Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Individual Tree Detection and Characterisation from UAV Data
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Individual Tree Detection and Characterisation from UAV Data

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 December 2020) | Viewed by 150595

Special Issue Editor

Dr. Luke Wallace

E-Mail Website
Guest Editor
School of Science, Geospatial Science, RMIT University, Melbourne, VIC 3000, Australia
Interests: 3D remote sensing; remote sensing of forested environments; laser scanning; vegetation structure; wildfire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) and associated sensors are providing us with data with spatial resolutions not previously available from an airborne source. One area of forest remote sensing that is benefiting greatly from this increased resolution is the detection and characterisation of individual trees. Individual tree information is required to inform a number of diverse fields, including forestry, habitat mapping and ecology, urban forestry and fire behaviour modelling.

Whilst UAV data has the potential to improve our understanding at the level of the individual tree, a number of challenges remain to be addressed. This Special Issue invites prospective authors to submit papers that address challenges within the field of individual tree detection and characterisation using UAVs. Potential topics include:

  • new algorithms for the extraction of individual tree information from UAV-based data;
  • new methods for characterising the three-dimensional (3D) tree structure once individual trees have been identified;
  • species and health identification of individual trees;
  • the impact of data collection parameters on individual tree detection and characterisation;
  • comparisons of detection and characterisation accuracies between different sensors as well as between UAVs and other platforms; and
  • sensor fusion to improve individual tree detection and characterisation.

Dr. Luke Wallace
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • individual tree detection
  • forest remote sensing
  • LiDAR
  • unmanned aerial vehicle (UAV)
  • drones
  • sensor fusion

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (20 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 12524 KiB  
Article
Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park
by Ebadat Ghanbari Parmehr and Marco Amati
Remote Sens. 2021, 13(11), 2062; https://doi.org/10.3390/rs13112062 - 24 May 2021
Cited by 43 | Viewed by 6913
Abstract
Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds [...] Read more.
Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R2 of 99.54% and the estimated tree canopy parameters are correlated with R2 of higher than 95%. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

18 pages, 1742 KiB  
Article
Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests
by Anton Kuzmin, Lauri Korhonen, Sonja Kivinen, Pekka Hurskainen, Pasi Korpelainen, Topi Tanhuanpää, Matti Maltamo, Petteri Vihervaara and Timo Kumpula
Remote Sens. 2021, 13(9), 1723; https://doi.org/10.3390/rs13091723 - 29 Apr 2021
Cited by 13 | Viewed by 5299
Abstract
European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence [...] Read more.
European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

26 pages, 10719 KiB  
Article
Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)
by Veronika Kopačková-Strnadová, Lucie Koucká, Jan Jelének, Zuzana Lhotáková and Filip Oulehle
Remote Sens. 2021, 13(4), 705; https://doi.org/10.3390/rs13040705 - 16 Feb 2021
Cited by 25 | Viewed by 5217
Abstract
Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In [...] Read more.
Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: (i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and (ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

22 pages, 5082 KiB  
Article
Effects of Differences in Structure from Motion Software on Image Processing of Unmanned Aerial Vehicle Photography and Estimation of Crown Area and Tree Height in Forests
by Shohei Kameyama and Katsuaki Sugiura
Remote Sens. 2021, 13(4), 626; https://doi.org/10.3390/rs13040626 - 9 Feb 2021
Cited by 29 | Viewed by 5710
Abstract
This study examines the effects of differences in structure from motion (SfM) software on image processing of aerial images by unmanned aerial vehicles (UAV) and the resulting estimations of tree height and tree crown area. There were 20 flight conditions for the UAV [...] Read more.
This study examines the effects of differences in structure from motion (SfM) software on image processing of aerial images by unmanned aerial vehicles (UAV) and the resulting estimations of tree height and tree crown area. There were 20 flight conditions for the UAV aerial images, which were a combination of five conditions for flight altitude, two conditions for overlap, and two conditions for side overlap. Images were then processed using three SfM programs (Terra Mapper, PhotoScan, and Pix4Dmapper). The tree height and tree crown area were determined, and the SfM programs were compared based on the estimations. The number of densified point clouds for PhotoScan (160 × 105 to 50 × 105) was large compared to the two other two SfM programs. The estimated values of crown area and tree height by each SfM were compared via Bonferroni multiple comparisons (statistical significance level set at p < 0.05). The estimated values of canopy area showed statistically significant differences (p < 0.05) in 14 flight conditions for Terra Mapper and PhotoScan, 16 flight conditions for Terra Mapper and Pix4Dmapper, and 11 flight conditions for PhotoScan and Pix4Dmappers. In addition, the estimated values of tree height showed statistically significant differences (p < 0.05) in 15 flight conditions for Terra Mapper and PhotoScan, 19 flight conditions for Terra Mapper and Pix4Dmapper, and 20 flight conditions for PhotoScan and Pix4Dmapper. The statistically significant difference (p < 0.05) between the estimated value and measured value of each SfM was confirmed under 18 conditions for Terra Mapper, 20 conditions for PhotoScan, and 13 conditions for Pix4D. Moreover, the RMSE and rRMSE values of the estimated tree height were 5–6 m and 20–28%, respectively. Although the estimation accuracy of any SfM was low, the estimated tree height by Pix4D in many flight conditions had smaller RMSE values than the other software. As statistically significant differences were found between the SfMs in many flight conditions, we conclude that there were differences in the estimates of crown area and tree height depending on the SfM used. In addition, Pix4Dmapper is suitable for estimating forest information, such as tree height, and PhotoScan is suitable for detailed monitoring of disaster areas. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

18 pages, 6559 KiB  
Article
Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning
by Romain Neuville, Jordan Steven Bates and François Jonard
Remote Sens. 2021, 13(3), 352; https://doi.org/10.3390/rs13030352 - 20 Jan 2021
Cited by 106 | Viewed by 18396
Abstract
Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter [...] Read more.
Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

24 pages, 27782 KiB  
Article
Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning
by Ha Trang Nguyen, Maximo Larry Lopez Caceres, Koma Moritake, Sarah Kentsch, Hase Shu and Yago Diez
Remote Sens. 2021, 13(2), 260; https://doi.org/10.3390/rs13020260 - 13 Jan 2021
Cited by 42 | Viewed by 5215 | Correction
Abstract
Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics [...] Read more.
Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

14 pages, 20093 KiB  
Article
Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms
by Jun Qin, Biao Wang, Yanlan Wu, Qi Lu and Haochen Zhu
Remote Sens. 2021, 13(2), 162; https://doi.org/10.3390/rs13020162 - 6 Jan 2021
Cited by 82 | Viewed by 7313
Abstract
Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it [...] Read more.
Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

22 pages, 7011 KiB  
Article
Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard
by José Luis Gallardo-Salazar and Marín Pompa-García
Remote Sens. 2020, 12(24), 4144; https://doi.org/10.3390/rs12244144 - 18 Dec 2020
Cited by 41 | Viewed by 8213
Abstract
Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis [...] Read more.
Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis included different vegetation indices estimated with a high-resolution orthomosaic. Statistically reliable results were found through a three-phase workflow consisting of image acquisition, canopy analysis, and validation with field measurements. Of the 117 trees in the field, 112 (95%) were detected by the algorithm, while height, area, and crown diameter were underestimated by 1.78 m, 7.58 m2, and 1.21 m, respectively. Individual tree attributes obtained from the UAV, such as total height (H) and the crown diameter (CD), made it possible to generate good allometric equations to infer the basal diameter (BD) and diameter at breast height (DBH), with R2 of 0.76 and 0.79, respectively. Multispectral indices were useful as tree vigor parameters, although the normalized-difference vegetation index (NDVI) was highlighted as the best proxy to monitor the phytosanitary condition of the orchard. Spatial variation in individual tree productivity suggests the differential management of ramets. The consistency of the results allows for its application in the field, including the complementation of spectral information that can be generated; the increase in accuracy and efficiency poses a path to modern inventories. However, the limitation for its application in forests of more complex structures is identified; therefore, further research is recommended. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

20 pages, 2130 KiB  
Article
An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials
by Robin J. L. Hartley, Ellen Mae Leonardo, Peter Massam, Michael S. Watt, Honey Jane Estarija, Liam Wright, Nathanael Melia and Grant D. Pearse
Remote Sens. 2020, 12(24), 4039; https://doi.org/10.3390/rs12244039 - 10 Dec 2020
Cited by 37 | Viewed by 6274
Abstract
The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for [...] Read more.
The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R2 = 0.99, RMSE = 5.91%, and R2 = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R2 = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

31 pages, 15497 KiB  
Article
Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests
by Eric Hyyppä, Xiaowei Yu, Harri Kaartinen, Teemu Hakala, Antero Kukko, Mikko Vastaranta and Juha Hyyppä
Remote Sens. 2020, 12(20), 3327; https://doi.org/10.3390/rs12203327 - 13 Oct 2020
Cited by 115 | Viewed by 9304
Abstract
In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, [...] Read more.
In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

22 pages, 4067 KiB  
Article
Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model
by Alin-Ionuț Pleșoianu, Mihai-Sorin Stupariu, Ionuț Șandric, Ileana Pătru-Stupariu and Lucian Drăguț
Remote Sens. 2020, 12(15), 2426; https://doi.org/10.3390/rs12152426 - 29 Jul 2020
Cited by 68 | Viewed by 9499
Abstract
Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to [...] Read more.
Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to overcome these shortcomings due to its superior performance and versatility, proven with reported detection rates of ~90%. However, such models still find their limits in transferability across study areas, because of different tree conditions (e.g., isolated trees vs. compact forests) and/or resolutions of the input data. This study introduces a highly replicable deep learning ensemble design for ITC detection and species classification based on the established single shot detector (SSD) model. The ensemble model design is based on varying the input data for the SSD models, coupled with a voting strategy for the output predictions. Very high-resolution unmanned aerial vehicles (UAV), aerial remote sensing imagery and elevation data are used in different combinations to test the performance of the ensemble models in three study sites with highly contrasting spatial patterns. The results show that ensemble models perform better than any single SSD model, regardless of the local tree conditions or image resolution. The detection performance and the accuracy rates improved by 3–18% with only as few as two participant single models, regardless of the study site. However, when more than two models were included, the performance of the ensemble models only improved slightly and even dropped. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

25 pages, 14050 KiB  
Article
Individual Tree Detection from UAV Imagery Using Hölder Exponent
by Elena Belcore, Anna Wawrzaszek, Edyta Wozniak, Nives Grasso and Marco Piras
Remote Sens. 2020, 12(15), 2407; https://doi.org/10.3390/rs12152407 - 27 Jul 2020
Cited by 20 | Viewed by 4894
Abstract
This article explores the application of Hölder exponent analysis for the identification and delineation of single tree crowns from very high-resolution (VHR) imagery captured by unmanned aerial vehicles (UAV). Most of the present individual tree crown detection (ITD) methods are based on canopy [...] Read more.
This article explores the application of Hölder exponent analysis for the identification and delineation of single tree crowns from very high-resolution (VHR) imagery captured by unmanned aerial vehicles (UAV). Most of the present individual tree crown detection (ITD) methods are based on canopy height models (CHM) and are very effective as far as an accurate digital terrain model (DTM) is available. This prerequisite is hard to accomplish in some environments, such as alpine forests, because of the high tree density and the irregular topography. Indeed, in such conditions, the photogrammetrically derived DTM can be inaccurate. A novel image processing method supports the segmentation of crowns based only on the parameter related to the multifractality description of the image. In particular, the multifractality is related to the deviation from a strict self-similarity and can be treated as the information about the level of inhomogeneity of considered data. The multifractals, even if well established in image processing and recognized by the scientific community, represent a relatively new application in VHR aerial imagery. In this work, the Hölder exponent (one of the parameters related to multifractal description) is applied to the study of a coniferous forest in the Western Alps. The infrared dataset with 10 cm pixels is captured by a UAV-mounted optical sensor. Then, the tree crowns are detected by a basic workflow. This consists of the thresholding of the image on the basis of the Hölder exponent. Then, the single crowns are segmented through a multiresolution segmentation approach. The ITD segmentation was validated through a two-level validation analysis that included a visual evaluation and the computing of quantitative measures based on 200 reference crowns. The results were checked against the ITD performed in the same area but using only spectral, textural, and elevation information. Specifically, the visual assessment included the estimation of the producer’s and user’s accuracies and the F1 score. The quantitative measures considered are the root mean square error (RMSE) (for the area, the perimeter, and the distance between centroids) and the over-segmentation and under-segmentation indices, the Jaccard index, and the completeness index. The F1 score indicates positive results (over 73%) as well as the completeness index that does not exceed 0.23 on a scale of 0 to 1, taking 0 as the best result possible. The RMSE of the extension of crowns is 3 m2, which represents only 14% of the average extension of reference crowns. The performance of the segmentation based on the Hölder exponent outclasses those based on spectral, textural, and elevation information. Despite the good results of the segmentation, the method tends to under-segment rather than over-segment, especially in areas with sloping. This study lays the groundwork for future research into ITD from VHR optical imagery using multifractals. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

22 pages, 27296 KiB  
Article
Assessment of Tree Detection Methods in Multispectral Aerial Images
by Dagoberto Pulido, Joaquín Salas, Matthias Rös, Klaus Puettmann and Sertac Karaman
Remote Sens. 2020, 12(15), 2379; https://doi.org/10.3390/rs12152379 - 24 Jul 2020
Cited by 18 | Viewed by 6323
Abstract
Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. [...] Read more.
Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Figure 1

21 pages, 4220 KiB  
Article
A Transferable and Effective Method for Monitoring Continuous Cover Forestry at the Individual Tree Level Using UAVs
by Guy Bennett, Andy Hardy, Pete Bunting, Philippe Morgan and Andrew Fricker
Remote Sens. 2020, 12(13), 2115; https://doi.org/10.3390/rs12132115 - 1 Jul 2020
Cited by 9 | Viewed by 4506
Abstract
Transformation to Continuous Cover Forestry (CCF) is a long and difficult process in which frequent management interventions rapidly alter forest structure and dynamics with long lasting impacts. Therefore, a critical component of transformation is the acquisition of up-to-date forest inventory data to direct [...] Read more.
Transformation to Continuous Cover Forestry (CCF) is a long and difficult process in which frequent management interventions rapidly alter forest structure and dynamics with long lasting impacts. Therefore, a critical component of transformation is the acquisition of up-to-date forest inventory data to direct future management decisions. Recently, the use of single tree detection methods derived from unmanned aerial vehicle (UAV) has been identified as being a cost effective method for inventorying forests. However, the rapidly changing structure of forest stands in transformation amplifies the difficultly in transferability of current individual tree detection (ITD) methods. This study presents a novel ITD Bayesian parameter optimisation approach that uses quantile regression and external biophysical tree data sets to provide a transferable and low cost ITD approach to monitoring stands in transformation. We applied this novel method to 5 stands in a variety of transformation stages in the UK and to a independent test study site in California, USA, to assess the accuracy and transferability of this method. Requiring small amounts of training data (15 reference trees) this approach had a mean test accuracy (F-score = 0.88) and provided mean tree diameter estimates (RMSE = 5.6 cm) with differences that were not significance to the ground data (p < 0.05). We conclude that this method can be used to monitor forests stands in transformation and thus can also be applied to a wide range of forest structures with limited manual parameterisation between sites. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

21 pages, 9693 KiB  
Article
Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement
by Sean Krisanski, Mohammad Sadegh Taskhiri and Paul Turner
Remote Sens. 2020, 12(10), 1652; https://doi.org/10.3390/rs12101652 - 21 May 2020
Cited by 45 | Viewed by 7434
Abstract
The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest [...] Read more.
The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m2) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

20 pages, 5604 KiB  
Article
Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement
by Karel Kuželka, Martin Slavík and Peter Surový
Remote Sens. 2020, 12(8), 1236; https://doi.org/10.3390/rs12081236 - 13 Apr 2020
Cited by 83 | Viewed by 7818
Abstract
Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a [...] Read more.
Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

17 pages, 11917 KiB  
Article
Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR
by Juan Picos, Guillermo Bastos, Daniel Míguez, Laura Alonso and Julia Armesto
Remote Sens. 2020, 12(5), 885; https://doi.org/10.3390/rs12050885 - 10 Mar 2020
Cited by 66 | Viewed by 9223
Abstract
The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge [...] Read more.
The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

20 pages, 7554 KiB  
Article
A New Individual Tree Crown Delineation Method for High Resolution Multispectral Imagery
by Lin Qiu, Linhai Jing, Baoxin Hu, Hui Li and Yunwei Tang
Remote Sens. 2020, 12(3), 585; https://doi.org/10.3390/rs12030585 - 10 Feb 2020
Cited by 42 | Viewed by 6748
Abstract
In current individual tree crown (ITC) delineation methods for high-resolution multispectral imagery, either a spectral band or a brightness component of the multispectral image is employed in delineation with reference to edges or shapes of crowns, whereas spectra of tree crowns are seldom [...] Read more.
In current individual tree crown (ITC) delineation methods for high-resolution multispectral imagery, either a spectral band or a brightness component of the multispectral image is employed in delineation with reference to edges or shapes of crowns, whereas spectra of tree crowns are seldom taken into account. Such methods normally perform well in coniferous forests with obvious between-crown shadows, but fail in dense deciduous or mixed forests, in which tree crowns are close to each other, between-crown shadows and boundaries are unobvious, whereas adjacent tree crowns may be of distinguishable spectra. In order to effectively delineate crowns in dense deciduous or mixed forests, a new ITC delineation method using both brightness and spectra of the image is proposed in this study. In this method, a morphological gradient map of the image is first generated, treetops of multi-scale crowns are extracted from the gradient map and refined regarding the spectral differences between neighboring crowns, the gradient map is segmented using a watershed approach with treetops as markers, and the resulting segmentation map is refined to yield a crown map. Evaluated on images of a rainforest and a deciduous forest, the proposed method more accurately delineated adjacent broad-leaved tree crowns with similar brightness but different spectra than the other two typical ITC delineation algorithms, achieving a delineation accuracy of up to 76% in the rainforest and 63% in the deciduous forest. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

14 pages, 6678 KiB  
Article
A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data
by Wanqian Yan, Haiyan Guan, Lin Cao, Yongtao Yu, Cheng Li and JianYong Lu
Remote Sens. 2020, 12(3), 515; https://doi.org/10.3390/rs12030515 - 5 Feb 2020
Cited by 58 | Viewed by 5148
Abstract
Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The [...] Read more.
Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

15 pages, 1467 KiB  
Article
Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks
by Michael Fromm, Matthias Schubert, Guillermo Castilla, Julia Linke and Greg McDermid
Remote Sens. 2019, 11(21), 2585; https://doi.org/10.3390/rs11212585 - 4 Nov 2019
Cited by 86 | Viewed by 7569
Abstract
Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce [...] Read more.
Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-20
Back to TopTop