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.

Development and evaluation of an automated tree detection–delineation algorithm for monitoring regenerating coniferous forests

2005 ◽  
Vol 35 (10) ◽  
pp. 2332-2345 ◽  
Author(s):  
D A Pouliot ◽  
D J King ◽  
D G Pitt
Keyword(s):  
Absolute Error ◽  
Tree Regeneration ◽  
Tree Size ◽  
Detection Accuracy ◽  
Individual Tree ◽  
Commission Errors ◽  
Tree Detection ◽  
Crown Diameter ◽  
Accuracy Index ◽  
The Individual

An algorithm is presented for automated detection–delineation of coniferous tree regeneration that combines strategies of several existing algorithms, including image processing to isolate conifer crowns, optimal image scale determination, initial crown detection, and crown boundary segmentation and refinement. The algorithm is evaluated using 6-cm pixel airborne imagery in operational regeneration conditions typically encountered in the boreal forest 5–10 years after harvest. Detection omission and commission errors as well as an accuracy index combining both error types were assessed on a tree by tree basis, on an aggregated basis for each study area, in relation to tree size and the amount of woody competition present. Delineation error was assessed in a similar manner using field-measured crown diameters as a reference. The individual tree detection accuracy index improved with increasing tree size and was >70% for trees larger than 30 cm crown diameter. Crown diameter absolute error measured from automated delineations was <23%. Large crown diameters tended to be slightly underestimated. The presence of overtopping woody competition had a negligible effect on detection accuracy and only reduced estimates of crown diameter slightly.

scholarly journals Development of a Low-Cost Narrow Band Multispectral Imaging System Coupled with Chemometric Analysis for Rapid Detection of Rice False Smut in Rice Seed

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1209 ◽  
Author(s):  
Haiyong Weng ◽  
Ya Tian ◽  
Na Wu ◽  
Xiaoling Li ◽  
Biyun Yang ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Narrow Band ◽  
Low Cost ◽  
False Negative ◽  
False Negative Rate ◽  
Rice False Smut ◽  
Negative Rate ◽  
False Smut ◽  
Rice Seeds

Spectral imaging is a promising technique for detecting the quality of rice seeds. However, the high cost of the system has limited it to more practical applications. The study was aimed to develop a low-cost narrow band multispectral imaging system for detecting rice false smut (RFS) in rice seeds. Two different cultivars of rice seeds were artificially inoculated with RFS. Results have demonstrated that spectral features at 460, 520, 660, 740, 850, and 940 nm were well linked to the RFS. It achieved an overall accuracy of 98.7% with a false negative rate of 3.2% for Zheliang, and 91.4% with 6.7% for Xiushui, respectively, using the least squares-support vector machine. Moreover, the robustness of the model was validated through transferring the model of Zheliang to Xiushui with the overall accuracy of 90.3% and false negative rate of 7.8%. These results demonstrate the feasibility of the developed system for RFS identification with a low detecting cost.

scholarly journals Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches

2021 ◽  
Author(s):  
Eetu Kotivuori ◽  
Matti Maltamo ◽  
Lauri Korhonen ◽  
Jacob L Strunk ◽  
Petteri Packalen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Stem Volume ◽  
Prediction Errors ◽  
Individual Tree ◽  
Commission Errors ◽  
Tree Detection ◽  
Fundamental Properties ◽  
Aggregation Behaviour ◽  
Quartet Level

Abstract In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error ($\overline{\mathrm{RMSE}}$) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation ($\overline{\mathrm{RMSE}}$: 17.6 percent, 17.4 percent), followed by ABA ($\overline{\mathrm{RMSE}}$: 19.3 percent, 18.2 percent) and ITD ($\overline{\mathrm{RMSE}}$: 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level ($\overline{\mathrm{RMSE}}$ 15.5 and 15.3 percent), with poorer ITD performance ($\overline{\mathrm{RMSE}}$ 19.4 percent).

scholarly journals INDIVIDUAL TREE DETECTION FROM UAV LIDAR DATA IN A MIXED SPECIES WOODLAND

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 657-663 ◽  
Author(s):  
A. Zaforemska ◽  
W. Xiao ◽  
R. Gaulton
Keyword(s):  
Point Cloud ◽  
Voronoi Tessellation ◽  
Mean Shift ◽  
Region Growing ◽  
Single Species ◽  
Mixed Species ◽  
Individual Tree ◽  
Local Maxima ◽  
Tree Detection ◽  
Segmentation Algorithms

<p><strong>Abstract.</strong> The study evaluates five existing segmentation algorithms to determine the method most suitable for individual tree detection across a species-diverse forest: raster-based region growing, local maxima centroidal Voronoi tessellation, point-cloud level region growing, marker controlled watershed and continuously adaptive mean shift. Each of the methods has been tested twice over one mixed and five single species plots: with their parameters set as constant and with the parameters calibrated for every plot. Overall, continuous adaptive mean shift performs best across all the plots with average F-score of 0.9 with fine-tuned parameters and 0.802 with parameters held at constant. Raster-based algorithms tend to achieve higher scores in coniferous plots, due to the clearly discernible tops, which significantly aid the detection of local maxima. Their performance is also highly dependent on the moving size window used to detect the local maxima, which ideally should be readjusted for every plot. Crown overlap, suppressed and leaning trees are the most likely sources of error for all the algorithms tested.</p>

scholarly journals Differential evolution detection models for SMS spam

2021 ◽  
Vol 11 (1) ◽  
pp. 596
Author(s):  
Sarab M. Hameed
Keyword(s):  
Differential Evolution ◽  
Clustering Analysis ◽  
Short Message Service ◽  
Low Cost ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Short Message ◽  
Message Service ◽  
Positive Rate

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods.

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 &gt; 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 &gt; 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 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
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