THE ACTUALIZATION OF FOREST INVENTORY DATA BASE BY THE DEVELOPMENT OF FOREST GROWTH MODELS

Abstract Key message We examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester. Context Airborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data. Aims This study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data. Methods We examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient. Results In clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals. Conclusion The study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

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A System to Derive Optimal Tree Diameter Increment Models from the Eastwide Forest Inventory Data Base (EFIDB)

Southern Journal of Applied Forestry ◽

10.1093/sjaf/26.4.214 ◽

2002 ◽

Vol 26 (4) ◽

pp. 214-221 ◽

Cited By ~ 1

Author(s):

Don C. Bragg

Keyword(s):

Data Base ◽

Forest Inventory ◽

Computer Software ◽

Ordinary Least Squares ◽

Least Squares Regression ◽

Inventory Data ◽

Tree Diameter ◽

Diameter Increment ◽

Forest Inventory Data ◽

Optimal Tree

Abstract This article is an introduction to the computer software used by the Potential Relative Increment (PRI) approach to optimal tree diameter growth modeling. These DOS programs extract qualified tree and plot data from the Eastwide Forest Inventory Data Base (EFIDB), calculate relative tree increment, sort for the highest relative increments by diameter class, and generate an ASCII file for post-processing in any software package capable of customized ordinary least squares regression. South. J. Appl. For. 26(4):214–221.

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Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

Canadian Journal of Forest Research ◽

10.1139/x09-183 ◽

2010 ◽

Vol 40 (2) ◽

pp. 184-199 ◽

Cited By ~ 68

Author(s):

Michael J. Falkowski ◽

Andrew T. Hudak ◽

Nicholas L. Crookston ◽

Paul E. Gessler ◽

Edward H. Uebler ◽

...

Keyword(s):

Forest Inventory ◽

Nearest Neighbor ◽

Forest Growth ◽

Tree Level ◽

Lidar Data ◽

Inventory Data ◽

K Nearest Neighbor ◽

Forest Inventory Data ◽

Forest Growth Model ◽

Nearest Neighbor Imputation

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and species) across a 12 100 ha study area in northeastern Oregon, USA. The primary objective was to provide spatially explicit data to parameterize the Forest Vegetation Simulator, a tree-level forest growth model. The final imputation model utilized LiDAR-derived height measurements and topographic variables to spatially predict tree-level forest inventory data. When compared with an independent data set, the accuracy of forest inventory metrics was high; the root mean square difference of imputed basal area and stem volume estimates were 5 m2·ha–1 and 16 m3·ha–1, respectively. However, the error of imputed forest inventory metrics incorporating small trees (e.g., quadratic mean diameter, tree density) was considerably higher. Forest Vegetation Simulator growth projections based upon imputed forest inventory data follow trends similar to growth projections based upon independent inventory data. This study represents a significant improvement in our capabilities to predict detailed, tree-level forest inventory data across large areas, which could ultimately lead to more informed forest management practices and policies.

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The Eastwide forest inventory data base: users manual.

10.2737/nc-gtr-151 ◽

1992 ◽

Author(s):

Mark H. Hansen ◽

Thomas Frieswyk ◽

Joseph F. Glover ◽

John F. Kelly

Keyword(s):

Data Base ◽

Forest Inventory ◽

Inventory Data ◽

Forest Inventory Data

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Updating Forest Inventory Data by Remote Sensing or Growth Models to Characterise Maritime Pine Stands at the Management Unit Level

Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring - Forestry Sciences ◽

10.1007/978-94-017-0649-0_8 ◽

2003 ◽

pp. 97-109

Author(s):

J. S. Uva ◽

M. Tomé ◽

J. Moreira ◽

P. Soares

Keyword(s):

Remote Sensing ◽

Forest Inventory ◽

Growth Models ◽

Management Unit ◽

Maritime Pine ◽

Inventory Data ◽

Unit Level ◽

Forest Inventory Data ◽

Pine Stands

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Evaluation of tree and stand-level growth models using national forest inventory data

European Journal of Forest Research ◽

10.1007/s10342-017-1025-8 ◽

2017 ◽

Vol 136 (2) ◽

pp. 251-258 ◽

Cited By ~ 5

Author(s):

Andrew McCullagh ◽

Kevin Black ◽

Maarten Nieuwenhuis

Keyword(s):

Forest Inventory ◽

Growth Models ◽

National Forest Inventory ◽

National Forest ◽

Inventory Data ◽

Forest Inventory Data

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Rapid Assessment of Tree Damage Resulting from a 2020 Windstorm in Iowa, USA

Forests ◽

10.3390/f12050555 ◽

2021 ◽

Vol 12 (5) ◽

pp. 555

Author(s):

Thomas C. Goff ◽

Mark D. Nelson ◽

Greg C. Liknes ◽

Tivon E. Feeley ◽

Scott A. Pugh ◽

...

Keyword(s):

Forest Inventory ◽

Damage Zone ◽

Satellite Image ◽

Rapid Assessment ◽

Aerial Survey ◽

Inventory Data ◽

Tree Damage ◽

Forest Inventory Data ◽

Damage Susceptibility ◽

The Impact

A need to quantify the impact of a particular wind disturbance on forest resources may require rapid yet reliable estimates of damage. We present an approach for combining pre-disturbance forest inventory data with post-disturbance aerial survey data to produce design-based estimates of affected forest area and number and volume of trees damaged or killed. The approach borrows strength from an indirect estimator to adjust estimates from a direct estimator when post-disturbance remeasurement data are unavailable. We demonstrate this approach with an example application from a recent windstorm, known as the 2020 Midwest Derecho, which struck Iowa, USA, and adjacent states on 10–11 August 2020, delivering catastrophic damage to structures, crops, and trees. We estimate that 2.67 million trees and 1.67 million m3 of sound bole volume were damaged or killed on 23 thousand ha of Iowa forest land affected by the 2020 derecho. Damage rates for volume were slightly higher than for number of trees, and damage on live trees due to stem breakage was more prevalent than branch breakage, both likely due to higher damage probability in the dominant canopy of larger trees. The absence of post-storm observations in the damage zone limited direct estimation of storm impacts. Further analysis of forest inventory data will improve understanding of tree damage susceptibility under varying levels of storm severity. We recommend approaches for improving estimates, including increasing spatial or temporal extents of reference data used for indirect estimation, and incorporating ancillary satellite image-based products.

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Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

Remote Sensing ◽

10.3390/rs13081592 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1592

Author(s):

Nikolai Knapp ◽

Andreas Huth ◽

Rico Fischer

Keyword(s):

Remote Sensing ◽

Forest Inventory ◽

Canopy Height ◽

Biomass Estimation ◽

Inventory Data ◽

Border Effects ◽

Tree Crowns ◽

Forest Inventory Data ◽

Estimation Uncertainty ◽

Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

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