 1.84% 0.92% 7.82% * 13.14%

Tot Ref 57.44% 35.56% 7.00% 1 Sites Overall Map Accuracy 60.16%

Hardwood (Upland and Riparian) Canopy Closure Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producers Hardwood Canopy Closure % Area Sample HWL HWM HWH Tot Map Accuracy Weight Sites

HWL = Hdwd (10%-40%) 3.02% 0.0101 2.01% 1.01% 0.00% 3.02% 4.61% HWM = Hdwd (41%-70%) 34.40% 0.0164 16.38% 16.38% 1.64% 34.40% 32.65%

MAP DATA MAP HWH = Hdwd (71%+) 62.58% 0.0149 25.33% 32.78% 4.47% 62.58% * 73.18%

Tot Ref 43.72% 50.17% 6.11% 1 Sites Overall Map Accuracy 22.87%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producer's Hardwood Canopy Closure % Area Sample HWL HWM HWH Tot Map Accuracy Weight Sites

HWL = Hdwd (10%-40%) 3.02% 0.0101 3.02% 0.00% 0.00% 3.02% 7.53%

HWM = Hdwd (41%-70%) 34.40% 0.0164 14.74% 19.66% 0.00% 34.40% 45.19%

MAP MAP DATA HWH = Hdwd (71%+) 62.58% 0.0149 22.35% 23.84% 16.39% 62.58% 100.00%

Tot Ref 40.11% 43.50% 16.39% 1 Sites Overall Map Accuracy 39.07%

[Appendix I: Producer’s & Overall Accuracy Results] 107 Woodland Canopy Closure Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producer's Woodland Canopy Closure % Area Sample WDL WDM WDH Tot Map Accuracy Weight Sites

WDL = Woodland (10%-20%) 33.13% 0.0042 25.91% 6.80% 0.42% 33.13% 54.88% WDM = Woodland (21%-40%) 52.04% 0.0051 15.66% 30.82% 5.56% 52.04% 69.36%

MAP MAP DATA WDH = Woodland (41%+) 14.83% 0.0030 5.63% 6.82% 2.37% 14.83% 28.40% Tot Ref 47.21% 44.44% 8.36% 1 Sites Overall Map Accuracy 59.10%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producer's Woodland Canopy Closure % Area Sample WDL WDM WDH Tot Map Accuracy Weight Sites

WDL = Woodland (10%-20%) 33.13% 0.0042 28.88% 3.82% 0.42% 33.13% 69.44%

WDM = Woodland (21%-40%) 52.04% 0.0051 7.07% 42.95% 2.02% 52.04% 82.42% 3.86% MAP DATA WDH = Woodland 41%+) 14.83% 0.0030 5.63% 5.34% 14.83% 61.18% Tot Ref 41.59% 52.11% 6.30% 1 Sites Overall Map Accuracy 75.68%

Shrubland Canopy Closure Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producer's Shrubland Canopy Closure % Area Sample SHL SHM SHH Tot Map Accuracy Weight Sites

SHL = Shrubland (10%-20%) 58.34% 0.0043 37.16% 15.99% 5.19% 58.34% 68.04% SHM = Shrubland (21%-30%) 25.08% 0.0031 10.66% 9.72% 4.70% 25.08% 30.93%

MAP DATA MAP SHH = Shrubland (31%+) 16.58% 0.0027 6.80% 5.71% 4.08% 16.58% 29.20%

Tot Ref 54.62% 31.42% 13.97% 1 Sites Overall Map Accuracy 50.96%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producer's Shrubland Canopy Closure % Area Sample SHL SHM SHH Tot Map Accuracy Weight Sites

SHL = Shrubland (10%-20%) 58.34% 0.0043 42.78% 10.37% 5.19% 58.34% 78.36% SHM = Shrubland (21%-30%) 25.08% 0.0031 5.02% 18.18% 1.88% 25.08% 56.66% 6.25% MAP DATA MAP SHH = Shrubland (31%+) 16.58% 0.0027 6.80% 3.53% 16.58% 46.94%

Tot Ref 54.59% 32.09% 13.32% 1 Sites Overall Map Accuracy 67.22%

108 [Appendix I: Producer’s & Overall Accuracy Results] Woodland Canopy Closure Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producer's Riparian Shrubland Canopy Closure % Area Sample RSL RSH Tot Map Accuracy Weight Sites RSL = Riparian Shrub (10%-40%) 16.85% 0.0562 5.62% 11.24% 16.85% * 25.25% RSH = Riparian Shrub (41%+) 83.15% 0.0554 16.63% 66.52% 83.15% 85.55% MAP DATA MAP Tot Ref 22.25% 77.75% 1 Sites Overall Map Accuracy 72.14%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producer's Riparian Shrubland Canopy Closure % Area Sample RSL RSH Tot Map Accuracy Weight Sites RSL = Riparian Shrub (10%-40%) 16.85% 0.0562 5.62% 11.24% 16.85% * 33.63%

DATA MAP RSH = Riparian Shrub (41%+) 83.15% 0.0554 11.09% 72.06% 83.15% 86.51%

Tot Ref 16.70% 83.30% 1 Sites Overall Map Accuracy 77.68%

[Appendix I: Producer’s & Overall Accuracy Results] 109 Conifer Size Class Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producer's Conifer Tree Size Class % Area Sample CFS CFM CFL Tot Map Accuracy Weight Sites

CFS=Conifer--Small(<9"DBH) 29.67% 0.0989 9.89% 19.78% 0.00% 29.67% 27.32% CFM=Conifer--Med(9-21"DBH) 56.67% 0.0111 17.78% 37.78% 1.11% 56.67% 60.27%

MAP DATA CFL=Conifer--Large(>21"DBH) 13.66% 0.0171 8.53% 5.12% 0.00% 13.66% * 0.00%

Tot Ref 36.20% 62.68% 1.11% 1 Sites Overall Map Accuracy 47.67%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producer's Conifer Tree Size Class % Area Sample CFS CFM CFL Tot Map Accuracy Weight Sites

CFS=Conifer--Small(<9"DBH) 29.67% 0.0989 19.78% 9.89% 0.00% 29.67% 46.26%

CFM=Conifer--Med(9-21"DBH) 56.67% 0.0111 14.45% 42.23% 0.00% 56.67% 73.77%

MAP DATA CFL=Conifer--Large(>21"DBH) 13.66% 0.0171 8.53% 5.12% 0.00% 13.66% * ----

Tot Ref 42.76% 57.24% 0.00% 1 Sites Overall Map Accuracy 62.01%

Hardwood (Upland and Riparian) Size Class Producer’s Map Accuracy and Overall Map Accuracy

Area Weighted Error Matrix Minimum Correct REFERENCE DATA (Includes 1st call only) Producer's Hardwood & Riparian Tree Size Class % Area Sample HRS HRM HRL Tot Map Accuracy Weight Sites

HRS=Hardwood & Rip--Small(<9"DBH) 76.81% 0.0213 61.87% 14.93% 0.00% 76.81% 78.58% HRM=Hardwood & Rip--Med(9-21"DBH) 19.14% 0.0106 13.82% 5.32% 0.00% 19.14% 25.00%

MAP DATA HRL=Hardwood & Rip--Large(>21"DBH) 4.05% 0.0101 3.04% 1.01% 0.00% 4.05% ----

Tot Ref 78.74% 21.26% 0.00% 1 Sites Overall Map Accuracy 67.19%

Area Weighted Error Matrix Maximum Correct REFERENCE DATA (Includes 1st or 2nd call) Producer's Hardwood & Riparian Tree Size Class % Area Sample HRS HRM HRL Tot Map Accuracy Weight Sites

HRS=Hardwood & Rip--Small(<9"DBH) 76.81% 0.0213 61.87% 14.93% 0.00% 76.81% 81.90%

HRM=Hardwood & Rip--Med(9-21"DBH) 19.14% 0.0106 10.63% 8.51% 0.00% 19.14% 34.79%

MAP DATA HRL=Hardwood & Rip--Large(>21"DBH) 4.05% 0.0101 3.04% 1.01% 0.00% 4.05% ----

Tot Ref 75.55% 24.45% 0.00% 1 Sites Overall Map Accuracy 70.38%

110 [Appendix I: Producer’s & Overall Accuracy Results]