Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

Abstract

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions.

Impact of Urban Land-Cover Changes on the Spatial-Temporal Land Surface Temperature in a Tropical City of Mexico

“…In the case of Mérida, the difference in LST between urban areas and the peripheral areas with vegetation (scattered or dense) is on average 3.5 °C at the beginning and end of the study season, and is maintained in both seasons, similar to that observed by Adeyeri et al [78], Ferreira and Duarte [71], and Dang et al [79], among other authors.…”

“…Likewise, specific sites with extreme temperatures were identified, associated with the cement industry, the sanitary landfill, fires, and burned areas as part of agricultural activities, in the same way it was observed that, in recent years, the temperature between these hot spots and the rest of city is increasingly similar, which shows the occurrence of the UHI. In big cities where the UHI phenomenon has been recorded, the heat island is concentrated in the center of the cities [22,24,78,79,81]. However, for Mérida's urban zone, the heat island is not located in the center, but in the periphery, where the new developments are located, as pointed out by Pérez-Medina and López-Falfán [68].…”

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

“…It is observed that LST changes could be abnormal in some areas [7], resulting into droughts, increased frequency of fires, but simultaneously more rainfall and severe storms. LST and other parameters such as the normalized difference vegetation index (NDVI) or bare soil index (BSI) have been mostly studied in relation to urbanization [8,9]. Several studies have been conducted to analyze how changes in LST affect the variability of the microclimate of an area, the phenological cycle of vegetation [5,6], the agricultural production [10], and the land cover changes [11] using time series analysis of data.…”

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

“…LST is a very useful variable, which found its application in many different fields of science, including hydrology, climatology, geophysics, and specifically, in the assessment of the surface urban heat island [16,17]. LST is also recognized as complementary to nearsurface air and spatial temperature data in nature, thus helping to achieve the sustainable development of climate action goals [18,19]. One of the characteristics of the LST is that it shows very high spatial and temporal variation, mostly because of the heterogeneity of factors influencing its value, such as characteristics of the vegetation cover, precipitation, soil moisture, area topography, and geology [20][21][22][23].…”