Using Satellite-Borne Remote Sensing Data in Generating Local Warming Maps with Enhanced Resolution

Abstract

Warming, i.e., increments of temperature, is evident at the global, regional, and local level. However, understanding the dynamics of local warming at high spatial resolution remains challenging. In fact, it is very common to see extremely variable land cover/land use within built-up environments that create micro-climatic conditions. To address this issue, our overall goal was to generate a local warming map for the period 1961–2010 at 15 m spatial resolution over the southern part of the Canadian province of Alberta. Our proposed methods consisted of three distinct steps. These were the: (i) construction of high spatial resolution enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) maps; (ii) conversion of air temperature (Ta) normal (i.e., 30 years average) at higher spatial resolution using vegetation indices (VI); and (iii) generation of a local warming map at 15m spatial resolution. In order to execute this study, we employed MODIS-driven air temperature data, EVI and NDVI data, and Landsat-driven vegetation indices. The study uncovered that around 58% (up to positive 1 °C) of areas in the considered study region were experiencing increased temperature; whereas only about 4% of areas underwent a cooling trend (more than negative 0.25 °C). The remaining 38% did not exhibit significant change in temperature. We concluded that remote sensing technology could be useful to enhance the spatial resolution of local warming maps, which would be useful for decision-makers considering efficient decisions in the face of increments in local temperature.

Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region

“…The Markov chain is a stochastic-based model and commonly used for short term projection of a large area [21][22][23]. The CA model is a model with set transition rules and work on the principle of a neighborhood where the current state of each cell depends on the previous one and its functionality pattern is similar to LULC [24][25][26]. The validation of the CA model is done through Kappa coefficient by comparing observed and simulated LULC map [27].…”

“…Many studies indicated the relationship between LULC and LST [25,[31][32][33][34]. Some researchers studied long-term and seasonal changes in temperature in relation to urban growth [35,36].…”

Consequences of covid-19: urban thermal variation and social repercussion

“…These changes in urban surfaces affect the thermal variation on a fine spatial scale (Rahaman et al, 2018), therefore, making it difficult to map by the conventional method, using climatological stations. One of the problems that can be accentuated by this difficulty of monitoring, refers to the vulnerability of certain social groups to extreme heat fluctuations, as is the case of people over the age of 65 years.…”

“…It is extremely important to obtain empirical evidence for predicting temperature changes at the local scale, especially regarding situations susceptible to natural disasters and the management of natural resources (Rahaman et al, 2018).…”

“…Over time, this can be translated to significant increases in micro and mesoscale temperatures compared to rural areas in the same region (Golden, 2004). Changes in land cover pattern interferes with the temperature on a local scale (Rahaman et al, 2018), as the characteristics of each surface affects energetic flows and balance, which ultimately results in alterations in the urban climate (Ferreira & Andrade, 2021). This happens because of factors such as mobility, consisting in the locomotion of people among spaces, and the radiation emitted or reflected by the diverse materials and surfaces that compose urban landscapes (Neves et al, 2018).…”