PROJECT 4 : LAND SURFACE TEMPERATURE ANALYSIS IN CAMERON HIGHLANDS
LAND SURFACE TEMPERATURE ANALYSIS USING QGIS
Traditionally, surface temperature has been measured by weather station in many parts of the globe. It has been long running ever since the weather monitoring station have been set up and known to provide accurate and sufficient data in temporal scale. Nevertheless, there are many locations that have very few weather monitoring stations. This led to gaps in surface temperature datasets and disrupts our understanding on how surface temperatures are changing over the years and the impacts of extreme events in spatial scale. Furthermore, LST dataset does depicts the observe air temperature (Shi et al., 2016), a good indicator of urban heat island, and provides wide coverage with good resolution (Pepin et al.,
2016). Therefore, land surface temperature can be an alternative way to obtained temperature conditions. It can be defined as the skin temperature of the land surface and is measured by the satellite.
HOW ?
Satellite carrying thermal infrared sensors capable in providing high spatial and temporal resolution of temperature datasets. Figure 1 illustrates the mechanism of remote sensing on obtaining land surface temperature. The sensors will detect the electromagnetic radiation (EMR) emit by the Earth’s
surface. The EMR received can be quantified in the form of measurements of Top of Atmosphere (TOA) radiances. The inverse of Planck’s Law is used to derive brightness temperatures from TOA radiances and converted to LST by correcting three main effects: angular effects, atmospheric attenuation and spectral emissivity values at the surface. There is a number of different satellite
remote sensing platforms with multiple sensors in the TIR spectrum.
DATA SETS OF LAND SURFACE TEMPERATURE (LST)
Datasets are available for different time periods, at different resolutions with varying accuracy. The most popular sensors that is used in measuring land surface temperature is Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat (ETM+, OLI), Advanced Very High-Resolution Radiometer (AVHRR) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). MODIS is the most widely used source for air temperature estimation compared
to other remote sensing satellite because of its availability and resolution (Phan et al., 2018). MODIS was launched in 1999 and consists of two onboard satellites which are Terra and Aqua. The instruments capture image in 36 spectral bands ranging from 0.405 μm until 14.385 μm. The products were taken in both daytime (Terra: 10.30am and Aqua: 1.30pm) and night-time (Terra: 10.30pm and 1.30am).
Various temporal and spatial resolutions of LST help in wide range of field such as air temperature estimation, urban heat island and drought vulnerability assessment.
The LST value can be extracted through pixel-based calculation. The value of each pixel will be calculated by using equation:
I was assign to identify the Land Surface Temperature of Cameron Highlands and analyze the changes between given temporals which throughout the year 2006 and 2022 day and night. The datasets of land surface temperature that used to perform this analysis was retrieved from MODIS which widely provided by NASA. The LST map which results from my analysis is below:
The data was then converted into graph and table which as shown below:
The land surface temperature clearly differs from the year 2006 to 2022. Based on the provided table comparing land surface temperature values (in °C) between the years 2006 and 2022 at Cameron Highlands, we can analyze the differences in the minimum, maximum, range, and average temperatures for both day and night. In 2006, the minimum temperature during the day was 20.74°C, whereas in 2022, it was 14.95°C. Therefore, the minimum daytime temperature was higher in 2006. For nighttime temperatures, the minimum temperature was 11.04°C in 2006, compared to 8.48°C in 2022. Once again, the minimum nighttime temperature was higher in 2006. In 2006, the maximum daytime temperature reached 31.55°C, while in 2022, it was slightly lower at 29.37°C. Thus, the maximum daytime temperature was higher in 2006. For nighttime temperatures, the maximum temperature was 20.42°C in 2006, and 20.65°C in 2022, with a negligible difference. Therefore, there was not much variation in the maximum nighttime temperature between the two years. The temperature range refers to the difference between the maximum and minimum temperatures. In 2006, the temperature range during the day was 10.81°C, which decreased to 14.42°C in 2022. Thus, the temperature range was higher in 2022. For nighttime temperatures, the range was 9.38°C in 2006, and it increased to 12.17°C in 2022. Therefore, the temperature range was higher in 2022. The average temperature is calculated by summing the temperatures and dividing by the number of data points. In 2006, the average daytime temperature was 25.58°C, while in 2022, it decreased to 24.02°C. Hence, the average daytime temperature was higher in 2006. Similarly, the average nighttime temperature was 15.91°C in 2006, which decreased to 14.93°C in 2022. Thus, the average nighttime temperature was higher in 2006. Based on the provided data, it appears that, on average, land surface temperatures were higher in 2006 compared to 2022 for both daytime and nighttime.
Based on the comparison of land surface temperature values between 2006 and 2022, there can be several possible reasons for the observed differences
Natural climate variability can cause fluctuations in land surface temperature from year to year. In my perception, Factors such as variations in atmospheric conditions, weather patterns, and regional climate systems can influence temperature differences between different years. Climate change is a gradual shift in long-term weather patterns and can contribute to changes in land surface temperature over time. While the data provided is for a relatively short period, it's possible that broader climate trends influenced the temperature differences between 2006 and 2022. Local factors specific to the Cameron Highlands region may have played a role. These could include changes in vegetation cover, alterations in land management practices, or variations in cloud cover and precipitation patterns. Such factors can affect the local microclimate and, subsequently, land surface temperature. There is always the possibility of measurement or data variability, including differences in data collection methods, equipment, or station locations. It's important to ensure data consistency and accuracy when comparing temperature values between different years. Other than that, Covid-19 might have played role in this reduction of LST in Cameron Highlands. During lockdowns and travel restrictions, there was a significant reduction in transportation, industrial activities, and energy consumption. This resulted in lower emissions of greenhouse gases (GHGs) and pollutants, such as nitrogen dioxide (NO2) and particulate matter (PM), leading to improved air quality and reduction in trapped heat in the atmosphere. The temporary halt in human activities allowed some wildlife to reclaim habitats and reduced disturbances to natural ecosystems. There were reports of increased sightings of wildlife in urban areas and improved conditions for certain species which might have increased the vegetation cover of lands in Cameron Highlands that results in reduction in land surface temperature.
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