The Effect of Urbanization and Associated Climate Change on GNSS Positioning: A Case Study in Addis Ababa City, Ethiopia

doi:10.21203/rs.3.rs-5256884/v1

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The Effect of Urbanization and Associated Climate Change on GNSS Positioning: A Case Study in Addis Ababa City, Ethiopia

https://doi.org/10.21203/rs.3.rs-5256884/v1

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Fast urbanization and associated micro-climate change in urban settings affect the day-to-day activities of contemporary dwellers. This can also affect the positioning of points using the Global Navigation Satellite System(GNSS) technique. To investigate the association between urbanization, change in the urban climate, and long-term change in the propagation of GNSS signal through the atmosphere in Addis Ababa City, the data from the International GNSS Service (IGS) station ADIS was processed for the years from 2008 to 2019. Similarly, the built-up area expansion, vegetation cover change, and land surface temperature were computed from 2005 to 2019. For all the data sets, the low-frequency variations are then estimated using a polynomial fitting technique, through the least-square approach. The comparison of the resulting long-wavelength data, using the linear correlation technique showed that there is a very high correlation between the long-term changes in the Up direction of the IGS station ADIS, and the built-up expansion, the vegetation coverage changes, and land surface temperature with correlation values − 0.9227, + 0.9489, and − 0.9862 respectively. Even though, the level of impact has not been quantified a conclusion is drawn that urbanization and its impact on climate change have an effect on the positional information of a station.

Urbanization

Land Cover Change

Micro-climate Change

and GNSS

Urbanization is an activity, which is an inevitable and dominant issue of our time that is increasing alarmingly. The world’s urbanization status indicates these realities. According to a United Nation report, since 2007 the world's urban population exceeds the world's rural population and the residents of the world's urban population are expected to reach 68 percent in 2055(UN 2019). Addis Ababa, the capital city of Ethiopia, is one of the fastest-growing cities in Africa and 25 percent of the urban population of Ethiopia is living in Addis Ababa (World Bank Group 2015).

Even though the driving forces of urbanization have many factors, population growth and economic developments are the leading ones. An increase in population density and demands due to economic development in urban areas are mostly related to the utilization of urban space. To fulfill both the internal and external driving needs of urban residents, the urban area expands physically (Tahir et al. 2014). The physical expansion of urban space can lead to diversified issues and effects. One of the effects is urban land cover change (Wang et al. 2007).

Urbanization is frequently observed through an increase in the land cover of built-up areas. On the other hand, the expansion of the built-up area often affects and replaces agricultural land, vegetated areas, bare surfaces, etc. The normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) are commonly used to study the expansion and degree of change in the urban landscape(Carlson and Arthur 2000; Cui and Shi 2012; Sun et al. 2012; Bhatti and Tripathi 2014; Deng et al. 2018; Potter 2019). In other words, NDVI and NDBI are well-known remote sensing techniques that are often applied to control and identify continuously changing urban land cover, which is caused by urbanization (Karanam and Babuneela 2017).

Urbanization and associated land cover change are one of the causes of urban climate change and it is also the central concern of many findings (Fang and QuanSheng 2012; HaiShan and Ye 2013; Zhang et al. 2016). According to Adeyeri et al. (2017), urban environmental change can be addressed by studying land cover change. The land cover change caused by urban expansion is the main factor for global, regional, and local environmental effects (Odindi et al. 2015). It also affects microclimate (Lim et al. 2005; Zhang et al. 2016; Naserikia et al. 2019), the spatial distribution, amount of precipitation, atmospheric composition, near-surface humidity, energy balance, wind speed, and temperature (Gallo et al. 1999; Han et al. 2013; Pathirana et al. 2014; Tahir et al. 2014; Souza et al. 2016; Choudhary and Tripathi 2018). Deng et al. (2018) also stated that land use land cover is one of the major factors which affects land surface temperature and they have a strong correlation (Ibrahim 2017). The extent of urbanization's effect on the environment varies with the size of the city. According to Gallo et al. ( 1996), the effect of dominant land cover on climate goes up to 10 kilometers radius. The major source of all this influence in urban areas is land use/land cover change (Linh and Chuong 2015; Odindi et al. 2015).

In general, the increment of an urban built-up area leads to a decrement in urban vegetation cover, and this affects the surface temperature of the region. The impact of the change in the urban built-up area on the atmosphere is observed through released particles, moisture, and heat (Molders and Olson 2004; Odindi et al. 2015). In addition to the greenhouse gases, anthropogenic factors related to changes in land cover are also the cause of changes in the surface and lower atmospheric temperatures (Laat and Maurellis 2006). As a result of the changes in the surface and lower atmosphere, the composition of the atmosphere alters (Pielke et al. 2002). This on the other hand affects the propagation of the signal through the atmosphere.

An electromagnetic wave passing through a nonhomogeneous medium can be reflected, diffracted, dispersed, or refracted. Refraction is a directional change of propagated wave when passes from one medium to the other (Hofmann-Wellenhof et al. 2008). Atmospheric refraction is the deviation of electromagnetic propagation within the atmosphere due to the difference in air mass and density (Jin et al. 2014). Electromagnetic waves typically move at the speed of light when they are transmitted in a vacuum, but this is not always the case. The medium in which waves travel can cause them to delay.

Satellite-based positioning uses electromagnetic waves in the radio frequency range and it is a well-known fact that the use of satellite-based positioning plays a significant role in our daily lives. For the last 20 years or more, it was also used to understand the effect of the atmosphere on radio signals (Hordyniec et al. 2018). The basic working principle of satellite-based positioning is mainly dependent on the position of the satellites and the distance of the satellite from the end-user's receiver. This information is acquired by the reception of a radio signal that traveled from the satellite to the receiver, through the atmosphere. Studying how the propagation medium affects the propagated signal is necessary to acquire precise and accurate positioning. The dynamic behavior of the atmosphere is yet another factor that continuously and differently affects the GNSS signal as it passes through the atmosphere (Hordyniec et al. 2018).

The propagation of a signal from the satellite to the user’s receiver is delayed by particles in the atmosphere. The neutral part of the atmosphere, which is also known as the troposphere, contains dry gas and water vapor. The delay due to the neutral atmosphere is called tropospheric delay and it causes the geometric lengthening of propagated signals (Jin et al. 2007). The tropospheric delay plays a vital role in studying weather, climate, and the vertical motion of atmospheres (Jingyong et al. 2005; Jin et al. 2007; Jin et al. 2009; Hordyniec et al. 2018). The land surface temperature directly or indirectly affects atmospheric composition. Vegetation cover and built-up area are on the other hand related to land surface effects (Malik et al. 2019) and they are interrelated. Because of these, it is logical to presume the indirect effect of urban expansion on the propagated GNSS signal. Therefore, a study was conducted using data from the IGS station ADIS, which is located on the premises of the Institute of Geophysics, Space Science, and Astronomy (IGSSA) of Addis Ababa University (AAU), as well as remotely sensed data on the expansion of the built-up area, changes in vegetation cover, and land surface temperature of Addis Ababa city, used to investigate this presumption. The location of the study area is shown in Fig. 1.

Data sources

To test the presumption that urbanization might have an impact on the positioning of points using the GNSS technique four data sets were used in this study. One is Landsat data, which is used to measure the physical expansion of the study area. The other data set is MODIS data, which is used to quantify changes in vegetation coverage and to investigate the Land Surface Temperature of Addis Ababa city. The Up directional of the GNSS data from the IGS site ADIS was used to observe the long-term variation in positioning.

Landsat Data

Landsat, the former Earth Resource Satellite Technology (ERST) is designed to study the earth's surface. Since 1972 Landsat satellite has continuously provided information about the changes occurring on the earth’s surface in a multispectral image (USGS 2019). It delivers moderate-resolution multispectral data of the earth's surface on a global scale. In this study, Landsat 5 TM (Thematic Mapper), Landsat 7 ETM+(Enhanced Thematic Mapper Plus), and Landsat 8 OLI(Operational Land Imager) products were used to measure the urban physical expansion of Addis Ababa city, from 2005 to 2019. To overcome the problem of cloud cover and avoid any seasonal effects, the images used were from January. In rare cases, it was necessary to search for cloud-free data from December to April, to get cloud-free images.

MODIS Data

Landsat has a good spatial resolution to measure land cover change. However, it has a limitation on temporal resolution and is highly affected by clouds. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors enable measuring surface change by providing daily worldwide observation (Weng 2011). MODIS data will improve our insight into the earth’s surface variability at a larger scale and process global changes, such as in oceanography, biology, and the atmosphere(Gao 2009). From those vast application areas and data sources of MODIS, in this study vegetation index and Land Surface Temperature (LST) were used to study urban vegetation cover and land surface temperature of Addis Ababa city, from 2005 to 2019.

The vegetation index (VI) is one of the MODIS data sets, which is used to study vegetation covers of the earth. It empirically measures vegetation coverage at the land surface. In this study, the Terra MODIS Vegetation Indices (MOD13Q1), with 16 days temporal and 250m spatial resolution was used. The other MODIS data used in this study is LST. LST is one important measure of surface energy (Assiri 2017). It gauges the temperature of the earth's surface at a specific location. MODIS provides LST data daily in global coverage. In this study, MODIS land surface temperature and emissivity MOD11A1, version 6 product, with 1km spatial resolution was used to generate information about the LST of Addis Ababa city. On some specific days, there were however missing data. Both NDVI and LST were downloaded from NASA’s FTP server.

GNSS Data

The positional information generated from GNSS data can be computed at different levels using different types of approaches. In those areas of application that demand very high accuracy, it is common to use a differential technique in the data processing. This on one hand requires the availability of reference stations and an accurate reference location. The IGS is one of the pioneering organizations, which stands to deliver quality reference locations and data. IGS is dedicated to advancing society and science by providing high-precision-free GNSS data (Johnston et al. 2017).

ADIS is one member station of the IGS network, which is run by the Institute of Geophysics, Space Science, and Astronomy (IGSSA) of Addis Ababa University (AAU). Since 2007 ADIS station played its role by providing continuous data with 30-second temporal resolution. In this work, the data from ADIS is used to analyze the effect of urbanization and the associated climate change on the GNSS positioning. In addition to ADIS other 11 IGS stations (HERS, HYDE, MAL2, MAS1, NKLG, NOT1, RAMO, REUN, TEHN, and YIBL) data were used during the processing to precisely generate the daily solutions. The GNSS data was processed using the GAMIT scientific software (Herring et al. 2010) of the Massachusetts Institute of Technology (MIT). Although GNSS data for ADIS was available since July 2007, this research used data from 2008 to 2019 to avoid the effect of station monument settlement, which is common at the early stage of any station operation. This precaution is made, as the expected long-term station coordinate change is in the order of a few millimeters. All the required information for data analysis was downloaded from the archives of IGS and IGSSA.

To process and analyze the data, different software was used. Urbanization and related issues were analyzed using ArcGIS, and python (in addition to other python modulus PyModis was used for MODIS data analysis). On the other hand, GAMIT/ GLOBK software was used to analyze GNSS data.

The level of urbanization in this study is measured using urban physical expansion as a parameter. The physical expansion of Addis Ababa city was measured from Landsat images. NDBI is used to automatically detect and map built-up areas(Ghosh et al. 2018). The automated process of mapping built-up areas through NDBI was first used by Zha (2003) to close the gap, in the laborious process of converting satellite imagery into a land-cover map using manual interpretation techniques and parametric image classification.

The NDVI and LST for Addis Ababa city were extracted from the MODIS dataset. The data were projected to the WGS84 reference system and converted to TIFF format, by using pyModis. NDVI was further analyzed using ArcGIS and a python script. By using ArcGIS zonal statistics LST data was also generated. Then from the processed data of NDVI and LST, statistical information was generated for further correlation analysis. One advantage of the ArcGIS software is that it has a python scripting extension. The development of the script played an important role in the analysis of the time-series data in an automated manner.

To get the daily solutions for the ADIS station, GNSS data was processed using Gamit/Globk with 11 IGS reference stations. As an output, daily solutions for ADIS and the other 11 IGS reference stations as well as the associated root mean square errors were obtained. After a thorough control of the first processing stage, yearly time series results were generated. Next, by removing outliers and jumps, due to different causes, from the time series a second round processing was conducted, and by combining the yearly solutions, the final time series is produced for the years 2008 to 2019.

To study the effect of urbanization and associated climate change on GNSS positioning, the interrelation between four parameters (urban physical expansion, vegetation cover, LST, and daily GNSS data for ADIS) was analyzed. The built-up area, vegetation cover, LST, and GNSS data from 2008 to 2019 were taken and the long-term changes were modeled using regression analysis through the least-squares curve fitting method. For this, the second-degree polynomial was used in the analysis. The parameters obtained from the least-squares fits are then used to generate the long-term trends for the year 2008 to 2019 and are checked for any correlation using Pearson’s correlation method. The main aim of fitting the second-order polynomial function of the data is to remove the seasonal effects and be able to analyze the long-term trend. In addition, for the LST and GNSS data, the Savitzki-Golay filter was applied to suppress very high-frequency effects that at times have large amplitude and visually mask the middle- and low-frequency part of the data.

Urbanization is measured by the physical expansion of Addis Ababa city, which is obtained from the Landsat image. The yearly built-up coverage of the study area, which was generated from the built-up index is shown in Fig. 2a. A sample result nearly with a 5-years interval is presented in the figure. The result of the 15-year urban physical expansion processed data was illustrated graphically in Fig. 3a as the blue points indicate the yearly result and the red line of the long-term curve fitting of the physical expansion of the city. As can be seen from the figures (map and graph), the built-up area has shown fast expansion during the period from 2005 to 2019.

The vegetation cover of the study area is derived from the MODIS data for 15 years period. Figure 2b displays maps for the city of Addis Ababa, created using data from the January Vegetation Index from 2005 to 2019. The vegetation coverage with sixteen-day intervals, which was processed for the whole period, is graphically illustrated in Fig. 3b. The blue line shows the 16-day vegetation cover of the study area from 2005 up to 2019. The red line is the modeled data for the same duration. As one can see from the graph, the long-term trend of vegetation coverage is monotonously decreasing throughout the study year. The blue line graph gives some insight into the seasonal effects that are periodic in nature.

LST for the study area is also analyzed from the MODIS data. The obtained LST is presented in Fig. 2c. The daily LST data, of Addis Ababa city, for the period 2005 to 2019, is graphically illustrated in Fig. 3c. The graph derived from the raw data is the superposition of effects from different frequencies. To remove the very high-frequency effects the Savitzki-Golay filter was applied. The blue color represents the original data, the cyan filtered data and the red the long-term trend. Like the vegetation coverage, LST data has also periodic nature. From the long-term trend result, we can observe an increment in the graph even if it is small compared with built-up expansion and vegetation coverage. Here it is important to highlight the difference between LST and air temperature, which can be as large as 12^OC depending on season and land cover (Do Nascimento et al. 2022).

The three-dimensional (North, East, and Up), daily solutions of the GNSS data for the station ADIS were produced for the period 2008 to 2019. The final time series result for the North, East, and Up directions were extracted. The Up direction is presented graphically in Fig. 3d. The graph with blue color shows the plot of the residual for the Up direction, while the red color shows the modeled long-term trend. Similar to the LST the Up direction also has high-frequency effects. Thus, the Savitzki-Golay smoothing filter was applied to the observed data, to generate the graph in cyan color.

The long-term trends are used to study the effect of urbanization and associated climate change on GNSS positioning. Figure 4 shows the long-term trends of the four variables from 2008 to 2019. The result of the correlation analysis among the different data sets (between the Built-up and Vegetation cover, between Built-up and GNSS-Up direction, between Vegetation cover and LST, between Vegetation cover and GNSS-Up direction, between LST and GNSS-Up direction, between LST and Built-up) is shown on Table 1.

The urban physical expansion which is derived from satellite image data is one of the parameters used to measure urbanization (Fang and QuanSheng 2012). In this study, the result showed that there is a significant increment in the built-up area, which caused a physical expansion of Addis Ababa City in the last fifteen years. According to a Study by the Swiss Agency for Development and Cooperation (2017) report, the expansion took place both in a planned and unplanned manner. From two decades of urban expansion, relatively the highest rate of expansion was observed in the last ten years.

Due to the physical expansion, other land covers such as agricultural land, barren land, and vegetation cover of Addis Ababa city are replaced by built-up areas. This is also the case in China (Cui and Shi 2012) and elsewhere. As a result, vegetation covers in the current urbanized areas are affected. The rate of decrement in vegetation cover significantly increased in the last decades (2008–2019), which is similar to the pattern of urban expansion, but in the opposite manner. The individual results and the very strong correlation (Table 1) between built-up and vegetation cover which amounts to -0.9972, clearly indicate that the city is indeed expanding. The strong negative correlation shows that as the city is expanding, the vegetation coverage is decreasing at a comparable rate.

The expansion of urban built-up areas and the decline of vegetation cover affect the urban land surface temperature (Adeyeri et al. 2017). The trend of the LST also followed the pattern of other parameters starting from 2008 onwards. The increment in the land surface temperature of the city is about 0.93⁰C in the last 10 years. This increment is correlated with the built-up area and vegetation cover. The correlation between the built-up area and LST is + 0.9738 and this shows that as the built-up area is increasing the temperature of the city also increases. The − 0.9881 correlation between the vegetation cover and LST shows that as the vegetation coverage is decreasing, the temperature of the city is increasing. This result agreed with different scholars' findings (Li et al. 2011; Linh and Chuong 2015; Ibrahim 2017; Deng et al. 2018). The study by Fang & QuanSheng (2012) indicates that the effect of urbanization on climate changes with respect to temperature trends varies with the size of the city (small and large cities), at a local and regional level, and also by season.

From the result of GNSS data for ADIS, the three-dimensional position is continuously changing in time, with the North and East component almost following a linear trend with a rate almost similar, but not exactly the same, to that of the Nubian plate movement. However, the Up component shows, both seasonal and non-linear but a low-frequency movement. It is optically obvious that the station on average was not showing any low-frequency movement until 2011, but subsided noticeably after 2012 until 2019. These changes share a similar trend with the other three parameters in the same or opposite manner. Both the physical expansion of urban area and vegetation cover change is related to the change in the Up direction (Fig. 4). However, as both cannot directly affect the change in the location of the station, it was necessary to study urban climate change through the LST. Because according to Jamei et al. (2019), built-up expansion and vegetation cover contribute to urban microclimate change.

The amount of correlation between the Up direction of ADIS and LST over the study area is -0.9861 (Table 1). This also shows that as the temperature is increasing the Up direction of the station position has changed. This on the other hand will lead to the impression that the station is subsiding. Dodo et al. (2008) stated that the computation of the Up direction of station position is relatively affected by changes in the lower atmosphere. The result, which showed that there is a very good correlation between LST and the Up direction, will be in good agreement with their statement. Also According to Trenberth (2011), the increment of temperature by 1^oC has a significant effect on the propagation of signals in the lower atmosphere. Therefore, the 0.93^oC increase in LST has its own impact on positional data for the Up direction of ADIS.

The very good correlation between built-up area and vegetation cover with the UP direction of GNSS amount to -0.9226 and + 0.9489 respectively confirming that the long-term subsidence observed in the Up direction of the station ADIS is related to the expansion of the city. The correlation of the LST signal with the other data sets during the same period shows that the expansion of the urban built-up area and the associated depletion of vegetation cover affect the LST of the study area.

Table 1

Correlation coefficient between the different data pairs
No.	Variables		Pearson’s Correlation Coefficient
1	Built-up Area	Vegetation Cover	− 0.9972
2	Built-up Area	GNSS, UP Direction	− 0.9227
3	Vegetation Cover	LST	− 0.9881
5	Vegetation Cover	GNSS, Up Direction	+ 0.9489
6	LST	GNSS, Up Direction	− 0.9861
7	LST	Built-up Area	+ 0.9738

Although there are some other factors, including the extraction of groundwater, subsurface magmatic movements, earthquakes, and the placement of large masses close to the station, that could affect the station's movement in the upward direction, it is believed that these are not the circumstances in this work because the closest significant water extraction point in the city is about 25 kilometers away. Furthermore, there isn't significant mass loading in the vicinity other than the few high-rise buildings about 3 km away from the station. Apart from these, the temporal signature of earthquakes and volcanic-related deformations are also very different from what has been recorded at ADIS station. Moreover, if one is even inclined to relate the changes observed at the station to such phenomena, there were none during the test period.

Given the association between the change in the Up direction and the temperature change, we have deduced from all of this that the change in the Up direction of the GNSS station position is caused by changes in atmospheric conditions. Therefore, one can learn from this study that urbanization and associated climate change affect the Up direction position of a station that is derived from GNSS station measurements.

However, further investigation is needed to fully ascertain if urbanization and associated climate change is the only factor that has caused the long-term change in the Up direction of the station position that is derived from GNSS measurement.

The research work was conducted to study the effect of urbanization and urban micro-climate change on the GNSS positioning by correlating the GNSS processed result from the IGS station ADIS with the urban built-up expansion, vegetation cover change, and land surface temperature for the city of Addis Ababa. The result exhibits that there is a strong correlation between urbanization, micro-climate change, and the change in the vertical position of the ADIS station.

The measured result shows that the urban built-up area of Addis Ababa city has expanded in the last almost two decades. Because of the expansion of the urban built-up area, the reduction of vegetation cover was observed. Visual observation in Fig. 3 and Fig. 4, and the correlational result of Table 1 clearly showed that, because of the fast expansion in built-up areas in the last ten years, vegetation cover is highly depleted accordingly.

A notable change in the LST was also observed in Addis Ababa city in the last fifteen years. The correlation of this change with vegetation cover and built-up area indicated that the urban expansion and associated depletion of vegetation have raised the land surface temperature of the city slightly. This on the other hand has its influence on the propagation of the GNSS signal in the lower part of the atmosphere.

The Up direction of the GNSS signal at the IGS station ADIS was also showing slow but persistent subsidence, without a definable cause. This study illustrated that the station might not be fully physically subsiding, but the processing of data is influenced by changes in the lower atmosphere, that on the other hand is caused by urbanization (physical expansion of the city). The strong correlation between built-up area, vegetation cover, and LST on one hand and the Up direction of the daily solutions, on the other hand, exhibited that urbanization affects the Up direction position of the ADIS GNSS station. Even though further investigation is required, the correlation between changes in urbanization, land surface temperature, and the up direction of the GNSS station's position, might imply that associated long-term changes in tropospheric conditions could have affected the propagation of the GNSS signal. Thus, when processing GNSS data for very high precision work, one should take into consideration the effect of urbanization and urban climate change on positioning.

Acknowledgment

This research was conducted with the support of a grant obtained from Addis Ababa University, Addis Ababa, Ethiopia. We would like to thank the Institute of Geophysics, Space Sciences, and Astronomy (IGSSA) of Addis Ababa University (AAU) for its all-rounded and continued support during the whole research activity. The authors also acknowledge that the remote sensing data is downloaded from NASA's home page. The GNSS data is generated by IGSSA and downloaded from the SOPAC archive.

Data Availability

The main data that is used for this research were satellite images (Landsat and MODIS) and GNSS Data. All Landsat (Landsat 5 TM (Thematic Mapper), Landsat 7 ETM+ (Enhanced Thematic Mapper), and Landsat 8 OLI(Operational Land Imager) ) and MODIS data(MOD13Q1 and MOD11A1) were obtained from NASA’s FTP server (via http://earthexplorer.usgs.gov/). The GNSS data was obtained from the SOPAC/IGSSA’s archive. In addition to this, the administrative boundary of the study area was obtained from Addis Ababa municipality.

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The Effect of Urbanization and Associated Climate Change on GNSS Positioning: A Case Study in Addis Ababa City, Ethiopia

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Introduction

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Landsat Data

MODIS Data

GNSS Data

Processing Methods

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