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Relationship analysis of environmental factor change on the evidence of dengue fever diseases using image transformation (Case Study: Surakarta City)

Environmental factors change such as built-up area density, vegetation density, moisture and temperature can affect the health problems. Dengue Fever Disease (DFD) that are still high rates of incidence because of environmental factors change in Indonesia including Surakarta City. The purpose of this study are mapping environmental factors consisting of built-up area density, vegetation density, land surface moisture (LSM) and land surface temperature (LST) in Surakarta City 2002 and 2015 through image transformation and correlation analysis of environmental factors change on the evidence of DFD in Surakarta City. This research uses Landsat-7 ETM+, Landsat-8 OLI/TIRS and the evidence of DFD data on 2002 and 2015. Landsat image is processed by using LST, VGNIR-BI to be built-up area density, NDVI for vegetation density, NDMI for LSM. Based on the result of image transformation, built-up area density and LST in Surakarta City increased from 2002-2015, while vegetation density and LSM tended to decrease. The result of this research shows that the environmental factor change in the form of built-up area density, vegetation cover density, LSM and LST have correlation are 74%, 68%, 75% and 69% of the evidence of DFD in Surakarta, respectively. Overall, the environmental factors correlated 86.1% of the evidence of DFD in Surakarta City.


Introduction
Utilization of sustainable natural resources increase in line with the rate of development for the fulfillment of human needs. The environment continues to change, such as the change from bare or vegetated land to built-up [1]. These changes of environment factor also have an impact on changes in climate components such as land surface temperature (LST) and land surface moisture (LSM) [2]. On the other hand, the emergence of various types of diseases that attack humans with increasing number of cases into a separate question, that there is a correlation of environmental factors changes with increasing incidence of disease [3].
One of the disease that still be a problem is Dengue Fever Disease (DFD). The disease is caused by the bite of Aedes aegipty mosquito that carries dengue virus and cause death if the patient doesn't get the right treatment immediately. Ministry of Health Indonesia recorded the number cases of DFD in Indonesia from January to February 2016 as many as 8487 people with the number of deaths are 108 people. The case of DFD is closely related to environmental factors [4]. It is necessary to examine whether changes in environmental factors have an effect on the development of DFD. Research on the Environmental factors change monitoring needs to be control and there are new technologies that can accelerate environmental factors change monitoring such as remote sensing technology [5]. One of the imagery processing methods that can be used to identify objects is image transformation. Image transformation is an algorithm involving multiple bands in the image used to sharpen the appearance of one objects in the surface. The transformation to shows the vegetation object called the vegetation transformation index. This transformation will change the digital image number to the index value, where the index value reflects the vegetation conditions [6]. There are other types of image transformations that can highlight objects other than vegetation, such as built-up objects. The transformation that accentuates the building object is called VGNIR-BI. Similarly, image data processing to the LST and LSM. There is a special algorithm to get temperature and moisture data.
Based on background then conducted a study by taking the research about relationship analysis of environmental factor change on the evidence of dengue fever diseases using image transformation. Not all environmental factors are examined, but only environmental factors are associated with DFD cases such as built-up area density, vegetation density, LSM and LST. For this research, we use Surakarta City and surrounding area, Central Java Province, Indonesia. The population density in Surakarta is 11,675 people/km 2 , which is the highest density in Central Java (the density of Central Java is only 1,038 people/km 2  City. The coverage area of each evidence of DFD is located in every Puskesmas in Surakarta City. The number of Puskesmas in Surakarta City are 17.

Landsat-7 ETM+ and Landsat-8 OLI/TIRS data pre-processing
Satellite imagery data should be preprocessing first before used to find the indices value. The aim of preprocessing data to make pixel value in ideal condition so that it can be used for both visual and mathematical analysis [5]. Before performing image processing into a spectral indices, Landsat-7 ETM+ and Landsat-8 OLI/TIRS images should be performed radiometric calibration and atmospheric correction. The raw image value calibrated pixel value or digital number (DN) of the multispectral sensor must be converted into Top of Atmosphere (TOA) surface reflectance. The TOA spectral radiance of multispectral bands of Landsat-7 ETM+ and Landsat-8 OLI imageries calculated using the formula. In this study, we used the radiometric calibration module available in the Environment for Visualizing Images (ENVI Ver. 5.2, Exelis Visual Information Solutions, Boulder, CO, USA) software to derive the TOA spectral reflectance.
The image processed into these spectral indices required atmospheric correction to the level of atsensor reflectance. Atmospheric correction of Landsat-8 OLI/TIRS and Landsat-7 ETM+ image using histogram adjustment. The histogram evaluated is a histogram of reflected values on the sensor in the form of fractional numbers [8]. The method used in histogram adjustment is dark subtraction [9].

Image transformation
Land surface temperature (LST) obtained from calculation of image brightness value. It's assumed that the water content in the atmosphere is constant for a small region, so that atmospheric conditions can be considered uniform and the effect of atmospheric conditions on radiance temperatures can be ignored [10]. Normalized Difference Vegetation Index (NDVI) is used to determine the coverage of vegetation in the surface of a study area. Normalized Difference Moisture Index (NDMI) shows values that estimate the level of moisture content in any object recorded by the sensor [11]. VgNIR-BI is used to determine the built-up area in the surface of study area [12].
After processing into TOA surface reflectance, so it can be process to get LST, NDVI, NDMI, and VgNIR-BI values. Calculated LST using this equation: (1) where T value is obtained from satellite sensor recording which shows the temperature value in Kelvin units. Lλ is the value obtained from the radiance spectral. K1 are the values of calibration constants one for Landsat ETM+ (666.09 wm -2 sr -1 m -1 ), Landsat OLI band 10 (774.8853 wm -2 sr -1 m -1 ), and Landsat OLI band 11 (480.8883 wm -2 sr -1 m -1 ), K2 is the calibration constant two for Landsat ETM+ (1282.71 K), Landsat OLI band 10 (1321.0789 K) and Landsat OLI band 11 (1201.1442 K).
Calculated NDVI, NDMI and VgNIR-BI using this equation: where and refer to the atmospherically corrected surface reflectance values of band 2, band 3, band 4, band 5 and band 7 in Landsat-7 ETM+ image, while band 3, band 4, band 5, band 6 and band 7 in Landsat-8 OLI/TIRS image, respectively. The result of image transformation (NDVI, NDMI and VgNIR-BI) is done by linear regression analysis using field survey data to get NDVI information into vegetation cover density, NDMI becomes land surface moisture (LSM) and VgNIR-BI becomes built-up area density.

Correlation statistic analysis
Correlation analysis in this research use two scenario that is correlation analysis and multiple correlation analysis. Correlation analysis performs statistical analysis of the relationship between dependent variable and independent variable separately on each independent variable [13], while multiple correlation analysis performs statistical analysis of the relationship between two dependent variables or more with independent variables simultaneously [14].
The correlation test was performed on 17 sample selected by total of Puskesmas in Surakarta City. The sample has the information of the evidence of DFD, land surface temperature (LST), built-up area density, vegetation cover density, and land surface moisture (LSM). Correlation test using Pearson Product Moment correlation performed on the sample where the result obtained is a graph correlation between the evidence of DFD on LST, built-up area density, vegetation cover density and LSM.
Correlation analysis used in this research is using Pearson Product Moment method. Formulation for Pearson Product Moment Correlation [14] is shown as follows: Multiple correlation analysis in this study using correlation coefficient. Decision making in multiple correlation test can compare the probability value of 0.05 with the probability value of significance. A more detailed explanation of the probability significance can be found in [15]. Formulation for multiple correlation test is shown as follows: (6) where R x1x2x3x4y is the coefficient of multiple correlation, X 1 is the independent variable (LST), X 2 is the independent variable (LSM), X 3 is the independent variable (built-up area density), X 4 is the independent variable (vegetation cover density), rX 1 Y is the correlation coefficient between X 1 and Y, rX 2 Y is the correlation coefficient X 2 and Y, rX 3 Y is the correlation coefficient between X 3 and Y, rX 4 Y is the correlation coefficient between X 4 and Y, and Y is variable dependent (the evidence of DFD).
Based on calculation of correlation coefficient (r) will show the relationship between the dependent variable with the independent variable with the range of values 0 to 1 and a detailed description of the classification of the range of correlation coefficient values can be found in [16].

Built-up area density
Built-up area density data is obtained from VGNIR-BI. The advantages of this transformation are on the green, red and near infrared band. On the green and red band, the built-up area will appear brighter and on the near infrared band the appearance of vegetation will be seen more clearly. Based on the shape, can be distinguished between the object of building and vegetation. Built

Vegetation cover density
This research uses NDVI to present vegetation density in Surakarta City. The NDVI is a combination of deterrence techniques with image reduction techniques and very often used in various studies. The band used in the NDVI transformation indice are the red band and the near infrared band. Based on result of data procesing between NDVI and data from high resolution spatial imagery or field survey, the precentage of vegetation density on 2002 and 2015 is obtained. Figure 3 shows a change of vegetation density over 13 year period. The degradation of vegetation density on 2002 to 2015 most located in the middle of Surakarta City. Almost all district in Surakarta City has change vegetation density. This change is most often because the land convertion from vegetation area to built-up area. Figure 3 shows the change of color in some location from green to white, indicating that there has been a reduction in vegetation density up a density of 0 -12,5%.

Land surface moisture (LSM)
The moisture value is obtained by using NDMI. NDMI using the near infrared and middle infrared band. The result of transformation indice and field survey resulted in moisture map which is a description of the moisture condition of the object in the field. The results in Figure 4 shows that there

Land surface temperature (LST)
Land surface temperature (LST) is generated from processing image radiance value. Satellite images of previously applied atmospheric corrections are further processed using formulas (1) and (2)   The changes surface temperature object shown in figure 5 can be interpreted that over a period of 13 years there has been a change of landcover from vegetated land to built-up area or from low builtup area density to high built-up area density. The number of buildings, especially residential buildings, indicates that more and more people are living in the area. Increasingly people makes available places used by aedes mosquitoes to breed and lay eggs in the tubs of every home and other water reservoirs increasly. Increasingly people also indicated food for mosquitoes increasly. Therefore, it is very reasonable if there is an increase number of DFD in the city of Surakarta in 2015 better than previous years.

Correlation analysis of enviromental factor change on the evidence of DFD changes
Assessment to determine the effect of environmental factor change on DFD evidence is done by looking at the change of environmental factors that occur in every coverage area of Puskesmas. This is done considering the available DFD data is the total DFD cases data per coverage area of Puskesmas. Data DFD recorded in each Puskesmas is a disease case that occurred in the region. Thus, it can be said that the case of DFD recorded in each Puskesmas in Surakarta City is a reflection of the factors that affect the evidence of DFD in the region.

Table 1. Enviromental factors change and the evidence of DFD based on Puskesmas
It should be noted that factors affecting DFD evidence are not only environmental factors but, including human habit factors and health care factors. However, this study will only be done to see how big the influence of environmental factors on DFD cases in Surakarta City. Table 1 is a data of environmental factor changes consisting of changes in built-up area density, vegetation cover density, LSM and LST.
Correlation of environmental factor change such as changes of LST, vegetation cover density, LSM, and built-up area density with the changes of DFD cases can be done with pearson product moment analysis. Correlation analysis value between the changes of environmental factor and DFD cases shows strong correlation value. Based on table 2, the lowest correlation value shows by the correlation value of vegetation cover density is 0.69, while LSM has 0.75 on the highest correlation value. LSM show the highest correlation value, means that LSM has the strongest correlation with the changes of DFD cases. The value of LSM getting lower in 2002 to 2015. This case also makes the higher value of mousquito that being a vector of DFD.   The effect of built-up area density on the evidence of DFD is the highest built-up area density can be connected with the increasing population. High population numbers can affect the evidence of DFD, which will provide enough food for mosquitoes. Built-up area density factors will impact on other environmental factors such as drainage conditions and other environmental health. Figure 6 shows the results of the linear regression between changes in built-up area density and changes in DFD cases. Increased built-up area density can increase DFD cases if not balanced with public awareness to create cleanliness of the environment and changes in living habits.
Changes in vegetation cover density affect 45.9% of DFD cases change in Surakarta City. As it is known that the condition of vegetation in Surakarta City from 2002 -2015 has decreased from the area and its density. This condition is a result of changes in land cover from vegetated land to built-up area. Vegetation will affect the temperature and moisture of the air, where temperature and moisture affect the metabolism and life cycle of mosquitoes.
The relationship between LST change and DFD case change can be seen in figure 6, LST gives 47% effect to DFD case change. Based on the temperature aspect, although mosquitoes can survive at low temperatures, the ability of the mosquito's metabolism process decreases or even stops when the air temperature falls below the critical temperature. At temperatures above 35°C have an impact on the physiological processes of mosquitoes. While the optimum temperature of the average growth of mosquitoes between 25°C-27°C. The growth of the mosquito will stop completely at temperatures of less than 10°C or more than 40°C. While for the larva growing process requires a temperature between 25°C-30°C. The regression between the LSM change of the object and the change of DFD case can be seen in figure 6, The LSM gives 56% effect to the change of DFD case. Moisture is certainly the effect of air temperature, so that can be drawn relation between moisture objects, air temperature and mosquito metabolism. While the LSM aspect is an important factor in the growth of mosquitoes. Optimum moisture required for mosquito growth between 60% and 80%. If the LST and LSM are optimal, the age of the mosquito can reach one month (age Aedes aegypti female mosquitoes on average 10 days). LSM factors can indirectly affect the age of mosquitoes. For example, the high moisture causes the mosquito to become so rapid that it can cause death. Whereas in moisture less than 60% of age mosquitoes will be short.

Multiple correlation analysis of enviromental factor change on the evidence of DFD changes
Correlation analysis is not only done by using correlation of each environmental factor to the number of dengue cases. Correlation analysis between changes in environmental factors and the change of DFD cases can be done simultaneously using multiple correlation analysis. The results shown can be used to strengthen the relationship between environmental change and the change of DFD cases. The result of multiple correlation analysis is shown by table 3. The table 3 shows R that the correlation between environmental factors such as LST, LSM, vegetation cover density, and built-up area density on the evidence of DFD in Surakarta City is 86.1% or in other words indicates a very strong influence. The value of R 2 shows a value of 0.742 which states that these environmental factors simultaneously have an effect of 74.2% on the evidence of DFD, while 25.8% are factors other than those environmental factors. In addition, the level of significance (Sig. F Change) has a value of 0.002 < 0.05 so that, environmental factors affect simultaneously and significantly to the evidence of DFD. The table 4 that shows the information that can be used to determine the role of each environment factor against the evidence of DFD. Based on the table 4, it is known that the regression equation is Y = -15.698+(0.764*X1)+(5.990*X2)+(2.373*X3)+(3.371*X4). Value X1, X2, X3 and X4 shows vegetation cover density, LSM, built-up area, and LST, respectively. The regression equation shows that LSM has the highest influence on the evidence of DFD in Surakarta City from unstandardized coefficients (B) is 5.99, followed by LST, built-up area density and vegetation cover density.  Result of correlation analysis using pearson product moment are change of environmental factor of built-up area density influenced 55%, vegetation cover density influential equal to 45.9%, land surface moisture influential equal to 47.8% and land surface temperature of influence object 56.9% to spread of DFD in Surakarta City. Meanwhile, the results of multiple correlations indicate that the relation between environmental factors such as LST, LSM, vegetation cover density, and built-up area density on the evidence of DFD in Surakarta City is 86.1% or in other words shows very strong influence. These environmental factors simultaneously have an impact of 74.2% of the evidence of DFD in the Surakarta City. LSM has the highest influence on the evidence of DFD in Surakarta City from unstandardized coefficients (B) is 5.99.