scholarly journals Verification of Accuracy of Unmanned Aerial Vehicle (UAV) Land Surface Temperature Images Using In-Situ Data

2020 ◽  
Vol 12 (2) ◽  
pp. 288 ◽  
Author(s):  
Bonggeun Song ◽  
Kyunghun Park
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Land Surface ◽  
Urban Areas ◽  
Thermal Characteristics ◽  
In Situ Measurements ◽  
Atmospheric Conditions ◽  
In Situ Data ◽  
Aerial Vehicle

The accuracy of land surface temperatures (LSTs) acquired by an unmanned aerial vehicle (UAV) was verified by comparison with in-situ LSTs of various land cover materials at the Changwon National University Campus, Changwon City, South Korea. UAV imaging and in-situ measurements were performed on 31 July and 2 August 2019. During the in-situ measurements, LST was measured at 160 points using an infrared thermometer. The linear regression model between the UAV and in-situ measurements exhibited a very high correlation on both days, with R2 values greater than 0.7004. The root mean square error (RMSE), however, was 4.030 °C on 31 July and 5.446 °C on 2 August and it also varied depending on the land cover type. These results may depend on various factors, such as the field of view and performance of the TIR (Thermal infrared radiance) camera, as well as the weather and atmospheric conditions. Accurately diagnosing the thermal characteristics of urban areas based on the spatial elements can be used to accurately analyze the thermal characteristics of urban areas and to make effective policy decisions. Techniques for verifying and improving the accuracy of UAV TIR LST data for various land cover materials are required to enable precise investigation of the thermal characteristics of urban areas.

SMART – Space monitoring of Arctic Tundra landscapes

2021 ◽  
Author(s):  
Jennifer Sobiech-Wolf ◽  
Tobias Ullmann ◽  
Wolfgang Dierking
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Surface ◽  
In Situ Measurements ◽  
German Research Foundation ◽  
Marine Research ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Infrared Measurements

<p>Satellite remote sensing as well as in-situ measurements are common tools to monitor the state of Arctic environments. However, remote sensing products often lack sufficient temporal and/or spatial resolution, and in-situ measurements can only describe the environmental conditions on a very limited spatial scale. Therefore, we conducted an air-borne campaign to connect the detailed in-situ data with poor spatial coverage to coarse satellite images. The SMART campaign is part of the ongoing project „Characterization of Polar Permafrost Landscapes by Means of Multi-Temporal and Multi-Scale Remote Sensing, and In-Situ Measurements“, funded by the German Research Foundation (DFG).  The focus of the project is to close the gap between in-situ measurements and space-borne images in polar permafrost landscapes. The airborne campaign SMART was conducted in late summer 2018 in north-west Canada, focussing on the Mackenzie-Delta region, which is underlain by permafrost and rarely inhabited. The land cover is either dominated by open Tundra landscapes or by boreal forests. The Polar-5 research-aircraft from the Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Germany, was equipped with a ground penetrating radar, a hyperspectral camara, a laserscanner, and an infrared temperature sensor amongst others. In parallel to the airborne acquisition, a team collected in-situ data on ground, including manual active layer depth measurements, geophysical surveying using 2D Electric Resistivity Tomography (ERT), GPR, and mapping of additional land cover properties. The database was completed by a variety of satellite data from different platforms, e.g. MODIS, Landsat, TerraSAR-X and Sentinel-1.  As part of the project, we analysed the performance of MODIS Land surfaces temperature products compared to our air-borne infrared measurements and evaluated, how long the land surface temperatures of this Arctic environment can be considered as stable. It turned out that the MODIS data differ up to 2°C from the air-borne measurements. If this is due to the spatial difference of the measurements or a result of data processing of the MODIS LST products is part of ongoing analysis.</p>

scholarly journals Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

2021 ◽  
Vol 13 (22) ◽  
pp. 4526
Author(s):  
Ali S. Alghamdi ◽  
Ahmed Ibrahim Alzhrani ◽  
Humud Hadi Alanazi
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Urban Areas ◽  
Thermal Characteristics ◽  
Arid Climate ◽  
Landsat 8 ◽  
Design Strategies ◽  
Local Climate ◽  
Riyadh City ◽  
Local Climate Zones

Using the local climate zone (LCZ) framework and multiple Earth observation input features, an LCZ classification was developed and established for Riyadh City in 2017. Four land-cover-type and four urban-type LCZs were identified in the city with an overall accuracy of 87%. The bare soil/sand (LCZ-F) class was found to be the largest LCZ class, which was within the nature of arid climate cities. Other land-cover LCZs had a lower coverage percentage (each class with <7%). The compact low-rise (LCZ-3) class was the largest urban type, as urban development in arid climate cities tends to extend horizontally. The daytime surface thermal characteristics of the developed LCZs were analyzed at seasonal timescales using land surface temperature (LST) estimated from multiple Landsat 8 satellite images (June 2017–May 2018). The highest daytime mean LST was found over large low-rise (LCZ-8) class areas throughout the year. This class was the only urban-type LCZ class that demonstrated a positive LST departure from the overall mean LST across seasons. Other urban-type LCZ classes showed lower LSTs and negative deviations from the overall mean LSTs. The overall thermal results suggested the presence of the surface urban heat island sink phenomenon as urban areas experienced lower LSTs than their surroundings. Thermal results demonstrated that the magnitudes of LST differences among LCZs were considerably dependent on the way the region of interest/analysis was defined. This was related to the types of LCZ classes presented in the study area and the spatial distribution and abundance of these LCZ classes. The developed LCZ classification and thermal results have several potential applications in different areas including planning and urban design strategies and urban health-related studies.

scholarly journals In situ measurements of the ice flow motion at Eqip Sermia Glacier using a remotely controlled unmanned aerial vehicle (UAV)

2020 ◽  
Vol 9 (1) ◽  
pp. 1-10
Author(s):  
Guillaume Jouvet ◽  
Eef van Dongen ◽  
Martin P. Lüthi ◽  
Andreas Vieli
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
In Situ Measurements ◽  
In Situ Monitoring ◽  
High Temporal Resolution ◽  
Ice Flow ◽  
Flow Motion ◽  
Aerial Vehicle ◽  
Matching Techniques

Abstract. Measuring the ice flow motion accurately is essential to better understand the time evolution of glaciers and ice sheets and therefore to better anticipate the future consequence of climate change in terms of sea level rise. Although there are a variety of remote sensing methods to fill this task, in situ measurements are always needed for validation or to capture high-temporal-resolution movements. Yet glaciers are in general hostile environments where the installation of instruments might be tedious and risky when not impossible. Here we report the first-ever in situ measurements of ice flow motion using a remotely controlled unmanned aerial vehicle (UAV). We used a quadcopter UAV to land on a highly crevassed area of Eqip Sermia Glacier, West Greenland, to measure the displacement of the glacial surface with the aid of an onboard differential GNSS receiver. We measured approximately 70 cm of displacement over 4.36 h without setting foot onto the glacier – a result validated by applying UAV photogrammetry and template matching techniques. Our study demonstrates that UAVs are promising instruments for in situ monitoring and have great potential for capturing continuous ice flow variations in inaccessible glaciers – a task that remote sensing techniques can hardly achieve.

scholarly journals Comparison of Accuracy of Surface Temperature Images from Unmanned Aerial Vehicle and Satellite for Precise Thermal Environment Monitoring of Urban Parks Using In Situ Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1977
Author(s):  
Dongwoo Kim ◽  
Jaejin Yu ◽  
Jeongho Yoon ◽  
Seongwoo Jeon ◽  
Seungwoo Son
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Thermal Environment ◽  
Green Spaces ◽  
Urban Park ◽  
Land Cover Type ◽  
Land Cover Types ◽  
Cover Type ◽  
Aerial Vehicle

Rapid urbanization has led to several severe environmental problems, including so-called heat island effects, which can be mitigated by creating more urban green spaces. However, the temperature of various surfaces differs and precise measurement and analyses are required to determine the “coolest” of these. Therefore, we evaluated the accuracy of surface temperature data based on thermal infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs), which have recently been utilized for the spatial analysis of surface temperatures. Accordingly, we investigated land surface temperatures (LSTs) in green spaces, specifically those of different land cover types in an urban park in Korea. We compared and analyzed LST data generated by a thermal infrared (TIR) camera mounted on an unmanned aerial vehicle (UAV) and LST data from the Landsat 8 satellite for seven specific periods. For comparison and evaluation, we measured in situ LSTs using contact thermometers. The UAV TIR LST showed higher accuracy (R2 0.912, root mean square error (RMSE) 3.502 °C) than Landsat TIR LST accuracy (R2 value lower than 0.3 and RMSE of 7.246 °C) in all periods. The Landsat TIR LST did not show distinct LST characteristics by period and land cover type; however, grassland, the largest land cover type in the study area, showed the highest accuracy. With regard to the accuracy of the UAV TIR LST by season, the accuracy was higher in summer and spring (R2 0.868–0.915, RMSE 2.523–3.499 °C) than in autumn and winter (R2 0.766–0.79, RMSE 3.834–5.398 °C). Some land cover types (concrete bike path, wooden deck) were overestimated, showing relatively high total RMSEs of 4.439 °C and 3.897 °C, respectively, whereas grassland, which has lower LST, was underestimated—showing a total RMSE of 3.316 °C. Our results showed that the UAV TIR LST could be measured with sufficient reliability for each season and land cover type in an urban park with complex land cover types. Accordingly, our results could contribute to decision-making for urban spaces and environmental planning in consideration of the thermal environment.

scholarly journals Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years

2019 ◽  
Vol 11 (5) ◽  
pp. 479 ◽  
Author(s):  
Maria Martin ◽  
Darren Ghent ◽  
Ana Pires ◽  
Frank-Michael Göttsche ◽  
Jan Cermak ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Quantitative Information ◽  
Data Availability ◽  
Data Sets ◽  
In Situ Data

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.

scholarly journals A Study of UVER in Santiago, Chile Based on Long-Term In Situ Measurements (Five Years) and Empirical Modelling

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 368
Author(s):  
Lisdelys González-Rodríguez ◽  
Amauri Pereira de Oliveira ◽  
Lien Rodríguez-López ◽  
Jorge Rosas ◽  
David Contreras ◽  
...  
Keyword(s):  
Urban Areas ◽  
Solar Spectrum ◽  
Sun Exposure ◽  
Great Accuracy ◽  
In Situ Measurements ◽  
Uv Index ◽  
Empirical Modelling ◽  
The People

Ultraviolet radiation is a highly energetic component of the solar spectrum that needs to be monitored because is harmful to life on Earth, especially in areas where the ozone layer has been depleted, like Chile. This work is the first to address the long-term (five-year) behaviour of ultraviolet erythemal radiation (UVER) in Santiago, Chile (33.5° S, 70.7° W, 500 m) using in situ measurements and empirical modelling. Observations indicate that to alert the people on the risks of UVER overexposure, it is necessary to use, in addition to the currently available UV index (UVI), three more erythema indices: standard erythemal doses (SEDs), minimum erythemal doses (MEDs), and sun exposure time (tery). The combination of UVI, SEDs, MEDs, and tery shows that in Santiago, individuals with skin types III and IV are exposed to harmfully high UVER doses for 46% of the time that UVI indicates is safe. Empirical models predicted hourly and daily values UVER in Santiago with great accuracy and can be applied to other Chilean urban areas with similar climate. This research inspires future advances in reconstructing large datasets to analyse the UVER in Central Chile, its trends, and its changes.

scholarly journals Monitoring of compliance with fuel sulfur content regulations through unmanned aerial vehicle (UAV) measurements of ship emissions

2019 ◽  
Vol 12 (11) ◽  
pp. 6113-6124 ◽  
Author(s):  
Fan Zhou ◽  
Shengda Pan ◽  
Wei Chen ◽  
Xunpeng Ni ◽  
Bowen An
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Sulfur Content ◽  
Measurement System ◽  
Yangtze River Delta ◽  
Exhaust Gas ◽  
Ship Emissions ◽  
Chemical Examination ◽  
Airborne Measurements ◽  
Aerial Vehicle

Abstract. Air pollution from ship exhaust gas can be reduced by the establishment of emission control areas (ECAs). Efficient supervision of ship emissions is currently a major concern of maritime authorities. In this study, a measurement system for exhaust gas from ships based on an unmanned aerial vehicle (UAV) was designed and developed. Sensors were mounted on the UAV to measure the concentrations of SO2 and CO2 in order to calculate the fuel sulfur content (FSC) of ships. The Waigaoqiao port in the Yangtze River Delta, an ECA in China, was selected for monitoring compliance with FSC regulations. Unlike in situ or airborne measurements, the proposed measurement system could be used to determine the smoke plume at about 5 m from the funnel mouth of ships, thus providing a means for estimating the FSC of ships. In order to verify the accuracy of these measurements, fuel samples were collected at the same time and sent to the laboratory for chemical examination, and these two types of measurements were compared. After 23 comparative experiments, the results showed that, in general, the deviation of the estimated value for FSC was less than 0.03 % (m/m) at an FSC level ranging from 0.035 % (m/m) to 0.24 % (m/m). Hence, UAV measurements can be used for monitoring of ECAs for compliance with FSC regulations.

Plot-scale retrieval of land surface temperature and emissivity estimation

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Kaniska Mallick ◽  
Ivonne Trebs
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
The Earth ◽  
Downwelling Longwave Radiation

&lt;p&gt;The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth&amp;#8217;s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth&amp;#8217;s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using&amp;#160; satellites.&amp;#160; At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST&amp;#160; using the grey body equation :&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4&lt;/sup&gt; + (1 &amp;#8722; &amp;#949;) R&lt;sub&gt; ldw&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; &lt;/sub&gt;(1)&lt;br&gt;where R&lt;sub&gt;lup&lt;/sub&gt; is the upwelling longwave radiation, R&lt;sub&gt;ldw&lt;/sub&gt; is the downwelling longwave radiation, &amp;#949; is the surface emissivity, &lt;em&gt;T&lt;sub&gt;s&lt;/sub&gt;&amp;#160; &lt;/em&gt;is the surface temperature and &amp;#963;&amp;#160; is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to:&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4 &lt;/sup&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&amp;#160; (2)&lt;br&gt;Despite&amp;#160; widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct &amp;#949; needed for in-situ LST retrievals using tower-based measurements.&lt;br&gt;The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST&amp;#160; obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.&lt;/p&gt;

scholarly journals Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

2020 ◽  
Author(s):  
Juan Carlos Laso Bayas ◽  
Linda See ◽  
Hedwig Bartl ◽  
Tobias Sturn ◽  
Mathias Karner ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Survey Data ◽  
Target Location ◽  
Low Cost ◽  
Mobile App ◽  
Sample Survey ◽  
In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

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