The application of a prediction model on land surface temperature using Artificial Neural Network and Scenario

Ⅰ. Introduction

The increase in the artificial environment, which is the main cause of the urban heat island, is directly associated with changes in land use and land cover(Streutker, 2003). Land use and land cover can be viewed as resulting from constant change by human activities from the past to the present, and these changes can be considered the fundamental cause of the destruction of the ecosystem(Singh, 1995; Voogt and Oke, 2003). Therefore, for accurate judgment of the urban environment, climate, response, management, collection, and accumulation of highly precise land use and land cover data of the latest information is required(Assefa, 2004).

Analysis tools in GIS(Geographic Information System) and RS(Remote sensing) are used to analyze urban climate and identify spatial characteristics and environmental characteristics; in particular, land surface temperature(LST) extracted from satellite images is being used to identify temperature rises and heat island phenomena in cities(Voogt and Oke, 2003; Small, 2006; Hais and Kučera, 2009).

However, despite the number of studies, in thermal environment analysis of a city through land use and land cover, high-resolution data have not been easily accessed so far due to data limitations. In addition, impact factors on the thermal environment have been identified in several previous studies but there are few studies predicting the changes in LST according to the changes in the urban environment in terms of urban and environmental planning.

Thus, this study develops the prediction models of LST by using detailed environmental factors extracted from a high-resolution land cover and land use map, and other indexes extracted from satellite images. Also, it analyzes the change in the LST depending on changes of spatial characteristics through the optimal model and the scenario analysis. Through this study, we will be able to identify the influence of the thermal environment in a city by using highly precise spatial data and predicting LST in the future.

Ⅱ. Literature Review

Looking into major research using LST, Carnahan and Larson(1990) identified the temperature difference between urban and suburban areas of Indianapolis on a meso-scale unit by using Landsat Thematic Mapper images, and Weng(2003) studied the pattern of LST targeting Guangzhou, China, and analyzed the relationship between LST and land cover. Also, Li et al.(2009) identified that the expansion of industrial areas and the Manhattanization of commercial buildings are the main causes of increasing LST; they did this by analyzing the change pattern of LST according to changes in land use using Landsat TM images of two periods targeting Shanghai. In addition, studies applying LST are under way in various fields of study such as the impact of terrain type on LST(Geiger et al., 2003; McCune, 2007; Hais and Kučera, 2009; Li et al., 2010), the impact of types and characteristics of land use and land cover on LST(Voogt and Oke, 2003; Weng et al., 2004; Xian and Crane, 2006; Amiri, 2009; Zhou et al., 2011), the impact of vegetation status such as NDVI(Normalized Difference Vegetation Index) on LST(Gallo and Owen, 1999; Weng et al., 2004; Wang et al., 2006; Yuan and Bauer, 2007; Raynolds et al., 2008), among other areas.

On the other hand, in general to analyze the relationship between LST and other impact factors, linear regression analysis is used a lot. Linear regression analysis has the advantages that it can realize a model within a relatively short time through simple statistical processing and can be easily applied through various statistical packages(Lin et al., 1986; Moore, 2008). However, it has limitations in that the accuracy of the model is low in the prediction of the complex relationship and important variables tend to be excluded depending on the level of significance and various applications of the model is difficult(Deshpande and Subbarayan, 2000; Kagie and Van Wezel, 2007). The predictive model technique recently highlighted is an artificial neural network(ANN) designed to perform the same function as the human brain structure. ANN is used to find the optimal mathematical algorithm model through numerous repetitive learning processes(Haykin, 1999)¹⁾. For example, ANN has proved to have higher prediction accuracy than linear regression analysis, logit model, ARIMA model, and others in many previous studies(Ottenbacher et al., 2001; Xu et al., 2005; Wang and Elhag, 2007; Yilmaz, 2009).

In comparing with previous studies, this study has some different characteristics. The high-resolution spatial data with land use and land cover were used, the highly precise LST was predicted by comparing ANN model and linear regression model, and the change in the LST depending on changes of spatial characteristics was quantitatively predicted by the scenario analysis.

Ⅲ. Materials and methods

1. Study area

This study was set in Changwon²⁾, South Gyeongsang Province, which is South Korea's first planned city. Changwon is located in the Southeastern part of the Korea peninsula; the city is surrounded by the Naknam Mountains starting from Mt. Jiri and has excellent natural resources because the south-west side faces the southern sea(Figure 1). The area of Changwon, the target site, is approximately 292.7km² and is divided into 15 administrative district; approximately 502,727 people live there. Regarding the city's climate, it shows the characteristics of an oceanic climate that has four seasons and is heavily influenced by the monsoon. The annual average temperature is about 15℃, and the annual average precipitation is about 1,395 mm.

Figure 1.

The location of study area

On the other hand, for Changwon, a planned city, the characteristics of land use are clearly classified. In this way, Changwon emphasized efficient use of land by planning land use separately by space but since spatial characteristics have a negative impact on the urban environment and are concentrated, many environmental problems such as the heat island phenomenon, regional disproportion of green area, etc. are occurring. Hence, Changwon was selected as study area in order to explode the change of LST within a planned city.

2. Research design

The research process was largely divided as shown inFigure 2. In the first step, basic data required for analysis were collected and built. LST, NDBI(Normalized Difference Built-up Index), and NDVI data were extracted from satellite images, and building area, impervious area, and vegetation area were generated from the land cover map and eight details such as residential area, commercial area, industrial area, etc. were generated from the land use map. The raw data for land use and LST were based on the data of previous study(Lee et al., 2011). Since LST is measured based on thermal infrared emitted from the earth's surface, LST data corrected through the process of correction and temperature extraction were built.

Figure 2.

The process of this study

In the second step, to product GIS-based thematic maps with the thermal environment and built spatial data, the space unit was set at 500m×500m of the vector grid. This was done because the minimum space range at which spatial characteristics of one point affect LST can be seen at 500 m and this was determined as the size at which characteristics of land use can be classified clearly(Ng et al., 2006; Yoon & An, 2009). Total vector grid of the target site is 1301 and thematic maps by each spatial characteristic were made based on this.

In the third step, the optimal LST predictive model was developed using ANN. Since prediction accuracy can be changed significantly in the predictive model according to input data, input data were classified into video data, land cover data, land use data, and the predictive model was created by changing a hidden layer of neural network structures into 1~3.

Among various models exist in ANN³⁾, this study used the Back Propagation Algorithm(BPA) of MLP in SPSS Clementine 11.1 as an analysis tool. Also, Multiple Linear Regression(MLR) was carried out to compare it with the existing traditional predictive technique. On the other hand, accuracy analysis of a model is the basis on which to judge the similarity based on the degree of observed value and predictive value. This study used RMSE and coefficient of determination(R²) of predictive value. After comparing the accuracy of the model estimated in this way, the optimal neural network to predict LST was selected.

In the last step, after setting 3 scenarios by considering the direction of urban planning of Changwon, the change in the thermal environment of a city according to scenarios was predicted. Prediction of LST progressed by setting the occupation ratio of changed land use as input data and applying it to an optimal neural network model that showed the highest prediction accuracy; then the result was derived.

6. Setting of Scenario

Scenario 1 assumed that the industrial complex was expanded into an area adjacent to a city(Table 1;Figure 3). This area is where industrial complex development was started in 2010; its size is 11ha and completion is set at 2013. The land use map to be changed was made based on industrial complex planning drawing provided by Changwon, and temperature was predicted based on it.

Table 1.

The setting of 3 scenario types

Figure 3.

The applying area and change of land use by each scenario

Scenario 2 was established that urban development was progressed to the outskirts of a city. The main purpose of this area is to develop residential complexes and its area is about 108ha; development was started in 2008 and will be completed in 2012. The land use map was built based on the urban development plan provided for Changwon, and variation was calculated by comparison with current land use.

Scenario 3 was set that some space in the industrial area was converted into green space. Although development is not scheduled for this area, it is where prediction was conducted on a pilot basis to reduce a high LST for an industrial complex; the total area is approximately 35ha.

Ⅳ. Results

1. Status of thermal environment and spatial characteristics

To perform analysis by a spatial unit of 500m×500m vector grid targeting Changwon, 14 basic theme maps such as LST and others were produced from Landsat 5 TM images, land cover, and the land use map(Figure 4). In examining the LST distribution map, the average temperature at 10:00 A.M. on May 13, 2007, in Changwon was shown in 19.93℃. The area that showed the highest LST was the Ungnam region in the central city, and it was analyzed as about 23.94℃; conversely, a low temperature distribution of about 17.8℃ was shown in southeastern Mt. Beolmo(Figure 3a). Examining characteristics by area, a significant temperature difference between the outskirts area and the central city can be checked, and the slight difference between east and west of the northern area can be observed. In particular, the temperature appeared high along the southwestern industrial area in the southern portion of the central city and the northeastern residential area showed a relatively low distribution. Also, the northeastern area where farmland facilities such as greenhouses are located showed a higher temperature distribution than surrounding areas.

Figure 4.

The base map for spatial analysis

Looking at the distribution pattern of spatial characteristics, NDBI, an impervious area showed the form similar to distribution of LST; in particular, NDBI was analyzed to show a high value in the large northeastern area and central city. Also, the temperature of the building area appeared high, centering on the urban area located in the south. On the other hand, unlike the above factors, NDVI and vegetation area showed a low value in the urban central part and a high value was distributed along forests on the outskirts. In the case of land use, the temperatures of the residential area, commercial area, industrial area, and traffic area appeared high along the urban central part but showed different distribution patterns. This is because Changwon was deliberately planned to place the same type of land use intensively to increase the spatial efficiency of land. On the other hand, green space showed a high proportion in the northwestern area and on the outskirts of a city, and the agricultural area showed a high proportion in the northern area. Also, barren areas were scattered across the city, and there was a high proportion of water in Junam Reservoir and Nakdong River, the large river in the north, and a relatively high proportion along Changwon River and Nam River in the urban center.

2. Development of the prediction model

ANN was driven by using input data, initial specifying function, parameters, etc., and the optimal model according to each case was derived asTable 2through numerous repetitive processes within a model. The node number of the hidden layer was set automatically by selecting the model with the best accuracy according to the condition of case. For Case 1, the model with the node structure of 5, 5-2, 9-8-2, respectively, as hidden layer increases was analyzed to be optimal. For Case 2, the structure of 10, 9-9, 11-11-7 was derived as the optimal model, and for Case 3, the structure of 11, 34-7, 39-39-4 was derived as the optimal model according to the change in hidden layer.

Table 2.

The node structure and prediction accuracy of optimized ANN by case

Next, to select the optimal model, accuracy was calculated as shown inTable 2by using RMSE and R2. For Case 1, RMSE appeared low, 0.564, in Case 1-3 where there are 3 hidden layers, and R2 was also analyzed as the highest in Case 1-3. Also, for Case 2, the same as for Case 1, RMSE and R2 of Case 2-3 were assessed to have a high accuracy, 0.477 and 0.798, respectively. Also, for Case 3, Case 3-3 with 3 hidden layers was analyzed as the model with the highest accuracy. Examining differences according to case, the result of Case 3 using land use data was shown to have higher accuracy than other cases. Of course, since the number of input data differs, comparison of accurate results may be difficult. However, since it was reported that if the number of input data is more than 2, the impact on accuracy of a model is not that great(Zhang et al.. 2001; Kim, 2008), it can be determined that land use data can be more accurate to predict LST compared to other input data. On the other hand, as a result of comparing the results of neural network and MLR results, the traditional method, the prediction result of MLR was also shown to be high and, therefore, there are cases having higher predictive power according to the node number of the hidden layer, but as a whole, in all cases, prediction accuracy was assessed lower than for the optimal neural network model. These findings have already been proved in several studies(especially Ottenbacher et al., 2001; Xu et al., 2005; Yilmaz, 2009), in the study of Xie et al.(2009), as a result of comparing prediction accuracy of LST of ANN and MLR, it was found that ANN has a remarkably higher accuracy. According to the time taken to realize a model, the time of Case 1-1 was the shortest, 1 second; Case 3-3 was the longest at 6 hours 1 minute 27 seconds. Case 3-3 was analyzed to have the highest prediction accuracy, but actually, the difference between Case 3-2 and R2 is just 0.002. Therefore, in considering temporal efficiency of a model, Case 3-2 can be said to be the more efficient model compared to Case 3-3.

Since the purpose of this study is to predict LST accurately, Case 3-3, which showed the highest accuracy, was selected as the optimal neural network model. RMSE of the predicted results for Case 3-3 is 0.455 and R²is 0.816.

In examining relatively important analysis results between input variables through sensitivity analysis of the optimal neural network model, traffic area(0.282) and industrial area(0.275) were shown to be the variables with the greatest impact on LST(Figure 5). Since the importance of the two variables was analyzed as two times higher than that of other variables, it can be determined that the impact on LST is high. Actually, the study of Amiti et al.(2009) showed that traffic area and industrial area increased LST; in particular, LST was analyzed as the highest in the areas such as road area.

Figure 5.

The importance7) of each variable

3. Prediction of change of LST using scenario analysis

1) Scenario 1

Scenario 1 represents area A adjacent to the city and predicted a change in LST in that new industrial complexes are expanded or developed near the existing industrial area(Figure 5b). As the change pattern of land use, agricultural area, residential area, green space, etc. decreased and industrial and traffic areas increased in most areas(Table 3). Especially in the case of area ID1412, while agricultural area decreased by 18.73%, industrial area increased by 28.78%, and therefore this area was shown to be the biggest area of change in land use.

Table 3.

The change of land use and LST at scenario 1

In the predictions of LST according to scenario 1, it was analyzed that LST of area ID1412, where there were many changes from agricultural to industrial area, would rise by 1.61℃, and in area ID1413, it was predicted that LST would rise by 1.03℃ due to an increase in the industrial area(Table 3;Figure 6a).

On the other hand, in the case of area ID1467, changes to the industrial area were not much, but LST was shown to decrease by 0.26℃, and this is the result of errors in the model and is occurs because existing green space is greater than in other areas.

Figure 6.

The change of LST by each scenario

2) Scenario 2

Scenario 2 is area B, which is located on the outskirts of the city, and the predicted change of LST in case that land use pattern is changed by new urban development(Figure 4c). According to the change pattern of land use, it was shown that residential area increased by 50% in areas ID250, ID304, and ID358 but that agricultural area, green space, barren area, etc. were shown to decrease(Table 4). For areas ID303, ID357, and ID412, it was confirmed that other land use was decreasing due to an increase in the residential area. On the other hand, in the case of area ID303, since LST for the industrial area and the traffic area appeared large and were shown to increase by 28.41% and 5.65%, respectively, the rise of LST after urban development can be expected.

Table 4.

The change of land use and LST at scenario 2

Examining the prediction results of LST throughTable 4 and Figure 6b, LST of ID250 area was predicted to show the greatest change by rising by 1.08℃. This was because land use having a correlation with the amount of LST such as residential area, traffic area, and commercial area, etc. increased about 70% in this area; and green space, barren area, and agricultural area, etc. decreased. Next, it was determined that LST of areas ID303 and ID304 will rise by 0.73℃ and 0.74℃, respectively. Area ID303 was expected to raise LST due to industrial area development and area ID250 was similarly predicted with area ID250. On the other hand, though the small size of the residential area increased, LST was predicted to decrease in areas ID305 and ID359. LST was predicted to be low because these areas hold a relatively large amount of green space compared to other areas: 65% of green space.

3) Scenario 3

Scenario 3 is area C, which is located in the industrial complex area of the city, with change of LST when part of the industrial complex is removed and green space is created(Figure 5d). According to the change pattern of land use of Scenario 3, the industrial area was decreased in all of 4 areas and green space was increased as much as the industrial area decreased(Table 5). Especially in area ID1093, the percentage of green space converted from industrial area appeared the highest, 69.17%.

Table 5.

The change of land use and LST at scenario 3

Looking at the reduction effect of LST in the industrial area followed by the creation of green space, it was predicted that approximately 3.61℃ was reduced in area ID1093 where the percentage of conversion to green space was high, and it was also analyzed that there was more than 1℃ of reduction effect in area 3(Table 5;Figure 6c). On the other hand, it was found that although the area converted into a green space in area ID1038 was less than that in area ID1092, 0.3℃ of LST was a greater reduction. This is because the percentage of traffic area, which was shown to have the greatest impact on LST, is 4.71%, relatively lower than 14.74% of area ID1092.

As above, it could be judged that the green space in the city relieves the heat island phenomenon by reducing ambient temperature and is an essential requirement for a pleasant urban environment(Spronken -Smith and Oke, 1998; Maco and McPherson, 2003; US EPA, 2007). Also, it is judged that it will be necessary to remove artificial areas such as buildings and roads and to convert them into pervious areas such as green space and water.

Ⅴ. Discussion and conclusions

This study develope the prediction models of LST based on high-resolution land cover and land use map targeting Changwon, South Korea, and predicted changes of LST according to the scenarios of urban development. The results and discussion from the analysis can be summarized as follows.

As a result of exploring the prediction models of LST through a neural network, it was analyzed that using land use data(Case 3) shows higher accuracy than if using data extracted from image data(Case 1) or land cover data(Case 2). Of course, for land use data, the condition that there are many variables is different, but according to the previous studies, it was analyzed that the number of data input layers in a neural network did not have a significant impact on accuracy(Zhang et al.. 2001; Kim, 2008), and therefore, it can be judged that land use had a significant impact on the LST. Also, the optimal ANN was analyzed to have higher accuracy than MLR.

According to the result of Case 3-3 selected as the optimal neural network, the importance of the traffic area appeared the highest, 0.282, followed by industrial area and green space, 0.275 and 0.155, respectively. These findings appeared to be similar in previous studies(Weng et al., 2004, Amiti et al., 2009), but in the case of Changwon, the city was created by separate land use intentionally and, therefore, it was judged to show a more remarkable difference than was seen in other studies.

In the prediction result of LST, if a new industrial complex is created in the area adjacent to the existing industrial complex, it was shown that LST would rise due to the increase in artificial areas such as buildings and roads, etc. Also, if a large-scale housing complex is created on the outskirts of the city, it was analyzed that LST would rise according to the ratio of artificial surface occupied per unit space. On the other hand, if green space is created in the industrial area that showed high LST, it was analyzed that LST would decrease by showing a significant difference.

As above, this study built the optimal model to predict LST through various approaches and LST changes were predicted according to changes in land use. Especially, it could be judged that the green space reduces ambient temperature in the city. Thus, these findings form this study can be used a basic data to propose the green spaces of proper scale and proportion in the planning stage, when trying to create a new large-scale housing and industrial complex. Also, it can be utilized a guideline to develop a high accurate prediction model on LST.

However, it did not consider the impact of structure or layout pattern of spatial characteristics such as land use, etc., on LST. Also, it has the limitation that factors shown to greatly affect LST such as elevation, slope, air pollution, height of artifacts, and shade(Nichol and Wong, 2005; Qin et al., 2011; Geiger et al., 2003) were not considered. Thus, in the future, it is judged that accurate models should be developed by considering more diverse indicators and a specific direction should be taken for a reduction in LST. In the case of ANN, it has the advantage that it can be generally analyzed with high prediction accuracy but has the disadvantage that it is difficult to understand the fundamental relationships between indexes and identify the exact internal structure of the model. Therefore, in order to apply the neural network model correctly, it is judged that the analysis of relationship for each parameter should be preceded preferentially.

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