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

Abstract

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.

A Review of Insect Monitoring Approaches with Special Reference to Radar Techniques

“…These cameras took pictures at 5-min intervals from sunset to sunrise, with only 10% of them containing insects (a median number of 2). In contrast to the light-trap solutions, some can notice that the described approach with sky-oriented cameras belongs to the remote sensing realm (as well as unmanned-aerial-vehicle (UAV) cameras [ 72 , 73 ]).…”

Presence and Biomass Information Extraction from Highly Uncertain Data of an Experimental Low-Range Insect Radar Setup

“…We varied the duration of acts during the experiment and the number of shaking events from 0 to around 20. During acts 22 (16) and 27(0), we even stopped shaking from the middle of acts to increase uncertainty. Events of shaking are distinguishable on the charts; the upper threshold of 3000 works well for recognizing shaking events here.…”

“…Among them, nowadays, automatic light camera traps are starting to play a central role due to technical progress [10][11][12][13]. A modern tendency to use the camera with light traps bridges trapping approaches with remote sensing methods of insect monitoring, where sky-oriented and unmanned aerial vehicleborne cameras are becoming more distinguishable due to the recent progress in robotics and computer vision [14][15][16]. Despite this, among remote sensing methods for insect monitoring, radar technology historically plays a central role.…”

Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images

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