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

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.

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

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.

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

2020 ◽  
Vol 12 (10) ◽  
pp. 1633 ◽  
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.

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

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.

Region of Attraction for a Vehicle Pose Estimator Utilizing Monocular Vision and Lane Marker Maps

2016 ◽  
Author(s):  
Robert D. Leary ◽  
Sean Brennan
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
Detection Methods ◽  
Region Of Attraction ◽  
Vehicle Localization

Currently, there is a lack of low-cost, real-time solutions for accurate autonomous vehicle localization. The fusion of a precise a priori map and a forward-facing camera can provide an alternative low-cost method for achieving centimeter-level localization. This paper analyzes the position and orientation bounds, or region of attraction, with which a real-time vehicle pose estimator can localize using monocular vision and a lane marker map. A pose estimation algorithm minimizes the residual pixel-level error between the estimated and detected lane marker features via Gauss-Newton nonlinear least-squares. Simulations of typical road scenes were used as ground truth to ensure the pose estimator will converge to the true vehicle pose. A successful convergence was defined as a pose estimate that fell within 5 cm and 0.25 degrees of the true vehicle pose. The results show that the longitudinal vehicle state is weakly observable with the smallest region of attraction. Estimating the remaining five vehicle states gives repeatable convergence within the prescribed convergence bounds over a relatively large region of attraction, even for the simple lane detection methods used herein. A main contribution of this paper is to demonstrate a repeatable and verifiable method to assess and compare lane-based vehicle localization strategies.

scholarly journals Efficiency of Individual Tree Detection Approaches Based on Light-Weight and Low-Cost UAS Imagery in Australian Savannas

2018 ◽  
Vol 10 (2) ◽  
pp. 161 ◽  
Author(s):  
Grigorijs Goldbergs ◽  
Stefan Maier ◽  
Shaun Levick ◽  
Andrew Edwards
Keyword(s):  
Low Cost ◽  
Light Weight ◽  
Individual Tree ◽  
Tree Detection

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

scholarly journals Rapid and Sensitive Detection of miRNA Based on AC Electrokinetic Capacitive Sensing for Point-of-Care Applications

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3985
Author(s):  
Nan Wan ◽  
Yu Jiang ◽  
Jiamei Huang ◽  
Rania Oueslati ◽  
Shigetoshi Eda ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Point Of Care ◽  
Low Cost ◽  
Clinical Diagnostics ◽  
Detection Methods ◽  
Capacitive Sensing ◽  
Application Potential ◽  
Mirna Detection ◽  
Mirna 25 ◽  
Low Sensitivity

A sensitive and efficient method for microRNAs (miRNAs) detection is strongly desired by clinicians and, in recent years, the search for such a method has drawn much attention. There has been significant interest in using miRNA as biomarkers for multiple diseases and conditions in clinical diagnostics. Presently, most miRNA detection methods suffer from drawbacks, e.g., low sensitivity, long assay time, expensive equipment, trained personnel, or unsuitability for point-of-care. New methodologies are needed to overcome these limitations to allow rapid, sensitive, low-cost, easy-to-use, and portable methods for miRNA detection at the point of care. In this work, to overcome these shortcomings, we integrated capacitive sensing and alternating current electrokinetic effects to detect specific miRNA-16b molecules, as a model, with the limit of detection reaching 1.0 femto molar (fM) levels. The specificity of the sensor was verified by testing miRNA-25, which has the same length as miRNA-16b. The sensor we developed demonstrated significant improvements in sensitivity, response time and cost over other miRNA detection methods, and has application potential at point-of-care.

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

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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