A Transferable and Effective Method for Monitoring Continuous Cover Forestry at the Individual Tree Level Using UAVs

Guy Bennett; Andy Hardy; Pete Bunting; Philippe Morgan; Andrew Fricker

doi:10.3390/rs12132115

A Transferable and Effective Method for Monitoring Continuous Cover Forestry at the Individual Tree Level Using UAVs

Remote Sensing ◽

10.3390/rs12132115 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2115

Author(s):

Guy Bennett ◽

Andy Hardy ◽

Pete Bunting ◽

Philippe Morgan ◽

Andrew Fricker

Keyword(s):

Low Cost ◽

Training Data ◽

Detection Methods ◽

Tree Level ◽

Test Accuracy ◽

Individual Tree ◽

Tree Detection ◽

Management Interventions ◽

Continuous Cover Forestry ◽

Wide Range

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.

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Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests

10.32942/osf.io/p7ygu ◽

2021 ◽

Author(s):

Derek Jon Nies Young ◽

Michael J Koontz ◽

Jonah Weeks

Keyword(s):

Low Cost ◽

3D Structure ◽

Ground Truth ◽

Processing Parameters ◽

Detection Methods ◽

Conifer Forests ◽

Individual Tree ◽

Processing Technologies ◽

Tree Detection ◽

Imagery Processing

Recent advances in remotely piloted aerial system (“drone”) and imagery processing technologies have enabled individual tree mapping in forest stands across broad areas with low-cost equipment and minimal ground-based data collection. One such method, “structure from motion” (SfM), involves collecting many partially overlapping aerial photos over a focal area and using photogrammetric analysis to infer 3D structure and detect individual trees. SfM-based forest mapping involves myriad decisions surrounding the selection of methods and parameters for imagery acquisition and processing, but no studies have comprehensively and quantitatively evaluated the influence of these parameters on the accuracy of the resulting tree maps.We collected and processed drone imagery of a moderate-density, structurally complex mixed-conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch, and image overlap), 12 imagery processing parameterizations, and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23-ha ground-truth map of 1,916 trees > 5 m tall that we created using traditional field survey methods.We found that the accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image-to-image overlap, photogrammetrically processing images into a canopy height model (CHM) with a 2-fold upscaling (coarsening) step, and detecting trees from the CHM using a variable window filter after first applying a moving-window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy that exceeds expectations for our structurally complex forest based on other recent results (for overstory trees > 10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely-measured tree heights corresponded to ground-measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understory trees, and it is likely to be higher in lower density and less structurally-complex stands.Our results may guide others wishing to efficiently produce individual-tree maps of conifer forests over broad extents without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and increasing the efficiency of forest management.

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Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

Remote Sensing ◽

10.3390/rs13010077 ◽

2020 ◽

Vol 13 (1) ◽

pp. 77

Author(s):

Tianyu Hu ◽

Xiliang Sun ◽

Yanjun Su ◽

Hongcan Guan ◽

Qianhui Sun ◽

...

Keyword(s):

Forest Inventory ◽

Low Cost ◽

Canopy Cover ◽

Tree Height ◽

Flying Height ◽

Tree Level ◽

Forest Site ◽

Lidar System ◽

Individual Tree ◽

Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

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International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2016.2543225 ◽

2016 ◽

Vol 54 (9) ◽

pp. 5011-5027 ◽

Cited By ~ 61

Author(s):

Yunsheng Wang ◽

Juha Hyyppa ◽

Xinlian Liang ◽

Harri Kaartinen ◽

Xiaowei Yu ◽

...

Keyword(s):

Laser Scanning ◽

Forest Ecology ◽

Canopy Structure ◽

Airborne Laser Scanning ◽

Detection Methods ◽

Individual Tree ◽

Tree Detection ◽

International Benchmarking ◽

Airborne Laser ◽

The Individual

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Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models

Remote Sensing ◽

10.3390/rs12101633 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1633 ◽

Cited By ~ 1

Author(s):

Daniel G. García-Murillo ◽

J. Caicedo-Acosta ◽

G. Castellanos-Dominguez

Keyword(s):

Low Cost ◽

False Negative ◽

False Negative Rate ◽

Seed Selection ◽

Individual Tree ◽

Local Maxima ◽

Commission Errors ◽

Tree Detection ◽

Surface Models ◽

Cumulative Summation

Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do.

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Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Scientific Reports ◽

10.1038/s41598-019-53797-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 20

Author(s):

Teja Kattenborn ◽

Jana Eichel ◽

Fabian Ewald Fassnacht

Keyword(s):

Neural Networks ◽

High Resolution ◽

Convolutional Neural Networks ◽

Low Cost ◽

Training Data ◽

Observation Data ◽

Visual Interpretation ◽

Fine Grained ◽

Wide Range ◽

Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

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The transferability of airborne laser scanning based tree-level models between different inventory areas

Canadian Journal of Forest Research ◽

10.1139/cjfr-2018-0128 ◽

2019 ◽

Vol 49 (3) ◽

pp. 228-236 ◽

Cited By ~ 4

Author(s):

Tomi Karjalainen ◽

Lauri Korhonen ◽

Petteri Packalen ◽

Matti Maltamo

Keyword(s):

Laser Scanning ◽

Nearest Neighbor ◽

Airborne Laser Scanning ◽

Tree Level ◽

Stem Volume ◽

K Nearest Neighbor ◽

Individual Tree ◽

Tree Detection ◽

Airborne Laser ◽

Crown Base Height

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh ≥ 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree-level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar.

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Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners

Open Geosciences ◽

10.1515/geo-2020-0290 ◽

2021 ◽

Vol 13 (1) ◽

pp. 1028-1039

Author(s):

Midhun Mohan ◽

Rodrigo Vieira Leite ◽

Eben North Broadbent ◽

Wan Shafrina Wan Mohd Jaafar ◽

Shruthi Srinivasan ◽

...

Keyword(s):

Accuracy Assessment ◽

Low Cost ◽

Stem Volume ◽

Biomass Estimation ◽

Visual Interpretation ◽

Individual Tree ◽

Local Maxima ◽

Tree Detection ◽

High Resolution Imagery ◽

Lm Algorithm

Abstract Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpretation from high-resolution imagery and field-data-based accuracy assessment. As the intended audience are beginners in remote sensing, we have adopted a very simple methodology and chosen study plots that have relatively open canopies to demonstrate our proposed approach; the respective R codes and sample plot data are available as supplementary materials.

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A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Sensors ◽

10.3390/s18103243 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3243 ◽

Cited By ~ 19

Author(s):

Marcin Bernas ◽

Bartłomiej Płaczek ◽

Wojciech Korski ◽

Piotr Loska ◽

Jarosław Smyła ◽

...

Keyword(s):

Road Traffic ◽

Low Cost ◽

Pedestrian Detection ◽

Traffic Monitoring ◽

Detection Methods ◽

Detection Accuracy ◽

Active Sensors ◽

Wide Range ◽

Passive Sensors ◽

Supporting Structures

This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.

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Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Forests ◽

10.3390/f13010003 ◽

2021 ◽

Vol 13 (1) ◽

pp. 3

Author(s):

Aaron M. Sparks ◽

Alistair M.S. Smith

Keyword(s):

Supply Chain ◽

Tree Species ◽

Coniferous Forest ◽

Canopy Cover ◽

Forest Products ◽

Tree Canopy ◽

Tree Level ◽

Individual Tree ◽

Tree Detection ◽

Canopy Position

Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

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