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

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.

Download Full-text

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

Journal of Arid Land ◽

10.1007/s40333-013-0191-x ◽

2013 ◽

Vol 6 (1) ◽

pp. 80-96 ◽

Cited By ~ 12

Author(s):

Carlos A. Aguirre-Salado ◽

Eduardo J. Treviño-Garza ◽

Oscar A. Aguirre-Calderón ◽

Javier Jiménez-Pérez ◽

Marco A. González-Tagle ◽

...

Keyword(s):

Aboveground Biomass ◽

Forest Inventory ◽

Nearest Neighbor ◽

Central Mexico ◽

Inventory Data ◽

K Nearest Neighbor ◽

North Central ◽

Forest Inventory Data

Download Full-text

Economic losses in carbon forestry due to errors in inventory data

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0251 ◽

2020 ◽

Cited By ~ 1

Author(s):

Roope Ruotsalainen ◽

Timo Pukkala ◽

Annika Kangas ◽

Mari Myllymäki ◽

Petteri Packalen

Keyword(s):

Forest Inventory ◽

Laser Scanning ◽

Net Present Value ◽

Nearest Neighbor ◽

Carbon Price ◽

Wood Products ◽

Total Carbon ◽

Economic Losses ◽

Inventory Data ◽

Forest Inventory Data

Forestry can help to mitigate climate change by storing carbon in trees, forest soils and wood products. Forest owners can be subsidized if forestry removes carbon from the atmosphere and taxed if forestry produces emissions. Errors in forest inventory data can lead to losses in net present value (NPV) if management prescriptions are selected based on erroneous data but not on correct data. This study assesses the effect of inventory errors on economic losses in forest management when the objective is to maximize the total NPV of timber production and carbon payments. Errors similar as in airborne laser scanning based forest inventory were simulated in stand attributes with a vine copula approach and nearest neighbor method. Carbon payments were based on the total carbon balance of forestry (incl. trees, soil and wood-based products) and calculations were carried out for 30 years using carbon prices of € 0, 50, 75, 100, 125 and 150 t-1. The results revealed that increasing the carbon price and decreasing the level of errors led to decreased losses in NPV. The inclusion of carbon payments for the maximization of the NPV decreased the effect of errors on the losses, which suggests that the value of collecting more accurate forest inventory data may decrease when the carbon price increases.

Download Full-text

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

Успехи современного естествознания (Advances in Current Natural Sciences) ◽

10.17513/use.36702 ◽

2018 ◽

pp. 52-57

Author(s):

S.S. Zubova ◽

V.S. Vorozhnin

Keyword(s):

Data Base ◽

Forest Inventory ◽

Growth Models ◽

Forest Growth ◽

Inventory Data ◽

Forest Inventory Data ◽

Forest Growth Models

Download Full-text

Estimation of Aboveground Biomass in Alpine Forests: A Semi-Empirical Approach Considering Canopy Transparency Derived from Airborne LiDAR Data

Sensors ◽

10.3390/s110100278 ◽

2010 ◽

Vol 11 (1) ◽

pp. 278-295 ◽

Cited By ~ 28

Author(s):

Andreas Jochem ◽

Markus Hollaus ◽

Martin Rutzinger ◽

Bernhard Höfle

Keyword(s):

Empirical Model ◽

Forest Inventory ◽

Volume Estimation ◽

Airborne Lidar ◽

Stem Volume ◽

Lidar Data ◽

Inventory Data ◽

Forest Inventory Data ◽

Semi Empirical ◽

Airborne Lidar Data

In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can be measured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R2 (R2 = 0.70 to R2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation.

Download Full-text

Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China

Remote Sensing ◽

10.3390/rs11172005 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2005 ◽

Cited By ~ 1

Author(s):

Yuanyuan Fu ◽

Hong S. He ◽

Todd J. Hawbaker ◽

Paul D. Henne ◽

Zhiliang Zhu ◽

...

Keyword(s):

Forest Inventory ◽

Environmental Variables ◽

Northeast China ◽

Nearest Neighbor ◽

Forest Biomass ◽

Remote Sensing Data ◽

Species Level ◽

Estimation Accuracy ◽

Inventory Data ◽

K Nearest Neighbor

Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.

Download Full-text

Fusing tree-ring and forest inventory data to infer influences on tree growth

10.1101/097535 ◽

2016 ◽

Author(s):

Margaret E. K. Evans ◽

Donald A. Falk ◽

Alexis Arizpe ◽

Tyson L. Swetnam ◽

Flurin Babst ◽

...

Keyword(s):

Tree Ring ◽

Tree Growth ◽

Forest Inventory ◽

Forest Dynamics ◽

Carbon Accounting ◽

Data Sources ◽

Tree Level ◽

Inventory Data ◽

Forest Inventory Data ◽

Reduced Data

AbstractBetter understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources – tree-ring and forest inventory data. A Bayesian hierarchical model is used to gain inference on the effects of many factors on tree growth – individual tree size, climate, biophysical conditions, stand-level competitive environment, tree-level canopy status, and forest management treatments – using both diameter at breast height (DBH) and tree-ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. The model was applied to a dataset developed at a single, well-studied site in the Jemez Mountains of north-central New Mexico, U. S. A. Inferences from the model included positive effects of seasonal precipitation, wetness index, and height ratio, and negative effects of seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model compared well to results from a dendroclimatic analysis. Combining the two data sources did not lead to higher predictive accuracy (using the leave-one-out information criterion, LOOIC), either when there was a large number of increment cores (129) or under a reduced data scenario of 15 increment cores. However, there was a clear advantage, in terms of parameter estimates, to the use of both data sources under the reduced data scenario: DBH remeasurement data for ~500 trees substantially reduced uncertainty about non-climate fixed effects on radial increments. We discuss the kinds of research questions that might be addressed when the high-resolution information on climate effects contained in tree rings are combined with the rich metadata on tree- and stand-level conditions found in forest inventories, including carbon accounting and projection of tree growth and forest dynamics under future climate scenarios.

Download Full-text

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.

Download Full-text

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.

Download Full-text