3.1.

Data Selection

The data source of the TM data is taken (days, months, years) as shown in Table 1.

Table 1

Data source for the Thematic Mapper (TM).

Images of the vegetation growing season and low water are selected since they are useful for improving the recognition accuracy.

3.2.

Data Processing

Remote sensing image preprocessing includes atmospheric correction, geometric precision correction, and feature extraction. The FLAASH atmospheric correction module, which is based on MODTRAN4 and retrieves spectral surface reflectance from multispectral remote sensing images, is the preferred atmospheric correction model. This method is based on observations by Kaufman et al.³² of a nearly fixed ratio between the reflectance for pixels at 660 and 2100 nm.^33–35 The FLAASH module in ENVI probably provides the most accurate means of compensating for atmospheric effects. For geometric correction, 12 to 20 ground control points are selected, usually including the locating object, which is explicitly identified.³⁶ The quadratic polynomial resampling technology correction refers to the total error control in a given pixel. A training area and an appropriate band combination was selected by considering the band wavelength of TM imagery, the region of interest (ROI) for the spectral reflectance of different types, the normalized difference vegetation index (NDVI), and the ecological characteristics (ecological niche and the color of the vegetation leaves in September). We made an exploration of TM band reflectance characteristics of each band for each land-cover type through the ROI training area. The ROI includes 1316 Spartina alterniflora Loisel, 633 mudflats, 1253 Suaeda salsa, 1002 Phragmites cammunis, 525 water bodies, and 4803 road samples (Fig. 2).

Fig. 1

The study area.

Fig. 2

The statistics of Thematic Mapper (TM) band reflectance characteristics of each band for each type.

The NDVI and spectral reflectance values of different features were collected. The NDVI has been widely used for vegetation application, and the calculation of NDVI is as follows:

Red and NIR stand for the spectral reflectance in the red and near-infrared bands, respectively. Normalized NDVI values lie between ($-1$, 1). Under normal circumstances, values for water are less than 0 and values for vegetation are larger during the growing season. A large amount of the biomass in this study has a large NDVI value.^37,38

3.3.

Decision Tree Classification Method

Decision tree methods for land cover classification are more convenient and efficient to solve the basic classification problem. The decision tree is based on a multistage or hierarchical decision scheme or a tree-like structure. The tree is composed of a root node containing all data, a set of internal nodes (splits), and a set of terminal nodes (leaves). Each node of the decision tree’s structure makes a binary decision that separates either one class or some of the classes from the remaining classes. The processing is generally carried out by moving down the tree until the leaf node is reached. Thus, the basic concept of a decision tree is to split a complex decision into several simpler decisions, which may lead to a solution that is easier to interpret. In a decision tree approach, features of data (i.e., bands) are predictor variables, whereas the class to be mapped is referred to as the target variable.³⁹ When the target variable is discrete (e.g., class attribute in a land cover classification), it is known as a decision tree classification. A decision tree classification system should confirm a threshold value based on the distribution of features in the feature space. The classification processing is conducted using ENVI4.7 software. The regional overlay is visualized using ArcGIS9.3 software.

3.4.

Selection of the Remote Sensing Band Combination

The band selection is done through data exploration. It helps to display and analyze remote sensing data. Remote sensing data at different wavelengths can be used to distinguish different vegetation types.

The results of feature selection indicate the range (maximum minus minimum) of bands 1 and 2 are small; the spectral reflectance area is narrow, which is unfavorable for feature recognition. Moreover, the spectral reflectance of P. cammunis and water bodies have almost identical mean values and hence cannot be distinguished from one another. However, the standard deviation value of P. cammunis is higher than that of water bodies, revealing that feature separability is stronger for P. cammunis than water bodies. A comparison of the reflectance standard deviation values of all seven bands shows that bands 1 and 2 have the lowest values, so these two bands should not be chosen. Band 3 (0.63 to 0.69 μm) reflects not only plant chlorophyll information but also beta-carotene and pigment information in autumn. Band 4 (0.76 to 0.90 μm) is in a high-reflection region for plants and is hence used for vegetation classification, biomass investigation, and determination of crop growth; accordingly, it is a general band in plant classification. Band 5 (1.55 to 1.75 μm) includes a large amount of information and thus has a high utilization rate. Bands 5 and 7, the spectral reflectance minima of water body, mudflat, and road, appear to be negative. These three bands (bands 5, 4, and 3) were used to separate vegetation from nonvegetation land-cover types.

3.5.

Tidal Flat Wetland Hierarchical Classification

This research considers NDVI, the band reflectance characteristics, and ecological features (especially ecological niche and the color of the vegetation leaves in September) of six typical tidal flat items, namely, water body, roads, mudflat, S. alterniflora, S. salsa, and P. cammunis. The first three types are nonvegetation. The last three types belong to vegetation. In the tidal flat wetland in the study region, the environmental characteristics are that the plants have predominant zonal distribution characteristics. Vegetation and nonvegetation land-cover information are precisely extracted by utilizing NDVI index segmentation. The process of selecting a value was repeated, and the value of NDVI was finally determined to be zero after comparing several results. This means that pixels with $\mathrm{NDVI}>0$ represent vegetation regions and those with $\mathrm{NDVI}<0$ represent nonvegetation regions.

3.5.1.

Classification of wetland vegetation types

The tidal flat vegetation can be mainly divided into three types: S. alterniflora, S. salsa, and P. cammunis. Their NDVI values are shown in Table 2.

Table 2

Statistics of normalized difference vegetation index values of zonal vegetation distribution.

Vegetation types	Min	Max	Mean
P. cammunis	0.172336	0.704160	0.509434
S. salsa	$-0.180490$	0.459669	0.084258
S. alterniflora	0.103365	0.753506	0.550454

The NDVI mean value (0.084258) for S. salsa was significantly lower than the values (0.509434, 0.550454) for the other two types of vegetation in the TM images. This is mainly because S. salsa’s biomass was relatively small and its color was red, while the biomasses of S. alterniflora and P. cammunis were higher and were shown in green on the image of September 21, 2010a. S. salsa can therefore be extracted through its NDVI value.

Zonal distribution vegetation classification

The distributions of other types of vegetation in the area were not determined only on the basis of analysis of the NDVI values. Since the NDVI values for the other two plants are almost identical, it is difficult to distinguish between them only through the NDVI values; hence the plants must be identified by their different environmental characteristics and reflectance properties.

A spectral reflectance curve represents the spectral selective absorption of the incident light, the light scattering, and the comprehensive characteristics of specular reflection on the surface of the object. Figure 3 shows the spectral reflectance response values of different wave bands for all three plants, obtained from the reflectance image acquisition of pixels, comprising purely pixel of each plant.

Fig. 3

Spectral reflectance characteristics of the vegetation.

The closer the spectral curves, the more difficult it is to distinguish the features. From the typical feature spectrum curve in Fig. 2, the three vegetation types are distinguishable. However, because of the phenomenon of “foreign body with same spectrum” and the ecotone that exists in the case of S. alterniflora, S. salsa, and P. cammunis, it is difficult to further distinguish between them. Meanwhile, it is important to find a way to more accurately interpret vegetation types in the ecotone.

Ecotones vegetation classification

The ecotones bandwidth for S. alterniflora and S. salsa is $\sim 2.503\mathrm{km}$, while that for P. cammunis and S. salsa is $\sim 1.655\mathrm{km}$. Pure pixels, i.e., those with only one vegetation type, were used to establish ROI. The spectral response curve was collected repeatedly from these pure pixels through the whole reflectivity spectrum to finally determine the value from the spectral response curve of vegetation (Figs. 4 and 5).

Fig. 4

The ecotones’ spectral reflectance of S. salsa and S. alterniflora.

Fig. 5

The ecotones’ spectral reflectance of S. salsa and P. cammunis.

Since the separability of each plant’s spectral reflectance value in band 5 is strong, band 5 spectral reflectivity value was chosen to effectively separate S. alterniflora and P. cammunis. After repeated tests, it was found that the band 5 reflectance value should be 0.12 to effectively distinguish between these plant types (Fig. 6).

In the classification results, a small proportion of P. cammunis was classified as S. alterniflora. From the ecological feature point of view, this is located in the west side of S. salsa. The field test was used to verify that the S. alterniflora was P. cammunis in fact. Accordingly, the classification was changed to P. cammunis. Through the above methods, it is possible to identity the distribution of zonal vegetation types in ecotones and scattered patches of vegetation types.

4.1.

Classification Results

The other three phases of image classification in the study area are realized through hierarchical classification, with the results shown in Fig. 8.

Fig. 6

The decision tree of vegetation classification.

Fig. 7

The decision tree of nonvegetation.

Fig. 8

Classification map of landscape from TM images at different times.

Wetland land cover types are complex, especially for wetland vegetation. Because of “same object with different spectrum” and “different objects with same image” phenomenons, misclassification and leakage of the pixels occur more frequently. These factors lead to the low classification accuracy. There are different amounts of the pixels of vegetation types that have been misclassified only based on spectral characteristics. Therefore, ecological niche and vegetation growth characteristics in different belts as the ancillary information were used to analyze the image of TM. This can identify a scattered distribution of vegetation types and vegetation patches. It can distinguish between patches as small as 0.09 ha, of which there are 345, accounting for 27% of the total number of patches.

4.2.

Classification Accuracy Test

A confusion matrix was established to analyze and evaluate the hierarchical classification accuracy. The ROI training method and 55 field data were used to perform a classification accuracy test. For this statistical classification and evaluation, there are six classes of land-cover type: water body, roads, S. alterniflora, S. salsa, P. cammunis, and mudflat. The sample space has a total of 3126 pixels in the 2010a image. After inspection, the overall accuracy was (2997/3126) 95.87%. The result from the same period as the image and the maximum likelihood method classification results (overall accuracy is 91.43%) are compared in Tables 3 and 4.

Table 3

Accuracy assessment of hierarchical classification of the 2010 TM image.

Table 4

Accuracy assessment of maximum likelihood classification of the same time image (in Table 3).

From a comparison of Tables 3 and 4, it can be seen that among the three types of vegetation, the user accuracy is highest for S. salsa, at 93.59%. The pixels correspond to the actual vegetation in Table 3 and to only 83.92% in Table 4. User precision indicates a high reliability of the classification diagram. Production shows that the actual accuracy for S. salsa is 94.62%, which is properly assigned to this kind of vegetation in Table 3, while 4.59% were classified as P. cammunis and 1.81% as S. alterniflora. Meanwhile, 96.79% is properly assigned to S. salsa with 7.38% classified as P. cammunis in Table 4 and 6.41% as S. alterniflora. In the classification statistical classification accuracy, mudflat and water body have the highest classification accuracy. The number of false points for road classification was larger, mainly because dirt roads are covered with vegetation and their width is narrower than other land-cover types, and some of them have ditches.