2008 International Workshop on Earth Observation and Remote Sensing Applications

1-4244-2394-1/08/$20.00 ©2008 IEEE.

A comparison and evaluation of four vegetation

analysis approaches based on remote sensing imagery

Peijun Du, Yan Luo, Wen Cao and Huapeng Zhang

Institute of Surveying and Spatial Information Engineering

China University of Mining and Technology

Xuzhou City, Jiangsu Province, P.R. China

dupjrs@cumt.edu.cn, dupjrs@gmail.com

Abstract—Some analytical approaches have been developed and

widely used for vegetation remote sensing, among which four

popular methods are vegetation analysis via NDVI and other VIs,

vegetation analysis using the vegetation abundance derived from

unmixing, vegetation analysis by land cover classification, and

the greenness component derived from K-T transform. There

four approaches are used to extract vegetation information from

Landsat TM image taking Xuzhou City as an example, and their

performance is compared. Association analysis among vegetation

types, NDVI values, vegetation abundance and greenness is

conducted at first. It is found that the association among NDVI,

vegetation abundance and greenness is quite obvious. Vegetation

coverage ratio is derived based on different vegetation extraction

approaches, and their consistency is analyzed. It is found that the

unmixing-based approach outperforms others in terms of

vegetation coverage ration estimation. By comparing the

performance and effectiveness of four approaches, some

suggestions are given for selecting suitable analytical approaches.

I. INTRODUCTION

As one of the most important components of terrestrial ecology system, vegetation plays significant roles in regional ecological system, environmental protection and human settlement environment optimization. Many researches on the pattern, structure, function and development of vegetation has been conducted in recent years. It has been shown that remote sensing imagery is the most effective information source to monitor, analyze and assess vegetation pattern, status, trends and functions owing to its superiority to traditional data acquisition means, overcoming the limitations of other techniques, such as limited monitoring stations, incomplete data, expensive cost and low efficiency. Especially, remote sensing technology can provide vegetation information with multi-temporal, multi-spectral, multi-resolution and wide spatial coverage capacities. Many studies have been done to use remote sensing technology to vegetation monitoring [1-3]. Shefali et al classified vegetation cover in central Asia using multi-temporal remote sensing data [4]; Pech et al estimated the vegetation coverage ratio of Australia by spectral mixture analysis [5]; Goetz et al estimated the coverage ratio using high resolution IKONOS data and decision tree classification method [6]. Among those vegetation remote sensing methods, four widely used approaches are: vegetation analysis based on

NDVI and other VIs, vegetation analysis using the vegetation abundance derived from spectral mixture analysis, vegetation identification via land cover classification, and vegetation analysis based on the greenness component derived from K-T transform. They are widely used for different case studies, but how to assess the performance of different methods and select the effective one for specific aims is still a question to be addressed. This topic is investigated in this paper. Landsat TM image captured on August 12 2005 is used as data source, and Xuzhou City located at the northwestern part of Jiangsu Province of China is chosen as the case study area. Four approaches are used to extract vegetation information from Landsat TM data and their effectiveness is compared from different aspects.

II. DATA AND STUDY AREA

A. Study Area

Xuzhou City, located at the northwestern part of Jiangsu Province, belongs to the Yellow River-Huaihe River plain. Its

geographical scope is: 116°22′～118°40 E and 33°43′～ 34°58′

N [7]. It has the semi-humid monsoon climate, with the annual average temperature of 14 degree centigrade, and rainfall of 800-930 millimeters. The city's forest coverage rate is about 22.5% and volume of live trees savings is 9.8 million cubic meters. The main crops include wheat, corn, rice, beans etc., while with other economic crops such as cotton, peanuts, sesames, etc. Xuzhou is a densely populated urban area (729 persons per square kilometer in average according to Xuzhou Sampling Census in 2005) and has limited green space. It has become a pressing problem to make the best use of limited land resources, refine urban vegetation system, optimize the distribution of green space to exert its ecological function [8].

B. Data

The Landsat TM image captured on 12 August 2005 at orbit of 122/36 is chosen as the data source of vegetation analysis. The weather was fine when satellite passed the area and vegetation was in good growth condition, so vegetation information can be extracted from the remotely sensed data. Figure 1 is the false color composite image of Band 5, Band 4 and Band 3.

Supported by the New Century Excellent Talents Program in University by Ministry of Education (NCET-06-0476), Jiangsu Provincial Natural Science Foundation (No. BK2006505), National High-Tech Development Program of China (No. 2007AA12Z162) and the China-UK Science Networks.

2008 International Workshop on Earth Observation and Remote Sensing Applications

1-4244-2394-1/08/$20.00 ©2008 IEEE.

Figure 1. The false color composite image of study area

III. VEGETATION INFORMATION EXTRACTION

Four methods are used for vegetation analysis from Landsat TM image: analysis based on NDVI and other VIs, estimation using the vegetation abundance derived from spectral mixture analysis, identification via classification, and analysis using the greenness component derived from K-T transform.

A. NDVI

Normalized Difference Vegetation Index (NDVI) is the most widely used Vegetation Index owing to its effectiveness and easy usability. NDVI can be derived by (1) [9]:

-NIR RED

NIR RED

R RNDVI

R R

（）

Where, NIRR is the reflectance of a pixel at the near

infrared band--Band 4 of Landsat TM image, and REDR is the

reflectance of the red spectrum--Band 3 of Landsat TM data.

B. Greeness by K-T transform

K-T transform is also effective to vegetation analysis via Landsat TM image because the greenness component derived from K-T transform can represent the coverage and growth status of vegetation [10]. The greenness component can be calculated by (2):

GREENNESS= -0.2848TM1-0.2435TM2-0.5436TM3

+0.7243TM4+0.084TM5-0.18TM7 （2）

C. Vegetation Abundance by Unmixing

Spectral mixture analysis is widely used for vegetation analysis as well. The steps of acquiring vegetation abundance are as follows [11]:

Using Minimum Noise Fraction (MNF) to original 6 bands of Landsat TM image (Band 6 is not used

because of its low spatial resolution) to getting 6 independent components.

Using the former 5 components of MNF to calculate pixel purity index (PPI).

Selecting endmembers by the n-Dimensional visualization tool of ENVI software and deciding the spectra of endmembers.

Deriving the abundance of each endmember by linear spectral mixture model.

Taking into account the possible errors caused by unreasonable endmember selection, four endmembers are chosen: low reflectance objects (mainly, water and shadow), vegetation, soil, and high reflectance objects (mainly, buildings and roads).

D. Vegetation Classification Using Decision Tree

The decision tree software, SEE 5.0, is used to construct the decision tree for classification. The land cover is classified into 6 types: non-vegetation land, forest land, cropland, mixed vegetation, grassland, and public green space. The classification result by decision tree is demonstrated in Figure 2. The overall accuracy of classification is 89% and the Kappa coefficient is 0.8654.

Figure 2. The classification result by decision tree

IV. COMPASION AND ANALYSIS

A. Correlation Analysis

For different representation ways of vegetation information derived by the previous four approaches, the NDVI, Greenness and Vegetation abundance are described by a data plane (like an image) respectively. Correlation analysis is conducted to check their consistency. The land cover types from decision tree are used as the reference data and the correlation coefficients of different expression ways are calculated to every land cover, so the correlation among vegetation

2008 International Workshop on Earth Observation and Remote Sensing Applications

1-4244-2394-1/08/$20.00 ©2008 IEEE.

abundance, greenness and NDVI can be analyzed. Table I is the results of correlation coefficient.

TABLE I. CORRELATION COEFFICIENT BETWEEN GREENNESS, NDVI AND ABUNDANCE

Type

correlation coefficient

Greenness-

Abundance

Greenness-

NDVI

Abundance-

NDVI

Water 0.974277 0.991559 0.984851

Building land 0.727317 0.868945 0.677735

Forest land 0.995721 0.980608 0.974684 Public green

space 0.984772 0.975183 0.975066

Cropland 0.973432 0.792439 0.706447 Mixed

vegetation 0.982383 0.972544 0.945283

Grassland 0.993065 0.971429 0.961556

Total 0.991662 0.984577 0.975152

It can be found that the three vegetation quantitative

descriptions extracted through different approaches have strong correlativity for different vegetation types, but the correlation coefficients of other land cover types including water, built-up land and non-use land on Greenness, Abundance and NDVI data plane are quite weak in contrast with those among other vegetation types. That means the three components, greenness, abundance and NDVI, have strong consistency in representing vegetation information, but their suitability to non-vegetation land cover is not so good.

B. Vegetation Coverage Ratio

Vegetation coverage ratio is the most popular indicator to describe regional vegetation distribution and pattern, so it is used to compare the four approaches further.

For classification-based method, the vegetation coverage ratio is derived by the proportion of pixels belonging to vegetation to total pixel amounts. For unmixing-based method, the abundance of vegetation endmember in each pixel is viewed as the vegetation proportion in that pixel so the sum of vegetation abundance of all pixels is regarded as vegetation coverage ratio. For NDVI-based and greenness-based approaches, the maximum and minimum values of NDVI and greenness are regarded as 100 percent and 0 percent vegetation coverage respectively, so the coverage ratio of each pixel can be derived by linear equation. Finally, the vegetation coverage ratio of the study area is summarized in Table II.

Except classification-based approach, the other three approaches assign each pixel a value representing the proportion of vegetation within it, so those pixels can be segmented into different levels based on their values. Figure 3 is the segmentation results of vegetation coverage ratio derived by the three approaches.

It can be found that the vegetation coverage ratio derived from the four methods differs from each other obviously, among which the coverage ratio derived from NDVI is the largest-64.5389%, and the others are result of unmixing-based

approach-60.4331 %, result of classification—based approach-58.652% and result of Greenness-based approach-48.2153 %.

TABLE II. THE VEGETATION COVERAGE RATIO BY DIFFERENT METHODS

Methods adopted Vegetation coverage

ratio

Classification-

based approach 58.652 %

Unmixing-based

approach 60.433 %

NDVI-based

approach 64.538 %

Greenness- based

approach 48.215 %

(a) Result of unmixing-based approach

(b) Result of NDVI-based approach

2008 International Workshop on Earth Observation and Remote Sensing Applications

1-4244-2394-1/08/$20.00 ©2008 IEEE.

（c）Result of Greenness-based approach

Figure 3. Segmentation level of vegetation coverage ratio

Comparing the land cover classification map with vegetation coverage ratio results, it is obvious that the vegetation coverage ratio calculated by the three approaches can reflect the vegetation distribution and pattern, urbanization and the scope and intensity of human activities with reliable information. Those areas with low coverage are located at water area, urban built-up area, hills and towns with crowd population. In the city center area, the vegetation coverage is quite low, and only some parks and ecological protection areas have high coverage, so it is urgent to enhance the vegetation coverage ratio in the city center to improve urban ecological environment. With the extension from city center to suburban, the vegetation coverage ratio tends to increase, and the southeastern and northwestern parts that are less affected by urbanization have the largest coverage ratio. But the situation is changing in recent years because a new urban administration center and urban area is developed in the southeastern part of the city.

It should be pointed that the direct quantitative comparison of the three coverage results can’t provide much useful information because both the NDVI and greenness-based approaches are based on the assumption of 1 for maximum and 0 for minimum of the values, which are usually affected by different factors such as vegetation saturation, values of non-vegetation pixels and so on. Comparatively, the abundance derived from unmixing has its own physical meaning so it is more suitable to describe vegetation coverage ratio.

C. Discussions

Based on the comparisons to principles, algorithms, uses and properties of different methods, some insightful conclusions can be drawn and then used to further study.

Classification-based method is effective to discriminate vegetation with non-vegetation, and further discriminate different subclass of vegetation, so it is suitable to vegetation identification, change and distribution analysis. Decision tree

classifier is useful to vegetation classification. Its limitations are strong dependence on training samples, easy confusion of different subclass and the assumption of pure pixel. Despite those limitations, classification is still the most popular way of vegetation remote sensing.

Spectral mixture analysis provides vegetation abundance information at sub-pixel level and addresses the nature of pixels being mixed by different endmembers. Its theoretical basis is much closer to the structure, pattern and distribution of vegetation, so the estimation result is meaningful and easy to be understood. Two limitations still exist: selection of endmembers, and establishment of unmixing model.

Vegetation indexes are constructed based on the characteristic spectral signature of vegetation on red and near infrared spectral scope. NDVI, as the most widely used vegetation index, is a good indicator of vegetation growth, distribution density and biophysical and biochemical parameter. One significant merit of NDVI is that it can be used to retrieve vegetation biophysical and biochemical parameters by statistical analysis, regression and other models. But NDVI is easy affected by environmental factors and the properties of different vegetation types, so how to improve the reliability of vegetation indexes need to be investigated further, and the uses of some other VIs should be experimented.

K-T transform to Landsat TM data can compress data volume and emphasize the important information contained in original data. As one of the most important outputs of K-T transform, greenness can be used to classification as an auxiliary feature, but its physical implication is not clear and its value may change in an uncertain interval, so its usability is still difficult in contrast with other approaches.

Therefore, the integrated strategy for urban vegetation remote sensing monitoring is proposed in our experiments and studies. Direct classification is used to distinguish vegetation with non-vegetation and then discriminate different subclass of vegetation, and greenness or NDVI can be used as an ancillary feature of classification. NDVI is used to retrieve some quantitative parameters about urban growth and pattern. Vegetation abundance information derived by spectral mixture analysis is used to estimate the coverage ratio of vegetation, and further to analyze the relationship between vegetation and other ecological and environmental phenomena, such as urban thermal heat island, human-settlement quality, and so on.

V. CONCLUSION

In this paper, four approaches of extracting and analyzing vegetation information from Landsat TM remotely sensed data are experimented and compared. Based on those comparisons and discussions, some conclusions can be drawn:

All the four methods are suitable to vegetation distribution and pattern analysis and discrimination of vegetation with non-vegetation, and the vegetation components derived by unmixing, NDVI and greenness are highly correlated in terms of vegetation representation.

2008 International Workshop on Earth Observation and Remote Sensing Applications

1-4244-2394-1/08/$20.00 ©2008 IEEE.

For vegetation coverage ratio retrieval, clear differences exist among different approaches, and the unmixing-based approach can get better results than other methods in the study area.

For the specific research, it is necessary to select effective vegetation remote sensing approach by considering different factors. Usually an integrated strategy is helpful, among which direct classification is effective to distinguish vegetation with non-vegetation and then discriminate different subclass of vegetation, and greenness or NDVI can be used as an ancillary feature of classification; NDVI is a strong indicator to retrieve quantitative parameters; and vegetation abundance information is suitable to analyze the relationship between vegetation and other ecological and environmental phenomena.

Although some experiments and comparisons were made in our past studies, the preliminary results and conclusions should be checked by ground-truth, field campaign and more case studies. That will be our research foci in the near future.

ACKNOWLEDGMENT

The project is supported by grants from the New Century Excellent Talents Program in University of the Ministry of Education (NCET-06-0476), Jiangsu Provincial Natural Science Foundation (No. BK2006505), the National High-Tech Development Program of China (No. 2007AA12Z162) and the China-UK Science Networks.

REFERENCES

[1] H. Larsson, Linear regressions for canopy cover estimation in Acacia wood lands using Landsat-TM/MSS and SPOT HRV-XS data, International Journal of Remote Sensing, vol. 14, no. 11, pp. 2129-2136, 1993.

[2] J. Duncan, D. Stow, and J. Franklin, Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico, International Journal of Remote Sensing, vol. 14, no. 18, pp. 3395-3416, 1993.

[3] P. Mayaux, G. D. Grandi, and J. P. Malingreau, Central African forest cover revisited: a multi satellite analysis, Remote Sensing of Environment, vol. 71, no. 2, pp. 183-196, 2000.

[4] S. Agrawal, P. K. Joshi, Y. Shukla, and P. S. Roy, SPOT-VEGETATION multi-temporal data for classifying vegetation in south central Asia. Current Science, vol. 84, no. 11, pp. 1440-1448, 2003.

[5] R. P. Pech, R. D. Graetz, and A. W. Davis, Reflectance modeling and the derivation of vegetation indices for an Australian semi-arid shrub land. International Journal of Remote Sensing, vol. 7, no. 3, pp. 389-403, 1986.

[6] S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region. Remote Sensing of Environment, vol. 88, no. 1, pp. 195-208, 2003.

[7] Summarize of Xuzhou City, http://baike.baidu.com/view/5469.htm

[8] Jiangsu environment protection website, http://www.jshb.gov.cn/

[9] G. Tang et al., Remote sensing image digital processing, Beijing: Science Press, 2004.

[10] Y. Zhang, Priciples and methods of remote sensing application analysis, Beijing:Science Press, 2003.

[11] P. Zou, Y. Wang, Z. Wang, and F. Fan, Accessing the linear spectral un-mixing approach for extreacting vegetation information using Landsat ETM+ data in Macao, Journal of South China Normal University (Natural Science Edition), vol. 5, no. 2, pp. 132-133, 2007.