Remote Sensing of Local Warming Trend in Alberta, Canada during 2001–2020, and Its Relationship with Large-Scale Atmospheric Circulations

Abstract

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months.

Using satellite imagery to assess the changes in land use and land cover in Diyarbakır city (SE Turkey)

“…While forest areas increase in the Arctic and alpine terrain, dry and semi-arid regions see a decline in vegetation. Furthermore, it has the potential to create local extreme temperature variations (Hassan et al, 2021), resulting in frequent and severe floods (Rahman et al, 2021). Additionally, LULC and agricultural activities (e.g., crops and livestock) are significant sources of greenhouse gas emissions that must be regulated under the Paris Agreement (Kalnay and Cai, 2003;Hong et al, 2021).…”

“…They are impacting the Earth's energy balance, as well as the climate, hazards, and ecosystems both locally and globally (Reis, 2008;Song et al, 2008;Altürk and Konukçu, 2020;Hong et al, 2021;Yohannes et al, 2021;Yohannes et al, 2021). For efficient environmental protection, sustainable development, and the supply of necessary information to decision makers, time-series trend analysis and continuous monitoring of LUCL and future mitigation are needed (Alqurashi and Kumar, 2013;Baynard, 2013;Abd El Hamid, 2020;Fawad et al, 2021;Hassan et al, 2021).…”

“…Land use and Land cover (LULC) changes have the potential to change the Earth energy balance (Song et al, 2018), to change hydrological ecosystem (Yohannes et al 2011), to fragment and transform the ecosystem (Hong et al, 2011), and to trigger the natural hazards such as flooding, landslides, rock fall and subsidence (Rahman et al, 2011;Mossa and James, 2013;Gill and Malamud, 2017). Land use and land cover changes and some other natural factors rain fall initially responsible for the changes in local temperature, water balance, greenhouse emission (Kalnay and Cai, 2003;Hassan et al, 2021;Tuladhar et al, 2019;Dewan et al, 2021). Those local changes have formed the global effect (Kalnay and Cai, 2003;Reis, 2008;Song et al, 2018;Altürk and Konukçu, 2020;Hong et al, 2021).…”

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

“…In contrast to traditional slope analysis, Sen’s slope is not disturbed by a few abnormal values and is widely used for the trend analysis of time series [ 52 , 53 ]. The basic principle is as follows: where i and j are the number of time series; and are the LST values of the i -th and j -th time series, respectively; and the slope Q > 0 indicates a warming trend, while Q < 0 indicates a cooling trend.…”

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

“…Unquestionably, water resources are vital for the survival of humans and other creatures and contribute substantially to the development of various agricultural, industrial, recreational, and environmental activities worldwide [1]. Population growth, urbanization, and industrialization have increased water demand, requiring effective management and monitoring of water resources to ensure food security, especially in countries with arid or semi-arid climates such as Iran [2,3]. There have been many problems in different regions of Iran in the last few years, mainly in the central and southern provinces, due to the decrease in the average annual precipitation, mismanagement, and misuse of water resources [4,5].…”