Effects of Measurement Errors on Individual Tree Stem Volume Estimates for the Austrian National Forest Inventory

Canada’s National Forest Inventory (NFI) relies on photo-interpreted forest resource data provided by provincial and territorial agencies. NFI data are collected at regular intervals in time from a nominal 20 × 20 km network of 2 × 2 km photoplots. Attribute-specific NFI estimates of precision include contributions from sampling errors and uncertainty in the source data. We assessed this uncertainty in NFI photo-interpreted forest attribute data from New Brunswick and Nova Scotia. Attributes examined were: cover type, age, maturity (class), crown closure, height, volume, and area associated with an attribute. Monte-Carlo simulations, with measurement errors superimposed on NFI data assumed to be error-free, showed that estimates of precision were inflated by an average of 7% (range 0%–36%) due to the uncertainty in the source data. Species misclassification and age determination were the largest sources of uncertainty.

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

The Forestry Chronicle ◽

10.5558/tfc2017-030 ◽

2017 ◽

Vol 93 (03) ◽

pp. 213-225 ◽

Cited By ~ 1

Author(s):

Steen Magnussen ◽

Graham Stinson ◽

Paul Boudewyn

Keyword(s):

Missing Data ◽

Forest Inventory ◽

Unit Area ◽

National Forest Inventory ◽

Current Data ◽

Stem Volume ◽

National Forest ◽

Multiple Imputations ◽

Type Area ◽

Study Sites

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 T2 and change between T1 and T2 in cover-type area proportions and in per unit area stem volume. At each location we found significant T1 differences in these attributes between plots with and without T2 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 T2 inference was based exclusively on plots with T2 data. Possible differences between the T2 and T1 photointerpretation, paired with no efficient stratification of disturbed and undisturbed plots, largely eliminated expected gains in precision from the MI boosting of the effective T2 sample size. Despite recognized limitations, we recommend MI as an effective tool to counteract an emerging spatial imbalance in the NFI.

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Country and regional carbon stock in forest cover – estimates based on the first cycle of the Czech National Forest Inventory data (2001–2004)

Central European Forestry Journal ◽

10.1515/forj-2017-0018 ◽

2017 ◽

Vol 63 (2-3) ◽

pp. 113-125 ◽

Cited By ~ 1

Author(s):

Ján Merganič ◽

Katarína Merganičová ◽

Bohdan Konôpka ◽

Miloš Kučera

Keyword(s):

Forest Inventory ◽

Carbon Stock ◽

Forest Cover ◽

Regional Distribution ◽

National Forest Inventory ◽

National Forest ◽

Individual Tree ◽

The Individual ◽

Species Specific ◽

Total Tree

AbstractSince forests can play an efficient role in the mitigation of greenhouse gas emissions, objective information about the actual carbon stock is very important. Therefore, the presented paper analysed the carbon stock in the living merchantable trees (with diameter at breast height above 7 cm) of the Czech forests with regard to groups of tree species and tree compartments (wood under bark with diameter above 7 cm, wood under bark with diameter below 7 cm, bark, green twigs, foliage, stump and roots). We examined its regional distribution and relationship to the number of inhabitants and the gross domestic product. The data used for the analysis originated from 13,929 forest plots of the first Czech National Forest Inventory performed between 2001 and 2004. The total tree carbon stock was obtained as a sum of the carbon stock in the individual tree compartments estimated from the biomass amount in the compartments multiplied by the relative carbon content. Wood biomass amount was calculated by multiplying a particular part of tree volume with species-specific green wood density. The total amount of carbon stored in forest trees in the Czech Republic was over 327 mill. t, which is about 113 t of carbon per ha of forests. The highest carbon amount (160 mill. t, i.e. 49.0% of the total amount) was fixed in spruce. The minimum carbon amount fixed in the forest cover (14.35 mill. t) was calculated for Ústecký kraj (region), while the maximum carbon amount (51.51 mill. t) was found in Jihočeský kraj.

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Integrating remote sensing and past inventory data under the new annual design of the Swiss National Forest Inventory using three-phase design-based regression estimation

Canadian Journal of Forest Research ◽

10.1139/cjfr-2014-0152 ◽

2014 ◽

Vol 44 (10) ◽

pp. 1177-1186 ◽

Cited By ~ 24

Author(s):

Alexander Massey ◽

Daniel Mandallaz ◽

Adrian Lanz

Keyword(s):

Forest Inventory ◽

Estimation Procedure ◽

National Forest Inventory ◽

Aerial Photographs ◽

Stem Volume ◽

Permanent Plots ◽

National Forest ◽

Special Focus ◽

Second Phase ◽

Three Phase

In 2009, the Swiss National Forest Inventory (NFI) turned from a periodic into an annual measurement design in which only one-ninth of the overall sample of permanent plots is measured every year. The reduction in sample size due to the implementation of the annual design results in an unacceptably large increase in variance when using the standard simple random sampling estimator. Thus, a flexible estimation procedure using two- and three-phase regression estimators is presented with a special focus on utilizing updating techniques to account for disturbances and growth and is applied to the second and third Swiss NFIs. The first phase consists of a dense sample of systematically distributed plots on a 500 m × 500 m grid for which auxiliary variables are obtained through the interpretation of aerial photographs. The second phase is an eightfold looser subgrid with terrestrial plot data collected from the past inventory, and the third and final phase consists of the three most recent annual subgrids with the current state of the target variable (stem volume). The proposed three-phase estimators reduce the increase in variance from 294% to 145% compared with the estimator based on the full periodic sample while remaining unbiased.

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