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Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK)., Competing Interests: The authors declare no conflict of interest.

Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) equipped with digital cameras have attracted much attention from the forestry community as potential tools for forest inventories and forest monitoring. This research fills a knowledge gap about the viability and dissimilarities of using these technologies for measuring the top of canopy structure in tropical forests. In an empirical study with data acquired in a Guyanese tropical forest, we assessed the differences between top of canopy models (TCMs) derived from TLS measurements and from UAV imagery, processed using structure from motion. Firstly, canopy gaps lead to differences in TCMs derived from TLS and UAVs. UAV TCMs overestimate canopy height in gap areas and often fail to represent smaller gaps altogether. Secondly, it was demonstrated that forest change caused by logging can be detected by both TLS and UAV TCMs, although it is better depicted by the TLS. Thirdly, this research shows that both TLS and UAV TCMs are sensitive to the small variations in sensor positions during data collection. TCMs rendered from UAV data acquired over the same area at different moments are more similar (RMSE 0.11-0.63 m for tree height, and 0.14-3.05 m for gap areas) than those rendered from TLS data (RMSE 0.21-1.21 m for trees, and 1.02-2.48 m for gaps). This study provides support for a more informed decision for choosing between TLS and UAV TCMs to assess top of canopy in a tropical forest by advancing our understanding on: (i) how these technologies capture the top of the canopy, (ii) why their ability to reproduce the same model varies over repeated surveying sessions and (iii) general considerations such as the area coverage, costs, fieldwork time and processing requirements needed., Competing Interests: The authors declare that they have no competing interests.

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm -2 ), leaf area index (RMSE = 0.67 m²·m -2 ), canopy chlorophyll (RMSE = 0.24 g·m -2 ) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm -2 , 0.85 m²·m -2 , 0.28 g·m -2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CI g provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system., Competing Interests: The authors declare no conflict of interest.

Objective: Meniscal injuries can lead to mechanical overloading of articular cartilage and eventually to knee osteoarthritis. The objective was to evaluate the potential of contrast enhanced computed tomography (CECT) to image contrast agent (CA) diffusion in human menisci with a clinical cone beam CT scanner., Design: Isolated human menisci (n = 26) were imaged using magnetic resonance imaging (MRI) and CECT in situ. Diffusion of anionic CA into the meniscus was imaged for up to 30 h. The results of CECT were compared with water, collagen and proteoglycan (PG) contents, biomechanical properties, age and histological and MR images of the samples., Results: Diffusion of CA required over 25 h to reach equilibrium. The CA partition (the CA concentration in the tissue divided by that in the bath) at the 40 min time point correlated significantly with that at the 30 h time point in both lateral (r = 0.706, P = 0.007) and medial (r = 0.669, P = 0.012) menisci. Furthermore, CA partition in meniscus after 30 h of diffusion agreed qualitatively with the distribution of PGs., Conclusion: The cross-sectional distribution of CA was consistent with that reported in a previous μCT study on bovine meniscus. The time required to reach diffusion equilibrium was found impractical for clinical applications. However, based on the present results, shorter delay between injection and imaging (e.g., 40 min) could be feasible in clinical diagnostics of meniscal pathologies., (Copyright © 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.)

We present the design of a full waveform hyperspectral light detection and ranging (LiDAR) and the first demonstrations of its applications in remote sensing. The novel instrument produces a 3D point cloud with spectral backscattered reflectance data. This concept has a significant impact on remote sensing and other fields where target 3D detection and identification is crucial, such as civil engineering, cultural heritage, material processing, or geomorphological studies. As both the geometry and spectral information on the target are available from a single measurement, this technology will extend the scope of imaging spectroscopy into spectral 3D sensing. To demonstrate the potential of the instrument in the remote sensing of vegetation, 3D point clouds with backscattered reflectance and spectral indices are presented for a specimen of Norway spruce.

Recent advances in nonlinear fiber optics and compact pulsed lasers have resulted in creation of broadband directional light sources. These supercontinuum laser sources produce directional broadband light using cascaded nonlinear optical interactions in an optical fibre framework. This system is used to simultaneously measure distance and reflectance to demonstrate a technique capable of distinguishing between a vegetation target and inorganic material using the Normalized Difference Vegetation Index (NDVI) parameters, while the range can be obtained from the waveform of the echoes. A two-channel, spectral range-finding system based on a supercontinuum laser source was used to determine its potential application of distinguishing the NDVI for Norway spruce, a coniferous tree, and its three-dimensional parameters at 600 nm and 800 nm. A prototype system was built using commercial components.

The design, operation, and properties of the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO) are presented. FIGIFIGO is a portable instrument for the measurement of surface Bidirectional Reflectance Factor (BRF) for samples with diameters of 10 - 50 cm. A set of polarising optics enable the measurement of linearly polarised BRF over the full solar spectrum (350 - 2,500 nm). FIGIFIGO is designed mainly for field operation using sunlight, but operation in a laboratory environment is also possible. The acquired BRF have an accuracy of 1 - 5% depending on wavelength, sample properties, and measurement conditions. The angles are registered at accuracies better than 2°. During 2004 - 2008, FIGIFIGO has been used in the measurement of over 150 samples, all around northern Europe. The samples concentrate mostly on boreal forest understorey, snow, urban surfaces, and reflectance calibration surfaces.

We present experiments on spectral bidirectional reflectance distribution function (BRDF) effects at backscatter and discuss the feasibility of new methods for laboratory and field simulations of remote sensing of land surfaces. The extreme sharpness of the intensity peak allows both directional and comparative experimental spectral studies of hot spots. We demonstrate wavelength-dependent features in the hot-spot reflectance signatures that facilitate extension of spectral and directional BRDF measurements of natural targets (such as forest understories and ice surfaces) into retroreflection to exploit their hot-spot characteristics in the interpretation of spaceborne and airborne data.