scholarly journals Glacier Dynamics in Changme Khangpu Basin, Sikkim Himalaya, India, between 1975 and 2016

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 259 ◽  
Author(s):  
Manasi Debnath ◽  
Milap Chand Sharma ◽  
Hiambok Jones Syiemlieh
Keyword(s):  
Land Surface ◽  
Average Rate ◽  
Late Summer ◽  
Recent Decade ◽  
Landsat 8 ◽  
Lower Altitude ◽  
Debris Cover ◽  
Summer Temperatures ◽  
Composite Maps ◽  
Sentinel 2A

This study provides a high resolution glacier database in the Changme Khangpu Basin (CKB) using LANDSAT 8 (2014) and Sentinel-2A image (2016), mapping of 81 glaciers that cover a 75.78 ± 1.54 km2 area. Composite maps of land surface temperature, slope and Normalized differential Snow Index have been successfully utilized in delineating near accurate debris cover boundary of glaciers. The cumulative controlling parameters of aspect, elevation, slope, and debris cover have been assessed to evaluate the nature of glacier distribution and dynamics. The local topographic settings seem to have significantly determined the glacier distribution in the CKB. Almost 20% area erstwhile under glacier cover has been lost since 1975 at an average rate of −0.453 ± 0.001 km2a−1. The recent decade (2001–2016) has witnessed a higher rate of area shrinkage (−0.665 ± 0.243 km2a−1), compared to a relatively lower rate of recession (−0.170 ± 0.536 km2a−1) between 1988 and 2001. The lower rates of glacial recession can most likely be induced regionally due to relatively cooler decadal late summer temperatures and peak in the monsoon spell. Glaciers with western and north-western aspects showed more vulnerability to area loss than the rest of the aspects. Lower altitude glaciers have receded faster than ones perched up on higher elevations. The rate of glacier area recession has been nearly twice that on clean glaciers as compared to debris-covered glaciers in the CKB.

scholarly journals Assessment of the Hail Damage on Agricultural Crops from North of Baragan Plain, Romania, Using Remote Sensing Data

2021 ◽  
Author(s):  
Claudiu Valeriu Angearu ◽  
Irina Ontel ◽  
Anisoara Irimescu ◽  
Burcea Sorin
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Weather Radar ◽  
Radar Data ◽  
Landsat 8 ◽  
Water Index ◽  
Hail Damage ◽  
Sentinel 2A

Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.

scholarly journals Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model

2021 ◽  
Vol 13 (6) ◽  
pp. 1186
Author(s):  
Saiping Xu ◽  
Qianjun Zhao ◽  
Kai Yin ◽  
Guojin He ◽  
Zhaoming Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Landsat 8 ◽  
Extreme Gradient Boosting ◽  
Sentinel 2A ◽  
Spatial Nonstationarity

Land surface temperature (LST) is a critical parameter of surface energy fluxes and has become the focus of numerous studies. LST downscaling is an effective technique for supplementing the limitations of the coarse-resolution LST data. However, the relationship between LST and other land surface parameters tends to be nonlinear and spatially nonstationary, due to spatial heterogeneity. Nonlinearity and spatial nonstationarity have not been considered simultaneously in previous studies. To address this issue, we propose a multi-factor geographically weighted machine learning (MFGWML) algorithm. MFGWML utilizes three excellent machine learning (ML) algorithms, namely extreme gradient boosting (XGBoost), multivariate adaptive regression splines (MARS), and Bayesian ridge regression (BRR), as base learners to capture the nonlinear relationships. MFGWML uses geographically weighted regression (GWR), which allows for spatial nonstationarity, to fuse the three base learners’ predictions. This paper downscales the 30 m LST data retrieved from Landsat 8 images to 10 m LST data mainly based on Sentinel-2A images. The results show that MFGWML outperforms two classic algorithms, namely thermal image sharpening (TsHARP) and the high-resolution urban thermal sharpener (HUTS). We conclude that MFGWML combines the advantages of multiple regression, ML, and GWR, to capture the local heterogeneity and obtain reliable and robust downscaled LST data.

scholarly journals Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale

2020 ◽  
Vol 12 (15) ◽  
pp. 2508 ◽  
Author(s):  
Ifeanyi R. Ejiagha ◽  
M. Razu Ahmed ◽  
Quazi K. Hassan ◽  
Ashraf Dewan ◽  
Anil Gupta ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Landscape ◽  
Single Channel ◽  
Late Summer ◽  
Early Spring ◽  
Landsat 8 ◽  
Urban Neighbourhoods ◽  
Structural Configurations

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.

Combined Use of Landsat 8 and Sentinel 2A Images for Enhanced Spatial Resolution of Land Surface Temperature

2021 ◽  
pp. 45-63
Author(s):  
Le Hung Trinh ◽  
V.R. Zablotskii
Keyword(s):  
Thermal Radiation ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Forest Fires ◽  
Land Surface ◽  
Extreme Values ◽  
Underlying Surface ◽  
Landsat 8 ◽  
Small Areas ◽  
Sentinel 2A

Underlying surface temperature is an important parameter of underlying surface thermal radiation and can be used in monitoring forest fires, coal fires, urban thermal radiation and developing climate models. Ground-based observations provide temperature information for small areas around weather stations and in fact cannot provide a high density of surface temperature data. Remote sensing technologies are promising in this respect. However, due to the low spatial resolution in the infrared channel, the surface temperature calculated from Landsat and Aster images does not always have the required detail needed when studying small areas. The results of images from Sentinel 2A and Landsat 8 satellites combination (joint digital processing) made in order to increase spatial resolution of underlying surface temperature are presented. Comparison of surface temperature extreme values shows that in spite of small difference in extreme values of temperature, the spatial field of temperature in case of combined images was more detailed and variable. This is evidenced by a significant increase in the variability of the temperature standard deviation. Direct visual observations of image fragments also confirm that combining Sentinel 2A and Landsat 8 images increases the spatial resolution of the surface temperature when compared to the Landsat 8 image

scholarly journals Contextualizing Thermal Effluent Impacts in Narragansett Bay Using Landsat-Derived Surface Temperature

2021 ◽  
Vol 8 ◽  
Author(s):  
Jonathan Benoit ◽  
Baylor Fox-Kemper
Keyword(s):  
Surface Temperature ◽  
Average Rate ◽  
Late Summer ◽  
Surface Fluxes ◽  
Narragansett Bay ◽  
Landsat 8 ◽  
River Input ◽  
Thermal Effluent ◽  
Before And After ◽  
Hope Bay

This work utilizes remotely sensed thermal data to understand how the release of thermal pollution from the Brayton Point Power Station (BPPS) affected the temperature behavior of Narragansett Bay. Building upon previous work with Landsat 5, a multi-satellite analysis is conducted that incorporates 582 scenes from Landsat 5, Landsat 7, and Landsat 8 over 1984–2021 to explain seasonal variability in effluent impacts, contrast data after the effluent ceased in 2011, identify patterns in temperature before and after effluent ceased using unsupervised learning, and track how recent warming trends compare to the BPPS impact. Stopping the thermal effluent corresponds to an immediate cooling of 0.26 ± 0.1°C in the surface temperature of Mt. Hope Bay with respect to the rest of Narragansett Bay with greater cooling of 0.62 ± 0.2°C found near Brayton Point; though, cooling since the period of maximal impact (1993–2000) totals 0.53 ± 0.2°C in Mt. Hope Bay and 1.04 ± 0.2°C at Brayton Point. During seasons with lower solar radiation (winter) and lower mean river input (autumn and late summer), the BPPS effluent impact is more prominent. The seasonal differences between the high impact and low impact periods indicate that river input played an important role in the heat balance when emissions were lower, but surface fluxes dominated when emissions were higher. Putting the BPPS effluent in context, Landsat data indicates that Narragansett Bay warmed 0.5–1.2°C over the period of measurement at an average rate of 0.23 ± 0.1°C/decade and that net warming in Mt. Hope Bay is near zero. This trend implies that Narragansett Bay has experienced climatic warming over the past four decades on the scale of the temperature anomaly in Mt. Hope Bay caused by the BBPS effluent.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

scholarly journals An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2730
Author(s):  
Animesh Chandra Das ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
Land Surface ◽  
Climatic Factors ◽  
Standardized Precipitation Index ◽  
Landsat 8 ◽  
Drought Frequency ◽  
Moisture Index ◽  
Area Index ◽  
Drought Period

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

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