Estimation of single-tree characteristics using the kNN method and plotwise aerial photograph interpretations

Holmström, Hampus

doi:10.1016/s0378-1127(01)00720-4

Cited by 23 publications

(17 citation statements)

References 17 publications

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“…The parameters in the simulations were set in order to resemble the quality of data that can be expected in practise. Parameters for stand record information based on ocular methods were derived from Eriksson (1990), Ståhl (1992), Holmström (2002), and Holmström et al (2001). Parameters for plot level aerial photo interpretation were derived from Holmström (2002) and Holmström et al (2001), corresponding to results actually obtained in the two test areas in previous studies.…”

Section: Carrier Data Simulationmentioning

confidence: 99%

“…Using plot level aerial photograph interpretations, the values of v t 1 and v t 2 were derived from regression functions using the aerial photo interpreted and stand record variables as regressors. The details are given in Holmström (2002) and Holmström et al (2001). Site index was set equal to the simulated stand record value, while the proportions of pine and spruce at the target plots were set as a weighted average value of the simulated aerial photo interpretations (weight 2/3) and the stand record information (weight 1/3).…”

Section: Knn Imputationmentioning

confidence: 99%

“…Tokola and Heikkilä 1997). Applications based on aerial photograph interpretation and stand record information are presented by Holmström (2002) and Holmström et al (2001).…”

Section: Introductionmentioning

confidence: 99%

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Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Holmström¹,

Kallur²,

Ståhl³

2003

Silva Fenn.

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The usefulness of kNN (k Nearest Neighbour)-assigned reference sample plot data as a basis for forest management planning was studied. Cost-plus-loss analysis was applied, whereby the inventory cost for a specifi c method is added to the expected loss due to non-optimal forestry activities caused by erroneous descriptions of the forest state. Four different strategies for data acquisition were evaluated: 1) kNN imputation of sample plots based on traditional stand record information, 2) imputation based on plot-wise aerial photograph interpretation in combination with stand record information, 3) sample plot inventory in the fi eld with 5 plots per stand, and 4) sample plot inventory with 10 plots per stand. Expected losses were derived as mean values of differences between the maximum net present value and the corresponding value obtained when the treatment schedule believed to be optimal (based on data simulated according to method 1-4) was selected. The optimal choice of method was found to depend on factors such as stand maturity, stand area, and time to next treatment (thinning or clearcutting). In general, the fi eld sample plot methods were competitive in large mature stands, especially when the time to the next (optimal) treatment was short. By in each stand (within an estate) employing the method with the lowest cost-plus-loss rather than choosing the method that performed best on average for the entire estate, the total cost for inventory at the estate level could be decreased by 15-50%. However, it was found diffi cult to identify what method should optimally be employed in a stand based on general stand descriptions.

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Section: Carrier Data Simulationmentioning

confidence: 99%

Section: Knn Imputationmentioning

confidence: 99%

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Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Holmström¹,

Kallur²,

Ståhl³

2003

Silva Fenn.

Self Cite

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“…Imputations could have been improved considerably with information from covariates on the presence/absence of disturbances. In an inventory based on 400 ha photo-interpreted plots, a second kind of 'correlation-breaker' appears in the form of interpreter-specific factors or through changes in the media or guidelines governing an interpretation (Holmström et al 2001, Holmström 2002, Ståhl et al 2011, Magnussen and Russo 2012. The best strategy to minimize the impact of 'correlationbreakers' is to form strata of disturbed plots and use a logicmodel for the anticipated consequence of a stand-replacing disturbance (Reams andMcCollum 2000, Massey et al 2014).The Canadian NFI is actively in pursuit of this option but an operational system will not be in place for some years to come.…”

Section: Discussionmentioning

confidence: 99%

Updating Canada’s National Forest Inventory with multiple imputations of missing contemporary data

Magnussen

StinsonGraham

BoudewynPaul

2017

The Forestry Chronicle

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Canada's National Forest Inventory (NFI) is facing an issue of spatial imbalance in photo interpreted data from 400 ha photo-plots available for estimation of state and change. Multiple imputations (MI) of missing data is therefore considered as a means to mitigate a potential bias arising from spatial imbalance, and-to a lesser degree-improve the precision relative to what can be achieved with the subset of plots having current data. In this study we explored MI with data from three study sites located in the provinces of Quebec, Ontario, and Saskatchewan. Specifically, we looked at state at time T 2 and change between T 1 and T 2 in cover-type area proportions and in per unit area stem volume. At each location we found significant T 1 differences in these attributes between plots with and without T 2 data. A MI procedure with 20 replications of stochastic model-based imputations of missing data was therefore effective as a way to mitigate a bias that would arise if T 2 inference was based exclusively on plots with T 2 data. Possible differences between the T 2 and T 1 photointerpretation, paired with no efficient stratification of disturbed and undisturbed plots, largely eliminated expected gains in precision from the MI boosting of the effective T 2 sample size. Despite recognized limitations, we recommend MI as an effective tool to counteract an emerging spatial imbalance in the NFI.Key words: forest cover-types, area proportions, stem volume, disturbance rates, ratio estimators. RÉSUMÉL'inventaire forestier national du Canada (NFI) est confronté à une problématique de déséquilibre dans les données de placettes photographiques de 400 ha disponibles pour évaluer l' état de la forêt et son changement. On envisage donc d'utiliser l'imputation multiple (MI) des données manquantes afin d'amoindrir le biais occasionné par le déséquilibre spatial et, dans une moindre mesure, améliorer la précision par rapport à ce qu'il est possible d'atteindre avec le sous-ensemble de placettes pour lesquelles nous disposons de données à jour. Dans cette étude, nous avons exploré les possibilités de MI avec les données provenant de trois sites d' étude situés dans les provinces de Québec, de l'Ontario et de la Saskatchewan. De façon plus particulière, nous nous sommes intéressés à l' état à temps T 2 et au changement survenu entre T 1 et T 2 dans les proportions des surfaces par type de couvert et le volume par tige par unité de surface. À chaque endroit nous avons observé des différences significatives pour ces paramètres à T 1 selon qu' on utilisait ou non les données de T 2 . Une procé-dure MI avec 20 répétitions d'imputations des données manquantes basées sur un modèle stochastique s' est avérée efficace pour atténuer le biais qui résulterait si l'inférence à T 2 reposait uniquement sur les placettes disposant des données à T 2 . Toutefois, les différences dans la technique de photo-interprétation à T 1 et à T 2 combinées à l'absence de stratification des placettes perturbées et intactes a masqué la majeure partie du g...

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“…The method was assessed to be statistically straightforward comparing to the conventional landcover estimation. (Hölmstrom, 2002) used a set of panchromatic aerial photos and field based information from 255 circular sample plots measured within the boreal forests of Sweden. Stem volume and age were modelled and validated, through which 14 % and 17 % of prediction errors (RMSE) for volume and age of the trees were observed, respectively.…”

Section: Remote Sensing For Modelling Forest Structure 21 Forest Attmentioning

confidence: 99%

Characterizing Forest Structure by Means of Remote Sensing: A Review

Latifi¹

2012

Remote Sensing - Advanced Techniques and Platforms

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Estimation of single-tree characteristics using the kNN method and plotwise aerial photograph interpretations

Cited by 23 publications

References 17 publications

Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Updating Canada’s National Forest Inventory with multiple imputations of missing contemporary data

Characterizing Forest Structure by Means of Remote Sensing: A Review

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