scholarly journals Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

2016 ◽  
Vol 8 (12) ◽  
pp. 1034 ◽  
Author(s):  
Songqiu Deng ◽  
Masato Katoh ◽  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
Tian Gao
Keyword(s):  
Tree Species ◽  
Tree Level ◽  
Individual Tree ◽  
Rgb Images ◽  
The Individual

Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV

2020 ◽  
Vol 8 (4) ◽  
pp. 310-333
Author(s):  
Sowmya Natesan ◽  
Costas Armenakis ◽  
Udayalakshmi Vepakomma
Keyword(s):  
High Resolution ◽  
Species Identification ◽  
Tree Species ◽  
Tree Canopy ◽  
Tree Level ◽  
Eastern Canada ◽  
Convolutional Network ◽  
Individual Tree ◽  
Rgb Images ◽  
The Individual

Tree species identification at the individual tree level is crucial for forest operations and management, yet its automated mapping remains challenging. Emerging technology, such as the high-resolution imagery from unmanned aerial vehicles (UAV) that is now becoming part of every forester’s surveillance kit, can potentially provide a solution to better characterize the tree canopy. To address this need, we have developed an approach based on a deep Convolutional Neural Network (CNN) to classify forest tree species at the individual tree-level that uses high-resolution RGB images acquired from a consumer-grade camera mounted on a UAV platform. This work explores the ability of the Dense Convolutional Network (DenseNet) to classify commonly available economic coniferous tree species in eastern Canada. The network was trained using multitemporal images captured under varying acquisition parameters to include seasonal, temporal, illumination, and angular variability. Validation of this model using distinct images over a mixed-wood forest in Ontario, Canada, showed over 84% classification accuracy in distinguishing five predominant species of coniferous trees. The model remains highly robust even when using images taken during different seasons and times, and with varying illumination and angles.

scholarly journals RESNET-BASED TREE SPECIES CLASSIFICATION USING UAV IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 475-481 ◽  
Author(s):  
S. Natesan ◽  
C. Armenakis ◽  
U. Vepakomma
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Tree Species ◽  
Classification Accuracy ◽  
Forest Tree ◽  
Tree Level ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Rgb Images

<p><strong>Abstract.</strong> Tree species classification at individual tree level is a challenging problem in forest management. Deep learning, a cutting-edge technology evolved from Artificial Intelligence, was seen to outperform other techniques when it comes to complex problems such as image classification. In this work, we present a novel method to classify forest tree species through high resolution RGB images acquired with a simple consumer grade camera mounted on a UAV platform using Residual Neural Networks. We used UAV RGB images acquired over three years that varied in numerous acquisition parameters such as season, time, illumination and angle to train the neural network. To begin with, we have experimented with limited data towards the identification of two pine species namely red pine and white pine from the rest of the species. We performed two experiments, first with the images from all three acquisition years and the second with images from only one acquisition year. In the first experiment, we obtained 80% classification accuracy when the trained network was tested on a distinct set of images and in the second experiment, we obtained 51% classification accuracy. As a part of this work, a novel dataset of high-resolution labelled tree species is generated that can be used to conduct further studies involving deep neural networks in forestry.</p>

scholarly journals SISFLOR: A COMPUTATIONAL SYSTEM TO DETERMINE THE OPTIMAL TREE BUCKING

2020 ◽  
Vol 28 ◽  
pp. 192-201
Author(s):  
Rodrigo Freitas Silva ◽  
Marcelo Otone Aguiar ◽  
Mayra Luiza Marques Da Silva ◽  
Gilson Fernandes Da Silva ◽  
Adriano Ribeiro De Mendonça
Keyword(s):  
Optimization Technique ◽  
Wood Products ◽  
Tree Level ◽  
Economic Losses ◽  
Computational System ◽  
Individual Tree ◽  
Optimal Tree ◽  
Cutting Pattern ◽  
Forest Enterprises ◽  
The Individual

A continuously competitive forest market and tied to the demands for wood products promotes the study and development of applications that increase the revenue of the forest enterprises. At harvesting, the cutting pattern (forest assortment) in which the trees are traced is traditionally determined by the experience of the chainsaw operator without using any optimization technique, which may result in economic losses in relation to the commercialized products. In general, there are numerous distinct assortments that can be chosen and hardly processed by a brute-force algorithm. This is the forest assortment problem at the individual tree level with the objetice of maximizing the commercial values of the felled trees. stem-level bucking optimization problem. The aim is to maximize the sales value of harvested trees. Dynamic Programming (DP) is an efficient optimization technique to determine the optimum bucking tree as it significantly reduces the number of calculations to be made. Thus, the objective of this work was to develop a modern and intuitive computational system that is able to find the optimum tree stem bucking through DP to help companies over the bole tracing, therefore, characterizing itself as a tool that supports decision making. After the execution of the system, the optimum assortment is shown by sequentially detailing all products that should be removed from the analyzed bole as well as their respective volumes and revenue.

scholarly journals AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

2019 ◽  
Vol 11 (12) ◽  
pp. 1413 ◽  
Author(s):  
Víctor González-Jaramillo ◽  
Andreas Fries ◽  
Jörg Bendix
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Tree Level ◽  
Mountain Forest ◽  
Multispectral Images ◽  
Individual Tree ◽  
Tropical Mountain ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

scholarly journals Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 871 ◽  
Author(s):  
Qiu ◽  
Wang ◽  
Zou ◽  
Yang ◽  
Xie ◽  
...  
Keyword(s):  
Tree Level ◽  
Inference Method ◽  
Mangrove Species ◽  
Individual Tree ◽  
Model Method ◽  
Lower Accuracy ◽  
Field Samples ◽  
The Individual ◽  
Mangrove Biomass ◽  
Grid Based

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha–1 vs. 38.95 Mg ha–1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

scholarly journals Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1047 ◽  
Author(s):  
Ying Sun ◽  
Jianfeng Huang ◽  
Zurui Ao ◽  
Dazhao Lao ◽  
Qinchuan Xin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Species Diversity ◽  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Diversity Index ◽  
Tree Species Diversity ◽  
Individual Tree ◽  
Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

Flexible open-source software for modelling wind disturbance and damage to trees and forests

2020 ◽  
Author(s):  
Tom Locatelli ◽  
Sophie Hale ◽  
Bruce Nicoll ◽  
Barry Gardiner
Keyword(s):  
Risk Model ◽  
Mechanistic Model ◽  
Wind Damage ◽  
Tree Level ◽  
Projection Data ◽  
Wind Disturbance ◽  
Experimental Conditions ◽  
Individual Tree ◽  
Tree Stability ◽  
The Individual

&lt;p&gt;Wind disturbance to forests extends across spatial and temporal scales and encompasses direct and indirect wind effects on the dynamics of forest ecosystems. It is detrimental to the provision of ecosystem services and reduces forest resistance and resilience to future natural disturbances. Historically, in the ecological and land-use scientific communities, forecasting the extent and probability of wind disturbance to forests has represented a serious challenge, with most studies electing to adopt qualitative or statistical approaches. The low degree of portability of statistical assessments of vulnerability to wind has limited their applicability and use, but it is recognised that they have a role in building hypotheses of the processes involved in wind damage that can be subsequently tested under experimental conditions. Results from tree stability experiments have contributed, in the last two decades, to the development of a mechanistic model of wind damage - ForestGALES. This is a process-based wind risk model that was originally created to inform the management of commercial forest plantations in the UK. Built on principles of forest science, physics, and ecology, ForestGALES requires a simple set of inputs and it has now been expanded to cover more than 20 common conifer species from across three continents, and multiple broadleaved species (e.g. Oak, Beech, Birch, and Eucalypts). Two methods of assessing vulnerability to wind damage are available in ForestGALES, one designed for application at stand level, and a novel approach that estimates vulnerability to wind at the individual tree within a stand &amp;#8211; the latter allowing for use in complex forest stands, and for the effect of competition between trees in a stand. Until recently, ForestGALES was only available as desktop software and as an online tool as part of forest decision support systems (only for selected countries and species). These formats can be limiting for research and academic projects that aim to model and understanding wind disturbance dynamics across diverse landscapes, and that require a bespoke approach with a high degree of flexibility. To accommodate these broader requirements, ForestGALES has recently been redeveloped and released as a FOSS R package (&amp;#8220;&lt;em&gt;fgr&lt;/em&gt;&amp;#8221;) that is fully customisable and easily integrated in R and modelling workflows and FOSS GIS frameworks. With this poster we present two exemplar studies of assessing wind damage risk to forested landscapes, one for each ForestGALES method (stand- and individual trees level), to showcase the capabilities and flexibility of the model in working with e.g. climate projection data, with other process-based models (e.g. 3PG) within an R modelling framework, and with LiDAR data, at the individual tree level.&lt;/p&gt;

Agrilus auroguttatus exit hole distributions on Quercus agrifolia boles and a sampling method to estimate their density on individual trees

2012 ◽  
Vol 144 (6) ◽  
pp. 733-744 ◽  
Author(s):  
Laurel J. Haavik ◽  
Tom W. Coleman ◽  
Mary Louise Flint ◽  
Robert C. Venette ◽  
Steven J. Seybold
Keyword(s):  
Southern California ◽  
Tree Level ◽  
Reliable Estimate ◽  
Hole Density ◽  
Exit Hole ◽  
Individual Tree ◽  
Quercus Agrifolia ◽  
Goldspotted Oak Borer ◽  
The Individual ◽  
Agrilus Auroguttatus

AbstractIn recent decades, invasive phloem and wood borers have become important pests in North America. To aid tree sampling and survey efforts for the newly introduced goldspotted oak borer, Agrilus auroguttatus Schaeffer (Coleoptera: Buprestidae), we examined spatial patterns of exit holes on the boles (trunks) of 58 coast live oak, Quercus agrifolia Née (Fagaceae), trees at five sites in San Diego County, southern California, United States of America. Agrilus auroguttatus exit hole densities were greater at the root collar than at mid-boles (6.1 m above ground). Dispersion patterns of exit holes on lower boles (≤1.52 m) were random for trees with low exit hole densities and aggregated for trees with high exit hole densities. The mean exit hole density measured from three randomly chosen quadrats (0.09 m2) provided a statistically reliable estimate of the true mean exit hole density on the lower bole, with <25% error from the true mean. For future sampling and survey efforts in southern California oak forests and woodlands, exit hole counts within a 0.09 m2 quadrat could be made at any three locations on lower Q. agrifolia boles to accurately estimate A. auroguttatus exit hole densities at the individual tree level.

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