scholarly journals Comparison of Land Surface Temperature During and Before the Emergence of Covid-19 using Modis Imagery in Wuhan City, China

2020 ◽  
Vol 34 (1) ◽  
Author(s):  
Hamim Zaky Hadibasyir ◽  
Seftiawan Samsu Rijal ◽  
Dewi Ratna Sari
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Anthropogenic Activities ◽  
Temporal Analysis ◽  
The Past ◽  
Modis Imagery ◽  
Rapid Transmission ◽  
The Mean ◽  
Spatio Temporal

Coronavirus disease (COVID-19) was firstly identified in Wuhan, China. By 23rd January 2020, China’s Government made a decision to execute lockdown policy in Wuhan due to the rapid transmission of COVID-19. It is essential to investigate the land surface temperature (LST) dynamics due to changes in level of anthropogenic activities. Therefore, this study aims (1) to investigate mean LST differences between during, i.e., December 2019 to early March 2020, and before the emergence of COVID-19 in Wuhan; (2) to conduct spatio-temporal analysis of mean LST with regards to lockdown policy; and (3) to examine mean LST differences for each land cover type. MODIS data consist of MOD11A2 and MCD12Q1 were employed. The results showed that during the emergence of COVID-19 with lockdown policy applied, the mean LST was lower than the mean LST of the past three years on the same dates. Whereas, during the emergence of COVID-19 without lockdown policy applied, the mean LST was relatively higher than the mean LST of the past three years. In addition, the mean LST of built-up areas experienced the most significant differences between during the emergence of COVID-19 with lockdown policy applied in comparison to the average of the past three years.

scholarly journals SPATIO-TEMPORAL ANALYSIS OF LAND USE DYNAMICS AND ITS POTENTIAL IMPLICATIONS ON LAND SURFACE TEMPERATURE IN LAHORE DISTRICT, PUNJAB, PAKISTAN

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 359-367
Author(s):  
M. Sohail ◽  
S. S. F. Ali ◽  
E. Fatima ◽  
D. A. Nawaz
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Kappa Statistics ◽  
Spatio Temporal

Abstract. The rapid population growth and the urge in people to move to big cities for their settlement upshot in urban expansion. While stepping into the corridor of the 21st century, the utility of remote sensing and GIS techniques in various fields has made things understandable and thus enhances the ways of investigation for better decision making and management. The paper presents the Landsat Satellite series based Land Surface Temperature retrieval concerning land use/ land cover changes over Lahore District, Punjab, Pakistan. The Spatio-temporal analysis was performed from 1980–2020. We availed high-resolution Landsat and Sentinel-2 Satellite imagery to perform Normalized Difference Vegetation Index and Supervised classification. Cloud-free satellite data was acquired from June, July, or August. Data pre-processing including atmospheric and terrain corrections were performed using ERDAS Imagine. The Red, NIR, and Thermal bands were utilized for LST estimation. ArcGIS 10.22 was used for making maps, analysis, and interpretations. The Spatio-temporal analysis of LULC and LST for the area indicates a great urbanization trend over the past forty years. People are migrating from small towns and villages to the metropolitan city of Pakistan for their livelihoods, and settlements. The built-up/urban land has expanded over the period with excessive construction that has affected the Land surface temperature. The area where human activity has increased shows higher LST’s as compared to green lands. The excessive construction has taken off the agricultural land, while the River Ravi still flows with a changing course and less water table. The COVID-19 pandemic hit in 2020 put everything on lockdown had an impact on environmental restoration due to fewer emissions and human activities. The overall classification accuracy of the images yielded substantial-high Kappa statistics of 80 %, 88%, 82%, 82.41%, and 87.76% for 1980, 1990, 2000, 2010, and the 2020 images, respectively. The unplanned urbanization is leading the Lahore District to serious environmental issues and climate change impacts. The need of the hour is to properly plan and manage the area for the coming generations to have a healthier and sustainable place to breathe in.

Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature

2017 ◽  
Vol 193 ◽  
pp. 225-243 ◽  
Author(s):  
C. Berger ◽  
J. Rosentreter ◽  
M. Voltersen ◽  
C. Baumgart ◽  
C. Schmullius ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Analysis ◽  
Urban Site ◽  
Site Characteristics ◽  
Spatio Temporal ◽  
The Relationship

scholarly journals A Two-Step Integrated MLP-GTWR Method to Estimate 1 km Land Surface Temperature with Complete Spatial Coverage in Humid, Cloudy Regions

2021 ◽  
Vol 13 (5) ◽  
pp. 971
Author(s):  
Zhen Gao ◽  
Ying Hou ◽  
Benjamin F. Zaitchik ◽  
Yongzhe Chen ◽  
Weiping Chen
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
South China ◽  
Land Surface ◽  
Absolute Error ◽  
Temporal Analysis ◽  
Integrated Method ◽  
Spatial Coverage ◽  
Spatio Temporal

There is an increasing demand for a land surface temperature (LST) dataset with both fine spatial and temporal resolutions due to the key role of LST in the Earth’s land–atmosphere system. Currently, the technique most commonly used to meet the demand is thermal infrared (TIR) remote sensing. However, cloud contamination interferes with TIR transmission through the atmosphere, limiting the potential of space-borne TIR sensors to provide the LST with complete spatio-temporal coverage. To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model. Finally, the 1-km all-weather LST for cloudy pixels was fused with Aqua MODIS clear-sky LST via bias correction. This method was applied to produce the all-weather LST products for both daytime and nighttime during the years 2013–2018 in South China. The evaluations showed that the accuracy of the reproduced LST on cloudy days was comparable to that of the MODIS LST in terms of mean absolute error (2.29–2.65 K), root mean square error (2.92–3.25 K), and coefficients of determination (0.82–0.92) against the in situ measurements at four flux stations and ten automatic meteorological stations with various land cover types. The spatial and temporal analysis showed that the MLP-GTWR LST were highly consistent with the MODIS, in situ, and ERA5-Land LST, with the satisfactory ability to present the LST pattern under cloudy conditions. In addition, the MLP-GTWR method outperformed a gap-filling method and another TIR-PMW integrated method due to the local strategy in MLP and the consideration of temporal non-stationarity relationship in GTWR. Therefore, the test of the developed method in the frequently cloudy South China indicates the efficient potential for further application to other humid regions to generate the LST under cloudy condition.

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