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An accurate onboard irradiance measurement on drones is a requirement direct reflectance transformation of aerial images and remote sensing data without use of on-ground reference targets. One of the major error sources in onboard irradiance measurement is the effect of sensor tilt to the observed irradiances. In this paper/presentation, we will present an intercomparison of two methods: the FGI AIRS method and the Köppl method. Both methods require an irradiance sensor with good cosine response and IMU system providing accurate tilt angle for each irradiance measurement. Additionally to this, the AIRS method requires special hardware with multiple tilted irradiance sensors, which effectively allow interpolation of a virtual horizontal sensor. The Köppl method requires the irradiance sensor to be a spectral sensor, which allows the direct and diffuse fractions of irradiance spectrum to be deducted from shape of the measured spectrum using the spectral unmixing between direct and diffuse spectrum shapes. We present a field experiment where the AIRS sensor was flown in a typical mapping pattern flight and evaluate the tilt correction methods during it. During a fully sunny period of the flight, the standard deviations of the irradiances was ±6.4% for the uncorrected tilted irradiances, ±1.8% for the AIRS method, and ±2.0% for the Köppl method. The data shows that an accurate irradiance measurement onboard a drone is feasible using a well-calibrated irradiance sensor system and both methods are capable to produce quite similar results. [ABSTRACT FROM AUTHOR]

Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the "green attack" phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of "bark beetle green attack", "root-rot", and "healthy". Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data. [ABSTRACT FROM AUTHOR]

Many natural land surfaces, such as sand or snow, consist of densely packed grains, often covered by dust, water droplets, contaminated with other materials such as possible oil leaks, hoar frost, and can also be internally cracked, porous, and heterogeneous. Most scattering models ignore these complications, but here a more detailed approach is taken to test all these effects. The current model is composed of three techniques: 1) Monte Carlo-based electromagnetic volume integral equation technique for non-spherical wavelength scale dust particles, 2) Monte Carlo ray tracing for stochastic-shaped grains much larger than the wavelength, with optional point scattering from dust cover, internal inclusions, and liquid surface layer, in a layer of an optical depths of few units, and 3) adding-doubling to combine smaller layers into an arbitrary, thick and vertically inhomogeneous medium. The model allows the medium to be built in a modular way, and after initialisation, rather complicated layered structures can be computed quickly and flexibly. The computed results are compared against experimental measurements of snow and sand. The model agrees with measurements usually within the measurement accuracy (∼ 0:05). The scattering is observed to depend significantly on grain size, shape, orientation, composition, fine structures, dust, and some other properties that need to be defined. Both, measurement and modelling, require much deeper attention to these properties. [ABSTRACT FROM AUTHOR]

We study and analyse performance of a system for direct reflectance measurements from a drone. Key instruments of the system are upwards looking irradiance sensor and downwards looking imaging spectrometer. Requirement for both instruments is that they are radiometrically calibrated, the irradiance sensor has to be horizontally stabilized, and the sensors needs to be accurately synchronized. In our system, irradiance measurements are done with FGI Aerial Image Reference System (FGI AIRS), which uses novel optical levelling methodology and can compensate sensor tilting up to 15°. We performed SI-traceable spectral and radiance calibration of FPI hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). After the calibration, the radiance accuracy of different channels was between ±4 % when evaluated with independent test data. Sensors response to radiance proved to be highly linear and was on average 0.9994 for all channels. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI and highlighted the importance of accurate calibration. The drone-based direct reflectance measurement system showed promising results with imagery collected over Jokioinen agricultural grass test site, Finland. AIRS-based image- and band wise image adjustment provided homogenous and seamless image mosaics even under varying illumination conditions and under clouds. [ABSTRACT FROM AUTHOR]

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites. [ABSTRACT FROM AUTHOR]

Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10% better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40% compared to approaches where only spectral features were applied. The best estimation RMSE of 239 kg/ha (6.0%) was obtained with multiview anisotropy corrected data set and the 3D features. [ABSTRACT FROM AUTHOR]

We developed a mobile remote sensing measurement facility for spectral and anisotropic reflectance measurements. We measured reflection properties (BRF) of over 100 samples from most common land cover types in boreal and subarctic regions. This extensive 5 data set serves as a unique reference opportunity for developing interpretation algorithms for remotely sensed materials as well as for modelling climatic effects in the boreal and subarctic zones. Our goniometric measurements show that the reflectances of the most common land cover types in the boreal and subarctic region can differ from each other by a factor of 100. Some types are strong forward scatterers, some backward scatterers, some reflect specularly, some have strong colours, some are bright in visual, some in infrared. We noted that spatial variations in reflectance, even among the same type of vegetation, can be well over 20%, diurnal variations of the same order and seasonal variation often over a factor of 10. This has significant consequences on the interpretation of satellite and airborne images and on the development of radiation regime models in both optical remote sensing and climate change research. We propose that the accuracy of optical remote sensing can be improved by an order of magnitude, if better physical reflectance models can be introduced. Further improvements can be reached by more optimised design of sensors and orbits/flight lines, by the effective combining of several data sources and better processing of atmospheric effects. We conclude that more extensive and systematic laboratory experiments and field measurements are needed, with more modelling effort. [ABSTRACT FROM AUTHOR]

Systematic laboratory measurements of laser backscatter intensity are presented for brightness calibration targets, and a calibration scheme for airborne laser scanner intensity data is proposed. Thus far, the use of these data has been partly hampered by the variability of the intensity with time, and no test fields have been available for airborne reflectance calibration. Portable brightness targets (tarps), with nominal reflectances from 5% to 70%, were manufactured, and, based on these measurements, found suitable for lidar reflectance standards. Furthermore, the variability of the recorded intensity from the tarps as a function of incidence angle was low. The measurements also provide new information on the surface albedo dependence of backscattering effects: as the surface brightness increases from 5% to 70%, the hotspot brightness peak amplitudes increase by 20% to 30%, and their apparent widths reduce to a half, which implies that hotspots could be used as an albedo discriminator. [ABSTRACT FROM PUBLISHER]