International Journal of Applied Earth Observations and Geoinformation (Feb 2021)
A new Global Navigation Satellite System (GNSS) based method for urban heat island intensity monitoring
Abstract
The Urban Heat Island (UHI) effect occurs when an urban area experiences higher temperatures than its rural surrounding because of heat being absorbed by built structures and heat being released by anthropogenic sources. UHIs can cause adverse effects to human health and increase energy consumption used for cooling buildings. Therefore, it is important to monitor accurately the UHI effect. The intensity of UHIs are usually monitored using satellite imagery, airborne sensors, and surface temperature sensors. Satellite imagery can cover a large area but requires a clear sky to obtain good images. Moreover, airborne sensors are expensive and also require a clear sky to obtain good data. A large network of surface temperature sensors is required to monitor the UHI of an entire region, which can also be expensive. In this paper, we present a three-step algorithm to monitor UHI intensity using data from Global Navigation Satellite Systems (GNSS). The advantages of using GNSS data to monitor the UHI effect are the increased availability of observation data, high temporal resolution and high geographical resolution. The first step of the algorithm is the calculation of a priori environmental parameters (i.e., water vapour partial pressure, troposphere height, surface pressure, and the vertical profile of refractivity) from radiosonde data. The second step is the calculation of temperature from GNSS data. The last step is the UHI intensity computation. The algorithm presented in this paper has been tested and validated using publicly available GNSS and meteorological data from Los Angeles, California, USA. The validation of the algorithm is done by comparing the UHI intensity estimated from the algorithm with temperature data obtained from weather stations. In the validation, the proposed algorithm can achieve an accuracy of 1.71 °C at 95 % confidence level.
