U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2020–5053

Using Remotely Sensed Data to Map Joshua Tree 

Distributions at Naval Air Weapons Station China Lake, 

California, 2018
Cover Photo. Joshua tree (Yucca brevifolia) photographed on Bureau of Land Management lands—

Coolgardie Mesa near the Naval Air Weapons Station China Lake, San Bernardino County, California. 

Photographed May 7, 2020, by Todd C. Esque, U.S. Geological Survey.
Using Remotely Sensed Data to Map 

Joshua Tree Distributions at Naval 

Air Weapons Station China Lake, 

California, 2018

By Todd C. Esque, Patrick E. Baird, Felicia C. Chen, David Housman, and 

J. Tom Holton

Scientific Investigations Report 2020–5053

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Esque, T.C., Baird, P.E., Chen, F.C., Housman, D., and Holton, J.T., 2020, Using remotely sensed data to map Joshua 

Tree distributions at Naval Air Weapons Station China Lake, California, 2018: U.S. Geological Survey Scientific 

Investigations Report 2020–5053, 13 p., https://doi.org/10.3133/sir20205053.

ISSN 2328-0328 (online)
iii

Acknowledgments

We thank Brandon Barr and Lisa Vanamburg—Natural Resources Specialists, Desert Integrated 

Products Team, Naval Facilities Engineering Command Southwest—for providing financial and 

logistical support, and manuscript review. Daniel J. Leavitt, Ph.D., and Cynthia Hopkins of Naval 

Air Weapons Station China Lake (NAWS–CL), contributed substantial time and effort ensuring 

we had safe access to remote sites at NAWS–CL, providing relevant background to the work, 

and reviewing this manuscript. Bill Linsdale safely guided us through NAWS–CL backcountry. 

We thank B. Gottsacker, A. McDonald, G. Olson, and J. Swart for assisting in remotely surveying 

more than 4,500 km2 of habitat for Joshua trees. This work was funded through the Department 

of the Navy, Military Interdepartmental Purchase Request N47609-17-MP-001XY.

v

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Methods...........................................................................................................................................................2

Study Area..............................................................................................................................................2

Error and Error Reduction ...................................................................................................................2

Model Comparison................................................................................................................................4

Use of Lidar to Detect Joshua Trees .................................................................................................5

Results .............................................................................................................................................................7

Descriptions of Joshua Tree Presence .............................................................................................7

Distributions in the North Range ........................................................................................................7

Distributions in the South Range ........................................................................................................7

Comparison Among Models ................................................................................................................9

Sources and Management of Error ...................................................................................................9

Cost.................................................................................................................................................................10

Discussion.....................................................................................................................................................10

Next Steps.....................................................................................................................................................11

References Cited..........................................................................................................................................11

Figures

1. Map of the Naval Air Weapons Station China Lake study area for determining 

the distribution of Joshua trees at 1-km2 resolution in 2018 .................................................3

2. Schematic view of the scanning technique used to determine presence or 

absence of Joshua trees at Naval Air Weapons Station China Lake in 2018 on 

1-km2 survey areas .......................................................................................................................4

3. Image depicting different scalar views used to determine presence/absence of 

Joshua trees in Google Earth Pro ..............................................................................................5

4. Image showing error analysis of Joshua tree distributions at Naval Air 

Weapons Station China Lake ......................................................................................................6

5. Image showing final Joshua tree distribution map at Naval Air Weapons 

Station China Lake in comparison with a recent previous distribution model ...................8

6. Image showing comparison of Joshua tree distributions using satellite images 

versus draft Joshua tree distributions derived from lidar data at Naval Air 

Weapons Station China Lake, 2018 ............................................................................................9

Tables

1. Results of Joshua tree presence and absence during mapping surveys across 

the range of the Naval Air Weapons Station China Lake in 2018 .........................................7
vi

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

U.S. customary units to International System of Units

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

Datum

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Abbreviations

AUC area under the curve

CHM Canopy Height Model

FINTC Fort Irwin National Training Center

lidar light detection and ranging

NAWS–CL Naval Air Weapons Station China Lake

SDM species distribution model

USFWS U.S. Fish and Wildlife Service

USGS U.S. Geological Survey

UTM Universal Transverse Mercator
Using Remotely Sensed Data to Map Joshua Tree 

Distributions at Naval Air Weapons Station China Lake, 

California, 2018

By Todd C. Esque,1 Patrick E. Baird,1 Felicia C. Chen,1 David Housman,1 and J. Tom Holton2

Abstract

Species distribution models (SDMs) that are derived 

through inference have been used to provide important 

insights toward species distributions. Their inferences can be 

robust in relation to known presences, but SDMs have error 

rates that cannot be quantified with certainty. For large plant 

species with unique signatures and in sparsely vegetated 

habitats, object-oriented satellite image interpretation provides 

a useful alternative to the more commonly used SDM 

approach. We tested visual image interpretation techniques in 

a pilot project to map the distribution of the Joshua tree (Yucca 

brevifolia), an arborescent succulent plant endemic to the 

Mojave Desert of North America. Naval Air Weapons Station 

China Lake (NAWS–CL) required assistance in mapping the 

distribution of Joshua trees across the 4,715 square kilometer 

(km2) military installation in support of their national security 

mission. Joshua trees were present on 1,307 1-km2 cells in 

the species distribution model, or 27.7 percent of the military 

installation. This increases the published range of Joshua trees 

at NAWS–CL by 90 percent and corrects for two stands of 

Joshua trees that were previously identified but do not exist. 

Remotely sensed satellite data in combination with ground 

surveys of Joshua trees produced a more accurate distribution 

map at a 1-kilometer resolution than did previous SDMs based 

on correlative modeling (area under the curve [AUC] 0.9064 

versus 0.5848, respectively). Ancillary comparison with light 

detection and ranging (lidar) data indicated that satellite and 

lidar data were equally successful with slightly different 

sources of error, but that using them in combination produced 

the best results.

1U.S. Geological Survey.

2Department of Defense.

Introduction

Accurate species distribution information is fundamental 

to (1) evaluating species’ distributional changes in response 

to habit disturbances and other environmental variation and 

(2) determining conservation status for conservation planning 

and resource management activities (Guisan and Thuiller, 

2005; Elith and Leathwick, 2009). Species distribution 

modeling (SDM) can be biased by the location and number 

of presence points available for modeling, while lacking 

absence data. Such biases may reduce SDM accuracy in areas 

with restricted access. Object-based image interpretation 

using remotely sensed data is a feasible alternative to SDM 

and is capable of producing more accurate maps in cases 

where large focal subjects with unique characteristics occur 

in open vegetation types. One such plant is the Joshua tree 

(Yucca brevifolia).

The Joshua tree is a slow-growing arborescent succulent 

in the Asparagaceae family (Angiosperm Phylogeny Group 

III, 2009; Chase and others, 2009), in a subgroup commonly 

known as yuccas. It is endemic to the Mojave Desert of 

the southwestern United States, at elevations ranging from 

700 to 2,100 meters (m) in California, Nevada, Utah, and 

Arizona (Rowlands, 1978). Joshua tree populations are at risk 

from wildfires associated with invasive grasses (Loik and 

others, 2000; DeFalco and others, 2010) and from climate 

change (Barrows and Murphy-Mariscal, 2012). Joshua tree 

recruitment is episodic and dependent on an array of favorable 

environmental conditions; thus, changes in climate that lead to 

less precipitation and higher temperatures could be detrimental 

to Joshua trees (Reynolds and others, 2012).
2 Using Remotely Sensed Data to Map Joshua Tree Distributions at NAWS China Lake

This study encompasses the Department of Defense, 

Naval Air Weapons Station China Lake (NAWS–CL), near 

Ridgecrest, San Bernardino County, California, United States 

of America. Habitat enhancement and the protection of native 

plant communities and the native wildlife (including any 

Federally listed species) that depend on these communities is 

an integral part of NAWS–CL’s natural resources ecosystem 

management strategy detailed in the installation’s Integrated 

Natural Resources Management Plan (Tierra Data, Inc., 

2014). Areas of dense Joshua tree woodland are identified 

as a sensitive habitat warranting special consideration in 

land-use planning.

Ground-based mapping of Joshua trees at NAWS–CL is 

challenging because of limited road access, the distribution 

of high security areas that are off-limits to non-essential 

personnel, and the presence of unexploded ordnance in 

backcountry areas. Because of restricted access, fewer species 

occurrence records are available from this and most other 

large military installations in the desert southwest, potentially 

reducing the accuracy of species distribution models within 

these areas. Therefore, we developed a cost-effective 

remote sensing technique using publicly accessible satellite 

imagery—Google Earth Pro. This technique was of sufficient 

resolution to plan for future demographic surveys and was not 

hampered by access constraints. We used a combination of 

object-oriented analyses in three stages: (1) reviewing satellite 

imagery; (2) conducting ground surveys to validate remotely 

sensed results; and (3) conducting subsequent inspection of 

satellite imagery using the combination of previous satellite 

imagery analysis, ground surveys, and a small sample of 

light detection and ranging (lidar) data provided to us by the 

Department of Defense, Fort Irwin National Training Center.

The objective for this study was to conduct Joshua tree 

distribution mapping as a baseline to establish a statistically 

rigorous sampling protocol toward determining the status 

of Joshua tree populations at NAWS–CL. The results of this 

study are important to determine current species status and to 

establish a baseline for monitoring future trends in Joshua tree 

populations. The success of this study will determine potential 

future uses of the technology toward a rangewide distribution 

map for Joshua trees.

Methods

Study Area

The study area encompasses the entirety of NAWS–CL 

with up to a 1-kilometer (km) buffer outside the boundary 

and covers 4,715 square kilometers (km2) of desert basin and 

range habitat (fig. 1). Elevation ranges from 448 m in the 

Panamint Valley to 2,695 m at the summit of Maturango Peak 

in the Argus Range.

Publicly available Google Earth satellite imagery was 

used exclusively to create a provisional map of Joshua tree 

distributions prior to ground truthing the data on NAWS–CL at 

a 1-km2 resolution. To conduct the survey, the Fishnet toolbox 

in ArcMap 10.5 was used to create a grid of 4,715 1-km2 cells 

that covered the entire NAWS–CL facility, which consists 

of a North Range and a South Range (fig. 1). The grid was 

compatible with the global Universal Transverse Mercator 

(UTM) coordinate system. Cells inside and intersecting 

the boundary of the installation were visually inspected in 

satellite imagery for the presence or absence of adult Joshua 

trees at a standardized eye elevation of 150–300 m for initial 

surveys. The eye elevation (the altitude above the land surface 

provided to the user as a view of the scene in the imagery) 

that was used provided a view of each cell with sufficient 

resolution to perform a thorough survey of each cell by 

scanning systematically from the northwest corner along the 

northern boundary and following around the perimeter of 

the cell, then placing the scanning window in the northwest 

corner and moving diagonally to the southeast corner, and 

likewise placing the scanning window in the southwest corner 

and moving to the northeast corner to complete the scan 

with 100 percent coverage (fig. 2). Satellite imagery survey 

was limited to adult Joshua trees because their branches 

are easily recognizable in medium to high quality satellite 

imagery (fig. 3), especially where other large yucca species 

such as Yucca schidigera are absent. By contrast, single stem 

Joshua trees are difficult or impossible to distinguish from 

shrubs, trees (pinyon pines and junipers—Pinus spp. and 

Juniperus spp., respectively), or other yucca species. Areas 

that were difficult to search owing to variability of imagery 

quality sometimes required varying the eye elevation or using 

different time frames within the historical satellite imagery 

(from time frame including 2013 to 2017 in the North Range 

and only 2014 in the South Range because of poor image 

quality in years prior to 2014), providing higher resolution or 

better contrast than more recent imagery.

A team of six biologists inspected a total of 4,979 1-km2

cells. Each observer inspected 830 cells. To calculate 

observational error, 264 cells (5.6 percent of all cells) were 

inspected separately by different observers. Visual scans 

of each cell were targeted as 2-minute time intervals to 

standardize the work and maintain cost effectiveness. If no 

Joshua trees were identified within the allotted time, then 

the cell was designated as an absent cell. Observers denoted 

whether Joshua trees were present or absent in each cell 

by placing waypoints, either at the location of an identified 

Joshua tree for a present cell or near the cell center for an 

absent cell (fig. 4).

Error and Error Reduction

Field visits to NAWS–CL were conducted to validate and 

correct the remotely sensed results. This activity was limited 

by road and hiking access because of base restrictions for 
Methods 3

118° 117°30' 117°

36°

35°30'

SAN BERNARDINO

COUNTY

INYO COUNTY

KERN COUNTY

SAN BERNARDINO

COUNTY

P a n a m i n t Va l l e y

Slocum

Mountain

Maturango 

Peak

Coso Range

Wild Horse Mesa

Black Hills

Pilot

Knob

Granite

Mountain

G r a s s

Va l l e y

Straw 

Peak

Brown

Mountain

Quail Mountains

Ridgecrest

Parrot 

Point

A r g u s R a n g e

Coso

Peak

Superior

Valley

Fort Irwin

National

Training

Center

NAWS China Lake

North Range

NAWS China Lake

South Range

Naval Air Weapons Station (NAWS) China Lake boundary Joshua tree presence Joshua tree absence

0 5 10 20 MILES

0 5 10

15

15 20 KILOMETERS

CALIFORNIA

ARIZONA

NEVADA UTAH

Mojave 

Desert

NAWS

China

Lake

Base modified from U.S. Geological Survey and other Federal digital datasets, various scales; 

Universal Transverse Mercator projection, zone 11; North American Datum of 1983

EXPLANATION

178

395

395

14

178

Figure 1. Naval Air Weapons Station China Lake, California, study area for determining the distribution of Joshua trees at 

1-square-kilometer (km2) resolution in 2018.
4 Using Remotely Sensed Data to Map Joshua Tree Distributions at NAWS China Lake

security and safety. We visited 256 random points along the 

access roads of NAWS–CL with binoculars, and each was no 

closer than 500 m from the next. In addition to the 256 sites 

that were randomly selected for inspection, we also evaluated 

an additional 315 cells that were intersected during our 

travels among the sites. All these data were used to evaluate 

errors. At each point, the compass bearings and distances 

to the nearest Joshua trees were recorded. For each cell that 

was driven through, regardless of containing random points, 

Joshua tree presence or absence within view of the road was 

noted. Special care was taken to note the location of any trees 

found in cells identified as absent during the satellite imagery 

inspection, and vice versa—the absences of trees in cells that 

had been identified as present were also recorded.

To further reduce error in the development of the Joshua 

tree distribution map, we prioritized field surveys for cells that 

were difficult to evaluate during satellite imagery inspections, 

thus having increased potential for error (fig. 4). Such areas 

included the edges of the Joshua tree stands, isolated cells that 

were designated with presence in otherwise large expanses of 

absence, cells with conflicting results identified by multiple 

observers, and cells that simply had poor imagery quality. 

We predicted that Joshua trees on the edges of stands would 

be more difficult to locate in satellite imagery than those in 

areas of dense Joshua tree presence or absence. To test this, we 

focused on areas that transitioned between Joshua tree absence 

and presence according to the provisional map. The results of 

field surveys were used to correct and validate the provisional 

map. The increased experience in interpreting remotely 

sensed data with field validation improved our ability to make 

additional changes to cell status (presence versus absence) 

by what we termed as informed inspection on the provisional 

map—subsequent to field validation. If a change of status was 

required based on the informed inspection, then it was termed 

an informed correction.

Informed corrections were performed using satellite 

imagery in cells suspected of being incorrect for any of 

the reasons mentioned above but where visitation was not 

possible during the field surveys because of distance from 

roads or safety or security restrictions. For absence cells 

that were adjacent to presence cells, we inspected the areas 

where presence was noted, and if we felt confident that we 

had previously identified Joshua trees correctly, we followed 

topographical and geological features (for example, unique 

soil texture visible in the image) from where trees were 

found into adjacent cells where Joshua trees were previously 

undetected. The waypoints created in the database by the 

original observer were inspected and either confirmed 

by the informed inspection or discarded as erroneous. A 

complete record for all cells was maintained and archived. All 

individual presence cells that were isolated by absence cells 

were reconsidered using informed inspection and corrected 

as necessary. Thus, the final Joshua tree map is the result of 

the initial satellite imagery survey that was reinforced by 

two additional phases of corrections: field validation and 

informed inspections of imagery in areas that were otherwise 

inaccessible owing to large distances from roads or mission 

safety related site access restrictions.

Model Comparison

The final distribution map that was created for this 

report was compared to currently used models of Joshua tree 

distributions for this region, including Rowlands, 1978; Cole 

and others, 2003; and Godsoe and others, 2008, to determine 

differences in the amount of Joshua habitat between this 

model and the others. Area under the curve (AUC) statistics 

were used to compare data from ground-truthed cells (total 

number = 571) with the data collected from satellite data 

versus the same cells from a recently published Joshua tree 

habitat map (Godsoe and others, 2009). We compared the 

remotely sensed model of Joshua tree distribution and a 

previously published SDM model of Joshua tree distribution 

Movement through raster pixels

Move 1

Move 2

Move 4

Move 3

Move 5

Move 6

Cell 1 Cell 2 Cell 3

Cell 4

Cell 7 Cell 6 Cell 5

Cell 8

Cell 9

Figure 2. Scanning technique used to determine presence 

or absence of Joshua trees at Naval Air Weapons Station 

China Lake, California, in 2018 on 1-square-kilometer (km2) 

survey areas. (1) The scanning view is moved eastward 

across the top of the cell to the northeast boundary of the 

cell; (2) then scanned southward to the southeast corner; 

(3) westward to the southwest corner; and (4) northward, 

back to the northwest corner. Next, (5) the scanning window 

is placed in the northwest corner and moved diagonally 

across the cell to the southeast corner and (6) finally placed 

in the southwest corner and shifted diagonally to the 

northeast corner to complete the coverage.
Methods 5

(Godsoe and others, 2009) with 571 ground-based validation 

sites of Joshua tree distribution using AUC statistics (Hanley 

and McNeil, 1982; Fielding and Bell, 1997).

Use of Lidar to Detect Joshua Trees

Performed concurrently but separately from this study, 

the neighboring Department of Defense, U.S. Army–Fort 

Irwin National Training Center (FINTC) conducted successful 

experiments to identify Joshua trees using lidar. This lidar 

database overlapped with small areas of NAWS–CL that 

coincided with the detailed ground surveys.

Fusion Version 3.6 (McGaughey, 2018) was used for lidar 

processing, and ArcMap 10.5 was used for final data scrubbing 

of lidar outputs to identify Joshua trees ≥12 feet tall within the 

FINTC and some areas in adjacent NAWS–CL. The Fusion 

software package developed by the U.S. Forest Service was 

used to create a Normalized Canopy Height Model (CHM) 

discussed in the Fusion manual (McGaughey, 2018, p. 29). A 

Normalized CHM creates a high-resolution raster to determine 

the height of objects above ground. This was based on the 

distance from the Earth’s surface to the top of the object 

(EarthDefine, 2016). All final shapefile outputs were inspected 

and cleaned manually to remove false identifications such 

as rock outcrops, Populus fremontii (cottonwood) trees, and 

man-made structures.

The following set of programs within Fusion were 

executed to determine Yucca brevifolia point locations and 

heights. The program GroundFilter was used to filter out 

all bare-earth points used to create the bare-earth model. 

These bare-earth points within each land analysis system 

(LAS) tile were used to create the bare-earth model with 

GridSurfaceCreate, which was used to normalize the CHM. 

CanopyModel was then used to generate the Normalized 

CHM from the lidar point cloud. A watershed algorithm called 

TreeSeg was then applied to the normalized CHM to generate 

basins to create local minima that represented the dominant 

clumps of trees, or in this case, individual Yucca brevifolia

(McGaughey, 2018, p. 143). The Integrate geoprocessing tool 

was also used in ArcMap 10.5 to clean up point locations 

that were identifying the same trees that were within 10 m of 

each other. All outputs were projected to WGS 1984 UTM 

Zone 11N, which was the original projection of the LAS files.

Approximately 1 square kilometer

Approximately 1 hectare

Joshua tree

Joshua tree

Figure 3. Scalar views used to determine presence/absence of Joshua trees in Google Earth Pro at Naval Air Weapons Station 

China Lake, California. The large square on the left is a 1-square-kilometer (km2) grid square that was used to identify search areas. 

Visual inspection of a focal area (for example, the small white square) of potential Joshua tree habitat at 1,600-meter eye altitude. 

Note two examples of adult Joshua trees in the view at this resolution are circled.
6 Using Remotely Sensed Data to Map Joshua Tree Distributions at NAWS China Lake

118° 117°30' 117°

36°

35°30'

Ridgecrest

Fort Irwin

National

Training

Center

NAWS China Lake

North Range

NAWS China Lake

South Range

P a n a m i n t Va l l e y

G r a s s

Va l l e y

Superior

Valley

0 5 10 20 MILES

0 5 10

15

15 20 KILOMETERS

Lidar correction

Naval Air Weapons Station (NAWS) China Lake boundary

Joshua tree presence

Joshua tree absence

Observer conflict

Field confirmation

Field correction

Informed correction

Base modified from U.S. Geological Survey and other Federal digital datasets, various scales; 

Universal Transverse Mercator projection, zone 11; North American Datum of 1983

EXPLANATION

CALIFORNIA

ARIZONA

NEVADA UTAH

Mojave 

Desert

NAWS

China

Lake

Map

area

178

395

395

14

178

Figure 4. Error analysis of Joshua tree distributions at Naval Air Weapons Station China Lake, California. Colors represent areas 

where the determination of presence (green areas) or absence (light gray areas within the military boundary) were changed based 

on field surveys (yellow areas), informed corrections (blue areas), or more recently acquired light detection and ranging (lidar) data 

(turquoise areas). Field surveys resulting in a change of plot status are the magenta areas.
Results 7

Results

Descriptions of Joshua Tree Presence

All 4,715 1-km2 cells within the boundaries of NAWS–

CL were evaluated. The final map product shows that 

27.7 percent (1,307 cells) of the naval installation is currently 

occupied by Joshua trees (table 1). Joshua trees are present 

on 39.4 percent of the North Range (1,029 of 2,612 cells) 

and cover only 13.2 percent of the South Range (278 of 

2,103 cells). The elevational range of Joshua trees across all 

of NAWS–CL is from 764 to 2,450 m (2,468 to 7,916 feet 

[ft]). The median elevation at which Joshua trees occur differs 

between North and South Ranges; the median elevation at 

which Joshua trees are present in the North Range is 1,726 m 

(5,576 ft), whereas Joshua trees grow at a median 1,192 m 

(3,851 ft) in the South Range. The overall elevation of South 

Range is lower than the North Range, with the highest point 

of the South Range only reaching 1,638 m. The southwestern 

foothill region of the Argus Mountain Range on the North 

Range is the lowest elevational area where Joshua trees were 

located (764 m; table 1).

Distributions in the North Range

In the North Range, there is one large nearly continuous 

population of Joshua trees covering 1,045 km2, occurring 

between 1,025 and 2,450 m above sea level in the vicinity 

of the southwestern foothills of the Argus Range and Parrot 

Point, respectively (fig. 5). Joshua trees cover most of the 

Argus and Coso Ranges except small areas at the highest 

elevations of Maturango and Coso Peaks. However, the map 

indicates Joshua tree occupancy of the grid sections containing 

these peaks because lower elevations areas within the 1 km2

are occupied by Joshua trees. Field surveys found Joshua trees 

intermixed in pinyon/juniper habitats at elevations as high 

as 2,450 m. It is possible that Joshua trees are present in low 

densities above the stated elevational maximum but could not 

be identified using satellite imagery and were inaccessible 

during field surveys. The southern edge of this population 

is delimited by steep south- and southwest-facing slopes 

of Wild Horse Mesa and the southeast facing slopes of the 

Coso Range. The northern, eastern, and western edges of this 

population continue beyond the boundaries of NAWS–CL 

but were not surveyed. A smaller population of Joshua trees 

covering 36 km2 is present between 764 and 1,192 m elevation 

in the far southwest foothills of the Argus Range. This 

population is only separated by a 2-km gap from the larger 

stand previously described. It is possible that Joshua trees 

are present in the cells between the two populations but were 

not identified in satellite imagery. Alternatively, it is possible 

that Joshua trees are unable to occupy the steep, rocky 

south-facing slope as it drops 400 m in elevation between the 

two populations.

Distributions in the South Range

The South Range of NAWS–CL contains six distinct 

populations of Joshua trees that range in size from 11 to 

83 km2 (fig. 6). The largest population occupies areas on 

and surrounding the Black Hills, Pilot Knob, and Granite 

Mountain, extending westward into Grass Valley and beyond 

the NAWS–CL boundary where it thins out and was not 

surveyed further. This population occupies an elevational 

range of 871 to 1,456 m. A second population occupies 

the northwest part of Slocum Mountain between 1,112 and 

1,239 m in elevation covering 11 km2 and is only separated 

from the Granite Mountain population by a narrow unnamed 

valley. Three of the South Range populations are restricted 

to high elevational areas: Straw Peak, Brown Mountain, and 

the Quail Mountains. The Straw Peak population covers 

28 km2, with an elevation range from 1,154 to 1,638 m and is 

completely contained within the boundaries of NAWS–CL. 

The Brown Mountain population covers 13 km2, ranges from 

1,141 to 1,511 m in elevation, and exists entirely within the 

boundary of NAWS–CL. The Quail Mountains population is 

the second largest, occupying 54 km2. This population extends 

east into the FINTC and was not surveyed via satellite or field 

past the boundary of NAWS–CL. The Quail Mountains and 

Brown Mountain populations are only separated by 1 km and 

may be a continuous population that remains unsubstantiated. 

The last population present in the South Range is in the 

southeasternmost corner of the NAWS–CL. This stand covers 

38 km2 and has the smallest Joshua tree elevational range, 927 

to 1,074 m. The population extends both south and east into 

the FINTC and was not surveyed beyond the boundaries of 

NAWS–CL. This population stands out as the only one that 

occupies entirely flat habitat in lower elevational terrain.

Table 1. Results of Joshua tree presence and absence during mapping surveys across the range of the Naval Air Weapons Station 

China Lake (NAWS–CL), California, in 2018.

NAWS–CL

ranges

Total number

cells

surveyed

Number of cells

Joshua tree

absence

Number of cells

Joshua tree

presence

Joshua tree

presence

as percentage

Both Ranges 4,715 3,408 1,307 27.7%

North Range 2,612 1,583 1,029 39.4%

South Range 2,103 1,825 278 13.2%
8 Using Remotely Sensed Data to Map Joshua Tree Distributions at NAWS China Lake

117°30' 117°

36°

35°30'

117°45' 117°15'

P a n a m i n t Va l l e y

G r a s s

Va l l e y

Ridgecrest

Superior

Valley

Fort Irwin

National

Training

Center

NAWS China Lake

North Range

NAWS China Lake

South Range

0 5 10 20 MILES

0 5 10

15

15 20 KILOMETERS

Naval Air Weapons Station (NAWS)

 China Lake boundary

Joshua tree presence

Joshua tree absence

Modeled Joshua tree range

Base modified from U.S. Geological Survey and other Federal digital

datasets, various scales; Universal Transverse Mercator projection, 

zone 11; North American Datum of 1983

EXPLANATION

CALIFORNIA

ARIZONA

NEVADA UTAH

Mojave 

Desert

NAWS

China

Lake

178

395

395

14

178

Figure 5. Final Joshua tree distribution map at Naval Air Weapons Station China Lake, California, in comparison with a previous 

distribution model (Godsoe and others, 2008). The dark green areas represent Joshua tree presence, and the light gray areas (within 

the military boundary) represent Joshua tree absence according to the present document. In contrast, the overlaid blue-gray pixelated 

areas represent Joshua tree presence, and all other areas within the military boundary, but without blue-gray pixelated patterns 

indicate Joshua tree absence according to Godsoe and others (2008).
Results 9

Comparison Among Models

Ground-based validation survey data for the presence/

absence of Joshua trees (N = 571 cells) were compared to the 

remotely sensed survey of Joshua trees and with a recently 

published SDM model (Godsoe and others, 2009) of Joshua 

tree distribution in the same 571 cells. The remotely sensed 

model had an AUC of 0.0964 with a point biserial correlation 

of 0.8456. The published SDM model did not perform as 

well, with an AUC of 0.5848 with a point biserial correlation 

of 0.3323.

Sources and Management of Error

There were 264 cells inspected by two different observers 

to quantify observer error in the satellite imagery survey. 

Observers only had differing results in 15 (5.7 percent) of the 

EXPLANATION

117°15' 117°00'

35°

45'

35°

30'

Base modified from U.S. Geological Survey and

other Federal digital datasets, various scales; 

Universal Transverse Mercator projection, zone 

11; North American Datum of 1983 CALIFORNIA

ARIZONA

NEVADA UTAH

Mojave 

Desert

NAWS

China

Lake

Map

area

A

A

B

B

Lidar identified object

Incorrect lidar result

Joshua tree presence

Joshua tree absence

Lidar informed correction

Naval Air Weapons Station (NAWS)

 China Lake boundary

0 5 10 MILES

0 5 10 KILOMETERS

178

Figure 6. Comparison of Joshua tree distributions using satellite images versus draft Joshua tree distributions derived from light 

detection and ranging (lidar) data at Naval Air Weapons Station China Lake, California, 2018.
10 Using Remotely Sensed Data to Map Joshua Tree Distributions at NAWS China Lake

cells checked for error. Of those, the correct classification 

for three cells was determined during field surveys, and the 

correct classification for the remaining 12 cells was made 

as a result of informed inspections of the satellite imagery 

because they were not accessible on the ground. All 15 of the 

cells with conflicting initial survey results had low Joshua 

tree presence. Error can also be quantified as the number of 

cells that changed from the original satellite imagery survey 

to the final map product. Cells were corrected either based 

on field observations or by an informed inspection of the 

satellite imagery (table 1). Field survey inspection covered 

571 cells, 528 of which were determined to have been 

correctly identified during the satellite imagery survey. The 

43 misidentified cells (7.5 percent error) were corrected as 

a result of field surveys, and 40 of the 43 field corrections 

changed a previously absent cell to a presence cell. As 

hypothesized, those that were corrected were often located on 

the edge of a Joshua tree population, and those cells that were 

correctly identified often occurred either within the interior of 

a Joshua tree population or within large area of absences.

The informed inspection corrected 59 cells in total 

with 27 cells changed to presence and 32 cells converted 

to absence. Two cells were corrected based on lidar data 

provided by FINTC. During production of the final map, a 

total of 104 (2.2 percent) of all cells were corrected from the 

original provisional Joshua tree distribution map based solely 

on satellite imagery.

Cost

The remote sensing survey required approximately 

157 hours of personnel time. Field surveys were performed 

with three employees over five 10-hour days, adding 

150 work-hours. The informed inspection was performed on 

an estimated 400 cells with an average of 5 minutes spent in 

each cell, or a total of 33 work-hours. The cumulative time 

spent on this map product is estimated to be 340 work-hours.

Discussion

The current distribution of Joshua trees delineated in the 

new distribution map substantially increases the documented 

presence of Joshua trees at NAWS–CL. A comparison with 

previous Joshua tree distributions (Rowlands, 1978; Cole and 

others, 2003; Godsoe and others, 2008) indicates that the new 

distribution map increases the known presence of Joshua trees 

within the boundaries of NAWS–CL by 90.5 percent (fig. 5). 

The previously published distribution of Joshua trees for the 

North Range of NAWS–CL was characterized as three small 

distinctive stands of Joshua trees. By contrast, the revised 

map, presented here (fig. 5), indicates that all three of these 

stands are part of a contiguous population, and additionally, 

there are two small stands of limited distribution in the 

southern part of the North Range that were previously not 

identified. The South Range of NAWS–CL was previously 

characterized by four very small and disjunct stands of Joshua 

trees in the southern half of the range, the largest of which 

straddled the western boundary of the installation. The revised 

map substantially increases the areal extent of Joshua trees, 

which are now represented in all four quadrants of the South 

Range, albeit represented by less extensive stands than in the 

North Range. Additionally, we found no evidence of Joshua 

trees in two polygons of Joshua tree presence depicted in the 

previous distribution maps. However, the resolution of the 

remotely sensed data for those locations is of lower quality 

than most other areas at NAWS–CL; therefore, the potential 

for error in that region of the installation is greater. We 

attribute the improvement in the new distribution mapping 

over previous mapping and modeling efforts to the use of 

remotely sensed data as well as to increased access to the 

installation that we were provided. There is no evidence that 

previous mapping efforts had the benefit of site visits to the 

NAWS–CL facility (Rowlands, 1978; Cole and others, 2003; 

Godsoe and others, 2008).

Areas at the edge of dense Joshua tree stands were 

consistently the most difficult to classify from satellite 

imagery. This is likely because at higher elevations such 

areas are represented by smaller, younger growth forms of 

Joshua trees. Smaller, younger growth form Joshua trees 

are more difficult to detect in images that also have more 

well-developed vegetation communities of large plants such 

as junipers that can confound visual detection of similarly 

sized Joshua trees, and this illustrates one of the limitations to 

this method. This may explain why more cells were changed 

from absence to presence than vice versa. The three cells that 

were corrected to absence cells were isolated and contained 

structures or large creosote bushes that were previously 

misidentified for Joshua trees in the satellite imagery.

A combination of remotely sensed satellite data (for 

example, Google Earth) and lidar data with ground-truthing 

surveys appears to be ideal for identifying Joshua trees with 

the least error in landscapes. While we were remotely sensing 

Joshua trees, researchers at FINTC were simultaneously using 

lidar to detect and map Joshua trees. Lidar uses laser detection 

technology to identify the distribution of Joshua trees. From 

the sample of overlapping data, it appears that both lidar 

and the remote sensing technique were successful and had 

similar error values although with different sources of error. 

By ground truthing both lidar and satellite imagery in the 

field, we found that the lidar imagery improperly classified a 

dead deciduous tree as a Joshua tree and possibly a tall pole 

potentially placed during historical mining activities. The 

latter is inferred from the area that was traversed, but a pole 

was not actually observed. Conversely, some mapping cells 

that were up to 2 km from traveled routes had Joshua trees 

detected by lidar that were missed by initial remote sensing 

surveys and by ground searches. Furthermore, by using the 
References Cited 11

lidar data to review sites in satellite imagery, it was possible 

to locate and confirm individual Joshua trees that were 

previously undetected.

Another approach is to conduct species distribution 

modeling, which has been used extensively as a management 

and research tool to define sensitive habitats, identify 

previously unknown habitats, and direct searches for species 

(Graham and others, 2004; Elith and others, 2006). Species 

distribution modeling has been previously used in Joshua tree 

research as well (Godsoe and others, 2009; Smith and others, 

2011; Cole and others, 2011; Barrows and Murphy-Mariscal, 

2012). However, each of the previous uses of SDM for Joshua 

trees was to answer different questions than we addressed. 

The SDM method divides the landscape into many cells and 

then makes correlations between known presence/absence of a 

target species and environmental variables in each of the many 

cells. Then, using information about areas where presence/

absence is not known, the modeling algorithm assigns the 

probability of the species of interest being present, based on 

the correlation with environmental values that are assigned to 

each cell. This requires large scale geographic correlational 

assignment of species presence. The new species distribution 

map based on object detection differs from SDM methods 

because the majority of presence/absence determinations were 

made by visual validation of presence either remotely, or in 

situ, or both. We propose that once an empirical map such 

as we developed at NAWS–CL is available for the range of 

Joshua trees, SDM can be used to provide greater insight into 

environmental relationships with Joshua tree distributions and 

demographic dynamics. The object-oriented remote sensing 

methods that we used on the Joshua tree distribution map for 

NAWS–CL are superior to the inferential distribution maps 

that can be created using SDM because the remotely sensed 

mapping product is based on empirical inspection of the 

habitat, thus accumulating fewer errors.

Manned or unmanned aircraft surveillance of habitats 

is a third alternative for mapping species distributions; this 

approach could potentially refine results from the previously 

described techniques. Although relatively straightforward 

to accomplish on public lands such as those administered 

by the Bureau of Land Management or the U.S. National 

Park Service, special planning may be required to secure 

access to sensitive sites at NAWS–CL and other such secure 

Federally administered lands. We propose that the use of 

lidar or near-remote sensing aircraft could be used to increase 

substantially the resolution of mapping adult Joshua trees.

It is important to note that the quality of the satellite 

images we used varied through space and time, influencing the 

accuracy of identifying Joshua trees consistently over large 

areas. Characteristics of the associated vegetation community 

may also influence the ability of observers to correctly identify 

Joshua trees. In locations where multiple arborescent yucca 

species co-occur (for example, Y. schidigera in parts of the 

east Mojave Desert), it may be particularly difficult to visually 

distinguish the different species of yucca. It is likely that such 

areas will require more extensive field and aerial surveys 

for ground truthing. In most cases, it will not be possible to 

detect Joshua trees that occur within the canopies of shrubs. 

Consequently, none of the currently available remote sensing 

methods are likely to facilitate demographic modeling 

completely because the smaller size classes of trees (that is, 

less than 1 m tall and possibly 30 years of age; Esque and 

others, 2015) will frequently not be visible from remotely 

sensed data of any type. However, it is possible that higher 

resolution surveillance methods such as lidar could be used to 

account for all but the shortest Joshua trees. For successful and 

complete demographic modeling, it will still be necessary for 

personnel to conduct ground-based demographic surveys.

Next Steps

The completion of the preliminary distribution map 

comes at a time when information about Joshua trees is of 

concern to management agencies across the Mojave Desert 

ecoregion. We found that the distribution of Joshua trees in a 

representative area of habitat could be successfully mapped 

using satellite imagery that is publicly available. Moreover, 

this technique provided a more accurate distribution map than 

was previously achieved using statistical modeling (SDM) 

approaches. Based on the results of this study, a consortium 

of academics, government researchers, and non-governmental 

agencies have proposed that similar methods be used to 

establish a rangewide map of Joshua tree distribution. At 

4,715 km2, NAWS–CL is approximately 3.4 percent of the 

entire Mojave Desert (for example, MacMahon and Wagner, 

1985, estimate the Mojave Desert at 140,000 km2) and likely 

constitutes 6 to 8 percent of the Joshua tree’s entire range. 

The provisional distribution map required approximately 

340 hours of work, suggesting that thousands more hours may 

be required to map the full distribution of Joshua trees in the 

Mojave Desert.

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For more information concerning the research in this report, contact the

 Director, Western Ecological Research Center

U.S. Geological Survey

3020 State University Drive East

Sacramento, California 95819

https://www.usgs.gov/centers/werc

Publishing support provided by the U.S. Geological Survey 

Science Publishing Network, Sacramento Publishing Service Center
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ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20205053