Identifying the genus or species of individual trees using a three-wavelength airborne lidar system

Budei, Brindusa Cristina; St-Onge, Benoît; Hopkinson, Chris; Audet, Félix-Antoine

doi:10.1016/j.rse.2017.09.037

Cited by 124 publications

(99 citation statements)

References 55 publications

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“…Budei et al [33] classified the species and tree genera of individual trees from multispectral ALS data. The error was 3-5% for classification of broadleaf and coniferous trees, 13% for four genera, 20% for seven genera and 24% for ten different species.…”

Section: Discussionmentioning

confidence: 99%

“…When using such a laser scanner, colors further down in the canopy are revealed [30], and this information might be very well suited for tree species estimations [31]. Data from multispectral ALS have already been shown to provide a good basis for tree species classification [32][33][34]. Sensors providing an inherent combination of structure and reflectance may be the future for remote sensing in forestry.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Multispectral ALS Data for Tree Species Classification

2018

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Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58 • 27 18.35 N, 13 • 39 08.03 E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user's and producer's accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Multispectral ALS Data for Tree Species Classification

2018

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“…This sensor was released in the commercial market in 2015, and data acquired with this system was subject to scientific research shortly after the release. However, published studies are still few in number (e.g., [17][18][19][20][21]), and also for forestry and ecology applications [17,22]. In particular Budei et al [17], Yu et al [22], and Axelsson et al [20] focused on tree species classification.…”

Section: Introductionmentioning

confidence: 99%

“…However, published studies are still few in number (e.g., [17][18][19][20][21]), and also for forestry and ecology applications [17,22]. In particular Budei et al [17], Yu et al [22], and Axelsson et al [20] focused on tree species classification. Budei et al [17] showed that species identification accuracy can be improved by using a three-wavelength ALS compared to single-channel ALS systems, especially when tree species diversity is fairly large (seven classes or more).…”

Section: Introductionmentioning

confidence: 99%

“…In particular Budei et al [17], Yu et al [22], and Axelsson et al [20] focused on tree species classification. Budei et al [17] showed that species identification accuracy can be improved by using a three-wavelength ALS compared to single-channel ALS systems, especially when tree species diversity is fairly large (seven classes or more). The authors found that the channel at 1550 nm is the most useful for this task.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

et al. 2018

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Abstract:In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R 2 being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate.

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Remote Sensing at Local Scales for Operational Forestry

Vepakomma

Cormier

Hansson

et al. 2023

Advances in Global Change Research

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The success of current and future forest management, particularly when dealing with triggered changes stemming from extreme climate change–induced events, will require prompt, timely, and reliable information obtained at local scales. Remote sensing platforms and sensors have been evolving, emerging, and converging with enabling technologies that can potentially have an enormous impact in providing reliable decision support and making forest operations more coherent with climate change mitigation and adaptation objectives.

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Identifying the genus or species of individual trees using a three-wavelength airborne lidar system

Cited by 124 publications

References 55 publications

Exploring Multispectral ALS Data for Tree Species Classification

Exploring Multispectral ALS Data for Tree Species Classification

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Remote Sensing at Local Scales for Operational Forestry

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