# An Inventory of Vacant Land Soil Stability and Unpaved Private Roads in the Las Vegas Valley Using Remote Sensing Imagery

Submitted to:



## Clark County Department of Finance Purchasing and Contracts

500 S. Central Parkway Las Vegas, Nevada 89155-1217



## December 2006 Submitted by:



### Environmental Quality Management, Inc. 6340 McLeod Drive, Suite 1 Las Vegas, Nevada 89120 

### Midwest Research Institute in conjunction with:AN INVENTORY OF VACANT LAND SOIL STABILITY AND PRIVATE UNPAVED ROADS IN THE LAS VEGAS VALLEY USING REMOTE SENSING IMAGERY

Prepared by:
Environmental Quality Management, Inc. 6340 McLeod Drive, Suite 1

Las Vegas, Nevada 89120

and

Midwest Research Institute 425 Volker Blvd. Kansas City, Missouri 64110-2241

PN 70204.001

December 2006ii CONTENTS

Section Page
Acknowledgement ................................................................................................................ ......... vi Executive Summary.............................................................................................................. ........ vii

1.0 Introduction............................................................................................................... ....... 1-1 1.1 Background.......................................................................................................... 1-1 1.2 Overview of Methodology................................................................................... 1-4
2.0 Training Site Selection and Characterization .................................................................. 2-1 2.1 Site Selection Methodology................................................................................. 2-4 2.2 Site Characterization Selec tion Methods and Results ......................................... 2-7 2.2.1 Site Selection ........................................................................................... 2-7 2.2.2 Site Characterization Methods................................................................. 2-8 2.2.3 Ground Truthing Results........................................................................ 2-18
3.0 Satellite Imagery Selection and Processing ..................................................................... 3-1 3.1 Remote Sensing Data Sources ............................................................................. 3-1 3.2 Other Studies........................................................................................................ 3-2 3.3 Landsat vs IKONOS and QuickBird.................................................................... 3-2 3.4 Rationale for Selection of Landsat....................................................................... 3-4
4.0 Spectral Analysis Procedures........................................................................................... 4-1 4.1 Land Category Characteristics............................................................................. 4-4 4.2 Supervised Classification..................................................................................... 4-5 4.3 Choice of Mahalanobis Method........................................................................... 4-5 4.4 Definition of Land Subcategories ........................................................................ 4-6 4.5 Special Challenges............................................................................................... 4-6 4.6 Accuracy Assessment .......................................................................................... 4-7
5.0 Results of Satellite Imagery Analysis.............................................................................. 5-1 5.1 Control of Misregistration Errors......................................................................... 5-1 5.2 Mapping of Land Categories ............................................................................... 5-1 5.3 Accuracy Assessment .......................................................................................... 5-4
6.0 Private Unpaved Road Traffic Counts............................................................................. 6-1 6.1 Identified Roads and Areas.................................................................................. 6-1 6.2 Delineating the Private Unpaved Roads for Traffic Counts ................................ 6-1 6.3 Conducting the Traffic Counts on Private Unpaved Roads................................. 6-2iii CONTENTS (continued)

Section Page
7.0 Results of Private Unpaved Road Analysis ..................................................................... 7-1 7.1 Identified Roads and Areas of Concentration...................................................... 7-1 7.2 Average Daily Traffic Results ............................................................................. 7-1

8.0 Conclusions................................................................................................................ ...... 8-1 8.1 Vacant Lands ....................................................................................................... 8-1 8.2 Applicability of Limitations of WEG Classification ........................................... 8-2 8.3 Private Unpaved Roads........................................................................................ 8-2 8.4 Inventory Accuracy Assessment.......................................................................... 8-3 8.5 Protocol for Inventory Updating.......................................................................... 8-3

9.0 References................................................................................................................. ....... 9-1
Appendix A – Glossary Appendix B – Unpaved Road Traffic Count Locations Appendix C – Dr. James Wind Tunnel Classifications Appendix D – Ground Truthing Data Form Field Sheets Appendix E – Table 1 & Table 2-1 of Sec tion 90 Clark County Air Quality Regulations Appendix F – Clark County Air Quality Regulation Section 91.4.1.2 Appendix G – Wind Erodibility Groups Appendix H – Traffic Count Data Form Field Sheetsiv FIGURES

Number Page 1-1 Hydrographic Area/Airshed Basemap ............................................................................. 1-2 1-2 Proposed Test Site Areas Based U pon Wind Erodibility by Groups (WEG).................. 1-6 1-3 Coverage of Geometry of a Satellite in Orbit Above a Spherical Planet with a Nadir-Pointing Conical Sens or or Field-of-View............................................................ 1-8 2-1 Decision Tree for Vacant Land Classification................................................................. 2-3 2-2 Example Map Labels with Wind Erodibility Groups ...................................................... 2-5 2-3 Proposed Test Site Areas for Pilot Study......................................................................... 2-6 2-4 Test Sites (Northern)...................................................................................................... .. 2-9 2-5 Test Sites (Southern)...................................................................................................... 2-10 2-6 Example Test Sites......................................................................................................... 2-12 2-7 Aerial Photo Showing One Traini ng Site and Soil Test Locations ............................... 2-13 3-1 Landsat 5 TM Coverage of Clark County........................................................................ 3-3 3-2 Reflectance Response Curves of Natural and Manmade Features Using Landsat Imagery.................................................................................................... 3-5 4-1 Visible Spectra from Landsat TM Imagery ..................................................................... 4-2 4-2 Mahalanobis Supervised Classification ........................................................................... 4-3 5-1a Mahalanobis Supervis ed Classification ........................................................................... 5-2 5-1b Mahalanobis Supervised Classification ........................................................................... 5-3 5-2 Sun-Synchronous Orbit of Landsat 4 and 5..................................................................... 5-6 5-3 Non-Concrete Runway at McCa rran International Airport ............................................. 5-7 5-4 Concrete Runway at McCarra n International Airport ..................................................... 5-8 5-5a Land Classification at Nellis Air Force Base................................................................... 5-9 5-5b Runway at Nellis Air Force Base................................................................................... 5-10 5-6 Native Desert .............................................................................................................. ... 5-11 5-7 Natural Drainage........................................................................................................... . 5-12 6-1 Unpaved Road Traffic Count Sites.................................................................................. 6-5 7-1 Overview of 160 Miles of Private Unpaved Roads ......................................................... 7-4 7-2 Example of Unpaved Roads Identifi ed by Aerial Photograph Analysis.......................... 7-5v TABLES

Number Page I Project Chronology ........................................................................................................... vii II Land Categories ............................................................................................................. .... ix III Classificatio n Error Matrix ................................................................................................ ..x IV Average Daily Traffic Counts............................................................................................ xi 2-1 Study Sites ................................................................................................................ ....... 2-7 2-2 Ground Truthing Locations............................................................................................ 2-11 2-3 Wind Erodibility Group (WEG) Categories for Selected Ground Truthing Areas ....... 2-17 2-4 Erodibility Ground Truthing Results ............................................................................. 2-19 3-1 Satellite Features Landsat TM vs. IKONOS vs. QuickBird ............................................ 3-4 4-1 Issues and Resolution...................................................................................................... . 4-7 4-2 Risk Area, Impact, and Mitigation................................................................................... 4-8 5-1 Area of Land Categories .................................................................................................. 5- 4 5-2 Error Matrix ............................................................................................................... .... 5-13 5-3 Accuracy Assessment .................................................................................................... 5-14 6-1 Unpaved Roads Selected for Traffic Counts ................................................................... 6-3 7-1 Average Daily Traffic Counts.......................................................................................... 7-3 8-1 Land Categories ............................................................................................................ ... 8-1vi ACKNOWLEDGEMENT

This project involved a close collaboration between the contractor team and the Clark County DAQEM Project Team. The outcome w ould not have been possible without the guidance and technical advice r eceived from the Project Officer Mr. Rodney Langston and the other members of the team, John Koswan, Russ Me rle, Chuck Richter, and Michael Uhl. The Clark County GIS Management Office was also cr itical to the success. Mr. Clint Woods and Ms. Sharon Rice provided invaluable assistance in providing and interpre ting the large database of maps, road layers, and other information needed for this project. A special thanks to Chuck Richter whose practical knowledge of the County and its road network and construction activity
was essential to the ground truthing effort.vii EXECUTIVE SUMMARY

Wind blown dust from vacant land is a significant source of PM-10 emissions in the semiarid Las Vegas Valley. Th e potential of vacant land to emit PM-10 during high wind events is related to soil stability at the surface, whic h depends on land characteristics. The purpose of this study was to develop an inventory of native desert land, disturbed va cant land, stabilized vacant land, and private unpaved roads in Hydr ographic Basin 212 (HB 212) containing the Las Vegas Valley, as defined by Nevada State Plane coordinates. Table I presents a time line, or project chr onology, for significant milestones over the 20- month duration of this project.
TABLE I. PROJECT CHRONOLOGY Item Date Activity

1 May 4, 2004 Kickoff Meeting and Preliminary Windshield Surveys 2 May 7, 2004 Obtained Spring 2004 Aerial Photography 3 June 2, 2004 Draft Work Plan 4 June 25, 2004 Revision 1 Work Plan – changed from PEP groups to WEG field training site selection methodology test procedures 5 August 18, 2004 Project Briefing 6 September 20, 2004 Revision 2 Work Plan – add Uhl methodology for vegetation WTE 7 October 6 – 8, 2004 Selected sites in North and South pilot areas 8 October 11 – 13, 2004 Took soil samples North and South 9 October 15, 2004 Revision 3 WP – defined pilot test areas 10 November 18, 2004 IKONOS Flyover for North Test Area 11 November 19, 2004 Groundtruthing North Test Area 12 December 14, 2004 Project Briefing and Project Hold– due to wet weather and wet soil conditions 13 February 9, 2005 Obtained Fall 2004 Aerial Photography 14 March 7, 2005 Project Briefing and Changed from IKONOS to Landsat approach. Reviewed North Pilot area results. 15 March 18, 1005 Selected Asphalt Sites and Gravel Sites for Further Classification 16 March 18 – 23, 2005 Groundtruthing for Asphalt and Gravel Sites 17 April 8, 2005 Revision 4 Work Plan – Change to Landsat Imagery 18 May 10-11, 2005 Attended WRAP Conference in Palm Springs 19 June 10, 2005 Met with GIS Management Office to Get Road Layers for unpaved roads 20 June 23 - 24, 2005 Selected 24 New Training Sites Throughout HB 212 21 June 26, 2005 Landsat Flyover 22 June 27 – 28, 2005 Groundtruthing 23 June 28, 2005 Obtained Road Layers from GIS Management Office 24 August 26, 2005 Project Briefing – reviewed classification of HB212 with Landsat 25 August 29 – 31, 2005 Selected Road Segments for Counting 26 October 10, 2005 Final Work Plan for Road Traffic Counts 27 October 13 – 29, 2005 Conducted Traffic Counts 28 December 13, 2005 Project Briefing, Review Draft Report

May June July Aug Sep Oct Nov Dec May June July Aug Sep Oct Nov Dec Jan Feb Mar Apr 2004 2005

1 2 3 4 5 6 8 9
7 10 11
12 13 14 15 16
17 18 19
22 23 20 21 24 25 26 27 28viii The methodology for developing this inventory used multi-spectral satellite imagery and ground truthing to develop unique sp ectral signatures that allowed mapping of land areas of the
stated surface charac teristics within HB 212. The important conclusions drawn by the investigators are as follows:

• The Landsat satellite imagery is more cost-e ffective for this purpose than high spatial resolution imagery, and avoids cl assification errors due to confounding from small pixel size.

• Aerial photography is an important tool to supplement imagery.

• There is far less unstable land in the La s Vegas Valley than previously thought.

• Soil chemistry is an important variable in classification of di sturbed, unstable land.

• Erodibility is not closely correlated with Wind Erodibility Groupings (WEG), but more so the condition of the surface.

• The enforcement of regulations controlling construction-related dus t has significantly decreased unstable land across the Valley.
The inventory of land areas based on soil stab ility using satellite imagery is based on a computer algorithm called supervised classificati on. This algorithm requires a suite of “training sites” that are representative of the various soil stability categories, or classes. The training sites were established through aerial photography and fiel d evaluation, ensuring th at the training sites accurately represented the soil stability catego ries. Additional subcategories such as urban landscape, wash areas, and concrete were devel oped primarily to eliminate these areas from the
overall land area of interest and enhance accuracy. Severa l classification schemes were evaluated, but the Mahalanobis di stance scheme proved to be th e most effective for this particular project. It was not feasible to classify the disturbe d unstable vacant land category directly because the training sites exhibited a variation in signatures which is believed to be attributable to differences in soil chemistry. A large expanse of disturbed unstable area might provide a more reliable signature, but no such large areas exis ted. Therefore, the pe rcentage of disturbed unstable vacant land was derived by inference – when all other land cover types (native desert, urban, etc.) were classified, then the remain ing unclassified pixels represent an upper bound on
the amount of disturbed unstable vacant land.ix Table II summarizes the land classifications for HB 212 de rived from the classified imagery based on the Landsat view of June 26, 2005.

TABLE II. LAND CATEGORIES Land Category Percent of HB 212 Square Kilometers Native Desert 47.6 1,897.1 Disturbed Unstable Vacant Land Less than 1.1 Less than 45.3 Disturbed Stable Vacant Land 3.1 122.8 All Other 48.2 1,918.6

The “all other” category includes wash areas (9%), concrete (0.7%), urban area (4.8%), vegetation (0.9%), and barren shadow (32.8%). Barren shadow is largely comprised of mountainous and/or rock areas plus shadowed areas. The amount of disturbed unstable vacant land is significantly less than previously reported . This is attributed to several factors:

1. A probable over-estimation in the past given the qualitative met hods available at that time.

2. The success of the enforcement program requi ring contractors to utilize stabilization techniques during construction.

3. The higher than average rainfall in the Valley during 2004 and 2005.
An accuracy assessment was performed to quantify the success of the supervised
classification. Reference or validation sites were derived from field observations, and also were obtained from aerial photography an d map data. An error matrix indicates the mapping accuracy of each soil stability category by showing the percent of correctly classified pixels. Table III provides the error matr ix for the classification process. Error matrices are very effective representations of map accuracy because the indi vidual accuracies of each map category are plainly described al ong with both the errors of in clusion (commission errors) and errors of exclusion (omission errors) present in the map. A commission error occurs when an area is included in an incorrect category. An omission error occurs when an area is excluded from the category to which it belongs. Overal l accuracy is the sum of the major diagonal (i.e., the correctly classified pixels or samples) divided by the total number of pixels or samples in the error matrix (Congalton and Green, 1999). The overall accuracy for the classification of HB 212 in this project is 89% (313/350).x TABLE III. CLASSIFI CATION ERROR MATRIX B/S Concrete DS Veg Wash Urban ND Row Total B/S 43 6 49 Concrete 45 45 DS 5 50 18 73 Vegetation 50 50 Wash 31 31 Urban 7 1 44 52 ND 50 50 Column Total 50 50 50 50 50 50 50 350

A second phase of the projec t involved the identificatio n of unpaved roads in HB 212 which were not already in the Clark County GIS. Roads were identifie d through 2-foot pixel aerial photography supplemented by field surveys. By definition, roads had to be a minimum of 22 feet wide. A total of 160 miles of unpaved ro ads were identified. A sample of 30 road segments was selected on which to perform traffic counts. The 30 segments were selected to be representative based on usage. All 30 segments were in the urban area and in areas of expanding home building. Average daily traffic (ADT) c ounts by segment are summarized in Table IV.
The reader is advised to read the entire report because there were many areas of complexity and judgments throughout the project that cannot be adequate ly reflected in this Executive Summary. The reader is also referred to the glossary for definition of terms unique to
this field.xi TABLE IV. AVERAGE DAILY TRAFFIC COUNTS Traffic Count Data Site No. Sun Mon Tues Wed Thur Fri Sat Counter ADT

1 6 13 22 EQ01 13.7 2 9 27 23 J 1 19.7 3 21 60 54 J 2 45.0 4 Construction area - Road extremely active with earthmoving equipment - Did not use 5&6 12 32 28 J 3 24.0

7 23 24 26 EQ02 24.3 8 50 140 89 J 5 93.0 9 a 34 32 29 J 4 31.7 9 b 34 36 28 EQ03 32.7 10 27 28 79 J 7 44.7 11 203 493 511 J 8 402.3 12 Did not use 13 40 43 53 J 6 45.3 14 96 105 80 J 8 93.7 15 a 249 172 208 J 7 209.7 15 b 189 153 177 EQ03 173.0 16 Berm added (Dirt) - Access to unpaved road blocked 17 58 84 137 EQ02 93.0 18 16 12 28 J 6 18.7 19 33 33 30 J 5 32.0 20 17 18 26 EQ 01 20.3 21 4 4 7 J 4 5.0 22 45 43 43 J 3 43.7 23 13 20 12 J 2 15.0 24 89 73 53 J 1 71.7 25 11 15 5 J 3 10.3 26 70 45 61 J 2 58.7 27 6 7 10 J 1 7.7 28 Old pavement - broken up - did not use 29 Did not use 30 Dead End street = did not use 31 68 61 19 J 6 49.3 32 a 10 12 1 J 4 7.7 32 b 10 9 2 EQ01 7.0 33 642 693 463 J 5 599.3 34 11 11 12 EQ02 11.3 35 Did not use 36 a 91 76 50 J 1 72.3 36 b 84 74 56 EQ01 71.3 37 35 33 23 J 7 30.3 38 3 3 0 J 8 2.0

ADT Excluding Sites 11, 15, and 33 (ADT >150) 36.4 ADT Including Sites 11, 15, and 33 (ADT >150) 73.21-1 1.0 INTRODUCTION

1.1 Background
The Las Vegas Valley is one of the fastest growing metropolit an areas in the nation. The population expanded from about 400,000 in 1980 to 1.4 million in 2000 (U.S. Census Bureau, Census 2000). As of April 2005, the population of the entire Las Vegas Valley was about 2 million people. The cities of Las Vegas, North Las Vegas, and Henderson are located in the Las Vegas Valley within Clark County, in the southern tip of Nevada. The city estimates a new home is comple ted every 20 minutes, as 5,000 people move to Las Vegas every month. Las Vegas is currently building 88 new schools, and the number of hospitals has tripled in the past several years. Commercial building activity maintains pace with new shopping centers, warehousing, service stations , casinos and other entertainment, and the full complement of related businesses required to support th e population growth.
The U.S. EPA in accordance with the Clean Ai r Act (CAA) has classified the Las Vegas Valley, also known as Hydrographic Basin 212 (H B 212), within Clark County as a “serious” nonattainment area for PM-10 (particulate matte r having an aerodynamic diameter of 10 microns or less). Figure 1-1 illustrate s the Hydrographic Basins which ar e used to define the airsheds within Clark County. HB 212 is sometimes called the “BLM Disposal Area.” The BLM Disposal Area refers to the land which has b een sold by the Bureau of Land Management through auction under the Southe rn Nevada Public Lands Mana gement Act. These Lands represent a smaller subset of HB 212, but th ey represent the area where most of the anthropogenic activity takes place, and consequen tly most of the PM-10 emissions. HB 212 includes significant land area that is still largely undeveloped, including mountainous areas. The elevation of the valley floor is about 2,200 feet. HB 212 has experienced violations of the 24-hour National Ambient Air Quality Standards (NAAQS) for PM-10 that are largely due to windblow n dust. During 2002, four highwind events occurred resulting in exceedances of the PM-10 NAAQS. During 2003, two highwind events occurred that resulted in exceed ances of the PM-10 NAAQS, and during 2004 two high-wind events occurred that resulted in exceedances of the PM-10 NAAQS.1-2 Figure 1-1. Hydrographic Area/Airshed Basemap1-3 Conditions that create high-wind PM-10 excee dances vary from area to area and are based on soil condition (as influenced by precip itation) and wind speed. Wind erosion occurs where natural soil stability has been disturbed by human activities. This is because natural soils have a tendency to form a mineral and organic cr ust that is resistant to erosion by wind. Human activities can remove or break this crust, allo wing dust to become airborne. Even the sparse desert vegetation found in the va lley acts as a windbreak and provide s some protection to the soil surface. When human activities remove vegetati on, the soil is more susceptible to erosion, and as a result, airborne dust is produced. The purpose of this study was to develop a remote sensing method for inventorying vacant land categories in the Las Vegas Valley . The methodology for developing the inventory used multi-spectral satellite imagery and ground tr uthing to determine unique spectral signatures
for land surface characteristics. Pr ior studies of land erodibility in arid areas of the West have also been assessed using satellite imagery:

• Owens Lake Revegetation

– Test for compliance with 50% coverage requirement

– Use Landsat 7 TM and Quickbird data to char acterize extent of ve getation (16 training sites)

• Antelope Valley Blowsand Areas

– Identify blowsand areas that driv e wind erosion of abandoned farmland

– Use Landsat data to map blowsand areas (8 training sites)
The four main categories of la nd surfaces considered in this project are: (1) native desert, (2) disturbed and unstable vacant land, (3) disturbe d and stabilized vacant land, and (4) private unpaved roads that have not been identified in existing Clark C ounty data. Unpaved roads were identified through high-resolution aerial photogra phy which proved to be more effective than satellite imagery. In addition, private unpave d average daily road tra ffic (ADT) was determined using traffic counters at selected locations. Both pneumatic and magnetic counters were utilized with data loggers. At each site, traffic count s were obtained over a consecutive 3-day period, including one weekend day. Stabilized surfaces include rocky soils, soils protected by vegetation, and soils that have re-crusted following disturbance. Unstabilized surfaces include active construction sites and areas traveled by 4-wheel drive and all-terrain vehicles (ATVs) where no rocky surface condition exists and no surface crust has been developed.1-4 The strategy for performing this project utiliz ed a pilot study to assess all elements of data gathering and analysis before proceeding with the main study.

• Pilot Study (two 100-km 2 areas of HB 212)

– Utilize limited study area to develop and te st predictive algorithms derived from satellite imagery.

– Identify training sites for specified land categories.

– Develop ground truth training site s to verify land surface condition.

• Main Study (Remaining Area of HB 212)

– Perform supervised classification of satellite imagery for full study area.

– Identify and characterize verification sites for accuracy assessment of supervised classification.
Given the relatively small c ontribution of the area outside the BLM disposal area to the overall emission inventory, emphasis was placed on th e BLM disposal area because this is where virtually all of the anthropogenic activity takes place to create land disturbances and thereby substantially increases the wind erodibility of the land. The BL M disposal area was therefore viewed as the key to developing an overall em ission inventory for open areas and vacant land, for which this study will be used.

1.2 Overview of Methodology
The inventory of land areas for soil stability using satellite imagery was initially to be based on high-resolution imagery from either th e IKONOS or Quickbird sa tellites. Due to the many uncertainties that had to be resolved in obtaining imagery, evaluating various classification algorithms, and correlating imagery to actual cond itions on the ground, it was decided to utilize a pilot study of relatively small areas before attempting to st udy the entire hydrographic basin which consists of 1,524 square miles. Another im portant factor was the hi gh cost of high-spatial resolution imagery which ranges from $35-$40/km 2

. During the evaluation of the first pilot ar ea, the designated North Area (Figure 1-2), imagery from the IKONOS satellite was used as well as Landsat imagery for comparison. This analysis revealed that the greater resolution of IKONOS actually was a complicating factor in classification of the larger land areas of interest because of la rge pixel-to-pixel variation. Landsat, however, provided an in herent averaging of the minor va riations in surface conditions1-5 across its 30-m pixel size. In addition, Landsat ha s six visible/near-IR sp ectral bands compared
to IKONOS’s four bands. One of the important conclusions from the pilot study was that the small pixel size and limited spectrum of this high-resolution im agery actually confounded the classification algorithms. It was then determined that the La ndsat satellite (specifical ly Landsat 5 TM) would not only provide better classificat ion for this project, but also pr ovided imagery at a fraction of
the cost. This will result in si gnificant future savings for the C ounty as the process is repeated. Any classification algorithm re quires a suite of “training sites” which are representative of the particular soil stability categories bein g studied. The training sites were established through aerial photography and fi eld evaluation, thereby ensuri ng that the training sites accurately represent the soil stabil ity categories. Each site had to be uniform, accessible, and clean of debris. (Addi tional subcategories such as urban landscape, concrete, and wash areas were ultimately developed to increas e the accuracy of the classification.) The ground truthing was initially performed as part of the Pilot Study focused on two
100-km 2 pre-selected areas within HB 212 contai ning land surface conditions and private roads representative of the four specified erodibility classifications and the eight WEG categories as shown in Figure 1-2. Some training sites were us ed to develop the spectral signatures and others were used as verification sites for testing the reliab ility of the spectral signatures. These training sites were approximately 60 m x 60 m. The Pilo t Study was used to develop the most efficient ways to perform supervised classification before final classification was attempted. The Pilot Study also allowed the Project Team to evalua te and refine the ground truthing methodology and data collection forms, and understand the comple xities of satellite imagery acquisition. The imagery was obtained when there had not been a rain event with in the past 7 days. Ground truthing was completed within 48 hours of imager y acquisition to ensure the conditions did not change between the time the imagery was obtained and the ground measurements were taken. After the Pilot Study was successfully comple ted, the most recent Landsat satellite imagery was purchased. This set of satellite imag ery was processed to complete the inventory of vacant land. Twenty-four additiona l training sites were then used to develop the classification algorithms over wider expanses of HB 212.
The initial focus was on the 12 USDA soil texture categories a nd the different wind erodibility groups (WEG). The soil groups are based on a set of parame ters collected during a1-6Figure 1-2. Proposed Test Site Areas Based Upon Wind Erodibility Groups (WEG)1-7 comprehensive soil survey conducted by Speck and McKay in the early 1980s. 1 Training sites were selected to represent the WEG categories to determine if classification of the imagery could detect differences by WEG. One soil sample was taken at each site from the surface to a depth of 3 inches and analyzed for silt, clay, a nd sand by a geotechnical la boratory in Las Vegas (Geotechnical and Environmental Services, Inc.). The results of the testing did not correlate well with the WEG mapping of Speck and McKay. One reason may be the difference between
conventional soil classification data which focuses on soil at depth versus the study of windblown dust which is a surface phenomenon. It was further determined that differences in WEG could not be accurately cl assified through the imagery.
Selection of sites was based on Wind Erodibi lity Groups (WEG) consistent with Speck and McKay and the previous wind tunnel tests by Dr. James 2 and the emission inventory approach developed by MacDougall and Uhl. 3 Suspended wind-blown dust was estimated by James using an empirical formulation derived from a series of field observations made with a mobile wind tunnel over a representative set of "w ind-erodible" soils and soil conditions. Soils covering eight distinct wind erodibility groups were sampled in the Las Vegas Valley, and the results of this study are provided in Appendix C. The phenology of vegetation in the region wa s also studied to determine chlorophyll concentrations during the course of the year. It was initially believed that chlorophyll might enhance satellite recognition of ve getation because of the distinct signature it provides relative to un-vegetated areas. In the end, no specific conc lusions could be drawn regarding the optimum

1 SIP June 2001 PM 10 State Implementation Plan for Clark County, Appendix B - Emission Inventories, Methodology, Emission Factors, and Emission Estimates, Pa ge B-37 Native Desert Fugitive Dust, and Appendix C – Section II, Estimation of Valley-Wide PM 10 emissions using UNLV 1995 wind tunnel-derived emission factors, 1998-1999 emission factors, revised vacant land classifications, and GIS-based mapping of vacant lands, – Draft Final Report, David James, et al., Civil and Environmental Engineering Department, University of Nevada Las Vegas, dated September 12, 2000.

2 SIP June 2001 PM 10 State Implementation Plan for Clark County, Appendix B - Emission Inventories, Methodology, Emission Factors, and Emission Estimates, Pa ge B-37 Native Desert Fugitive Dust, and Appendix C – Section II, Estimation of Valley-Wide PM 10 emissions using UNLV 1995 wind tunnel-derived emission factors, 1998-1999 emission factors, revised vacant land classifications, and GIS-based mapping of vacant lands, – Draft Final Report, David James, et al., Civil and Environmental Engineering Department, University of Nevada Las Vegas, dated September 12, 2000.

3 MacDougall, C. R. and M.F. Uhl. 2002. Empirical method for determining Fugitive dust emissions from wind erosion of vacant land: “The MacDougall Method.” Clark County Department of Air Quality Environmental Management.1-8 time of year for imagery acquisition. A more significant factor for obt aining imagery was clear
weather conditions with minimal haze. The effect of the nadir angle was determined to not be as significant as previously thought due to imagery technology and software enhancements since 2000. A larger angle actually provides more opportunitie s for image acquisition. Figure 1-3 illustrates the relationship of the geometry of satellite imagery.

Figure 1-3. Coverage Geometry of a Sate llite in Orbit Above a Spherical Planet with a Nadir-Pointing Conical Sensor or Field-of-View

An accuracy assessment was performed to quantify the success of the supervised
classification. Reference or validation sites were derived from field obser vations, and also from aerial photography and map data. An error ma trix was developed to indicate the mapping accuracy of each soil stability ca tegory by showing the percentage of correctly classified pixels.2-1 2.0 TRAINING SITE SELECTIO N AND CHARACTERIZATION

This section describes the selection and char acterization of training and verification sites for the Pilot Study. Ground truthing tests document gradations of erodi bility for the sites selected as representative for each specific cate gory of land inventoried. Three different field test procedures were used to confirm the erodibil ity of a particular soil surface area as described in Clark County Air Quality Regulations Section 90.4: drop ball, rock test, and threshold friction velocity test. The number and size of training sites was based upon the number of soil stability categories, the degree of variation within a category, and the pixel size of the satellite imagery. For example, native desert is considered a co mposite category and multiple training sites were needed to fully capture this vari ability (vegetation and desert pavement). Th e actual number of training sites for the study was selected through an iterative process between the field crew and the image analyst. Thirty sites were selected as training sites: 15 in the northern pilot area and 15 throughout Hydrographic Area 212. Ground truthing tests documented the gradations of erodibility for the sites that were selected as representative for each specific categor y of land to be inventoried. (The categories of vacant land included in this study are native desert, disturbed stabilized, and disturbed unstabilized.) The drop ball test , rock test, and threshold friction velocity test were used to confirm the erodibility of a par ticular soil surface area. Field data was collected during dry conditions (i.e., no precipitation for a period for at least 5 to 7 days prior to collection of field data). Weather data for the project was obtai ned from the National W eather Bureau, the Clark County Flood Control rain gauge data, and local weather reports. These vacant land categories
are defined as follows:

• Native desert : desert land in its natural state with no evidence of disturbance by anthropogenic activities. Native desert is characterized by (a) stable soil that is highly consolidated and (b) natural vegetation that is scattered across the surface except in wash areas with rocky surfaces . Typically native desert soil is impregnated with small rocks that in conjunction with natura l mineralogical cementation, form a desert pavement. In any case, the soil is crusted so that it passes the ball drop test (i.e., the2-2 ball does not penetrate the crust and form a crater containing loose dust). Even if there are small soil areas that do not pass th e drop ball test, these areas are likely to pass the rock test (more than 20 percent coverage with non- erodible elements such as rocks or stones larger than 1 cm). Finall y, any pockets of loose soil are likely to pass the threshold friction velocity test (TFV e qual to or greater than 100 cm/sec). It should be noted that small amounts of loose dust may have been deposited on stable soil crust (especially in wind-depletion sh adows of vegetative structures) during or after high wind events; such amounts, howev er, are usually much smaller than the minimum (significance threshold) required for the TFV test.

• Disturbed unstable vacant land : native desert that has been disturbed by removal of vegetative groundcover and soil surface layers such that the natural crust on the soil is destroyed. Typically this occurs in association with construction or other land development activities. Disturbed unstabl e land by definition is characterized by unstable soil, i.e., unconsolidated soil that is unprotected against wind erosion. In order to classify vacant land as disturbed un stable, the soil must fail all three tests conducted in sequence: the drop ball te st, the rock test, and the TFV test.

• Disturbed stable vacant land : native desert that has been disturbed by removing natural crusts and vegetation and re -stabilized by natu ral phenomena or anthropogenic measures. Disturbed stable land by definition is characterized by stable soil, i.e., soil that is protected against wind erosion. If the soil is sufficiently rocky (such that it passes the rock test or the TFV test), it remains stable, even immediately after mechanical disturbance. If the soil is not suffi ciently rocky, it may take years to reform a stable surface by natural processes, or the soil may be
stabilized after the next rain event. Non-erodible surface cover (e.g., vegetation or soil stabilizers of a physical or chemical nature) can be applied to stabilize the soil immediately, but stabilizers may lose effec tiveness in time. In order to classify vacant land as stable, it must pass only one of the drop ball drop tests, the rock test, or
the TFV test . When these tests are conducted in sequence, one passing test negates the need for performing any additional tests. Figure 2-1 presents a decision tr ee for vacant land classification.2-3 Figure 2-1. Decision Tree for Vacant Land Classification Is the area uniform in appearance?

Is there any evidence of disturbance? - Significant loose soil - Tire tracks - Absence of vegetation (except on rocky soils) - Disprupted topography

Is the area covered by sand dunes? Is there debris on the surface? YES NO

NO

NO

NO YES

YES

YES Visual Inspection

Reject the area as test site

Classify the area as “sand dunes” Reject the area as test site Acceptance/Classification

NO YES NO YES Classify as:

Disturbed Vacant Land
Stabilized Vacant Land
Native Desert Does the surface pass any of the tests? Surface stability tests - Drop ball - Rock - TFV2-4 2.1 Site Selection Methodology
Surveys were made to visually evaluate and access site conditions, uniform areas of sufficient size, and other factors at potential tr aining sites. Windshield surveys were performed to locate potential training sites. Aerial photogra phs from the Clark County GIS files were also reviewed with the County to evaluate site locations. Cl ark County DAQEM representatives provided site suggestions and assistan ce in selecting the training sites.
In addition to field observations , another resource used to sele ct test sites was the soil
classification schemes such as the USDA Soil Textural Triangles and the USDA Wind Erodibility Groups (WEG) used for the characteri zation of vacant land and determining fugitive dust emissions using emission factors based on wind tunnel data. The WEG groups, however, were developed to characterize total soil m ovement rather than PM-10 emissions. PM-10 emission potential is more related to clay part icles which have less mobility than the larger particles that make up sand and silt.
The sites were located within the BLM di sposal area (Figure 1-2) because of its dominance in the estimated wind generated emission inventory fo r Hydrographic Area 212. Most land outside the BLM Disposal area cons ists of native desert, and therefore was not selected for training sites. Sites were selected by the WEGs based on the soil classifications and aerial photographs annotated with the WEGs as shown in Figure 2- 2. Bulk soil samples were taken in the first phase of the pilot study at a depth of 3 inches and analyzed for silt, sand, and clay content to verify the WEG and classify th e ground truthing sites relativ e to the soil textural triangle. Thirty locations with uniform surface character istics were selected as training sites to determine their relative potential wind erodibilit y using the procedures outlined in Section 2.3.
These test locations were used either for traini ng or validation of the remote sensing algorithms. (Some additional validation sites were selected and evaluated from available aerial photography. For example, native desert land could be identi fied from photography re presenting large areas (many pixels) and of obvious uniform characteristics. Such sites could then be compared to the corresponding satellite imagery classification.) Two initial study areas were se lected to represent the full range of WEG groups (Figure 2-3). Fourteen additional sites were selected outside of these areas to complete the ground truthing. (Please see Table 2-1 for the total number and type of ground areas that were characterized.)2-5 Figure 2-2. Example Map Labeled with Wind Erodibility Groups2-6Figure 2-3. Proposed Test Site Areas for Pilot Study2-7 TABLE 2-1. STUDY SITES Type of Land Surface No. of Sites (spanning major WEGs) Disturbed unstable land 4 Native desert 5 Disturbed stable land 18 Unvegetated rocky surface (e.g., w ashes) 10 (by Aerial Photographs) Anthropogenic areas: urban landscaping and asphalt 10 (by Aerial Photographs)
Unpaved roads 30 Water soaked areas (construction watering or heavy rains) 4

Site inspections performed in this study indi cated a high degree of difficulty in locating
disturbed unstable land. The select ed test locations were used eith er for training or validation of the classification algorithms. Ground truthing was typically conducted within 24 hours of verified image acquisition.

2.2 Site Characterization Selection Methods and Results

2.2.1 Site Selection
In the north pilot study area, training and ve rification test locations were selected to represent surface uniformity over an area of at leas t 60 m x 60 m in size. Approximately 15 test areas were characterized for gr ound truthing. In the south st udy area and other Hydrographic area 212 training sites, the primary tools for site selection were aerial photography and recommendations from Clark County DAQEM, with no prescribed limits as to how large a site could be, as long as uniformity of surface conditions was verified by site inspection. Once the satellite imagery was collected over an area, ground truthing was performed at each of the applicable test sites within 48 hour s following the generation of the images, as long as atmospheric conditions were acceptable. It wa s critical to minimize the time between imagery collection and ground truthing in order to minimize the like lihood of uncontrolled events changing the land surface. Such events include d rainfall, wind events, or human activity disturbing the surface. To limit the effort that was required in the 48-hour period after the recording of the satellite images, the test surfaces were preselected as described above, and surveyed in advance. Within the 48-hour period, vi sits were made to each pre-selected site to perform the ground truthing inspection. If the site changed during the brief period between the initial survey and actual testing, the site was reclassified. For example, if a stable site had2-8 become disturbed by human activity, we reclassifi ed the site as disturbed unstable. Digital photographs were taken during ground truthing at each site to document soil condition, vegetative cover, a nd surrounding land area. Specific ground truthing methods were used to classify each pilot study site into one of the three specified vacant land categories. It was important that each ground truthing site was sufficiently homogeneous that its entire ground su rface fit the characteristics on only one of the three vacant land categories. In addition no de bris from construction or other activities was present at a ground truthing site. Dry runs of the ground truthing pro cedures were done to refine the approach and determine the time required for implementation. Twelve ground truthing sites were selected for the pilot study on October 6 through 8, 2004, and 15 additional sites were selected on J une 16 and 17, 2005. Fifteen test sites were selected in the north pilot test area, and 12 si tes were selected in th e south test area and throughout the BLM area of Hydrographic Basin 212. The test sites were selected based on WEG soil categories and land category (i.e., na tive desert, disturbed stable, and disturbed unstable land). The pilot study was performed usin g the sites in the north pilot area. Due to weather delays (rain and wet groun d), most of the sites (10) in the south area were not used and other sites were added. All ground truthing sites were inside HB 212. Figures 2-4 and 2-5 show the test sites selected for the north pilot area, and the locations of the remaining test sites. Table
2-2 lists the locations and clas sifications of these sites.

2.2.2 Site Characterization Methods
During ground truthing, three in terior test areas measuri ng 1 m x 1 m were visually selected at each test site to be representative of the overall site. (Figure 2-6 shows an illustrated view of what a typical te st site should look like, a nd Figure 2-7 depicts an act ual test site used in the study, with the 1-m 2 squares denoted in red.) Each 1-m 2 square was characterized by using the following methods: The drop ball test, the rock test, and the th reshold friction velocity (TFV) test were performed as described in Section 90 of the Clark County Air Quality Regulations. The TFV was done only when applicable, i.e., when there was sufficient loose material on the surface to perform the test and when the rock cover test fail ed. In addition, an enhanced version of the drop ball test using a five-point scale was used to better characterize the soil crust.2-9Figure 2-4. Test Sites (Northern)2-10Figure 2-5. Test Sites (Southern)2-11 TABLE 2-2. GROUND TRUTHING LOCATIONS

Sample

Area Site No. Location Date Ground Truthing Disturbed Stabilized Disturbed Unstable Native Desert GPS Coordinates

SW corner GPS Coordinates

NW corner GPS Coordinates

NE corner GPS Coordinates

SE corner North 1 N. 5th Street and Craig Road North section of vacant lot 11/18/04 X 007-88-666 E 267- 88-258 N 007-88-883 E 267

- 88-282 N 007-88-940 E 267

- 88-084 N 007-88-723 E 267

- 88-030 N North 2 N. 5th Street and Craig Road South section of vacant lot 11/18/04 X 007-88-759 E 267

- 87-062 N 007-88-967 E 267

- 87-077 N 007-88-993 E 267

- 86-861 N 007-88-759 E 267

- 86-832 N North 3 N. Losee, North of 215 11/19/94 X 007-94-261 E 268

- 12-941 N 007-94-471 E 268

- 12-950 N 007-94-489 E 268

- 12-749 N 007-94-288 E 268

- 12-706 N North 4 N. 5th Street and Centenniel 11/19/04 X 007-88-415 E 268

- 03-056 N 007-88-618 E 268- 03-073 N 007-88-626 E 268

- 02-865 N 007-88-415 E 268

- 02-849 N North 5 N. Losee, South of 215 11/19/04 X 007-93-225 E 268

- 04-335 N 007-93-438 E 268

- 04-377 N 007-93-461 E 268

- 04-125 N 007-93-258 E 268

- 04-132 N North 6 Deer Springs and Lawrence Northwest corner 11/19/04 X 007-90-801 E 268

- 05-609 N 007-91-005 E 268

- 05-633 N 007-90-997 E 268

- 05-397 N 007-90-792 E 268

- 05-397 N North 7 E. Washburn Road and N. Pecos Road 11/19/04 007-99-979 E 267

- 95-114 N 008-00-230 E 267

- 95-045 N 008-00-181 E 267

- 95-841 N 007-99-982 E 267

- 94-909 N North 8 E. Washburn Road and N. Pecos Road 11/19/04 008-00-087 E 267

- 95-398 N 008-00-287 E 267

- 95-393 N 008-00-293 E 269

- 95-196 N 008-00-087 E 267

- 95-202 N North 9 E. Washburn Road and N. Pecos Road 11/19/04 X 008-00-125 E 267

- 96-432 N 008-00-324 E 267

- 96-432 N 008-00-326 E 267

- 96-223 N 008-00-124 E 267

- 96-224 N North 10 E. Lone Mountain Road and Berg Street 11/19/04 X 007-94-658 E 267

- 91-649 N 007-94-857 E 267

- 91-652 N 007-94-873 E 267

- 91-457 N 007-94-664 E 267

- 91-451 N North 11 Craig Road East of Losee 11/18/04 X 007-95-213 E 267

- 89-140 N 007-95-412 E 267

- 89-134 N 007-95-403 E 267

- 88-933 N 007-95-211 E 267

- 88-939 N North 12 Craig Road and Clayton Street, North of Canal 11/19/04 X high veg 007-78-588 E 267

- 90-687 N 007-78-792 E 267

- 90-683 N 007-78-790 E 267

- 90-478 N 007-78-596 E 267

- 90-485 N North 13 Craig Road and Clayton Street, NE Corner 11/19/04 X low veg 007-78-042 E 267

- 89-955 N 007-78-246 E 267

- 89-960 N 007-78-243 E 267

- 89-757 N 007-78-040 E 267

- 89-755 N North 14 Retention Basin, N. Losee, North of 215 11/19/04 X 007-89-870 E 268

- 09-769 N 007-90-070 E 268

- 09-759 N 007-90-068 E 268

- 09-561 N 007-89-867 E 268

- 09-563 N North 15 Craig Road and Allen Lane, SE Corner 11/19/04 X almost native 007-72-648 E 267

- 88-742 N 007-72-845 E 267

- 88-740 N 007-72-848 E 267

- 88-539 N 007-72-646 E 267

- 88-541 N South 1.1 Fort Apache & Long Boat Key Ave. - SW corner 6/28/05 X 007-40-541 E 267- 15-444 N 007-40-546 E 267

- 15-653 N 007-40-734 E 267

- 15-641 N 007-40-721 E 267

- 15-423 N South 2.1 Durango & Blue Diamond Rd. - NW
corner 6/28/05 Standing water 007-45-790 E 267

- 09-757 N 007-45-810 E 267

- 09-931 N 007-45-993 E 267

- 09-904 N 007-45-966 E 267

- 09-694 N South 3.1 Cimmeron & Blue Diamond Rd. - SW
corner 6/28/05 X 007-48-426 E 267

- 09-016 N 007-48-424 E 267

- 09-112 N 007-48-699 E 267

- 09-088 N 007-48-711 E 267

- 08-991 N South 4.1 Jones & Oleta - SE corner 6/28/05 H2O H2O 007-62-011 E 267

- 09-813 N 007-62-017 E 267- 09-966 N 007-62-159 E 267

- 09-973 N 007-62-197 E 267

- 09-813 N South 5.1 Las Vegas Blvd. & St. Rose Pkwy. (SR 146) - Across from auction lot 6/28/05 X 007-74-541 E 266-80-885 N 007-74-626 E 266-81-024 N 007-74-844 E 266-80-950 N 007-74-747 E 266-80-817 N South 6.1 St. Rose Pkwy. (SR 146) & Bermuda - W of Vegas Rock 6/28/05 X 007-81-487 E 266

- 92-883 N 007-81-394 E 266-93-002 N 007-81-566 E 266

- 93-078 N 007-81-618 E 266

- 92-992 N South 7.1 Stephanie & Russell Rd. - NE corner 6/28/05 X 008-15-364 E 267

- 33-089 N 008-15-360 E 267

- 33-176 N 008-15-555 E 267

- 33-204 N 008-15-557 E 267

- 33-119 N South 8.1 Hollywood & Desert Inn Rd. - NE
corner 6/28/05 X 008-20-087 E 267

- 49-352 N 008-20-111 E 267

- 49-506 N 008-20-286 E 267

- 49-479 N 008-20-256 E 267

- 49-310 N North 9.1 Hualapai & Alexander (N of Cheyenne) - Lone Mountain WRB 6/27/05 X 007-36-472 E 267-85-530 N 007-36-535 E 267-85-732 N 007-36-699 E 267-85-658 N 007-36-660 E 267-85-491 N North 10.1 2-15 & Charleston - Red Rock WRB 6/27/05 X 007-22-673 E 267

- 57-957 N 007-22-637 E 267

- 58-125 N 007-22-875 E 267

- 58-154 N 007-22-832 E 267

- 57-992 N North 10.2 2-15 & Charleston - Red Rock WRB 6/27/05 X 007-22-150 E 267

- 58-456 N 007-22-160 E 267

- 58-586 N 007-22-670 E 267

- 58-569 N 007-22-643 E 267

- 58-494 N North 11.1 2-15 & Lone Mountain - Quarry 6/27/05 X 007-34-415 E 267

- 91-250 N 007-34-407 E 267

- 91-445 N 007-34-583 E 267

- 91-514 N 007-34-634 E 267

- 91-237 N North 12.1 Torrey Pines & Grand Teton - SW corner - S of school 6/27/05 X 007-58-698 E 268

- 12-737 N 007-58-692 E 268

- 12-891 N 007-59-046 E 268

- 12-894 N 007-59-035 E 268

- 12-747 N North 13.1 N. Jones & N of Iron Mountain @ end of road 6/27/05 X 007-61-455 E 268

- 21-260 N 007-61-479 E 268

- 21-382 N 007-61-730 E 268

- 21-366 N 007-61-679 E 268

- 21-161 N North 14.1 N. Decatur and Iron Mountain - NE
corner 6/27/05 X 007-67-118 E 268

- 19-544 N 007-67-108 E 268

- 19-758 N 007-67-274 E 268

- 19-739 N 007-67-279 E 268

- 19-560 N North 15.1 Range Rd. & 2-15 - NE of Army Nat. Guard - section of unpaved road 6/27/05 X 008-13-041 E 268-02-906 N 008-13-035 E 268-02-985 N 008-13-526 E 268-03-003 N 008-13-543 E 268-02-920 N Construction watering - Site saturated Construction watering - Site saturated2-12 Figure 2-6. Example Test Site Example Test Site (Three 1m Test Areas, at Each Site) 2 2 Meter Buffer to Avoid Edge Effects 5 Major Types of Vegetation throughout 30m x 30m Test Site 60m

58m

Drop Ball Test (5 Ball Drops) TFV/Silt in 30cm x 30cm (where applicable) Rock Count in 1m 2 60m

1m

30cm2-13 Figure 2-7. Aerial Photo Showing One Tr aining Site and Soil Test Locations2-14 Each test site was tested three times each in representative areas, as discussed below for the drop ball, rock test, and TFV tests. Within each test area, the drop ball test was done five times in a 30-cm x 30-cm (1-foot) square area in th e lower left-hand corner of the 1-meter square area, the rock cover test was done one time, and the TFV was done one time, as applicable. The average of the replicate tests was used to char acterize each training site. All field sheets completed during ground truthing are available in Appendix D a nd summarized in Table 2-2. Each ground truthing method is descri bed below and included in Figure 2-6.

Drop Ball Test
In addition to the regulatory pass-fail drop ba ll test, the drop ball test was scored on a 5- point scale as follows:
Drop Ball Penetration Index

1. Surface consists of powdery soil with no clods. 2. No crust on surface, but clods are present. 3. Ball breaks/penetrates crust leaving a powdery crater. 4. Ball leaves dimple in crust. 5. Ball does not penetrate crust.
As shown in Figure 2-6, the drop ball test wa s performed within a 30-cm x 30-cm (about 1 square foot) area within each of the three 1-square -meter areas at each test site. Within this 30-cm x 30-cm area, the ball was dropped three times and assigned both a pass-fail score as well as a drop ball penetration index (1 through 5) for each drop. The sample area passed if at least two of three drops indicated sufficient crust, m eaning the ball does not sink into the surface. This is represented by a pe netration index of 4 or 5.

Rock Test

The rock test was used to indicate surface stability by es timating the rocks and other nonerodible elements on a given disturbed surface. Such elements provide a degree of wind
resistance and decrease er odibility. Vegetation was not counted as a nonerodible element. The 1-meter by 1-meter square area was used for the rock test. Within this 1-meter square area, rocks with a diameter greater than 1- cm were mentally grouped into small, medium, and large size categories and the number of rocks were counted in each group. This is a visual qualification process, but generally the classification is:2-15 Small = 1 to 2 cm Medium = 2 to 4 cm Large = > 4cm

Following the count, one or two typical rocks we re selected in each group, and the actual length and width measured to determine an aver age size of each category. The average area of the small, medium, and large size rocks were multiplied by the estimated number of rocks in each category, and the total area of each size category was then divided by two to estimate the
frontal area of the rocks. This calculation determines the percent of estimated rock cover. The
surface was considered stable if the rock cover exceeded 20%.

Threshold Friction Velocity Test
To perform the threshold friction velocity test , sieves were used w ith screen sizes of 4 mm, 2 mm, 1 mm, 0.5 mm, 0.25 mm. They were attached in size order with the larger screen sizes on top and the catch pan on the bottom. For this test, only loose surface dust up to a depth of 1 cm (other than sandy areas, most loose surface dust will not be present at a depth of 1 cm) was collected over an area of at least 30 cm by 30 cm. The test was not conducted for hard, nonerodible surfaces where little or no loose material ex ists. Prior to sieving the dust, rocks larger than 1-cm in diameter were removed. The dust was then placed on top of the sieve stack, the stack covered, and rotated at least 20 circ ular arm movements ( 10 clockwise and 10 counterclockwise). Following th e sieving, the stacked sieves were separated and the relative quantities of each catch were inspected. After a visual determination was made of the sieve catch with the greatest volume, Table 1 of Section 90 of Clark County Air Quality Regulations was used to determine TFV provided in Appendi x E. The TFV was corrected for non-erodible elements using Table 2-1 of Section 90 of the Regulations (Appendix E). The silt content was estimated using a fractional part of the sieve pa n contents as described in the Clark County Air Quality Regulation Section 91.4.1.2 (Appendix F).

Vegetation
The extent of vegetative cover (vegetation density) was estimated for each training site. The umbrella and height of the vegetation was r ecorded and the percent of vegetative cover for each site was visually estimated. This was done by dividing the site into quadrants and using a 6-foot step ladder to get a visual perspective of the area. The predominant species was also2-16 identified. This data was used for background in formation and possible insight into differences in imagery that could be caused by differing greenness of each specie s at various times of year. Digital photo documentation wa s taken at each site.
A literature search was conducted on the s ubject of discriminating vegetative cover relative to the greenness of vegetation. The chlorophyll cycle of vegetation in the Mohave species is complicated, but can generally be charact erized as short cycles of greenness occurring shortly after rainfall. It is optimal to wait 5-7 da ys after a rainfall to avoi d the effects of rain on
soil conditions. A literature search was conducted on phenol ogy, but no quantitative data was found. Qualitative information sources all agree that chlorophyll peaks during the rainy season, and desert plants quickly decrease activity in the summer.

Soil Texture
Overall, soil texture classi fication (e.g., wind erodibility groups or WEGs) is typically based on sampling down to a depth of several feet and may not be a good predictor of the erodibility of the surface soil that is exposed to the wind. Field samp ling to characterize the
textural classification of the soils at the ground truthing site s was beyond the scope and funding of this project. Fifteen soil samples were collected and analyzed from the test sites in the north pilot area and 10 from the south pilot area. These samples were submitted to Geotechnical and Environmental Services (GES) in Las Vegas to determine sand, si lt, and clay analysis. The samples were taken from the top 3 inches of the surface. Samples were placed in 1-quart cans and transported with a chain-of-custody form. Table 2-3 summarizes the GES lab results for the percentage of sand, silt, and cl ay content of these 25 samples. The calculated WEG group for
each soil shown in the “Lab WEG” column is ba sed on the laboratory results and the application of the WEG Soil Triangle in the Uhl/MacDouga ll paper, “Determining Aeolian Fugitive Dust Emissions for the Western U.S.: Methodologies and Assumptions). The WEG for each site based on the existing maps are shown in the “Map WEG column.” Note that only 3 of the 25 mapped WEGs and laboratory-determined WEGs agr ee (Sites 4 and 9 in the North area and Site 7 in the South area). Also note that adjacent Site s 7 and 8 in the North area (the sites eliminated from ground truthing because of construction ac tivity and the water-saturated condition of the soil from watering) show very different soil types, i.e., WEG 7 and WEG 4.2-17 TABLE 2-3. WIND ERODIBILITY GROUP (WEG) CATEGORIES FOR SELECTED GROUND TRUTHING AREAS Laboratory Soil Analysis, % Sample

Area Site No. Map WEG Lab WEG Sand Silt Clay Native Desert Disturbed Stabilized Disturbed Unstable
N orth 1 4L 6 25.5% 56.7% 17.8% X N orth 2 3 3 62.2% 24.4% 13.4% X N orth 3 6 4L 52.3% 37.1% 10.6% X N orth 4 4L 2 54.2% 25.0% 20.8% X N orth 5 7 2 59.8% 14.1% 26.1% X N orth 6 7 5 24.5% 58.7% 16.8% X N orth 7 7 7 11.1% 79.7% 9.2% X N orth 8 7 4 28.5% 21.2% 50.3% X N orth 9 4 4L 47.0% 42.7% 10.3% X N orth 10 5 4L 40.6% 40.3% 19.1% X N orth 11 5 3 61.2% 25.4% 13.4% X N orth 12 4L 6 3.1% 69.4% 27.5% X high veg N orth 13 4L 3 60.8% 28.3% 10.9% X low veg N orth 14 6 4L 51.1% 35.2% 13.7% X N orth 15 3 4L 48.6% 28.5% 22.9% X almost native South 1 Sand 1 78.5% 5.9% 15.6% X South 2 3 1 86.9% -0.2% 13.3% X South 3 4 1 88.1% -1.8% 13.7% X South 4 3 2 79.0% -3.9% 24.9% X South 5 3 2 72.1% 5.3% 22.6% X South 6 2 1 80.9% 3.6% 15.5% X South 7 2 2 66.2% 13.2% 20.6% X South 8 8 3 71.1% 10.4% 18.5% X South 9 8 3 67.2% 20.3% 12.5% X South 10 5 3 73.3% 11.3% 15.4% X Note: Information in this table is based on Gismo records (MAP WEG) and laboratory results (LAB WEG). See Figures 2-4 and 2-5 for site locations. See Appendix G for descripti on of WEG classifications.2-18 2.2.3 Ground Truthing Results
Initial ground truthing began after IKONOS satellite imagery of the north area was collected on Thursday, November 18, 2004. Th e ground truthing for the North Pilot area was completed by end of day Friday, November 19, we ll within the 48-hour pe riod allowed. Sites 7
and 8 were not tested because of site preparation for constructi on activity. A water truck was continuously watering the area, and these two s ites were wet with standing water in several places.
Several sites in the South P ilot area were surveyed prior to subsequent IKONOS and Landsat flyovers, but the ground was so wet and hardened by rain from December 2004 through March 2005 that further satellite imagery a nd ground truthing in the South was halted.
Based on the results of the pilot study of the north area and comparis on of the results of the space imagery analysis comparing IKONOS w ith Landsat 5 TM, the decision was made to use Landsat 5 TM imagery for the remainder of the study. Landsat imagery included additional spectra (7 vs. 4), and the less-detailed resolution of Landsat (30-meter pixels vs. 1-meter pixels) provided an automatic averaging eff ect that was very eff ective in avoiding th e need to subdivide the feature classes such as native vegetation. Se ction 3 provides additional rationale for selecting Landsat over IKONOS imagery. In June 2005 the soil in the Las Vegas Valley was judged to be sufficiently dry to resume ground truthing. Prior to the Landsat imagery of June 26 and July 12 (Landsat TM imagery of the Las Vegas Valley is taken ev ery 16 days), 24 new ground truthi ng sites, including several in the disturbed unstable category, were selected w ith the assistance of DAQEM personnel. Table 2-2 shows the ground truthing sites used for this study. Ground trut hing was performed at 15 of these sites within 48 hours following each of th e Landsat TM flyovers on June 27 and 28 and July 13, 2005. Based on the ground tr uthing tests of all 30 sites, 3 sites were disturbed unstable, 18 sites were disturbed stable, 5 sites were native desert, and 4 sites were stable with standing water due to construction watering or recent rain s. A summary of the results of these tests is presented in Table 2-4.2-19 TABLE 2-4. ERODIBILITY GROUND TRUTHING RESULTS Soil Stability Drop Ball Site Site Description Test Area P/F No. Rock Test TFV Site Stability, Stable/Unstable Disturbed Stabilized 1 P 4 0 NA S Map WEG 4L 2 P 4 <1% NA S Lab WEG 4L 3 P 4 1.20% NA S N-1

Summary P 4 <1% NA S Disturbed Stabilized 1 P 4 <1% NA S Map WEG 3 2 P 4 <1% NA S Lab WEG 3 3 P 5 <1% NA S N-2

Summary P 4 <1% NA S Native Desert 1 P 5 50 NA S Map WEG 6 2 P 5 >45 NA S Lab WEG 3 3 P 5 50% NA S N-3

Summary P 5 >45 NA S Disturbed Stabilized 1 P 5 <1% NA S Map WEG 4L 2 P 5 <1% NA S Lab WEG 2 3 P 5 <1% NA S N-4

Summary P 5 <1% NA S Native Desert 1 P 4 40% NA S Map WEG 7 2 P 4 40% NA S Lab WEG 2 3 P 4 45% NA S N-5

Summary P 4 42% NA S Disturbed Stabilized 1 P 4 45% NA S Map WEG 7 2 P 4 42% NA S Lab WEG 3 3 P 4 45% NA S N-6

Summary P 4 44% NA S N-7 Soil saturated with water. Preconstruction site watering being performed. Site stable, no tests run. Map WEG 7 Lab WEG 4L N-8 Soil is saturated with water. Preconstruction site watering is being performed. Site is stable, no tests

run. Map WEG 7 Lab WEG 1 Native Desert 1 P 5 42% NA S Map WEG 4L 2 P 4 35% NA S Lab WEG 3 3 P 5 45% NA S N-9

Summary P 5 40% NA S Disturbed Stabilized 1 P 4 1.9% NA S Map WEG 5 2 P 4 1.0% NA S Lab WEG 2 3 P 4 0.2% NA S N-10

Summary P 4 1.0% NA S Disturbed Stabilized 1 P 5 <1% NA S Map WEG 5 2 P 5 3.8% NA S Lab WEG 1 3 P 4 0.0% NA S N-11

Summary P 5 1.6% NA S Nat. Des. Hi Veg. 1 P 5 <1% NA S Map WEG 4L 2 P 5 <1% NA S Lab WEG 4L 3 P 4 <1% NA S N-12

Summary P 5 <1% NA S Nat. Des. Low Veg. 1 P 4 <1% NA S Map WEG 4L 2 P 4 <1% NA S Lab WEG 3 3 P 5 <1% NA S N-13

Summary P 4 <1% NA S Disturbed Stabilized 1 P 5 2.3% NA S Map WEG 6 2 P 4 6.8% NA S Lab WEG 1 3 P 4 5.3% NA S N-14

Summary P 4 4.8% NA S Dis. Stab. Near Nat. 1 P 4 <1% NA S Map WEG 3 2 P 4 <1% NA S Lab WEG 4L 3 P 4 <1% NA S N-15

Summary P 4 <1% NA STABLE 2-4. (continued)

2-20 Soil Stability Drop Ball Site Site Description Test Area P/F No. Rock Test TFV Site Stability, Stable/Unstable
Disturbed Stabilized 1 P 5 <1% NA S 2 P 4 <1% NA S 3 P 5 <1% NA S 1-1

Summary P 5 <1% NA S Disturbed Stabilized 1 NA NA NA NA S Stable with standing 2 NA NA NA NA S water 3 NA NA NA NA S 2-1

Summary NA NA NA NA S Disturbed Stabilized 1 P 5 1.2 NA S 2 P 4 1.2 NA S 3 P 5 <1% NA S 3-1

Summary P 5 <1.1% NA S Disturbed Stabilized 1 NA NA NA NA S Stable with standing 2 NA NA NA NA S water 3 NA NA NA NA S 4-1

Summary NA NA NA NA S Disturbed Unstable 1 F 3 4.3 60 cm/s U 2 F 3 3.1 60 cm/s U 3 F 3 2.6 60 cm/s U 5-1

Summary F 3 3.3% 60 cm/s U Disturbed Stabilized 1 P 4 <1% NA S 2 P 4 1.8 NA S 3 P 5 2.8 NA S 6-1

Summary P 4 <1.9% NA S Disturbed Stabilized 1 P 5 0 NA S 2 P 5 <1% NA S 3 P 5 <1% NA S 7-1

Summary P 5 <1% NA S Disturbed Unstable 1 F 3 0.5 30 cm/s U 2 F 3 2.0 60 cm/s U 3 F 3 0.4 30 cm/s U 8-1

Summary F 3 <1% 30 cm/s U Disturbed Stabilized 1 P 5 1% NA S 2 P 4 2.5 NA S 3 P 4 <1% NA S 9-1

Summary P 4 <1.5% NA S Disturbed Stabilized 1 P 5 0 NA S Some areas unstable 2 P 4 0 NA S 3 P 5 0 NA S 10-1

Summary P 5 0.0% NA S Disturbed Stabilized 1 F 3 2.7 NA S 2 P 5 42 NA S 3 P 4 35 NA S 11-1

Summary P 4 27.0% NA S Disturbed Stabilized 1 P 4 2.5 NA S 2 P 5 2.5 NA S 3 P 5 1 NA S 12-1

Summary P 5 2.0% NA S Disturbed Unstable 1 F 1 <1% 30 U 2 P 4 2.5 NA S
3 F 3 1.5 86 U 13-1

Summary F 3 <1.7% 86 U Disturbed Stabilized 1 P 4 22.5 NA S 2 P 5 10 NA S 3 P 5 20 NA S 14-1

Summary P 5 17.5% NA S Disturbed Unstable 1 P 4 7.5 NA S 2 F 2 0.7 43 U 3 F 2 0.4 43 U 15-1

Summary F 3 2.9% 86 U3-1 3.0 SATELLITE IMAGERY SELECTION AND PROCESSING

3.1 Remote Sensing Data Sources
IKONOS satellite imagery of the north pilot ar ea was obtained for evaluation as part of the Pilot Study. In addition, Landsat TM data was purchased because of its low cost and applicability to the large, relatively hom ogeneous area outside the BLM disposal area. Imagery at various nadir angles was received from Space Imaging and evaluated by Midwest Research Institute (MRI) and Clark Count y. It was clear that there was no significant difference in the quality of the imagery from different nadir angles, and that the wider angle was acceptable for this project. The use of Landsat 5 TM was investigated for the largely homoge neous native desert land outside the BLM disposal area. Based on Landsat’s longe r wavelength bands for broader spectral signatures, larger pixels to remove undesirable influence of unimportant micro-features, and less cost, it was decided that the Landsat 5 TM satellite imager y would be used for the entire Hydrographic Area 212 including the BLM Disposal Area (see detaile d rationale in Sections 3.3 and 3.4). Landsat TM imagery was acquired from a period when there had not been a rain event within the past 7 days and clear sky conditions were present (i.e., no cl oud cover). Days with fog, haze, and smog were avoided. Landsat TM imagery was obtained, and the registration accuracy was verified using aerial photography. Use of Landsat TM imagery wa s much more useful and cost-effective than high spatial resolution imagery (IKONOS or Quic kBird) that lacks the additional infrared wavelength bands. High spatial resolution imager y can pick up micro-feat ures (e.g., vehicles, large trash piles, individual rocks) that are difficult to categorize by selective classification and have only localized effects on wind erodibility. The larger pixel size provided by the Landsat TM sensor (30m x 30m) is more appropriate for la nd areas with micro-features that do not affect wind erodibility. The larger pixel size for Landsat TM provides an averaging eff ect that is very useful for characterizing the vast areas of native desert outs ide of the BLM disposal area. Because there is no advantage to distinguishing subcategories of native desert with varying amounts of3-2 vegetation, the averaging of refl ectance across different vegetative densities greatly simplifies the process of identifying and mapping native vegetation. In addition, the extra IR wavelength ba nds available from Landsat TM have been demonstrated to be very useful in distinguishin g senesced vegetation and soil moisture. Landsat TM in conjunction with QuickBird is being used effectively in evalua ting vegetation and other dust control strategies at Owens Dry Lake in California. The ot her obvious advantage of Landsat TM imagery is the much lower cost, which make s periodic updates of land category inventories economically feasible. Figure 3-1 shows the Landsat TM coverage of the Clark County area and the BLM area.

3.2 Other Studies
Prior studies of land erodibility in arid areas of the West have also been based on satellite imagery. Two relevant studies are summarized below. The first study dealt with the tracking of th e revegetation of parts of Owens Dry Lake in California. On the revegetated part of the lake surface, it was necessary to test for compliance with the 50 percent coverage requirement. Fo r this purpose, satellite imagery was collected using Landsat 7 TM and Quickbird to characte rize the South Farm area where 16 training sites were located. The results are reported in the Owens Lake Vegetation Compliance Report (2003). The second study was directed to the mapping of blowsand areas in the Antelope Valley of California. For the past 5 y ears, MRI has been performing res earch on the role of blowsand in driving wind-generated emissions from abandone d farmland. This has involved the use of a portable wind tunnel and the instru mentation of two large blowsa nd sites to determine sand fluxes and vertical fluxes of PM-10 during high wind events. In the satellite imagery study, Landsat data was used to map blowsand areas based on signatures developed from eight training
sites.

3.3 Landsat vs IKONOS and QuickBird
The major issues for the Clark County study were the classification methodology for vacant land, and which of the available satellite imagery sources would be most appropriate to use. In the early stages of the project, Quickbird or I KONOS were targeted for characterizing the BLM3-3 Figure 3-1. Landsat 5 TM Coverage of Clark County3-4 disposal area, and Landsat for the remainder of Hydrographic Area 212. Th e features of each of
these satellites are described in Table 3-1.

TABLE 3-1. SATELLITE FEATURES LANDSAT TM VS. IKONOS VS. QUICKBIRD Landsat TM IKONOS QuickBird Spatial Resolution 30 meters 1 meter 0.6 meter Spectral Resolution 6 bands 4 bands 4 bands Coverage of Imagery 49,425.82 km 2 200 km 2 200 km 2 Cost for Project $425.00 $7785.00 >$7785.00 Cost per km 2 per band $0.00143 $9.73 >$9.73

• IKONOS (Space Imaging)

– 3 visible bands (blue, green, red), 1 near IR band

– Resolution: 4-m multispectral, pan-sharpened to 1 m

• QuickBird (Digital Globe)

– 3 visible bands (blue, green, red), 1 near IR band

– Resolution: 2.4-m multispectral, pan-sharpened to 0.6 m

• Landsat 5/7 TM (NASA/NOAA/USGS)

– 6 visible/near-IR bands (same as above plus 3 others)

– Resolution: 30-m multispectral sharpened to 15 m
The main advantage of the IKONOS or QuickB ird imagery is the high spatial resolution. However, the cost of the imagery is three orders of magnitude higher than the cost of the Landsat
TM data as shown in Table 3- 1. IKONOS and QuickBird costs ar e very similar; a formal quote was only obtained from IKONOS.

3.4 Rationale for Selection of Landsat
In the course of the project, tests of the e ffectiveness of Landsat imagery showed it to have many advantages over IKONOS or Quic kBird imagery. The expanded wavelength components proved very useful in isolating distin ctive fingerprints of the vacant land categories of interest. The response to vege tation was particularly important. An example is illustrated in Figure 3-2 (Band 7 is the 6 th band for Landsat). In addition, the larger pixel size provides an automatic averaging effect is very effective in avoiding the need to subdivide the feature classes such as native vegetation. For example, it was anti cipated that desert shrubs that stabilize soils and provide windbreaks might be difficult to char acterize by satellite imagery because of theirFigure 3-2. Reflectance Response Curves of Natural and Manmade Features Using Landsat ImageryWavelength (μm)

ReflectanceBand 7Band 5

Band 4Bands 1-3SagebrushDolomiteQuartzConcreteNatural and Manmade Features in a Landsat TM Context

3-53-6 indistinct spectral signatures, mostly small si ze (< 1 sq m), and sparseness. Also, it was anticipated that disturbed land with small rocks might provide a challenge for identification by satellite imagery. However, these potential prob lems were overcome with use of imagery from
Landsat 5 TM. Another advantage was the ability to incorpor ate the entire study area in a single Landsat scene collected at one point in tim e. Finally, the very affordable cost of the imagery produced a much higher cost-effectiveness for this project. It is important to note that the available highresolution aerial photography provided a necessary element in the accuracy assessment process.4-1 4.0 SPECTRAL ANAL YSIS PROCEDURES

The purpose of the Pilot Study was to establis h firm relationships between the ground surface data (as described in Sect ion 2) and remote satellite imag es of the same area. Landsat TM satellite imagery was used to remotely characterize ground surface types for wind erodibility. The satellite imager y was provided as GEOTIFF files. Satellite images were obtained within 2 da ys of the verified field data collection described in Section 2. The BLM Disposal Area wa s an important focus of the imagery in order
to ensure the identification of the vacant land sc attered throughout the urban area. This area is also the highest contributo r to the overall emission i nventory for Clark County. IKONOS satellite imagery of the pilot test areas was attempted several times (October 11, 16, and 19, 2004) but cloud cover in the Las Ve gas Valley prevented clear imagery of the pilot test areas. IKONOS satellite imagery of the north pilot test area was finally collected Thursday, November 18. Additional data was collected on asphalted ar eas to add the surface drainage category to the imagery analysis. Urban vegetation was al so added as a category by using the spectral signature of green areas (e.g., golf courses) in the Landsat TM imagery. Figure 4-1 shows the visible spectra (red, green, and blue) from the Landsat TM imagery, and Figure 4-2 shows the supervised classification of the Landsat TM im agery using Mahalanobis Distance classification. Unpaved road data from the various municipalities and the spring 2005 aerial photography of the Las Vegas Valley were pr ovided by the County Geographic Information System Management Office (GISMO). The delin eation of the unpaved roads was initiated using the available unpaved road da ta along with the Las Vegas Valley aerial photography. A sampling of the unpaved roads not included in the available GIS data was investigated by direct on-site observation. Many of the unpaved roads were located in areas under development. These roads will be paved as the development is completed. Other roads were extensions of single-lane utility roads or roads to private residences located in the rural areas of the Las Vegas Valley. These roads are generally less than 12 f eet wide (observed roads of this type ranged from 6 to 12 feet wide). Many of these roads ar e rarely traveled. In other cases, the GIS data4-2 Figure 4-1.Visible Spectra from Landsat TM ImageryFigure 4-2. Mahalanobis Supervised ClassificationUrbanNative DesertDisturbed StableBarren/ShadowVegetationSurface DrainageConcreteUnclassified

4-34-4 showed unpaved roads that no longer existed. These unpaved roads were shown in heavily developed areas where all the st reets were paved. These roads were probably old utility roads once maintained by the county, but were built ove r as Las Vegas grew and power lines were moved underground. These roads no longer exis t, but the GIS data has not been updated. The input and output files from the classifi cation process are rast er images: i.e., each image is treated as an array of values. The classification results were converted to polygon vector layers that are compatible with Envi ronmental Systems Research Institute (ESRI) products.

4.1 Land Category Characteristics
Four categories of erodible land surfaces were to be mapped in this project: (1) native desert, (2) disturbed stable vacant land, (3) disturbed unstable vacant land, and (4) private unpaved roads or other unpaved roads not previously identified in existing Clark County data. The first two categories were mapped with superv ised classification techniques. An analyst using aerial photography mapped the fourth category, unpaved roads.
The disturbed unstable land cate gory was problematic because of a lack of training sites (reference sites) to use for the classification proce ss. For Landsat TM, a training site must be at least 210 square meters (210 x 210 m), so that no pixel overlaps into anot her land classification. In the disturbed unstable category, however, only one training site was large enough to meet this
criterion. The results were incons istent with this one training si te because of the wide range of soil reflectance that can occur with disturbed unstable soil. As a consequence, the percent of disturbed unstable vacant land was derived by inference; when all other land cover types (native desert, urban, etc.) were classified, then the re maining unclassified pixels represent an upper
bound on the amount of disturbed vacant land. Five other land cover types were also mapped. Because of their abundance, nonvegetated rocky surfaces (washes), concrete, ur ban areas, urban vegetation, and barren/shadow were included in the supervised classification procedure. The last cat egory, barren/shadow, was added to account for land cover types that were not classified in one of the other categories, and to lower the upper bound of the estimate for the disturbed vacant land category. This category
includes shadows from mountains , buildings, and rocky areas.4-5 4.2 Supervised Classification
Multi-spectral classification is the process of sorting pixe ls into a finite number of individual classes, or categorie s of data, based on their spectral reflectance value. If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to those
criteria. The analyst closely controls s upervised training. In this process, image pixels were selected that represent the land cove r features listed above , i.e. training sites. These pixels were identified primarily from ground truth data , with GISMO layers as ancillary data. By identifying these representative ground tr uth sites, the computer algorithim was trained (calibrated) to identify pixels with similar characteristics. Based on the classification accuracy, the resulting classes re present the land surface categories within the Landsat TM data. It was important that training samples be representative of the classes that are being identified. This does not necessarily mean that th ey must contain a large number of pixels or be dispersed across a wide region of the data. The selection of training samples depended largely upon knowledge of the data, the pilot study ar ea, and the classes to be extracted. Once a set of reliable signatures was created and evaluated, the next step was to classify the data. Each pixel was analyzed independentl y. The measurement vector for each pixel was compared to each signature, according to a decisi on rule, or algorithm. Pixels that passed the criteria that were established by the decision rule were then assigned to the class for that signature.

4.3 Choice of Mahalanobis Method
The Mahalanobis distance classifier is designed so that clusters that are highly varied will lead to similarly varied classes, and vice-ver sa. For example, when classifying urban areas, typically a class whose pixels va ry widely, correctly classified pixels may be farther from the mean than those of a class for water, which is us ually not a highly varied class (Swain and Davis, 1978). The classifier was chosen because it was superior in its ability to classify the highly varied land cover types in Hydrographic Basin 212.4-6 4.4 Definition of Land Subcategories
The following procedure was used to define land subcategories:

1. Use aerial photography to define large suba reas of distinct a ppearance (“training subareas”) within the pilot study area.

2. Inspect and collect soil samples within training subareas to determine the land category of each subarea.

3. Acquire Landsat 5 TM imagery for the pilot study area and perform supervised classification.

4. Use analysis results to characterize land categories (disconnected areas with same appearance).

5. Apply cross checks with IKONOS imagery.

6. Perform field checks to determine the re liability of the la nd classification.

7. Complete the pilot study using the new approach to identify training areas.

8. Evaluate Landsat imagery as the basis for the vacant land classification (with IKONOS cross checks).

9. Acquire Landsat imagery for the whole st udy area and develop the inventory for erodible lands.

10. Perform final field veri fication of the inventory.

4.5 Special Challenges
In the course of the project, it was found th at areas of disturbed unstable lands were widely scattered and small in size in compar ison to the requirements for determining unique spectral signatures. Some of this difficulty related to unusually wet weather conditions during the study period that tended to restabilize distur bed areas. In addition, there was wide use of intensive watering on constr uction sites to comply with permit conditions.
The few disturbed unstable areas that were identified as marginally suitable for ground truthing tended to exhibit differi ng spectral signatures because of differences in soil types and associated chemical compositions. When areas such as native desert are crusted, it appears that the surface chemistry is more uniform and relativel y independent of the parent soil type beneath the crust. In addition a high wi nd event during a dry period typi cally produces a large area of4-7 deposition as the well-mixed atmospheric particul ate loading settles to the ground at the end of
the wind event. Consequently, there were insufficient, unstabl e areas to reliably train the classification software. An acceptable option was to determine the disturbed, unstable land area by difference. This involved subtracting all of the other identifie d land area types from th e total land area being considered. The impact of this change in procedure is believed to be small because the prevalence of this land type is a relatively insign ificant part of the total study area. Table 4-1 presents the issues and resolutions.

TABLE 4-1. ISSUES AND RESOLUTIONS

Issue Resolution

Incidental variations of land condition are accentuated by IKONOS imagery Landsat TM imagery has superior spectral resolution (more wave length bands).
Large Investment and Uncertainties in Imagery Acquisition/Analysis July 2004 – Modify technical approach to incorporate focus area pilot study within ground truthing at 30 locations.
Prohibitive Cost to Acquire High-Resolution Imagery for Entire Las Vegas Valley August 2004 – Propose use of Landsat TM imagery for areas of low land disturbance (outside of BLM Disposal Area).
Intrusion of Rain Events November 2004 to February 2005 – Postpone imagery acquisition until soil dries.
Registration Errors in Imagery February 2004 – Make linear shifts in imagery positional files to match ground truthing site coordinates.

Stay Within Cost Ceiling on Imagery Acquisition for Main Study March 204 – Rely primarily on Landsat for imagery datawith IKONOS imagery for quality assurance crosschecking.

Other areas of special challenge and the steps ta ken to resolve them are summarized in Table 4-2.

4.6 Accuracy Assessment
Accuracy assessment is a general term for co mparing the classifica tion to geographical
data that are assumed to be true in order to de termine the accuracy of the classification process. For the pilot study, the assumed-true data were derived from aerial photography and ground truth data. This validation procedure determined the quality of the image processing algorithms used to distinguish vacant land categ ories of varying erodibility.4-8 TABLE 4-2. RISK AREA, IMPACT, AND MITIGATION Risk Area Risk Impact Risk Mitigation Extension of ground truthing sites to entire Hydrographic Area 212 Unrepresentative erosion sites used for ground truthing would result in spectral signatures that could not reliably identify wind erosion sites in entire area of interest. Use of Pilot Study sites selected to contain samples of the largest areas susceptible to wind erosion. Pilot Study sites contained representative sites of the most erodible lands in the Las Vegas Valley. Excessive costs to purchase complete imagery for State Hydrographic Area 212 Purchase of imagery of nonerodible urban land is not costeffective. Purchase of expensive satellite imagery for subareas of proven non-erodible land in urban Clark County was eliminated, and lower resolution imagery was used for these areas to reduce costs. Substantial costs to analyze complete satellite imagery for State Hydrographic Area 212 Excessive costs may not allow for proper development of the spectral signatures for erosion areas or to complete other parts of the study. A two-phase study was proposed to make sure that all analysis procedures were able to be efficiently managed before comprehensive analysis of Area 212.

Lack of correlation between spectral signatures and ground truth data on erodibility (as occurred in the prior study) The validity of the technical approach will be compromised if good correlations are not obtained in the Pilot Study. Lack of correlation in the prior study was likely related to the inability of pass/ fail erosion tests to characterize the type of wind eroded lands. Ground truthing tests included continuous gradations of erodibility (e.g., drop test will have 5 options); moreover, at least three different field test procedures confirmed the erodibility of a particular soil surface. Seasonal cycles of surface erodibility, due to moisture Soil moisture inhibits wind erosion and develops a crust to prevent future wind erosion and causes the satellite imagery not to identify source areas of erosion. Ground truthing performed during “dry” periods.

Computer processing time for very large satellite imagery data file As demonstrated in prior study, very large processing times are required. The relatively small Pilot Study was used to develop the most efficient ways to process data before large-scale data processing was attempted. Delays in collecting and delivering satellite data for analysis (as occurred in 2000 study) Poor correlation of ground truth data with out-of-date satellite spectral signatures. Ground truthing performed within 48 hrs of satellite imagery collection.4-9 It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of re ference pixels were used. Refe rence pixels are points on the classified image for which actual data was (o r will be) known. The reference pixels were initially randomly selected, and then accep ted or rejected based on accessibility. If the analyst selects the reference pixels, it is often tempting to select the same pixels for testing the classification as were used in th e training samples. This biases the accuracy assessment because the training samples were the basis of the classification. By allowing the reference pixels to be selected at random, we co uld lessen or eliminate the possibility of bias. The number of reference pixels was an importa nt factor in determining the accuracy of
the classification. It has been shown that a pproximately 50 reference pixels are needed to estimate the accuracy of a class. Therefore, 50 reference pixels were used in this study.5-1 5.0 RESULTS OF SATELLITE IMAGERY ANALYSIS

5.1 Control of Misregistration Errors
Because of problems with the Landsat 7 TM sate llite (failure of the scan line corrector), imagery from Landsat 5 TM was used. The Landsat 5 TM satellite was launched more than 20 years ago, and there can be prob lems with the registration of the imagery. The Landsat 5 TM imagery that was purchased was evaluated and an unacceptable amount of misregistration was
observed. For the August 2004 Landsat 5 TM scene, the registration error was corrected by collecting a series of “tie points” from the 2- foot pixel GISMO aerial photography. These tie points were used to “warp” the Landsat TM image back to the correct registration.
The June 2005 Landsat 5 TM scene was also mi sregistered. Since th e tie point collection and warping process were very time consuming, a decision was made to reposition the training sites so that they still represen ted the correct land cover type. So me training sites, such as native desert, were not repositioned because the land c over category was much larger than the Landsat 5 TM registration error.

5.2 Mapping of Land Categories
As described above, it was not fe asible to classify the dist urbed vacant land category because of a lack of training sites and the effects of highly variable soil chemistry. Therefore, the percent of disturbed vacant land was derive d by inference; when all other land cover types (native desert, urban, etc.) were classified, then the remaining unclassifie d pixels represent an upper bound on the amount of disturbed vacant land. Figures 5-1a and 5-1b show comparisons of high resolution aerial photogra phy with Mahalanobis supervised classification of example
subareas within HB 212. Tabl e 5-1 shows the classification re sults, with the area for each category, for both HB 212 and the BLM disposal area. The BLM disposal area is a boundary, mostly within HB 212, adopted by the Bureau of Land Management, that identifies federally-owned la nd that is available for purchase, trade, or5-2 Figure 5-1a. Mahalanobis Supervised Classification5-3 Figure 5-1b. Mahalanobis Supervised Classification5-4 TABLE 5-1. AREA OF LAND CATEGORIES Hydrographic Region 212, BLM Only, Land Category km 2 % of area km 2 % of area Native Desert 1897.11 47.6 284.95 21.2 Disturbed Stabilized 122.78 3.1 94.86 7.1 Disturbed Unstable Vacant Land < 45.26 < 1.1 < 45.98 < 3.4 Wash – Drainage 357.15 9.0 150.61 11.2 Concrete 26.11 0.7 25.09 1.9 Urban 192.32 4.8 171.21 12.8 Vegetation 35.79 0.9 34.18 2.6 Barren/Shadow 1307.2 32.8 534.51 39.9
lease by public or private interests. The BL M disposal area contains nearly all of the anthropogenic sources within the non-attainment ar ea, and this area was used for attainment demonstration in the PM10 State Implemen tation Plan for Clark County (June 2001). One issue with the supervised classification was the orient ation of elongated concrete
surfaces in relation to the orbit of Landsat 5. As shown in Figure 5-2, the orbit of Landsat is skewed in relation to the compass directions. In classifying concrete runways at McCarran International Airport, th e runway in Figure 5-3 wa s not classified as concrete for two reasons. First, much of the runway was covered with a laye r of rubber tire fragments. Second, the narrow width of the runway in relation to the Landsat pixel size and the orientation of the runway in relation to the Landsat orbit pr oduced the results that most pi xels were not overlayed onto a predominately concrete surface. In contrast, the runway in Figure 5-4 was readily classified as
concrete because of its orientation and th e absence of the tire particle layer. Similarly, the primary runway at Nellis Air Force Base was successfully classified as concrete because of the runway orientation and re lative lack of a tire particle layer. This is illustrated in Figure 5-5a and Figure 5-5b.

5.3 Accuracy Assessment
An accuracy assessment was performed with an error matrix. An error matrix compares
information from reference sites to information on the map for a number of sample areas. For this purpose, comparison pixels were randomly selected from widely spaced areas within HB 212. This is illustrated in Figures 5-6 and 5-7. Th e error matrix is a square array of numbers set out in rows and columns that express the labels of samples assigned to a particular category in one classification relative to the labels of sample s assigned to a particul ar category in another5-5 classification. On the error matrix in Table 5-2, the columns were assumed to be correct and are termed the reference data. The rows were used to display the map labels or classified data generated from the remotely sensed data. To gauge the accuracy, two labels from each sample are compared to one another (Congalton and Green, 1999). Error matrices are very effective representa tions of map accuracy because the individual accuracies of each map category are plainly descri bed along with both the errors of inclusion (commission errors) and errors of exclusion (omi ssion errors) present in the map. A commission
error occurs when an area is included in an in correct category. An omission error occurs when an area is excluded from the category to which it belongs. In addition, the error matrix can be used to compute overall accuracy. Overall accuracy is the sum of the major diagonal (i.e., the correctly classified pixels or sa mples) divided by the total number of pixels or samples in the error matrix (Congalton and Green, 1999). The overall accuracy is 89% (313 / 350). The accuracy assessment effort also involve d a calculation of producer’s accuracy and user’s accuracy. Producer’s and user’s accuracies are ways of representing individual category accuracies instead of just the overall classifi cation accuracy. For example, producer’s accuracy was calculated by dividing the tota l number of correct sample un its in a given category by the total number of sample units as indicated by the reference data. The user’s accuracy is calculated by dividing the total number of correc t pixels in a given cat egory by the total number of pixels classified as the cate gory. Table 5-3 shows the producer’s and user’s accuracy for each land cover category.Figure 5-2. Sun-Synchronous Orbit of Landsat 4 and 5

5-65-7 Figure 5-3. Non-Concrete Runway at McCarran International Airport5-8 Figure 5-4. Concrete Runway at McCarran International AirportFigure 5-5a. Land Classification at Nellis Air Force Base

5-95-10 Figure 5-5b. Runway at Nellis Air Force BaseFigure 5-6. Native Desert

5-11Figure 5-7. Natural Drainage

5-12TABLE 5-2. ERROR MATRIX

5-13TABLE 5-3. ACCURACY ASSESSMENT

5-146-1 6.0 PRIVATE UNPAVED ROAD TRAFFIC COUNTS

6.1 Identified Roads and Areas
The private unpaved road traffic count consisted of three major activities:

1. Delineating the private unpave d roads for traffic counts, 2. Conducting the traffic counts, and 3. Interpreting and reporting the data.

6.2 Delineating the Private Unpaved Roads for Traffic Counts
The extent of “private” unpaved roads is de fined by subtracting currently identified unpaved roads under Public Works from the unpaved roads identified through image analysis. RS 2477 roads were not considered private and therefore were subtra cted along with public
roads. Stretches of non-maintained roads were cons idered private if they are not currently in the
Public Works database.
It should be noted that all unpaved roads in Clark County with average daily traffic (ADT) of greater than 150 vehicles were projected to be paved. Many roads with ADT of less than 150 vehicles were also paved. Most uninventoried private unpaved roads have surfaces of compacted native soil, and most unpaved roads appeared to overlap with the disturbed stable vacant land category. The unpaved roads identified for traffic counts were selected using the available unpaved road data from North Las Vegas, Las Vegas, Henderson, and the BLM along with the Las Vegas Valley aerial photography and ground surveys by Clark County DAQEM and EQ. The unpaved roads already identified in the existing GIS data were excluded from traffic counts. A sampling of the unpaved roads not included in the availabl e GIS data was investigated by direct on-site observation. Many of the unpaved roads were locat ed in areas under development. These roads will be paved as the development is completed. Other roads were extensions of single-lane utility roads or roads to privat e residences located in the rura l areas of the Las Vegas Valley. These roads are generally less than 12 feet wide ( observed roads of this type ranged from 6 to 12 feet wide). Many of these roads are rarely tr aveled. In other cases, the GIS data showed unpaved roads that no longer existed. These unpa ved roads were shown in heavily developed6-2 areas where all the streets were paved. Thes e roads were probably old utility roads once maintained by the county but were built over as Las Vegas grew and power lines were moved underground. These roads no longer exist but the GIS data has not been updated. In a few cases, unpaved roads between busy paved roads appear ed to have traffic counts above 150 ADT.
Based on the above considerations and a ground survey by Chuck Richter of Clark County DAQEM and Victoria Hansen of EQ, the road s presented in Table 6- 1 were selected for traffic counting. Traffic counts were performed on approximately 30 of these unpaved roads as
described in the next section. Figure 6-1 shows the relative loca tions of these unpaved roads. Appendix B shows maps with the detailed locations.

6.3 Conducting the Traffic Count s on Private Unpaved Roads

Vehicle count data for 72-hr periods was obtained using bo th eight pneumatic counters which are rubber tubes positioned across the roa d, and three magnetic sensor counters which respond to the metal in a vehicle. Eight Jama r TRAX 1 Plus pneumatic traffic counters were used for the majority of the traffic counts. To minimize damage to the rubber tubes placed across the unpaved roads by abrasion from heav y vehicles rolling over rough ground, gravel, or sharp stones, Jamar RoadRAMP Rural tm pneumatic tubes were used in place of the standard road tubes where necessary. This product consists of a rubber ramp, covered by wear-resistant tape,
that contains a built-in road tube. Three Trafx magnetic vehicle counters were al so used for vehicle counts. These units
were buried in the middle or on the side of an unpaved road. To verify the proper operation of the Trafx vehicle counters, comparative counts were done using both a Jamar pneumatic counter and a Trafx counter on a sample of one out of ten unpaved roads. Each traffic count sampling period was 3 days, and the equipment was checked twice daily during the counts for proper performance and placement. Each 3-day count included 2 weekdays and 1 weekend day. Eleven traffic counters were used to produce approximately 30 traffic counts for approximately 30 re presentative private unpaved roads. The traffic counts were performed over a pe riod of 2 to 3 weeks beginning October 11,
2005 as follows:6-3 TABLE 6-1. UNPAVED ROADS SELECTED FOR TRAFFIC COUNTS

Site No. GPS Coordinates Starting Point Starting Point Location GPS Coordinates Ending Point Ending Point Location 1 007-54-207 E 267-27-485 N W Sunset Rd & S Tenaya Way 007-54-135 E 267-29-113 N Sundown Glen Ave & S Tenaya Way 2 00751-954 E 267-30-084 N W Patrick Ln & Buffalo Dr 007-51-186 E 267-30-053 N W Patrick Ln & W of Maccan St 3 007-51-415 E 267-32-686 N W Russell Rd & Buffalo Dr 007-51-506 E 267-28-698 N N of W Sunset Rd & Buffalo Dr 4 007-40-665E 267-22-705 N Boulder Opal Ave & S Fort Apache Rd 007-40-701 E 267-22-055 N W Warm Springs Rd & S Fort Apache Rd 5 007-46-123 E 267-14-040 N W Wigwam Ave & S Durango Dr 007-44-935 E 267-13-969 N W Wigwam Ave & S Riley St

6 007-44-935 E 267-13-969 N W Wigwam Ave & S Riley St 007-44-817 E 267-12-723 N W Ford Ave & S Riley St

7 007-29-018 E 267-11-968 N W Torino Ave & W of Fortney Rd 007-29-985 E 267-11-959 N W Torino Ave & Kulka Rd

8 007-35-215 E 267-10-233 N Blue Diamond Rd. & S Hualapai Way 007-35-205 E 267-08-527 N Serene Ave & S Hualapai Way 9 007-35-205 E 267-08-527 N Serene Ave & S Hualapai Way 007-33-178 E 267-08-552 N Serene Ave & Orduno St

10 007-67-129 E 267-07-450 N Gary Ave & S Decatur Bl vd 007-65-930 E 267-07-433 N Gary Ave & Edmond St

11 007-82-971 E 266-94-902 N N of St. Rose Pkwy & Bermuda Rd 007-82-933 E 266-96-935 N Liberty Heights & Bermuda Rd 12 007-80-277 E 266-97-699 N Chartan Ave & Gillespie St 007-80-274 E 266-96-465 N Starr Ave & Gillespie St

13 007-71-164 E 267-14-783 N Wigwam Ave & Hinson St 007-72-935 E 267-14-822 N Wigwam Ave & W of Procyon Ave 14 008-23-366 E 267-62-752 N Stewart Ave & Los Feliz St 008-24-011 E 267-62-751 N Stewart Ave & Probst Way

15 008-03-262 E 267-77-293N Cartier Ave & Lincoln Rd 008-03-586 E 267-77-296 N Cartier Ave & Desert Edge St

16 007-83-400 E 267-79-772 N Brooks Ave & Revere St 007-34-404 E 267-79-471 N N of Rev Wilson Ave & Revere St 17 007-67-009 E 268-18-722 N N Decatur Blvd @ NE corner of power station 007-66-888 E 268-23-308 N N Decatur Blvd & S of water tower section 18 007-66-896 E 268-23-897 N N Decatur Blvd & N of water tower section 007-66-976 E 268-25-028 N N Decatur Blvd & short distance N towards mine 19 007-64-571 E 268-19-988 N Gilbert Ln & Bradley Rd 007-61-767 E 268-21-377 N Gilbert Ln & Jones Blvd

20 007-59-870 E 268-17-882 N Maggie Ave & Mustang St 007-59-851 E 268-18-522 N Iron Mountain Rd & Mustang St 21 007-59-235 E 268-18-583 N Iron Mountain Rd & N Torrey Pines Dr 007-61-771 E 268-18-436 N Iron Mountain Rd & Jones Blvd 22 007-59-896 E 268-15-728 N Horse Dr & Mustang St 007-57-996 E 268-15-770 N Horse Dr & Gareheim St

23 007-56-003 E 268-10-902 N Donald Nelson Ave & Balsam St 007-55-995 E 268-10-306 N Farm Rd & Balsam St6-4 TABLE 6-1. (continued)

Site No. GPS Coordinates Starting Point Starting Point Location GPS Coordinates Ending Point Ending Point Location 24 007-55-305 E 268-04-941 N Deer Springs Way & Rio Vista St 007-55-284 E 268-03-762 N Rome Blvd & Rio Vista St

25 007-54-410 E 268-01-296 N W Azure Dr & Starlight Dr 007-54-424 E 268-00-114 N W Tropical Pkwy & Starlight Dr 26 007-54-856 E 268-00-109 N W Tropical Pkwy & Moonlight Dr 007-54-861 E 268-00-741 N S of W Azure Dr & Moonligh t Dr 27 007-61-078 E 267-98-956 N Corbett St & N Bronco St 007-61-582 E 267-98-894 N Corbett St & Jones Blvd

28 007-61-020 E 267-93-782 N S of W Washburn Rd & N Bronco St 007-61-020 E 267-92-992 N W La Madre Way & N Bronco St 29 007-32-545 E 268-05-347 N Deer Springs Way & Alpine Ridge Way 007-32-554 E 268-04-741 N Deer Springs Way & Bath Dr

30 007-33-917 E 268-04-366 N Deer Springs Way & Egan Crest Dr 007-33-917 E 268-04-725 N Tate & Egan Crest Dr

31 007-39-244 E 268-13-393 N Gilcrease Ave & N Tee Pee Ln 007-39-242 E 268-12-740 N Grand Teton Dr & N Tee Pee Ln 32 007-45-860 E 268-02-132 N Regena Ave & Grand Montecito 007-44-228 E 268-02-108 N Regena Ave & E of N Juliano Rd 33 007-45-620 E 268-00-179 N W Tropical Pkwy & W of N Durango Dr 007-45-250 E 268-00-168 N W Tropical Pkwy & N Bonita Vista St 34 007-42-800 E 267-99-451 N Corbett St & W of N El Capitan Way 007-42-135 E 267-99-432 N Corbett St & E of Campbell Rd 35 007-42-472 E 267-99-439 N Corbett St & N Kevin Way 007-42-476 E 267-99-743 N S of W Tropical Pkwy & N Kevin Way 36 007-40-571 E 268-01-417 N Fort Apache Rd & Azure Dr 007-40-878 E 268-01-426 N Dapple Gray and Azure Dr

37 007-22-802 E 268-21-429 N Rantool St & Log Cabin Way 007-21-867 E 268-21-415 N Nickelson St & Log Cabin Way 38 007-31-246 E 268-23-682 N Trails End Way & Power Transfer Station 007-33-260 E 268-24-104 N Trails End Way & Frontage Road6-5Figure 6-1. Unpaved RoadTraffic Count Sites6-6 1. Receive and check equipment October 11-12, 2005 2. Set up equipment for counts 1 through 10 October 13-15 3. Traffic counts 1 through 10 October 16-18 4. Set up for counts 11 through 20 October 19 5. Traffic counts 11 through 20 October 20-22 6. Set up for counts 21 through 30 October 24-26 7. Traffic counts 21 through 30 October 27-29 8. Makeup counts November 3-5
The one-tube layout was used with the pneumatic counters to collect the traffic data for the unpaved roads. One tube was placed and anchored across both lanes of traffic. This layout collected traffic volume information only and did not detect direction, speed, or type of vehicle. The traffic counter was set for a straight axle count (as opposed to the divide-by-two setting). For the unpaved roads with a high traffic count, it was assumed that each two counts denote one
vehicle. For traffic counts less than about 50 ADT, the data was reviewed and vehicles were
counted based on the time interval between axles crossing the tube. The Trafx magnetic counter parameters were set for low volume conditions to prevent double counting for large vehicles. Vehicle types (i.e., two axle, four axle, etc.) traveling the road will be noted during the twice per day equipment checks if applicable. Because of the small traffic volume expected on most unpaved roads, it was not prac tical to develop detailed tr affic-type information.
The traffic count data from the TRAX 1 Plus and Trafx traffic coun ters were downloaded to a computer following each traffic count. Each traffic counter time stamps the raw data and
also stores the traffic count data until cleared from the memory. The data survey forms shown in Appendix H were used to record traffic counts and auxiliary information as follows:

C Traffic counter locati on (GPS coordinates)

C Date and start/stop times

C Individual person develo ping traffic count data

C Road width and condition (e.g., 12-ft width w ith vegetated center area; well-used road)

C Road length (measured by truck odometer)

C Vegetation surrounding private road (e.g., type, character, height, coverage)

C Land identification including land use (e.g., c onstruction site, mining area, open desert)

C Likely purpose of road (e.g., recreati onal traffic; cons truction traffic)

C Traffic count between start and st op times, and translated to ADT

C Comments (e.g., vehicle types using road, developing or unde veloped area, speed limit if available)

C Photographs of road area where counter is located7-1 7.0 RESULTS OF PRIVATE UNPAVED ROAD ANALYSIS

7.1 Identified Roads and Areas of Concentration

A GIS layer of private unpaved roads was produ ced for this project. The GIS layer was developed in conjunction with GI S layers (provided by Sharon Rice of GISMO) that showed the location of a subset of private unpaved roads. Thus, the GIS laye r produced through this project was only for previously unma pped private unpaved roads. The mapping of private unpaved roads was done with the 2-foot pixe l aerial photography
from the GISMO archive. The roads were ma pped by visually examining the digital aerial photography (outside the boundaries of Las Vegas, North Las Ve gas, Henderson, and Nellis Air Force Base) and digitizing all unpaved roads 22 feet wide or greater. A total of 158 miles of unpaved private roads were identified. Even though aerial photography was not available for the entire HB 212 area, analysis of the photography showed the absence of roads well before the boundary of the photography was reached. Therefore, it can be concluded that the roads identified represent the entire HB 212 area. To be considered as a road, a minimum wi dth of 22 feet was required. This eliminated bike paths, trails, and off-road disturbances.

7.2 Average Daily Traffic Results
ADT for each of the roads selected is presente d in Table 7-1. For those roads using both the Jamar and Trafx counters, the data from the Jamar counters was use d. Figure 7-1 shows an overview of the 160 miles of private unpaved roads. Figure 7-2 presents an example of unpaved roads identified by the aerial pho tography analysis. The red lin es represent unpaved roads not identified in the county GIS system. ADT ranged from 2 to 599 ADT. Three of the counts were greater than 150 ADT (402 ADT for Site 11, 210 ADT for Site 15, and 599 ADT for Site 33). The ADT agreement between the Jamar a nd Trafx counters was very good for those roads with ADT less than 150 ( 32 vs. 33 ADT for Site 9, 8 vs. 7 ADT for Site 32, and 72 vs. 71 ADT for Site 36). At Site 15, ADT based on the Jamar count was 210 and ADT based on the7-2 Trafx count was 173. This difference is believed to be due to the low traffic settings on the Trafx counters. A few of the sites originally selected were not used for traffic counts due to the road segment being paved (Sites 12, 28, 29, and 35) and heavily used by construction equipment and blocked on a dead end street (Sites 16 and 30).7-3 TABLE 7-1. AVERAGE DAILY TRAFFIC COUNTS Traffic Count Data Site No. Sun Mon Tues Wed Thur Fri Sat Counter ADT

1 6 13 22 EQ01 13.7 2 9 27 23 J 1 19.7 3 21 60 54 J 2 45.0 4 Construction area - Road extremely active with earthmoving equipment - Did not use 5&6 12 32 28 J 3 24.0

7 23 24 26 EQ02 24.3 8 50 140 89 J 5 93.0 9 a 34 32 29 J 4 31.7 9 b 34 36 28 EQ03 32.7 10 27 28 79 J 7 44.7 11 203 493 511 J 8 402.3 12 Did not use 13 40 43 53 J 6 45.3 14 96 105 80 J 8 93.7 15 a 249 172 208 J 7 209.7 15 b 189 153 177 EQ03 173.0 16 Berm added (Dirt) - Access to unpaved road blocked 17 58 84 137 EQ02 93.0 18 16 12 28 J 6 18.7 19 33 33 30 J 5 32.0 20 17 18 26 EQ 01 20.3 21 4 4 7 J 4 5.0 22 45 43 43 J 3 43.7 23 13 20 12 J 2 15.0 24 89 73 53 J 1 71.7 25 11 15 5 J 3 10.3 26 70 45 61 J 2 58.7 27 6 7 10 J 1 7.7 28 Old pavement - broken up - did not use 29 Did not use 30 Dead end street = did not use 31 68 61 19 J 6 49.3 32 a 10 12 1 J 4 7.7 32 b 10 9 2 EQ01 7.0 33 642 693 463 J 5 599.3 34 11 11 12 EQ02 11.3 35 Did not use 36 a 91 76 50 J 1 72.3 36 b 84 74 56 EQ01 71.3 37 35 33 23 J 7 30.3 38 3 3 0 J 8 2.0

ADT Excluding Sites 11, 15a, 15b, and 33 (ADT >150) 36.4 ADT Including Sites 11, 15a, 15b, and 33 (ADT >150) 73.27-4 Figure 7-1. Overview of 160 Mile s of Private Unpaved RoadsFigure 7-2. Example of Unpaved Roads Identified by Aerial Photography Analysis

7-58-1 8.0 CONCLUSIONS

• The Clark County emission inventory calculatio n procedure requires an inventory of erodible land areas in Las Vega s Valley (Hydrographic Area 212)

– Native desert (natural state)

– Disturbed, unstabilized vacant la nd (loss of surface protection)

– Disturbed, stabilized vacant land (restoration of surface protection)

– Private unpaved roads (dirt or gravel)

• WEG values developed by USDA/NRCS to indi cate the susceptibility of surface soil to blowing (nine WEG values) are usef ul only for disturbed unstable land.

• Soil blowing is correlated with:

– Soil texture

– Organic matter content

– Effervescence due to carbonate reaction with HCl

– Rock and pararock fragment content

– Mineralogy

– Soil moisture and frozen soil

• Supervised classification gives reliable results for mapping all land categories except for disturbed unstable lands that constitute less than 1 percent of the total land area.

• Stratified random sampling, used with aerial photography, is an e ffective approach to generate reference data for accuracy assessment.

8.1 Vacant Lands

Table 8-1 summarizes the land classifications for HB 212 de rived from the classified imagery based on the Landsat view of June 26, 2005.

TABLE 8-1. LAND CATEGORIES Land Category Percent of HB 212 Square Kilometers Native Desert 47.6 1,897.1 Disturbed Unstable Vacant Land Less than 1.1 Less than 45.3 Stabilized Vacant Land 3.1 122.8 All Other 48.2 1,918.6

The “all other” category includes wash areas (9%), concrete (0.7%), urban area (4.8%), vegetation (0.9%), and barren shadow (32.8%). Barren shadow is largely comprised of8-2 mountainous and/or rock areas plus shadowed areas. The amount of disturbed unstable vacant land is significantly less than previously reported . This is attributed to several factors:

1. A probable over-estimation in th e past given the qualitative methods available at that time.

2. The success of the enforcement program requi ring contractors to utilize stabilization techniques during construction.

3. The higher than average rainfall in the Valley during 2004 and 2005.

8.2 Applicability Limitations of WEG Classification
As part of the subject project, data obtai ned from UNLV wind tunnel tests was analyzed to investigate the dependence of the PM-10 fl ux rate on the WEG category. Results for both undisturbed and disturbed (manually raked) surfaces were evaluated. The results of the analysis showed that no clear dependence of the flux rate on WEG is observable fo r either disturbed or undisturbed surfaces. The spike results also sh owed no observable correlation with WEG.
It should be noted that the WE G classification of the wind tunne l test areas appears to be based on soil classification maps, an d not on actual analysis of the soils that were tested. In the ground truthing work under the current project, little if any correlation was found between the WEG map classification and the actual analysis results for soil samples collected from the test
sites as noted earlier in Table 2-3. It is important to note that the WEG cla ssification was developed in an agricultural context and was designed to predict total soil er osion and not PM-10 emissions. For example, loose sandy soils (WEG 1) are the easiest to move by the wind, but typically do not have a large PM-10 component, in comparison with other soil t ypes. This observation is supported by field tests using the MRI wind tunnel in California’s Antelope Valley (w estern Mojave Desert).

8.3 Private Unpaved Roads
A second phase of the projec t involved the identificatio n of unpaved roads in HB 212 which were not already in the Clark County GIS. Roads were identifie d through 2-foot pixel aerial photography supplemented by field surveys. Ro ads had to be a minimum of 22 feet wide to be considered as a road. A sample of 30 road segments was selected on which to perform8-3 traffic counts. The 30 segments were selected to be representative based on usage. All 30 segments were in the urban ar ea and in areas of expanding home building. A total of 160 miles of unpaved roads was identified. Excluding th ree sites with ADT counts exceeding 150, the average daily traffic count was 36.

8.4 Inventory Accuracy Assessment
The accuracy assessment indicates a high level of classification accuracy. Lower values of classification accuracy were achieved in the us er’s accuracy for the dist urbed stable category. The error matrix indicated that samples for the “wash” category (natural drainage) were included in the disturbed stable category (an error of commission). Considering the definition of disturbed stable, the wash/natural drainage land cover can also sa tisfy the definition of disturbed stable. Wash/natural drainage areas are distur bed by infrequent overland drainage and then can stabilize during the interval between rainfalls. In summary, the overall , producer’s, and user’s accuracy indicate an acceptable le vel of confidence in the classi fication process and the derived areas of the land cover types (Table 5-2).

8.5 Protocol for Inventory Updating
The mathematical procedure that Clark Count y uses to calculate PM-10 emissions from
wind erosion of vacant lands utilizes a series of emission factor equations for the various land categories: native desert, disturbed stable vaca nt land, and disturbed uns table vacant land. Each of these categories is assigned a set of emi ssion rates that depend on the wind speed, as developed from on-site wind tunnel tests. Th e extent of source measures utilized in these
calculations is the areal exte nt of each land category. This project has generated procedures that can be replicated to de velop updated land area values as development in the Las Vegas Valley c ontinues. The procedures utilize Landsat 5 TM imagery that can be purchased for all of Clark County at a modest price. Then the spectral signatures of the different land categories can be used to map each land category corresponding to the time of imagery acquisition. This provide s not only the total area of each land category, but also the spatial distribution of each catego ry within the boundaries of Hydrographic Area
212.8-4 The same is true for the procedure used to locate uninventoried segments of private unpaved roads. However, the procedure is much mo re labor intensive in that it involves careful inspection of aerial photographs to find road segments that meet the identification criteria and are not contained in the GIS layer for private unpaved roads. It should be noted that private unpaved roads tend to cluster around land development proj ects, and many such ro ads are paved as the area development continues. This project has provided a protocol for updati ng the source extent measures needed to develop future inventories of wi nd generated particulate emissions from vacant lands and trafficgenerated emissions from private unpaved roads. The same source extent measures are also appropriate for the development of particulate emission inventories for other particle size fractions, provided that the applicable emission f actor equations contain adjustment factors for application to the desired size fractions.9-1 9.0 REFERENCES

Congalton, R.G., and K. Green. 1999. Assessing th e Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.

June 2001 PM 10 State Implementation Plan for Cl ark County, Appendix B - Emission Inventories, Methodology, Emission Factors, an d Emission Estimates, Page B-37 Native Desert Fugitive Dust, and Appendix C – Section II, Estimation of Valley-Wide PM 10 Emissions Using UNLV 1995 Wind Tunnel-Derived Emission Fact ors, 1998-1999 Emission Factors, Revised Vacant Land Classifications, and GIS-Based Ma pping of Vacant Lands, – Draft Final Report, David James, et al., Civil and Environmental Engi neering Department, University of Nevada Las Vegas, September 12, 2000.
MacDougall, C.R., and M.F. Uhl. 2002. Empiri cal Method for Determining Fugitive Dust Emissions from Wind Erosion of Vacant La nd: “The MacDougall Method.” Clark County Department of Air Quality Management.
Speck, R.L., and T.R. McKay. 1985. Soil Survey of Las Vegas Valley Area, Nevada — Part of Clark County. U.S. Department of Agricultu re, Soil Conservation Service, Las Vegas.
Swain, P.H., and S.M. Davis. 1978. Remote Se nsing: The Quantitative Approach, New York: McGraw Hill Book Company.APPENDIX A

GLOSSARYGLOSSARY
Airport Disposal Lands - That property owned and contro lled by the Clark County Department of Aviation used primarily as a buffer fo r the airport proper for flight paths.

Desert Pavement - The relatively strong and durable crust of native desert land formed by a combination of natural mineralogical cementatious processes in combination with rock or stone fragments. A varnish on the rocks is a coa ting built up over many decades by windblown clay particles containing iron and manganese combined with bacteria. Several theories exist on the formation of the stony surface ranging from defla tion of finer particles to erosion by water to repeated wetting and drying cycles.
Disturbed Vacant Land (or Disturbed Soil) - Vacant land that has been disturbed by removal of the vegetation and grading, or by vehicle movement or in some other way such th at the natural crust on the soil is destroyed. Di sturbed land can become stabilized by natural processes such as rainfall or revegetation if it is not repeatedly disturbed.

GEOTIFF Files - A standard for storing georeference a nd geocoding information in a TIFF 6.0 compliant raster file.
Ground Truthing - Observations and testi ng performed at ground level to verify the properties and parameters of a given land area fo r correlation to satellite imagery.

IKONOS - The trade name of the satellite used in this study, operated by a private consortium.
Multi-Spectral Satellite Imagery - Satellite data simultaneously collected in several wavelength bands.
Nadir Angle –Native Desert (or Native Desert Soil) - Desert land in its natural state that has not been disturbed by anthropogenic activities.

NITF Files - Electronic files using the National Im agery Transmission Format Standard (NITFS). NITFS is the standard for formatting digital imagery and imag ery-related products and exchanging them among members of the Intel ligence Community (IC), the Department of Defense (DOD), and other departments and agen cies of the United States Government as governed by Memoranda of Agreement (MOA) with those departments and agencies.
Private Unpaved Roads - Roadways that are at least 22 feet in width, used for transiting from point to point with no paving of the surface. U npaved roads may be surfaced with native soil or with aggregate materials brought into the area fo r road construction. Pathways used as hiking trails and off-road vehicle recr eation are not considered roadwa ys. Roadways typically will follow a property boundary, have a destination point, or provide a shortcut between unconnected paved roads. Private means roads that are not ma intained by a government al authority such as Clark County, the cities of Cl ark County, or NDOT. Roads designated as RS that are under BLM control are not considered private.
QuickBird – The trade name of a satellite with imagery capabilities, operated by a private consortium.
Soil Stability

- That characteristic of soil that relates to the tendency to resi st wind erosion. The more dust that is generated at a given wind spee d, the more the soil is considered unstable. Stability is not specifically defined by a univers ally accepted wind speed, but a soil which emits no dust at a sustained wind speed of 25 mph would generally be considered stable.
Soil Stability Categories - Any set or grouping of soil types th at is used to define the relative stability (i.e., resistance to wind erosion) of vari ous soils. Formalized categorizations include the Soil Textural Triangle and Wind Erodibility Groups formulated by the U.S. Department of Agriculture.

Stabilized Vacant Land (or Stabilized Soil) - Vacant land that has been stabilized either by the application of palliatives, or by natural crusting or re-vegetation, or that is protected by rock cover. Stationary soils are considered stable wh en they are in compliance with the standard set forth in Regulation Section 90.4.
Supervised Classification - A type of classification where the image analyst “supervises” the pixel categorization process by sp ecifying, to the computer algorith m, numerical descriptors of the various land cover types present in a scene.
Training Site

- A specific area on the ground that is defined by various tests to be of a certain class of soil that can then be used to “train,” or calibrate, the software that processes satellite imagery so a given spectral si gnature relates to a given type of land surface or cover type.

Verification Site

- A verification site is the same as a training site except it is used after the imagery is classified, as an independent verifica tion to develop an accuracy assessment of the classification procedure.APPENDIX B

UNPAVED ROAD TRAFFIC COUNT LOCATIONSAPPENDIX C

DR. JAMES WIND TUNNEL CLASSIFICATIONSAPPENDIX D

GROUND TRUTHING DATA FORM FIELD SHEETSAPPENDIX E

TABLE 1 & TABLE 2-1 OF SECTION 90 CLARK COUNTY AIR QUALITY REGULATIONSAPPENDIX F
CLARK COUNT AIR QUALITY REGULATION SECTION 91.4.1.2APPENDIX G

WIND ERODIBILITY GROUPS (Source: U.S. Department of Agriculture, Natural Resources Conservation Services, 2005.

National Soil Survey Handbook, title 430-VI. Exhibit 618-16. Online: http://soils.usda.gov/technical/handbook/)APPENDIX H

TRAFFIC COUNT DATA FORM FIELD SHEETS