scholarly journals Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018

2020 ◽  
Vol 8 (2) ◽  
pp. 15-21
Author(s):  
Azad Rasul ◽  
Luqman W. Omar

Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M >6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected.

2021 ◽  
Author(s):  
Getachew Bayable ◽  
Getnet Alemu

Abstract The aggravating deforestation, industrialization and urbanization are increasingly becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal discrepancies in Land Surface Temperature (LST) and the link with elevation in Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001–2020) was used. The pixel-based linear regression model was employed to explore the spatiotemporal discrepancies of LST changes pixel-wise. Furthermore, Sen's slope and Mann-Kendall were used for determining the extent of temporal shifts of the areal average LST and evaluating trends in areal average LST values, respectively. Coefficient of Variation (CV) was calculated to examine spatial and temporal discrepancies in seasonal and annual LST for each pixel. The distribution of average seasonal LST spatially ranged from 43.45–16.62℃, 39.89–14.59℃, 50.39-21.102℃ and 43.164–20.39℃ for autumn (September-November), summer (June-August), spring (March-May) and winter (December-February) seasons, respectively. The seasonal LST CV varied from1.096-10.72%, 0.7–11.06%, 1.29–14.76% and 2.19–10.35% for average autumn, summer, spring and winter seasons, respectively. The seasonal spatial LST trend varied from − 0.7 − 0.16, -0.4-0.224, 0.6 − 0.19 and − 0.6 − 0.32 for average autumn, summer, spring and winter seasons, respectively. Besides, the annual spatial LST slope varied from − 0.58 − 0.17. An insignificantly declining trend in LST shown at 0.036℃ yr− 1, 0.041℃ yr− 1, 0.074℃ yr− 1, 0.005℃ yr− 1 in autumn, summer, spring and winter seasons (P < 0.05), respectively. Moreover, the annual variations of mean LST decreased insignificantly at 0.046℃ yr− 1. Generally, the LST is tremendously variable in space and time and negatively correlated with an elevation.


2021 ◽  
Author(s):  
Munawar Munawar ◽  
Tofan Agung Eka Prasetya ◽  
Rhysa McNeil ◽  
Rohana Jani

Abstract Increased temperature is one of the signals of global warming. Trends in land surface temperature can be used to measure climate change. This research aimed to investigate the variation of land surface temperature in Borneo island using a cubic spline method and a multivariate regression model. The island was divided into 8 regions each comprising 9 subregions. Land surface temperatures for each subregion from 2000 to 2019 was obtained from the National Aeronautics and Space Administration moderate resolution imaging spectroradiometer database. The average increase in temperature was 0.2oC per decade with a 95% confidence interval of (0.14, 0.27). The changes differed by region; a significant increase was seen in Sarawak, North Kalimantan, West Kalimantan, West-central Kalimantan, Central-east Kalimantan region, a slight decrease in Sabah and Brunei Darussalam (Sabah & Brunei) region, a slight increase in East Kalimantan and a stable trend in South Kalimantan.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Chunlei Meng ◽  
Huoqing Li

AbstractFengyun-4A is the new generation of Chinese geostationary meteorological satellites. Land surface albedo, land surface emissivity and land surface temperature are key states for land surface modelling. In this paper, the land surface albedo, land surface emissivity and land surface temperature data from Fengyun-4A were assimilated into the Integrated Urban land Model. The Fengyun-4A data are one of the data sources for the land data assimilation system which devoted to produce the high spatial and temporal resolution, multiple parameters near real-time land data sets. The Moderate-Resolution Imaging Spectroradiometer (MODIS) LSA and LSE data, the Institute of Atmospheric Physics, China Academy of Sciences (IAP) 325 m tower observation data and the observed 5 cm and 10 cm soil temperature data in more than 100 sites are used for validation. The results indicate the MODIS land surface albedo is much smaller than the Fengyun-4A and is superior to the Fengyun-4A for the Institute of Atmospheric Physics, China Academy of Sciences 325 m tower site. The Moderate-Resolution Imaging Spectroradiometer land surface emissivity is smaller than the Fengyun-4A in barren land surface and the differences is relatively small for other land use and land cover categories. In most regions of the research area, the Fengyun-4A land surface albedo and land surface emissivity are larger than those of the simulations. After the land surface albedo assimilation, in most regions the simulated net radiation was decreased. After the land surface emissivity assimilation, in most regions the simulated net radiation was increased. After the land surface temperature assimilation, the biases of the land surface temperature were decreased apparently; the biases of the daily average 5 cm and 10 cm soil temperature were decreased.


2020 ◽  
Vol 52 (2) ◽  
pp. 239
Author(s):  
Tofan Agung Eka Prasetya ◽  
Munawar Munawar ◽  
Muhammad Rifki Taufik ◽  
Sarawuth Chesoh ◽  
Apiradee Lim ◽  
...  

Land Surface Temperature (LST) assessment can explain temperature variation, which may be influenced by factors such as elevation, land cover, and the normalized difference vegetation index (NDVI). In this study, a multiple linear regression model of LST variation was constructed based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite, relating to the period, 2000-2018. The highest LST variation of nearly 1.3 °C/decade was found in savanna areas while the lowest variation was in the evergreen broadleaf forest and woody savanna, which experienced a decrease of 2.1 °C/decade. The overall mean change of LST was -0.4 °C/decade and the regression model with LST as the dependent variable and elevation, land cover type, and NVDI as independent variables produced an R square of 0.376. The variation in LST was different depending upon the NDVI.


2019 ◽  
Vol 2 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Nayan Zagade ◽  
Ajaykumar Kadam ◽  
Bhavana Umrikar ◽  
Bhagyashri Maggirwar

Drought assessment for agricultural sector is vital in order to deal with the water scarcity in Ahmednagar and Pune districts, particularly in sub-watersheds of upper catchment of the River Bhima. Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite data (2000, 2002, 2009, 2014, 2015 and 2017) for the years receiving less rainfall have been procured and various indices were computed to understand the intensity of agricultural droughts in the area. Vegetation health index (VHI) is computed on the basis of vegetation moisture, vegetation condition and land surface temperature condition. Most of the reviewed area shows moderate to extreme drought conditions.


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