a combined approach for estimating vegetation cover

8/7/2019 A combined approach for estimating vegetation cover

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Computers & Geosciences 32 (2006) 12991309

A combined approach for estimating vegetation cover in urban/

suburban environments from remotely sensed data

Chen Yunhao, Shi Peijun, Li Xiaobing, Chen Jin, Li Jing

College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China

Received 24 August 2005; received in revised form 23 November 2005; accepted 24 November 2005

Abstract

The spatio-temporal distribution of vegetation is an important component of the urban/suburban environment.

Therefore, correct estimation of vegetation cover in urban/suburban areas is fundamental in land use studies. In this study,

the potential of extracting fractional vegetation cover (FVC) from remotely sensed data and ground measurements is

explored. Based on the assumption that pixel has a mosaic structure, sub-pixel models for FVC estimation are first

introduced. Then a combined approach of using different sub-pixel models for FVC estimation based on land cover

classification is proposed. The experimental result, derived from a case study in Haidian district, Beijing, indicates that the

accuracy of FVC estimation using the proposed method can be up to 80.7%. The results suggest that this method may be

generally useful for FVC estimation in urban and suburban areas.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Remote sensing; Vegetation cover; Land use classification; Urban spread

1. Introduction

Recent estimates indicate that over 45% of the

worlds human population now lives in urban areas,

with this figure rising to over 60% projected by 2030

(United Nations, 1997). Monitoring urban environ-

ment change will therefore become increasingly

important as the number and proportion of urbanresidents continue to increase. The spatio-temporal

distribution of vegetation is a fundamental compo-

nent of the urban/suburban environment (Small,

2001). Research has shown that vegetation influ-

ences urban environmental conditions and energy

fluxes by selective reflection and absorption of solar

radiation. The presence of fractional vegetation

cover (FVC), which is defined as the percentage of

vegetation occupying a unit area, in urban areas

may also influence air quality and even human

health (Wagrowski and Hites, 1997).

Table 1 shows the different methods used now in

FVC estimation. Field measurements such as

methods of sampling, instrumental methods andocular estimation are the traditional methods used

to determine the FVC (Zhou et al., 1998; White

et al., 2000). This approach is usually too expensive,

and remote sensing is an effective tool for observing

the distribution and fraction of the vegetation cover.

Two categories of methods, namely spectral mixture

analysis (SMA) and vegetation indices (VIs), are the

most frequently used techniques to estimate FVC

(Camacho-de et al., 2004).

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doi:10.1016/j.cageo.2005.11.011

Corresponding author.

E-mail address: [email protected] (C. Yunhao).
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There are several SMA methods (Adams et al.,

1986; Smith et al., 1990; Settle and Campbell, 1998;

Oki et al., 2004) that allow a user to estimate

coverage at a sub-pixel level. However, the spectral

radiance (end member) of each category covered

within a pixel must be precisely known before

carrying out the unmixing method (Quarmby et al.,

1992; Foody and Cox, 1994). Despite major

advances made in the field of remote sensing, the

number of parameters that interact in the problem isstill greater than the intrinsic dimension of the

spectral data that are provided by these sensors

(Camacho-de et al., 2004). Thus, traditional SMA

methods are difficult to apply to regions with urban

and suburban areas with a homogeneous land

surface.

The methods of FVC estimation using VI data

can be grouped into two sub-categories: empirical

models and transformed models (Table 1). In the

situation of empirical models, the FVC is derived

from established empirical relationships between

FVC data obtained on the ground and vegetation

indices. Graetz et al. (1988) measured FVC in a

semi-arid soil region, and proposed a linear regres-

sion formulation to estimate the FVC. Many other

empirical FVC models have been presented accord-

ing to the features of specified study areas using

multi-scale remotely sensed data (Dymond et al.,

1992; Wittich and Hansing, 1995; Purevdorj et al.,

1998). These empirical models can produce a

reliable estimation of the FVC in specified areas,

but are often difficult to use because of their

dependency on good ground data.

The main aim of transformed models is to

establish the relationship between the vegetation

index and vegetation fraction to estimate the FVC

directly. Gutman and Ignatov (1998) presented

uniform-pixel model and mosaic-pixel models

(including the dense vegetation model, non-dense

vegetation model, variable density vegetation mod-

el) according to probable vegetation distributed

features in pixels, and estimated global FVC from

the NOAA AVHRR data using the dense vegeta-tion model. Extensive fieldwork is unnecessary in

applying the transformed methods, but their accu-

racy is often lower than that of empirical models.

Thus, improving the accuracy of transformed

models has become a key problem in the develop-

ment of these methods.

The main objective of this study is to develop a

new approach for urban/suburban FVC estimation

from remotely sensed data and field surveys,

combining the dense vegetation and non-dense

vegetation models. Two steps are involved in this

approach: choosing appropriate sub-pixel models,

and determining parameters for these models

(especially in the non-dense vegetation model). A

quantitative validation is to examine the applic-

ability of the combined approach to the estimation

of the vegetation fraction in suburban of Beijing

using Landsat thematic mapper (TM) data.

2. Theoretical basis

NDVI is an integrative reaction parameter of

vegetation types, cover fraction and growth status

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Table 1

Comparisons between different FVC estimation methods

Methods Advantage Disadvantage Used image

Field

measurements

Sampling Need much manpower and

financial resources; dependency

on personal experience

Digital camera data

Instrumental method Higher accuracy

Ocular estimation

Spectral mixture

analysis

Linear mixture models Calculation simple with

reasonable accuracy

Cannot broadly apply to the

urban and suburban

TM/ Spot/ Ikonos

Nonlinear mixture

models

Vegetation indices Empirical models Reasonable accuracy According to the features of

specified study areas

TM/ Spot

Transformed models Less fieldwork with

reasonable accuracy

Dependency on good ground

data

NOAA/TM/Spot

C. Yunhao et al. / Computers & Geosciences 32 (2006) 129913091300


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in a unit pixel. Its value is determined by leaf area

index (LAI) (vertical density) and FVC (horizontal

density) (Price, 1992). Many empirical models have

shown that there is a close relationship between

FVC and NDVI, but the same NDVI signal may

result from different sub-pixel vegetation structures(vertical and horizontal densities). Hence, to explore

the numerical relationship between NDVIand FVC

further, it is necessary to analyze the sub-pixel

structure of an image pixel. According to different

land cover structures, pixels can be divided into two

types: uniform pixels and mosaic pixels (Gutman

and Ignatov, 1998). When a pixel is fully covered by

green vegetation, it is considered that the pixel is a

uniform pixel, whereas a pixel partly covered by

green vegetation is a mosaic pixel. This research

simplified the sub-pixel types of mosaic pixels

into: dense vegetation and non-dense vegetation,illustrated in Table 2.

2.1. Uniform pixel

In Table 2, depending upon the pixel type,

relationships between the vegetation index (NDVI),

fractional vegetation cover (FVC) and leaf area

index (LAI) can be described as follows:

In the situation of a uniform pixel, the pixel is

fully covered by green vegetation (FVC 1) with acertain vertical density (Table 2, top). Hence, the

value of NDVI is determined mainly by the LAI.

The equation of LAI and NDVI is presented based

on a modified Beers law (Baret and Guyot, 1991):

NDVI NDVI1 NDVI1 NDVI0

expk LAI, 1

where NDVI0 and NDVIN are the signals of bare

soil (LAI-0) and dense green vegetation (LAI-N),

respectively, and k is the extinction coefficient.

2.2. Mosaic pixel

Mosaic pixels are partly covered by green

vegetation, so the NDVI observed is a weighted

average of the vegetated (NDVIg) and non-vege-

tated (NDVI0) parts. According to the features of

mosaic pixels on the vegetation coverage, two sub-

pixel models can be described as follows:

2.2.1. Dense vegetation model

In this model, an assumption is made that

the vegetation type is single and the density

of the vegetated part of the pixel is very high(LAI-N, and NDVI-NDVIN

) correspondingly,

so that

NDVI FVC NDVI1 1 FVCNDVI0.

(2)

Hence, the FVC yields

FVC NDVI NDVI0

NDVI1 NDVI0. (3)

2.2.2. Non-dense vegetation modelSimilar to the dense vegetation model, an

assumption is made that the vegetation type is

single but the density of vegetated part of the pixel is

low LAI51. A combination of Eqs. (1) and (2)

yields

NDVI FVC NDVIg 1 FVCNDVI0, (4)

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Table 2

Schematic representation of sub-pixel models for FVC estimation

Pixel types Sub-pixel structure of vegetation Schematic map Formulation of FVC

Uniform pixel Uniform full vegetation fg 1, NDVI value is determined by LAI mainly

Mosaic pixel Dense vegetation fg NDVINDVI0

NDVI1NDVI0

Non-dense vegetation fg NDVINDVI0

NDVIgNDVI0

Gutman and Ignatov (1998), modified.

C. Yunhao et al. / Computers & Geosciences 32 (2006) 12991309 1301


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where

NDVIg NDVI1 NDVI1 NDVI0

expk LAI. 5

Thus,

FVC NDVI NDVI0

NDVIg NDVI0. (6)

From Eqs. (2)(6) we note that calculation of

FVC is more complicated in the non-dense than in

the dense vegetation model. In the former, not only

NDVI0 and NDVIN, but also the extinction

coefficient k and LAI, must be determined.

3. Model

3.1. Model selection

It is straightforward to estimate the FVC with the

dense vegetation model, which is suitable for

estimating the FVC of a region covered by a single

vegetation type with high vertical density. The non-

dense vegetation model is suitable for estimating the

FVC of a region covered by a single vegetation type

with low vertical density. However, it is difficult to

determine the parameters in this model.

Taking into account the spatial resolution ofLandsat TM data, it is reasonable to choose a sub-

pixel model among the uniform and mosaic-pixel

models for urban and suburban FVC estimation.

Considering the characteristics of the above-men-

tioned sub-pixel models and any constraints, an

appropriate sub-pixel model based on land cover

classification should improve the precision of

estimation. The models that follow were derived

from our study and represent a range of vegetation

structure.

Orchard: single vegetation type with high vertical

density, (LAI-N). Hence, the dense vegetation

model is adopted.

Woodland: complicated vegetation types with

high vertical density. Accounting for limited spatial

resolution of image, the dense vegetation model is

chosen approximately.

Meadow: is a single vegetation type and the value

of LAI is suitable for the non-dense vegetation

model. Hence, the non-dense vegetation model is

adopted.

Cropland: our study area is in the outskirts of

Beijing. Vegetable gardens and lawns take up most

of the land area, and winter wheat and broomcorn

are rare, so the non-dense vegetation is adopted.

City zone: its sub-pixel character is complex and

arrays of trees are the main green vegetation part:

except for large lawn areas where the non-dense

vegetation model is applied, the dense vegetationmodel is adopted.

3.2. Determination of parameters

The parameters common to the dense and non-

dense vegetation models are: NDVI0 and NDVIN,

which are defined by Sellers et al. (1996, 1997) as the

lower and upper 25% NDVI for each biome.

Furthermore, it is required to determine k and LAI

in the non-dense vegetation model. k is the light

extinction coefficient, characterizing the vegetation

type (Choudhury et al., 1994):

k lnAPAR=PAR

LAI, (7)

where PAR relates to the visible part of the

spectrum between 0.4 and 0.7mm, where chloro-

phyll absorbs solar radiation, namely PAR is thus a

fraction of the incoming solar radiation. APAR is

absorbed PAR by the vegetation.

The k values for meadow and cropland were

determined using Eq. (7), and the parameters used

for calculation of k were observed by field survey.PAR and APAR were taken by a SUNSCAN

Canopy Analysis System and the LAI measurement

was taken by an LAI-2000 plant canopy analyzer

(PCA). The operation instructions for the PAR and

LAI instrument were followed carefully to ensure

that each point was measured accurately. Each

PAR, APAR and LAI measurement represents an

average of 10 PCA readings which were taken in a

sample.

Directly measured LAI may provide a reliable

result, however, it is difficult to estimate a regional

LAI by using this method, because there of the

problem of transferring the data from a point scale

to a surface scale (Chen et al., 2001). Price (1993)

developed a two-stream approximation model for

LAI estimation from remotely sensed data. Para-

meters needed in the estimation of LAI are

estimated by using a scattergram of visible and

near-infrared reflectance measurements of vegeta-

tion, along with the reflectance measurements

derived from remotely sensed data. The LAI can

be obtained using Prices method, but this requires

knowledge of a number of constants: (1) the soil line

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constants (see Fig. 3A), a and b, are required (under

some conditions, information about the soil line

may be identified from a scattergram of remotely

sensed data); (2) the coefficients c1 and c2 used to

describe the attenuation of radiation as it passes

through successive layers of leaves, which differbetween vegetation types; (3) the reflectance values

for dense vegetation rN

, should also be estimated

from remotely sensed data.

Price (1993) has proposed an approach to identify

parameters needed from remotely sensed data. The

relationship between LAI and DNi, the measured

values from a satellite sensor, can be written as

(i 1,2 relating to TM band 3 and TM band 4

respectively)

DNsi

DNie2ciLAI r21i DN1i1 e

2ciLAI

1 r21ie2ciLAI DNir21i1 e2ciLAI=DN1i,

(8)

where the DNNi can be estimated from remotely

sensed data, for rN1 the value 0.05 and for rN2 the

value 0.7 can be used. Putting Eq. (8) into the soil

line equation, which can be obtained from satellite

data in visible and near-infrared, we can express

LAI as a function of DNi and c1, c2

DNs2 a0DNs1 b

0, (9)

where a0 and b0 can be regressed from the

scattergram of visible and near-infrared reflectance

measurements.

Although the functional equation of LAI and

DNi can be expressed as a polynomial equation

and be solved for certain values of c1, c2, a

numerical solution is generally satisfactory, so a

look-up table of LAI values corresponding to an

array of DNi values is constructed, followed by

interpolation.

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Fig. 1. Location of study area.



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3.3. Study area and data

Haidian district, an area in the northeast of Beijing,

was chosen to examine the effect of the FVC

estimation method (Fig. 1). Its hypsography is low

in the east, high in the west, and average horizon levelis about 50 m. Of the total area, plains occupy 75%,

and mountains and hills occupy 25%. Land cover

types and the FVC are variable over the study area.

A Landsat TM scene image of the study area,

which was acquired on 19 May 2004 (path and row

numbers are 123 and 32), was adopted as the main

data source. After image preprocessing, land cover

classification, model selection for FVC estimation,

and FVC calculations for the Haidian district were

carried out. The flowchart for the FVC estimation is

illustrated in Fig. 2.

4. Methodology

4.1. Geometric registration

Image-to-map registration was carried out using

1:50 000 topographic maps. Geometric accuracy is

less than 1 pixel at all land areas.

4.2. Land cover classification

Supervised classification of the TM image was

carried using the maximum likelihood algorithm in

ERDAS 8.7 applied to the six non-thermal bands.

The training sites were defined using a false colourcomposite of channels 4, 3 and 2 displayed as red,

green and blue, respectively. Three to five training

areas were randomly chosen for each class. The

following classes were used: meadow, cropland,

woodland, orchard, urban, water zone, and bare

area. The correctness of the land cover types was

evaluated in relation to the ground data collected by

field survey in study area in May, 2004. The field

data were positioned with a GPS and classified by

visual estimation. The digital interpretation indi-

cates a total accuracy of 85.8% (Table 3).

4.3. FVC estimation

Sub-pixel models for the FVC estimation were

selected based on the land cover classification. A

value of 0 is assigned to the value of the FVC in

water zones and bare areas, and the non-dense

vegetation model was applied to estimate meadow

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Model selection for vegetation fraction based onland cover classification and field survey

Woodland Orchard Urban Meadow Cropland Water zone Bare area

Dense vegetation model Non-dense vegetation model

A two stream approximation model

Urban vegetation fraction map

TM image

Geometric registration/ Study zone selection

Land cover classification

Field survey

Fig. 2. Flowchart for FVC estimation based on remotely sensed data and field survey.



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and cropland. Meanwhile, the dense vegetation

model was adopted for woodland, orchard and

urban sites. The parameters for these models are

illustrated in Table 4. In the non-dense vegetation

model, the two-stream approximation method is

used to determine the LAI. The scattergram of the

TM band 4 (infrared) and TM band 3 (visible) is

given in Fig. 3A. Constants needed for the LAI

estimation are: DNs11 19, DNs2

1 9; DNs12 102,

DNs2

2 95 for the soil line; and DNN

1 16,

DNN2 122 for the dense vegetation, where the

subscript 1, 2 refers to the grey level value of the TM

band 3 and 4, respectively. Two coefficients, c1 and

c2, are chosen as 0.6 and 0.21 for the TM data,

which are applicable to cropland and meadow with

low vertical density (Price, 1993). Using these

parameters, a look up table, relating LAI to DN1and DN2, is constructed and shown in Fig. 3B. The

distribution of the LAI map for study area can be

obtained by interpolation from the look up table.

Fig. 4A shows the distribution of the FVC map

for Haidian district on May 19, 2004. The spatial

distribution features of the FVC map may be

summarized as follows: (1) Generally, the FVC is

low over the whole region. Only in the western

narrow mountainous regions and hills does the

vegetation cover exceed 50%, (2) in the city zone,

only the FVC of scattered parks can achieve

4055%, the other regions are mainly 1025%.

This indicates that green space protection is lacking

during urbanization of the Haidian district,

(3) crops in the study area are younger in the

beginning of May, so the FVC of northern cropland

is low.

5. Results and discussions

5.1. Results analysis

To analyze accuracy of above-mentioned FVC

estimation quantitatively, we carried out ground

FVC measurements in the middle ten days of May,

2004. In order to evaluate the effect of accuracy

improvement by using the combined approach, the

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Table 3

Error matrix calculated for the supervised classification results

Ground data Class Users accuracy

Image 1 2 3 4 5 6 7 Total

1 16 2 1 0 0 0 0 19 0.842 1 14 3 0 0 0 0 18 0.78

3 1 2 26 1 0 0 0 30 0.87

4 0 0 2 35 3 0 0 40 0.88

5 0 0 0 2 22 1 0 25 0.88

6 0 0 0 1 0 4 0 5 0.8

7 0 0 0 0 0 0 4 4 1

Total 18 18 32 39 25 5 4 141

Producers accuracy 0.89 0.78 0.81 0.9 0.88 0.8 1 TA 0.858

Classes: 1 meadow grassland; 2 cropland; 3 orchard; 4 woodland; 5 urban; 6 water zone; 7 bare area. Users and Producers accuracy are

shown in last column and last row, respectively. TA is total accuracy.

Table 4

Parameters used for the FVC estimation in Haidian district, Beijing

Land cover types Sub-pixel types NDVI0 NDVIN k

Meadow Non-dense vegetation model 0.05 0.646 0.82

Cropland Non-dense vegetation model 0.05 0.656 0.94

Woodland Dense vegetation model 0.05 0.718

Orchard Dense vegetation model 0.05 0.713

Urban Dense vegetation model 0.05 0.627

Water zone, bare soil 0



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FVC was also calculated using the dense vegetation

model, and the result is shown in Fig. 4B.

Forty-five sample patches of different land-cover

type were selected, including meadows (8 patches),

croplands (6 patches), woodlands (12 patches),

orchards (7 patches) and urban (12 patches). The

smallest sample patch was 30 30 m. The FVC field

survey was carried out with a digital camera

following the procedure described by Calera et al.

(2001) and using the line-intercept method (Duncan

et al., 1993). Table 5 presents a comparison of the

FVC values observed on the ground and those

estimated by the model.

The analysis showed that estimated FVC values are

highly correlated with those observed on the ground,

with an average accuracy of 80.7%. We compared the

use of a single model (the dense vegetation model)

with the combined approach. Due to the dense model

results are already available for the woodland,

orchard and urban land cover types, the dense model

results would only have to be compiled for the

meadow and cropland types (Table 5).

Table 5 shows that the accuracy of meadow and

cropland FVC using the dense model are 73 and

68%, whereas the FVC using the combined

approach are 86 and 78%, respectively. The results

show that the combined model as compared to the

dense model is more reasonable over the hetero-

geneous land surface.

Fig. 5 presents a comparison of FVC values

observed on the ground and those estimated by the

combined approach. Calculated FVC had a bias

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Dense vegetation model Combined approach

0.9

0.7~0.8

0.8~0.9

0.1~0.2

0.4~0.5

0.2~0.3

0.5~0.6

0.3~0.4

FVC

N 40009

E 116003

N 40009

E 116023

N39035E 116003

N39035E 116023

N 40009

E 116003

N 40009

E 116023

N39035E 116003

N39035E 116023

N

0 5 km 0 5 km

N

(a) (b)

Fig. 4. Estimated FVC in Haidian District, Beijing.

Fig. 3. (A) Scattergram of Landsat/TM scene for Haidian District; (B) Look-up table relating LAI to digital values of TM3 and TM4.



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(mean estimated FVCobserved FVC) of 6.89%.

Regression analysis was carried out to find the

linear trend and bias of the curve from the 1:1 line.

The analysis showed that estimated FVC are highly

correlated with those observed (r2

0:83, n 45).Comparison of estimated FVC and observed data

demonstrates that this approach may be an im-

portant tool for estimating FVC.

5.2. Comparison with soil adjusted vegetation index

(SAVI)

Huete (1988) suggested soil adjusted vegetation

index (SAVI) based on NDVI and various observed

to eliminate the effect from background soils:

SAVIrNIR rredrNIR rred

1 L, (10)

where L is a constant that is empirically determined

to minimize the vegetation index sensitivity to soil

background reflectance variation. L varies with

vegetation density, ranging from 0 for very high

vegetation cover to 1 for very low vegetation cover.

For intermediate vegetation cover ranges, L is

typically around 0.5 (Schowengerdt, 1997). The

SAVI index can be used to estimate the amount of

pixel mixing between vegetation and non-vegetation

portions. The objectives of the FVC approach and

the SAVI approach are similar, but they are

different in the following respects:

(1) The aim of the SAVI is to calculate a vegetation

index with minimizing the influence of soil on

canopy vegetation reflectance. However, the

objective of the FVC approach is to develop a

new method for fractional vegetation cover

estimation;

(2) this paper demonstrated that the FVC estima-

tion approach is suitable for urban/suburban

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Table 5

Error analysis based on field survey

Sample

no.

Woodland (%) Urban (%) Orchard (%) Meadow (%) Cropland (%)

Obs. Est. 1 Err. 1 Obs. Est. 1 Err. 1 Obs. Est. 1 Err. 1 Obs. Est. 1 Err. 1 Est. 2 Err. 2 Obs. Est. 1 Err. 1 Est. 2 Err. 2

1 53 42 0.21 18 13 0.28 44 37 0.16 53 41 0.23 44 0.17 30 36 0.20 22 0.272 75 68 0.09 14 18 0.29 75 63 0.16 39 31 0.21 29 0.26 31 22 0.29 19 0.39

3 37 30 0.19 14 10 0.29 36 41 0.14 44 52 0.18 58 0.32 43 32 0.26 32 0.26

4 43 51 0.19 26 23 0.12 43 52 0.21 40 45 0.13 27 0.33 52 46 0.12 40 0.23

5 51 44 0.14 20 25 0.25 45 36 0.20 51 48 0.06 41 0.20 33 41 0.24 47 0.42

6 33 37 0.12 29 37 0.28 37 45 0.22 33 38 0.15 48 0.45 34 26 0.24 21 0.38

7 74 82 0.11 15 12 0.20 71 61 0.14 74 67 0.09 60 0.19

8 62 73 0.18 13 9 0.31 52 46 0.12 40 0.23

9 44 52 0.18 20 17 0.15

10 33 38 0.15 26 21 0.19

11 49 41 0.16 23 16 0.30

12 28 36 0.29 20 15 0.25

Avg1 0.17 0.24 0.17 0.14 0.27 0.22 0.32

Avg2 0.193

Obs. observed value, Est. 1 estimated value 1 using the combined approach, Est. 2 estimated value 1 using the dense vegetation model, Err.1 error using the combined approach, Err. 2 error using the dense vegetation model, Avg1 the absolute mean errors for each cover type,

Avg2 the absolute mean error over all samples using the dense vegetation model.

y = 0.9045x + 5.0635

R2 = 0.8321

0

10

20

30

4050

60

70

80

90

0 20 40 60 80 100

Estimated Vegetation Fraction Values (%)

ObservedVegetationF

ractionValues(%)

Fig. 5. Relationship between FVC values observed and estimated

from remotely sensed data.



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areas with a homogeneous land surface. The

SAVI approach is generally a superior method

for estimating a green vegetation index over

rural areas at broad spatial scales;

(3) since vegetation density is usually unknown, it is

difficult to optimize the SAVI index (Liang,2004). On the other hand, the SAVI was shown

to be sensitive to NIR variations induced by

sensor/sun geometry. In comparison to the

SAVI, the FVC approach had reasonable

parameters based on land cover classification.

Thus the new approach has been shown to be a

good estimator of the urban FVC.

6. Conclusions

In this study, a new method for fractional

vegetation cover estimation in urban/suburban

areas is derived. This method takes into account

the sub-pixel vegetation structure, and deals with

the parameters of the dense and non-dense vegeta-

tion models specifically. Through a case study in

Haidian district, Beijing, a good agreement (the

accuracy of FVC estimation is about 80.7%) is

obtained between estimated and the ground-based

measurements, which is higher than using the dense

vegetation model only. This combined approach

may be an effective tool in monitoring the fractionalvegetation cover in urban and suburban areas.

Acknowledgments

This work was supported by the Natural Science

Foundation of China (no. 40201036), the National

Key Developing Program for Basic Sciences of

China (no. 2006CB701302) and the Foundation of

Key Lab of Resources Environment and GIS of

Beijing, Capital Normal University. We are grateful

to the anonymous referees for their thoughtful andhelpful comments.

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