As a first-year MSc student at the University of Toronto Mississauga, I wrote a paper for a planning course where I integrated remote sensing data with planning documents to understand environmental change in the rapidly growing Town of Milton, Ontario. In this blog post, I will summarize the paper including my methods and findings. However, I will not touch on the integration with planning documents.

Climate change is creating a shift in climate patterns, caused by natural systems and human activity (Fawzy et al., 2020). The Earth has been warming for decades, suggesting that global temperatures will increase between 0.8 and 1.5 °C between 2030 and 2052 (Field et al., 2012; Masson-Delmotte et al., 2018). One of the contributions caused by human activity is the change in land use/land cover (LULC), as it results in lost agricultural land and forest area, leading to an increase in barren area and impermeable surfaces (Buyadi et al., 2013; Fall et al., 2010; Hussain et al., 2014; Kant et al., 2009; Kumar et al., 2012). Cities are losing greenery due to rapid urbanization from LULC change, contributing to the urban heat island (UHI) effect (Buyadi et al., 2013). The UHI effect explains the condition where urban areas are hotter than their surrounding rural areas, due to the increase of heat absorbing surfaces from human infrastructure (Buyadi et al., 2013; Guo et al., 2012; Weng, 2001; Xiao & Weng, 2007; Shahmohamadi et al., 2011). It’s important to analyze historic and current LULC to understand and manage the UHI effect (Abd El-Kawy et al., 2011; Buyadi et al., 2013; Das, 209; Pelorosso et al., 2009).

Land surface temperature (LST) data from remote sensing satellite imagery allows us to understand environmental change associated with land development and UHI effects (Kant et al., 2009; Anderson et al., 2008; Arnfield, 2003). Luckily, with long-term satellite data, we can detect, quantify, and monitor change (Chen et al., 2005; Lu et al., 2004). Many studies have determined that LST and LULC are strongly related to the UHI effect (Kant et al., 2009).

In this paper, I investigated remote sensing data to understand environmental change within the Town of Milton, Ontario, an area undergoing rapid landscape change. I wanted to discover if the relationship between LST and the Normalized Difference Vegetation Index (NDVI) can determine if a decrease in vegetation increases urban heat exposure.

North America has experienced extreme weather conditions such as rising temperatures, droughts, and wildfires, resulting in morbidity, mortality, respiratory health issues, and diseases (United Nations, n.d.). Drought is the main concern for agriculture because of the loss of moisture in the ground (Cook et al., 2018). The Province of Ontario will see 20-30% of plant species at high risk of extinction because of the increase in global temperatures (Office of Auditor General of Ontario, n.d.). However, Ontario has been implementing nature-based solutions to help mitigate climate change, such as the Ontario Greenbelt Plan (2005), aiming to protect 2 million acres of land from urban sprawl (Green Belt, n.d.).

The Town of Milton is a compact suburban community in the Greater Toronto Area (GTA), within which the Niagara Escarpment creates distinct urban and rural areas (Statistics Canada, n.d.; Town of Milton, 2008). This study investigates the correlation between temperature and vegetation to understand the possible risks of rapid urban expansion.

A True Colour Composite (TCC) image of Milton, Ontario, derived from Landsat 8.
Figure 1: A True Colour Composite (TCC) image of Milton, Ontario, derived from Landsat 8.

Remote sensing was used to extract LST and NDVI data for Milton using Google Earth Engine (GEE) obtained from USGS (Gorelick et al., 2017; USGS, n.d.). The images were obtained for the summer months in 10-year increments from 1989, 1999, 2009, and 2019, using Landsat 5-TM (1989, 1999, and 2009) and Landsat 8-OLI/TIRS (2019). The best and most available data from June 10th to August 25th were selected. The LST images were converted from Kelvin to Celsius in ArcGIS Pro. The NDVI images were calculated using the red and near-infrared bands, also in ArcGIS Pro. I also obtained historic ground-level meteorological data from Toronto Pearson Airport to validate the LST data (Government of Canada, n.d.).

Average Mean Temperature (°C)Average Maximum Temperature (°C)
Table 1: Average mean and average maximum temperature readings obtained from the Toronto Lester B. Pearson International Airport (1989, 1999, 2009) and Toronto International Airport (2019) weather stations from June 10th to August 25th.

Starting with the LST results, the maximum temperatures from all four years are between 38 and 54 °C, and the minimum temperatures are between 5 and 25 °C. Overall, the coolest temperatures are in the rural area (west) and the warmest temperatures are in the urban area (east). However, 1999 has hot areas in the rural area (west), suggesting a hot year. The weather station data corroborates this finding. 2009 looks like a cold year, as seen through the LST results and the weather station data. In 2019, the size of the hot area expands within the urban area (east).

Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from June 10th to August 25th in 1989, 1999, 2009, and 2019, in Milton, Ontario
Figure 2: Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from June 10th to August 25th in 1989, 1999, 2009, and 2019, in Milton, Ontario

The LST and weather station data show a similar pattern. Temperatures increased from 1989 to 1999, decreased in 2009, and increased in 2019. There is a 5 to 7 °C variation between the two datasets. It is important to note that the weather station data is from 30 kilometres away from the center of the Town of Milton.

Temperature readings from the LST and the Toronto Pearson weather station from 1989 to 2019.
Figure 3: Temperature readings from the LST and the Toronto Pearson weather station from 1989 to 2019.

Moving on to the NDVI results, the maximum NDVI for all four years is between 0.88 and 0.91, and the minimum is between -0.16 and 0.1. Overall, higher NDVI is found in the rural area (west) and lower NDVI in the urban area (east). We can see this trend in 1989 (Figure 2). Within each year, we can see a decrease in NDVI occurring in the urban area (east). However, we can see that the Niagara Escarpment has consistent dense vegetation (Figure 2).

200 random ground points were used to extract LST and NDVI to create linear regression models to investigate their relationship (Figure 4). Land type was determined by using unsupervised classification and was subsequently used for analysis. The linear regression models showed a significant negative relationship between LST and NDVI. From the overall results, for every 0.1 decrease in NDVI, LST increases about 1.17 °C. Visually, vegetation points see higher NDVI values and lower LST values, while non-vegetation points, such as urban, residential, and bare soil areas, see lower NDVI values and higher LST values.

The relationship between NDVI and LST for 1989, 1999, 2009 and 2019.
Figure 4: The relationship between NDVI and LST for 1989, 1999, 2009, and 2019.

Overall, the remote sensing results recognize a relationship between temperature and vegetation density. From 1989 to 2019, we saw a decrease in dense vegetation in the urban area (east). These results can be combined with known development information. Between 1996 and 2016, Milton built 23,700 housing units to accommodate its growing population (Malone Given Parsons Ltd., 2017). The Greenbelt Plan (2005) protects the Niagara Escarpment and its agricultural areas (Green Belt, n.d.). Using the maps (Figure 2), we can see a visual relationship between LST and NDVI as areas with high temperatures are associated with low NDVI, and vice versa.

This study used remote sensing data to understand how urban expansion is linked to increasing temperatures. I hope this information leads to a discussion on how to implement green mitigation strategies in urban areas to mitigate the UHI effect.

– Scarlett Rakowska


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