Estimating wetland vegetation abundance based on ...

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Estimating wetland vegetation abundance based on spectral mixture analysis: a comparison between LSMA and FCM classification methods abcd

Zhaoning Gong

, Tianxiang Cui

abcd

abcd

& Huili Gong

a

College of Resource Environment and Tourism, Capital Normal University, Beijing, PR China b

Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Beijing, PR China c

Key Laboratory of Resources Environment and GIS of Beijing Municipal, Beijing, PR China d

Base of the State Laboratory of Urban Environmental Processes and Digital Modelling, Beijing 100048, PR China Published online: 24 Dec 2013.

To cite this article: Zhaoning Gong, Tianxiang Cui & Huili Gong (2014) Estimating wetland vegetation abundance based on spectral mixture analysis: a comparison between LSMA and FCM classification methods, International Journal of Remote Sensing, 35:1, 189-203, DOI: 10.1080/01431161.2013.866292 To link to this article: http://dx.doi.org/10.1080/01431161.2013.866292

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International Journal of Remote Sensing, 2014 Vol. 35, No. 1, 189–203, http://dx.doi.org/10.1080/01431161.2013.866292

Estimating wetland vegetation abundance based on spectral mixture analysis: a comparison between LSMA and FCM classification methods Zhaoning Gonga,b,c,d, Tianxiang Cuia,b,c,d*, and Huili Gonga,b,c,d a

College of Resource Environment and Tourism, Capital Normal University, Beijing, PR China; Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Beijing, PR China; cKey Laboratory of Resources Environment and GIS of Beijing Municipal, Beijing, PR China; dBase of the State Laboratory of Urban Environmental Processes and Digital Modelling, Beijing 100048, PR China

b

(Received 2 April 2013; accepted 14 October 2013) Vegetation abundance is a critical indicator for measuring the status of vegetation. It is also important for evaluating the eco-environment of wetland. In this article, linear spectral mixture analysis (LSMA) and fuzzy c-means (FCM) classification methods were applied to estimate vegetation abundance in Wild Duck Lake Wetland, one of the typical freshwater wetlands in North China, based on Landsat Thematic Mapper (TM) data acquired on 27 June 2011. Due to its effectiveness in characterizing vegetation activity and greenness, the normalized difference vegetation index (NDVI) was incorporated into the six reflective bands of the Landsat TM image to provide enough dimensionality to support the use of the a five-endmember LSMA model, which includes terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil. Then, a fully constrained LSMA algorithm was performed to obtain vegetation abundance in our study area. An FCM classification algorithm was also used to generate vegetation abundance. Finally, both results were modified using the extracted water area of Wild Duck Lake Wetland, which was obtained with the combination of NDVI and normalized difference water index. The root mean square error (RMSE) and the coefficient of determination (R2) were calculated to assess the accuracy of vegetation abundance by using a WorldView-2 multispectral image. Validation showed that although there were slight differences between the vegetation abundance images, they shared similar spatial patterns of vegetation distribution: high vegetation abundance values in agricultural areas and riparian areas, moderate in grassland areas, and low in residential areas. The FCM classification generated an R2 of 0.791, while the LSMA yielded a result with an R2 of 0.672. Additionally, the RMSE also indicated that the FCM classification can obtain a much better result than LSMA: the former’s RMSE is 0.091 and the latter is 0.172. The result suggests that the FCM classification based on the nonlinear assumption can handle mixed pixels more effectively than LSMA.

1. Introduction Wetlands are essential ecologic land areas with abundant biodiversity, providing important living environments for human beings (Zhang et al. 2011). As one of the critical components of wetland ecosystems, vegetation plays an important role in the exchanges of water, carbon, and energy at the land surface (Hoffmann and Jackson 2000). Vegetation abundance is a crucial element that can be used to evaluate wetland health. It is also a sensitive indicator of land degradation in wetland regions (Guo et al. 2007). Thus, *Corresponding author. Email: [email protected] © 2013 Taylor & Francis


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understanding and measuring vegetation abundance is a prerequisite for analysing wetland ecosystems. Traditionally, vegetation abundance is measured using field observations or photography interpretation, which are time-consuming and costly (Xing et al. 2009). Satellite remote-sensing imagery has been used for the estimation of vegetation estimation due to its suitability for large-area mapping and relatively low cost. Numerous techniques have been developed for estimating vegetation abundance from remotely sensed imagery, including regression analysis (Gitelson et al. 2002; Yang, Weisberg, and Bristow 2012), spectral mixture models (Myint and Okin 2009), the decision tree classifier (Goetz et al. 2003; Tooke et al. 2009), and artificial neural networks (Boyd, Foodty, and Ripple 2002). Among these techniques, the simple regression method and spectral mixture analysis have been widely used (Chen, Li, and Shi 2002). The former was usually developed based on training data with known vegetation index values and actual vegetation abundance and often used in a particular region with certain vegetation types; in comparison, the latter shows its advantage in the estimation of vegetation abundance due to its rational physical meaning. Among types of spectral mixture models, linear spectral mixture analysis (LSMA) has been successfully used in many studies. Small and Lu (2006) found that LSMA with a three-endmember vegetation–impervious-surface–soil (V–I–S) model can facilitate the mapping and monitoring of vegetation abundance in urban areas. The same three-endmember model was also applied in Brisbane, Australia (Phinn et al. 2002), and Bangkok, Thailand (Madhavan et al. 2001). In an analysis of the spectral variability of impervious surface, Wu and Murray (2003) modified the V–I–S model to a four-endmember vegetation–high-albedo–low-albedo–soil (V–L–H–S) model for Columbus, OH, USA, to detect an urban environment. Elmore et al. (2000) identified another four-endmember model which included vegetation–light-soil–dark-soil–shade for LSMA in a semi-arid environment by analysing a time series of Landsat Thematic Mapper (TM) images of Owens Valley, CA, USA. Okin et al. (2001) also used a four-endmember model for LSMA with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data to retrieve vegetation and soil information in an arid and semi-arid environment. Although LSMA has been widely used, it has fundamental limitations (Foody 1996). LSMA is based on the assumption that the mixtures of spectral signatures of land-cover features are linear. When the scattered photons interact with multiple features, however, the mixture may become nonlinear. In many cases, the nonlinearity characteristics of the mixture are very significant and cannot be neglected (Ray and Murray 1996). To tackle this problem, nonlinear unmixing techniques were developed. Fuzzy classification can be used as a nonlinear unmixing technique (Tang, Wang, and Myint 2007), which has been widely applied in existing research. For example, Bastin (1997) conducted a series of techniques including fuzzy c-means (FCM) classification, LSMA, and maximum likelihood classification to generate sub-pixel land-cover maps and found that the FCM classifier can obtain the best predictions of sub-pixel land cover. LSMA and fuzzy classifier were also applied to three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (Hu and Weng 2011), and the latter was found to be more effective than LSMA. Almost the same conclusion can be found in the study by Hu, Hu, and Zhao (2010) in Haidian district, Beijing. Although numerous studies have been performed to generate vegetation abundance, the analysis of vegetation abundance of wetlands and the use of more than four endmembers in a spectral mixture analysis with Landsat data have both received little attention. Additionally, relatively few comparisons have been conducted between linear and nonlinear spectral mixture models. One purpose of this study was to derive the vegetation abundance of wetland using linear spectral mixture analysis with Landsat TM imagery. In this process, the normalized difference vegetation index (NDVI) was

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calculated and added to the Landsat TM image to extend the dimensionality of the data to support the use of a five-endmember model. Another objective was to apply an FCM classification to generate vegetation abundance, which may handle nonlinear mixture natures more effectively. The results of both LSMA and FCM classification models were evaluated by conducting an accuracy assessment procedure using a WorldView-2 multispectral image.

2. Study site and data 2.1. Study site Wild Duck Lake Wetland is located in the northwest part of Yanqing District, Beijing (40° 22′04″–40°30′31″ N, 115°46′16″–115°59′48″ E) (Figure 1). The topography of the study area is complex, with high mountainous areas located in the northwest and flat areas in the southeast. The Wild Duck Lake Wetland is one of the typical freshwater wetlands of North China; it is also the biggest wetland in Beijing. The area covers about 6837 hectares, of which wetland accounts for over 50%. This region has a continental semihumid and semi-arid monsoon climate in a warm temperate zone. Thus, precipitation is of monsoonal origin and the rainy season lasts from June to August. Overall, the average annual precipitation is 538 mm and the mean annual temperature is 12.83°C (Lin et al. 2013). Aquatic plants, hygrophytes, mesophytes, and halophytes account for over 71 families, 213 genera, and 357 species (Cui et al. 2013). The abundance of vegetation promotes its important role in maintaining and managing biodiversity.

2.2. Data and processing A cloud-free Landsat TM image (Path 123, Row 32) acquired on 26 July 2011 was used in this study. The Landsat TM image consists of six reflective bands with a spatial resolution of 30 m and one thermal band with 120 m spatial resolution. The six reflective bands were used in our study to obtain vegetation abundance based on both linear and

Figure 1. Location and Landsat TM image of Wild Duck Lake Wetland, Beijing. Display band combination: R, Band 5; G, Band 4; B, Band 3.


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nonlinear spectral mixture models. A WorldView-2 multispectral image taken at the same time was used to produce training and validation data sets to evaluate our two models; this image has eight multispectral reflective bands (1.8 m) and one panchromatic band (0.5 m). The Landsat TM image was georeferenced using ground control points (GCPs) selected with reference to a 1.8 m WorldView-2 multispectral image. In this process, 52 GCPs were collected on positions such as road intersections, edges, building corners, and confluences of rivers. The geometric correction was performed using a second-degree polynomial with a nearest neighbourhood resampling algorithm, and the overall root mean square error (RMSE) was equal to 0.32 pixels. The digital numbers of the geometrically corrected Landsat TM image were converted to at-sensor radiance following the methods proposed by Chander, Markham, and Helder (2009). Second simulation of the satellite signal in the solar spectrum (6S) (Vermote et al. 1997) software package (http://6s.ltdri.org/) was used to generate atmospheric parameters necessary for the conversion of radiance to reflectance. These parameters were then applied to the image to perform atmospheric correction. Finally, the reflectance values were achieved.

3. Methods 3.1. Linear spectral mixture analysis LSMA assumes that the spectral signature of a given pixel is the linear proportionweighted combination of the spectra signatures of land-cover features within a single pixel – the effect of multiple scattering between cover types is neglected (Zhang and Baas 2012). The pure spectra of these land-cover features are called endmembers. Mathematically, LSMA is expressed as n X

Ri ¼

fj Cij þ ei ;

(1)

j¼1

where Ri is the reflectance for band i, n is the number of endmembers, fj is the fraction of endmember j, Cij is the reflectance of endmember j at band i, and ei is the residual error for band i. The derived fractions of endmembers are often subject to the unity constraint: n X

fj ¼ 1;

(2)

j¼1

and another constraint is that each endmember fraction fj should lie between 0.0 and 1.0: 0 fj 1:

(3)

Model fitness can be assessed by the RMSE, which is defined as follows: RMSE ¼

X M

1=2 e2i =M

;

(4)

i¼1

where M is the number of bands and ei is the residual error at band i (i = 1, 2, …, M).


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A five-endmember model was developed with both the sum-to-one constraint and the non-negativity constraint in our study in order to access how LSMA performed in the estimation of vegetation abundance.


3.1.1. Endmember selection The number of endmembers used in LSMA is usually constrained by the dimensionality of the remote-sensing image. For Landsat TM/ETM+ (Enhanced TM Plus) images, a maximum of four endmembers is usually used due to the strong correlations among three visible bands (Xiao and Moody 2005). In our study area, the correlation coefficients among the first three visible bands of the Landsat TM image were 0.935 (bands 1 and 2), 0.912 (bands 1 and 3), and 0.940 (bands 2 and 3). Thus, a maximum of four endmembers can be used in the LSMA when using the original Landsat TM data. Four endmembers, however, may be inadequate to spectrally characterize the complex and heterogeneous landscapes of wetlands. The NDVI describes the contrast between the visible red and near-infrared reflectance of vegetation canopies, which is defined as follows: NDVI ¼

RNIR Rred ; RNIR þ Rred

(5)

where Rred and RNIR are the reflectance of the visible red and near-infrared wavelength regions corresponding to Landsat TM bands 3 and 4, respectively. In previous studies, it has been indicated that NDVI is usually strongly correlated to the fraction of photosynthetically active radiation (fPAR) and also closely associated with vegetation activity (Myneni et al. 1995). Additionally, an area is considered to be covered with vegetation when NDVI is greater than 0.2, and vegetation abundance increases as NDVI rises. When NDVI is negative, this can be considered as either water or ice (Shabanov et al. 2002). In our study area, NDVI is moderately correlated with the visible red and near-infrared bands, with coefficient of determination, R2, equal to 0.547 and 0.389, respectively. Thus, as a nonlinear combination of visible red and near-infrared, NDVI can provide new information that is linearly independent from the original Landsat TM image bands. By including NDVI along with the six reflective bands, this extended the spectral dimensionality of the Landsat TM data and allowed the use of a fifth endmember in the LSMA model. A five-endmember model can capture the spectral variability of our study region more effectively than the three- or four-endmember model of traditional studies. Several techniques have been used in identifying endmembers from remotely sensed images, including the use of principal component analysis (Small 2001), two-dimensional feature space plots (Lee and Lathrop 2005), and identification of pure pixels with reference to field survey data (Kameyama, Yamagata, and Nakamura 2001). In our study, a combination of automatic and supervised endmember selection was performed on the seven-band Landsat TM image. The minimum noise fraction (MNF) transformation was performed on the image to minimize the influence of band to band correlation and to separate noise from the image. MNF transformation consists of two cascaded principal component transformations: the first is performed to calculate the noise covariance matrix in order to rescale and decorrelate the noise in the image, which generates a noisewhitened image; the second is performed on the noise-whitened image (Meer and Jong 2000). By conducting an MNF algorithm, we obtained seven MNF components from the image (Figure 2). The first two components clearly illustrated the separation of terrestrial

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160,000 120,000 80,000 40,000 0 1

2

3 4 5 Eigenvalue number

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Figure 2. Seven MNF components of the Landsat TM image and MNF eigenvalue plot of the study area. (a) MNF component 1; (b) MNF component 2; (c) MNF component 3; (d) MNF component 4; (e) MNF component 5; (f) MNF component 6; (g) MNF component 7; (h) MNF eigenvalue plot. The first two MNF components clearly illustrate the separation of high albedo, terrestrial plants, and low albedo (water), the third component is essential in identifying aquatic plants, and the fourth MNF component can provide information on separation of bare soil.

plants, high albedo, and low albedo (water), and the third component is essential in the identification of aquatic plants. In addition, the fourth MNF component provided information on separating bare soil. However, higher-order MNF components provided little information. The MNF eigenvalue plot also suggests that the first four MNF components contain almost 97% of the variance. Thus, the first four MNF components were used to calculate the pixel purity index (PPI) image. PPI can help to identify the most spectrally pure pixels in the image. PPI is computed by repeatedly projecting n-dimensional scatter plots in the spectral feature space on a random unit vector. It records the extreme pixels in each projection and notes the total number of times each pixel is marked as extreme (Li et al. 2010). The PPI image is created where each pixel value corresponds to the number



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Figure 3. Landsat TM-based spectral signatures of the five selected endmembers of the study area. The individual symbols on the right represent NDVI values of endmembers.

of times that pixel was recorded as extreme. Hence, the pixels with the higher PPI values correspond to spectrally unique features and can be selected as candidate endmembers. The final endmembers were determined by selecting extreme pixel clusters which bound almost all other pixels in the n-D visualizer tool by referring to the Landsat TM image using ENVI. Finally, five endmembers were identified: terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil. The average reflectance and NDVI values of the selected representative pixels with the n-D visualizer tools were used as endmember spectral signatures. The scatter plots of the mean reflectance values and NDVI values of the endmembers are shown in Figure 3. The reflectance of the terrestrial plants corresponds to the vegetation spatial signatures: a peak at the green band in the visible spectrum and a rapid increase between the red band and the near-infrared band. In addition, the NDVI value of terrestrial plants is the highest, at 0.85. The NDVI value of aquatic plants is also higher than the other non-vegetated endmembers. The reflectance, however, is relatively low. The high-albedo endmember is mainly derived from the top of buildings and its reflectance is between 0.2 and 0.4. The low-albedo endmember corresponds to water pixels and shadows in the area whose reflectance is the lowest. Bare soil’s reflectance is between 0.04 and 0.3. Because the reflectance and NDVI values of the five endmembers can easily be distinguished, they were used to characterize the heterogeneous landscapes in our study area.

3.1.2. Fraction image calculation The endmember fraction images (Figure 4) were calculated by solving a fully constrained five-endmember linear mixture model using the seven-band Landsat TM image. The terrestrial plants fraction image correlates with known vegetated areas within the study area. That is, the fraction is low in residential areas, while increasing to 0.4–0.6 in grassland areas, and to over 0.8 in agricultural lands. Moreover, the high values of the aquatic plants fraction are typically distributed along the riparian areas. Fraction images of


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(a)

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(d)

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Figure 4. Fraction images derived from Landsat TM image using five-endmember LSMA: (a) terrestrial plants, (b) aquatic plants, (c) high albedo, (d) low albedo, and (e) bare soil.

high albedo and low albedo are also correlated with their distribution, the former high values located in urban areas and the latter in water areas. The bare soil fraction image has its highest values in grassland areas. Overall vegetation abundance can be generated by adding terrestrial and aquatic plant fraction images. 3.2. FCM classification FCM classification is a classifier based on fuzzy set theory. Fuzzy sets are sets without sharp boundaries and can be used to represent uncertain and qualitative information. A fuzzy set is measured by fuzzy membership possibilities which range from 0.0 to 1.0. The FCM algorithm is an iterative clustering method (Li, Ling, and Du 2012), which minimizes the objective function: Jm ¼

N X C X

2 μm ij dij ;

(6)

i¼1 j¼1

where N is the total number of the pixels; C is the number of classes; μij is the fuzzy membership value of the ith pixel (i = 1, 2, …, N) for class j; m is the fuzzy weight, which controls the level of fuzziness; and dij is the Euclidean distance between the ith pixel and the mean vector of class j (j = 1, 2,…, C), which can be described as follows: dij ¼ xi cj ;

(7)

where xi is the vector of the ith image pixel value and cj is the mean vector of class j. The fuzzy membership value μij satisfies the constraints:


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0 μij 1;

(8)

C X

μij ¼ 1;

(9)

μij > 0;

(10)

j¼1


N X i¼1

and its value is calculated with the following: 1

μij ¼ C P k¼1

1 dij2 ðm 1Þ dik2

;

(11)

2 where dij2 ¼ xi cj and dik2 ¼ kxi ck k2 . In the FCM clustering process, it is necessary to determine the value of m. In previous studies, Zhang and Foody (1998) state that in most clustering cases, m = 2.0 can achieve the most accurate fuzzy classification, and therefore in our study, m = 2.0 was used. In our research, FCM classification was conducted to extract vegetation abundance. The initial training data were manually selected from the seven-band Landsat image. Five fuzzy membership images (terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil) were then yielded. The overall vegetation abundance image was then generated by adding the terrestrial and aquatic plant fuzzy membership images (Figure 5). (a)

(b)

(d)

(e)

(c)

Figure 5. Fuzzy membership images of Wild Duck Lake Wetland based on fuzzy c-means classification: (a) terrestrial plants, (b) aquatic plants, (c) high albedo, (d) low albedo, and (e) bare soil.

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3.3. Water removal Results obtained from the water regions in our study area were greater than 0.0 (pure water pixels) due to the spectral distinction between low-albedo endmember and water, which indicates that both LSMA and FCM classification models cannot handle the water problem well. Thus, a water removal procedure was performed to rationalize our results. Former research has used either a single band (Ryu, Won, and Min 2002) or a ratio of two bands (Ouma and Tateishi 2006) of Landsat TM data to delineate water features and enhance their presence in the image. In our study, water pixels were extracted using the combination of normalized difference water index (NDWI) and NDVI. NDWI is one of the ratio water information extracting models which can maximize the reflectance properties of water (Mcfeeters 1996), and is defined as follows: NDWI ¼

Rgreen RNIR ; Rgreen þ RNIR

(12)

where Rgreen and RNIR are the reflectance of the visible green and near-infrared wavelength regions. In regard to Landsat TM images, these correspond to bands 2 and 4. This index is designed to maximize the reflectance of water by using green wavelengths, minimizing the low reflectance in NIR by water features, and taking advantage of the high reflectance in NIR by vegetation and soil features. As a result, water features have positive values and thus are enhanced, while vegetation and soil usually have zero or negative values and therefore are suppressed. In our study, we defined pixels where the NDVI was less than −0.03 and NDWI greater than 0.1 as water by manually selecting pure water pixels referring to the Landsat TM image and a series of statistics. By defining the vegetation abundance value in water regions as 0.0, the results generated based on both LSMA and FCM classification methods will be more rational.

4. Accuracy assessment A total of 60 sites were randomly sampled from the Landsat TM image for model validation. A 3 × 3 window for each site was used to decrease the co-registration errors between the Landsat TM image and the WorldView-2 multispectral image. The vegetation areas were manually digitized on the high-resolution image, and the proportion of vegetation was then calculated for each site as inspection data. Both the RMSE and R2 were used to assess the accuracy of vegetation abundance estimation. Their equations are the following: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uN uP ^ u ðIi Ii Þ2 ti¼1 ; RMSE ¼ N N P

R2 ¼

i¼1 N P i¼1

(13)

ðÎi IÞ2 ðIi IÞ2

;

(14)


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where Îi is the estimated vegetation abundance for sample i, Ii is the vegetation proportion computed from the WorldView-2 image, I is the mean value of all of the samples, and N is the number of samples. The 60 sites for validation were randomly based on areas like residential areas, agricultural areas, grasslands, and river coasts.

5. Results and discussion Both LSMA and FCM classification models were applied to the Landsat TM image for vegetation abundance estimation. Five fraction images and five fuzzy membership images consisting of terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil were yielded to describe the spatial pattern of each land-cover type within the study area. Vegetation abundance images were generated by adding the terrestrial and aquatic plant images, then a process of removing water was performed on the two vegetation abundance images to rationalize the results. Figure 6 shows the vegetation abundance extracted from the Landsat TM image by the two models. Although there were slight differences between the vegetation abundance images, they shared similar spatial patterns of vegetation distribution. That is, the vegetation abundance values are high in agricultural areas and river banks (Figure 6(b)-A), which are greater than 0.8, moderate in grassland areas (Figure 6(b)-B), and low in residential areas (Figure 6(b)-C). After an accuracy assessment procedure, Table 1 suggests that FCM classification can yield better results than LSMA. The FCM classification generated a RMSE of 0.091 and R2 of 0.791, while LSMA yielded RMSE of 0.172 and R2 of 0.672. These results indicate that the nonlinear method can obtain better results of vegetation abundance estimation than the linear method; the nonlinear mixture property was significant in the Landsat TM image and cannot be neglected. Scatter plots (Figure 7)

Figure 6. Final vegetation abundance images of Wild Duck Lake Wetland, Beijing. The images were produced by classifying data into five categories of equal interval. Both images share similar spatial patterns of vegetation distribution: high in the agricultural areas and river banks (Figure 6(b)-A), moderate in grassland areas (Figure 6(b)-B) , and low in residential areas (Figure 6(b)-C). (a) Vegetation abundance generated by LSMA. (b) Vegetation abundance generated by FCM classification.

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LSMA FCM classification

Figure 7.

R2

RMSE

0.672 0.791

0.172 0.091

Scatter plots of (a) LSMA and (b) FCM classification assessment results.

of the relationship between estimated and actual data showed that with LSMA, there was over- and under-estimation in our study area, while with FCM classification, over- and under-estimation were not clear and therefore that result was much better. LSMA has been widely used as a sub-pixel classification technique in earlier studies due to its effectiveness in handling mixed pixels. However, LSMA is based on an assumption that nonlinear mixture is insignificant and can be neglected. The inherent drawbacks of LSMA limit its accuracy in the estimation of vegetation abundance. Endmember selection is another challenge due to the limited spectral dimensionality of the image and the complex and heterogeneous landscapes of wetlands. For Landsat images, a maximum of four endmembers is usually used due to the strong correlation among three visible bands. In our study area, terrestrial plants and aquatic plants should be spectrally represented by two types of endmembers as their spectral signatures are quite different, especially in the near-infrared bands: the reflectance value of terrestrial plants in the near-infrared bands is relatively higher than that of aquatic plants. Thus, the use of only one single type of vegetation endmember may reduce the capability of unmixing due to the image spectra being under-sampled. Our research suggests that NDVI can be incorporated into the Landsat TM image to help the spectral separation of the two types of vegetation and the establishment of five-endmember models. To tackle the inherent limitation of LSMA, FCM classification was adopted to obtain vegetation abundance. The advantage of FCM classification over LSMA is that FCM classification has no assumption about the nature of spectral mixture. Furthermore, it allows mixed pixels to be chosen as signatures. Therefore, the selection of training data for the FCM classification is more flexible and easier. Our results indicated that FCM classification can improve the accuracy of vegetation abundance estimation better than LSMA. In this study, the data used were obtained during the growing season of vegetation. Having a multi-date remotely sensed image to show both the growing and fallow seasons of vegetation can provide a dynamic characterization of vegetation abundance for the


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whole year. Similarly, using Landsat TM data from 1984 (when Landsat 5 was launched) can obtain changes in the status of vegetation abundance in a long time series. Moreover, in traditional land-use classifications, each pixel of the remotely sensed image is assigned to a single class. Most pixels, however, contain information on multiple ground cover types, which affect the capacity of traditional classification. In contrast, spectral mixture analysis, which details the information within each single pixel, can help to obtain a more reasonable and accurate result for land-use studies.

6. Conclusion In this study, both LSMA and FCM classification were evaluated to address their ability to estimate vegetation abundance in wetland using medium-resolution remotely sensed imagery. Both methods were applied to a Landsat TM image over the Wild Duck Lake Wetland, Beijing, China, acquired on 26 July 2011. By adding NDVI to the original Landsat TM data, five fraction images and five fuzzy membership images consisting of terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil were yielded. The overall vegetation abundance images were then generated by adding terrestrial and aquatic plant images. To make our results more rational, pure water pixels in the study area were removed using a combination of NDVI and NDWI, which enhanced the difference of pure water and other features and helped to identify pure water pixels. Finally, an accuracy assessment procedure was carried out and RMSE and R2 were calculated to evaluate the two models. The number of endmembers used in LSMA is determined by the spectral dimensionality of the image and the correlations between image bands. Our study area is too spectrally variable for it to be possible to characterize it using only three or four endmembers. Our research suggests that NDVI can be incorporated into the Landsat data set to provide enough dimensionality to support the establishment of the fiveendmember model. LSMA is based on an assumption that mixtures of reflectance of land-cover features are linear. The linear nature of LSMA has become a fundamental obstacle for improving the accuracy level of sub-pixel classification. Furthermore, LSMA is also limited by the endmember selection procedure. FCM classification has no assumption about the nature of mixing and it considers linear mixing as a special case of nonlinear mixing. Therefore, it can yield a better vegetation abundance estimation result.

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