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Advanced Applications of UAV Remote Sensing in Forest Structure

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

Deadline for manuscript submissions: closed (3 March 2023) | Viewed by 20612

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Special Issue Editors

Prof. Dr. Liyong Fu

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Guest Editor

Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China
Interests: forest measurement; forest growth and yield modeling; forest biometrics
Special Issues, Collections and Topics in MDPI journals

Dr. Qingwang Liu

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Guest Editor

Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: LiDAR; forest remote sensing

Prof. Dr. Hua Sun

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Guest Editor

Research Center of Forestry Remote Sensing and Information Engineering, College of Forestry, Central South University of Forestry and Technology, Changsha 410000, China
Interests: digital forest resource monitoring; spatial statistics; LiDAR
Special Issues, Collections and Topics in MDPI journals

Dr. Qiao Chen

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Co-Guest Editor

Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: remote sensing monitoring of forest resources; UAV data forest parameter extraction and tree species identification

Special Issue Information

Dear Colleagues,

Forest structure is essential for forest management and ecosystem dynamics research. Many forest structure parameters, such as diameter at breast height (DBH) and total tree height, are the most important tree or stand characteristics and can be directly measured on the ground. They are indicators of tree vigor and used to describe stand structure, estimate tree volume and biomass, and select sample trees in forest inventory. However, collecting forest structure parameters, e.g., DBH data at a large scale, can be costly and time consuming. Substantial research has thus been conducted to advance the methods for obtaining DBH values at the individual tree or stand levels through development and use of estimation models based on satellite or airborne remote sensing technologies. Especially, with the emergence and advancement of unmanned aerial vehicles (UAV) remote sensing technology over the past decade, automated individual tree detection and crown delineation using high spatial resolution data provides good opportunities for estimation of forest structure parameters at a large scale. We encourage studies from all fields, including experimental studies, monitoring approaches and models, to contribute to this Special Issue in order to promote knowledge and adaptation strategies for the forest inventory, management, health protection, dynamic monitoring, carbon sequestration, and future development of forest ecosystems.

Prof. Dr. Liyong Fu
Dr. Qingwang Liu
Prof. Dr. Hua Sun
Dr. Qiao Chen
Guest Editors

Manuscript Submission Information

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Keywords

forest modeling
forest inventory
forest management
forest carbon sequestration
forest dynamic monitoring
airborne light detection and ranging (LiDAR)
forest structure
unmanned aerial vehicles (UAV)

Published Papers (9 papers)

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Research

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13 pages, 58565 KiB

Open AccessArticle

Cost-Effective Aerial Inventory of Spruce Seedlings Using Consumer Drones and Deep Learning Techniques with Two-Stage UAV Flight Patterns

by Eugene Lopatin and Pasi Poikonen

Forests 2023, 14(5), 973; https://doi.org/10.3390/f14050973 - 08 May 2023

Viewed by 1866

Abstract

Traditional methods of counting seedling inventory are expensive, time-consuming, and lacking in spatial resolution. Although previous studies have explored the use of drones for seedling inventory, a cost-effective and accurate solution that can detect and identify missing seedlings at a high spatial resolution using consumer drones with traditional RGB cameras is needed. This study aims to address this research gap by developing such a solution using deep learning techniques. A two-stage drone flight pattern was employed to collect high-resolution data (2.22 mm). Firstly, a flight was conducted at a 120 m altitude to generate an obstacle map. This map was then used to conduct a second flight at a 5 m altitude, avoiding collision with larger trees. Convolutional neural networks were used to detect planted spruce seedlings with high accuracy (mean average precision of 84% and detection accuracy of 97.86%). Kernel density estimation was utilized to identify areas with missing seedlings. This study demonstrates that consumer drones and deep learning techniques can provide a cost-effective and accurate solution for taking aerial inventories of spruce seedlings. The two-stage flight pattern used in this study allowed for safe and efficient data collection, while the use of convolutional neural networks and kernel density estimation facilitated the accurate detection of planted seedlings and identification of areas with missing seedlings. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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16 pages, 6998 KiB

Open AccessArticle

Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests

by Lili Lin, Zhenbang Hao, Christopher J. Post and Elena A. Mikhailova

Forests 2023, 14(2), 233; https://doi.org/10.3390/f14020233 - 26 Jan 2023

Cited by 6 | Viewed by 1503

Abstract

Casuarina equisetifolia L. plays a significant role in sandy, coastal regions for sand stabilization and windbreaks. However, C. equisetifolia forests are susceptible to plant diseases and insect pests, resulting in mortality due to pure stands and a harsh natural environment. Mapping the distribution of C. equisetifolia and detecting its height can inform forest-management decisions. Unmanned aerial vehicle (UAV) imagery, coupled with the classical detection method, can provide accurate information on tree-level forest parameters. Considering that the accuracy of a forest-parameter estimation is impacted by various flight altitudes and extraction parameters, the purpose of this study is to determine the appropriate flight altitude and extraction parameters for mapping C. equisetifolia using UAV imagery and the local maxima algorithm in order to monitor C. equisetifolia more accurately. A total of 11 different flight altitudes and 36 combinations of circular smoothing window size (CSWS) and fixed circular window size (FCWS) were tested, and 796 trees with corresponding positions in the UAV image and ground–tree heights were used as reference. The results show that the combination of a 0.1 m CSWS and a 0.8 m FCWS for individual tree detection (ITD) and tree-height detection achieved excellent accuracy (with an F1 score of 91.44% for ITD and an estimation accuracy (EA) of 79.49% for tree-height detection). A lower flight altitude did not indicate a higher accuracy for individual tree and tree-height detection. The UAV image obtained within a flight altitude of 60 m–80 m can meet the accuracy requirements for the identification of C. equisetifolia tree-height estimation (F1 score > 85% for ITD; EA > 75% for tree-height estimation). This study provides a foundation for monitoring C. equisetifolia by using UAV imagery and applying the local maxima algorithm, which may help forestry practitioners detect C. equisetifolia trees and tree heights more accurately, providing more information on C. equisetifolia growth status. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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13 pages, 4375 KiB

Open AccessArticle

Modeling the Missing DBHs: Influence of Model Form on UAV DBH Characterization

by Wade T. Tinkham, Neal C. Swayze, Chad M. Hoffman, Lauren E. Lad and Mike A. Battaglia

Forests 2022, 13(12), 2077; https://doi.org/10.3390/f13122077 - 06 Dec 2022

Cited by 1 | Viewed by 1863

Abstract

The reliability of forest management decisions partly depends on the quality and extent of the data needed for the decision. However, the relatively high cost of traditional field sampling limits sampling intensity and data quality. One strategy to increase data quality and extent, while reducing the overall sample effort, is using remote sensing-based data from unmanned aerial vehicles (UAV). While these techniques reliably identify most tree locations and heights in open-canopied forests, their ability to characterize diameter at breast height (DBH) is limited to estimates of a fraction of trees within the area. This study used UAV-derived DBHs and explanatory variables to test five model forms in predicting the missing DBHs. The results showed that filtering UAV DBHs using regionally derived height to DBH allometries significantly improved model performance. The best predicting model was slightly biased, with a 5.6 cm mean error and a mean absolute error of 6.8 cm. When applied across the stand, the number of trees was underestimated by 26.7 (3.9%), while the basal area and quadratic mean diameter were overestimated by 3.3 m² ha⁻¹ (13.1%) and 1.8 cm (8.3%), respectively. This study proposes a pathway for remotely sensed DBHs to predict missing DBHs; however, challenges are highlighted in ensuring the model training dataset represents the population. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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13 pages, 3051 KiB

Open AccessArticle

Precious Tree Pest Identification with Improved Instance Segmentation Model in Real Complex Natural Environments

by Ying Guo, Junjia Gao, Xuefeng Wang, Hongyan Jia, Yanan Wang, Yi Zeng, Xin Tian, Xiyun Mu, Yan Chen and Xuan OuYang

Forests 2022, 13(12), 2048; https://doi.org/10.3390/f13122048 - 01 Dec 2022

Cited by 2 | Viewed by 1087

Abstract

It is crucial to accurately identify precious tree pests in a real, complex natural environment in order to monitor the growth of precious trees and provide growers with the information they need to make effective decisions. However, pest identification in real complex natural environments is confronted with several obstacles, including a lack of contrast between the pests and the background, the overlapping and occlusion of leaves, numerous variations in pest size and complexity, and a great deal of image noise. The purpose of the study was to construct a segmentation method for identifying precious tree pests in a complex natural environment. The backbone of an existing Mask region-based convolutional neural network was replaced with a Swin Transformer to improve its feature extraction capability. The experimental findings demonstrated that the suggested method successfully segmented pests in a variety of situations, including shaded, overlapped, and foliage- and branch-obscured pests. The proposed method outperformed the two competing methods, indicating that it is capable of accurately segmenting pests in a complex natural environment and provides a solution for achieving accurate segmentation of precious tree pests and long-term automatic growth monitoring. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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27 pages, 1061 KiB

Open AccessArticle

The Application of UASs in Forest Management and Monitoring: Challenges and Opportunities for Use in the Miombo Woodland

by Hastings Shamaoma, Paxie W. Chirwa, Abel Ramoelo, Andrew T. Hudak and Stephen Syampungani

Forests 2022, 13(11), 1812; https://doi.org/10.3390/f13111812 - 31 Oct 2022

Cited by 3 | Viewed by 1844

Abstract

The Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays an important role in supplementing field methods to fill in data gaps. Traditional satellite and manned aircraft remote sensing platforms have their own advantages and limitations. The advent of unmanned aerial systems (UASs) has made it possible to acquire forest data at unprecedented spatial and temporal scales. UASs are adaptable to various forest applications in terms of providing flexibility in data acquisition with different sensors (RGB, multispectral, hyperspectral, thermal and light detection and ranging (lidar)) at a convenient time. To highlight possible applications in the Miombo woodlands, we first provide an overview of the Miombo woodlands and recent progress in remote sensing with small UASs. An overview of some potential forest applications was undertaken to identify key prospects and challenges for UAS applications in the Miombo region, which will provide expertise and guidance upon which future applications in the Miombo woodlands should be based. While much of the potential of using UASs for forest data acquisition in the Miombo woodlands remains to be realized, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of forest applications. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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21 pages, 13954 KiB

Open AccessArticle

Examining the Angular Effects of UAV-LS on Vegetation Metrics Using a Framework for Mediating Effects

by Yang Liu, Yu Shan, Hong Ying, Du Wala, Xiang Zhang, A Ruhan, Su Rina and Su Rina

Forests 2022, 13(8), 1221; https://doi.org/10.3390/f13081221 - 02 Aug 2022

Cited by 1 | Viewed by 1518

Abstract

Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe). Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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13 pages, 4531 KiB

Open AccessArticle

Measuring the Tree Height of Picea crassifolia in Alpine Mountain Forests in Northwest China Based on UAV-LiDAR

by Siwen Chen, Yanyun Nian, Zeyu He and Minglu Che

Forests 2022, 13(8), 1163; https://doi.org/10.3390/f13081163 - 22 Jul 2022

Cited by 4 | Viewed by 1662

Abstract

Forests in alpine mountainous regions are sensitive to global climate change. Accurate measurement of tree height is essential for forest aboveground biomass estimation. Unmanned aerial vehicle light detection and ranging (UAV-LiDAR) in tree height estimation has been extensively used in forestry inventories. This study investigated the influence of varying flight heights and point cloud densities on the extraction of tree height, and four flight heights (i.e., 85, 115, 145, and 175 m) were set in three Picea crassifolia plots in the Qilian Mountains. After point cloud data were classified, tree height was extracted from a canopy height model (CHM) on the basis of the individual tree segmentation. Through comparison with ground measurements, the tree height estimations of different flight heights and point cloud densities were analyzed. The results indicated that (1) with a flight height of 85 m, the tree height estimation achieved the highest accuracy (R² = 0.75, RMSE = 2.65), and the lowest accuracy occurred at a height of 175 m (R² = 0.65, RMSE = 3.00). (2) The accuracy of the tree height estimation decreased as the point cloud density decreased. The accuracies of tree height estimation from low-point cloud density (R² = 0.70, RMSE = 2.75) and medium density (R² = 0.69, RMSE = 2.80) were comparable. (3) Tree height was slightly underestimated in most cases when CHM-based segmentation methods were used. Consequently, a flight height of 145 m was more applicable for maintaining tree height estimation accuracy and assuring the safety of UAVs flying in alpine mountain regions. A point cloud density of 125–185 pts/m² can guarantee tree height estimation accuracy. The results of this study could potentially improve tree height estimation and provide available UAV-LiDAR flight parameters in alpine mountainous regions in Northwest China. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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14 pages, 3459 KiB

Open AccessArticle

The Potential of Widespread UAV Cameras in the Identification of Conifers and the Delineation of Their Crowns

by Jan Komárek, Petr Klápště, Karel Hrach and Tomáš Klouček

Forests 2022, 13(5), 710; https://doi.org/10.3390/f13050710 - 30 Apr 2022

Cited by 7 | Viewed by 2437

Abstract

With the ever-improving advances in computer vision and Earth observation capabilities, Unmanned Aerial Vehicles (UAVs) allow extensive forest inventory and the description of stand structure indirectly. We performed several flights with different UAVs and popular sensors over two sites with coniferous forests of various ages and flight levels using the custom settings preset by solution suppliers. The data were processed using image-matching techniques, yielding digital surface models, which were further analyzed using the lidR package in R. Consumer-grade RGB cameras were consistently more successful in the identification of individual trees at all of the flight levels (84–77% for Phantom 4), compared to the success of multispectral cameras, which decreased with higher flight levels and smaller crowns (77–54% for RedEdge-M). Regarding the accuracy of the measured crown diameters, RGB cameras yielded satisfactory results (Mean Absolute Error—MAE of 0.79–0.99 m and 0.88–1.16 m for Phantom 4 and Zenmuse X5S, respectively); multispectral cameras overestimated the height, especially in the full-grown forests (MAE = 1.26–1.77 m). We conclude that widely used low-cost RGB cameras yield very satisfactory results for the description of the structural forest information at a 150 m flight altitude. When (multi)spectral information is needed, we recommend reducing the flight level to 100 m in order to acquire sufficient structural forest information. The study contributes to the current knowledge by directly comparing widely used consumer-grade UAV cameras and providing a clear elementary workflow for inexperienced users, thus helping entry-level users with the initial steps and supporting the usability of such data in practice. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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Review

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31 pages, 4562 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review

by André Duarte, Nuno Borralho, Pedro Cabral and Mário Caetano

Forests 2022, 13(6), 911; https://doi.org/10.3390/f13060911 - 10 Jun 2022

Cited by 39 | Viewed by 5579

Abstract

Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented. Full article

(This article belongs to the Special Issue Advanced Applications of UAV Remote Sensing in Forest Structure)

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