Discussion on possibility of the identification of karst vegetation communities based on OLI data

Rui Zhang, Hongxia Luo*, Yangqing Zou Guangpeng Liu

School of Geographical Science School of Resources & Environment Southwest university Southwest university

Chongqing, 400715, China Chongqing, 400715, China E-mail:Ruizh0921@163.com

Abstract—Desertified karst region is a focal area of vegetation recovery and ecological restoration in southwest China, and vegetation is an important and sensitive factor to reflect the changes of ecological environment in karst region. Recently, with the development and application of imaging spectrometer, remote sensing technology plays an important role in large-scale karst vegetation investigation. Remote sensing data have the advantage of macroscopic, real-time, dynamic and make vegetation investigation of large areas more convenient.

Zhongliang Mountian was taken as the study area, which is an area that vegetation is recovering in karst rocky desert area of Chongqing. The image of Landsat 8 OLI data in August 19, 2013 was applied as the test data in the study. we tried to discuss the effect and feasibility of BP artificial neural network which is a kind of neural networks using multi-spectral images to classify and identify the karst vegetation, the method includes neural network based on the multi-spectral bands of remote sensing image, or accompanied by non-remote sensing information, including texture information, slope, aspect and elevation. Finally, the supervised classification based on maximum likelihood was applied to extract the vegetation classes which were compared with the classes extracted by BP neural network. The results showed that Cupressus funebris, Cunninghamia lanceolata (Lamb.) Hook and mixed broadleaf-conifer forest couldn’t be identified well with neural network when only multi-spectral data were employed. With the application of other data, such as ratio band, texture, slope, aspect and elevation, the total accuracy was improved gradually, and the three vegetations were well identified. It was concluded that the effect of neural network classification(NNC) based on multi-spectral bands of OLI image and DEM is the best in which the total accuracy reached 87.42%, the Kappa coefficient 0.85. Compared the results with that of traditional supervised classification, the total accuracy of NNC has shown a growth of 5.57%. For mapping accuracy, the identification accuracy of Pinus massoniana, bamboo forest, broadleaved forest, shrubland and hassocks were relatively high in which the mapping accuracy reached 91.03%, 94.03%, 84.39%, 80.50% and 99.21% respectively. While one of Cupressus funebris, Cunninghamia lanceolata (Lamb.) Hook and mixed forest was low, which might be because the area of three vegetation communities in the study area was relatively small, which led to self learning effect is poor. As a result, the accuracy wasn't always ideal.

In general, the OLI multi-spectral remote sensing image using BP neural network is feasible to identify the Karst vegetation communities, and provide a scientific method for vegetation mapping. At the same time, it should be pointed out that the difference of spectrum characteristic of vegetation community is small or the remote sensing image has mixed pixel, this will cause loss of some precision. Aiming at the deficiency, we can combine multi-sensor remote sensing data with non-spectral information to extract information efficiently.

Keywords—multi-spectral remote sensing; karst area; vegetation

community; feature extraction; neural network model

I. INTRODUCTION The Karst rocky desertification region is the key area of

vegetation restoration and ecological reconstruction in southwest China, and the vegetation is an important and sensitive indicative factor of ecological environment changes in karst regions[1]. Therefore, it is particularly significant to figure out the classification and distribution of vegetation population in karst regions. In recent years, with the continuous improvement of spatial, temporal and spectral resolution of remote sensing data, the massive remote sensing image data sets come into being. Noticeably, extracting the surface features information, especially vegetation information, with the aid of remote sensing image, has become a hot subject in the research of remote sensing.

Artificial neural network (ANN) [2], with the advantages of self-adaption, self-learning, parallel processing and fault tolerance, can comprehensively analyze the various types of multivariate data (non-spectral data) in an effective way so that it is paid much attention to in remote sensing image classification. For example, by means of extracting image texture, Chen Qihao [3]used BP neural network classification to obtain surface features based on aviation remote sensing image resolution of 0.2 m; Foody [4]used artificial neural network model with priori knowledge to improve classification accuracy. But these studies mainly chose high spatial resolution remote sensing image as the test material. Besides, from the perspective of classification target, the focus is primarily put on the classification of land use, scarcely on the classification of vegetation community scale.

This article, based on the importance of acquiring the vegetation community information and the advantage of artificial neural network classification, selects the Zhongliang Mountain as the experimental zone, Landsat-8 OLI multispectral remote sensing image as the experimental material through the analysis of different combinations of input vectors of BP neural network model recognition effect for karst vegetation community, to discuss the feasibility of using the multispectral remote sensing image to extract the vegetation information extraction in community scale.

II. STUDY AREA SURVEY AND DATA PREPARATION

A. Survey of Study Area Zhongliang Mountain, located in the north of Chongqing

municipality, belongs to the subtropical moist monsoon This work was supported by The National Natural Science Foundation of

China (41201436). Corresponding author (*): Hongxia Luo.

climate with high relative humidity, an annual average temperature of 18℃ and an annual average precipitation of 1000-1300 mm. Its geomorphic types are strongly influenced by its geological structure and lithology and make the pattern of "three mountain two groove ridge". It belongs to the low mountain hilly region with an altitude of 360-700 m and the slope of 0 ~ 40 °.The steep flanks are covered with hard sandstone while the axis of undulating rolling hills is composed of purple shale. Between them, there is limestone which is karstified to valleys and then developed as the karst landform. In uneven soil layer thickness, all have different levels of calcium carbonate, and more developed yellow soil, brown lime soil and calcareous purple soil[5]. To explore multispectral remote sensing image based on artificial neural network method which is used to identify whether the karst vegetation community has the feasibility and reliability, this research chose an area of richer vegetation community types in Zhongliang mountain (106°23′03.64″-106°28′19.43″ E ， 29 ° 39 ′ 13.38 ″ -29 ° 49 ′ 01.90 ″ N) as the experimental zone, covering an area of 7350.93 hm2. Geographical location is shown in figure 1.

Figure 1 The geographical location of the study area Due to lack of soil, water and alkaline karst environment,

the plants in the region have the characteristics of drought, saxicole and calciphilous. The main vegetation types distributed in the axis of Zhongliang Mountain are grassland, scrub-grassland, rattan thorn bushes and some scattered deciduous broadleaf tree species. Both flanks are dominated by pinus massoniana, secondary theropencedrymion and cunninghamia lanceolata, which form a large size of evergreen coniferous forest and some mixed forests. Near the residential area and parts of piedmont piece grow subtropical bamboo forests including many neosinocalamus affinis and a bit of dendrocalamus latiflorus and moso bamboo.

B. Data source and Preprocessing The LANDSAT-8 OLI multispectral data includes visible

light - near infrared - short wave infrared in total 9 bands, and is 30-meter space resolution. OLI land imager on the basis of ETM + adds the new bands (band1:0.433-0.433 microns, band9:1.360-1.390 microns) used for observing coastal zone aerosol and cirrus cloud. Therefore, this research mainly adopts OLI2, 3, 4, 5, 6, 7 bands. The track number of OLI image is 128/39. The image was taken on August 19, 2013

which is the best period for the growth of vegetation. These data are provided by the computer network information center, Chinese academy of sciences, China international scientific data mirror site (http://www.gscloud.cn). In order to extract more accurate information, we took means of some other relevant auxiliary materials, including the secondary survey data of forest resources in Chongqing, wild vegetation investigation data in situ and high spatial resolution aerial films.

Since the obtained remote sensing data is L1T level products which have taken radiation system error correction, geometric correction, the ground control points geometric correction and digital elevation model (DEM) for terrain correction, the data just need to be preprocessed by radiation calibration, atmospheric correction, image enhancement and the study area clipping. The data processing is completed with the help of envi5.0 SP3 software.

III. RESEARCH METHODS

A. Classification Scheme Through the investigation of the actual situation of the

wild vegetation and reference to the classification system from the book "China vegetation” written by Wu Zhengyi [6], 1:40,000,000 Chinese Vegetation map classification system and 1:1,000,000 land surface cover classification system[7], the research ultimately determines the remote sensing classification scheme in the study area, mainly including two categories and 12 small classes(table Ⅰ). One category is vegetation classification scheme, involving 8 small classes: subtropical evergreen needle forest pinus massoniana, Cunninghamia lanceolata, Cupressus funebris, subtropical sinocalamus affinis forest, subtropical evergreen and deciduous broad-leaved forest, theropencedrymion, brush and grassland. They respectively represent the different succession stages of vegetation types and different vegetation community types in the same succession stage. The other is non-forest classification scheme, involving 4 small classes: water, farm land, artificial construction and quarry.

B. Classification Method This experiment mainly used the artificial neural network

classification method based on the theory of the self learning. Artificial neural network (ANN) is nonlinear complex network system composed of a large number of simple connected neurons [8].Compared with traditional statistical methods, information distributed storage and parallel processing characteristics of ANN can more convieniently introduce the non-spectral information in classification. The three-layer Back-propagation Network model with the topological structure shown by figure 2 is selected. From the spectral bands and bans arithmetic, texture information, slope, slope direction and elevation information, this article aims to explore whether these different combinations of input vector neural network on the basis of multispectral images can be reliable to identify the types of vegetation community. Finally, based on the same image, the BP neural network classification result will be compared with the maximum likelihood of

supervised classification result to find out the optimal method of vegetation information extraction.

TABLE I. THE OLI IMAGE CLASSIFICATION SCHEME IN ZHONGLIANG MOUNTAIN

Figure 2 The model structure of BP neural network

C. Training Samples Selection The quality of training samples selection is directly related

to learning and classification accuracy [9]. In this study, we took Chongqing Municipality secondary survey data of forest resources and the Google Earth high spatial resolution image as auxiliary materials, mainly referring to the large area feature location information during the period of wild vegetation survey in order to determine the training area. It is required that sample area be more than 3600 square meters, namely with at least four pixels, and that ensure the training sample is composed of one single type, such as what is shown

in figure 3). Finally, the 2678 training samples are selected to involve the learning process.

Figure 3 The selection of training samples

D. Accuracy Validation Method Confusion matrix is a matrix r * r which is the most

extensive method to measure classification accuracy. Therefore, this article selects the confusion matrix for quantitative evaluation of classification results [10]. Evaluation index includes commission errors (CE), omission errors (OE), user's accuracy (UA), produce's accuracy (PA), overall accuracy (OA) and kappa coefficient (KC).

IV. RESULTS AND ANALYSIS

A. BPNN Classification Just Based On Spectrum Because the independence is stronger and the amount of

information is the largest between the 3, 5, 6 band of the OLI image, firstly, the research used the three spectrum bands as input vectors to conduct neural network classification. According to the neural network classification steps, it sets the three bands as the input layer nodes, and the 12 class features in classification scheme as output layer nodes. First of all, network number of hidden layer nodes starts from 6, and then adjusts itself after the network training. The transfer function uses the logistic nonlinear function. Finally, after a great number of studies and relevant parameters adjustment for many times, the classification accuracy achieved 75.5% when the study rate (η)is 0.2, the momentum factor (α) is 0.9, training times is 1500 and the hidden layer is 3. That is best precision of the parameters adjusted for many times. The final classification result is shown in figure 4(a), and the classification accuracy is shown in table 2.

In the neural network classification accuracy, it used three independent multispectral bands classification but doesn’t achieve good classification effect, which is probably due to less spectral band for network learning. Thus the research tries to join OLI 2, 3, 4, 5, 6, 7 bands as the input nodes to explore whether the spectral bands can, more or less, affect the training consequence. According to the weight training results

classification scheme

vegetation community types

description

Vegetation Communities

Ⅰ- pinus massoniana

subtropical evergreen needle forest

Ⅱ-Cunninghamia lanceolata

subtropical evergreen needle forest

Ⅲ- Cupressus funebris

subtropical evergreen needle forest

Ⅳ-sinocalamus affinis

subtropical bamboo forest

Ⅴ-subtropical evergreen and

deciduous broad-leaved forest

corkoak, chestnut, acacia, citrus, liquidambar, camphor,

cyclobalanopsis, tianzhu laurel, flocculus banyan, DaYeYang, privet, mulberry, yellow oak

trees, etc Ⅵ-

theropencedrymion mixed needle and broadleaf

forest Ⅶ-brush evergreen shrubs, elfin forest,

evergreen broadleaf deciduous arbor sapling thickets and

scrub-grassland in acidic and alkaline soil

Ⅷ- grassland Annual or perennial herb such as saccharum arundinaceum, cogon, wild chrysanthemum,

etc

Non-forest

Ⅸ- artificial construction

towns, rural residential areas, roads

Ⅹ-quarry Ⅺ-farmland dry land, paddy field Ⅻ-water rivers, reservoirs, ponds

prior to the initial setting, network in the loop 1168 times is astringed. The classification result is shown in figure 4(b), and the classification accuracy is 76.3% (table 2). Compared with the input of OLI 3, 5, 6 bands, the accuracy only increases by 0.8%. Thus it is necessary to consider whether there is other information to improve the classification accuracy.

B. BPNN Classification Based on Spectrum and Ratio Bands Parts of the object in the image have shadow which affects

the spectral characteristics, so it causes similar bodies with different spectrum or different bodies with similar spectrum phenomenon. Perhaps shadow elimination can reduce the error phenomenon and improve the automatic recognition accuracy. Shadow is usually caused by sensor scans and angle of the sun. The band ratio method is an effective method to eliminate the shadow just by putting any two synchronizations. The way of accepting the same area of the band image to divide can get a new image to basically eliminate the shadow. In the experiment, the two ratio bands RA5/4 (OLI5 / OLI4) and RA7/3 (OLI7 / OLI3) are taken as new bands joined with OLI 2, 3, 4, 5, 6, 7 bands to carry out training and classification. The overall classification accuracy is 79.18% (table 2), increasing 2.8% than just using spectral bands. But the classification still does not reach the ideal effect.

C. BPNN Classification Based On Spectrum and Texture Information Texture information refers to the image features

information such as shape, size and direction among different plaques. It can make up for the defects of error classification and discrimination by the pure spectral information classification and improve the classification precision [11].

At present, the extraction of texture feature is dominated by the statistical analysis method. Meanwhile, gray level co-occurrence matrix is a hotspot method with better effect in recent years. In this part we adopt the gray level co-occurrence matrix to extract image texture feature in order to assist multispectral image classification. According to the principle of gray level co-occurrence matrix, the image of six texture measure in the study area are extracted by using the 3 x 3 mobile window, including the Mean, Variance, Homogeneity, Contrast, Dissimilarity and Entropy. After comparing one by one, the three most significant texture feature value based on the blue band, namely, variance, contrast and otherness are selected.

Then, the three texture measurements and OLI2, 3, 4, 5, 6, 7 spectral bands are input to the BPNN for training and classification. Classification result is shown in figure 4(d). The overall classification accuracy is 79.48% (table 2), increasing 3.11% than just using spectral bands.

D. BPNN Classification Based On Spectrum and The Elevation Related Data According to the landform and the topographic

differentiation phenomenon of wild vegetation, it can be speculated that the spatial distribution of vegetation in the study area may be affected by the gradient, slope direction and altitude [12]. Therefore, adding the elevation, gradient and

slope direction data in the neural network classification could distinguish vegetation communities which distribute in different altitude terrain or within the common features such as dankness or sun, so as to improve the classification precision.

Hence, the gradient, slope direction, elevation data together with OLI2, 3, 4, 5, 6, 7 spectral bands take part in the training and classification of BPNN. The overall classification accuracy is 79.48% (table 2) with an increase of 3.11%.

Classification accuracy is 82.36% (table 2), which is better than only using spectral bands classification accuracy with an increase of 5.99%. However, the classification accuracy of only the elevation data and OLI six bands as input vector of neural network classification have reached 87.42% with Kappa coefficient of 0.85 (table 2), better than only using spectral bands classification accuracy with an increase of 11.05%. Classification result is shown in figure 4(e). The study results show that the multiple spectral bands in the classification of the neural network is an important input vector, and slope, aspect to join there may be some interference, but elevation and the combination of multi-spectral bands for the neural network classification can yield ideal result, thus providing reliable ground vegetation communities information.

E. Classification Results of Maximum Likelihood Supervised Maximum likelihood classifier is based on the Bayesian

criterion of minimum classification error probability of the nonlinear classification, which is both a kind of completely phylogenetic tree reconstruction method based on statistics and a applied more extensive and mature supervised classification method [13]. The purposes of choosing this method and medium space resolution image of OLI to identity the vegetation communities and non-forest land is to test whether this method can identify the type of community scale and to verify the results of artificial neural network classification [14,15]. Maximum likelihood classification result is shown in figure 5, and the confusion matrix is shown in table 3.

It can be seen from table 3 that the overall accuracy of maximum likelihood is 81.8% and the Kappa coefficient is 0.78, which basically meets the requirements of classification. From the perspective of mapping, the classification accuracy of broad-leaved forest, Cupressus funebris and artificial building is poorer but the worst is theropencedrymion with only 35% due to more leakage pixels. In addition, the identification effect of vegetation community is seriously worse than non-forest features.

Figure 4.The different input vector of BP neural network classification

TABLE II. THE CLASSIFICATION ACCURACY OF DIFFERENT INPUT VECTOR OF BP NEURAL NETWORK

Input layer Nodes OA (%)

KC PA (%) Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ Ⅺ Ⅻ

OLI 3\5\6 3 75.58 0.70 97.93 0.00 0.00 87.79 27.51 0.00 76.88 92.13 31.50 91.75 91.86 98.59

OLI 2\3\4\5\6\7 6 76.37 0.72 98.64 40.35 0.00 87.17 36.43 0.00 81.34 98.43 30.31 92.78 85.15 99.30

OLI 2\3\4\5\6\7+ RA5/4+RA7/3 7 79.18 0.75 92.08 72.81 13.95 88.95 42.75 2.94 79.39 95.28 44.88 94.50 91.53 98.59

OLI 2\3\4\5\6\7 +variance +contrast

+dissimilarity

9 79.48 0.75 95.10 76.32 11.63 89.57 45.35 0.00 82.45 95.28 32.28 96.22 90.76 98.59

OLI 3\5\6+dem + slope + aspect 6 79.94 0.76 93.85 7.02 15.89 90.02 80.30 0.00 73.26 97.64 79.53 94.85 81.74 97.89

OLI 2\3\4\5\6\7+ dem+slope+aspect 9 82.36 0.79 92.39 95.61 18.99 94.03 50.19 28.43 74.09 98.43 58.27 90.03 92.08 98.59

OLI 2\3\4\5\6\7 +dem 7 87.42 0.85 91.03 39.47 51.94 94.03 84.39 36.27 80.50 99.21 78.35 91.41 98.68 99.30

Figure 5 The results of maximum likelihood supervised classification

V. COMPARISON OF THE TWO CLASSIFICATION METHODS Through the comparison of the best classification result of

BP neural network and maximum likelihood supervised classification result, it was found that the total accuracy of this two methods are more than 80% , and the precision of BP neural network method than maximum likelihood method is improved[16].But for the Cunninghamia lanceolata, Cupressus funebris, broad-leaved forest and theropencedrymion, the single recognition effect of two methods is not very good. The reason why it gains this result may be that the four types of features in the space distribution of area is relatively small, broken, which leads to the result that the extract sample pixel purity is not high. Thereby the learning effect is not good. And the similar spectrum of these four plant communities is another cause of commission and omission. Thus it is impossible to fully realize accurate interpretation and recognition of the vegetation communities by using remote sensing image. But from the regional scale research perspective, the classification results can be used in the vegetation mapping or other related study when the classification accuracy reaches 70% or above.

From the point of overall accuracy and Kappa coefficient, different input vectors of neural network classification accuracy, rises gradually with the increase of other types of information (ratio bands, texture, gradient, slope direction, elevation). Among them, the best classification effect is the artificial neural network based on the OLI multi-spectrum bands and elevation data with an overall accuracy of 87.42% and the Kappa coefficient of 0.85. At the same time, this method is better than maximum likelihood classification accuracy which increases 5.57%.

It can be found that BP neural network algorithm has high fault-tolerance ability and self-learning ability, and it is more

advantageous to distinguish similar spectral characteristics vegetation, such as Cunninghamia lanceolata, Cupressus funebris and theropencedrymion.

VI. CONCLUSIONS (1) In BP neural network classification method, the

vegetation classification accuracy used to OLI multi-spectral remote sensing image is significantly higher than the traditional maximum likelihood classification method. The use of the texture information, the DEM data and other auxiliary data to assist classification on the basis of the spectral characteristics, to a certain extent, can effectively solve the mixed phenomenon of different bodies with the same spectrum and improve the accuracy of feature extraction.

(2) It cannot identify Chinese fir, cypress and theropencedrymion that BP neural network classification just relies on the image spectrum characteristics. The three vegetation communities are gradually identified when added with the texture feature, gradient, slope direction and elevation data. And neural network classification effect joined with the DEM elevation data is best, which meets the needs of the actual mapping with the total classification accuracy of 87.42% and the Kappa coefficient of 0.85. It is feasible to extract the spatial information of vegetation communities from the medium spatial resolution and multi-spectral remote sensing image by the method of BP neural network classification based on taking spectral bands and elevation data as the input vector.

(3)BP neural network training number, relatively speaking, depends on the choice of learning rate— the low rate makes overtraining while the high rate makes training volatile—which leads to the low recognition accuracy of vegetation communities. The additional momentum method and the adaptive learning rate should be taken to improve these deficiencies.

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Attached Table:

TABLE Ⅲ. THE CONFUSION MATRIX OF MAXIMUM LIKELIHOOD SUPERVISED CLASSIFICATION

Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ Ⅺ Ⅻ Total OE (%)

UA (%)

Ⅰ 913 22 1 32 40 5 14 0 0 0 0 0 1027 11.10 88.9 Ⅱ 10 92 0 0 1 0 0 0 0 0 0 0 103 10.68 89.32 Ⅲ 0 0 167 0 47 5 0 0 0 0 0 0 221 24.43 75.57 Ⅳ 0 7 0 955 0 0 11 0 0 0 3 0 969 1.44 98.56

Ⅴ 7 0 18 104 143 22 42 0 2 0 25 0 363 60.61 39.39 Ⅵ 27 0 0 0 0 36 0 0 0 0 0 0 63 42.86 57.14 Ⅶ 0 0 54 31 26 17 274 2 4 2 89 0 499 45.09 54.91 Ⅷ 0 0 15 0 2 0 16 122 4 0 12 0 171 28.65 71.35

Ⅸ 0 0 3 0 0 0 0 0 168 38 13 0 222 24.32 75.68 Ⅹ 0 0 0 0 0 0 0 0 0 238 0 1 239 0.42 99.58 Ⅺ 0 0 0 0 10 17 2 3 76 13 766 0 887 13.64 86.36

Ⅻ 0 0 0 0 0 0 0 0 0 0 0 141 141 0.00 100 Total 959 114 258 1122 269 102 359 127 254 291 908 142 4905

CE (%) 4.8 19.3 35.27 14.86 46.86 64.71 23.68 3.94 33.86 18.21 15.64 0.70 PA (%) 95.2 80.7 64.73 85.12 53.16 35.29 76.32 96.06 66.14 81.79 84.36 99.3

OA 81.8552% KC 0.7887