Technical notes: A detailed study for the provision of measurement uncertainty and traceability for goniospectrometers

Peltoniemi, Jouni; Hakala, Teemu; Suomalainen, Juha; Honkavaara, Eija; Markelin, Lauri; Gritsevich, Maria; Eskelinen, Joona; Jaanson, Priit; Ikonen, Erkki

doi:10.1016/j.jqsrt.2014.04.011

Cited by 34 publications

(17 citation statements)

References 49 publications

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“…Reflectance panels of size 1 m × 1 m and with a nominal reflectivity of 0.03, 0.1 and 0.5 were installed in the area for reflectance transformation purposes [51]. The reference reflectance values were measured in a laboratory with an estimated accuracy of 2%-5% using the FIGIFIGO goniospectrometer [52].…”

Section: Remote Sensing Data Capturementioning

confidence: 99%

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2017

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Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.

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Section: Remote Sensing Data Capturementioning

confidence: 99%

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2017

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“…Following the classification given by the ISO9060:1660 and WMO (2012), CM11 and CMP6 pyranometers comply with the specifications defined for the secondary standards and high quality instruments, respectively. In addition to this set up, BRF measurements were acquired from the sooted spots and undisturbed snow during SoS2013, using the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO; Suomalainen et al, 2009;Peltoniemi et al, 2014). The FIGIFIGO system consists of a motor-driven moving measurement arm that tilts up to ±90…”

Section: Albedo Measurementsmentioning

confidence: 99%

Soot on snow experiments: light-absorbing impurities effect on the natural snowpack

Svensson

Virkkula

Meinander

et al. 2015

Preprint

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Abstract. Light-absorbing impurities affect snow and ice via a decrease in albedo and a consequent disturbance to the radiative energy balance. Experimentally, these matters have only been examined in a few studies. Here we present results from a series of experiments in which we deposited different soot concentrations onto natural snow in different regions of Finland, and thereafter monitored the changes of the snowpack through the melting season. Measurements of the particulates in the snow indicated concentrations in the range of thousands of ppb to have clear effects on the snow properties, including the albedo, the physical snow characteristics, and an increased melt rate. For soot concentrations in the hundreds of ppb range, the effects were not as clearly visible, and it was more difficult to attribute the effects solely to the soot on the snow. Comparisons between our experimental data and the widely used Snow, Ice and Aerosol Radiation (SNICAR) model showed a general agreement when the model was specifically tuned to our measurements. This study highlights the importance of additional experimental studies, to further articulate and quantify the effects of light-absorbing impurities on snow.

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“…We assessed the pointing accuracy of the UAV system and found it to be of acceptable accuracy for a GPS-aided flying system, although it is still not as precise as ground-based instrumentation [34,45]. Parameters of altitude, X/Y position and sensor tilt angle are highly accurate within the navigation grade GPS specifications.…”

Section: Discussionmentioning

confidence: 99%

Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer

et al. 2015

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Abstract:In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83-0.95; TCARI = 0.04-0.116; REIP = 729-735 nm. Our analysis highlights the necessity to consider angular effects in optical sensors when observing vegetation. We compare the measurements of the UAV goniometer to the angular modules of the SCOPE radiative transfer model. Model and measurements are in high accordance OPEN ACCESSRemote Sens. 2015, 7 726(r 2 = 0.88) in the infrared region at angles close to nadir; in contrast the comparison show discrepancies at low tilt angles (r 2 = 0.25). This study demonstrates that the UAV goniometer is a promising approach for the fast and flexible assessment of angular effects.

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Technical notes: A detailed study for the provision of measurement uncertainty and traceability for goniospectrometers

Cited by 34 publications

References 49 publications

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Soot on snow experiments: light-absorbing impurities effect on the natural snowpack

Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer

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