Online Global Land Surface Temperature Estimation from Landsat

Parastatidis, David; Mitraka, Zina; Chrysoulakis, Nektarios; Abrams, Michael J.

doi:10.3390/rs9121208

Cited by 156 publications

(84 citation statements)

References 43 publications

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“…Imja Lake might significantly exceed the 4 • threshold in some September and early October measurements, but for the most part it also remains near or below 4 • C. Satellite-based observations were sparse during the summer monsoon due to the presence of cloud cover but generally show a plateau or depression of temperatures during the monsoon (Figure 4B). Lake temperature comparisons between ASTER and Landsat thermal data had an RMSE (mean difference) of 1.1 • C (Figure 4D), which is comparable to the RMSE of 1.52 • C (between ASTER and Landsat sensors) found by Parastatidis et al (2017) for land surfaces. ASTER might be more accurate as temperature is derived from five bands, instead of two for Landsat 8, but Landsat 8 gives a much better frequency of measurements.…”

Section: Satellite Surface Temperature Measurementssupporting

confidence: 69%

“…To calculate the mean lake temperature for each image, we applied an internal buffer of 150 m to the lake boundaries and used the remaining pixels in the lake interior to derive the lake temperature, excluding any pixels containing icebergs. We also used the thermal infrared bands of Landsat 5, 7 and 8, which generally had a slightly earlier acquisition time compared to ASTER (∼30 min earlier), to derive lake temperatures 2000-2018 using the Google Earth Engine (GEE)based Landsat Land Surface Temperature tool (Parastatidis et al, 2017). The tool used a single channel algorithm applied to thermal infrared data and includes atmospheric and emissivity corrections, and the application of a cloud mask.…”

Section: Satellite Measurements Of Lake Temperaturementioning

confidence: 99%

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Mass Loss From Calving in Himalayan Proglacial Lakes

et al. 2020

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The formation and expansion of Himalayan glacial lakes has implications for glacier dynamics, mass balance and glacial lake outburst floods (GLOFs). Subaerial and subaqueous calving is an important component of glacier mass loss but they have been difficult to track due to spatiotemporal resolution limitations in remote sensing data and few field observations. In this study, we used near-daily 3 m resolution PlanetScope imagery in conjunction with an uncrewed aerial vehicle (UAV) survey to quantify calving events and derive an empirical area-volume relationship to estimate calved glacier volume from planimetric iceberg areas. A calving event at Thulagi Glacier in 2017 was observed by satellite from before and during the event to nearly complete melting of the icebergs, and was observed in situ midway through the melting period, thus giving insights into the melting processes. In situ measurements of Thulagi Lake's surface and water column indicate that daytime sunlight absorption heats mainly just the top metre of water, but this heat is efficiently mixed downwards through the top tens of metres due to forced convection by wind-blown icebergs; this heat then is retained by the lake and is available to melt the icebergs. Using satellite data, we assess seasonal glacier velocities, lake thermal regime and glacier surface elevation change for Thulagi, Lower Barun and Lhotse Shar glaciers and their associated lakes. The data reveal widely varying trends, likely signifying divergent future evolution. Glacier velocities derived from 1960/70s declassified Corona satellite imagery revealed evidence of glacier deceleration for Thulagi and Lhotse Shar glaciers, but acceleration at Lower Barun Glacier following lake development. We used published modelled ice thickness data to show that upon reaching their maximum extents, Imja, Lower Barun and Thulagi lakes will contain, respectively, about 90 × 10 6 , 62 × 10 6 and 5 × 10 6 m 3 of additional water compared to their 2018 volumes. Understanding lake-glacier interactions is essential to predict future glacier mass loss, lake formation and associated hazards.

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Section: Satellite Surface Temperature Measurementssupporting

confidence: 69%

Section: Satellite Measurements Of Lake Temperaturementioning

confidence: 99%

Mass Loss From Calving in Himalayan Proglacial Lakes

et al. 2020

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“…We topographically corrected the images and calculated the tasseled cap transformation to derive "brightness", "greenness", and "wetness" using sensor-specific coefficients [59,60]. Additionally, we derived the Normalized Difference Vegetation Index (NDVI, [61]) and downloaded the NDVI-based land surface temperature (LST) for each acquisition date [62].…”

Section: Satellite Remote Sensing Datamentioning

confidence: 99%

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

et al. 2019

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Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended.

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“…We hypothesized that the use more imageries produces clearer output images to understand the LST patterns in the study area. Past studies have shown the aptness of this method to generate LST in different areas [39] [40]. All extracted images were georectified using the WGS84/UTM 44N projection system before further processing.…”

Section: Mountain Cities In Asia Have Been Developing Since the Colonmentioning

confidence: 99%

“…The use of more satellite images captured in multiple time point can potentially provide more specific information to understand the changing pattern of LST [4]. Still, it is not easy to analyze extensive earth observation data set due to spatial and temporal resolution [39] [40]. Thus, big data analysis platforms can be used as an alternative for conducting accurate result [39].…”

Section: Introductionmentioning

confidence: 99%

Landscape Changes and their Impacts on Land Surface Temperature in Tropical Hill Stations in South Asia: A Case Study of Nuwara Eliya, Sri Lanka

Ranagalage¹,

Murayama²,

Dissanayake³

et al. 2019

Preprint

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Although urbanization has contributed to improving living conditions, it has had negative impacts on the natural environment in the urbanized areas. Urbanization has changed the urban landscape and resulted in increasing land surface temperature (LST). Thus, studies related to LST in various urban environments have become a popular research topic. However, few LST studies focusing on the mountain landscapes (i.e. hill stations) have been carried out. The primary objective of this study is to investigate changes in the landscape and their impacts on LST intensity (LSTI) in the tropical mountain city of Nuwara Eliya, Sri Lanka. The study utilized annual median temperatures extracted from Landsat data collected from 1996 to 2017 based on the Google Earth Engine (GEE) interface. The fractions of built-up (BL), forest (FL), and agricultural (AL) land were calculated using land use and cover maps based on the urban-rural zone (URZ) analysis. The urban-rural margin was demarcated based on the fraction of BL (<10%) and LSTI was measured using the mean LST difference in the urban-rural zone. In addition, the mixture of land use types was calculated using the AL/FL and BL/FL fraction ratios, and grid-based density analysis. The result shows that the BL in all URZ rapidly developed, while AL decreased during the period 1996 to 2017. There was minimal change in the forest area of the Nuwara Eliya owing to the government forest preservation policies. The fraction of the BL increased from 32.4% in 1996 to 58.7% in 2017 in the city center zone (URZ1) resulting in increased mean LST by 4.7 °C. Furthermore, the increase of the BL/FL fraction ratio and the decrease of the AL/FL fraction ratio were positively correlated with the mean LST. Grid-based analysis showed an increasing positive relationship between mean LST and density of BL. This indicated that BL density has been a crucial element in increasing LST in the study area. The results of this study will be a useful indicator to introduce improved landscape and urban planning in the future to minimize the negative impact of LST on urban sustainability.

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Online Global Land Surface Temperature Estimation from Landsat

Cited by 156 publications

References 43 publications

Mass Loss From Calving in Himalayan Proglacial Lakes

Mass Loss From Calving in Himalayan Proglacial Lakes

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Landscape Changes and their Impacts on Land Surface Temperature in Tropical Hill Stations in South Asia: A Case Study of Nuwara Eliya, Sri Lanka

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