Correcting for nondetection in estimating forest characteristics from single-scan terrestrial laser measurements

Kuronen, Mikko; Henttonen, Helena M.; Myllymäki, Mari

doi:10.1139/cjfr-2018-0072

Cited by 9 publications

(44 citation statements)

References 18 publications

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“…Both Liang et al [5] and Dassot et al [26], in their reviews, have underlined that several challenges still must be overcome to efficiently use TLS to replace manual field acquisition in forest mensuration. The main problem of TLS is the occluded areas, which occur using the single-scan approach, that can be reduced with a multi-scan but not completely eliminated [5,27]. Furthermore, the multi-scan approach requires more time for data acquisition and more effort in data processing [5].…”

Section: Introductionmentioning

confidence: 99%

Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning

Perugia

Giannetti

Chirici

et al. 2019

Forests

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Nowadays, forest inventories are frequently carried out using a combination of field measurements and remote sensing data, often acquired with light detection and ranging (LiDAR) sensors. Several studies have investigated how three-dimensional laser scanning point clouds from different platforms can be used to acquire information traditionally collected with forest instruments, such as hypsometers and callipers to detect single-tree attributes like tree height and diameter at the breast height. The present study has tested the performances of the ZEB1 instrument, a type of hand-held mobile laser scanner, for single-tree attributes estimation in pure Castanea sativa Mill. stands cultivated for fruit production in Central Italy. In particular, the influence of walking scan path density on single-tree attributes estimation (number of trees, tree position, diameter at breast height, tree height, and crown base height) was investigated to test the efficiency of field measures. The point clouds were acquired by walking along straight lines drawn with different spacing: 10 and 15 m apart. A single-tree scan approach, which included walking with the instrument around each tree, was used as reference data. In order to evaluate the efficiency of the survey, the influence of the walking scan path was discussed in relation to the accuracy of single-tree attributes estimation, as well as the time and cost needed for data acquisition, pre-processing, and analysis. Our results show that the 10 m scan path provided the best results, with an omission error of 6%; the assessment of single-tree attributes was successful, with values of the coefficient of determination and the relative root mean square error similar to other studies. The 10 m scan path has also proved to decrease the costs by about €14 for data pre-processing, and a saving of time for data acquisition and data analysis of about 37 min compared to the reference data.

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Section: Introductionmentioning

confidence: 99%

Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning

Perugia

Giannetti

Chirici

et al. 2019

Forests

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“…However, there is increasing interest in making the measurements using a terrestrial laser scanner TLS or mobile laser scanner MLS , and if those are used in the field data collection, the measurement errors are no more negligible. For instance, the tree heights and diameters measured with TLS or MLS can be seriously biased [51,52] and correct inclusion probabilities of trees may be difficult to obtain [53]. If such devices become common in NFI, including the measurement errors into the uncertainty analysis becomes important.…”

Section: Measurement and Model Errorsmentioning

confidence: 99%

Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories

Kangas

Räty

Korhonen

et al. 2019

Forests

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Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making.

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“…Kuronen et al (2019) improved on the work of Olofsson and Olsson (2018) by modifying the visible area based on what we call a detection condition, producing a weight for every tree that depends on its DBH and the detection condition: is the tree detected only if it is fully outside of the nonvisible area, or if the center point is visible, or if any small visible part of stem is enough for detection, or something in between, a partial visibility? The premise for the work of Kuronen et al (2019) was that the estimator of Olofsson and Olsson (2018) produced large under-and overestimation in Poisson forests when the detection condition was either full visibility or any visibility, respectively, and deduced that this was because the area from which trees could be detected in these cases was not the same as the area visible from the scanner. However, Kuronen et al (2019) also found that their estimator has a positive bias in the Poisson process case.…”

mentioning

confidence: 99%

“…The premise for the work of Kuronen et al (2019) was that the estimator of Olofsson and Olsson (2018) produced large under-and overestimation in Poisson forests when the detection condition was either full visibility or any visibility, respectively, and deduced that this was because the area from which trees could be detected in these cases was not the same as the area visible from the scanner. However, Kuronen et al (2019) also found that their estimator has a positive bias in the Poisson process case. Kansanen et al (2016) proposed estimators for stem density that correct a nondetection problem in the case of individual tree detection from airborne laser scanning (ALS) data.…”

mentioning

confidence: 99%

“…The detection probabilities are used in a Horvitz-Thompson-like estimator to produce estimates of a population total of interest, such as stem density or basal area. The main difference between our estimator and the estimator of Kuronen et al (2019) is that our detection probabilities are distance-based, whereas their construction is area-based. Hence, it could be easier to combine our method with other, possibly distance sampling based, methods that take into account some other effects on tree detection from TLS data that depend on the distance from the scanner.…”

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confidence: 99%

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Horvitz‐Thompson–like estimation with distance‐based detection probabilities for circular plot sampling of forests

et al. 2020

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In circular plot sampling, trees within a given distance from the sample plot location constitute a sample, which is used to infer characteristics of interest for the forest area. If the sample is collected using a technical device located at the sampling point, eg, a terrestrial laser scanner, all trees of the sample plot cannot be observed because they hide behind each other. We propose a Horvitz-Thompsonlike estimator with distance-based detection probabilities derived from stochastic geometry for estimation of population totals such as stem density and basal area in such situation. We show that our estimator is unbiased for Poisson forests and give estimates of variance and approximate confidence intervals for the estimator, unlike any previous methods. We compare the estimator to two previously published benchmark methods. The comparison is done through a simulation study where several plots are simulated either from field measured data or different marked point processes. The simulations show that the estimator produces lower or comparable error values than the other methods. In the sample plots based on the field measured data, the bias is relatively small-relative mean of errors for stem density, for example, varying from 0.3% to 2.2%, depending on the detection condition. The empirical coverage probabilities of the approximate confidence intervals are either similar to the nominal levels or conservative in these sample plots. K E Y W O R D S circular plot sampling, forest remote sensing, stochastic geometry, terrestrial laser scanning 1 INTRODUCTION Circular plot sampling is a commonly used method in forest inventory (Gregoire and Valentine, 2007). In this form of sampling, a location is selected as a center point of a plot and the surrounding area within a given distance is observed from that point. The objective is to gather information on forest characteristics of interest, such as stem This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Correcting for nondetection in estimating forest characteristics from single-scan terrestrial laser measurements

Cited by 9 publications

References 18 publications

Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning

Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning

Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories

Horvitz‐Thompson–like estimation with distance‐based detection probabilities for circular plot sampling of forests

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