scholarly journals A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 35
Author(s):  
Jean-François Prieur ◽  
Benoît St-Onge ◽  
Richard A. Fournier ◽  
Murray E. Woods ◽  
Parvez Rana ◽  
...  
Keyword(s):  
Species Identification ◽  
Laser Scanning ◽  
Time Series Data ◽  
Single Photon ◽  
Series Data ◽  
Tree Level ◽  
Potential Contribution ◽  
Individual Tree ◽  
Airborne Laser ◽  
The Individual

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.

scholarly journals Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

2021 ◽  
Vol 11 ◽  
Author(s):  
David Pont ◽  
Heidi S. Dungey ◽  
Mari Suontama ◽  
Grahame T. Stovold
Keyword(s):  
Laser Scanning ◽  
Management Practices ◽  
Spatial Models ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Residual Variance ◽  
Spatial Effects ◽  
Individual Tree ◽  
Airborne Laser ◽  
Individual Trees

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.

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

2019 ◽  
Vol 49 (3) ◽  
pp. 228-236 ◽  
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.

scholarly journals LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 550
Author(s):  
Dandan Xu ◽  
Haobin Wang ◽  
Weixin Xu ◽  
Zhaoqing Luan ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Forest Biomass ◽  
Remotely Sensed ◽  
Tree Level ◽  
Biomass Estimation ◽  
Future Perspectives ◽  
Individual Tree ◽  
The Individual ◽  
Individual Trees

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

scholarly journals Updating stand-level forest inventories using airborne laser scanning and Landsat time series data

2018 ◽  
Vol 66 ◽  
pp. 174-183 ◽  
Author(s):  
Douglas K. Bolton ◽  
Joanne C. White ◽  
Michael A. Wulder ◽  
Nicholas C. Coops ◽  
Txomin Hermosilla ◽  
...  
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Time Series Data ◽  
Airborne Laser Scanning ◽  
Series Data ◽  
Forest Inventories ◽  
Airborne Laser

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.

Predicting and calibrating tree attributes by means of airborne laser scanning and field measurements

2012 ◽  
Vol 42 (11) ◽  
pp. 1896-1907 ◽  
Author(s):  
Matti Maltamo ◽  
Lauri Mehtätalo ◽  
Jari Vauhkonen ◽  
Petteri Packalén
Keyword(s):  
Laser Scanning ◽  
Practical Importance ◽  
Field Measurements ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Best Linear Unbiased Predictor ◽  
Individual Tree ◽  
Airborne Laser ◽  
Dead Branch ◽  
Best Linear Unbiased

This paper examines the calibration of airborne laser scanning based tree attribute models to separate data by applying a best linear unbiased predictor. Firstly, single Scots pine ( Pinus sylvestris L.) trees were identified from dense airborne laser scanning data. Secondly, seemingly unrelated mixed-effects models for diameter at breast height, tree height, volume, dead branch height, and crown base height were constructed using airborne laser scanning based height metrics as predictors at both the area and individual tree level. Finally, these models were calibrated to validation stands using field measurements of some of the five abovementioned tree attributes. The models were calibrated by applying the best linear unbiased predictor to predict the random stand effects for the validation stand. In a system of several models, the correlation of random effects enabled the prediction of stand effects for all models, providing the response of at least one of the models was known for one or more sample trees of the validation stand. The results showed that the accuracy of tree attribute prediction improved in most cases as the number of sample trees increased. The level of improvement was highest for volume and dead branch height. The practical importance of the results of this study lies in applications where forest stands are visited in the field, for example, before making cutting decisions.

scholarly journals DETECTING FORESTS DAMAGED BY PINE WILT DISEASE AT THE INDIVIDUAL TREE LEVEL USING AIRBORNE LASER DATA AND WORLDVIEW-2/3 IMAGES OVER TWO SEASONS

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 181-184 ◽  
Author(s):  
Y. Takenaka ◽  
M. Katoh ◽  
S. Deng ◽  
K. Cheung
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Tree Level ◽  
Support Vector ◽  
Larix Kaempferi ◽  
Individual Tree ◽  
Tree Crowns ◽  
Airborne Laser ◽  
Pine Wilt ◽  
The Individual

Pine wilt disease is caused by the pine wood nematode (<i>Bursaphelenchus xylophilus</i>) and Japanese pine sawyer (<i>Monochamus alternatus</i>). This study attempted to detect damaged pine trees at different levels using a combination of airborne laser scanning (ALS) data and high-resolution space-borne images. A canopy height model with a resolution of 50&amp;thinsp;cm derived from the ALS data was used for the delineation of tree crowns using the Individual Tree Detection method. Two pan-sharpened images were established using the ortho-rectified images. Next, we analyzed two kinds of intensity-hue-saturation (IHS) images and 18 remote sensing indices (RSI) derived from the pan-sharpened images. The mean and standard deviation of the 2 IHS images, 18 RSI, and 8 bands of the WV-2 and WV-3 images were extracted for each tree crown and were used to classify tree crowns using a support vector machine classifier. Individual tree crowns were assigned to one of nine classes: bare ground, <i>Larix kaempferi</i>, <i>Cryptomeria japonica</i>, <i>Chamaecyparis obtusa</i>, broadleaved trees, healthy pines, and damaged pines at slight, moderate, and heavy levels. The accuracy of the classifications using the WV-2 images ranged from 76.5 to 99.6&amp;thinsp;%, with an overall accuracy of 98.5&amp;thinsp;%. However, the accuracy of the classifications using the WV-3 images ranged from 40.4 to 95.4&amp;thinsp;%, with an overall accuracy of 72&amp;thinsp;%, which suggests poorer accuracy compared to those classes derived from the WV-2 images. This is because the WV-3 images were acquired in October 2016 from an area with low sun, at a low altitude.

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