84 research outputs found
Correcting the nondetection bias of angle count sampling
No full textThe well-known angle count sampling (ACS) has proved to be an efficient sampling technique and has been applied in forest inventories for many decades. However, ACS assumes total visibility of objects; any violation of this assumption leads to a nondetection bias. We present a novel approach, in which the theory of distance sampling is adapted to traditional ACS to correct for the nondetection bias. Two new estimators were developed based on expanding design-based inclusion probabilities by model-based estimates of the detection probabilities. The new estimators were evaluated in a simulation study as well as in a real forest inventory. It is shown that the nondetection bias of the traditional estimator is up to -52.5%, whereas the new estimators are approximately unbiased
Spatial prediction of forest stand variables
No full textThis study aims at the development of a model to predict forest stand variables in management units (stands) from sample plot inventory data. For this purpose we apply a non-parametric most similar neighbour (MSN) approach. The study area is the municipal forest of Waldkirch, 13 km north-east of Freiburg, Germany, which comprises 328 forest stands and 834 sample plots. Low-resolution laser scanning data, classification variables as well rough estimations from the forest management planning serve as auxiliary variables. In order to avoid common problems of k-NN-approaches caused by asymmetry at the boundaries of the regression spaces and distorted distributions, forest stands are tessellated into subunits with an area approximately equivalent to an inventory sample plot. For each subunit only the one nearest neighbour is consulted. Predictions for target variables in stands are obtained by averaging the predictions for all subunits. After formulating a random parameter model with variance components, we calibrate the prior predictions by means of sample plot data within the forest stands via BLUPs (best linear unbiased predictors). Based on bootstrap simulations, prediction errors for most management units finally prove to be smaller than the design-based sampling error of the mean. The calibration approach shows superiority compared with pure non-parametric MSN predictions
Density estimation based on k-tree sampling and point pattern reconstruction
No full textIn k-tree sampling, also referred to as point-to-tree distance sampling, the k nearest trees are measured. The problem associated with k-tree sampling is its lack of unbiased density estimators. The presented density estimator based on point pattern reconstruction remedies that shortcoming. It requires the coordinates of all k trees. These coordinates are translated into a simulation window where they remain unchanged. Empirical cumulative distribution functions of intertree and location-to-tree distances estimated from the sample plots are set as target characteristics. Using the idea of simulated annealing, an optimal new tree pattern is constructed in the simulation window outside the k-tree samples. The reconstruction of the point pattern minimizes the contrast between the empirical cumulative distribution functions and their analogs for the simulated pattern. The density estimator is simply the tree density of the optimum pattern in the simulation window. The performance of the reconstruction-based density estimator is assessed for k = 6 and k = 4 based on systematic sampling grids regarding its potential application in forest inventories. Simulations are carried out using real stem maps (covering different stand densities and different types of spatial point patterns, such as regular, clustered, and random) as well as completely random patterns. The new density estimator proves to be empirically superior in terms of bias and root mean squared error compared with commonly used estimators. The reconstruction-based density estimator has biases smaller than 2%.En chantillonnage de k arbres, aussi appel chantillonnage de k arbres selon leur distance, on mesure les k arbres les plus proches. Le problme li lchantillonnage de k arbres est son incapacit fournir des estimateurs de densit sans biais. Lestimateur de densit bas sur la reconstruction du patron des points comble cette lacune. Il requiert les coordonnes de tous les k arbres. Ces coordonnes sont traduites dans une fentre de simulation o elles demeurent inchanges. Les fonctions empiriques de distribution cumulative de distances entre les arbres et entre un point et les arbres estimes partir des placettes chantillons sont les caractristiques cibles. En utilisant le recuit simul, un nouveau patron optimal des arbres est construit dans la fentre de simulation en dehors des k arbres chantillons. La reconstruction du patron de points minimise le contraste entre les fonctions empiriques et leurs analogues drivs du patron simul. Lestimateur de densit est tout simplement la densit des arbres de la structure optimale dans la fentre de simulation. La performance de lestimateur de densit bas sur la reconstruction est value pour k = 6 et k = 4 sur la base des grilles dchantillonnage systmatique quant son application potentielle dans les inventaires forestiers. Des simulations sont effectues en utilisant les cartes relles des tiges (couvrant diffrentes densits de peuplement et diffrents types de patrons spatiaux de points, tels que rgulier, en grappe et alatoire) aussi bien que des patrons compltement alatoires. Le nouvel estimateur de densit savre empiriquement suprieur en termes de biais et derreur quadratique moyenne par rapport aux estimateurs frquemment utiliss. Son biais est infrieur 2%
Spatio-temporal prediction of site index based on forest inventories and climate change scenarios
No full textA methodological framework is provided for the quantification of climate change effects on site index. Spatio-temporal predictions of site index are derived for six major tree species in the German state of Baden-Wurttemberg using simplified universal kriging (UK) based on large data sets from forest inventories and a climate sensitive site-index model. It is shown by a simulation study that, with the underlying large sample size, residual kriging using ordinary least squares (OLS) estimates of the mean function leads to an approximately unbiased spatial predictor. Moreover, the simulated coverage probabilities of resulting prediction intervals are quite close to the required level. B-spline regression techniques are applied to model nonlinear cause-and-effect curves for estimating site indexes at existing inventory plots dependent on retrospective climate covariates. The spatially structured error is modeled by exponential covariance functions. The mean model is then applied to downscaled climate projection data to spatially predict the relative changes of site index under perturbed climate conditions. Applying climate projections of an existing regional climate model based on IPCC emission scenarios A1B and A2, it is found that site index of all tree species would be decreased in lowland areas, and may increase in mountainous regions. Silver fir and common oak stands would also show increased site indexes in mountainous regions, but further extended to lower elevation levels. Site conditions in the Alpine foothills may remain highly productive for growth of Norway spruce, Baden-Wurttemberg's most dominant tree species. Whereas site index of common beech and Douglas-fir may decrease to almost the same relative amount and on nearly the same sites as Norway spruce, site index of Scots pine may be less affected by future climate change. (C) 2012 Elsevier B.V. All rights reserved
Dichteschätzung für N-Baum-Stichproben durch Reproduktion von Baumartenverteilungsmustern
No full textsol-LAUT – Point clouds and hemispherical photographs for solar radiation modeling from Austrian forest inventory plots
No full text<p>The amount of solar radiation reaching the forest floor is a crucial parameter for the abundance and vitality of regenerating trees. Therefore, its quantification and characterization are of particular importance for forest management. The calculation of below-canopy light regime from forest point clouds collected with personal laser scanners offers an opportunity to assess the necessary metrics without the requirement of any additional data collection.</p>
<p>This dataset contains 3D point cloud data collected on 40 forest inventory plots in Austria, which can be found in the folder ‘PointClouds’. The scanner used for data acquisition was a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK). Furthermore, hemispherical photographs were taken at the same forest plots using the Solariscope SOL 300 (Behlin, Wedemark, Germany) as well as a Canon EOS 5D camera (Canon Inc., Ōta, Tokyo, Japan) mounted on a SLM9 self-levelling mount (Delta-T Devices Ltd., Burwell, Cambridge, UK) together with HemiView software (Delta-T Devices Ltd., 2016). The raw and classified images can be found in the corresponding subfolders (‘HemiView’ or ‘Solariscope’) in the folder ‘HemisphericalPhotography’. Additionally, the results obtained with the classified images of Solariscope and HemiView are included in the output files. Note that the results from the Solariscope survey represent average values over a predefined time span, in this case from April 1 to September 30, 2022. Also, the range of solar zenith angles considered for the analyses was set from 0 to 50° in the device settings, meaning that radiation values originating from moments with a solar elevation angle below 40° were not included into the calculations. The file 'Plots.csv' contains the geographical coordinates of the inventory plots (columns 'X' and 'Y', EPSG:31286). The file 'treespecies.csv' gives information on the percentage of tree species at each plot, which is necessary for the calculation of a plot-wise canopy extinction coefficient.</p>
Comparison of stand volume predictions based on airborne laser scanning data versus aerial stereo images
No full textPractical forest management requires information on dendrometrical forest parameters in a high spatial resolution, particularly interesting is the timber volume. Nearest neighbor techniques and the random forest approach were employed in this study to predict timber volume per hectare (total stem volume and stem volume of large beech trees, DBH >= 60 cm) at forest stand level. The predictions were based on sample plot data from a regional forest inventory and selected sets of auxiliary variables derived from two different remote sensing data sources airborne laser scanning (ALS) data and aerial stereo images (ASI) to quantify and compare prediction precision. Existing studies conclude that ALS data provide more precise height information, but also that acquisition of ALS data is more expensive than of ASI data, which are often already available from other monitoring projects. Currently the cost of ASI data is about a half to a third of ALS data. To make spatial predictions we compared two frequently used methods for imputation: random forest and k-most similar neighbors. For both methods, the prediction precisions (RMSE) were similar. Most promising was the fact that the two different sources of auxiliary variables resulted in predictions of almost the same precision. The similarity between ASI and ALS predictions suggest that ASI may serve as a lower-cost alternative to ALS data for estimating many forest stand-level variables
Dichteschätzung für N-Baum-Stichproben durch Reproduktion von Baumartenverteilungsmustern
No full textComparison of stand volume predictions based on airborne laser scanning data versus aerial stereo images
No full textPractical forest management requires information on dendrometrical forest parameters in a high spatial resolution, particularly interesting is the timber volume. Nearest neighbor techniques and the random forest approach were employed in this study to predict timber volume per hectare (total stem volume and stem volume of large beech trees, DBH >= 60 cm) at forest stand level. The predictions were based on sample plot data from a regional forest inventory and selected sets of auxiliary variables derived from two different remote sensing data sources airborne laser scanning (ALS) data and aerial stereo images (ASI) to quantify and compare prediction precision. Existing studies conclude that ALS data provide more precise height information, but also that acquisition of ALS data is more expensive than of ASI data, which are often already available from other monitoring projects. Currently the cost of ASI data is about a half to a third of ALS data. To make spatial predictions we compared two frequently used methods for imputation: random forest and k-most similar neighbors. For both methods, the prediction precisions (RMSE) were similar. Most promising was the fact that the two different sources of auxiliary variables resulted in predictions of almost the same precision. The similarity between ASI and ALS predictions suggest that ASI may serve as a lower-cost alternative to ALS data for estimating many forest stand-level variables
Climate-sensitive radial increment model of Norway spruce in Tyrol based on a distributed lag model with penalized splines for year-ring time series
No full textA novel methodological framework is presented for climate-sensitive modeling of annual radial stem increment using year-ring width time series. The approach is based on a generalized additive model with penalized regression splines together with a distributed time lag model taking into account smooth nonlinear effects of a series of monthly temperature and precipitation values as well as interactions thereof. Climate effects are also assumed to vary smoothly with time lag. The model framework enables that both the detrending of the individual time series and the regression modeling can be performed simultaneously in a single model step. The approach is applied to year-ring width time series of Norway spruce (Picea abies (L.) H. Karst.) trees in Tyrol, Austria. The marginal response curves show that tree growth is mainly promoted by high temperatures in late spring and early summer and by precipitation in fall and winter. Summer drought does not have a negative influence on the current year's radial increment; however, when it is associated with high temperatures, it lowers the increment in the subsequent growth period. Higher winter precipitation in conjunction with lower temperatures have a positive effect. A significant non-climate related long-term growth trend is demonstrated, probably reflecting NOx and SO2 emission trends in Austria.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
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