Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics

Vauhkonen, Jari; Korpela, Ilkka; Maltamo, Matti; Tokola, Timo

doi:10.1016/j.rse.2010.01.016

Cited by 189 publications

(123 citation statements)

References 38 publications

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“…According to our knowledge, this study is the first one for detection of insect defoliation. These methods have been used in several studies in the estimation of forest characteristics other than defoliation at stand and tree level [29,30,38,47,48].…”

Section: Discussionmentioning

confidence: 99%

“…With its ability to directly measure forest structure, including canopy height and crown dimensions, ALS is increasingly used for forest inventories at different levels. Previous studies have shown that ALS data can be used to estimate a variety of forest inventory attributes including tree, plot and stand level estimates for tree height [15][16][17][18], biomass [19][20][21], volume [22][23][24], basal area [20,25,26] and tree species [21,[27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Kantola

Vastaranta

et al. 2010

Remote Sensing

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Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Kantola

Vastaranta

et al. 2010

Remote Sensing

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“…However, generating only a few points from the ground surface in dense forest may be problematic when interpolating the terrain (Wallace et al 2014b). Therefore, forest structure types become significant when it comes to detecting tree crowns or real tree heights (Vauhkonen et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Estimating tree heights with images from an unmanned aerial vehicle

Birdal

Avdan

Türk

2017

Geomatics, Natural Hazards and Risk

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Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

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“…This is usually achieved by allocating field plots in the forest area and measuring the position and DBH of all trees and the height of a subsample of trees (Vauhkonen et al 2010). The measurements are done manually and even though electronic equipment may be used to save the measurements automatically, the field workers still have to measure each individual tree.…”

Section: Introductionmentioning

confidence: 99%

Estimation of stem attributes using a combination of terrestrial and airborne laser scanning

et al. 2012

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Properties of individual trees can be estimated from airborne laser scanning (ALS) data provided that the scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial laser scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 × 80 m field plots in spruce-dominated forest (lat. 58° N, long. 13° E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1%) and the bias was 1.6 mm (0.5%). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-oneout cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4%) for DBH, 9.4 dm (3.7%) for tree height, and 197.4 dm 3 (34.0%) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.

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Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics

Cited by 189 publications

References 38 publications

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Estimating tree heights with images from an unmanned aerial vehicle

Estimation of stem attributes using a combination of terrestrial and airborne laser scanning

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