Integrating Spatial Modelling and Interpolation of Environmental Factors in Landscape Design Analysis
Wan Yusryzal Wan Ibrahim1*, Muhamad Solehin Fitry Rosley1, Mohamad Alif Abas1
1 Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Johor, Malaysia
*Corresponding Author: [email protected]
Accepted: 15 May 2022 | Published: 1 June 2022
DOI:https://doi.org/10.55057/ijarti.2022.4.2.3
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Abstract: Rapid landscape change for physical development has led to extensive environmental consequences. Analysing and simulating landscape change's impact is vital in understanding the consequence of existing development scenarios, particularly on the microclimate and thermal comfort. Spatial interpolation is one of the tools capable of visualising the characteristic of the environmental scenario in any area with empirical data.
This paper discussed the method of GIS integration in modelling the environmental characteristic (microclimate) in landscape design development. A spatial database is developed based on the input from empirical data, and IDW is used to simulate the environmental characteristic of the landscape in the analysis stage. Using campus landscape rehabilitation at UTMKL Pasir Gudang as an example, an empirical study was conducted to collect the microclimate data and the existing landscape composition. Both existing and future microclimate situations are visualised and simulated to strengthen the understanding of the functionality of the new landscape design proposal. It indicates the performance of the landscape design and demonstrates the thermal comfort level of the campus. This analysis goes beyond aesthetic value evaluation and gives designers the necessary idea to orientate the ideal design concept to optimise ecosystem service to the campus. The spatial interpolation clearly shows the expected environmental condition scenario as the landscape at the campus changes.
This simulation significantly improves the decision-making process in landscape design that contributes to an eco-friendly campus where the community will have a more comfortable environment. In conclusion, the spatial interpolation of the environmental characteristic is vital to formulate a substantial understanding of the landscape design consequence to maximise ecosystem service provision.
Keywords: GIS, Spatial Interpolation, Landscape design, Ecosystem Friendly
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1. Introduction
The urban landscape has significantly changed the environment, particularly the microclimate condition at local and global scales (Sharma et al., 2021). Thermal comfort is an element that has always been referred to in measuring the comfortability level of the local environment. The degree of temperature, humidity, surface temperature, and wind velocity are the microclimate parameters used to indicate the comfortability of the environmental surrounding. Those elements are closely related to the condition of the surrounding landscape scenario.
Anthropogenic development particularly has significant changes in landscape scenario and
contributes to the unpleasant microclimate. Landscape design and development are normally executed to normalise the extreme microclimate scenario with the invention of green infrastructure (Zhaoa and Fong, 2017).
Nevertheless, understanding the changes scenario is difficult without properly presenting the information. Thus, this paper discusses geo-spatial interpolation's ability to demonstrate the microclimate scenario in the landscape design process. The methodology and tools in simulating the environmental condition are elaborated in this paper to strengthen understanding of the impact of landscape design on the environment.
2. Research Background
Landscape Change and Environmental Impact
Anthropogenic development mainly promotes to change of natural landscape composition and configuration. It influences local microclimate, and this irreversible conversion causes a long term adverse effect at the regional and local levels. At the local level, microclimate is an essential element that contributes to thermal comfort. The thermal comfort scenario is highly dependent on the structure and material used in the development (Liu et al., 2021). Building, asphalt, roof, and existing landscape design contribute to different temperature, surface temperature, and humidity conditions. Those elements absorb and release heat which causes extreme microclimate to the environment. This unpleasant condition tremendously affects human outdoor activities. Poor design of surrounding landscape and building structure influences the indoor environment, which requires internal lighting and high usage of conditional air system (Yaoa et al., 2020). Increased use of the supporting facilities causes more money expansion and increases carbon dioxide emissions. Therefore designers and stakeholders have to understand the site condition precisely and should be able to project the probability of consequences from the landscape development.
Geo-spatial Modelling in Landscape Design
Spatial visualisation is vital to understand site conditions, primarily intangible environmental factors such as microclimate data (temperature, humidity, wind and surface temperature). GIS is one of the efficient tools in modelling the environmental landscape condition. Nowadays, GIS interpolation is used to present the existing condition and simulate the situation after landscape development (Doorga et al. 2019, Fischer et al. 2021 and Orhan, 2021). The visual approach on the existing microclimate condition strengthens the understanding of issues at the micro-level of any study area. The shortcoming of any niche area can be interpreted, and empirical data in the GIS database can interpolate the spatial dimension of microclimate conditions (Ma, et al. 2019, Tercan, et al. 2021 and Urech et al. 2022). This comprehensive understanding is essential to support site analysis and better decision-making in future landscape development. The result of GIS spatial interpolation will orient the concept plan and schematic design. The expected landscape design output can also be interpreted using GIS simulation.
GIS is currently growing in multidisciplinary applications as its ability to integrate with many sources of data as well as solve a lot of spatial issues. Moreover, the emergence of open-source GIS with continuous improvement on the spatial analysis engine alleviate GIS as a common tool for spatial data handling. In landscape application, geo-spatial analysis, spatial representation and data integration are the key factors that allocate GIS to be included in landscape design analysis.
The geostatistical analysis is a valuable tool for presenting the landscape condition on the specific function of GIS in landscape planning. It is broadly used in many disciplines, and Inverse Distance Weightage (IDW) is one of the best interpolation methods which has been used in visualising the environmental scenario. The model can simulate and produce a reliable map series with spatial variation (John et al. 2020 and Ahmad et al. 2021). It is a deterministic interpolation model that creates an estimated surface using measured checkpoints. The IDW model estimates the unsampled area by employing the sample points at the neighbourhood of the point. The weight value and distance from the points are used to indicate the value at the unsampled area (Magesh et al. 2019 and Zeb et al. 2021).
Thermal Comfort Importance at Institution
Higher education institutions are one of the focus areas with a large compound with diverse landscape elements and human activities. Studies on sustainable campuses were comprehensively conducted where so many approaches, indicators, and themes were implemented to achieve a sustainable campus (Washington-Ottombre, 2018 and Adenle, 2020). Carbon reduction, thermal comfort and microclimate equilibrium and green technology awareness improve landscape planning and management on campus (Chen et al. 2018). Those factors are directly related to the composition and configuration of landscape design and spatial features at the campus area. Thus, this paper is presented to formulate the concept and method to apply GIS modelling and interpolation in landscape design analysis. Every stage of the process is discussed to highlight the implementation of the GIS application and geostatistical interpolation in site analysis and design. Using the campus environment for the landscape rejuvenation study, the output of the analysis presented and the expected changes formulated at the study area. Good landscape composition and configuration in a sustainable campus environment is important to provide the campus with educational benefits and help students learn and appreciate good ambient (Amaral et al., 2020). Landscape technology will help reduce the impact of extreme microclimate on campus and increase the value of green space in archiving the well-being in campus areas (Abdelaal, 2019). Preserving ecosystems will further enhance the awareness and perception of the campus residents and visitors to the area. The approach used in this project will be a model for comprehensive landscape analysis in the future study.
3. Study Area
UiTM Campus in Pasir Gudang is used as a study area to show the integration of GIS modelling and interpolation in site analysis and design. Pasir Gudang is known as an industrial zone and port city at Johor Bahru and nowadays has rapid physical development that is changing the overall landscape of Pasir Gudang. The study area is under the Municipal Council of Pasir Gudang, and the campus area is about 70 acres (Figure 1). The campus was fully operational in May 2014 with five different departments consisting of civil engineering, electrical engineering, mechanical, chemical, and business and management. The campus is surrounded by mixed land-use types such as industrial, housing, commercial, municipal, water, and education for the site context. The active surrounding activities and rapid physical development changes mainly influence the microclimate at Pasir Gudang and the campus. Moreover, the unpleasant existing landscape in the campus negatively contributes to the extreme microclimate in the campus area. Therefore this study area is used as an outdoor laboratory for this spatial GIS modelling and interpolation for the landscape design rehabilitation process.
Figure 1: The location of UTMKL Pasir Gudang at Johor Bahru.
4. Methodology
Landscape design analysis consists of several main stages, and good output will be achieved from thorough analysis and integrated with appropriate techniques. This study clarifies the method used to handle the microclimate study at the analysis stage. The first stage is planning and organising the time, duration, date, type of microclimate data that need to be collected and devices required for data collection. Secondly, identify the location of checkpoints to collect the microclimate data and record detailed information about the landscape features and characteristics from each point. The spatial point data is developed and coordinated for each location recorded by using GPS receiver or refer to Google Map application. Apart from that, other spatial data like building, road, river, land use are also developed for this exercise. The database is adequately designed to accommodate the empirical and projected data in the database.
Figure 2: The research process of the study
5. Data Collection
On the microclimate aspects, three microclimate data are identified and measured in the study:
temperature, surface temperature, and humidity (Table 1). These attributes are essential to relate to the community's thermal comfort level and the effectiveness of the energy used in the campus (Figure 2). Thirdly, those data were collected using tools such as an anemometer, thermometer and infrared laser.
Table 1: Microclimate data for landscape design analysis Microclimate data Unit Duration Description
a. Temperature Degree 7am to 7pm (2 days)
The data is collected every 1/2 hour at the station 1 meter above the earth's surface.
b. Surface
Temperature
Degree 7am to 7pm (2 days)
The data is collected by measuring the earth surface by using laser measurement.
c. Humidity Percentage 7am to 7pm (2 days)
The humidity data is collected 1meter above than earth surface.
a) Surface temperature
The data was collected at each checkpoint and taken at different times. The data collection aims to find out the areas with high and low surface temperatures. It is related to the types of materials used or land cover.
b) Relative-Humidity
The temperature and pressure of the environment are related to relative humidity. A greater relative humidity in cool air has resulted from the same amount of water vapour. It will
influence comfort level with moisture in the environment with a cooling effect. Vegetation is closely related to humidity level and the data is collected for the interpolation.
c) Area-temperature
Temperature data was collected for every checkpoint, and several areas were focused that showed a significant degree of temperature. Hostel areas, hostel parking, academic zone and sport area were among the areas that could have substantial microclimate.
Apart from current data, projection of microclimate data is developed based on the future scenario after the new landscape design. This will be based on the references to indicate the percentage of the expected microclimate changes. The data is embedded in each point attribute table, and the interpolation is conducted using IDW model.
Figure 2: Map of UTMKL Pasir Gudang at Johor Bahru.
6. Data Analysis
Data analysis was conducted after all the collected data were transferred into a digital GIS database. The point data carries all the microclimate data in the attribute table. It will be an input for a model to run the interpolation. Interpolation of the microclimate scenario is conducted using the Inverse Distance Weightage (IDW) model and interpolated using GIS. The interpolation of the data was performed throughout the study area with the distribution location of points. The IDW calculates the whole microclimate area around the points by considering the value at the points. The output results show the scenario of the microclimate for the entire study area in raster format. Every cell represents the value for related microclimate, and this continuous raster data indicates the situation in the whole study area.
The microclimate scenarios are visualised and interpreted to evaluate the microclimate's relationship with the landscape features. Building structure, landscape elements and vegetation are among the objects related to the microclimate. ArcGIS software is used to interpolate the microclimate scenario. A series of maps show the difference between the current microclimate situation and the projection of future scenarios at the campus.
7. Result
The collected data is tabulated, and descriptive analysis shows the current situation of the study area. The future scenario embedded in the table and interpolated graph compares both
conditions before and after rehabilitated design (Figure 3). Table 2 and Figure 3 show the current microclimate data and scenario at the UTMKL campus by referring to the most extreme microclimate along the study. The current situation shows the temperature is about 31 to 36 degrees in the study area. This situation exceeds the comfortable level for humans (20 to 30 degrees) to conduct outdoor activities. At the same time, the surface temperature generally is about 41 to 48 degrees which contribute to the high temperature and uncomfortable situation.
The heat is absorbed by the surface and will release at night. This situation contributed to the urban heat island. Asphalt, concrete and building façade are the medium that absorbs heat and releases heat. The situation generates an uncomfortable environment during daytime and at night as the heat will out from the medium.
Table 2: Result of microclimate data from empirical survey
Point Station Air Temperature Surface Temperature Humidity
1 34 46 24
2 33 48 20
3 33 43 31
4 31 47 19
5 33 41 34
6 35 44 22
7 35 45 33
8 34 45 25
9 38 48 34
10 34 33 45
11 36 35 30
12 34 30 41
13 31 41 35
Figure 3: Simulation of existing microclimate condition at UTMKL Pasir Gudang at Johor Bahru.
The overall humidity level is relatively low, between 19 to 45 percent, out of a good range between 45 to 85 percent. Some areas show a significant dry with low humidity and high surface temperature, such as station 2 and station 4. Other extreme situations can be interpreted in several locations in this study area where there is a lack of soft landscape features, and the hard surface elements contribute to significant heat scenarios such as at stations 8, 9 and 11.
The visualisation of the scenario can be seen in their surroundings.
Figure 4: The planting design and the rehabilitation of spaces at UTMKL Pasir Gudang.
Other stations like stations 5, 6 and 7 also show a significantly high temperature with 33 to 35 degrees out from the comfortable range. The value displayed that the negative impact of the existing landscape elements on their surroundings. The extreme temperature is contributed by a lack of vegetation and hard surfaces such as building roofs and concrete. Those extreme high- temperature areas are supposed to be used for other outdoor activities; however, the current scenario does not attract humans for organising the outdoor activity. Therefore, the new landscape design was invented to rehabilitate the study area's current situation (Figure 4 and Figure 5). The expectation of changes is made for the value of temperature and surface temperature reduction and humidity improvement.
The interpolation of the existing scenario oriented the potential design to achieve the standard comfort level for the campus environment. The IDW model was used again to visualise the future microclimate after the landscape change. The expected changes of the temperature, surface temperature, and humidity are stored in each point's similar attribute table. The simulation is executed, and the future microclimate is visualised. The interpolation shows the reduction of the temperature and surface temperature and the improvement of humidity level (Figure 6).
Figure 5: The design landscape rehabilitation of the academic zone at UTMKL Pasir Gudang
Figure 6: The result visualized the expected microclimate scenario at the study area after design implementation.
Figure 7: The graph shows a significant changes of microclimate at the study area after design implementation.
8. Discussion and Conclusion
Visualisation is a significantly reliable approach to portraying the implication of environmental surroundings from landscape design changes. Simulation and mapping of expected scenarios better understand the design function. The composition and configuration of hard and soft landscape elements can be measured and mapped out to strengthen the knowledge of landscape design.
Furthermore, the simulation of the expected outcome gives more opportunity for designers to generate alternative design ideas and justify their views. With the visualisation of microclimate impact, the performance of alternative design can be measured and determined the best strategy for the development. The selection and arrangement of hard and soft landscapes will be more effective with the computer simulation. Experimental on the response of those landscape features towards the positive environmental impact can be conducted with a series of simulations. This will make the design process visible as more information and facts can be visualised in the design process.
The simulation above strengthens the expected output of the proposed design. Implementation of landscape technology such as rooftop garden, permeable surface, and good coordination on planting will contribute to a conducive environment for the campus. Consequently, the environment will be more sustainable for students to do their daily activities as they live within
an excellent thermal comfort area for the whole campus. Finally, after succession, it is expected that the master plan will be beneficial for the environment and finally for the entire campus community.
Simulation of the microclimate scenario for the study area has stimulated the justification of landscape design. It allows designers and stakeholders to be more critical and innovative in significantly composing and selecting landscape features to reduce heat and improve thermal comfort. The methodology used in this study enhances the existing landscape design process and could be applied in other landscape applications. The availability of open-source GIS software will increase designers' opportunities to explore the implication of their design.
Spatial visualisation and simulation of the impact specifically improve the decision-making process and the accuracy of the decision. Understanding the microclimate behaviour is vital for future landscape development as it strengthens the expected output simulation and landscape design proposal.
Acknowledgements
The authors sincerely acknowledge Research Management Centre (RMC) Of Universiti Teknologi Malaysia (UTM), Pusat Kajian Lingkungan Alam Melayu (KALAM) UTM, for The funding of this research through research grant no. Q.J130000.3852.20J47.
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