Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching

Kansanen, Kasper; Lähivaara, Timo; Seppänen, Aku; Maltamo, Matti; Mehtätalo, Lauri

doi:10.1016/j.isprsjprs.2019.04.007

Cited by 23 publications

(20 citation statements)

References 29 publications

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“…Also regression analysis can be used to predict the stand characteristics (Hyyppä et al 2008). Some of the methods developed recently, such as distribution matching, cannot be easily classified to represent individual tree or area-based approach (Vauhkonen and Mehtätalo 2015;Kansanen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Delineating forest stands from grid data

Pukkala

2020

For. Ecosyst.

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Background: Forest inventories are increasingly based on airborne laser scanning (ALS). In Finland, the results of these inventories are calculated for small grid cells, 16 m by 16 m in size. Use of grid data in forest planning results in the additional requirement of aggregating management prescriptions into large enough continuous treatment units. This can be done before the planning calculations, using various segmentation techniques, or during the planning calculations, using spatial optimization. Forestry practice usually prefers reasonably permanent segments created before planning. These segments are expected to be homogeneous in terms of site properties, growing stock characteristics and treatments. Recent research has developed methods for partitioning grids of ALS inventory results into segments that are homogeneous in terms of site and growing stock characteristics. The current study extended previous methods so that also the similarity of treatments was considered in the segmentation process. The study also proposed methods to deal with biases that are likely to appear in the results when grid data are aggregated into large segments. Methods: The analyses were conducted for two datasets, one from southern and the other from northern Finland. Cellular automaton (CA) was used to aggregate the grid cells into segments using site characteristics with (1) growing stock attributes interpreted from ALS data, (2) predicted cutting prescriptions and (3) both stand attributes cutting prescriptions. The CA was optimized for each segmentation task. A method based on virtual stands was used to correct systematic errors in variable estimates calculated for segments. Results: The segmentation was rather similar in all cases. The result is not surprising since treatment prescriptions depend on stand attributes. The use of virtual stands decreased biases in growth prediction and in the areas of different fertility classes. Conclusions: Automated stand delineation was not sensitive to the type of variables that were used in the process. Virtual stands are an easy method to decrease systematic errors in calculations.

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Section: Introductionmentioning

confidence: 99%

Delineating forest stands from grid data

Pukkala

2020

For. Ecosyst.

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“…One could attempt to improve the quality of the diameter distribution predictions. However, although it is acknowledged that many methods exist for compensating for the stem number left undetected, not as many methods extend the correction to cover frequencies of the diameter distribution (but see Vauhkonen and Mehtätalo 2015;Kansanen et al 2019). On the other hand, it could be possible to accept the errors and not to aim for a high overall accuracy, but to develop prediction methods to optimally respond to management questions at hand, as also suggested by Mauro Gutiérrez et al (2019).…”

Section: Discussionmentioning

confidence: 99%

“…The first case could reflect a higher success rate of detecting trees due to improved algorithms (cf. Kansanen et al 2019) and the latter a lower rate due to using poorer remote sensing material, for instance. As illustrated in Fig.…”

Section: Simulated Diameter Distributionsmentioning

confidence: 99%

Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Vauhkonen

2020

Annals of Forest Science

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& Key Message Tree-level forest inventory data are becoming increasingly available, which motivates the use of these data for decision-making. However, airborne inventories carried out tree-by-tree typically include systematic errors, which can propagate to objective function variables used to determine optimal forest management. Effects of under-detection focused on the smallest trees on predicted immediate harvest profits and future expectation values were assessed assuming different sites and interest rates. Management decisions based on the erroneous information caused losses of 0-17% of the total immediate and future expected income of Scots pine stands. & Context Optimal decisions on how to manage forest stands can depend on the absence or presence of intermediate and understory trees. Yet, these tree strata are likely prone to inventory errors. & Aims The aim of this study is to examine implications of making stand management decisions based on data that include systematic errors resembling those typically observed in airborne inventories carried out tree-by-tree. & Methods Stand management instructions were developed based on theoretical diameter distribution functions simulated to have different shape, scale, and frequency parameters corresponding to various degrees of under-detection focused on the smallest trees. Immediate harvest income and future expectation value were derived based on various management alternatives simulated. & Results Errors in diameter distributions affected the predicted harvest profits and future expectation values differently between the simulated alternatives and depending on site type and interest rate assumptions. As a result, different alternatives were considered as optimal management compared to the use of the error-free reference distributions. In particular, the use of no management or most intensive management alternatives became preferred over alternatives with intermediate harvesting intensities. Certain harvesting types such as thinning from below became preferred more often than what was optimal. The errors did not affect the selection of the management alternative in 71% of the simulations, whereas in the remaining proportion, relying on the erroneous information would have caused losing 2%, on average, and 17% at maximum, of the total immediate and future expected income. & Conclusion The effects above might not have been discovered, if the results were validated for inventory totals instead of separately considering the immediate and future income and losses produced by the erroneous decisions. It is recommended not to separate but to integrate the inventory and planning systems for well-informed decisions.

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“…Importantly, the estimate of the stand-specific mean squared errors, which play a large part in determining the reliability of s-ITC or ABA for stand-level reporting, can be conducted using only stands where field plots exist, greatly reducing the computational effort required to compare the two approaches in an initial screening phase. We recognize that s-ITC is one of many possible methodologies to conduct forest inventory assessments using tree segmentation [3,45] and view the incorporation of these other methods into SAE methodology as a basis for further research.…”

Section: Implications For Forest Management Inventoriesmentioning

confidence: 99%

Model-Based Estimation of Forest Inventory Attributes Using Lidar: A Comparison of the Area-Based and Semi-Individual Tree Crown Approaches

2020

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The use of individual tree detection methods to support forest management inventories has been a research topic for over two decades, but a formal assessment of these methods to produce stand-level and region-level predictions of forest attributes and measures of error is lacking. We employed model-based estimation methods in conjunction with the semi-individual tree crown approach (s-ITC) to produce predictions and measures of error for tree volume (VOL), basal area (BA), stem density (DEN), and quadratic mean diameter (QMD) at the scale of forest stands and the entire study region. We compared the s-ITC approach against the area-based approach (ABA) for predictions of region-level and stand-level attributes via model-based root mean squared errors (RMSEs). The study was conducted at the Panther Creek watershed in Oregon, USA using a set of 78 field plots and aerial lidar information. For region-level attributes, s-ITC RMSEs demonstrated changes between −31% and 17% relative to ABA models. At the stand level, median s-ITC RMSEs generally increased, with changes between −29% and 414% relative to ABA models, but demonstrated important reductions in stands where segmentation provided large increases in sample size and was less prone to extrapolation than ABA models. The ABA demonstrated smaller RMSEs in stands without sampled population units for all variables. Our findings motivate further research into niche applications where s-ITC models may consistently outperform ABA models.

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Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching

Cited by 23 publications

References 29 publications

Delineating forest stands from grid data

Delineating forest stands from grid data

Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Model-Based Estimation of Forest Inventory Attributes Using Lidar: A Comparison of the Area-Based and Semi-Individual Tree Crown Approaches

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