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Application of UAS-Based Spectral Imaging in Agriculture and Forestry

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 7212

Special Issue Editors


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Guest Editor
Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
Interests: UAS-based photogrammetry; RGB; multispectral and hyperspectral imaging; environmental remote sensing; machine learning

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Guest Editor
UAV Research Centre, Ghent University, 9000 Ghent, Belgium
Interests: artificial intelligence; hyperspectral imaging; LiDAR; data fusion; UAS
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Special Issue Information

Dear Colleagues,

Following trends in the increasing miniaturization and automation of both platforms and sensors over the last 5–10 years, unoccupied aircraft system (UAS)-based spectral imaging has grown from pure research to operational deployment in agriculture and forestry. From detecting crop issues such as drought and disease at an early stage to quantifying zonal or plant-specific characteristics, supporting phenotyping or variable treatment, refining and filling gaps in satellite measurements, and generalizing in-situ and proximally sensed data, the variety in applications reported in the literature continues to expand. In addition to innovations in sensor technology, recent advancements in machine learning are playing an important role in opening new applications. This shows the continuing relevance of UAS-based spectral imaging in understanding local vegetation processes, as a crucial aspect in dealing with climate change and environmental pressures in agriculture and forestry.

Despite these recent advancements, important questions remain. Some of these include:

  • Which scientific or operational applications in agriculture and forestry still require narrowband VNIR multi- and hyperspectral imaging, given that learning-based approaches have sparked a renewed interest in exploiting the generally wider availability, lower acquisition cost and higher spatial resolutions of drone-based RGB cameras (even for applications that previously seemed unachievable without higher spectral resolution)?
  • Can fusion with thermal, LiDAR or non-imaging spectrometer data and machine learning approaches help to overcome deficiencies in the spatial resolution of drone-based spectral cameras?
  • How can insights gained from advanced drone-based spectral imaging techniques be transferred to more operationally feasible methods that would increase uptake in agriculture and forestry in resource-limited areas?
  • What are the trade-offs between multiple available drone-based hyperspectral imaging techniques, and will there be an evolution towards a single optimal all-round solution?
  • Which new areas of research in agriculture and forestry can benefit from recent advances in drone-based spectral imaging, for example, using SWIR cameras?

This Special Issue aims to answer some of these questions by:

  • Bundling state-of-the-art scientific experimental results and novel methods that promote operational capabilities and increased integration with in-situ, airborne and satellite measurements.
  • Providing a forum for reviews which identify outstanding knowledge gaps and compelling future research paths.

We also aim to maximize diversity in drone-based spectral imaging in agriculture and forestry literature by encouraging contributions from all kinds of research backgrounds that are typically underrepresented in this field.

Dr. Klaas Pauly
Prof. Dr. Hiep Luong
Guest Editors

Manuscript Submission Information

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Keywords

  • UAS-based spectral imaging
  • agriculture
  • forestry
  • multispectral
  • hyperspectral
  • data fusion
  • machine learning

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Published Papers (3 papers)

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Research

18 pages, 4347 KiB  
Article
Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales
by Jie Pan, Jiayi Lin and Tianyi Xie
Remote Sens. 2023, 15(9), 2281; https://doi.org/10.3390/rs15092281 - 26 Apr 2023
Cited by 7 | Viewed by 1995
Abstract
Pine wilt disease (PWD), caused by pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a serious threat to the coniferous forests in China. This study used unmanned aerial vehicle (UAV)-based hyperspectral imaging conducted at different altitudes to investigate the impact of spatio-temporal scales [...] Read more.
Pine wilt disease (PWD), caused by pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a serious threat to the coniferous forests in China. This study used unmanned aerial vehicle (UAV)-based hyperspectral imaging conducted at different altitudes to investigate the impact of spatio-temporal scales on PWD detection in an monoculture Masson pine plantation. The influence of spatio-temporal scales on hyperspectral responses of pine trees infected with PWD and detection accuracies were evaluated by Jeffries–Matusita (J-M) distances and the random forest (RF) algorithm. The optimal vegetation indices (VIs) and spatial resolutions were identified by comparing feature importance and model accuracy. The main results showed that the VIs and J-M distances were greatly affected by spatio-temporal scales. In the early, mid-, and late infection stages, the RF-based PWD detection model had accuracies ranging between 72.05 and 79.48%, 83.71 and 89.59%, and 96.81 and 99.28%, peaking at the 10 cm, 8 cm, and 4 cm spatial resolutions, respectively. The green normalized difference vegetation index (GNDVI) and red edge position (REP) were the optimal VIs in early and mid-infection stages, respectively. This study can be important to improve the efficiency of PWD detection and reducing the loss of forests resources. Full article
(This article belongs to the Special Issue Application of UAS-Based Spectral Imaging in Agriculture and Forestry)
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18 pages, 30753 KiB  
Article
A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds
by Michiel Vlaminck, Laurens Diels, Wilfried Philips, Wouter Maes, René Heim, Bart De Wit and Hiep Luong
Remote Sens. 2023, 15(6), 1524; https://doi.org/10.3390/rs15061524 - 10 Mar 2023
Cited by 4 | Viewed by 2954
Abstract
Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. [...] Read more.
Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few fully fledged drone systems exist that combine different sensing modalities in a way that complements the strengths and weaknesses of each. In this paper, we present our multimodal drone payload and sensor fusion pipeline, which allows multispectral point clouds to be generated at subcentimeter accuracy. To that end, we combine high-frequency navigation outputs from a professional-grade GNSS with photogrammetric bundle adjustment and a dedicated point cloud registration algorithm that takes full advantage of LiDAR’s specifications. We demonstrate that the latter significantly improves the quality of the reconstructed point cloud in terms of fewer ghosting effects and less noise. Finally, we thoroughly discuss the impact of the quality of the GNSS/INS system on the structure from the motion and LiDAR SLAM reconstruction process. Full article
(This article belongs to the Special Issue Application of UAS-Based Spectral Imaging in Agriculture and Forestry)
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17 pages, 5030 KiB  
Article
Use of Geostatistics for Multi-Scale Spatial Modeling of Xylella fastidiosa subsp. pauca (Xfp) Infection with Unmanned Aerial Vehicle Image
by Antonella Belmonte, Giovanni Gadaleta and Annamaria Castrignanò
Remote Sens. 2023, 15(3), 656; https://doi.org/10.3390/rs15030656 - 22 Jan 2023
Cited by 4 | Viewed by 1431
Abstract
In recent years, the use of Unmanned Aerial Vehicles (UAVs) has been spreading widely, as in plant pest control. The collection of huge amounts of spatial data raises various issues including that of scale. Data from UAVs generally explore multiple scales, so the [...] Read more.
In recent years, the use of Unmanned Aerial Vehicles (UAVs) has been spreading widely, as in plant pest control. The collection of huge amounts of spatial data raises various issues including that of scale. Data from UAVs generally explore multiple scales, so the problem arises in determining which one(s) may be relevant for a given application. The objective of this work was to investigate the potential of UAV images in the fight against the Xylella pest for olive trees. The data were a multiband UAV image collected on one date in an olive grove affected by Xylella. A multivariate geostatistics approach was applied, consisting firstly of estimating the linear coregionalization model to detect the scales from the data; and secondly, of using multiple factor kriging to extract the sets of scale-dependent regionalized factors. One factor was retained for each of the two selected scales. The short-range factor could be used in controlling the bacterium infection while the longer-range factor could be used in partitioning the field into three management zones. The work has shown the UAV data potential in Xylella control, but many problems still need to be solved for the automatic detection of infected plants in the early stages. Full article
(This article belongs to the Special Issue Application of UAS-Based Spectral Imaging in Agriculture and Forestry)
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