Environmental Monitoring and Assessment (****) ***: 65 83

c Springer 2006

DOI: 10.1007/s10661-006-1071-z

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA USING

AERIAL PHOTOGRAPHS ALONG THE CACHE CREEK, CALIFORNIA

SHAOKUI GE1,2,, RAYMOND CARRUTHERS2, PENG GONG1

and ANGELICA HERRERA1,2

1

Department of Environmental Science, Policy, and Management, University of California,

Berkeley, California, USA; 2 Exotic and Invasive Weeds Research Unit, Western Regional Research

Center USDA, Agricultural Research Service, 800 Buchanan Street Albany,

California, USA

( author for correspondence, e-mail: abpyy9@r.postjobfree.com)

(Received 17 September 2004; accepted 24 January 2005)

Abstract. Natural color photographs were used to detect the coverage of saltcedar, Tamarix parvi ora,

along a 40 km portion of Cache Creek near Woodland, California. Historical aerial photographs from

2001 were retrospectively evaluated and compared with actual ground-based information to assess

accuracy of the assessment process. The color aerial photos were sequentially digitized, georeferenced,

classi ed using color and texture methods, and mosaiced into maps for eld use. Eight types of ground

cover (Tamarix, agricultural crops, roads, rocks, water bodies, evergreen trees, non-evergreen trees

and shrubs (excluding Tamarix)) were selected from the digitized photos for separability analysis

and supervised classi cation. Due to color similarities among the eight cover types, the average

separability, based originally only on color, was very low. The separability was improved signi cantly

through the inclusion of texture analysis. Six types of texture measures with various window sizes

were evaluated. The best texture was used as an additional feature along with the color, for identifying

Tamarix. A total of 29 color photographs were processed to detect Tamarix infestations using a

combination of the original digital images and optimal texture features. It was found that the saltcedar

covered a total of 3.96 km2 (396 hectares) within the study area. For the accuracy assessment, 95

classi ed samples from the resulting map were checked in the eld with a global position system (GPS)

unit to verify Tamarix presence. The producer s accuracy was 77.89%. In addition, 157 independently

located ground sites containing saltcedar were compared with the classi ed maps, producing a user s

accuracy of 71.33%.

Keywords: invasive species, saltcedar, tamarix parvi ora, monitoring, detection, aerial photograph,

remote sensing, California, texture analysis

1. Introduction

Biological invasions have increasingly become an important topic for environmental

scientists and natural resource managers, worldwide. Invasive species alter commu-

nity composition and ecosystem function, and cause severe threats to biodiversity

in the invaded areas. In order to understand the invasion mechanism and better con-

trol invasive plants and their spread, it is helpful to characterize the abundance and

distribution of an invasive species in areas of concern (Mooney, 1999). Although

ground-based investigation can accurately measure invasive species in a limited

66 SHAOKUI GE ET AL.

area, it is a time-consuming, labor-intensive work. In particular, ground-based as-

sessment is not always practical for inaccessible areas where invasive species often

exist. Thus, remote sensing can be an important tool to detect and characterize

invasive species over large areas (Everitt et al., 1995, 1996; Lass et al., 1996).

Saltcedars, Tamarix spp., were introduced to the United States in the 1800 s for

a variety of reasons including soil and wind erosion control. This shrub escaped

from cultivation and now poses a signi cant threat to the natural ecosystems in the

western USA (Brock, 1994; Di Tomaso, 1998; Bailey et al., 2001). It grows most

successfully along riparian zones, where it out-competes native vegetation.

This invasive shrub has spread widely along Cache Creek and other riparian

areas in Northern California (Figure 1). Its roots extend deeply into the soil where

it depletes surface and underground water. In addition to its extensive water use,

its invasion causes several other negative impacts such as salinization of soils, high

stream bank erosion, increased ooding and high re hazard to localized habi-

tats. Multiple stems and slender branches characterize the general morphology of

saltcedar, which may grow over 10 meters in height (Figure 2). It out-competes

and replaces native vegetation, resulting in high-density monospeci c-stands of

saltcedar along riparian corridors. In some areas, water consumption and salt

Figure 1. Study area (Cache Creek, California, USA, after California NRCS, (A) down part; (B)

middle part; and (C) up part for this study).

67

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

Figure 2. Showing Saltcedars patches.

buildup have highly altered native ecosystems to the point that few other species can

survive. It is therefore critical that effective techniques to limit saltcedar growth and

control its spread be developed and implemented (Di Tomaso, 1998; Cleverly et al.,

1997; Zavaleta, 2000; Bailey et al., 2001). Such attempts closely depend upon ac-

curate detection of this plant on a regional scale, thus control actions can be applied

widely throughout entire watersheds. However, except for some preliminary tests

using digital videography, no research has been reported on the use of aerial photog-

raphy in monitoring saltcedar distributions across wide areas or entire watersheds.

It was demonstrated that high spatial resolution remotely sensed data was a valu-

able data source to detect invasive species infestations across wide areas (Gausman

et al., 1977; Everitt et al 1995; Lass et al., 1996). Wide area assessment is of-

ten important because invasive species are commonly distributed in an aggregated

fashion providing a patchwork pattern in speci c habitats within an invaded area.

In particular, for invasive and noxious plant weed, small-patches of infested habitat

are very important and cannot be ignored, since they often are newly infected areas

under high-intensity control or eradication. Existing studies of invasive species have

primarily been based on the re ectance difference of targets with the expectation

that vegetation types could be distinguished from surrounding neighboring plants

based on color or color infrared photographs (CIR). For example, having compared

the application of color photos with CIR aerial photography, Everitt and Deloach

(1990) found that the foliage of Tamarix ramosissima turned golden brown during

the late fall/ early winter period so that its re ectance curve was much different

from those of the associated native vegetation and soil background. Both types of

photography (color and CIR) were used to distinguish Tamarix ramosissima from

associated native vegetation. Color photography, however, is more widely avail-

able than CIR using both lm and digital technology. High resolution commercial

satellite images are usually taken along polar orbits with a narrow footprint. It takes

multiple overpasses of those satellites to acquire images over a river system that

could take many different directions in their meanderings. Therefore, the cost of

68 SHAOKUI GE ET AL.

data acquisition is high. In consideration of these factors, color aerial photogra-

phy was thought to be the most cost-effective remote sensing tool for the study of

saltcedar in our riparian environment and several sets of historical photographs of

Cache Creek were available.

In this study, color aerial photography was used to map invasive Tamarix parv-

i ora along an east running portion of Cache Creek, near Woodland, California

(see Figure 1). Traditionally, for speci c feature extraction and delineation, manual

photo interpretation is the most common method used by landmanagers interested

in invasive plant control. However, manual interpretation is both time-consuming

and labor-intensive, and it is almost impossible to recognize small patches of vege-

tation over wide areas or at a watershed scale of interest. Therefore, we developed

an image based classi cation procedure to extract Tamarix covers for large areas

along riparian corridors. Here, not only color but also image texture was used to

detect these invasive plants. Texture analysis was considered important because

color alone was not suf cient to separate Tamarix cover from adjacent bene cial

plant species. In general, texture refers to the tonal variation in an image; and thus

texture features help enhance separability among classes (Anys et al., 1995; Mather

et al., 1998; Smits et al., 1999; Jonathan et al., 2001).

Texture extraction algorithms have been developed from remotely sensed data

for various spatial resolutions (Haralick et al., 1973; Haralick, 1979; Gong et al.,

1992; Hassan and He 1995; Riou et al., 1997; Saatchi et al., 2000, Nyoungui

et al., 2002; Xu et al., 2003). All these studies demonstrated that texture in a

local pixel neighborhood can be used to improve vegetation discrimination in high-

resolution images (Hudak et al., 1998; Podest and Saatchi, 2002). In this paper

we report our analysis methods and experimental results. In addition, we evaluate

some commonly used texture measures for improving land cover classi cation from

aerial color photographs and assess the accuracy of extracted Tamarix parviflora

distributions through ground-based veri cation.

2. Materials and Methods

Historical aerial photographs were used retrospectively to assess saltcedar infes-

tations along an important waterway in Northern California and then linked back

to ground based validation sampling conducted following this analysis. The pho-

tographs were actually taken for assessing gravel distribution along Cache Creek

but were timed such that they correspond with peak bloom and thus allowed an

assessment of the saltcedar infestation in this area. A total of 29 color photos were

taken along Cache Creek in April 2001 using a Zeiss RMK TOP 15 camera with

a Zeiss Pleogon A3/4 lens. The camera was physically mounted in a twin-engine

aerial platform that was piloted by American Aerial Survey, Inc. The scale of the

photos was 1:12,000. At the time of photography, the saltcedar was not yet leafed out

and only owers were present on these plants. The owers were pink in color, mak-

69

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

ing them distinctive from other associated riparian vegetation and other background

materials in the study area. Because the photographs were taken in response to ow-

ering, it was possible to mix saltcedar with some blooming fruit trees in adjacent

agricultural orchards and thus careful discrimination between riparian zones and

agricultural elds was needed to ensure that only saltcedar was extracted. Although

it was possible to scan the photos in great detail, we chose to scanned the photos to

1 meter resolution. The scanned photos were georeferenced to 1-meter resolution

Digital Ortho Quarter Quads (DOQQ) from USGS with a second order polynomial

function. After classi cation, they were mosaiced together to provide a continuous

photographic map. Based on the color similarity and relationships among various

types of plants in the area, vegetation was initially divided into eight categories:

saltcedar, evergreen trees, non-evergreen trees, shrubs, crops, bare elds and hills

(including agriculture and rangeland), water bodies (including wetlands), rocks and

roads. The procedure used for mapping the saltcedar cover is outlined in Figure 3.

The image processing used to distinguish the tamarix from associated vegetation

and other background substrates required four distinct steps: (1) Texture extraction

using six algorithms; (2) Texture subset selection based on separability measured

by Bhattacharya distances (BD, Shaban and Dikshit, 2001); (3) Best texture deter-

mination based on classi cation accuracy; and (4) A post-classi cation processing

procedure, were used as the basis of invasive habitat determination. Following this

classi cation procedure, the original eight categories were reduced to six cate-

gories, by merging rocks and roads with bare elds and hills. Individual classi ed

photos were then mosaiced into three discrete sections of a 40km long portion of

Cache Creek: the downstream, middle and up-stream segments of the riparian cor-

ridor. Finally, using completely random samples 100 points were extracted from

these classi ed maps and used to conduct overall accuracy assessments, including

calculating producer s and user s accuracies.

2.1. C OMPARING COLOR SEPARABILITY

The original image was scanned in three layers (blue, green and red) using 8 bits per

layer. Then, twenty 1-meter pixels of each cover type were randomly sampled from

these digital images, and then was replicated three times. The color differences

among land covers were determined using analysis of variance, and a Tukey s

multiple range test was used to examine the statistical signi cance between means

at a 0.95 probability level.

2.2. TEXTURE MEASUREMENTS

Texture measurements were derived from the digitally processed photographs based

on a gray level co-occurrence matrix (GLCM) (Gong et al., 1992; Jensen, 1996).

GLCM is usually a probability matrix whose elements are indexed by gray-level

values of any pixel-pair (a, b) at a xed distance and angle in a pixel neighborhood.

70 SHAOKUI GE ET AL.

Scan photo with 1000 dpi to form 3-layer-images of 1-foot pixel size

Resampling scanned photos to pixel sizes of 1.0 meter

Try different texture algorithms with 3*3,5*5,7*7 and 9*9 windows.

Overall and tamarix

separabilities

Determine texture for inclusion in supervised classification by MLC

Based on overall and tamarix accuracy to determine the best texture

Aggregate into six categories including trees, shrubs, bare fields, tamarix,

crops, and water

Based on habitat info to calibrate and validate classified results manually

interpretation.

Mosaicing and outputting map of invasion cover for Tamarix distribution

Figure 3. Procedures for the tamarix detection.

Given N as the number of gray levels and Pa,b (i, j) as the joint probability of pixel

a with gray level i and pixel b with gray level j. For each pixel neighborhood of

a certain size, a GLCM was constructed with its ith row and jth column having

probability Pa,b (i, j). The relative spatial relationship between a and b is xed for

each GLCM. For simplicity, we will use P(i, j) instead of Pa,b (i, j). The following

71

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

functions were used to develop texture algorithms:

p (i, j )

Homogeneity: Homo = (1)

1 + (i j )2

Contrast: Con = (i j )2 p (i, j ) (2)

Dissimilarity: Dis = p (i, j ) abs (i j ) (3)

Mean: Mean = i p (i, j ) (4)

2

i

Standard deviation: Sdev = p (i, j ) i (5)

n

Entropy: Ent = p (i, j ) log p (i, j ) (6)

2.3. B ETWEEN -CLASS SEPARABILITY WITH DIFFERENT WINDOW SIZES

Bhattacharya distance (BD) was applied to represent the separability between class

pairs of training samples. BD values range between 0.0 and 2.00. If it is less than

1.00, the two cover classes are very similar and thus hard to separate. When this is

the case, the classes should be merged or one of the cover classes discarded. BD

values between 1.00 and 1.9 indicate that the two classes could be separated at least

to some extent. BD values between 1.90 and 2.00 indicate very good separation

between the two classes in question. In this study, texture measures were calcu-

lated and compared at different window sizes to determine the optimal window

size for texture selection to distinguish invasive saltcedar from other surface cov-

ers. Comparisons were based on maximum, minimum and average separabilities of

training samples with different texture measures obtained in window sizes of 3 * 3,

5 * 5, 7 * 7, and 9 * 9 pixels. A texture set was determined to be appropriate for

classi cation when the separability was greater than 1.90. Speci cally, the separa-

bility between Tamarix and other classes were given speci c attention in order to

determine whether it was separable from all other cover types. Based on the over-

all separability and Tamarix separability with each non-tamarix class, the optimal

window size and texture feature were chosen for mapping tamarix the distribution

along Cache Creek. This texture assessment was combined with the original image

layers to jointly classify different pixel-based patterns to extract the invasive cover

areas. Due to variation in light condition and topographical changes within our

large study area, texture could be different from location to location, even for the

same vegetation type. Therefore, in order to obtain accurate classi cation results,

different textures were assessed and used across individual photos. As an example,

a photo taken from the middle reach of the creek was used to illustrate such image

processing.

72 SHAOKUI GE ET AL.

2.4. S ELECTION OF THE OPTIMAL TEXTURE ALGORITHM

Various texture features selected as described previously, were added as additional

channels to the original image classi cation process. The maximum likelihood

classi er (MLC) was then used in the nal classi cation, and class signatures

were derived from images based on pixel color values and the selected texture

determined by training samples. Although this study focused primarily on tamarix

covers, the overall classi cation accuracy was still considered. Therefore, through

the balanced assessment of the overall classi cation accuracy and the classi cation

accuracy of tamarix, the optimal texture feature was selected for inclusion in the

nal classi cation.

2.5. H ABITAT - BASED CALIBRATION AND ACCURACY ASSESSMENT

Due to color similarities among different cover types, non-tamarix habitat areas

such as orchards had the potential to be misclassi ed as saltcedar. However, Tamarix

primarily grows only along the riparian corridor adjacent to the ood plane. Thus,

large rectangular orchard areas were masked out from image classi cation in order

to improve the classi cation process. After orchard exclusion, accuracy assessment

was analyzed using independent eld validation data. For producer s accuracy,

random samples were selected from the classi ed tamarix cover class. A differential

eld GPS unit was then used to locate speci c areas where pixels were identi ed

as saltcedars by the proposed classi ers. A eld crew located appropriate points

and made on-site determination as to whether saltcedar was present or absent at

each site. As the majority of the saltcedar in this location are medium to large

shrubby trees comprised of several square meters of cover area, the submeter GPS

accuracy was considered adequate for this assessment along with aerial photograph-

based maps that allowed individual tree recognition in the eld. For user s accuracy

assessment, we randomly located tamarix patches in the eld. The coordinates of

these locations were then measured and the sample sites were then compared with

the classi ed tamarix covers back in the laboratory where we veri ed how many

samples were found on the classi cation map to calculate user s accuracy. Based on

the calibrated and verify mapping of tamarix cover, the full area of invasive tamarix

within our study area was estimated.

3. Results and Analysis

3.1. O RIGINAL DATA ANALYSIS

Using twenty sampled pixels with three replications (60 total samples), all eight

cover types were compared for their gray-level values. Statistically, there were

high correlations among the three scanned color layers in each image. Correlation

73

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

Figure 4. Comparisons on separabilites of different covers by color composition.

coef cients of the rst layer versus the second and the third layers was 0.96 and

0.92, respectively. Therefore, to reduce the information redundancy, we only used

the blue layer to compare the gray-level difference of covers and derive the texture

features. Using analysis of variance, it was determined that there was signi cant

difference at the overall level, however, there existed tonal confusion among some

covers. The Turkey multi-comparison demonstrated that some covers partly over-

lap, which led to confusion in image classi cation (Figure 4). For example, tamarix

sometimes was confused with water and other shrubs, because of their color sim-

ilarity and spatial overlap where some mixing occurred within the 1 meter pixels.

Only when differences were far from the zero separation line, were these covers

easily separated. Therefore, just using gray-level or color alone, it was dif cult

to separate the tamarix from some other shrubs and water. There was also some

overlap of gray level among trees, shrubs and crops. Tamarix therefore was not

signi cantly different from shrubs, trees, or even water bodies. Clearly it was im-

possible to distinguish tamarix from its associated vegetation and background using

color alone, at this level of spatial resolution.

74 SHAOKUI GE ET AL.

3.2. E FFECTS OF WINDOW SIZES ON SEPARABILITY

Window size provided a functional range of texture features. The separability of

the original images was just 1.81 using the overall average. The separability of

Tamarix from other covers was 1.88. The separability between Tamarix and water

was only 1.03. When textures were combined with the original data, the separability

was signi cantly improved between different cover types. To some extent, the

level of change depended partly on the window size (Tables I IV). Therefore,

window size played an important role in capturing spatial pattern information to

improve separability among covers. If a window size is too small, some spatial

information contained in larger-scale patterns might be lost. However, if a window

size is too large, small cover patterns would be mixed with other patterns. Also,

the effect of a texture function may be in uenced by window size. For example,

TABLE I

Texture separabilities comparison with 3 3 windows

Tamarix separability

Overall separability

Covers of Min. Covers of Min.

Textures Max. Min. Average separability Max. Min. Average separability

Raw 2.00 1.03 1.81 Rocks-roads 2.00 1.03 1.88 Tamarix-water

Hom 2.00 1.20 1.91 Trees-shrubs 2.00 1.56 1.91 Tamarix-water

Mean 2.00 1.15 1.89 Tamarix-waters 2.00 1.15 1.89 Tamarix-water

Con 2.00 1.29 1.95 Trees-shrubs 2.00 1.76 1.95 Tamarix-shrubs

Dis 2.00 1.29 1.95 Trees-shrubs 2.00 1.78 1.94 Tamarix-water

SD 2.00 1.29 1.96 Shrubs-trees 2.00 1.91 1.98 Tamarix-trees

Entropy 2.00 1.30 1.91 Shrubs-trees 2.00 1.48 1.97 Tamarix-trees

TABLE II

Texture separability comparison with 5 5 windows

Tamarix separability

Overall separability

Covers of Min. Covers of Min.

Textures Max. Min. Average separability Max. Min. Average separability

Raw 2.00 1.03 1.81 Rocks-roads 2.00 1.03 1.88 Tamarix-water

Hom 2.00 0.95 1.87 Tamarixs-water 2.00 0.95 1.87 Tamarix-water

Mean 2.00 1.30 1.91 Tamarix-water 2.00 1.30 1.91 Tamarix-water

Con 2.00 1.23 1.96 Trees-shrubs 2.00 1.92 1.98 Tamarix-trees

Dis 2.00 1.36 1.95 Rocks-roads 2.00 1.91 1.98 Tamarix-trees

SD 2.00 1.23 1.96 Shrubs-trees 2.00 1.91 1.98 Taamrix-trees

Entropy 2.00 1.43 1.95 Rocks-roads 2.00 1.90 1.96 Tamarix-water

TABLE III

Texture separability comparison with 7 7 windows

Overall separability Tamarix separability

Textures Max. Min. Average Covers of Min. separability Max. Min. Average Covers of Min. separability

Raw 2.00 1.03 1.81 Rocks-roads 2.00 1.03 1.88 Tamarix-water

Hom 2.00 0.95 1.86 Tamarix-water 2.00 0.95 1.86 Tamarix-water

Mean 2.00 1.28 1.90 Tamarix-waters 2.00 1.28 1.90 Tamarix-water

Con 2.00 1.24 1.96 Nonevergreen trees-shrubs 2.00 1.92 1.98 Tamarix-nonevergreen trees

Dis 2.00 1.36 1.94 Nonevergreen Trees-shrubs 2.00 1.91 1.98 Tamarix-nonevergreen trees

SD 2.00 1.29 1.96 Shrubs-nonevergreen Trees 2.00 1.91 1.98 Tamarix-nonevergreen trees

Entropy 2.00 1.33 1.93 Shrubs-nonevergreen trees 2.00 1.83 1.94 Tamarix-nonevergreen trees

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

75

76

TABLE IV

Texture separability comparison with 9 9 windows

Overall separability Tamarix separability

Textures Max. Min. Average Covers of Min. separability Max. Min. Average Covers of Min. separability

Raw 2.00 1.03 1.81 Rocks-roads 2.00 1.03 1.88 Tamarix-water

Hom 2.00 1.15 1.93 Nonevergreen trees-shrubs 2.00 1.86 1.96 Tamarix-water

Mean 2.00 1.22 1.89 Tamarix-water 2.00 1.22 1.86 Tamarix-water

Con 2.00 1.26 1.95 Nonevergreen trees-shrubs 2.00 1.88 1.97 Tamarix-water

SHAOKUI GE ET AL.

Dis 2.00 1.31 1.96 Nonevergreen trees-shrubs 2.00 1.91 1.98 Tamarix-Nonevergreen trees

SD 2.00 1.29 1.96 Shrubs-nonevergreen trees 2.00 1.91 1.98 Tamarix-Nonevergreen trees

Entropy 2.00 1.30 1.92 Shrubs-nonevergreen trees 2.00 1.72 1.92 Tamarix-water

77

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

the mean texture decreased separability in large window sizes. On the other hand,

entropy increases the separability in large window sizes. For the 3 3 window,

the overall average separability was improved from 1.81 to the range of 1.89

1.96; and the Tamarix separability was improved to 1.89 1.98. At this window

size, the standard deviation improved separability the most. For the 5 5 window,

the overall average separability was improved to 1.87 1.96, while the average

separability of Tamarix from other covers was improved to 1.83 1.98. The 7 7

and 9 9 window sizes had similar effects, but the corresponding separabilities

were a little lower than that of the 5 5 window. Therefore, the 5 5 window size

was nally chosen as the optimal window size for analysis and was then used to

extract texture features with these photographs, and was set as the standard window

size in all texture measurements along the Cache Creek. These textures were then

applied to classify all the images, the images were then mosaiced and used for

mapping the saltcedar and estimating overall area of infestation. Among different

texture features, those measuring heterogeneity such as the standard deviation and

dissimilarity played a more important role in improving separability than the other

measures discussed. Based on an overall average and the speci c separability of

Tamarix compared to other classes, the two homogeneity measures gave the poorest

performance.

3.3. T HE DETERMINATION OF THE OPTIMAL TEXTURE ALGORITHM

The overall accuracy and the speci c accuracy for Tamarix ( Table V) were used to

determine the optimal texture for detection and classi cation of invasive saltcedar

across large areas along Cache Creek. A high separability did not necessarily corre-

spond to high accuracy in all categories, but was true for saltcedar cover detection

as follows. The overall accuracy was calculated for all eight covers from different

textures for comparison with the raw image data. The accuracy for Tamarix was

singled out separately from other classes as this was the target species of most

importance in our analysis. The overall accuracy and accuracy for Tamarix across

different wondow sizes were sometimes inconsistent. For instance, the dissimilarity

TABLE V

Accuracy assessment of the classi cation with 5 5 window size

Average Overall Tamarix Min. Target Max Target

accuracy accuracy accuracy accuracy of Min. accuracy accuracy of Max. accuracy

Raw 90.99 91.38 87.92 78.49 Nonevergreen trees 99.46 Bare elds

Con 93.46 94.74 98.61 88.81 Roads 99.37 Bare elds

Dis 93.72 95.34 84.95 86.17 Nonevergreen trees 100.00 Bare elds

SD 94.37 95.58 97.43 80.79 Roads 100.00 Bare elds

Entropy 96.70 97.41 98.22 87.34 Nonevergreen trees 100.00 Bare elds

78 SHAOKUI GE ET AL.

texture with a 3 3 window size led to a very high overall accuracy; however, the

corresponding saltcedar accuracy was poor. Therefore, the best texture algorithm

was judged by balancing among the average, maximum and minimum accuracies

for both overall separability and for Tamarix separability. For most of the images,

the contrast texture was the best choice for saltcedar mapping. Therefore, it was

included in all image classi cation processes. Although the degree of improvement

in separability may vary from photograph to photograph due to topography and

light conditions, it was shown that texture analysis always improved invasive cover

detection, and that consecutive images usually shared the same optimal texture set

(Table VI).

3.4. P OST PROCESSING EDITING AND ACCURACY ASSESSMENT

Tamarix, as the focal cover type, only exists along or near creeks. However, in the

initial classi cation, some orchards were also classi ed as Tamarix due to similar

bloom characteristics. Tamarix was also occasionally confused with shrubs and

trees in the riparian zone, but rarely. Based on its habitat characteristics, upland

areas where orchards occur and cause apparent classi cation errors, were corrected

and edited as non-invasive covers, and thus the classi cation results were further im-

proved. In addition, three other non-objective covers (roads, rocks, and bare elds)

were merged together for nal assessment and analysis. These post-classi cation

processed images were then mosaiced with increased accuracy for nal assessment

and eld use.

To estimate producer s accuracy, we rst randomly sampled 400 pixels from

classi ed Tamarix covers along the creek. However, most of these points were

inaccessible, due to water bodies blocking and steep banks limiting access. A total

of 95 sample points were reached in the eld. Among these, 74 samples were

veri ed to be Tamarix cover (accuracy was 77.89%). Only 12 misclassi ed pixels

were caused by dif culties in separating the classes with the color and texture

methods used in the analysis. For example, there were 4 willow pixels classi ed as

saltcedar. There were other objective factors causing some errors that we hope to

eliminate in future assessments. For example, there were 5 sample points classi ed

as Tamarix that were removed at the time of the eld veri cation, because it was

found that there were dead patches of saltcedar around speci c sample locations

where local landowners had chemically treated and killed these plants. There were

also 4 errors in the samples caused by georeferencing problems along steep slopes,

for example, near a dam, extreme creek banks, or uneven road edges. For user s

accuracy assessment, we used a differential submeter GPS to collect 157 randomly

selected eld points where saltcedar occurred in patches along the creek. These

samples were then checked and compared with the classi ed saltcedar covers back

in the laboratory. There were 112 samples classi ed as Tamarix; however, there

were 45 samples not found as tamarix in the map. The resulting user s accuracy

was 71.33%. In this case, it was found that the misclassi cation was caused to some

79

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

TABLE VI

The optimal texture measure with different photographs

Photo No. UTM coordinates Texture Photo no. UTM coordination Texture

1 605535E, 4288576N DEV 16 576180E, 4289000N CON

608430E, 4285739N 580283E, 4284835N

2 603344E, 4288607N DEV 17 574660E, 4290559N CON

606494E, 4285566N 578809E, 4286403N

CON + DEV

3 601775E, 4289077N 18 573141E, 4294140N CON

605690E, 4285193N 577164E, 4288138N

4 600170E, 4287628N CON 19 572622E, 4292646N CON

604096E, 4283738N 576464E, 4288832N

5 598642E, 4286268N CON 20 571687E, 4294644N CON

602592E, 4282305N 575469E, 4290885N

6 594683E, 4284239N CON 21 570776E, 4296627N CON

598181E, 4280859N 574429E, 4293042N

7 592608E, 4284969N CON 22 569786E, 4298835N CON

596098E, 4281569N 573439E, 4294552N

8 590332E, 4285671N CON 23 568570E, 4300738N

593838E, 4282197N 572346E, 4296928N

DIS + DEV

9 588410E, 4286137N 24 567690E, 4302862N

591860E, 4282713N 571462E, 4299014N

CON + DIS + DEV 25

10 586529E, 4286626N 566908E, 4303654N

590029E, 4283206N 570825E, 4300572N

11 584459E, 4287248N DEV 26 566424E, 4304561N CON

587874E, 4283773N 570175E, 4301795N

12 583178E, 4287125N DEV 27 565631E, 4306054N CON

586253E, 4284205N 569296E, 4302458N

13 581129E, 4286824N CON 28 564882E, 4306530N CON

584054E, 4283989N 568845E, 4302605N

14 579046E, 4286667N 29 563348E, 4307980N CON

582038E, 4283782N 567205E, 4304042N

15 577736E, 4287528N CON 581816E, 4283467N

581816E, 4283467N

Note: not using any texture.

degree by the spread of this invasive species after the photos were taken or small

patch sizes that may not have been easily visible in the 1 meter pixels. These photos

were taken in 2001, but the retrospective analysis and nal accuracy assessment

were not completed until the summer of 2004. Although saltcedar is a relatively

slow growing shrub, the three-year time delay certainly added to this error. In all, we

found that there were 13 misclassi ed samples near big patches of saltcedar on the

photos in 2001 that could have been caused by Tamarix dispersal after the photos

80 SHAOKUI GE ET AL.

were taken. If these samples were checked in 2001, the corresponding accuracy

would certainly have been higher. If we exclude these 13 samples for the accuracy

assessment, then the accuracy would have been 77.78%. In the future, in order

to have more exact invasion information, the invasive map should be updated and

veri ed each year. It was also determined that 32 samples of isolated saltcedar were

not found on the resulting classi cation map. Such errors were mainly caused by

small patches of saltcedar. These small infestation areas were not easy to capture

in the image analysis process due to our 1 meter pixel resolution and the texture

analysis conducted in the relatively large 5 5 window size. This might be improved

by conducting the analysis under ner image resolution, however, constraints of

image processing and handling made that level of detail in the analysis impractical

and cost prohibitive. Finally, using these analyses and assessing the overall classi ed

Tamarix cover along a 40 km section of Cache Creek, the total area of invasive

saltcedar was estimated to be 396 hectares (3.96 km2 Table VII).

It was found that there were many more saltcedar invaded areas in the middle

section of the creek and that the invaded areas in up-stream section were less in

area and less dense. Through correlation analysis, it was found that the invasive

areas were strongly linked with bare soil areas of and water adjacent to the creek

(including wetland) (Table VIII). Saltcedar infestation negatively correlates with

the amount of bare eld area, and positively with the area ooded by water. In the

middle section, the area of water in the creek is large but the water ow is slow.

TABLE VII

Invasive characteristics in different parts along Cache Creek in 2001

Tamarix areas Length of water Invasive degree

Creek parts (Square meters) Body (Meter) (Square meters / meter)

Down stream 522***-***** 40.00

Middle part 918***-***** 67.54

Up stream 521***-***** 36.11

TABLE VIII

Correlation coef cients among different cover types

Non-evergreen Bare Water bodies

Tamarix Evergreen (including shrubs) Crops elds (including wetland)

0.58 0.77 0.47 0.99

Tamarix 1.00 0.96

0.99 0.69 0.78

Evergreen 1.00 0.97

0.93 0.85 0.92

Non-evergreen 1.0000

1.00 0.59

Crops 0.70

1.00 0.99

Bare elds

Water bodies 1.00

81

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

Possibly, such water condition forms a habitat condition bene cial to saltcedar

seed germination and invasion along the creek The invasion of Tamarix also com-

petitively reduces native vegetation in infested areas leading to a further negative

correlation with both evergreen and non-evergreen plant covers in these areas.

4. Discussion

Previous studies of invasive plant species detection using remote sensing and image

analysis have primarily emphasized the use of re ectance or color properties alone.

In those studies results have provided some but limited success. In situations where

effective detection has been achieved, it has been attributed to substantial differences

in re ectance caused by changing phenological characteristics between the species

of interest and the associated environment (Everitt and Deloach, 1990; Everitt

et al., 1995; Lass et al., 1996). In this research, color differences from natural color

photographs were not suf ciently large between the invasive target species and

other associated background vegetation to allow accurate classi cation. Texture

analysis increased the separability between invasive cover and these background

plants and other habitat features.

We intended to nd proper texture(s) from six different measures. In fact, the

spatial dispersion patterns in plant ecology are typically scale-dependent, and thus

we hoped to nd an appropriate texture measure to describe the spatial pattern

of gray levels of a pixel neighborhood at different scales of study. That is, after

evaluating several different scales (different window sizes), we applied the optimal

texture assessment to extract the spatial neighbor relationships with a particular

window size (5 5 pixels). This texture was then used as an additional layer in image

classi cation and improved the average separability (minimum accepted value set

to 1.90). However, occasionally in some images, a selected window size, using only

one texture did not reach a separability of over 1.90. Thus we included two or three

different texture measurement to capture the optimal texture for comparison and

thus ensured that the separability was always greater than 1.90.

Sometimes the separability improvement was not much different among textures

producing similar results as seen in the contrast and dissimilarity texture measures.

The optimal texture assessment method could only be determined after the full

accuracy assessment was completed, and it was expected to have a higher overall

accuracy and the best accuracy for the speci c classi cation of Tamarix. During this

process, we also assessed the use textures other than the selected optimal texture(s)

to improve classi cation accuracy. In the assessment process, we found that none

of the other textures increased accuracy more than 2%, even when using all six

texture measures simultaneously to do the classi cation. Therefore in the end, we

used the optimal texture subset alone, instead of all the texture measures combined

to extract the Tamarix cover. These results suggest that gray-level values of digitized

photographs in combination with a single texture feature can be effectively used

82 SHAOKUI GE ET AL.

in an automatic procedure to detect invasive Tamarix in large areas of infestation.

Such detection is possible for not only large tracts of invasive areas but also for

small patches where local land managers are interested in detecting and eradicating

these plants.

5. Conclusions

In this study, we demonstrated that texture could be used to improve the separability

between invasive Tamarix parvi ora and associated vegetation along an example ri-

parian corridor in California. Texture features were useful in developing a relatively

automatic procedure for detection and recognition of Tamarix using historical color

aerial photographs. We derived six different texture algorithms from a gray-level

co-occurrence matrix (GLCM), which proved useful for improving classi cation

results. The optimal window size was 5 5 among 4 other bracketing window

sizes. This optimal size was chosen based on the separability of cover types with

special attention paid to the separability of Tamarix when compared to other cover

types. The producer s and user s accuracies were found to be 77.89% and 71.33%,

respectively. Totally, there was 396 hectares (3.96 km2 ) of Tamarix cover along the

invaded Cache Creek drainage.

Acknowledgments

We are grateful to Dr. Ruiliang Pu for his help on the use of PCI software and

discussions on the application of remote sensing for use in mapping invasive species.

We also thank Mr. Justin Weber, Mr. Joel Garza and Ms. Julie Garren for their

eld validation work. In particular, we thank USDA-ARS # 5325-22000-017-00D

(Biology and Control of invasive weeds on the Western United States) and USDA-

CSREES-IFAFS # 00-521**-**** (Biologically-based control for the area-wide

management of exotic and invasive weeds).

References

Anys, H. and He, D. C.: 1995, Evaluation of textural and multipolarisation radar features for crop

classi cation, IEEE Trans. on Geosci. Remote Sens. 33, 1130 1181.

Bailey, Joseph K., Schweitzer, J. A. and Whitham, T. G.: 2001, Salt cedar negatively affects biodi-

versity of aquatic macroinvertebrates, Wetlands 21, 442 447.

Brock, J. H.: 1994, Tamarix spp. (Salt Cedar), An Invasive Exotic Woody Plant in Arid and Semi-arid

Riparian Habitats of Western USA, In: L. C. de Waal, L. E. Child, C. P. M. Wade, J. H. Brock,

Landscape Ecology Series; Ecology and Management of Invasive Riverside Plants, John Wiley

and Sons Ltd.; John Wiley and Sons, Inc.; Chichester, UK/New York, USA, 1994, pp. 27 44.

Cleverly, J. R., Smith, S. D., Sala, A. and Devitt, D. A.: 1997, Invasive capacity of Tamarix ramo-

sissima in a Mojave desert oodplain: The role of drought, Oecologia (Berlin) 111, 12 18.

83

TEXTURE ANALYSIS FOR MAPPING TAMARIX PARVIFLORA

Di Tomaso, J. M.: 1998, Impact, biology and ecology of saltcedar (Tamarix spp.) in the southwest

United States, Weed Tech. 12, 326 336.

Everitt, J. H. and Deloach, C. J.: 1990, Remote sensing of Chinese Tamarisk (Tamarix chinensis)

and associated vegetation, Weed Sci. 38, 273 278.

Everitt J. H., Escobar, D.E. and Davis, M. R.: 1995, Using remote sensing for detecting and mapping

noxious plants, Weed Abstr., 44, 639 649. CAB internal.

Everitt, J. H., Escobar, D. E, Alaniz, M. A, Davis, M. R. and Richardson, J. V.: 1996, Using spatial

information techniques to map Chinese tamarisk (Tamarix chinensis) infestations, Weed Sci. 44,

194 201.

Gausman, H. W., Everitt, J. H., and Gerbermann Bowen R. L.: 1977, Canopy re ectance and lm

image relations among three south Texas rangelands plants, J. Range Manage. 30, 449 450.

Gong, P., Marceau, D. J. and Howarth, P. J.: 1992, A comparison of spatial feature extraction algo-

rithms for land-use classi cation with SPOT HRV data, Remote Sens. Environ. 40, 137 151.

Haralick, R. M.: 1979, Statistical and structural approaches to texture, Proc. IEEE, 67, 786 804.

Haralick, R. M., Shanmugam, K. and Dinstein, I.: 1973, Textural features for image classi cation,

IEEE Tran. Geosci. Remote Sens. 33, 1170 1181.

Hassan, A. and He, D. C.: 1995, Evaluation of textural and multi-polarization radar features for crop

classi cation, IEEE Tran. Geosci Remote Sens. 33, 1170 1181.

Hudak, A. T. and Wessman C. A.: 1998, Textural analysis of historical aerial photography to char-

acterize woody plant encroachment in South African Savanna, Remote Sens. Environ. 66, 317

330.

Jonathan, C.-W. C., Defries, R. S. and Townsend, R.G.: 2001, Improved Recognition of Spectrally

Mixed Land Cover Classes Using Spatial Textures and Voting Classi cations, CAIP 2001, LNCS

2124, 217 227.

Lass, L. W., Carson, H. W. and Callihan, R. H.: 1996, Detection of yellow starthistle (Centaurea sol-

stitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery,

Weed Tech. 11, 248 256.

Mather, P. M., Tso, B. C. K. and Koch, M.: 1998, An evaluation of Landsat TM spectral data and

SAR Derived textural information for Litho logical discrimination in the Red Sea Hills, Sudan,

Int. J. Remote Sens. 19, 587 604.

Nyoungui, A. N., Tonye, E. and Akono, A.: 2002, Evaluation of speckle ltering and texture analysis

methods for land covers classi cation from SAR images, Int. J. Remote Sens. 23, 1895 1925.

Podest, E. and Saatchi, S.: 2002, Application of multiscale texture in classifying JERS-1 radar data

over tropical vegetation, Int. J. Remote Sens. 23, 1487 1506.

Riou, R. and Seyler, F.: 1997, Texture analysis of tropical rain forest infrared satelliate images, PE

and RS. 63, 515 521.

Saatchi, S. S., Nelson, B., Podest, E. and Holt, J.: 2000, Mapping land cover types in the Amazon

Basin using 1 km JERS-1 mosaic, Int. J. Remote Sens. 21, 1183 1200.

Shaban,M. A. and Dikshit, O.: 2001, Improvement of classi cation in urban areas by the use of

textural features: The case study of Lucknow city, Uttar Pradesh Int. J. Remote Sens. 22, 565

593.

Smits, P. C. and Annoni, A.: 1999, Updating Land-cover maps by using texture information from

very high-resolution space-borne imagery, IEEE Trans. Geosci. Remote Sens. 37, 1244 1254.

Stohlgren, T. J., Binkley, D., Chong, G. W. Kalkhan, M. A., Schell, L. D., Bull, K. A. Otsuki, Y.,

Newman, G., Bashkin, M. and Son, Y.: 1999, Exotic plant species invade hot spots of native plant

diversity, Ecol. Monog. 69: 25 46.

Xu, B., Gong, P., Spear R. and Seto, E.: 2003, Comparison of different gray level reduction schemes

for a revised texture spectrum method for land-use classi cation using IKONOS imagery, PE.

RS. 69, 529 536.

Zavaleta, E.: 2000, The economic value of controlling an invasive shrub, Ambio 29, 462 467.

Contact this candidate