NASA/TM—2014–21857 

 

High Resolution Mapping of Soils and 

Landforms for the Desert Renewable Energy 

Conservation Plan (DRECP) 

Christopher S. Potter 

NASA Ames Research Center, Moffett Field, California 

Shuang Li 

California State University Monterey Bay, Seaside, California 

January 2014
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NASA/TM—2014–218357 

 

High Resolution Mapping of Soils and 

Landforms for the Desert Renewable Energy 

Conservation Plan (DRECP) 

Christopher S. Potter 

NASA Ames Research Center, Moffett Field, California 

Shuang Li 

California State University Monterey Bay, Seaside, California 

Prepared for 

The Renewable Energy Action Team 

U.S. Bureau of Land Management California Office 

National Aeronautics and 

Space Administration 

Ames Research Center 

Moffett Field, California 

January 2014
Acknowledgments 

The authors thank James Weigand, Tom Pogacnik, and Matthew Bobo of the U. S. Bureau 

of Land Management for technical guidance and support in the analysis of satellite imagery to 

monitor renewable energy development zones. 

Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
1

Acronyms Used 

IDL Interactive Data Language 

DEM Digital Elevation Model 

NED National Elevation Dataset 

NASA National Aeronautics and Space Administration 

NASA JPL NASA Jet Propulsion Laboratory 

ASF Alaska Satellite Facility 

Landsat 5 TM Landsat 5 Thematic Mapper 

NAIP National Agriculture Imagery Program 

DOQQs Digital Ortho Quarter Quad Tiles 

ALOS/PARSAR Advanced Land Observing Satellite / Phased Array L-band Synthetic 

Aperture Radar 

NLCD National Land Cover Database 

TIGER Topologically Integrated Geographic Encoding and Referencing 

SAR Synthetic Aperture Radar 

USGS United States Geological Survey 

USDA United States Department of Agriculture 

DN the quantized and calibrated Digital Numbers for each cell of satellite 

image 

FLAASH Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes 

CCM Compressed County Mosaics 

NDVI Normalized Difference Vegetation Index 

NIR band Near-infrared band 

R band Red band 

HH polarization Horizontal Transmit - Horizontal Receive Polarization 

HV polarization Horizontal Transmit - Vertical Receive Polarization 

BSCI Biological Soil Crusts Index 

BTLS Bi-temporal layer stack 

RGBC Red Green Blue Clustering 

COSICorr Co-registration of Optically Satellite Imagery and Correlation 
2

Glossary of Terms 

Interferometry A set of techniques that superimposes waves, usually electromagnetic 

waves, to extract information about the waves to measure small 

displacements, refractive index changes and surface irregularities 

Landsat Level 1T Standard Terrain Correction - provides systematic radiometric and 

geometric accuracy by incorporating ground control points while 

employing a DEM for topographic accuracy. Geodetic accuracy of the 

product depends on the accuracy of the ground control points and the 

resolution of the DEM used: Ground control points used for Level 1T 

correction come from the GLS2000 data set. DEM sources include SRTM, 

NED, CDED, DTED, and GTOPO 30. 

Reflectance The fraction of incident electromagnetic power that is reflected at an 

interface, in contrast to the reflection coefficient, which is the ratio of the 

reflected to incident electric field. 

Atmospheric The process of removing the effects of the atmosphere on the reflectance 

Corrections values of images taken by satellite or airborne sensors. 

HH polarization A mode of radar polarization where the microwaves of the electric field are 

oriented in the horizontal plane for both signal transmission and reception 

by means of a radar antenna. 

HV polarization A mode of radar polarization where the microwaves of the electric field are 

oriented in the horizontal plane for signal transmission, and where the 

vertically polarized electric field of the backscattered energy is received by 

the radar antenna. 

Terrain Correction The process to remove the geometric distortions from a radar image due 

to terrain relief. 

Incidence Angle The angle defined by the incident radar beam and the vertical (normal) to 

the intercepting surface. 

Histogram matching A method in image processing of color adjustment of two images using 

the data histograms. 
3

List of software tools used in this report 

Python 2.7 (Python Software Foundation, Download here) 

ArcGIS 10.1 (Environmental Systems Research Institute, Inc.) 

ENVI 5.0 (Exelis Visual Information Solutions, Inc.) 

ASF MapReady 3.1.22 (Alaska Satellite Facility, Download here) 

PolSARpro 4.2.0 (European Space Agency, Download here) 

Spectroradiometer data processing (GER 1500, Spectra Vista Corporation) 

List of remote sensing images used in this report 

DEM 10 m (from USGS) 

Landsat 5 TM (from USGS) 

NAIP DOQQs imagery (from USDA) 

ALOS/PALSAR (from Alaska Satellite Facility) 

List of auxiliary data 

National Land Cover Database 2006 (NLCD2006, Download here) 

Federal and State managed lands in California (Modified: 01/03/2014, Download here) 

TIGER/Line® All Roads County based Shapefile (2013, Download here) 

TIGER/Line® Linear Hydrography County based Shapefile (2013, Download here) 

All the Python/ArcPy scripts have been developed using Python 2.7, which couples to the 

spatial analysis model of ArcGIS 10/10.1. An IDL package from the NASA Jet Propulsion 

Laboratory has been used to investigate sand dune migration in Palen Dune area. This IDL 

package was developed for image analysis with the ENVI 4.x platform, but it works as well 

under the latest ENVI 5.x version. 
4

High Resolution Mapping of Soils and Landforms for the Desert 

Renewable Energy Conservation Plan (DRECP) 

Introduction

The Bureau of Land Management (BLM) is playing a key role in achieving national goals of 

generating more electricity from renewable energy sources. The BLM allows access to federal 

lands for renewable energy development, helping California and the rest of the country reach a 

secure and reliable energy future. The Desert Renewable Energy Conservation Plan (DRECP), 

a major component of California's renewable energy planning efforts, is intended to provide 

effective protection and conservation of desert ecosystems, while allowing for the sensible 

development of renewable energy projects. 

The DRECP was prepared through a collaborative effort between the California Energy 

Commission, California Department of Fish and Game, the BLM, and the U.S. Fish and Wildlife 

Service, also known as the Renewable Energy Action Team. The DRECP will result in efficient 

biological mitigation and conservation, providing renewable project developers with permit 

timing and cost certainty under the federal and California Endangered Species Acts, while at the 

same time preserving, restoring and enhancing natural communities. Approximately 22.5 

million acres (9.1 million hectares) of federal and non-federal southern California desert land are 

within the DRECP area. 

The California BLM is in the process of defining program plans for results-based adaptive 

biological mitigation and conservation related to renewable energy projects. From these 

program plans, developers of renewable projects will be provided greater certainty about 

required permits and permitting processes, time schedules in the planning and implementation 

phases, and costs for environmental compliance with the Federal and California Endangered 

Species Acts. Regulatory agencies and the general public will have greater assurance that 

natural communities and associated ecosystem functions will be preserved, restored, and 

enhanced for future generations. 

This NASA mapping report was developed to support the DRECP. We outline in this 

document remote sensing image processing methods to deliver new maps of biological soils 

crusts, sand dune movements, desert pavements, and sub-surface water sources across the 

DRECP area. We focused data processing first on the largely unmapped areas most likely to 

be used for energy developments, such as those within Renewable Energy Study Areas (RESA) 

and Solar Energy Zones (SEZs). We used imagery (multispectral and radar) mainly from the 

years 2009-2011. 
5

Figure 1.-- Area of desert features mapping with remote sensing. The DRECP boundary 

line is shown in blue and the outlines of available Landsat TM scenes is shown in dashed red 

lines. 

The geographic focus area for the DRECP includes parts of the Great Basin, Mojave, and 

western Sonoran (or Colorado) Deserts, as well as ecotones of these desert communities with 

the adjacent ecosystems in the Sierra Nevada, Tehachapi Mountains, Transverse Ranges 

(Western Transverse Ranges, San Gabriel, and San Bernardino mountains), and Peninsular 

Ranges (Baldwin et al., 2002; DRECP, 2010) (Figure 1). 

The geographic scope for this NASA mapping work was eastern Riverside County from 

Hayfield Dry Lake area east to the Colorado River with proposed renewable energy 

development areas, and Desert Conservation Lands under consideration as part of the DRECP. 

The mapping area totals to approximately 2.9 million acres (1.2 million hectares), or 12% of the 

total DRECP area (Figure 2). 
6

Figure 2.-- Landsat5 TM Mosaic Images of Colorado Desert. A true color image using TM 

bands 3(Red), 2(Green), and 1 (blue) (Images captured in Aug. 2011) 
7

Data Preparation for Desert Mapping 

Digital Elevation Models (DEM data) 

DEM data at 10-m spatial resolution were acquired from the National Elevation Dataset 

(NED) 1/3-arc-second dataset. NED is the primary elevation data product of the USGS 

(http://ned.usgs.gov). Slope (gradient, or rate of maximum change in z-value) from each cell of 

the DEM raster surface was calculated using the Spatial Analysis Model of ArcGIS 10.1 (Figure 

3). Only the regions with lower terrain fluctuation are considered possible locations to develop 

renewable energy facilities. We detected slopes less than 13 degrees as the natural break point 

to delineate these primary mapping areas (Figure 4). 

For each cell in the DEM layer, the ArcGIS slope tool calculates the maximum rate of 

change in value from that cell to its neighbors. Eight neighbor cells are used to determine the 

maximum change in elevation over a specified distance. The equation below shows how the 

rates of change (delta) of the surface in the horizontal (dz/dx) and vertical (dz/dy) directions 

from the center determine the slope. 

The value 57.29578 shown here is the truncated value of 180/. 

The slope layer was further processed to mask out all the cells with slopes greater than 13 

degrees and exclude them from further analysis (Figure 3). 

Figure 3.-- Slope Distribution across Colorado Desert 
8

Our rationale for this slope threshold selection of 13 degrees was as follows: 

According to the Executive Summary of the Solar Energy PEIS for BLM lands, 

"Applications may include some lands with up to 10% slope where higher slopes 

inclusions meet all of the following: (1) are proximate to variance lands in the application, 

(2) are not otherwise excluded from development, (3) allow for the avoidance or 

minimization of resource conflicts, and (4) do not create any significant new or additional 

conflicts. In such cases, a land use plan amendment would have to be adopted as part of 

the project-specific analysis to permit the slope exception." See: 

http://solareis.anl.gov/documents/fpeis/Solar_FPEIS_ExecutiveSummary.pdf

According to information in Table 4.1 – Summary of Key Solar Energy Technology 

Attributes in Solar Energy Development on DoD Installations in the Mojave and Colorado 

Deserts (January 2012) 

Anticipating that the technology for solar panels will advance quickly, so that a slightly 

broader range of undulating terrains will be suitable for use. 

Figure 4.-- Study areas with slope less than 13 degrees 

The study area was then further divided into sub-sections referred to in this study within 

Common Resource Area (CRA) maps (Figure 5). 
9

Figure 5.-- National Coordinated Common Resource Area geographic database for the 

DRECP area 

Landsat TM images 

The Landsat program has been providing uninterrupted imagery of Earth since 1972. 

Landsat 5 Thematic Mapper (TM) images were used in this project for mapping biological soil 

crust, desert pavements and varnishes, and active sand dune movements. The Landsat 5 TM 

image sets for the study area included path/row 038/036, 039/036, 040/036, 038/037, 039/037, 

and 040/037 (Figure 6). Images were selected according to the degree of atmospheric 

interference (primarily due to clouds). The sun elevation and sun azimuth were also considered 

as important factors for Landsat image selection (rationale are stated below). 

Vegetation cover, soil crusts, dune texture, morphological parameters and dune 

mineralogical composition are critical factors for desert mapping (Mohamed and Verstraeten, 

2012). Studies have shown that when the sun direction is perpendicular to a dune crest or at 

the stoss side and the sun elevation angle is greater than 30 degrees, the shading effect with 

regard to the dune profile is pronounced. Variations of sun elevation and sun azimuth of 

Landsat TM scenes were plotted in Figure 6. For southern California, it was concluded that the 

best time window for sand dune detection would be July and August. 
10

Figure 6.-- The sun elevation and sun azimuth of Landsat TM5 scenes (southern California 

coverage) 

As an example, Landsat images covering Palen Dunes were listed in Table 1. Sun azimuth 

in the area ranged from 116to 136, which is nearly perpendicular to the crest orientation of the 

dunes. Thus, important morphological features of the dunes may be identifiable using the 

spectral properties of the Landsat TM images. 

Table 1.-- Landsat 5 TM images for mapping of sand dune migration 

Acquisition Date Image ID Sun Azimuth Sun Elevation 

2000-09-16 LT50390372000260XXX02 136.69 50.88 

2001-08-02 LT50390372001214LGS01 114.80 60.38 

2002-08-21 LT50390372002233LGS01 122.13 56.24 

2003-08-08 LT50390372003220PAC04 116.35 58.96 

2004-08-26 LT50390372004239PAC01 127.90 56.73 

2005-08-13 LT50390372005225PAC01 122.49 60.04 

2006-08-16 LT50390372006228PAC01 125.84 60.52 

2007-08-19 LT50390372007231PAC01 127.26 59.97 

2008-08-05 LT50390372008218PAC01 118.12 61.01 

2009-08-08 LT50390372009220PAC01 120.46 61.16 

2010-08-11 LT50390372010223EDC00 122.19 60.88 

2011-08-14 LT50390372011226PAC01 123.08 60.13 

Landsat 5 TM scenes from the USGS EarthExplorer (http://earthexplorer.usgs.gov/) archive 

were processed with Standard Terrain Correction (Level 1T). Level 1T provides systematic 

radiometric and geometric accuracy by incorporating ground control points while employing a 

DEM for topographic accuracy. 

Image calibration, atmospheric correction, and scene mosaicing were applied to these 

Landsat scenes before further image transformation and analysis were conducted (Figure 7). 

We developed Interactive Data Language (IDL) scripts to process these Landsat scenes 

automatically. For calibration, we converted the 8-bit satellite-quantized digital numbers (DNs) 

into top-of-atmosphere (TOA) spectral reflectance using the radiometric gain and offset values 

associated with the Landsat TM image. For atmospheric correction, a Fast Line-of-sight 

Atmospheric Analysis of Spectral Hypercubes (FLAASH) module based on MODTRAN4 in the 

ENVI software package was applied (Adler-Golden et al., 1999). Atmospheric correction 

requirements were minimal for these Landsat scenes (Song et al., 2001), since aerosol 

contamination was extremely limited across south California desert in the summer months. 
11

Figure 7.-- The coverage of Landsat TM5 footprints for the study area (Images captured in 

Aug. 2011). 

Calibration 

Calibration attempts to compensate for radiometric errors from sensor defects, variations in 

scan angle, and system noise, resulted in images that represented true spectral radiance. ENVI 

5.0 reads parameters (e.g., gains, offsets, solar irradiance, sun elevation, and acquisition time) 

from the Landsat MTL file (Level-1 metadata). Reflectance was computed using the following 

equation: 

Where:

 
12



 (e.g. watts per square meter per steradian when the wavelength is given in um) 

d = Earth to sun distance, in astronomical unit (au, 1 au is equal to 149.6*106 km, or 

2.956*106 mi) 

ESUN= Solar irradiance in units of W/(m2*um) 

 = Sun elevation in degrees 

Atmospheric Corrections 

Atmospheric corrections included adjustments to account for Rayleigh scattering, gaseous 

absorption, and aerosol scattering in three visible channels (480 nm, 560 nm, and 660 nm), and 

the near-infrared (NIR) channel (830 nm). Atmospheric constituents such as water vapor and 

ozone are extracted from climatology data sets, while aerosol optical depths (AODs) are derived 

from the Landsat 5 TM scene itself by adopting the dark target approach. 

 The current FLAASH version includes the following features: 

1) Correction for the adjacency effect (pixel mixing due to scattering of surface-reflected 

radiance) 

2) An option to compute a scene-average visibility (aerosol/haze amount) for handling the 

presence of clouds and stress-inducing atmospheric conditions, such as cirrus and 

opaque cloud classification map, and adjustable spectral polishing for artifact 

suppression. 

FLAASH starts from a standard equation for spectral radiance at a sensor pixel, L, that 

applies to the solar wavelength range (thermal emission is neglected) and flat, Lambertian 

materials or their equivalents. The equation applied was as follows: 

Where: 

 = the pixel surface reflectance 

e = an average surface reflectance for the pixel and a surrounding region 

S = the spherical albedo of the atmosphere 

La = the radiance back scattered by the atmosphere 

A and B are coefficients that depend on atmospheric and geometric conditions, but not on 

surface conditions. 

NAIP Imagery 
13

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the 

agricultural growing seasons for the continental U.S. We collected three year (2005, 2009, and 

2010) of NAIP image data in Compressed County Mosaics (CCMs). CCMs are available for free 

download through the USDA Geospatial Data Gateway, http://datagateway.nrcs.usda.gov/ and 

provide high resolution (1-meter) seamless coverage images in Red, Green and Blue (or RGB) 

bands. 

We also ordered 2009 and 2012 NAIP digital ortho quarter quad tiles (DOQQs) across the 

study area. Each individual image tile covered a 3.75 x 3.75 minute quarter quadrangle plus a 

300-meter buffer on all four sides with 1-m resolution and 4 bands (RGB and Near Infrared). 

More than 2200 DOQQs image tiles in Geotiff were ordered from USDA - Aerial Photography 

Field Office (Figure 8) for the study area. The NAIP images were used in addition to Landsat 

imagery for mapping biological soil crusts and sand dune delineation. 

Figure 8.-- The coverage of NAIP DOQQs image tiles 

The Normalized Difference Vegetation Index (NDVI) used to map relative density of green 

vegetation cover. NDVI takes advantage of the contrast of the characteristics of two bands from 

multispectral remote sensing images: the chlorophyll pigment absorption in the red band and 

the high reflectivity of plant materials in the NIR band. 

The documented and default NDVI equation is: 

where NIR are pixel reflectance values from the near-infrared band and R values are the 

pixel reflectance values from the red band. 
14

The raster layer of NDVI had values between -1.0 and 1.0. In this study, any negative values 

are mainly generated from water, and values near zero are mainly generated from rock 

outcrops, sand sheets, and desert pavements. Moderate NDVI values (0.2 to 0.3) corresponded 

to desert shrub and grassland or cropland. 

To render the NDVI layer in ArcGIS with a specific color ramp, the equation used was as 

follows: 

 

A NDVI layer was created from NAIP imagery as an auxiliary mask map of 1-m resolution 

green vegetation cover (Figure 9). 
15

Figure 9.-- NAIP NDVI layers. (a)True color image, (b) NDVI layer, (c) Vegetation mask layer 

derived from NDVI. 
16

Radar (ALOS/PALSAR) data 

Spaceborne L-band SAR data from the ALOS/PALSAR sensor was acquired for the study 

area. We collected PALSAR data sets at 2008-4-14 with Fine Beam Single polarization (FBS, 

look angle 34.3, HH-polarization, and a 6.25m×6.25m ground resolution) and 2008-7-18 with 

Fine Beam Double Polarization (FBD, look angle 34.3, HH- and HV-polarization, ground 

resolution approximately 12.5m×12.5m). In FBS mode, ALOS/PALSAR can be operated with 

either HH- or VV-polarization with a bandwidth of 28 MHz. In FBD mode, the polarization 

options are HH/HV or VV/VH (HH/VV not possible) at 14-MHz bandwidth. FBS polarization (HH 

or VV) and FBD polarization (HH+HV or VV+VH) of PALSAR data have a varied look angle 

between 7° and 51° (8-60° incidence angle). The operating sensor frequency is 1.27GHZ, which 

corresponds to a wavelength of 23.6 cm (L-band). The Alaska Satellite Facility (ASF) provides 

range compression using Fast Fourier Transform (FFT), secondary range compression using 

range migration compensation, range migration curvature corrections, azimuth compression, 

multi-look processing and performed conversion from slant to ground range (PALSAR User’s 

Guide, 1st edition, March 2006). 

In order to eliminate the influence of (sub)surface soil moisture on radar signals, annual 

average temperature and precipitation records from the Thermal and Blythe weather stations 

were used to delimit the selection window of PALSAR images (Figure 10 & Figure 11, 

http://www.geog.ucsb.edu/~joel/g148_f09/, last accessed Feb 20, 2013). The average 

minimum precipitation time period across the study region was from April to June. Average 

temperature peaks appeared in July. PALSAR images captured within this time window (Apr - 

Jul) were collected for potential subsurface landform analysis (Figure 12). 

Figure 10.-- Thermal Temperature and Precipitation Salton Trough - Elevation = -34m 
17

Figure 11.-- Blythe Temperature and Precipitation - Elevation = 82m 

Figure 12.-- Footprint of PALSAR scenes in FBD mode 

L-band images from PALSAR were acquired for June/July 2010 (12.5m, HH and HV 

polarization) coinciding with dry season, and for November/December 2010 (12.5m, HH and HV 

polarization) coinciding with wet season (Table 2). 
18

Table 2. -- Listing of PALSAR imagery datasets collected. 

ID Name Observation Mode Polarization Capture Date 

1 ALPSRP231960640 FBD HH/HV 2010-06-02 

2 ALPSRP231960650 FBD HH/HV 2010-06-02 

3 ALPSRP231960660 FBD HH/HV 2010-06-02 

4 ALPSRP231960670 FBD HH/HV 2010-06-02 

5 ALPSRP234440640 FBD HH/HV 2010-06-19 

6 ALPSRP234440650 FBD HH/HV 2010-06-19 

7 ALPSRP234440660 FBD HH/HV 2010-06-19 

8 ALPSRP234440670 FBD HH/HV 2010-06-19 

9 ALPSRP236190640 FBD HH/HV 2010-07-01 

10 ALPSRP236190650 FBD HH/HV 2010-07-01 

11 ALPSRP236190660 FBD HH/HV 2010-07-01 

12 ALPSRP236190670 FBD HH/HV 2010-07-01 

13 ALPSRP236920640 FBD HH/HV 2010-07-06 

14 ALPSRP236920650 FBD HH/HV 2010-07-06 

15 ALPSRP236920660 FBD HH/HV 2010-07-06 

16 ALPSRP236920670 FBD HH/HV 2010-07-06 

17 ALPSRP254570640 FBD HH/HV 2010-11-04 

18 ALPSRP254570650 FBD HH/HV 2010-11-04 

19 ALPSRP254570660 FBD HH/HV 2010-11-04 

20 ALPSRP254570670 FBD HH/HV 2010-11-04 

21 ALPSRP257050640 FBD HH/HV 2010-11-21 

22 ALPSRP257050650 FBD HH/HV 2010-11-21 

23 ALPSRP257050660 FBD HH/HV 2010-11-21 

24 ALPSRP257050670 FBD HH/HV 2010-11-21 

25 ALPSRP258800640 FBD HH/HV 2010-12-03 

26 ALPSRP258800650 FBD HH/HV 2010-12-03 

27 ALPSRP258800660 FBD HH/HV 2010-12-03 

28 ALPSRP258800670 FBD HH/HV 2010-12-03 

SAR image processing steps 

MapReady software version 3.1.22 (http://www.asf.alaska.edu/downloads/software_tools), 

developed by ASF, has been used to process ALOS/PALSAR imagery. 

Step 1: Radiometric calibration 

Calibrating a SAR image involves the process of converting a linear amplitude image into a 

radiometrically calibrated power image. The input image is in units of digital numbers (DNs), 

where the output image is in units of , , or . Backscatter is the portion of the outgoing 

radar signal that the target redirects directly back towards the radar antenna. It is a measure of 

the reflective strength of a radar target (Figure 13). The normalized measure of the radar return 
19

from a distributed target is called the backscatter coefficient,  or sigma naught, and is defined 

per unit area on the ground. If the signal formed by backscatter is undesired, it is called clutter. 

Other portions of incident radar energy may be reflected and scattered away from the radar or 

absorbed. The radar backscatter coefficient  is the ratio of the power that comes back from a 

patch of ground to the power sent to the patch of ground, and serves as a quantitative reference 

measure to the ground. 

Figure 13.-- Geometric properties of radar signal 

 can be retrieved from SAR images by using the following equation: 



where DN = the digital number of the original pixel value 

Nr = the noise offset as a function of range 

a1 = the noise scale factor determined during processor calibration 

a2 = a linear conversion factor determined during processor calibration. 

The  value resulting from the equation above is in a power scale. Converting these values 

into decibel (dB) values uses the following relationship: 

Step 2: Terrain Correction 

SAR images are acquired with side-looking geometry. This format leads to a number of 

distortions in the imagery: foreshortening, layover, and shadow (Figure 14). Terrain correction 

removes these geometry-induced distortions by using a DEM. The process of terrain correction 

includes geometric terrain correction and radiometric terrain correction. Geometric terrain 

correction adjusts the individual pixels of an amplitude image to be in their proper locations. 

Radiometric terrain correction adjusts the brightness of the pixels with respect to the 
20

observation geometry, defined by the incidence angle as well as the slope and aspect of the 

local terrain. 

Figure 14.-- Schematic of how terrain can appear to lean left towards the satellite path with 

foreshortening, layover, and shadow effects 

Step 3: Geocoding 

The geocoding step is essential to correlate the SAR image geometry to the real world. By 

transforming the image from SAR geometry into one of the standard map projections, the SAR 

image can be imported into ArcGIS or ENVI for further analysis. The geocoding step is invoked 

by turning on the “Geocode” module from MapReady. A UTM projection was used in this study. 

For the PALSAR scene of the I-10 corridor, the parameters for UTM zone 11N and the WGS84 

datum have been used. As part of the transformation from SAR geometry into map-projected 

space, a bilinear method resampled the data through interpolation. Compared to nearestneighbor and bicubic approaches, the bilinear interpolation scheme considers four neighboring 

pixel values and typically leads to satisfactory results. 

Step 4: Speckle filtering 

SAR images were filtered to reduce the effect of speckle by utilizing a Kuan filter with a 3×3 

kernel in ENVI 5.0. The resultant imagery preserved the mean values, while decreasing the 

standard deviation of homogeneous targets, and visually preserving the feature edges. 
21

Desert Mapping Methodologies 

Ben-Dor el al (2009) presented an overview of the case studies that have used remote 

sensing technologies for desert mapping applications. After a review of more than 100 literature 

citations pertinent to the spectral characteristics of desert soils, we selected those 

methodologies most readily applicable to the DRECP areas in southern California. The 

algorithms adopted from these peer-reviewed methodologies have largely been field-validated 

for acceptable accuracy in the published studies cited below. 

Biological soil crusts 

Biological Soil Crusts (BSCs) consist of cyanobacteria, algae, microfungi, lichens and 

bryophytes in varying proportions, living within or immediately on top of the uppermost 

millimeters of soil. Over the past two decades, a number of research studies have been 

conducted to investigate the spectral characteristics of BSCs or its species components (Chen 

et al., 2005). Graetz and Gentle (1982) examined the relationship between reflectance in the 

Landsat wavebands and the composition of semi-arid shrub rangeland in Australia. Ager and 

Milton (1987) investigated the spectral reflectance of lichens and their effects on the reflectance 

of rock substrates. Jacobberger (1989) focused on the reflectance characteristics and surface 

processes in stabilized dune environments. Karnieli and his collaborators (Karnieli & Tsoar, 

1995; Pinker & Karnieli, 1995; Karnieli et al., 1996; Tsoar & Karnieli, 1996; Karnieli & Sarafis, 

1996; Karnieli et al., 1997; Karnieli et al., 1999; Karnieli et al., 2001) published a series of 

papers to explore the relationship between spectral characteristics and the structure and 

function of soil crusts in semi-arid and arid environments. 

The most popular vegetation index, the Normalized Difference Vegetation Index (NDVI), has 

been applied to capture the properties of BSCs during wet periods (Burgheimer et al., 2006; 

Karnieli et al., 1999). The Crust Index (CI) proposed by Karnieli et al. (1997, 1999) took 

advantage of the unique spectral phenomenon of BSCs. Not unlike NDVI, the CI was based on 

the normalized difference between the red and the blue spectral bands: 

Chen et al. (2005) developed an improved Biological Soil Crust Index (BSCI) to map soil 

crusts in desert area: 

The BSCI can be treated as an enhancement of spectral features of the biological crusts in 

green, red and NIR bands of Landsat TM images. It yields higher values for the presence BSCs, 

relative to the background of bare sand and dry plant material. We therefore selected the BSCI 

to map the BSC distribution across the study area of southern California. Compared to the CI, 

BSCI does not include a blue band, but is sensitive to lichen-dominated BSCs. The higher the 

lichen percent coverage, the higher the BSCI value would be expected. The parameter value of 
22

L and the threshold of BSCI (Equation 9 above) may be refined in the future, based on 

additional field-based spectral measurements of BSCs. 

Desert varnish and pavements 

Desert varnish is a hard, dark-brown or black coat, dull or lustrous (if wind polished) that 

accumulates on the exposed surfaces of rock fragments and outcrops. Its composition is 

independent of the composition of the host rock (Potter and Rossman, 1979). A desert 

pavement is a desert surface that is covered with closely packed, interlocking angular or 

rounded rock fragments of pebble and cobble size (Cooke and Warren, 1973; see Summary of 

Varnish and Summary of Desert Pavement). 

Beratan and Anderson (1998) developed an approach to map alluvial geomorphic surfaces 

in California. Visible bands and the NIR band of Landsat TM images were used to compose a 

newly developed spectral space, which provided information about the composition and 

topography of desert surfaces. This spectral space can readily be classified as young, 

intermediate, or old in terms of stages of development. This approach was successfully applied 

to mapping in the southern Whipple Mountains portion of the Mojave Desert. We applied the 

methods of Beratan and Anderson (1998) in this study. The image processing flowchart and 

definitions of surface classifications were provided in 

Figure 15 and Table 3. 

Table 3.-- Association of spectral categories with geomorphic surfaces definitions (after 

Beratan and Anderson, 1998) 

Image 

class 

Stage Description 

S1 1 Currently active drainage system. Pronounced bar-and-swale morphology. 

Sand, with scattered larger clasts. Minimal vegetation, concentrated along 

channel margins. 

S2 1 Abandoned drainages that have not been inactive long enough for soils, 

rock varnish or pavements to develop. Similar to S1, but with somewhat 

larger clasts. Vegetation along the margins. 

S3 1⁄2 Clasts dominate over sand, but clast-to-clast contacts are rare. Bar-andswale morphology less pronounced than in S1, S2. Rock varnish present 

as thin, discontinuous veneer. A small amount of scattered vegetation. 

S4 2 Well-developed rock varnish, pavements. Bar-and-swale morphology 

almost entirely subdued. Covered by interlocking mosaics of clasts. Some 

small cacti in spaces between clasts. 

S5 2 Similar to S4, but disrupted by gullies, with well-developed desert 

pavement preserved only on the ridges between gullies. 

S6 3 More heavily dissected than S5, with little preserved pavement. Clast 

mosaic disrupted, leaving a veneer of thickly varnished clasts. Scattered 

fragments of caliche. 

S7 3 Highly dissected, with no intact pavemented surface remaining. Typically 

sloping, forming banks of gullies and small washes. 
23

Figure 15.-- Image processing flowchart of desert pavements using the four-component 

technique 

Sand dune movements 

Through the 1980s and 1990s, studies of the geomorphology of desert sand dunes were 

dominated by field studies of wind flow and sand flow over individual dunes (Livingstone et al., 

2007). However, field-based approaches can rarely cover large areas of dune movements. 

Modeling methods have been shown to complement field-based approaches (Mohamed and 

Verstraeten, 2012). The combined use of remote sensing imagery, long-term GPS 

measurements, and GIS has enabled the mapping of large dune area movements. 

The research of Livingstone et al. (2007) and Mohamed and Verstraeten (2012) provided 

comprehensive reviews of recent progress in studies of desert dune movements (Table 4). The 

U.S. Geological Survey (USGS) scientists successfully conducted remote sensing research for 

monitoring of sand dune movement in the Sonoran Desert (Redsteer et al., 2011). The 

-Volcanic-dominated deposits 

-Plutonic-dominated deposits 

-Varnished surfaces 
24

comparison of GPS mapping results from these previous USGS studies in Arizona showed that 

optical remote sensing could provide a high level of accuracy for sand dune monitoring (see 

Appendix 1 of this report). Two latest published algorithms (from Mohamed and Verstraeten, 

2012; Hermas, et al., 2012) were selected for application to sand dune migration monitoring in 

this study. 

Table 4.-- Approaches used in geomorphological studies of desert sand dunes (Livingstone et 

al., 2007) 

Bi-Temporal Layer Stacking (BTLS) for multi-temporal Landsat TM images 

We have acquired 10 cloud free scenes each year over the Mojave Desert region and 

southern California for sand dune mapping. The RGB and NIR bands of TM images was used 

to determine the extent to which sand dunes have shifted over the past 20 years. This mapping 

can provide a baseline for years before and after the development of renewable energy 

infrastructure in the DRECP. 

Mohamed and Verstraeten (2012) presented a technique based on the analysis of multitemporal Landsat-TM images for studying the dynamics of different dune morphologies. The 

algorithm (RGB-clustering) was tested at (a) South-Rayan Dune-Field (SRDF), Egypt, (b) Namib 

Sand Sea (NSS), Namibia. (c) White Sand Dune Field (WSDF), USA, (d) Gran Desierto Sand 

Sea (GDSS), Mexico, (e) Megabarchanoid ridges, Al Ruba' Al khali (RKH), Saudi Arabia, and (f) 

Giant linear dunes, Al Ruba' Al Khali (RKH), Saudi Arabia. 

We first applied an automated procedure from Mohamed and Verstraeten (2012) in this 

work for a multi-temporal analysis of sand dune dynamics. The advantage of this methodology 

is that a variety of dune types, morphologies, and wind regimes can be mapped with high 

accuracy. This technique showed the good results with dunes developed under uni-directional 

wind pattern. It has proven easier to detect migration of simple dunes than in compound or 

complex dunes. 

FieldsurveyAttemptstomeasurethestressesimposedbythewindonthesandsurface

provedchallenging,andtheuseofshear(orfriction)velocityasasurrogate

forshearstressalsofailedtodelivereffectiveresults.

ModelingapproachesConsideringdesertsanddunesascomplexsystem,Werner(1995)modeled

thedevelopmentofdunefieldpatterns.KocurekandEwing(2005)described

nonlinearmodelingtoexaminesanddunesasselforganizedcomplex

systems.Hardwareinwindtunnelswasusedinthemodelingapproaches.

AerialphotographyHistoricalrecords

OpticalimageryAdvancedVeryHighResolutionRadiometer

(AVHRR),LandsatTMimagery,ASTER,SPOT,

Quickbird(MohamedandVerstraeten,2012)

Remotesensing

RadardetectionSpaceborneImagingRadar(SIRC),Radarsat,and

LIDARimages25

Figure 16.-- Processing scheme for sand dune movement mapping with Landsat TM5 

images 

COSI-Corr approach for high resolution aerial imagery 

The Co-registration of Optically Satellite Imagery and Correlation (COSI-Corr) (Leprince et 

al., 2007) is a potential alternative technique for the detection and measurement of sand dune 

migration. The technique has been developed to detect and measure the subtle crustal 

deformation in tectonically active areas with a high level of accuracy (Hermas et al., 2012). The 

COSI-Corr approach has been successfully applied for land surface processes such as glacier 

(Scherler et al., 2008) and dune movements (Vermeesch & Drake, 2008; Necsoiu et al., 2009; 

Hermas et al., 2012). In 2012, this technology was also used to map the dynamic of sand dune 

movement on Mars (Nature, http://www.caltech.edu/article/13511, last accessed Feb. 25, 2013). 

The COSI-Corr pre-processing steps involved ortho-rectification and co-registration 

processes of the pre- and post-event images. The correlation process produced a horizontal 

displacement image composed of three components: east-west, north-south, and signal-tonoise ratio (SNR) (Figure 17). An advantage of the COSI-Corr algorithms, unlike the traditional 

techniques using field measurements and remote sensing, is that its sub-pixel correlation 

techniques can provide measurements of sand dune movement over wide spatial coverage at 

high accuracy (Hermas et al., 2012). The spatial distribution, magnitude, and direction of sand 

dunes relative to other morphologic units can be well-identified using COSI-Corr algorithms. 

seriesMulti-temporal Landsat TM 

ti i

CalibrationRadiometric

lib i

Differencing

-Arithmetic subtraction 

-Highlighting invariant objects

-Comparing results before 

and after calibration 

RGBC

-32 partitions per cluster (max 

allowed) 

-Many iterations until the best results 

achieved 

-NIR4 (old image) in 

blueBTLS

-NIR4 (recent image) in RED 

-NIR4(old image) in Green 

-NIR4(old image) in blue 
26

Figure 17.-- Flow chart of COSI-Corr algorithm in producing displacement images from 

multi-temporal high resolution aerial images (from http://www.tectonics.caltech.edu, last 

accessed Feb. 25, 2013) 

Paleodrainage mapping 

Mccauley et al. (1982) first demonstrated the feasibility of subsurface moisture imaging with 

radar. Low frequency orbital Synthetic Aperture Radar (SAR) has the capability to probe the 

subsurface down to several meters in arid areas (Paillou et al., 2009). Previous studies have 

shown that L-band (25 cm or 1.2GHz) SAR is able to penetrate low electrical loss material such 

as sand (Mccauley et al., 1982; Elachi et al., 1984; Farr et al., 1986). Based on these 

methodologies, a PALSAR image processing flowchart was generated for this study (Figure 18). 
27

Figure 18. -- Flow chart of subsurface water mapping from ALOS-PALSAR 
28

Desert Map Product Results 

Biological Soil Crusts 

 BSCI maps were created for the entire DRECP study area. Figure 19 shows the 

distribution pattern of the BSCI in the Riverside East Solar Energy Zone. Four cover categories 

were included, based on the BSCI value of the pixels. Two categories of BSC cover were 

determined form the BSCI, one at >= 33% BSC cover, and the other more uncertain at < 33% 

BSC cover. In “Background” and “Gravel (desert pavement)” areas, the presence of BSC cover 

could not be detected. 

Figure 19.-- The pattern of the BSCI in Riverside East Solar Energy Zone (SEZ) 

Datasets listed below were used to mask out non-BSC features from the BSCI product: 

Non-BLM land derived from Land Ownership (updated 01/03/2014) 

Vegetation mask created from NDVI of high-resolution NAIP image. 

Terrain mask generated from the 10-m DEM. 

Buffered (100-m on each side) road and linear water features are from 2013 TIGER linear 

database 
29

Desert Pavements 

A desert pavement is a surface that is covered with closely packed, interlocking angular or 

rounded rock fragments of pebble and cobble size. Most pavements occur on alluvial fans or 

alluvial aprons. Desert varnish is visible across the northern Sonoran Desert. The varnish is a 

hard, dark-brown or black coating, either dull or lustrous (if wind polished), that accumulates on 

the exposed surfaces of rock fragments and outcrops. 

Vesicular horizons are found under the surfaces of many desert pavements ( Figure 20), 

their distinguishing characteristic being an abundance of unconnected spherical pores (Turk, 

2012). Vesicular pores are gas-bubbles that formed within the soil during periods of wetness. 

Moisture loss in arid soils can be rapid and subject to high evaporation rates. Pulled up through 

the soil profile via evaporation, these gas-bubbles become essentially “frozen” as the soils dries 

(Turk, 2012). 

Vesicular soil horizons are immediately apparent under the closely packed rock layer. The 

BSCI was used in this study to detect the distribution of vesicular horizons, defined also as 

Stage 2 desert pavement. A threshold value (5.4) of BSCI was defined to indicate the probable 

presence of vesicular horizons. Vesicular horizons detected along the Interstate I-10 highway 

corridor and across the northern Sonoran Desert were mapped in brown shades below ( 

Figure 21 and Figure 22). 

 Figure 21.--Vesicular horizons pattern along the I-10 

highway corridor Figure 20. -- Desert 

pavement with underlying
30

Figure 22. -- Vesicular soil horizons under Stage 2 desert pavements detected in Landsat 5 

TM images across the northwestern Sonoran Desert (Colorado Desert) 

Band ratios for desert pavement mapping were next calculated from Landsat 5 TM images. 

Before applying these image analysis steps, pixels with known ground features were masked 

out (e.g., vegetation, roads, rivers, human structures, and biological soil crusts; Figure 23). 

Figure 24 shows composite images with red, green, and blue assigned to the three designated 

band ratios. The RGB clustering approach was further used to divide the areas as Stages 1 

and 3 of desert pavement development. Stage 1 included young alluvial fan deposits with active 

channels and depositional fan surfaces. This stage of development is identified by the presence 

of distinct bar and swale topography. Stage 3 included old alluvial fan deposits that have been 

highly dissected, leaving virtually no trace of the original fan surface.
31

Figure 23.--True color Landsat 5 TM image ready for desert pavement mapping 

Figure 24.-- Sample of the band-ratio product for desert pavements (Riverside East Solar 

Energy Zone) 
32

Sand Dune Movements 

The near-infrared (NIR) band (0.76-0.90um) of the Landsat 5 TM images has been used to 

map sand dune migration (see Appendix 1 for field validation report). Two different approaches 

were applied: 

Approach 1 (BTLS and RGBC) 

Step 1: Extract Band 4 from each Landsat scene 

Step 2: Co-registration, histogram matching, histogram equalization, and normalization 

Step 3: Calculate difference between post- and pre-event of Band 4 layers. 

Step 4: Evaluate the effect of histogram matching, histogram equalization, and radiometric 

normalization by using image differencing. Spectrally stable or invariant objects were identified 

in the images to check pre-processing effects. 

Step 5: Layer stack recent and old Band 4 images into one ENVI DAT file for BTLS analysis. 

Step 6: Layer stack images in two specific time periods (post-, pre-event Band4 images) into 

one DAT file for RGB Classification. 

Step 7: Post-process (includes - identify clump, remove sieve (small clumps), and thematic 

pixel aggregation). 

Approach 2 (COSI-Corr) 

Step 1: Prepare the NIR scenes across the investigation time periods. 

Step 2: Correlate the pre-event ortho-rectified image (or master image) using the COSI-Corr 

Fourier correlation engine with the post-event ortho-rectified image. 

Step 2: Test correlation parameters with window size and shifting step size in image pixels. 

Step 3: Check the layer for signal-to-noise ratio to evaluate the first results. 

Step 4: Post-process the horizontal displacement layers and appraise the correlation quality 

of east-west displacement and north-south displacement. 

Figure 25.-- Palen Dune migration interpreted from Landsat 5 TM image (baseline 

boundaries of 1984) 
33

Figure 26.-- Palen Dune migration interpreted from Landsat 5 TM image (baseline 

boundaries of 2011) 

According to the results from BLTS and COSI-Corr approaches, the movement of sand at 

Palen Dune was delineated as vector layers. Polygon maps were produced that delineated the 

shift of the leading edges of Palen Dunes between 1984 and 2011 (Figure 25 and Figure 26, 

respectively). The results from this mapping showed that the rate of dune migration in active 

regions varied, mostly depending on the original land cover types. Before 1984, sand fields on 

Palen Dunes were separated by scattered bush and desert grass. Most active dune areas were 

covered by a thin sand sheet. Only the main dune fields were totally covered by deep sand. 

Dune coverage interpreted from 2011 Landsat 5 TM images showed that the two separate 

sand sheets had become connected and covered most of the areas that had merely scattered 

vegetation in 1984. We measured dune migration rates as high as 50 meters per year (1373 

meters in 27 years, 1984-2011), predominantly in the southern direction, depending on the 

locality. The overall size of active dune fields increased significantly from 1984-2011. Landsat 5 

TM image results showed that the Palen Dunes had grown in areal extent by 47% (from 31 km2

to 45 km2

) over the past three decades. Active dune areal extent expanded 60% (from 21.3 km2

to 34.12 km2

) over the same time period. The area covered by scattered bush decreased from 

9.2 km2

 to 7.5 km2. 
34

Suspected water sources under desert surfaces 

The results from PALSAR mapping of suspected water source identified numerous “hot 

spots” with soil moisture abnormalities or signs of a paleodrainage system for subsurface water 

from radar backscattering. A strong relationship between radar backscattering and soil moisture 

was demonstrated in Figure 27. 

Figure 27.-- Sensitivity of radar backscattering to soil roughness and moisture (after Morvan 

ea al., 2008) 

The following PALSAR analysis steps were followed: 

Step 1: A threshold value (greater than -15 dB) of the preprocessed radar backscatter 

signals was defined to detect soil moisture anomalies across the study area. Pixels of 

abnormally high soil moisture were exported into a new layer. 

Step 2: Single or scattered pixels were removed in the newly created soil moisture anomaly 

layer. Only clustered pixels were retained for further “hot spot” analysis. 
35

Step 3: Using visual inspection, pixel clusters with known ground features (i.e. high radar 

backscatter coefficient caused by terrain relief, transmission towers, communication towers, or 

single buildings) were eliminated from the anomaly layer. 

Step 4: All the suspected subsurface water sources were converted into point features. A 

total of 104 hot spot point locations were identified across the DRECP area (Figure 28). 

Figure 28.-- Suspected water sources (at blue point locations) from PALSAR image analysis 
36

Conclusions and Next Steps 

Methods of image processing were successfully implemented to deliver new maps of BSCs, 

desert pavements, sand dune movements, and suspected sub-surface water sources across 

the entire DRECP area. The peer-reviewed methodologies used to generate these new maps 

have been previously field-validated in the published literature for acceptable accuracy. 

Nevertheless, next steps for this study may be focused on additional field site visits and more 

extensive validation of these new map products, mainly in and around areas most likely to be 

used for renewable energy development. 

Close proximity to paved roads and relatively extensive area coverage of the surface 

features of interest constitute the two main criteria for selecting prime site locations for validation 

of the new map products. Several candidate locations meeting these criteria could be identified 

for BSC and desert pavements in the Riverside East SEZ (Figures 29 and 30, respectively). 

Close proximity to existing or planned renewable energy facilities was a secondary criteria used 

in selection of candidate locations for field validation. 

Figure 29. -- Candidate locations for field validation of BSCI mapping for the DRECP
37

Figure 30. -- Candidate locations for field validation of desert pavement mapping for the 

DRECP 

For the 104 suspected water sources identified from ALOS/PALSAR data, the distances 

between suspected water source points and a paved road were categorized in Table 5. The 

distances between suspected water source points and renewable energy facilities (solar and 

wind) were categorized in Table 6. We found that over 60 suspected water sources identified 

from ALOS/PALSAR data (see Figure 28) could be within relatively easy access (< 1km buffer 

zones) from paved roads, making these locations among the highest priority locations for new 

site visits. 
38

Table 5. -- Distances of the suspected water sources from paved road (US Census Bureau 

TIGER/Line, 2012) 

Buffer zone of 

paved roads 

1 km 2 km 5 km 10 km 

Number of water source points 61 94 103 104 

Table 6. -- Distances of the suspected water sources from a renewable energy facility 

(Renewable Energy Project Applications in California Modified: 01/24/2014) 

Buffer zone of 

renewable energy facility 

1 km 2 km 5 km 10 km Greater 

than 10 km 

Number of water source points 8 11 19 26 78 
39

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41

Appendix 1. Validation of Landsat Image Analysis for 

Mapping Sand Dune Movements

Background 

Landsat images have been used to study the mineralogy of sand dunes, dune 

morphological parameters, and dune migration rates in deserts around the world. The method 

of RGB-clustering with Landsat Thematic Mapper (TM) imagery (as reviewed by Mohamed and 

Verstraeten, 2012) is a technique to map the movement of sand dunes in arid regions from 

readily available satellite imagery. In order to apply this Landsat RGB-clustering methodology 

with confidence over the southern California desert region, we have carried out validation using 

measurements and analysis results from USGS scientists who are monitoring sand dune 

movement on Navajo Nation lands of Arizona (Figure 1; Redsteer et al., 2011). This USGS 

research revealed the dynamic migration rate of the Grand Falls dune field from 1953 to 2010. 

Global positioning system (GPS) and ground-based LiDAR (light detection and ranging) 

measurements were used to monitor the migration rate of the sand dunes in Arizona. 

Figure 1.-- Location of Grand Falls dune movement study site (from Redsteer et al., 2011) 
42

Multi-temporal Landsat-TM imagery 

Landsat TM5 scenes were collected from the USGS EarthExplorer 

(http://earthexplorer.usgs.gov/) and processed with Standard Terrain Correction (Level 1T). 

These Landsat images (Table 1) were selected to correspond (as closely as possible) to the 

measurement periods of Grand Falls dune field studies by Redsteer et al. (2011). 

Table 1. -- Landsat TM5 images used for the mapping of sand dune migration

Acquisition Date Image ID Sun Azimuth Sun Elevation 

07/4/1984 LT50370351984186PAC00 108.82 60.96 

08/27/1992 LT50370351992240XXX02 125.63 51.71 

06/22/1997 LT50370351997173XXX02 108.75 62.04 

08/31/2005 LT50370352005243PAC01 135.78 54.75 

08/13/2010 LT50370352010225PAC01 128.08 59.26 

Description of the RGB-clustering Methodology 

Criteria used in Landsat image selection (Figure 2 and Table 1) 

Best available radiometric, geometric, and atmospheric quality; 

Bi-temporal images were acquired at the same time of the year; 

Sun direction of a TM scene was perpendicular to the dune crest or at the stoss side, and 

the sun elevation angle was greater than 30 degrees 

Figure 2.-- Diagram of stoss and lee sides of a sand dune (Source: University of WisconsinMilwaukee) 
43

Bi-temporal layerstack (BTLS) 

The approach of a Bi-Temporal Layer Stack (BTLS) has its origin from Write Function 

Memory Insertion (WFMI) methods, which was first presented by Jensen (1996). WFMI is a 

visual change-detection technique, where single bands from multi-temporal satellite images are 

displayed in specific colors (typically red, green and blue). Mohamed and Verstraeten (2012) 

applied WFMI to multi-temporal Landsat images to detect the dynamics of sand dune movement 

(see Figure 3). 

Figure 3.-- Illustration of the basic principle of bi-temporal layer stacking (BTLS) for detecting 

dune migration (after Mohamed and Verstraeten, 2012) 
44

RGB-clustering for multi-temporal Landsat TM images 

RGB-Clustering methodology was used to compress three bands of data into one and to 

perform simple unsupervised classification. The RGB clustering method is more specialized 

than the Self-Organizing Data Analysis Technique (ISODATA) method, since it applies to threeband, 8-bit data (ERDAS, 2011; see Figures 4 and 5). 

Figure 4.--Diagram of RGB clustering methodology 

Figure 5.--Processing scheme for sand dune mapping with Landsat TM5 images 

Multi-temporal Landsat TM time 

Radiometric 

Image Differencing

-Arithmetic subtraction 

-Highlighting invariant 

objects 

-Comparing results before 

and after calibrationImage 

RGBC

-32 partitions per cluster (max 

allowed) 

-Many iterations until the best 

results achieved 

BTLS

-NIR4 (recent image) in 

RED 

-NIR4 (old image) in Green 
45

Results

Using the BTLS approach with Landsat TM images, we estimated the migration rate and 

direction of the Grand Falls dune field from 1984 to 2010. The post-event NIR band was 

displayed in the red color to express the increase in reflectance (Figure 6). The pre-event NIR 

band was displayed in both the green and blue channels to represent the stoss side of the sand 

dune in a cyan color (i.e., a decrease in reflectance). 

Figure 6.-- BTLS map results of Grand Falls dune field on the Navajo Nation 

 By adjusting the bin numbers of RGB classes during clustering, the pixels representing 

sand movement were matched to Landsat BTLS maps. The new raster layer from RGBclustering was used to estimate the migration rates of dunes in the Grand Falls dune field from 

1992 through 2010. We noted that Landsat pixels located on the southwest corner of the dune 

field were misclassified as sand. This area is apparently covered by riparian or wetland 

vegetation. The RGB-clustering approach cannot correctly discriminate this land cover type 

from sand erosion and deposition areas. We would recommend that a vegetation/barren mask 

layer from the National Land Cover Dataset (NLDC) be applied before the RGB-clustering 

method is used on a larger desert region for dune migration mapping. 
46

In order to evaluate the results from BTLS and RGB-clustering, we created more than 1600 

dot pairs of vertices along the four leading dune edge lines (1992, 1997, 2005, and 2010) for 

comparison to measured migration distances. The leading edges from USGS measurements 

were considered the baselines in this dot pair Euclidian distance comparison with Landsatestimated leading edges (Figure 7). 

For the leading-edge polyline comparisons shown in Figure 7, the predominant SW-to-NE 

direction of sand dune movements was the same between the baseline USGS measurements 

and Landsat image results. The smallest average difference between dot pairs was found for 

the 2005 leading edge at less than 1-meter difference (Table 2). Among the other comparison 

years, the largest difference between dot pairs occurred in 2010 at an average of 50 m, which is 

still less than a 2-pixel width error in the 30-m resolution Landsat TM images. The leading edge 

for 1992 detected from Landsat imagery was the only edge lagging behind the leading edge 

delineated by USGS measurements, by an average of 46 m. 

Table 2.-- Leading edge differences between dot pairs of Landsat RGB-clustering and 

USGS measurements of sand dune migration 

 Dot Pairs Max (m) Min (m) Average (m) StdDev 

2010 517 151.80 -64.85 50.15 48.58 

2005 399 58.69 -174.64 0.08 34.68 

1997 388 131.70 -64.85 39.73 53.20 

1992 390 59.08 -294.76 -45.62 82.40 
47

Figure 7.-- Difference of sand dune migration-rate between RGB-clustering algorithm and 

USGS measurements of dune leading edges 

Comparisons of Landsat-estimated maximum dune migration rates in the Grand Falls dune 

field with measured rates from USGS showed that USGS-measured maximum migration rates 

were 20-60% lower than the Landsat-estimated distances during all periods (except the 1997-

2005 time period; Table 3). The closest match of maximum annual dune migration rates 

between the two methods were found for the 1997-2005 time period and over the entire 1992-

2010 period, at less than 15 m difference, which is less than one-half the width of a Landsat TM 

pixel. The overall difference between the Landsat image and USGS measurement results in 

maximum dune migration rates between 1992 and 2010 was about 145 meters, which is 

equivalent to five Landsat pixels, compared to an equivalent of nearly 25 pixel distances 

detected in this overall (18-year) maximum migration distance. 
48

Table 3.-- Comparison of maximum migration rates in the Grand Falls dune field

Distance between two leading 

edges (meters) Annual migration-rate (m/yr) Time period 

USGS Landsat USGS Landsat 

1992-1997 156 260 31.2 52.0 

1997-2005 355 249 44.4 31.1 

2005-2010 105 253 21.0 50.6 

1992-2010 total 616 762 34.2 42.3 

There are several possible explanations for the modest differences detected between 

Landsat image results and USGS measurements of sand due migration in the Grand Falls dune 

field (Figure 7). First, the exact dates of the leading edges delineated by USGS researchers 

were not known to us. One winter/spring windy season may cause the dune fields to migrate 

more than 30 m, and since most of the Landsat images used in this study were captured in the 

cloud-free summer period, there may have been mismatches in timing of the satellite images 

and USGS measurements. Next, since the spatial resolution of Landsat images is coarser than 

the 1-m resolution measurements surveyed by USGS investigators, an average error level of 

just under two 30-m Landsat pixels would not be unexpected. Finally, the 30-50 m level of 

spatial accuracy in dune migration rates from 1992 to 2010 may be accounted for by the 

unavoidable shading effects of the leading edges of sand dunes captured by Landsat images. 

Conclusions 

Landsat image analysis of the Grand Falls dune field resulted in accurate mapping of sand 

dune migration rates, compared to measurements surveyed by USGS investigators from 1992 

to 2010. The predominant direction of sand dune movements was correctly mapped at this site 

from the Landsat satellite imagery, as well as the migration distances of the leading edges of 

sand dunes over the entire 1992-2010 monitoring period. Application of dune migration rate 

results from Landsat images collected for this validation study can be extended over a large 

portion of the Navajo Nation in Arizona and eventually over much of sandy desert areas of the 

southwestern United States. 

Acknowledgements

The authors thank USGS scientists Margaret Hiza Redsteer, John Vogel, and Rian 

Bogle for sharing sand dune migration data sets used for this evaluation study. 

References 

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movement and growth on the Navajo Nation, southwestern United States: U.S. 
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