remote sensing of eupatorium adenophorum spreng based on hj-a satellite data

7/31/2019 Remote Sensing of Eupatorium Adenophorum Spreng Based on HJ-A Satellite Data

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RESEARCH ARTICLE

Remote Sensing of Eupatorium Adenophorum Spreng Based

on HJ-A Satellite DataJun Chen & Wenting Quan & Kai Lu

Received: 24 October 2010 /Accepted: 25 May 2011 /Published online: 15 June 2011# Indian Society of Remote Sensing 2011

Abstract In the southwest of China, one of the

greatest threats to local ecosystem is the area

expansion of an invasive species, i.e., Eupatorium

adenophorum Spreng (EAS). In this study, the

remote-sensing technology was used to detect and

map the spatial distribution of EAS in Guizhou

Province, China. A series of vegetation indices,

including normalized difference vegetation index

(NDVI), simple ratio index (SRI) and atmospherically

resistant vegetation index (ARVI), were used to

identify EAS from HJ-A Chninese satellite data.According to the analysis results of fieldworks from

March 21 to 22, 2009, it was found that the vegetation

index of {1.9589SRI4.1095}{0.2359ARVI

0.5193} was the optimal remote-sensing parameter

for identifying EAS from HJ-A data. According to the

spatial distribution of EAS estimated from HJ-A data,

it was found that EAS was rather more in southwest of

Guizhou Province than in northeast. EAS became

sparse from southwest to northeast gradually, and the

central Guizhou Province was the ecological corridor

linking EAS in southwest to that in northeast. By

comparison with validated data collected by the

government of Guizhou Province, it was found that

the uncertainty of remote-sensing method was 18.52%,

29.31%, 8.77% and 9.46% in grassland, forest,

farmland and others respectively, and the mean

uncertainty was 13.29%. Owing to the lower heightof EAS than many plants in forest, the uncertainty of

EAS was the greatest in forest than that in grassland,

farmland and so on.

Keywords Remote sensing . Invasive species .

Eupatorium adenophorum Spreng

Introduction

Belonging to Asteraceae family, Eupatorium adenopho-rum Spreng (EAS) is mainly distributed in the tropical

and temperate zones of Central America (Sun et al.

2004). Owing to the superior endurance against various

environmental stresses, EAS has an advantage of

high ecological adaptation as well as an ability to

alter a natural ecosystem at an alarming rate (Wang

et al. 2007). In some fragile ecosystems, EAS is able

to change the biological diversity, alter the ecological

J Indian Soc Remote Sens (March 2012) 40(1):2936

DOI 10.1007/s12524-011-0133-z

J. Chen :K. LuThe key laboratory of marine hydrocarbon

resources and geology,

Qingdao 266071, China

J. Chen (*) :K. LuQingdao Institute of Marine Geosciences,

Qingdao 266071, China

e-mail: [email protected]

W. Quan

College of Resource Science and Technology,

Beijing Normal University,

Beijing 100875, China


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processes and services and even compete with other

native species. Additionally, EAS is a toxic species

causing serious damages to plants and animals

(Sharma et al. 1998; Katoch et al. 2000), e.g., the

foliage consumption by horses can lead to pulmonary

toxicity (OSullivan 1979); exposure of rats to feed

with the freeze-dried leaf powder of EAS may causehepatotoxicity (Ye 2001).

As its fruits introduced from Thailand to Yunnan

Province of China in 1940s (Xu and Wang 2004; Rymer

2008), EAS has dominated a large area of farmland,

national parks and forest land in Southeast China

during the past years (Ye 2001; Papes and Peterson

2003). If human beings still do nothing to solve this

problem, natural ecosystems may be replaced by a

monoculture of this invasive species. To effectively

prevent EAS spreading in Southeast China, compre-

hensive monitoring schemes for invasion detection arerequired. However, due to the lack of well-established

methods, EAS is rarely mapped on the large scale

spatial overview. Though providing accurate measure-

ments, traditional monitoring on EAS distribution is

costly, time consuming and often requires direct contact

with plants, which may result in further dispersal (Hestir

et al. 2008). Additionally, some land types are often

inaccessible or bring logistical difficulties to the field-

based monitoring methods. Satellite remote sensing

techniques may provide suitable tools to integrate

spectral data collected from traditional in situ measure-

ments. Since 1980s, with the improvement of sensor

spatial resolution, satellite remote sensing has been

widely used to monitor the spatial and temporal

changes of invasive plants in a large scale (Andrew

and Ustin 2008; Hestir et al. 2008).

The 4-channel HJ-A, a Peoples Republic of China

satellite launched on September 8, 2008, was selectedfor acquiring broad-band reflectance data by using the

following optical bands: blue-green (450520 nm),

green (520590 nm), red (630690 nm) and near

infrared (760900 nm). In this study, the HJ-A

satellite imageries were used to map the spatial

distribution of EAS. The main objectives of this

study were: (1) to develop the remote-sensing method

for identifying EAS from broad-band reflectance of

HJ-A satellite; (2) to extract EAS from HJ-A satellite

imageries; and (3) to use statistic data of field-based

reported in current literatures to access and validatethe accuracy of remote-sensing approach.

Materials and Methods

Study Area

Guizhou Province is the study area, located in Southeast

China (within 24372913N, 1033610935E) as a

representative region of EAS (Fig. 1). EAS has been

found in Guizhou Province since 1970s, of which the

Fig. 1 Study area

30 J Indian Soc Remote Sens (March 2012) 40(1):2936


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distribution range has extended from 9 cities to 26

cities with an extension rate of 50 km/y from

southwest to northeast (Luo and Xiong 2005). Duringpast several years, EAS has resulted in considerable

economic losses in agriculture and forestry. At present,

Guizhou Province has become one of the regions with

severely endangered EAS in China (Lu et al. 2006).

Data Used

The dataset consisting of spectral data and 4-channel HJ-

A imageries was used to map the spatial distribution of

EAS in Guizhou Province in this study. Fieldworks were

carried out in Xichang City on March 21 and 22, 2009, inorder to measure and acquire spectral reflectance data of

typical terrestrial materials in Lu Mountain (2753N,

10214E) and Yuanjia Mountain (2748N, 10221E),

Xichang City (Fig. 1). The field observation involved the

acquisition of the following information: spectra and

coordinates of the observed sites. The spectral data was

shown in Fig. 2. At each observation point, spectral

measurements were recorded by a spectroradiometer

with a 25fore-optic, covering a spectral domain

(Analytic Spectral Devices, Boulder, Colorado) of 400

900 nm. ASD had a spectral resolution of 3 nm (full-

width at half maximum, FWHM) and a 1.4 nm sampling

interval across the spectral range of 400 nm900 nm

(ASD 1999). Maximum reflectance spectra were mea-sured from nadir at a height of 0.5 m above the material.

Measurements were performed within 2 h at local solar

noon on clear days to reduce the effect from changes in

solar angle. The 4-channel HJ-A data were acquired on

March 22, 2009, when the fieldworks were carried out.

The HJ-A data had a spatial resolution of 30 m. The data

strip covered most landscape of Guizhou Province,

China, except for its left boundary (the bad weather).

Vegetation Indices

More than 150 vegetation indices (VIs) have been

published in scientific literatures, but only a small

subset of them have a substantial biophysical basis

or has been systematically tested. The selection on

the most important vegetation categories and the

optimal representative indices within each category

was done by Asner et al. (2008). The oldest, the

most famous and the most frequently used VIs

included Normalized Difference Vegetation Index

(NDVI) (Rouse et al. 1973; Tucker 1979; Jackson

et al. 1983; Sellers 1985), Simple Ratio Index (SRI)

0

10

20

30

40

50

60

70

80

400 500 600 700 800 900

Wavelength (nm)

Reflectance

EAS EASEAS EASEAS EASEAS EASEAS EASEAS EASEAS EASTree canopy Tree canopyTree canopy PYF

COA BackgroundBackgroundBackground

BackgroundBackground

Fig. 2 Spectral data of

typical terrestrial materials

in Xichang City

Vegetation indices Formula Common range

NDVI NDVI RNIRRREDRNIRRRED 0.2 to 0.8

SRI SRIRNIR

RRED2 to 8

EVI EVI 2:5 RNIRRREDRNIR6RRED7:5RBLUE1

0.2 to 0.8

ARVI ARVI RNIR 2RREDRBLUE RNIR 2RREDRBLUE 0.2 to 0.8

Table 1 The commonly

using VIs

J Indian Soc Remote Sens (March 2012) 40(1):2936 31


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(Rouse et al. 1973; Tucker 1979), Enhanced Vegeta-

tion Index (EVI) (Huete et al. 1997) and Atmospher-

ically Resistant Vegetation Index (ARVI) (Gitelson

and Merzlyak 1994; Kaufman and Tanre 1996; Sims

and Gamon 2002).

Due to the special absorption characteristics of

special plants at the near-infrared band, VIs are

usually designed for mapping the spatial distributionof plants species by using remote sensing approach

(Vogelmann et al. 1993). In this study, NDVI, SRI,

EVI and ARVI were used to construct the remote

sensing model for identifying EAS from HJ-A

satellite. Table 1 shows the equations and the common

green vegetation ranges of these four VIs. And then,

the focus of this study turned to finding out the

optimal threshold for these four VIs to detect EAS

from HJ-A satellite imageries.

Assessment Method for Selecting Optimal Parameters

It is assumed that

T X;Y 1

0

&VIMaxX VI

MinY

VIMinX VI

MaxY

< 0

VIMaxX VIMinY

VIMinX VI

MaxY

! 0

1

where, T(X,Y) is a parameter used to identify material

X from material Y; VIMaxX is the maximum VI of

material X; VIMinY is the minimum VI of material Y;

VIMinX is the minimum VI of material X; VIMaxY is the

maximum VI of material Y. According to Eq. (1), it is

found thatT(X,Y)=0 in case of the VI valued fields of

material Yintersected with that of material X, and T(X,

Y)=1 in case of the VI valued fields of material Y not

intersected with that of material X. Additionally, Eq.

(1) illuminates that it is possible to identify material X

from material Y by the values of VIs when T(X,Y)=1.

Conversely, it is difficult to identify material X from

material Y by the values of VIs if T(X,Y)=0.

In order to obtain optimal identification parameters

for EAS, the dividing degree of VIs, i.e., B(X,Y), was

used to illuminate the possibility that material Y

would be determined into material X.

B X;Y

VImaxX

VIminY

VIMaxX

VIMinX

VImax

Y

VImin

XVIMaxX

VIMinX

;whenVImaxX ! VI

minY

VImaxY > VImin

X

T X;Y 1

8>>>>>:

2

Results and Discussions

Atmospheric Correction

Atmospheric correction is an essential step in the

quantitative inversion of EAS based on remote

Table 2 NDVI

Sample size Minimum Maximum T B

EAS 15 0.3241 0.6086 /

Tree Canopy 3 0.6464 0.8372 1 13.29%

PYF 1 0.6113 0.6113 1 0.95%

COA 1 0.6686 0.6686 1 21.09%Background 4 0.1086 0.2287 1 33.53%

Table 3 SRI


EAS 15 1.9589 4.1095 /

Tree Canopy 3 4.6554 11.281 1 25.38%

PYF 1 4.1456 4.1456 1 1.68%

COA 1 5.0341 5.0341 1 42.99%

Background 4 0.1086 0.2287 1 80.45%

Table 4 EVI


EAS 15 0.7560 1.7826 /

Tree Canopy 3 1.7814 2.2919 0 /

PYF 1 1.7702 1.7702 0 /

COA 1 1.7972 1.7972 1 1.42%Background 4 0.1912 0.3552 1 39.04%

Table 5 ARVI


EAS 15 0.2359 0.5193 /

Tree Canopy 3 0.5731 0.7990 1 18.98%

PYF 1 0.5396 0.5396 1 7.16%

COA 1 0.5953 0.5953 1 26.82%

Background 4 0.0964 0.0156 1 7 7.73%



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sensing data. In this study, the dark target

atmospheric correction algorithm was used to

remove atmospheric influences on HJ-A data. Dark

targets usually refer to thick vegetation canopy,

clear water and dark soil. The light exiting the

atmosphere can be strongly polarized due to

Rayleigh scattering with effect ignored, leading to

significant errors in estimating remote sensing

reflectance. During correcting the HJ-A for removalof Rayleigh and aerosol scattering, Rayleigh scat-

tering with multiple scattering and polarization

correction has been computed and put into lookup

tables for all possible solar and viewing geometries

under a standard pressure (1,013.02 mbar) (Hu

and Carder 2002). Once Rayleigh scattering is

known, the problem is how to estimate aerosol

scattering and atmospheric diffuse transmittance.

And then, the aerosol lookup table constructed by

Gordon and Wang (1994) is used to estimate the

aerosol scattering from the reflectance of dark target

collected by HJ-A imageries.

Remote Sensing Model for EAS Identifying

Tables 2, 3, 4, and 5 show NDVI, SRI, EVI and ARVI

of the fieldwork respectively. According to the data

shown in Tables 2, 3, 4 and 5, NDVI, SRI and ARVI

characteristics of EAS were greatly different from

other materials. The differences existed as follows: (1)

NDVI of EAS was ranged from 0.3241 to 0.6086,

while those of other materials were beyond this range;

(2) SRI of EAS was ranged from 1.9589 to 4.1095,

while those of other materials did not show such

characteristics; (3) ARVI of EAS was ranged from

0.2359 to 0.5193, completely different from those ofother materials; and (4) EVI of EAS was ranged from

0.7560 to 1.7826, which was partly overlapped with

those of other materials.

To successfully map the spatial distribution of EAS

from HJ-A data, the VI dataset, composed of the VIs

with the maximum B(X,Y) for single material, was used

to identify EAS from other terrestrial materials. Table 6

shows the optimal VI dataset constructed by this study.

Table 6 Optimal combination of VIs identified parameters

Y Tree

canopy

COA Background PYF

VIs SRI SRI SRI ARVI

B(EAS,Y) 25.38% 42.99% 80.45% 7.16%

Threshold 1.9589SRI

4.1095

0.2359ARVI

0.5193

(a) Spatial distribution of EAS denoted by ARVI (b) Spatial distribution of EAS denoted by ARVI

Fig. 3 Regions detected from the HJ-A satellite data as having found EAS invading accidents



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According to Table 6, it was found that the maximum

B(X,Y) was 25.38%, 42.99%, 80.45% and 7.16% for

tree canopy, COA, background and PYF respectively,

and the corresponding VIs were SRI and ARVI. By

comparison with the study results as shown in Tables 2,

3, 4 and 5, the optimal parameters for EAS identified

from HJ-A data can be defined as follows:

0:2359 SRI 0:5193f g \ 0:2359 ARVI 0:5193f g

3

Mapping Spatial Distribution of EAS from HJ-A

Satellite Data

Equation (3) was used to map EAS from HJ-A

satellite data. Figure 3 shows the spatial distribution

of EAS denoted by VI values of ARVI and SRI.

According to Fig. 3, it was found that EAS was rather

more in southwest Guizhou Province than that in

northeast. EAS became sparse from southwest to

northeast gradually, and the central Guizhou Province

was the ecological corridor linking EAS in southwest

to that in northeast. Due to the tremendous space and

time of EAS propagation, traditional approaches

based on fieldworks were incapable for revealing the

distribution pattern and the propagation rules in such

a large mapping scale. However, the study results

showed that it was potential to use the remote sensing

approach to solve this problem.

The colors of EAS flowers are white, and the seeds

are hidden at the bottom of the f lowers l ike

Taraxacum. During the florescence, the fruits can be

easily carried by the south-west monsoon from

southwest to northeast of Guizhou Province. With

the high ecological adaptation, EAS gradually invades

farmland, forest, and grassland from southwest to

northeast of Guizhou Province. Figure 4 shows the

landscape pattern of Guizhou Province, China.

According to Figs. 3 and 4, it was found that EAS

was widely distributed in farmland, forest, wetland

and so on. Owing to human disturbance, EAS in

farmland and grassland was rather less than that in

other regions. Because of the serious impact on the

biodiversity and stability of ecosystems, critical

habits, nutrient cycles and environmental qualities

Landuse type Grass land Forest land Farm land Others Total

Estimated by Field words 0.0027 0.1184 0.0775 0.4315 0.63

Estimated by Remote Sensing 0.0032 0.1531 0.0843 0.4731 0.7137

Estimation Errors 18.52% 29.31% 8.77% 9.64% 13.29%

Table 7 EAS cover fraction

in Guizhou Province, China

Fig. 4 Landscape in

Guizhou Province, China



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should not be ignored. It is necessary to pay more

attention on supervising and controlling the propa-

gation trend of EAS.

Accuracy Validation

The landscape patterns in Guizhou Province, China, aredivided into four different types-grassland, forestland,

farmland and others. The landscape characteristics of

Guizhou Province of China were collected by the

government of Guizhou Province, as shown in Fig. 4.

The cover fraction of EAS was collected from in situ

measurement conducted by statistical departments of

the county governments in Guizhou Province. The

fieldwork mainly involved the investigation of the

cover fraction of EAS in these four different landscape

types. Table 7 shows the cover fraction of EAS, which

was estimated from the fieldworks and the remotesensing approach respectively, as shown in Fig. 3.

According to Table 7, it was found that the uncertainty

of the remote sensing method was 18.52%, 29.31%,

8.77% and 9.46% in grassland, forest, farmland and

others respectively, and the mean uncertainty was

13.29%. In general, the height of EAS plant was no

more than 1 m, but lower than those of many plants in

forest. As a result, the uncertainty of remote sensing

approach was the greatest in forest than those in

grassland, farmland and so on.

Conclusions

The initial results of an exploratory project on remote

sensing of the invasive speciesEAS were reported

in this study. Broadband multi-spectral methods were

successfully applied to map EAS for representing the

unique life forms in the ecological environment they

invaded. The following conclusions were obtained

from investigations:

(1) EAS was generally different from those native

plants in terms of their reflectance, NDVI, SRI and

ARVI. According to the study results, it was found

that the optimal spatial distribution of EAS

extracted from the remote sensing data was

{1.9589SRI4.1095}{0.2359ARVI0.5193}.

(2) The HJ-A CCD imageries were successfully

used to map EAS for representing the unique

life forms in the ecological environment they

invade. The spatial distribution pattern of EAS

obtained from HJ-A data showed that EAS was

rather more in southwest Guizhou Province than

that in northeast. EAS became sparse from

southwest to northeast gradually and the central

Guizhou Province was the ecological corridor

linking EAS in southwest to that in northeast.(3) The uncertainty of the remote sensing method

was 18.52%, 29.31%, 8.77% and 9.46% in

grassland, forest, farmland and others respec-

tively, and the mean uncertainty was 13.29%.

Owing to the lower height of EAS than those of

many plants in forest, the uncertainty of EAS

was the greatest in forest than those in grassland,

farmland, and so on.

Acknowledgements This study is supported by the China

National Great Geological Survey (GZH200900504).

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remote sensing of eupatorium adenophorum spreng based on hj-a satellite data

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