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 2006 
ii

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 Selection 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................................. 6-2 
iii

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 Section 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 Sheets 
iv

FIGURES 

Number Page

1-1 Hydrographic Area/Airshed Basemap ............................................................................. 1-2 

1-2 Proposed Test Site Areas Based Upon 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 Sensor 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 Training 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 Supervised 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 McCarran International Airport ............................................. 5-7 

5-4 Concrete Runway at McCarran 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 Identified by Aerial Photograph Analysis.......................... 7-5 
v

TABLES

Number Page

I Project Chronology ........................................................................................................... vii 

II Land Categories ................................................................................................................. ix 

III Classification 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 ............................................................................................................... 8-1 
vi

ACKNOWLEDGEMENT 

This project involved a close collaboration between the contractor team and the Clark 

County DAQEM Project Team. The outcome would not have been possible without the 

guidance and technical advice received from the Project Officer Mr. Rodney Langston and the 

other members of the team, John Koswan, Russ Merle, Chuck Richter, and Michael Uhl. The 

Clark County GIS Management Office was also critical to the success. Mr. Clint Woods and 

Ms. Sharon Rice provided invaluable assistance in providing and interpreting 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. The potential of vacant land to emit PM-10 during high wind events 

is related to soil stability at the surface, which depends on land characteristics. The purpose of 

this study was to develop an inventory of native desert land, disturbed vacant land, stabilized 

vacant land, and private unpaved roads in Hydrographic Basin 212 (HB 212) containing the Las 

Vegas Valley, as defined by Nevada State Plane coordinates. 

Table I presents a time line, or project chronology, 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 28
viii

The methodology for developing this inventory used multi-spectral satellite imagery and 

ground truthing to develop unique spectral signatures that allowed mapping of land areas of the 

stated surface characteristics within HB 212. The important conclusions drawn by the 

investigators are as follows: 

• The Landsat satellite imagery is more cost-effective for this purpose than high spatial 

resolution imagery, and avoids classification 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 Las Vegas Valley than previously thought. 

• Soil chemistry is an important variable in classification of disturbed, 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 dust has significantly 

decreased unstable land across the Valley. 

The inventory of land areas based on soil stability using satellite imagery is based on a 

computer algorithm called supervised classification. 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 field evaluation, ensuring that the training sites 

accurately represented the soil stability categories. Additional subcategories such as urban 

landscape, wash areas, and concrete were developed primarily to eliminate these areas from the 

overall land area of interest and enhance accuracy. Several classification schemes were 

evaluated, but the Mahalanobis distance scheme proved to be the most effective for this 

particular project. 

It was not feasible to classify the disturbed 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 existed. Therefore, the percentage of disturbed 

unstable vacant land was derived by inference – when all other land cover types (native desert, 

urban, etc.) were classified, then the remaining unclassified pixels represent an upper bound on 

the amount of disturbed unstable vacant land. 
ix

Table II summarizes the land classifications for HB 212 derived 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 methods available at that time. 

2. The success of the enforcement program requiring 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 and 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 matrix for the classification process. Error matrices are very 

effective representations of map accuracy because the individual accuracies of each map 

category are plainly described along with both the errors of inclusion (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. Overall 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. CLASSIFICATION 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 project involved the identification of unpaved roads in HB 212 

which were not already in the Clark County GIS. Roads were identified 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 roads 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) counts 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 adequately 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 

Site Traffic Count Data 

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) 73.2 
1-1

1.0 INTRODUCTION 

1.1 Background 

The Las Vegas Valley is one of the fastest growing metropolitan 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 completed 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 the population growth. 

The U.S. EPA in accordance with the Clean Air Act (CAA) has classified the Las Vegas 

Valley, also known as Hydrographic Basin 212 (HB 212), within Clark County as a “serious” 

nonattainment area for PM-10 (particulate matter having an aerodynamic diameter of 10 microns 

or less). Figure 1-1 illustrates the Hydrographic Basins which are 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 been sold by the Bureau of Land Management 

through auction under the Southern Nevada Public Lands Management Act. These Lands 

represent a smaller subset of HB 212, but they represent the area where most of the 

anthropogenic activity takes place, and consequently 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 windblown dust. During 2002, four highwind events occurred resulting in exceedances of the PM-10 NAAQS. During 2003, two highwind events occurred that resulted in exceedances 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 Basemap 
1-3

Conditions that create high-wind PM-10 exceedances vary from area to area and are 

based on soil condition (as influenced by precipitation) 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 crust that is resistant to erosion by wind. Human 

activities can remove or break this crust, allowing dust to become airborne. Even the sparse 

desert vegetation found in the valley acts as a windbreak and provides some protection to the soil 

surface. When human activities remove vegetation, 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 truthing to determine unique spectral signatures 

for land surface characteristics. Prior 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 characterize extent of vegetation (16 training 

sites) 

• Antelope Valley Blowsand Areas 

– Identify blowsand areas that drive wind erosion of abandoned farmland 

– Use Landsat data to map blowsand areas (8 training sites) 

The four main categories of land surfaces considered in this project are: (1) native desert, 

(2) disturbed and unstable vacant land, (3) disturbed and stabilized vacant land, and (4) private 

unpaved roads that have not been identified in existing Clark County data. Unpaved roads were 

identified through high-resolution aerial photography which proved to be more effective than 

satellite imagery. In addition, private unpaved average daily road traffic (ADT) was determined 

using traffic counters at selected locations. Both pneumatic and magnetic counters were utilized 

with data loggers. At each site, traffic counts 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 utilized a pilot study to assess all elements of 

data gathering and analysis before proceeding with the main study. 

• Pilot Study (two 100-km2 areas of HB 212) 

– Utilize limited study area to develop and test predictive algorithms derived from 

satellite imagery. 

– Identify training sites for specified land categories. 

– Develop ground truth training sites 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 contribution of the area outside the BLM disposal area to the 

overall emission inventory, emphasis was placed on the 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 BLM disposal area was therefore 

viewed as the key to developing an overall emission 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 the IKONOS or Quickbird satellites. Due to the 

many uncertainties that had to be resolved in obtaining imagery, evaluating various classification 

algorithms, and correlating imagery to actual conditions on the ground, it was decided to utilize a 

pilot study of relatively small areas before attempting to study the entire hydrographic basin 

which consists of 1,524 square miles. Another important factor was the high cost of high-spatial 

resolution imagery which ranges from $35-$40/km2

. 

During the evaluation of the first pilot area, 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 large pixel-to-pixel variation. 

Landsat, however, provided an inherent averaging of the minor variations in surface conditions 
1-5

across its 30-m pixel size. In addition, Landsat has six visible/near-IR spectral 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 imagery actually confounded the classification 

algorithms. It was then determined that the Landsat satellite (specifically Landsat 5 TM) would 

not only provide better classification for this project, but also provided imagery at a fraction of 

the cost. This will result in significant future savings for the County as the process is repeated. 

Any classification algorithm requires a suite of “training sites” which are representative 

of the particular soil stability categories being studied. The training sites were established 

through aerial photography and field evaluation, thereby ensuring that the training sites 

accurately represent the soil stability categories. Each site had to be uniform, accessible, and 

clean of debris. (Additional subcategories such as urban landscape, concrete, and wash areas 

were ultimately developed to increase the accuracy of the classification.) 

The ground truthing was initially performed as part of the Pilot Study focused on two 

100-km2

 pre-selected areas within HB 212 containing 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 used to develop the spectral signatures and others 

were used as verification sites for testing the reliability of the spectral signatures. These training 

sites were approximately 60 m x 60 m. The Pilot 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 evaluate and refine the ground truthing methodology and 

data collection forms, and understand the complexities of satellite imagery acquisition. The 

imagery was obtained when there had not been a rain event within the past 7 days. Ground 

truthing was completed within 48 hours of imagery 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 completed, the most recent Landsat satellite 

imagery was purchased. This set of satellite imagery was processed to complete the inventory of 

vacant land. Twenty-four additional 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 and the different wind 

erodibility groups (WEG). The soil groups are based on a set of parameters collected during a 
1-6

Figure 1-2. Proposed Test Site Areas Based Upon Wind Erodibility Groups 
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, and sand by a geotechnical laboratory 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 classified through the imagery. 

Selection of sites was based on Wind Erodibility Groups (WEG) consistent with Speck 

and McKay and the previous wind tunnel tests by Dr. James2 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 "wind-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 was also studied to determine chlorophyll 

concentrations during the course of the year. It was initially believed that chlorophyll might 

enhance satellite recognition of vegetation because of the distinct signature it provides relative to 

un-vegetated areas. In the end, no specific conclusions could be drawn regarding the optimum 

 

1

SIP June 2001 PM10 State Implementation Plan for Clark County, Appendix B - Emission Inventories, 

Methodology, Emission Factors, and Emission Estimates, Page B-37 Native Desert Fugitive Dust, and Appendix C 

– Section II, Estimation of Valley-Wide PM10 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 PM10 State Implementation Plan for Clark County, Appendix B - Emission Inventories, 

Methodology, Emission Factors, and Emission Estimates, Page B-37 Native Desert Fugitive Dust, and Appendix C 

– Section II, Estimation of Valley-Wide PM10 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 obtaining 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 opportunities for image acquisition. Figure 1-3 illustrates the relationship 

of the geometry of satellite imagery. 

Figure 1-3. Coverage Geometry of a Satellite 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 observations, and also from 

aerial photography and map data. An error matrix was developed to indicate the mapping 

accuracy of each soil stability category by showing the percentage of correctly classified pixels. 
2-1

2.0 TRAINING SITE SELECTION AND CHARACTERIZATION 

This section describes the selection and characterization of training and verification sites 

for the Pilot Study. Ground truthing tests document gradations of erodibility for the sites 

selected as representative for each specific category of land inventoried. Three different field 

test procedures were used to confirm the erodibility 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 composite category and multiple training sites were 

needed to fully capture this variability (vegetation and desert pavement). The 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 category 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 particular 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 obtained from the National Weather 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 natural mineralogical cementation, form a desert 

pavement. In any case, the soil is crusted so that it passes the ball drop test (i.e., the 
2-2

ball does not penetrate the crust and form a crater containing loose dust). Even if 

there are small soil areas that do not pass the 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). Finally, any pockets of loose soil are likely to pass 

the threshold friction velocity test (TFV equal 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 shadows of vegetative structures) during or 

after high wind events; such amounts, however, 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 unstable 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 unstable, the soil must fail all three tests 

conducted in sequence: the drop ball test, 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 natural 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 sufficiently 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 effectiveness 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 tree 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

YES NO YES NO Classify as:

Disturbed Vacant Land

Stabilized Vacant Land

Native Desert

Does the surface pass any of

the tests?

Surface stability tests

 - Drop ball

 - Rock

 - TFV
2-4

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 training sites. Windshield surveys were performed 

to locate potential training sites. Aerial photographs from the Clark County GIS files were also 

reviewed with the County to evaluate site locations. Clark County DAQEM representatives 

provided site suggestions and assistance in selecting the training sites. 

In addition to field observations, another resource used to select test sites was the soil 

classification schemes such as the USDA Soil Textural Triangles and the USDA Wind 

Erodibility Groups (WEG) used for the characterization 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 movement rather than PM-10 emissions. PM-10 

emission potential is more related to clay particles which have less mobility than the larger 

particles that make up sand and silt. 

The sites were located within the BLM disposal area (Figure 1-2) because of its 

dominance in the estimated wind generated emission inventory for Hydrographic Area 212. 

Most land outside the BLM Disposal area consists 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 the ground truthing sites relative to the soil textural 

triangle. 

Thirty locations with uniform surface characteristics were selected as training sites to 

determine their relative potential wind erodibility using the procedures outlined in Section 2.3. 

These test locations were used either for training 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 identified from photography representing 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 selected 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 Groups 
2-6

Figure 2-3. Proposed Test Site Areas for Pilot Study 
2-7

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., washes) 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 indicated a high degree of difficulty in locating 

disturbed unstable land. The selected test locations were used either 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 verification test locations were selected to 

represent surface uniformity over an area of at least 60 m x 60 m in size. Approximately 15 test 

areas were characterized for ground truthing. In the south study 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 hours following the generation of the images, as long 

as atmospheric conditions were acceptable. It was critical to minimize the time between imagery 

collection and ground truthing in order to minimize the likelihood of uncontrolled events 

changing the land surface. Such events included 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, visits 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 had 
2-8

become disturbed by human activity, we reclassified the site as disturbed unstable. Digital 

photographs were taken during ground truthing at each site to document soil condition, 

vegetative cover, and 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 surface fit the characteristics on only one of the 

three vacant land categories. In addition no debris from construction or other activities was 

present at a ground truthing site. Dry runs of the ground truthing procedures 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 June 16 and 17, 2005. Fifteen test sites were 

selected in the north pilot test area, and 12 sites were selected in the 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., native desert, disturbed stable, and disturbed 

unstable land). The pilot study was performed using the sites in the north pilot area. Due to 

weather delays (rain and wet ground), 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 classifications of these sites. 

2.2.2 Site Characterization Methods 

During ground truthing, three interior test areas measuring 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 test site should look like, and Figure 2-7 depicts an actual test site used in 

the study, with the 1-m2

 squares denoted in red.) Each 1-m2 square was characterized by using 

the following methods: 

The drop ball test, the rock test, and the threshold 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 failed. In addition, an enhanced version of the drop 

ball test using a five-point scale was used to better characterize the soil crust. 
2-9

Figure 2-4. Test Sites (Northern) 
2-10

Figure 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 Xhigh 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 Xlow 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 Xalmost 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 

saturated
2-12

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 1m2

60m

1m

30cm
2-13

Figure 2-7. Aerial Photo Showing One Training Site and Soil Test Locations 
2-14

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 the 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 characterize each training site. All field sheets 

completed during ground truthing are available in Appendix D and summarized in Table 2-2. 

Each ground truthing method is described below and included in Figure 2-6. 

Drop Ball Test 

In addition to the regulatory pass-fail drop ball 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 was 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, meaning the ball does not sink into the surface. 

This is represented by a penetration index of 4 or 5. 

Rock Test 

The rock test was used to indicate surface stability by estimating the rocks and other 

nonerodible elements on a given disturbed surface. Such elements provide a degree of wind 

resistance and decrease erodibility. 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 were selected in each group, and the actual 

length and width measured to determine an average 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 with 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 exists. 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 circular arm movements (10 clockwise and 10 

counterclockwise). Following the 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 Appendix 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 pan 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 recorded 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 also 
2-16

identified. This data was used for background information and possible insight into differences 

in imagery that could be caused by differing greenness of each species at various times of year. 

Digital photo documentation was taken at each site. 

A literature search was conducted on the subject 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 characterized as short cycles of greenness occurring 

shortly after rainfall. It is optimal to wait 5-7 days after a rainfall to avoid the effects of rain on 

soil conditions. 

A literature search was conducted on phenology, 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 classification (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 sampling to characterize the 

textural classification of the soils at the ground truthing sites 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, silt, 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 clay content of these 25 samples. The calculated WEG group for 

each soil shown in the “Lab WEG” column is based on the laboratory results and the application 

of the WEG Soil Triangle in the Uhl/MacDougall 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 agree (Sites 4 and 9 in the North area and Site 

7 in the South area). Also note that adjacent Sites 7 and 8 in the North area (the sites eliminated 

from ground truthing because of construction activity 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 

Sample Laboratory Soil Analysis, % 

Area 

Site 

No. 

Map 

WEG 

Lab 

WEG Sand Silt Clay 

Native 

Desert 

Disturbed 

Stabilized 

Disturbed

Unstable

North 1 4L 6 25.5% 56.7% 17.8% X 

North 2 3 3 62.2% 24.4% 13.4% X 

North 3 6 4L 52.3% 37.1% 10.6% X 

North 4 4L 2 54.2% 25.0% 20.8% X 

North 5 7 2 59.8% 14.1% 26.1% X 

North 6 7 5 24.5% 58.7% 16.8% X 

North 7 7 7 11.1% 79.7% 9.2% X 

North 8 7 4 28.5% 21.2% 50.3% X 

North 9 4 4L 47.0% 42.7% 10.3% X 

North 10 5 4L 40.6% 40.3% 19.1% X 

North 11 5 3 61.2% 25.4% 13.4% X 

North 12 4L 6 3.1% 69.4% 27.5% Xhigh veg 

North 13 4L 3 60.8% 28.3% 10.9% Xlow veg 

North 14 6 4L 51.1% 35.2% 13.7% X 

North 15 3 4L 48.6% 28.5% 22.9% Xalmost 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 description 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. The ground truthing for the North Pilot area was 

completed by end of day Friday, November 19, well within the 48-hour period allowed. Sites 7 

and 8 were not tested because of site preparation for construction activity. A water truck was 

continuously watering the area, and these two sites were wet with standing water in several 

places. 

Several sites in the South Pilot 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 and ground truthing in the South was halted. 

Based on the results of the pilot study of the north area and comparison of the results of 

the space imagery analysis comparing IKONOS with 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 effect that was very effective in avoiding the need to subdivide 

the feature classes such as native vegetation. Section 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 every 16 days), 24 new ground truthing sites, including several in 

the disturbed unstable category, were selected with the assistance of DAQEM personnel. Table 

2-2 shows the ground truthing sites used for this study. Ground truthing was performed at 15 of 

these sites within 48 hours following each of the Landsat TM flyovers on June 27 and 28 and 

July 13, 2005. Based on the ground truthing 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 rains. 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 S 
TABLE 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 U 
3-1

3.0 SATELLITE IMAGERY SELECTION AND PROCESSING 

3.1 Remote Sensing Data Sources 

IKONOS satellite imagery of the north pilot area 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 homogeneous 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 County. 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 homogeneous native desert 

land outside the BLM disposal area. Based on Landsat’s longer 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 imagery would be used for the entire 

Hydrographic Area 212 including the BLM Disposal Area (see detailed 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 cloud 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 was much more useful and cost-effective than 

high spatial resolution imagery (IKONOS or QuickBird) that lacks the additional infrared 

wavelength bands. High spatial resolution imagery can pick up micro-features (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 land areas with micro-features that do not affect 

wind erodibility. 

The larger pixel size for Landsat TM provides an averaging effect that is very useful for 

characterizing the vast areas of native desert outside of the BLM disposal area. Because there is 

no advantage to distinguishing subcategories of native desert with varying amounts of 
3-2

vegetation, the averaging of reflectance across different vegetative densities greatly simplifies 

the process of identifying and mapping native vegetation. 

In addition, the extra IR wavelength bands available from Landsat TM have been 

demonstrated to be very useful in distinguishing senesced vegetation and soil moisture. Landsat 

TM in conjunction with QuickBird is being used effectively in evaluating vegetation and other 

dust control strategies at Owens Dry Lake in California. The other obvious advantage of Landsat 

TM imagery is the much lower cost, which makes 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 the 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. For this purpose, satellite imagery was collected 

using Landsat 7 TM and Quickbird to characterize 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 years, MRI has been performing research on the role of blowsand in 

driving wind-generated emissions from abandoned farmland. This has involved the use of a 

portable wind tunnel and the instrumentation of two large blowsand 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 IKONOS were targeted for characterizing the BLM 
3-3

Figure 3-1. Landsat 5 TM Coverage of Clark County 
3-4

disposal area, and Landsat for the remainder of Hydrographic Area 212. The 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 km2 200 km2 200 km2

Cost for Project $425.00 $7785.00 >$7785.00 

Cost per km2 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 QuickBird 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 are 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 effectiveness of Landsat imagery showed it to 

have many advantages over IKONOS or QuickBird imagery. The expanded wavelength 

components proved very useful in isolating distinctive fingerprints of the vacant land categories 

of interest. The response to vegetation was particularly important. An example is illustrated in 

Figure 3-2 (Band 7 is the 6th 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 anticipated that desert shrubs that stabilize soils 

and provide windbreaks might be difficult to characterize by satellite imagery because of their 
Figure 3-2. Reflectance Response Curves of Natural and Manmade Features Using Landsa

Wavelength (μm)

Reflectance

Band 5

Band 4

Bands 1-3

SagebrushDolomiteQuartzConcrete

Natural and Manmade Features in a Landsat TM Cont

3-5 
3-6

indistinct spectral signatures, mostly small size (< 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 problems were overcome with use of imagery from 

Landsat 5 TM. 

Another advantage was the ability to incorporate the entire study area in a single Landsat 

scene collected at one point in time. 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 ANALYSIS PROCEDURES 

The purpose of the Pilot Study was to establish firm relationships between the ground 

surface data (as described in Section 2) and remote satellite images of the same area. Landsat 

TM satellite imagery was used to remotely characterize ground surface types for wind 

erodibility. The satellite imagery was provided as GEOTIFF files. 

Satellite images were obtained within 2 days of the verified field data collection 

described in Section 2. The BLM Disposal Area was an important focus of the imagery in order 

to ensure the identification of the vacant land scattered throughout the urban area. This area is 

also the highest contributor to the overall emission inventory 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 Vegas 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 areas to add the surface drainage category to 

the imagery analysis. Urban vegetation was also 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 imagery using Mahalanobis Distance classification. 

Unpaved road data from the various municipalities and the spring 2005 aerial 

photography of the Las Vegas Valley were provided by the County Geographic Information 

System Management Office (GISMO). The delineation of the unpaved roads was initiated using 

the available unpaved road data 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 feet wide (observed roads of this type ranged 

from 6 to 12 feet wide). Many of these roads are rarely traveled. In other cases, the GIS data 
4-2

Figure 4-1.Visible Spectra from Landsat TM Imagery 
Figure 4-2. Mahalanobis Supervised Classification 

Urban Native Desert Disturbed Stable Barren/Sh

Vegetation Surface Drainage Concrete Unclassifie

4-3
4-4

showed unpaved roads that no longer existed. These unpaved roads were shown in heavily 

developed areas where all the streets were paved. These 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. 

The input and output files from the classification process are raster 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 Environmental 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 supervised classification techniques. An analyst 

using aerial photography mapped the fourth category, unpaved roads. 

The disturbed unstable land category was problematic because of a lack of training sites 

(reference sites) to use for the classification process. For Landsat TM, a training site must be at 

least 210 square meters (210 x 210 m), so that no pixel overlaps into another land classification. 

In the disturbed unstable category, however, only one training site was large enough to meet this 

criterion. The results were inconsistent with this one training site 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 remaining 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, urban areas, urban vegetation, and barren/shadow 

were included in the supervised classification procedure. The last category, 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 pixels into a finite number of 

individual classes, or categories 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 supervised training. In this process, image pixels were 

selected that represent the land cover 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 truth sites, the computer algorithim was 

trained (calibrated) to identify pixels with similar characteristics. Based on the classification 

accuracy, the resulting classes represent 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 they 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 area, 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 independently. The measurement vector for each pixel was 

compared to each signature, according to a decision 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-versa. For example, when classifying urban areas, 

typically a class whose pixels vary widely, correctly classified pixels may be farther from the 

mean than those of a class for water, which is usually 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 subareas of distinct appearance (“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 reliability of the land 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 study area and develop the inventory for 

erodible lands. 

10. Perform final field verification of the inventory. 

4.5 Special Challenges 

In the course of the project, it was found that areas of disturbed unstable lands were 

widely scattered and small in size in comparison 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 disturbed areas. In addition, there was wide use of 

intensive watering on construction sites to comply with permit conditions. 

The few disturbed unstable areas that were identified as marginally suitable for ground 

truthing tended to exhibit differing 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 relatively independent of the parent soil type beneath 

the crust. In addition a high wind event during a dry period typically produces a large area of 
4-7

deposition as the well-mixed atmospheric particulate loading settles to the ground at the end of 

the wind event. 

 Consequently, there were insufficient, unstable 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 identified land area types from the 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 insignificant 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 data 

with IKONOS imagery for quality assurance crosschecking. 

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

4.6 Accuracy Assessment 

Accuracy assessment is a general term for comparing the classification to geographical 

data that are assumed to be true in order to determine 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 categories 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 reference pixels were used. Reference pixels are points on the 

classified image for which actual data was (or will be) known. The reference pixels were 

initially randomly selected, and then accepted 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 the 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 could lessen or eliminate the possibility of bias. 

The number of reference pixels was an important factor in determining the accuracy of 

the classification. It has been shown that approximately 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 satellite (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 problems 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 misregistered. Since the tie point collection 

and warping process were very time consuming, a decision was made to reposition the training 

sites so that they still represented the correct land cover type. Some training sites, such as native 

desert, were not repositioned because the land cover category was much larger than the Landsat 

5 TM registration error. 

5.2 Mapping of Land Categories 

As described above, it was not feasible to classify the disturbed 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 derived by inference; when all other land cover types 

(native desert, urban, etc.) were classified, then the remaining unclassified pixels represent an 

upper bound on the amount of disturbed vacant land. Figures 5-1a and 5-1b show comparisons 

of high resolution aerial photography with Mahalanobis supervised classification of example 

subareas within HB 212. Table 5-1 shows the classification results, 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 land that is available for purchase, trade, or 
5-2

Figure 5-1a. Mahalanobis Supervised Classification 
5-3

Figure 5-1b. Mahalanobis Supervised Classification 
5-4

TABLE 5-1. AREA OF LAND CATEGORIES 

 Hydrographic Region 212, BLM Only, 

Land Category km2 % of area km2 % 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 BLM disposal area contains nearly all of the 

anthropogenic sources within the non-attainment area, and this area was used for attainment 

demonstration in the PM10 State Implementation Plan for Clark County (June 2001). 

One issue with the supervised classification was the orientation 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, the runway in Figure 5-3 was not classified as concrete for two reasons. 

First, much of the runway was covered with a layer 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 produced the results that most pixels 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 the 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 relative 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. The 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 samples assigned to a particular category in another 
5-5

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 representations of map accuracy because the individual 

accuracies of each map category are plainly described along with both the errors of inclusion 

(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. 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 samples) 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 involved 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 classification accuracy. For example, producer’s accuracy 

was calculated by dividing the total number of correct sample units 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 correct pixels in a given category by the total number 

of pixels classified as the category. 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

5-6 
5-7 

Figure 5-3. Non-Concrete Runway at McCarran International Airport 
5-8 

Figure 5-4. Concrete Runway at McCarran International Airport 
Figure 5-5a. Land Classification at Nellis Air Force Base 

5-9 
5-10 

Figure 5-5b. Runway at Nellis Air Force Base 
Figure 5-6. Native Desert 

5-11 
Figure 5-7. Natural Drainage 

5-12 
TABLE 5-2. ERROR MATRIX 

5-13 
TABLE 5-3. ACCURACY ASSESSMENT 

5-14 
6-1 

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 unpaved 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 defined 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 subtracted along with public 

roads. Stretches of non-maintained roads were considered 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 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 feet wide (observed roads of this type ranged from 6 to 12 

feet wide). Many of these roads are rarely traveled. In other cases, the GIS data showed 

unpaved roads that no longer existed. These unpaved roads were shown in heavily developed 
6-2 

areas where all the streets were paved. These 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 appeared 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 roads 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 locations of these unpaved roads. 

Appendix B shows maps with the detailed locations. 

6.3 Conducting the Traffic Counts on Private Unpaved Roads

 Vehicle count data for 72-hr periods was obtained using both eight pneumatic counters 

which are rubber tubes positioned across the road, and three magnetic sensor counters which 

respond to the metal in a vehicle. Eight Jamar 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 heavy vehicles rolling over rough ground, gravel, or 

sharp stones, Jamar RoadRAMP Ruraltm 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 also 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 representative private unpaved roads. 

The traffic counts were performed over a period 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 Blvd 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 St 
6-4 

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 & Moonlight

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 

Road 
6-5 

Figure 6-1. Unpaved Road Traffic Count Sites 
6-6 

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 practical to develop detailed traffic-type information. 

The traffic count data from the TRAX 1 Plus and Trafx traffic counters 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 location (GPS coordinates) 

C Date and start/stop times 

C Individual person developing traffic count data 

C Road width and condition (e.g., 12-ft width with 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., construction site, mining area, open desert) 

C Likely purpose of road (e.g., recreational traffic; construction traffic) 

C Traffic count between start and stop times, and translated to ADT 

C Comments (e.g., vehicle types using road, developing or undeveloped area, speed limit 

if available) 

C Photographs of road area where counter is located 
7-1

7.0 RESULTS OF PRIVATE UNPAVED ROAD ANALYSIS 

7.1 Identified Roads and Areas of Concentration 

A GIS layer of private unpaved roads was produced for this project. The GIS layer was 

developed in conjunction with GIS layers (provided by Sharon Rice of GISMO) that showed the 

location of a subset of private unpaved roads. Thus, the GIS layer produced through this project 

was only for previously unmapped private unpaved roads. 

The mapping of private unpaved roads was done with the 2-foot pixel aerial photography 

from the GISMO archive. The roads were mapped by visually examining the digital aerial 

photography (outside the boundaries of Las Vegas, North Las Vegas, 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 width 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 presented in Table 7-1. For those roads using both 

the Jamar and Trafx counters, the data from the Jamar counters was used. 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 photography analysis. The red lines 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 and 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 the 
7-2

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 

Site Traffic Count Data 

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) 73.2 
7-4

Figure 7-1. Overview of 160 Miles of Private Unpaved Roads 
Figure 7-2. Example of Unpaved Roads Identified by Aerial Photography Analysis 

7-5 
8-1

8.0 CONCLUSIONS 

• The Clark County emission inventory calculation procedure requires an inventory of 

erodible land areas in Las Vegas Valley (Hydrographic Area 212) 

– Native desert (natural state) 

– Disturbed, unstabilized vacant land (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 indicate the susceptibility of surface soil 

to blowing (nine WEG values) are useful 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 effective approach to 

generate reference data for accuracy assessment. 

8.1 Vacant Lands 

Table 8-1 summarizes the land classifications for HB 212 derived 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 of 
8-2

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 the past given the qualitative methods available at that 

time. 

2. The success of the enforcement program requiring 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 obtained from UNLV wind tunnel tests was analyzed 

to investigate the dependence of the PM-10 flux 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 for either disturbed or 

undisturbed surfaces. The spike results also showed no observable correlation with WEG. 

It should be noted that the WEG classification of the wind tunnel test areas appears to be 

based on soil classification maps, and 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 classification was developed in an agricultural 

context and was designed to predict total soil erosion 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 types. This observation is supported by field 

tests using the MRI wind tunnel in California’s Antelope Valley (western Mojave Desert). 

8.3 Private Unpaved Roads 

A second phase of the project involved the identification of unpaved roads in HB 212 

which were not already in the Clark County GIS. Roads were identified through 2-foot pixel 

aerial photography supplemented by field surveys. Roads 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 perform 
8-3

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. A total of 160 miles 

of unpaved roads was identified. Excluding three 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 user’s accuracy for the disturbed 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 satisfy the definition of disturbed 

stable. Wash/natural drainage areas are disturbed 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 level of confidence in the classification process and the derived 

areas of the land cover types (Table 5-2). 

8.5 Protocol for Inventory Updating

The mathematical procedure that Clark County 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 vacant land, and disturbed unstable vacant land. Each 

of these categories is assigned a set of emission rates that depend on the wind speed, as 

developed from on-site wind tunnel tests. The extent of source measures utilized in these 

calculations is the areal extent of each land category. 

This project has generated procedures that can be replicated to develop updated land area 

values as development in the Las Vegas Valley continues. 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 provides not only the total area of each land category, 

but also the spatial distribution of each category 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 more 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 projects, and many such roads are paved as the 

area development continues. 

This project has provided a protocol for updating the source extent measures needed to 

develop future inventories of wind 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 factor equations contain adjustment factors for 

application to the desired size fractions. 
9-1

9.0 REFERENCES 

Congalton, R.G., and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: 

Principles and Practices, CRC Press. 

June 2001 PM10 State Implementation Plan for Clark County, Appendix B - Emission 

Inventories, Methodology, Emission Factors, and Emission Estimates, Page B-37 Native Desert 

Fugitive Dust, and Appendix C – Section II, Estimation of Valley-Wide PM10 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, September 12, 2000. 

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 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 Agriculture, Soil Conservation Service, Las Vegas. 

Swain, P.H., and S.M. Davis. 1978. Remote Sensing: The Quantitative Approach, New York: 

McGraw Hill Book Company. 
 

APPENDIX A 

GLOSSARY
 

GLOSSARY 

Airport Disposal Lands - That property owned and controlled by the Clark County Department 

of Aviation used primarily as a buffer for 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 coating 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 deflation 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 that the natural 

crust on the soil is destroyed. Disturbed 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 and geocoding information in a TIFF 6.0 

compliant raster file. 

Ground Truthing - Observations and testing performed at ground level to verify the properties 

and parameters of a given land area for 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 Imagery Transmission Format Standard 

(NITFS). NITFS is the standard for formatting digital imagery and imagery-related products and 

exchanging them among members of the Intelligence Community (IC), the Department of 

Defense (DOD), and other departments and agencies 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. Unpaved roads may be surfaced with native soil or 

with aggregate materials brought into the area for road construction. Pathways used as hiking 

trails and off-road vehicle recreation are not considered roadways. 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 maintained by a governmental authority such as 

Clark County, the cities of Clark 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 resist wind erosion. The 

more dust that is generated at a given wind speed, the more the soil is considered unstable. 

Stability is not specifically defined by a universally 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 that is used to define the relative 

stability (i.e., resistance to wind erosion) of various 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 when 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 specifying, to the computer algorithm, 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 signature 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 verification to develop an accuracy assessment of the 

classification procedure. 
 

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 SECTION 90 

CLARK COUNTY AIR QUALITY REGULATIONS
 

APPENDIX F 

CLARK COUNT AIR QUALITY REGULATION SECTION 91.4.1.2
 

APPENDIX 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