HySpex ODIN-1024: a new high-resolution airborne HSI system

Blaaberg, Søren; Løke, Trond; Baarstad, Ivar; Fridman, Andrei; Koirala, Pesal

doi:10.1117/12.2063502

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Using the full VSWIR range has been shown to significantly increase spectra-trait relationships [37,[79][80][81], as the majority of traits have significant expressions in the short-wave infrared spectrum. Airborne hyperspectral sensors covering the full 400-2500 nm range are commercially available [82]. Alternatively, combining information from multiple hyperspectral sensors is possible [83], and would also provide all the relevant bands.…”

Section: Discussionmentioning

confidence: 99%

Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

et al. 2018

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The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity.

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Section: Discussionmentioning

confidence: 99%

Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

et al. 2018

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“…This camera is equipped with a calibrated f/1.8 4.8 mm Schneider lens, which results in a 50.7 deg field of view over 1024 pixels. The collected hyperspectral data cubes are synchronised with a GPS/inertial navigation system (INS) positioning and orientation information in order to perform data cubes orthorectification and multiple data cube mapping; the detail of HySpex ODIN-1024 can be seen in [56]. The key to this model is visual and spatial smoothness.…”

Section: Generating Accurate Maps For Farmsmentioning

confidence: 99%

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Gao

Sun

et al. 2020

Sensors

112

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With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 • C and 16 • C, and high rainfall decreases the spread of wheat powdery mildew.

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“…The procedure in [12] is optimized for a field-programmable gate array (FPGA) with a digital signal processor and it is focused on geo-referencing rather than calibration. Real-time calibration has also been demonstrated by [13] for airborne hyperspectral imagery campaigns correcting for spectral shifts, keystone, and smile. Comparing the results to these other experiments suggests that it is possible to integrate the corrections into a more comprehensive data processing pipeline.…”

Section: Computation Timementioning

confidence: 99%

Real-Time Corrections for a Low-Cost Hyperspectral Instrument

Henriksen

Garrett

Prentice

et al. 2019

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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The development of a hyperspectral imager (HSI) made from commercial off-the-shelf (COTS) parts enables the use of hyperspectral imaging on smaller low-cost platforms such as cubesats, drones, or other autonomous vehicles. However, HSIs built from COTS parts often suffer from more pronounced optical distortions, such as 'smile' and 'keystone', due to the shifted balance between cost and image quality. In this proceeding, radiometric, spectral, and geometric calibrations of a COTS HSI are presented. Furthermore, the calibrations are used to develop a real-time software-based spectrogram correction. The corrections will enhance the capability of small, autonomous platforms in using hyperspectral imaging.

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HySpex ODIN-1024: a new high-resolution airborne HSI system

Cited by 5 publications

References 4 publications

Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Real-Time Corrections for a Low-Cost Hyperspectral Instrument

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