Airborne thermal remote sensing: the case of the city of Olomouc, Czech Republic

Pour, Tomas; Miřijovský, Jakub; Purket, Tomáš

doi:10.1080/22797254.2018.1564888

Cited by 20 publications

(14 citation statements)

References 23 publications

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“…However, the LWIR detector does not cover the entire thermal wavelength range. Thus, the camera manufacturer has used a modified PLANCK function [38], which is based on the sensor-specific spectral response curve and laboratory-derived PLANCK constants (Appendix A.1).…”

Section: Lst Retrieval Methodologymentioning

confidence: 99%

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

et al. 2020

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Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.

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Section: Lst Retrieval Methodologymentioning

confidence: 99%

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

et al. 2020

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“…These raw photos, in turn, needed to be corrected for the cold edges, or vignetting errors, associated with thermal data. To process these out, first vignetting masks were created (from four relatively still images in the drone flight, usually at the end of the flight) by subtracting the highest pixel value from the entire raster image [52]. Next, we clipped out anomalies such as GCPs, rocks, or landmines, and filled the missing data with the nearest neighbor method, so the mask is suitable across the entire flight; if this is not done artifacts are introduced with the mask.…”

Section: Image Processingmentioning

confidence: 99%

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

et al. 2020

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Recent advances in unmanned-aerial-vehicle-(UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection and identification of scatterable antipersonnel landmines in wide-area surveys. Our methodology is calibrated for the detection of scatterable plastic landmines which utilize a liquid explosive encapsulated in a polyethylene or plastic body in their design. We base our findings on analysis of multispectral and thermal datasets collected by an automated UAV-survey system featuring scattered PFM-1-type landmines as test objects and present results of an effort to automate landmine detection, relying on supervised learning algorithms using a Faster Regional-Convolutional Neural Network (Faster R-CNN). The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and 71.5% testing accuracy for a completely withheld testing set. Across multiple test environments, using centimeter scale accurate georeferenced datasets paired with Faster R-CNN, allowed for accurate automated detection of test PFM-1 landmines. This method can be calibrated to other types of scatterable antipersonnel mines in future trials to aid humanitarian demining initiatives. With millions of remnant PFM-1 and similar scatterable plastic mines across post-conflict regions and considerable stockpiles of these landmines posing long-term humanitarian and economic threats to impacted communities, our methodology could considerably aid in efforts to demine impacted regions.

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“…The post-processing was consisting of vignetting removal, radiometric corrections, atmospheric corrections and emissivity corrections (Pour, Miřijovský and Purket 2019). In the final step, photogrammetric processing was applied to geo-reference the images and create a seamless thermal mosaic.…”

Section: Fig 2 Visualisation Of the Afternoon Thermal Mosaic Of Olomentioning

confidence: 99%

Thermal data analysis for urban climate research: A case study of Olomouc, Czechia

Pour¹,

Voženílek²

2020

Geographia Cassoviensis

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Due to the recent advancements in thermal camera technology, it is easier than ever to acquire information about surface temperature in very-high or extreme spatial resolution from manned or unmanned airborne vehicles. The main objective of the paper is to investigate the possibilities of extreme resolution thermal data for urban climate research. The main dataset used for the work is thermal mosaic acquired by an aeroplane above the city of Olomouc in the morning and afternoon on 10 th July 2016. The spatial resolution of the dataset fluctuates between 90 and 100 cm per pixel edge. Auxiliary data used for the analysis included open data from European programmes Urban Atlas and CORINE Land Cover. For further analysis, building height information, material type and Local Climate Zones classification were used. The results show a possible comparison in various neighbourhoods and material types while maintaining reference units-Urban Atlas/CORINE Land Cover classes or Local Climate Zones. Verticality analysis shows a significant difference between ground and rooftop levels that need to be taken into account when investigating heat for quality of life. All the analyses confirmed knowledge about the difference between natural and artificial surfaces that most artificial surfaces tend to heat up much more during daytime and fail to cool down completely during the night-time. Moreover, this research lays the foundation to future urban climate research based on extreme resolution thermal images over multiple areas or during long-lasting campaigns.

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Airborne thermal remote sensing: the case of the city of Olomouc, Czech Republic

Cited by 20 publications

References 23 publications

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

Thermal data analysis for urban climate research: A case study of Olomouc, Czechia

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