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International Journal of Remote SensingPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713722504

A new vegetation index derived from the pattern decomposition methodapplied to Landsat-7/ETM images in MongoliaK. Muramatsu ab; Y. Xionga; S. Nakayama b; F. Ochiai c; M. Daigod; M. Hirata e; K. Oishif ; B. Bolortsetsegg;D. Oyunbaatar g; I. Kaihotsuha KYOUSEI Science Center for Life and Nature, Nara Women's University, Japan b Department of Information and Computer Sciences, Nara Women's University, Japan c Department of Living SpaceDesign, Tezukayama University, Japan d Department of Economics, Doshisha University, Japan e

Department of Agro-Environmental Science, Obihiro University for Agriculture and VeterinaryMedicine, Japan f Graduate School for Agriculture, Kyoto University, g Institute of Meteorology andHydrology, Ministry of Nature and Environment, Mongolia h Department of Natural EnvironmentalSciences, Hiroshima University,

First published on: 07 June 2007

To cite this Article Muramatsu, K. , Xiong, Y. , Nakayama, S. , Ochiai, F. , Daigo, M. , Hirata, M. , Oishi, K. , Bolortsetseg,B. , Oyunbaatar, D. and Kaihotsu, I.(2007) 'A new vegetation index derived from the pattern decomposition methodapplied to Landsat-7/ETM+ images in Mongolia', International Journal of Remote Sensing, 28: 16, 3493 — 3511, Firstpublished on: 07 June 2007 (iFirst)To link to this Article: DOI: 10.1080/01431160601024200URL:http://dx.doi.org/10.1080/01431160601024200

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A new vegetation index derived from the pattern decomposition methodapplied to Landsat-7/ETM + images in Mongolia

K. MURAMATSU* {{, Y. XIONG {, S. NAKAYAMA {, F. OCHIAI " ,M. DAIGO§, M. HIRATA E, K. OISHI # , B. BOLORTSETSEG {{ ,

D. OYUNBAATAR {{ and I. KAIHOTSU {{{KYOUSEI Science Center for Life and Nature, Nara Women’s University, Japan

{Department of Information and Computer Sciences, Nara Women’s University, Japan" Department of Living Space Design, Tezukayama University, Japan

§Department of Economics, Doshisha University, JapanEDepartment of Agro-Environmental Science, Obihiro University for Agriculture and

Veterinary Medicine, Japan# Graduate School for Agriculture, Kyoto University{{ Institute of Meteorology and Hydrology, Ministry of Nature and Environment,

Mongolia{{Department of Natural Environmental Sciences, Hiroshima University

(Received 17 November 2005; in final form 8 September 2006 )

The goal of this study was to estimate vegetation coverage and map the land-cover in an experimental field (60 6 60 km) near Mandalgobi, Mongolia using

Landsat-7/ETM + data for ground truthing in the Advanced Earth ObservingSatellite II (ADEOS-II) Mongolian Plateau Experiment (AMPEX). Wemeasured soil moisture, vegetation coverage, and vegetation moisture in thefield at 49 grid points around the time that the Aqua satellite passed over thearea. We also surveyed the land-cover in the field. Using ground-based data andcharacteristics of spectral reflectance, we attempted to extract vegetationinformation from satellite data using the pattern decomposition method, whichis a type of spectral mixture analysis. This method uses normalized spectralshapes as endmembers, which do not change between scenes. We defined anindex using the pattern decomposition coefficients to analyse sparsely vegetatedareas. The index showed a linear relationship with vegetation coverage. The

vegetation coverage was estimated for the study site, and the average coverage atthe site was 21.4%. Land-cover types were classified using the index and thepattern decomposition coefficients; the kappa coefficient was 0.75. The index wasuseful for estimating vegetation coverage and land-cover mapping for semiaridareas.

1. Introduction

The Mongolian Plateau is an arid to semiarid region, which has warmed substantiallybetween 1979 and 1997 (Chase et al . 2000). Several authors have studied thevegetation response to atmospheric warming and water availability in arid andsemiarid regions. In these areas, water-budget limitations determine the patterns of vegetation response to warming (Neilson 1995; Yu 2003), and grassland growth is

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing Vol. 28, No. 16, 20 August 2007, 3493–3511

International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160601024200


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strongly dependent on water availability (Kondoh and Kaihotsu 2003). On theMongolian Plateau, a detailed regional study of vegetation is important to understandthe role of vegetation in hydrological processes and thermal environments. Our studyarea is one of the validation sites for soil moisture estimations from remote sensingdata such as those provided by the Advanced Microwave Scanning Radiometer(AMSR) onboard the Advanced Earth Observing Satellite II (ADEOS-II) andAMSR-E onboard the Aqua satellite, which was planned by the Japan AerospaceExploration Agency (JAXA). The ground experiment area (60 6 60 km) was located ina semiarid and sparsely vegetated region near the city of Mandalgobi, Mongolia. Atthis site, soil moisture, vegetation coverage, species and vegetation moisture weremeasured every 10 km in a grid (Kaihotsu et al . 2001). Information on the vegetationin each grid was obtained from the ground experiment.

To detect vegetation signals from satellite optical sensor data, vegetation indicessuch as the normalized difference vegetation index (NDVI; Kriegler et al . 1969;Rouse et al . 1973) and spectral mixture analysis (SMA; Adams et al . 1986, 1995)have been widely used. In sparsely vegetated areas, however, vegetation indices areadversely influenced by variation in soil background reflectance (Elvidge and Lyon1985; Huete et al . 1985; Heilman and Boyd 1986; Tueller and Oleson 1989). Usingspectral mixture analysis, Huete (1986) investigated separation of vegetation fromthe background soil in a ground-based experiment. Background surface compo-nents, different illumination, atmosphere and instrument responses have beeninvestigated for desert vegetation using Landsat Thematic Mapper multispectralsatellite images (Smith et al . 1990). Spectral mixture analysis is a powerful tool forextracting vegetation information even in sparsely vegetated areas. In a comparisonof vegetation coverage derived from SMA and NDVI data, SMA was less sensitive

to soil background than NDVI (Garcia-Haro et al . 1996). In SMA, endmembers areselected scene by scene for the objects of study, and the fraction for each endmemberis obtained. In estimating the fraction using near infrared (NIR) spectral data, linearmixing overestimates green-leaf fractions because of nonlinear mixing, which iscaused by transmission and scattering by green leaves (Roberts et al . 1993). Modelsof the radiative transfer in vegetation canopies have been developed (Jacquemondand Baret 1990; Jacquemond 1993). Modelling is useful for estimating leaf areaindex (LAI) and biochemical characteristics, but the model parameters should bedetermined for each vegetation type.

The pattern decomposition method (PDM; Fujiwara et al . 1996; Muramatsu et al .

2000a,b; Daigo et al . 2004) is considered a type of spectral mixture analysis. In thismethod, three fixed spectral shape patterns that are used for every scene are used toextract spectral signatures. These standard spectral patterns are the typical spectralsignatures of water, vegetation and soil. The standard patterns are used asendmembers in spectral mixture analysis, but are regarded as axes of multi-dimensional data, and the three axes were selected as being almost orthogonal toeach other (Daigo et al . 2004). The same axes can be used for every scene. In SMA,the coefficient for each endmember is a fraction of a ground object. In contrast, thecoefficient for each standard pattern is simply the coefficient for each axis andreflects the spectral signature of each axis. Using the same axes, the data are

compared to the same standard. Using this method, changes in vegetation coveragehave been studied for flora of the temperate zone (Furumi et al . 1997).Our goal was to estimate vegetation coverage and land-cover mapping of the

experimental field using Landsat-7/ETM + images to obtain results for use as input

3494 K. Muramatsu et al .


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data for a study of the role of vegetation in hydrological processes and the thermalenvironment. We attempted to apply the method to estimate vegetation coverageand create land-cover classifications for a sparsely vegetated area using the samestandard pattern in the analysis. It is important to understand the relation betweenthe spectral signatures and land-cover/vegetation types for making clear themeaning of remote sensing data. Since, if the classification rule was determined, itmust be used widely. From this viewpoint, land-cover classification is studied usingthe the rule-based approach of spectral signatures.

2. Study area and field survey

2.1 Study area

The experimental field is one of the validation sites (Kaihotsu et al . 2005) used forsoil moisture estimation algorithms from ADEOS-II/AMSR data and Aqua/AMSR-E. The experimental site lies approximately 235km south-southwest of Ulaanbaatar, the capital of Mongolia, and is located in a semiarid and sparselyvegetated area. The dominant vegetation types are short grasses, tall grasses such asAchnatherum splendens , shrubs such as Caragana microphylla C. pygmaea , and salt-basin vegetation such as Salsora passerina and Reaumuria soongoria . Figure 1 showsphotographs of (a) short grasses, (b) C. microphylla , and (c) A. splendens .

The Enhanced Thematic Mapper Plus (ETM + ) image of the area on 12 June 2001is shown in figure 2. Red, green and blue correspond to radiance in bands 5, 4, and 3of ETM + , respectively. The area is 60 6 60 km, and the starting point atMandalgobi (M.G.) is indicated by the cross in the southwest corner of figure 2.

(a

)

(c )

(b

)

Figure 1. (a) Short grass, (b) Caragana microphylla , (c) Achnatherum splendens .

A new vegetation index derived from the pattern decomposition method 3495


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The field survey was conducted at each grid point at intervals of 10 km. The gridpoint at Mandalgobi was defined as ‘A’, and the other grid points were markedevery 10 km as ‘A, B, C, D, E, F, G’ in the west-east direction and ‘0, 1, 2, 3, 4, 5, 6’in the south–north direction. The longitude and latitude for each grid point aresummarized in table 1.

2.2 Field survey

Ground-based intensive observations were made around the time the Aqua satellitepassed over the area between 8 and 11 August 2001 (Kaihotsu et al . 2005). Soilmoisture, vegetation coverage, plant biomass and vegetation moisture weremeasured every 10km at each cross in figure 2. Here, we only describe thevegetation measurements (Hirata 2005). For short grasses, the vegetation coverage(%) and natural height (cm) were measured twice for each species using a 50 6 50 cmquadrat. One survey location was selected at a grid point determined by the projectusing a GPS receiver. Another location was randomly selected within 20 m of thisgrid point. If the vegetation was not uniform within a 60 6 60 m area, the land-cover

Figure 2. Experimental area near Mandalgobi, Mongolia.

Table 1. Longitude and latitude for each grid point. A0 represents Mandalgobi, Mongolia.

Longitude

A B C D E F G

106 u 15.878 9 106u 23.648 9 106u 31.365 9 106 u 39.082 9 106u 46.799 9 106u 54.516 9 107u 02.233 9

Latitude

0 1 2 3 4 5 6

45 u 44.488 9 45u 49.885 9 45u 55.282 9 46u 00.679 9 46u 06.076 9 46u 11.473 9 46u 16.870 9



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information was recorded. Vegetation samples were collected for each speciesfollowing these measurements. The dry weight was measured after drying for2 hours in an oven at 130 u C. For tall grasses and shrubs, the vegetation coverage andvolume were measured using a line method with three 25-m transects arrangedradially and 120 u apart. Natural height (cm) and crown and base area of thevegetation canopy were measured twice randomly for each species. After thesemeasurements, tall grasses were collected by the same process as the short grasses.

The vegetation coverage and plant biomass for each grid are summarized intable 2. In general, coverage by short grasses was higher than any other type of vegetation coverage, although the plant biomass of short grasses was lower thanthose of other types of vegetation. In particular, the plant biomass of Caraganaspecies was high in the east. The average vegetation coverage and plant biomass forthese 49 grid points was 23.8%, and 53.7 gDM/m 2 , respectively.

In addition to data from the 49 grid points, we also collected validation data forland-cover classification. A 60 6 60 m area (60 m is twice the spatial resolution of ETM + ) with uniform land-cover was selected for validation. The dominantvegetation type of the area was recorded, in addition to latitude and longitude, usinga global positioning system (GPS) receiver (GPS-IIIplus; Garmin). For short grass,tall grass such as A. splendens and shrubs such as C. microphylla and C. pygmaea , 14,13 and 6 positioning data were collected, respectively.

3. Satellite data and analysis

3.1 Satellite data

In this study area, the soil colour varies spatially and the soil reflectance is high. It is

important to extract the vegetation signal from the mixture of soil and vegetationsignals. Satellite data collected before the onset of germination in the spring areuseful for studying background soil reflectance. We used two set of satellite datafrom the ETM + sensor onboard Landsat-7: one collected before the onset of germination (25 April 2001) and one collected during the growing season (12 June2001). While the field survey was carried out in August, the ETM + data acquired inAugust were cloudy. Thus, data acquired in June were used as the data during thevegetation growing season in this analysis.

Registration of the June image with the April image was processed using groundcontrol points measured in the field with GPS receivers (GPS-IIIplus, Garmin; GPS

camera, Konica). We could not obtain a detailed map of the study area, and therewere few target control points because of a few artifacts. First, we selected severalpoints as clear targets for control points from the satellite image: for example, theintersection of a concrete runway or road, the edge of an open space in front of acity office, the edge of a village, etc. Next, we obtained the latitude and longitude of each point using GPS. Five sets of control points were used for registration: theseoccurred over a wide area of the experimental site. Two points were located at thenorthwest (A6) and southwest (A0: M.G. City) edges of the 60 6 60 km site, onepoint was about 90 km northeast of M.G. City, and two points were within the606 60 km site. The latitude and longitude were recorded for 400 points along the

roads connecting villages. These points were used as line data. The adequacy of the registration process was examined using the GPS data for the line data and theactual road lines in a image. The registration error was estimated from the differencebetween these data, and it was less than 1 pixel (28.5m).



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Table 2. Vegetation coverage (VC) and dry mass (DM) at each grid point based on observations made

Gridpoint

Short grass grass

Achnatherum

splendens Caragana microphylla Caragana phymgaea Salsora passeVC (%) DM (g/m2) VC (%) DM (g/m2) VC (%) DM (g/m2) VC (%) DM (g/m2) VC (%) DM

A0 13.3 12.0 0. 0. 0. 0. 0. 0. 0. A1 11.6 22.0 0. 0. 14.1 101.6 0. 0. 0. A2 19.6 31.6 0. 0. 0. 0. 0. 0. 0. A3 21.4 28.4 0. 0. 0. 0. 0. 0. 0. A4 21.3 37.2 0. 0. 0. 0. 0. 0. 0. A5 18.3 62.2 6.7 13.5 7.1 30.6 0. 0. 0. A6 24.7 41.2 0. 0. 0. 0. 0. 0. 0. B0 15.4 27.2 0. 0. 0. 0. 0. 0. 0. B1 9.1 30.7 55.1 63.6 0. 0. 0. 0. 0. B2 4.7 7.9 0. 0. 0. 0. 0. 0. 1.5 B3 19.6 43.2 0. 0.0 0. 0.0 0. 0. 0. B4 26.9 62. 0. 0.0 0. 0.0 0. 0.0 0. B5 14.5 44. 0. 0.0 0. 0.0 0. 0.0 0. B6 24.0 39.2 0. 0.0 0. 0.0 0. 0.0 0. C0 16.8 22.9 0. 0.0 1.2 4.9 0. 0.0 0. 0 C1 16.7 33.7 6.5 49.9 0. 0.0 0. 0.0 0. C2 11.5 13.6 0. 0.0 0. 0.0 0. 0.0 0. C3 14.6 22.4 0. 0.0 5. 4.1 0. 0.0 0. C4 19.0 34. 0. 0.0 0. 0.0 0. 0.0 0. C5 10.7 20. 0. 0.0 0. 0.0 0. 0.0 0. C6 13.3 28.4 0. 0.0 0. 0.0 0. 0.0 0. D0 6.4 7.8 1.7 9.8 0. 0.0 2.5 5.0 2.9 D1 9.8 10.7 0. 0.0 3.3 10.7 0. 0.0 0. D2 10.8 17.7 0. 0.0 3.7 3.5 0. 0.0 0. D3 19.8 52. 0. 0.0 0. 0.0 0. 0.0 0. D4 15.6 37.1 1.3 1.94 0. 0.0 0. 0.0 0. D5 18.3 36.8 0. 0.0 0. 0.0 0. 0.0 0. D6 23.8 62.4 0. 0.0 0. 0.0 0. 0.0 0. E0 22.5 37.6 0. 0.0 0. 0.0 0. 0.0 0.


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Gridpoint

Short grass grassAchnatherum

splendens Caragana microphylla Caragana phymgaea Salsora passe

VC (%) DM (g/m2) VC (%) DM (g/m2) VC (%) DM (g/m2) VC (%) DM (g/m2) VC (%) DM

E1 6.8 9.9 1.1 3.2 0. 0.0 0. 0.0 0. E2 22.7 28.7 4.5 1.2 0. 0.0 1.1 0.075 0. E3 35.1 43.4 2.4 3.39 0.3 1.8 0.5 0.87 0. E4 9.9 9.8 14. 10.8 0. 0.0 0. 0.0 0. E5 12.2 13.4 0. 0.0 0. 0.0 0. 0.0 7.7 E6 35.1 48.7 0. 0.0 0.4 0.6 0. 0.0 0. F0 19.5 38.3 0. 0.0 5.3 27.2 0. 0.0 0. F1 27.4 38.8 4.9 36.7 0. 0.0 5.4 20.2 0. F2 22.8 45.3 7.9 38.6 2.3 3.5 0. 0.0 0. F3 15.5 42.4 0. 0.0 0. 0.0 0. 0.0 0. F4 32.5 57.5 0. 0.0 8.5 76.2 0. 0.0 0. F5 21.4 19.9 5.9 53.4 1.3 0.49 0. 0.0 0. F6 18.7 24.3 0. 0.0 0. 0.0 0. 0.0 0. G0 27.0 38.3 6. 15.7 0. 0.0 2.9 26.1 0. G1 31.6 0.0 0. 0.0 0. 0.0 0. 0.0 0. G2 25.5 31.7 3.4 6.2 0. 0.0 0. 0.0 0. G3 26.8 67.6 0. 0.0 8.5 37.9 0.6 26.3 0. G4 16.5 40.5 1.8 1.75 0. 0.0 0. 0.0 0. G5 12.6 20.2 0. 0.0 17.1 68.4 0. 0.0 0. G6 17.2 24.7 0. 0.0 7.9 28.1 0. 0.0 0.

Table 2. Continued.


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The digital numbers (DN) of the original sensor data were converted to spectralreflectance (Muramatsu et al . 2000a). The calculation is as follows. The spectralreflectance ( R(l )) at satellite altitude for each band’s centre wavelength ( l (mm)) wascalculated as follows:

R lð Þ~ 1L solarD l

L lð ÞD l { L pathD ln o, ð1Þwhere D l is the spectral bandwidth, L(l )D l (Wm

2 2 sr2 1 ) is the spectral radiance

observed by the sensor, LpathD l Wm{ 2 sr { 1 is the spectral path radiance, and

L solarD l Wm{ 2 sr { 1 is the spectral solar radiance at satellite altitude over the spectral

bandwidth ( D l ).The spectral radiance L(l )at the centre wavelength l is converted from the DN

recorded by the sensor. The calibration constants from the Landsat-7 project areavailable to the public. The total in-band solar radiance ( L solarD l ) outside the

atmosphere is

L solarD l ~E meanD l cos h0

pd 2 : ð2Þ

Here E meanD l W m{ 2 is the solar spectral irradiance over the spectral bandwidth ( D l )

of the band at the mean Earth–Sun distance, and the value from the Landsat-7project is available to the public. h0 is the Sun’s angle from the zenith, and the valueis in the data header. d is the Earth–Sun distance on the observation day inastronomical units and was calculated manually.

The path radiance ( LpathD l ) depends on changes in solar illumination and

atmospheric conditions. The main process leading to the path radiance isRayleigh scattering (Rayleigh 1871). The cross-section (Hill and Sturm 1991) of Rayleigh scattering varies as l

2 4 and is calculated for each band using the solarzenith angle and the Earth–Sun distance for each image datum.

3.2 Analysis methods

3.2.1 Pattern decomposition method. We analysed the spectral reflectance datameasured on the ground and satellite data using the pattern decomposition method(PDM; Fujiwara et al . 1996; Muramatsu 2000a,b; Daigo 2003). In the PDM, a set of reflectance data was used. If the satellite data were given as DN values, they wereconverted to reflectance as described in the previous subsection. The outline of thePDM is that the set of spectral reflectance values ( R 1 , R2 , …, Rn ) of n bands for apixel is transformed into three coefficients such as water ( C w ), vegetation ( C v ) andsoil ( C s ) with three standard spectral shape patterns that correspond to typicalground objects, namely water ( P i w ), vegetation ( P i v ) and soil ( P i s ) as follows:

R i ? C wP i wz C vP i vz C sP i s: ð3Þ

The three standard spectral patterns are as follows:

Water pattern ~ P 1w , P 2w , . . . , P 6wð Þ,Vegetation pattern ~ P 1v , P 2v , . . . , P 6vð Þ,

Soil pattern ~ P 1s , P 2s , . . . , P 6sð Þ,

ð4Þ



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Xn

i ~ 1

P ik ~ 1, k ~ w, v, sð Þ:

The three standard spectral shapes were normalized to 1 and fixed for all analyses

with Landsat/TM or ETM+

data. The values of the three standard spectral patternsare shown in table 3 (Muramatsu et al . 2000a); they are the same as those previouslyused in the analysis of forested areas in Japan (Furumi et al . 1997; Muramatsu et al .2000a,b). These standard patterns were calculated using three image samples: awinter image of the sea, an early summer image of a deciduous forest, and an imageof bare soil from a development area. Each sample contained approximately 30– 40 pixels. For the analysis using spectral reflectance data measured in the field, thestandard patterns were calculated using the spectral reflectance data measured in thefield. If the atmospheric effects were corrected perfectly, we could use the samestandard patterns for ground-based data. For the standard samples, we used

seawater, 10 overlapping leaves of Quercus glauca Thunb, and soil samples fromMongolia as samples of water, vegetation and soil, respectively. The standardpatterns are summarized in table 4.

C w , C v , and C s are decomposition coefficients for water, vegetation and soilpatterns, respectively. The coefficients were found using the least squares methodbetween the reflectance of satellite data and the standard patterns.

3.2.2 Vegetation indices commonly used for remote sensing data analysis. TheNDVI (Kriegler et al . 1969; Rouse et al . 1973) is the most widely used index forvegetation monitoring:

NDVI ~r NIR { r redr NIR z r red , ð6Þ

where r represents reflectance in the NIR or red spectral region. NDVI is sensitiveto canopy background and is saturated at high-biomass conditions (Huete 1986).

To minimize the effect of soil brightness on the vegetation index, the soil-adjustedvegetation index (SAVI; Huete 1988) was developed as:

SAVI ~ r NIR { r redr NIR z r red z L

1z Lð Þ, ð7Þ

Table 4. Standard pattern for data measured by spectrometer with the spectral region of Landsat-7/ETM + .

Band 1 2 3 4 5 7

Water 0.38 0.32 0.17 0.09 0.03 0.02Vegetation 0.04 0.09 0.04 0.53 0.22 0.08Soil 0.07 0.10 0.15 0.19 0.25 0.24

Table 3. Standard pattern for Landsat-7/ETM + data analysis.

Band 1 2 3 4 5 7

Water 0.39 0.28 0.18 0.14 0.01 0.00Vegetation 0.03 0.05 0.03 0.60 0.21 0.09Soil 0.09 0.12 0.13 0.21 0.23 0.21

(5)



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where L is a correction factor ranging from 0 for very high vegetation coverage to 1for very low vegetation coverage. For Mongolia, we used a correction factor of 1.

The enhanced vegetation index (EVI; Huete et al . 1994, 1997, 2002) wasdeveloped to optimize the vegetation signal with improved sensitivity to highbiomass and lower sensitivity to canopy background signals and atmosphericeffects:

EVI ~ G r NIR { r red

r NIR z C 1 | r red { C 2 | r blue z L, ð8Þ

where L is the canopy background adjustment that addresses nonlinear, differentialNIR and red radiant transfer through the canopy, and C 1 ; and C 2 are thecoefficients of the aerosol resistance term, which uses the blue band to correct foraerosol effects in the red band.

4. A new index derived from the pattern decomposition method and vegetation

coverageNonlinear mixing effects have been reported in the NIR spectrum (Roberts et al .1993). Transmission and scattering by green leaves in the NIR region causenonlinear mixing. Linear mixing overestimates green-leaf fractions. In sparselyvegetated areas, this effect causes a weaker signal from vegetation. To studyvegetation coverage in sparsely vegetated areas, the relationship between spectralreflectance and vegetation coverage was investigated. The spectral reflectance curvesof samples were measured in the laboratory with vegetation coverage ranging from 0to 30%. Soil samples from the experimental area in Mongolia and short-grasssamples from Japan were used. The short grasses were planted in soil andphotographed to calculate the vegetation coverage. The light source was a 500Whalogen lamp and the field of view was restricted to a circle of 8-cm diameter using ablack cover. From each spectral reflectance curve, six Landsat-7/ETM + spectralbands were extracted and analysed using the PDM.

Figure3(a) shows the relationship between the spectral reflectance of the spectralbands of Landsat-7/ETM + and vegetation coverage. The reflectance of bands 1 to 3decreased as the vegetation coverage increased. The variation in band 3 was largerthan in bands 1 or 2. The reflectance of band 4 slightly decreased as vegetation coverratio increased to 0.25 (25%) and then slightly increased as vegetation coverageincreased further. This tendency was clear for bands 5 and 7, although the

reflectance was not as stable for vegetation coverage in bands 5 and 7.Pattern decomposition coefficients were then calculated using equation (3).

Figure3(b) shows the pattern decomposition coefficients of C v and C s with changesin vegetation coverage. Because the coefficient of C w was zero for most samples, it isnot shown. The vegetation coefficients decreased and the soil coefficients increasedas the vegetation coverage decreased. The variation was more remarkable in soilcoefficients than in vegetation coefficients. In sparsely vegetated areas, light wastransmitted through the vegetation and reflected off the soil, as was observed in thesparse vegetation sample. Thus, vegetation coefficients are less sensitive than soilcoefficients. The difference in the soil coefficients for soil covering 100% and a

mixture of soil and vegetation covering the field of view indicates the lightattenuation in vegetation. In this case, we were able to extract information onvegetation using not only the vegetation coefficients ( C v ) which originally containedvegetation information, but also the difference in the soil coefficients for soil



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covering 100% and a mixture of soil and vegetation covering the field of view

(C s (soil 100%) 2 C s ). To confirm this, the relationship between the value of C v + (C s (soil 100%) 2 C s ) and vegetation coverage was investigated. The equationthat fitted a straight line to the data was

Vegetation cover ratio ~ 0:215 | C vz C s soil 100%ð Þ{ C sð Þz 0:01, ð9Þ

and the experimental linear-correlation coefficient was 0.90. From this result, thevalue of C v + (C s (soil 100%) 2 C s ) showed a linear relationship with vegetationcoverage, with an intercept of almost zero.

Figure 4 shows the relationship between vegetation coverage and the index(C v + (C s (soil 100%) 2 C s ))6 0.215, and vegetation indices such as NDVI, SAVI and

EVI. For NDVI, clear linearity with vegetation coverage was observed, but theintercept was not zero. The tendencies of EVI and SAVI relative to vegetationcoverage were similar in that the intercept were not zero and linearity withvegetation coverage was less clear than with NDVI. The index ( C v + (C s (soil100%) 2 C s )) showed linearity to vegetation coverage and was zero when vegetation

Figure 3. (a) Spectral reflectance of the wavelengths of the Landsat-7/ETM + sensor and (b)pattern decomposition coefficients for different vegetation coverage.

Figure 4. Relationship between vegetation cover ratio and the index C v + (C s (soil100%) 2 C s ), and vegetation indices such as NDVI, SAVI, and EVI for spectral reflectancemeasurement data in the laboratory using Mongolian field soil and grasses from Japan.



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coverage was 0%. Although the index was slightly more sensitive to measurementconditions than NDVI, it is expected to be useful for analyses of sparsely vegetatedareas.

5. Results and discussion

5.1 Vegetation coverage

In the previous section, the index of C v + (C s (soil 100%) 2 C s ) was shown to be useful,but we still needed to determine the value of C s (soil 100%) for each pixel. Weexamined air temperatures before the beginning of May and recorded several dayswith air temperatures below 0 u C at the experimental site in Mongolia. Germinationdid not likely occur before 25 April. We visited Mongolia in June 2001, during theflowering season, and assumed that vegetation became vigorously active in June.

We attempted to analyse the index using the vegetation and soil coefficients inJune and soil coefficients in April as 100% soil coverage as follows:C v (June) + (C s (April) 2 C s (June)). The value of the index was about 0.15 overall,and 0.4 in the east where C. microphylla was dominant as previously indicated. Thespot value of a marsh was 0.8. The relationship between the vegetation coveragemeasured in the field and the index ( C v (June) + C s (April) 2 C s (June)) for ETM + datais shown in figure 5. The equation that fitted a straight line to the data was

Vegetation cover ratio ~ 1:13+ 0:07ð Þ| C v Juneð Þz C s Aprilð Þ{ C s Juneð Þð Þ, ð10Þ

and the experimental linear-correlation coefficient was 0.97. The slope of equation (10) differed from that of the ground-based experiment equation (9). It islikely that the difference between the reflectance observed by the satellite sensor andthat measured on the ground for the same sample is the primary cause of thedifference in the slope. The difference occurs because of the spectral response of thesensor and atmospheric effects. Here, we only corrected the spectral reflectance atthe satellite altitude for path radiance. A 60 6 60 m area of concrete is a relativelyunchanging place seasonally. The spectral reflectance of 23 samples for a concrete

Figure 5. Relationship between the vegetation cover ratio measured at grid points in theexperimental site and the values of C v (June) + (C s (April) 2 C s (June)) from Landsat-7/ETM +corresponding to each grid point.



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surface was measured on the ground. The average spectral reflectance of the 23samples was compared to the spectral reflectance observed by the satellite sensor.The shape was similar, but not exactly equal and, the ground-measured reflectancewas 0.1–0.15 higher than the reflectance observed by the satellite. To use the samerelationship between satellite and ground measurements for all samples, we shouldperfectly correct atmospheric effects and the spectral response of the sensor. Thesecorrections are not the main topic of this paper.

In addition, the difference in vegetation reflectance shape between humidtemperate vegetation in Japan and semiarid vegetation may be an alternativeexplanation for the difference in slope. We measured the spectral reflectance of C.micrioylla leaf and compared the shape of the reflectance with that of grass in Japan.There was a tendency toward lower and higher reflectance of ETM + sensor band 4and (NIR) band 5 (middle IR) 1.65 mm), respectively. Drier leaves reportedly havehigher middle IR reflectance (Furumi et al . 1998). We considered that this is one of causes for the slope difference. In the estimation of worldwide vegetation coverage,a slope for normalization factor should be introduced.

Figure 6 shows the results of the estimation of vegetation coverage using thisrelationship. The average vegetation coverage for the experimental area was 21.4%.The average vegetation coverage for the experimental area and that of the 49 gridpoints were almost the same within an acceptable margin of error.

In this study, we made a rough assumption that the vegetation condition did notchange dramatically between June and August. The total precipitation betweenthese two months was 56 mm during 11 rainy days, however, there was no rainfall

Figure 6. Vegetation coverage at the experimental site from Landsat-7/ETM + .



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during 20 days before the field survey on 8 August. Precipitation should encouragethe growth of vegetation. Thus, there is a possibility that vegetation coverage wasunderestimated. However, once germination occurs and vegetation grows to acertain degree, the vegetation coverage might not be very sensitive to vegetationvertical growth.

5.2 Classification

For determining how many categories are available especially for vegetation types,first, we investigate the characteristics of spectral reflectances, pattern decomposi-tion coefficients and the index for typical land-cover and vegetation species.

Figure7(a) shows the pattern decomposition coefficients and the sum of all bandreflectances for typical pond and cloud samples. For the pond sample, the value of C w is highest. For the cloud sample, the value of C w is also highest and the the sumof all band reflectances is approximately 2. Figure7(b) and (c) show the pattern

decomposition coefficients, the sum of all band reflectances, and the index fortypical soil and city samples, and vegetation samples, respectively. The sum of allband reflectances is lower for these samples than for the cloud sample; that for thesoil and city samples is > 1.5 and that for the vegetation sample is ( 1.5. The indexof the soil sample is , 0; that of the city sample is ( 0. For the vegetation sample, theindex is . 0 and the value varied with vegetation type. For vegetation, therelationship between the coefficient C v and the index was examined for eachvegetation type for the grid points of the field survey. Using the field survey datashown in table 2, the vegetation type was determined if the dry mass of thevegetation type was highest. If the grid point did not contain uniform land-cover,the point was omitted. Figure 8 shows the relationship between the C v in June andthe index for grid points. The value of C v was , 0 for short grass samples, that is> 0.02 for A. splendens , C. micriphylla and salt-basin vegetation such as Salsora passerina and Reaumurica soongoria . The indexes and C v for shrubs, tall grass andsalt-basin vegetation were similar. Thus, these vegetation type were considered tobelong in the same category.

Next, we delineated the land-cover categories as follows: (1) water, (2) shrubs, tallgrasses and salt-basin vegetation, (3) marsh, (4) short grasses, (5) soil and rocks, (6)urban areas and whitish mineral soils and (7) cloud. For this area, differentiation of tall grasses, shrubs and salt-basin vegetation was too difficult because of thesimilarity of their spectral characteristics; therefore, these three types of vegetationwere classified in the same category.

Figure 7. Pattern decomposition coefficients and the sum of all band reflectances for (a)pond and cloud samples and pattern decomposition coefficients, the sum of all bandreflectances and the index for (b) soil and city, and (c) vegetation samples.



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Third, from the results of investigation of the land-cover features, the thresholdsof pattern decomposition coefficients and the index were determined for each land-cover category as summarized in table 5.

Figure 9 illustrates the results of the classification. The results show a similartendency in the vegetation survey at the grid points. To validate the results, theaccuracy of the classification was examined using the data set for validation. For

short grasses, 14 sample points were investigated, of which 12 were classified asshort grasses and two were classified as tall grasses or shrubs. For tall grasses orshrubs, 19 sample points were checked, of which 17 were classified as tall grasses orshrubs and two were classified as short grasses. From these results, the kappacoefficient (Hudson and Ramm 1987; Congalton 1991) was calculated to be 0.75.

At the experimental site, vegetation types can be classified into two categories:one is short grasses and one is shrubs, tall grasses and salt-basin vegetation using thepattern decomposition coefficients reflected characteristics of the spectral responsedifferences. The index was useful for identifying grass area, and estimatingvegetation coverage for each land-cover type.

Figure 8. Relationship between the index of C v (June) + C s (April) 2 C s (June) and thevegetation coefficient C v (June).

Table 5. Classification criteria. Pi R i is the sum of all band reflectances.C v (Jun.)

+ C s (April) 2 C s (Jun.) C v (Jun.) Pi R i C w (Jun.)Cloud . 1.7 . 0.2Pond . 0.1Concrete orwhitish soil

. 1.5 . 0.04,0.1 .

Soil , 0.002Short grass > 0.002 , 0.02Tall grass andshrubs

. 0.002, 0.6 . > 0.02

Marsh > 0.6



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6. Conclusion

We conducted a field survey of vegetation at 49 grid points in an experimental fieldat the ADEOS-II Mongolian Plateau experimental site around the time the Aquasatellite passed over the area between 8 and 11 August 2001. Vegetation coverageand plant biomass were measured at each grid point. In addition to the field survey,validation data for land-cover classification were collected. This experimental sitewas located in a semiarid and sparsely vegetated region.

To study the spectral characteristics of vegetation in this semiarid area with redsoil, spectral reflectance was measured in the laboratory in Japan using Mongoliansoils with vegetation coverage varying from 0 to 30%. The measured spectralreflectance data were investigated using the PDM. From the results, we found thatthe index C v + (C s (soil 100%) 2 C s ) was useful for extracting the vegetation signalfrom sparsely vegetated samples.

The index was used for estimating vegetation coverage from Landsa-7/ETM +data. The average vegetation coverage in the field was 21.4% and was almost thesame as that determined for 49 grid points within an acceptable margin of error.

Using the index and pattern decomposition coefficients, land-cover was mappedin the experimental area. The accuracy of the land-cover mapping was estimatedusing the validation data measured in the field, and the kappa coefficient (Hudsonand Ramm 1987; Congalton 1991) was 0.75. At the experimental site in semiarid

Figure 9. Classification results. (1) Water, (2) tall grass, shrubs and salt-basin vegetation, (3)marsh, (4) short grass, (5) soil and rocks, (6) urban areas and whitish mineral soil, (7) cloud.



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and sparsely vegetated areas, vegetation was classified into two categories; one wasshort grasses, another was shrubs, tall grasses and salt-basin vegetation using thecharacteristics of the spectral response differences. The index was useful foridentifying grass area, and estimating vegetation coverage.

PDM is a type of spectral mixture analysis that uses the same three standardpatterns (endmembers) for all scenes, e.g., forested and sparsely vegetated areas,which is useful for multispectral data analysis. It is easy to understand images even if the location is different using the same axis as a reference axis.

AcknowledgementsLandsat-7/ETM + data were provided by the US Geological Survey (USGS). Thisstudy was supported by the National Space Development Agency (NASDA) of Japan as part of the Advanced Microwave Scanning Radiometer (AMSR) projectand the Global Imager (GLI) project; and MEXT (Ministry of Education, Culture,Sports, Science and Technology) of Japan, as part of the ‘Academic Frontier’

project for private Universities, 1999–2010.

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