This work was part of an internship project done by Ooi Juin Yan under Landscape Ecology and Conservation (LEC) Lab. The timeseries of Greater Kuala Lumpur, Peninsular Malaysia, and Greater Bandung, Indonesia was developed by exporting Landsat 5 and 8 surface reflectance images from Google Earth Engine and edited with tools from ArcGIS Pro 2.5.1. The project was supervised by Dr Alex Lechner and assisted by Michelle Ang and Darrel Tiang.
Authors: Juin Yan Ooi (1), Michelle Li Ern Ang (1), Darrel Chin Fung Tiang (1), Alex M. Lechner (1,2)
1 Landscape Ecology and Conservation Lab, School of Environmental and Geographical Sciences, University of Nottingham Malaysia, Semenyih, 43500, Malaysia
2 Lincoln Centre for Water and Planetary Health, School of Geography, University of Lincoln, Lincoln, LN6 7TS, UK
1.0 Environmental Justice
Environmental justice is based on a concept that all people have the right to be protected from environmental pollution and to live in a healthy environment regardless of their socioeconomic status such as income, race, age and occupation (Raymond, Gottwald, Kuoppa, & Kyttä, 2016). The idea was conceived in the United States coming out of the social movement fighting the uneven distribution of environmental hazards among racial groups (Raymond, Gottwald, Kuoppa, & Kyttä, 2016). Due to the increasing awareness of the importance in environmental justice, studies and research on environmental justice are no longer restricted to the US (Hobson, 2006). There should be a fair distribution of environmental burden and benefits across all groups. However, due to the lack of support from governing bodies and local planning authorities (Poon, 2018), inequal distribution of environmental benefits is still an issue.
Over the past two decades, uneven accessibility to environmental amenities such as those provided by urban green and blue spaces (UGBS) has been recognized as an environmental justice issue due to the increasing awareness towards the importance of UGBS for public health and well-being (Wolch, Byrne, & Newell, 2014). Urban green spaces can be defined as communal, private or publicly accessible areas which are naturally vegetated within urban landscapes (Nath, Sim, & Lechner, 2018). Examples of publicly accessible urban green spaces are parks, gardens, playgrounds, sporting fields, riparian areas and other open vegetated areas. Urban blue spaces represent surface water or aquatic environments such as ponds, lakes, rivers and other open waterbodies within the urban environments. Examples of these spaces are lakeside walkways, parks with lakes and other recreation areas. Waterscapes play an important role in attracting inhabitants to UGBS since the fascination and attraction of waterscapes are what motivates people to recreation areas (Völker & Kistemann, 2013).
Since UGBS are a form of environmental benefit, it should be distributed equally to all members of a community. Unplanned or rapid urbanization growth in the past may cause an imbalance in the distribution of UGBS, causing unequal access to these spaces by different communities in different locations. Despite efforts from many various sectors and organizations, many challenges must be overcome to further improve environmental justice on a global scale. The main issues faced are the lack of funds to initiate and maintain environmental projects; and a lack of environmental regulations with environmental protection laws being a relatively new concept in many countries (Harding 2007). Even where urban greening strategies are implemented in some cases the creation of new green spaces may create environmental justice issues as the proximity to green space can cause property value to increase, displacing the residents that the strategies were designed to benefit in the first place (Wolch et al., 2014).
2.0 Benefits of Urban Green and Blue Spaces to Public
Several studies have shown the importance of UGBS in combating health issues of users regardless of age, wealth and culture (Wolch et al., 2014; Weigand, Wurm, Dech, & Taubenböck, 2019; Claßen & Bunz, 2018). According to Wolch et al., (2014), access to urban spaces have been closely linked to mortality. These spaces provide a wide range of experiences and opportunities for recreation, relaxation and socializing with friends which have positive effects towards personal health. The concept of therapeutic landscape can be closely associated with UGBS which refer to landscapes that can help achieve physical, social and spiritual healing (Völker & Kistemann, 2013). UGBS are suitable locations for green exercising. Green exercising usually refers to exercises performed in natural environments such as parks (Nath et al., 2018). Just being in UGBS can have a positive effect to all people by providing a tranquil and stress-relieving atmosphere to improve their mental health (Weigand et al., 2019). UGBS are favoured meeting points for people to carry out social interactions and dynamic activities (Völker & Kistemann, 2013). This allows UGBS to provide a lively atmosphere and as a place for social contact which is important in maintaining networks between people as well as improving mental health.
Besides benefits in terms of health, UGBS are beneficial in terms of economy and the environment. UGBS are said to improve employment rates by providing more jobs to society; and with proper quality and management, UGBS could potentially improve tourism and other forms of marketing, further pushing the economy (Bolten, A.M., Kotter, T., Schuppe, 2019). Vegetation cover and water bodies help regulate urban temperature and humidity through the evapotranspiration process and by absorbing atmospheric carbon dioxide which is a greenhouse gas that contributes to climate change (Ramdani & Setiani, 2014).
3.0 Aims and Objectives
As awareness towards environmental justice continues to grow, this study aims to understand how urbanization impacts on UGBS in Greater Kuala Lumpur Malaysia and Bandung Indonesia. We developed a time series of UGBS using historical Landsat imagery processed and classified using Google Earth Engine. Since urban development is associated with the decline of UGBS, two major cities within Southeast Asia were selected as the study area for this project.
4.0 Study Area/Site Context
Urban green spaces have been declining in major cities of developing countries due to pressure in rapid urbanization, residential and commercial developments (Karuppannan, Baharuddin, Sivam, & Daniels, 2014). Many people migrate into city areas in search of better employment, increasing the local population. Major land conversion will occur due to increased pressure for housing areas and other infrastructures to support the growing population. As the population grows, the role of UGBS in resolving environmental justice related issues becomes more significant in countering the negative effects of urban development.
This led to major cities being the selection criteria for the project’s study area. The study was carried out in Greater Bandung in Indonesia (Figure 1) and Klang Valley in Malaysia (Figure 2). Both study areas are considered one of the top major cities in their respective countries and since development rates are usually faster in major cities, bigger land cover changes could be observed, causing environmental justice issues to be more prominent within these areas.
Figure 1. Greater Bandung in Java, Indonesia.
4.1 Greater Bandung
Greater Bandung or Bandung Basin is one of the five largest cities located in Indonesia. It was reported that urban green spaces within Greater Bandung were decreasing over time due to forest loss, land conversion and rapid urbanization (Agaton, Setiawan, & Effendi, 2016). Greater Bandung consists of Bandung Regency, West Bandung Regency, Cimahi City, Bandung City and 5 districts from Sumedang Regency and has a total of 8.2 million registered residents in 2014 (Tarigan et al., 2016). These areas form a metropolitan area that surrounds one of the most populated cities in Indonesia, Bandung City. Greater Bandung has a mountainous geography and is surrounded by volcanic highlands. The altitude of the central part of Greater Bandung is approximately 665 m and is surrounded by 2400 m-high volcanic terrain (Gumilar et al., 2015). Due to Greater Bandung’s topography and location in a geologically and seismically active area along with a tropical monsoon climate, the area is highly susceptible to natural hazards such as landslides, earthquakes and floods (OECD, 2018).
Figure 2. Greater Kuala Lumpur, Peninsular Malaysia.
4.2 Klang Valley
Malaysia is one of the top three countries with the highest national rates of deforestation (Yong, 2014). It is also reported that urban green spaces in Kuala Lumpur has declined since 2010 due to development and land expansion(Nath et al., 2018). Population concentration of Malaysia is focused around Kuala Lumpur in Peninsular Malaysia (Ahmad, 2020). Klang Valley is a metropolitan area that is centered around Kuala Lumpur, the capital of Malaysia. It is part of the state of Selangor and federal territories of Kuala Lumpur and Putrajaya. The name ‘Klang Valley’ originated from the Klang River which flows through Klang Valley from the Klang Gates Quartz Ridge in Gombak all the way into the Straits of Malacca in Port Klang. Districts and towns in Klang Valley began to develop due to tin mining activities in the late 19th century (Ooi, 2009). These mines were located close to the Klang River which evidently gave the valley its name (ExpatGo, 2014). There is no official boundary for Klang Valley however it usually comprises of several areas including the Selangor district of Petaling, Gombak, Klang, Hulu Langat, Sepang and the federal territories of Kuala Lumpur and Putrajaya. Klang Valley extends to Rawang in the northwest, Semenyih in the southeast, Port Klang in the southwest and with Kuala Lumpur as the center (Ooi, 2009).
5.0 Remote sensing analysis
5.1 Overview
To determine the distribution of UGBS, publicly available Landsat imagery was processed using Google Earth Engine (GEE) to generate NDVI, NDWI and true colour timeseries imagery. Timeseries are data sets collected at specific points over a period of time (Williams, 2020). In this study, a true colour and NDVI timeseries videos for both study areas were created to provide a general assessment in land cover change over time. Several timesteps where major development events had likely occurred from each study area were selected to represent changes in land cover. Each of the two study areas was mapped for a number of timesteps which represented dates of major development activities. The timesteps for Klang Valley are 1999, 2001, 2009, 2014 and 2019. The timesteps for Greater Bandung are 1998, 2008, 2014 and 2019. [AL1]
5.2 NDVI and NDWI
Since the normalized difference vegetation index (NDVI) is sensitive to chlorophyll content in vegetation, it is often used to monitor and differentiate vegetation from other land cover. NDVI is calculated based on the reflected visible or near-infrared light by vegetation, it can identify the health and the greenness of vegetation which allows it to even measure the density of vegetation (Shaharum et al. 2020). Healthier and dense vegetation absorbs more visible red light and reflects most near-infrared light while unhealthy or sparse vegetation reflects more red light and near-infrared light (NASA, 2000). Applications of NDVI in agriculture management and deforestation detection have led to mapping NDVI being one of the key methods used in remote sensing applications.
Normalized difference water index (NDWI) is sometimes used to monitor changes in water content of vegetation (Gao, 1996). Since NDWI has a quicker response to changes in water content and higher efficiency in retrieving vegetation water content information than NDVI, NDWI is good indicator for monitoring crop water content, drought conditions and surface waterbodies (JRC European Commission, 2011).
The formula for NDVI and NDWI are as follow:
NIR: near-infrared light
Red: visible red light
Green: visible green light
Both indexes have values which range from -1 to +1. Land cover classification was conducted using NDVI threshold values. The NDVI threshold classification for waterbodies was -1 to 0, bare soil and built-up area was 0 to 0.5 and vegetation 0.5001 to 1. The thresholds were set based on several studies with their respective NDVI threshold values taken in account (Hashim, Latif, Adnan, & Alam, 2019; Buyadi et al., 2014). In NDVI, negative values usually correspond to surface water, man-made structures or snow (EOS, 2019). Any sort of vegetation is always positive values which could range from 0.3 to 1, where the larger value indicates healthier and denser vegetation. However, there are no fixed optimal thresholds for each land cover which usually leads to differences in thresholds used in studies and projects (GIS Geography, 2020).
According to Ji, Zhang, & Wylie, 2009, NDWI values range from -1 to +1. In NDWI, waterbodies are indicated by positive values and values closer to -1 are classified as non-waterbodies (Sisay, 2016). Thus, 0 is usually set as the threshold to separate water and non-waterbodies. However, the NDWI threshold value in this study was set as -0.1 to distinguish non-water waterbodies and waterbodies. Optimal thresholds may vary due to difference in location, time and atmospheric conditions of the study area (Zhou et al., 2017). Therefore, minor alterations to the thresholds were made to improve the accuracy of the land cover classification.
5.3 Landsat Data processing
In the study, Landsat 5 and 8 surface reflectance archives were accessed through the GEE platform via the JavaScript API which allowed users to edit and control data through coding (Shaharum et al. 2020). The codes used in accessing and exporting the Landsat imagery are as follows:
Landsat 5: code
Landsat 8: code
Since the imagery was compared with one another, inter-calibrations between Landsat 5 and 8 were conducted due to the difference in band width between Landsat 5 and 8. The inter-calibrations were done through coding in GEE. According to Li, Jiang, & Feng, (2013) the coefficients used for inter-calibrating the Landsat is as follow:
The coefficients were produced based on the difference in values of similar bands of different satellites. Cross-comparison between bands of different satellites were done to measure the difference in value. Since Landsat 5 and 7 have similar band widths (USGS, n.d.), the coefficient above is suitable for Landsat 5, even though Landsat 7 is stated.
5.4 Timeseries video compilation
NDVI, NDWI and true colour imagery were processed and exported from GEE. The exported imagery were visualised and the outputs were used to compile a timeseries video from each study area as shown below.
6. Timeseries analysis outputs
The timestep maps and time-lapse videos have shown changes in land cover which indicates development trends from both study areas. The maps were useful in identifying locations with urban or residential areas and the changes in vegetation density. The timeseries represent locations where UGBS distribution are being lost across these cities and potential areas of concern for environmental justice.
Based on the Klang Valley timeseries (Video 3a and 3b), major development activities were visible in 2009. The expansion rate of Klang Valley during earlier timesteps (1999, 2001 and 2009) were much higher than those of later timesteps (2014 and 2019).
Video 3a. True-colour timeseries video of Klang Valley.
Video 3b. NDVI timeseries video of Klang Valley.
According to the Greater Bandung timeseries, no major changes were observed in the true colour maps (Video 4a and 4b). However, there was irregular land cover change shown on NDVI maps which could be influenced by hazy conditions. The city did expand over time when comparing the timestep maps of 1998 and 2019.
Video 4a. True-colour timeseries video of Klang Valley.
Video 3b. NDVI timeseries video of Klang Valley.
7.0 Future Works in UGBS
One of the main concerns faced in environmental justice is the uneven distribution of UGBS to different communities within an area. However, this study has not been able to support environmental justice, but future works and studies can be conducted to resolve them. Potentially, the next step in this study would be to identify and calculate distance between UGBS and certain residential areas to gain basic understandings towards the access and availability of UGBS. Socioeconomic indicators such as income, education and racial backgrounds obtained from government departments or other studies could be included to make comparisons and identify any trends of these indicators with the distribution of UGBS. Census data collected from surveys or questionnaires could be obtained as an alternative and used in future projects to incorporate opinions from the local inhabitants to better understand the perspective of the locals. The information obtained may help identify communities or groups that are more vulnerable where strategies can be designed to help them more efficiently in terms of environmental justice.
8.0 Conclusion
Cities from around the world are constantly expanding as more infrastructure and buildings are being built. Despite easier access to better health services, urban lifestyle has been negatively associated with health. Most health issues in urban areas are results of unhealthy lifestyle, stress and drug-use (Völker & Kistemann, 2013). Busy and stressful lifestyles, hectic environments and an unhealthy diet are what caused health conditions in urban areas to deteriorate. Cities around the world have begun to consider and integrate urban forest in city planning as awareness on health benefits of UGBS and impacts of climate change are increasing (Kanniah, K.D. Siong, 2017). Despite this the importance of UGBS is poorly understood in tropical countries and therefore, more research needs to be done (Nath et al., 2018). Environmental justice associated with access to UGBS is a complex topic and should be understood from multiple perspectives of different stakeholders. Geographical access alone is insufficient to capture the full impact of UGBS in environmental justice i.e. pollution is not captured. Ultimately, to solve environmental justice issues, it is important to understand the community structure and vulnerable groups.
Acknowledgments
Thanks to Teo Hoong Chen for his assistance with Google Earth Engine.
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