Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO

Siipilehto, Jouni; Lindeman, Harri; Vastaranta, Mikko; Yu, Xiaowei; Uusitalo, Jori

doi:10.14214/sf.1568

Cited by 36 publications

(47 citation statements)

References 40 publications

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“…Such an analysis, however, requires a distinctively more powerful computability than that applied here, as well as a totally new study design involving large-scale field measurements. Also, including random variation in tree height is an interesting issue, because it will result in a reasonable marginal distribution of tree heights (Siipilehto et al [62]) and simultaneously more realistic timber assortment and quality distribution. This, in turn, would further increase the needed sample size.…”

Section: Discussionmentioning

confidence: 99%

Effect of Stand Structure and Number of Sample Trees on Optimal Management for Scots Pine: A Model-Based Study

Ahtikoski

Siipilehto

Salminen

et al. 2018

Forests

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This study presents an attempt to discover the effect of sample size on the financial outcome derived by stand-level optimization with individual tree modeling. The initial stand structure was altered to reflect sparse, average, and dense Scots pine (Pinus sylvestris L.) stands. The stands had varying numbers of stems but identical weighted median diameters and stand basal areas. The hypothetical Weibull diameter distributions were solved according to the parameter recovery method. The trees were systematically sampled with respect to the tree basal area corresponding to sample sizes of 10, 20, or 40 trees. We optimized the stand management with varying numbers of sample trees and with varying stand structures and compared the optimal solutions with respect to the objective function value (maximum net present value) and underlying management schedule. The results for the pine stands in southern and central Finland indicated that the variations in the objective function value relating to sample size were minor (<2.6%) in the sparse and average stand densities but exceeded 3% in the dense stands. Generally, the stand density is not always known, and thus, we may need to generalize the average density for all cases in question. This assumption, however, resulted in overestimations with respect to the optimal rotation period and financial performance in this study. The overestimations in the net present value decreased along with the increasing sample size, from 22% to 14% in the sample sizes of 10 and 40 trees, respectively.

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Section: Discussionmentioning

confidence: 99%

Effect of Stand Structure and Number of Sample Trees on Optimal Management for Scots Pine: A Model-Based Study

Ahtikoski

Siipilehto

Salminen

et al. 2018

Forests

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“…In addition, some things are problematic for the area-based approach (ABA) that is currently used for providing raster-and stand-level forest information . For example, accuracy of the species-specific forest inventory attributes is rather limited and that leads to inaccurate predictions for tree size distributions (Siipilehto et al, 2016). Currently there aren't any theories or research ideas presented that could provide major improvements to ABA.…”

Section: Why Do We Need Information From Single Trees?mentioning

confidence: 99%

Individual tree detection and characterization using 3D remote sensing

Vastaranta¹,

Saarinen²,

Yrttimaa³

et al. 2020

Preprint

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Here, we will cover individual tree detection and characterization using 3D remote sensing. Simply, it means that point clouds are collected over a forested area using airborne laser scanning (ALS) or created using photogrammetric image interpretation and further used to detect individual trees using different algorithms. After the tree detection, the attributes of interest are predicted for each tree. We try to consistently use the terms "individual tree detection and characterization" or "individual tree detection" and "individual tree characterization" separately referring to different methodological steps. In the scientific remote sensing literature, terms individual tree detection (ITD), individual tree crown approach (ITC) and single tree inventory (STI) are also often used and most often they refer to the same thing. We'll start by discussing why we need information from single trees. Then we go through the methodological steps that are used in individual tree detection and characterization: 1) remote sensing and field data collection, 2) data types and processing, 3) tree detection algorithms, and 4) methods for prediction of tree attributes. The current methodological state-of-the-art in individual tree detection and characterization is described before we'll finally present some example applications in biodiversity monitoring, urban forestry and wood procurement planning.

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“…For the most effective use of harvester data, a necessary first step is to estimate the total height of each harvested tree that was bucked, measured and recorded by the harvester head. This necessity has been well recognised, and several attempts to do so have been made in the estimation of logging residue biomass and in the integration of harvester data with remote sensing data in forest inventory for predicting tree and stand attributes and estimating product recoveries (Varjo 1995;Kiljunen 2002;Maltamo et al 2010;Möller et al 2011;Söderberg 2015;Siipilehto et al 2016;Hauglin et al 2018;Lu et al 2018). So far, total tree height has been calculated by estimating the length of the unprocessed top section of individual trees using the only such existing model, that of Varjo (1995), or through an iterative search algorithm using a taper equation as demonstrated well by Lu et al (2018) and also briefly alluded to by Hauglin et al (2018).…”

Section: Introductionmentioning

confidence: 99%

A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations

Shan

Watt

et al. 2019

J. For. Res.

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performances of our new model in its linear and nonlinear form were evaluated through a leave-one-tree-out cross validation process and compared against that of the only such existing model. The evaluations and comparisons were made through benchmarking statistics both globally over the entire data space and locally within specific subdivisions of the data space. These statistics indicated that the nonlinear form of our model was the best and its linear form ranked second. The prediction accuracy of our nonlinear model improved when the total log length represented more than 20% of the total tree height. The poorer performance of the existing model was partly attributed to the high degree of multicollinearity among its predictor variables, which led to highly variable and unstable parameter estimates. Our new model will facilitate and widen the utilization of harvester data far beyond the current limited use for monitoring and reporting log productions in P. radiata plantations. It will also facilitate the estimation of bark thickness and help make harvester data a potential source of taper data to reduce the intensity and cost of the conventional destructive taper sampling in the field. Although developed for P. radiata, the mathematical form of our new model will be applicable to other tree species for which CTL harvester data are routinely captured during thinning and harvesting operations.

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Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO

Cited by 36 publications

References 40 publications

Effect of Stand Structure and Number of Sample Trees on Optimal Management for Scots Pine: A Model-Based Study

Effect of Stand Structure and Number of Sample Trees on Optimal Management for Scots Pine: A Model-Based Study

Individual tree detection and characterization using 3D remote sensing

A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations

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