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Drones
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Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring
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Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: 19 May 2024 | Viewed by 4184

Special Issue Editor

Dr. Tim Whiteside

E-Mail Website
Guest Editor
Department of Climate Change, Energy, The Environment and Water, Environmental Research Institute of the Supervising Scientist, Darwin, Australia
Interests: spatial analysis; vegetation mapping; landscape ecology; physical geography; remote sensing; vegetation; segmentation; satellite image analysis; UAV; image analysis

Special Issue Information

Dear Colleagues,

The application of Uncrewed Aerial Vehicles (UAVs) in vegetation monitoring has witnessed significant advancements, revolutionizing the way we observe and analyze plant life and condition. UAVs have the capacity to acquire data to monitor vegetation at very high spatial and temporal resolutions, is flexible, and cost-effective. At the same time, UAVs are equipped with advanced remote sensing equipment, which enables accurate observation and flexible deployment to measure vegetation cover, growth status, and terrain features. In addition, data acquired by UAVs can be analyzed in depth by combining AI and machine learning algorithms to support refined vegetation classification, change detection, and ecological environment assessment.

This Special Issue aims to collect high-quality and innovative scientific papers on the application of UAVs for vegetation monitoring. Specific topics include, but are not limited to:

  • 3D monitoring for structural change;
  • The monitoring of plant health/fate using imaging spectrometry;
  • The application of AI and machine learning models for classification and change detection;
  • Multi-modal sensing for monitoring;
  • Real-time and near-real time monitoring;
  • The development of novel but meaningful metrics for monitoring;
  • Automation of monitoring programs;
  • UAV data informing monitoring at larger scales (ie satellite).

By publishing this Special Issue, we hope to foster collaboration and knowledge exchange in the field of UAV-based vegetation monitoring, driving further innovation and progress in this dynamic area of research.We look forward to receiving your original research articles and reviews.

Dr. Tim Whiteside
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV
  • vegetation monitoring
  • vegetation mapping
  • image analysis
  • plant phenotyping
  • spatio-temporal analysis

Published Papers (4 papers)

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Research

22 pages, 8871 KiB  
Article
Early Drought Detection in Maize Using UAV Images and YOLOv8+
by Shanwei Niu, Zhigang Nie, Guang Li and Wenyu Zhu
Drones 2024, 8(5), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8050170 (registering DOI) - 24 Apr 2024
Viewed by 259
Abstract
The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the [...] Read more.
The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization. Full article
(This article belongs to the Special Issue Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring)
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22 pages, 35809 KiB  
Article
UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification
by Robert Van Alphen, Kai C. Rains, Mel Rodgers, Rocco Malservisi and Timothy H. Dixon
Drones 2024, 8(3), 113; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030113 - 21 Mar 2024
Viewed by 858
Abstract
As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and [...] Read more.
As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and species composition. However, this transition will likely be episodic, complicating monitoring efforts, as mangrove advances are countered by dieback from increasingly impactful storms. Coastal habitat monitoring has traditionally been conducted through satellite and ground-based surveys. Here we investigate the use of UAV-LiDAR (unoccupied aerial vehicle–light detection and ranging) and multispectral photogrammetry to study a Florida coastal wetland. These data have higher resolution than satellite-derived data and are cheaper and faster to collect compared to crewed aircraft or ground surveys. We detected significant canopy change in the period between our survey (2020–2022) and a previous survey (2015), including loss at the scale of individual buttonwood trees (Conocarpus erectus), a woody mangrove associate. The UAV-derived data were collected to investigate the utility of simplified processing and data inputs for habitat classification and were validated with standard metrics and additional ground truth. UAV surveys combined with machine learning can streamline coastal habitat monitoring, facilitating repeat surveys to assess the effects of climate change and other change agents. Full article
(This article belongs to the Special Issue Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring)
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15 pages, 17419 KiB  
Article
Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery
by Daihao Yin, Yijun Cai, Yajing Li, Wenshan Yuan and Zhong Zhao
Drones 2024, 8(3), 91; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030091 - 07 Mar 2024
Viewed by 1108
Abstract
Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health [...] Read more.
Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health status of old trees. All trees were classified according to health status into three classes: healthy, declining, and severe declining trees, based on the above-ground parts of the trees. Two traditional machine learning algorithms, Support Vector Machines (SVM) and Random Forest (RF), were employed to assess their health status. Both algorithms incorporated selected variables, as well as additional variables (aspect and canopy area). The results indicated that the inclusion of these additional variables improved the overall accuracy of the models by 8.3% to 13.9%, with kappa values ranging from 0.166 and 0.233. Among the models tested, the A-RF model (RF with aspect and canopy area variables) demonstrated the highest overall accuracy (75%) and kappa (0.571), making it the optimal choice for assessing the health condition of old trees. Overall, this research presents a novel and cost-effective approach to assessing the health status of old trees. Full article
(This article belongs to the Special Issue Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring)
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18 pages, 17666 KiB  
Article
Canopy Structural Changes in Black Pine Trees Affected by Pine Processionary Moth Using Drone-Derived Data
by Darío Domingo, Cristina Gómez, Francisco Mauro, Hermine Houdas, Gabriel Sangüesa-Barreda and Francisco Rodríguez-Puerta
Drones 2024, 8(3), 75; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030075 - 22 Feb 2024
Cited by 1 | Viewed by 1462
Abstract
Pine species are a key social and economic component in Mediterranean ecosystems, where insect defoliations can have far-reaching consequences. This study aims to quantify the impact of pine processionary moth (PPM) on canopy structures, examining its evolution over time at the individual tree [...] Read more.
Pine species are a key social and economic component in Mediterranean ecosystems, where insect defoliations can have far-reaching consequences. This study aims to quantify the impact of pine processionary moth (PPM) on canopy structures, examining its evolution over time at the individual tree level using high-density drone LiDAR-derived point clouds. Focusing on 33 individuals of black pine (Pinus nigra)—a species highly susceptible to PPM defoliation in the Mediterranean environment—bitemporal LiDAR scans were conducted to capture the onset and end of the major PPM feeding period in winter. Canopy crown delineation performed manually was compared with LiDAR-based methods. Canopy metrics from point clouds were computed for trees exhibiting contrasting levels of defoliation. The structural differences between non-defoliated and defoliated trees were assessed by employing parametric statistical comparisons, including analysis of variance along with post hoc tests. Our analysis aimed to distinguish structural changes resulting from PPM defoliation during the winter feeding period. Outcomes revealed substantive alterations in canopy cover, with an average reduction of 22.92% in the leaf area index for defoliated trees, accompanied by a significant increase in the number of returns in lower tree crown branches. Evident variations in canopy density were observed throughout the feeding period, enabling the identification of two to three change classes using LiDAR-derived canopy density metrics. Manual and LiDAR-based crown delineations exhibited minimal differences in computed canopy LiDAR metrics, showcasing the potential of LiDAR delineations for broader applications. PPM infestations induced noteworthy modifications in canopy morphology, affecting key structural parameters. Drone LiDAR data emerged as a comprehensive tool for quantifying these transformations. This study underscores the significance of remote sensing approaches in monitoring insect disturbances and their impacts on forest ecosystems. Full article
(This article belongs to the Special Issue Application of Uncrewed Aerial Vehicles (UAVs) in Vegetation Monitoring)
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