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Special Issue "Forestry Applications of Unmanned Aerial Vehicles (UAVs)"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Quantitative Methods and Remote Sensing".

Deadline for manuscript submissions: 20 May 2019

Special Issue Editor

Guest Editor
Dr. Alessandro Matese

IBIMET CNR–Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, via G. Caproni 8, 50145 Firenze, Italy
Website | E-Mail
Interests: remote sensing of agroecosystems, precision agriculture, precision forestry, UAV and Satellite remote sensing, wireless sensors network, biogeochemical cycles, data fusion, machine learning, computer vision and geostatistics

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral and thermal infrared. LiDAR sensors are becoming commonly-used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees and the primary objective is the conservation and protection of forests. Nevertheless forestry and agriculture involve the cultivation of renewable raw materials the difference is that forestry is less tied to economic aspects and this reflects the delay in using new monitoring technologies. The main forestry application aim to inventory resources, map diseases, species classification, fire monitoring and spatial gaps estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry.

Dr. Alessandro Matese
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 papers will be 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. Forests 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 1800 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 platforms
  • Sensors
  • Object recognition and machine vision
  • Estimation of plant traits including 3D measurements
  • Integration of UAVs with satellite images
  • Forestry applications

Published Papers (5 papers)

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Research

Open AccessArticle Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis
Forests 2019, 10(3), 235; https://doi.org/10.3390/f10030235
Received: 23 January 2019 / Revised: 11 February 2019 / Accepted: 28 February 2019 / Published: 6 March 2019
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Abstract
Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading [...] Read more.
Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading of black locust in short rotation coppices. This research study focused on seed germination, stump shoot resprout, and spreading by root suckering of black locust in ten short rotation coppices in Germany. Seed experiments and sample plots were analyzed for the study. Spreading was detected and measured with unmanned aerial system (UAS)-based images and classification technology—object-based image analysis (OBIA). Additionally, the classification of single UAS images was tested by applying a convolutional neural network (CNN), a deep learning model. The analyses showed that seed germination increases with increasing warm-cold variety and scarification. Moreover, it was found that the number of shoots per stump decreases as shoot age increases. Furthermore, spreading increases with greater light availability and decreasing tillage. The OBIA and CNN image analysis technologies achieved 97% and 99.5% accuracy for black locust classification in UAS images. All in all, the three reproduction strategies of black locust in short rotation coppices differ with regards to initialization, intensity, and growth performance, but all play a role in the survival and spreading of black locust. Full article
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs))
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Graphical abstract

Open AccessArticle Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests
Forests 2019, 10(1), 24; https://doi.org/10.3390/f10010024
Received: 27 November 2018 / Revised: 18 December 2018 / Accepted: 27 December 2018 / Published: 3 January 2019
Cited by 1 | PDF Full-text (7014 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer science technologies has provided classification methods for mapping at both pixel-based and object-based analysis, for increasingly complex environments. These thematic maps [...] Read more.
Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer science technologies has provided classification methods for mapping at both pixel-based and object-based analysis, for increasingly complex environments. These thematic maps then serve as vital resources for a variety of research and management needs. However, to properly use the resulting thematic map as a decision-making support tool, an assessment of map accuracy must be performed. The methods for assessing thematic accuracy have coalesced into a site-specific multivariate analysis of error, measuring uncertainty in relation to an established reality known as reference data. Ensuring statistical validity, access and time constraints, and immense costs limit the collection of reference data in many projects. Therefore, this research proposes evaluating the feasibility of adopting the low-cost, flexible, high-resolution sensor-capable Unmanned Aerial Systems (UAS, UAV, or Drone) platform for collecting reference data to use in thematic map accuracy assessments for complex environments. This pilot study analyzed 377.57 ha of New England forests, over six University of New Hampshire woodland properties, to compare the similarity between UAS-derived orthomosaic samples and ground-based continuous forest inventory (CFI) plot classifications of deciduous, mixed, and coniferous forest cover types. Using an eBee Plus fixed-wing UAS, 9173 images were acquired and used to create six comprehensive orthomosaics. Agreement between our UAS orthomosaics and ground-based sampling forest compositions reached 71.43% for pixel-based classification and 85.71% for object-based classification reference data methods. Despite several documented sources of uncertainty or error, this research demonstrated that UAS are capable of highly efficient and effective thematic map accuracy assessment reference data collection. As UAS hardware, software, and implementation policies continue to evolve, the potential to meet the challenges of accurate and timely reference data collection will only increase. Full article
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs))
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Open AccessArticle Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning
Forests 2018, 9(12), 736; https://doi.org/10.3390/f9120736
Received: 30 October 2018 / Revised: 22 November 2018 / Accepted: 23 November 2018 / Published: 26 November 2018
Cited by 1 | PDF Full-text (24730 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the [...] Read more.
One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the difficult access to these swamps. The contribution of this paper is twofold: the presentation of a dataset called MauFlex, and the proposal of a segmentation and measurement method for areas covered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method performs a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different environment and light conditions using three different RGB cameras. The MauFlex dataset was created from these images and it consists of 25,248 image patches of 512 × 512 pixels and their respective ground truth masks. The results over the test set achieved an accuracy of 98.143%, specificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation. Full article
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs))
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Open AccessArticle Application of UAV Photogrammetric System for Monitoring Ancient Tree Communities in Beijing
Forests 2018, 9(12), 735; https://doi.org/10.3390/f9120735
Received: 18 October 2018 / Revised: 22 November 2018 / Accepted: 23 November 2018 / Published: 24 November 2018
Cited by 1 | PDF Full-text (12791 KB) | HTML Full-text | XML Full-text
Abstract
Ancient tree community surveys have great scientific value to the study of biological resources, plant distribution, environmental change, genetic characteristics of species, and historical and cultural heritage. The largest ancient pear tree communities in China, which are rare, are located in the Daxing [...] Read more.
Ancient tree community surveys have great scientific value to the study of biological resources, plant distribution, environmental change, genetic characteristics of species, and historical and cultural heritage. The largest ancient pear tree communities in China, which are rare, are located in the Daxing District of Beijing. However, the environmental conditions are tough, and the distribution is relatively dispersed. Therefore, a low-cost, high-efficiency, and high-precision measuring system is urgently needed to complete the survey of ancient tree communities. By unmanned aerial vehicle (UAV) photogrammetric program research, ancient tree information extraction method research, and ancient tree diameter at breast height (DBH) and age prediction model research, the proposed method can realize the measurement of tree height, crown width, and prediction of DBH and tree age with low cost, high efficiency, and high precision. Through experiments and analysis, the root mean square error (RMSE) of the tree height measurement was 0.1814 m, the RMSE of the crown width measurement was 0.3292 m, the RMSE of the DBH prediction was 3.0039 cm, and the RMSE of the tree age prediction was 4.3753 years, which could meet the needs of ancient tree survey of the Daxing District Gardening and Greening Bureau. Therefore, a UAV photogrammetric measurement system proved to be capable when applied in the survey of ancient tree communities and even in partial forest inventories. Full article
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs))
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Open AccessArticle Detection of Coniferous Seedlings in UAV Imagery
Forests 2018, 9(7), 432; https://doi.org/10.3390/f9070432
Received: 26 June 2018 / Revised: 13 July 2018 / Accepted: 16 July 2018 / Published: 18 July 2018
Cited by 3 | PDF Full-text (9073 KB) | HTML Full-text | XML Full-text
Abstract
Rapid assessment of forest regeneration using unmanned aerial vehicles (UAVs) is likely to decrease the cost of establishment surveys in a variety of resource industries. This research tests the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest areas in [...] Read more.
Rapid assessment of forest regeneration using unmanned aerial vehicles (UAVs) is likely to decrease the cost of establishment surveys in a variety of resource industries. This research tests the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest areas in Alberta, Canada. In developing our protocols, we gave special consideration to creating a workflow that could perform in an operational context, avoiding comprehensive wall-to-wall surveys and complex photogrammetric processing in favor of an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling. Using simple spectral decision rules from a red, green, and blue (RGB) camera, we documented a seedling detection rate of 75.8 % (n = 149), on the basis of independent test data. While moderate imbalances between the omission and commission errors suggest that our workflow has a tendency to underestimate the seedling density in a harvest block, the plot-level associations with ground surveys were very high (Pearson’s r = 0.98; n = 14). Our results were promising enough to suggest that UAVs can be used to detect coniferous seedlings in an operational capacity with standard RGB cameras alone, although our workflow relies on seasonal leaf-off windows where seedlings are visible and spectrally distinct from their surroundings. In addition, the differential errors between the pine seedlings and spruce seedlings suggest that operational workflows could benefit from multiple decision rules designed to handle diversity in species and other sources of spectral variability. Full article
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs))
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