1,721,028 research outputs found
Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique
No full text
The need for harmonized estimates of forest biodiversity indicators
No full textThe investigations of Working Group 3 of COST Action E43 focused on assessing the ability of NFI's to report harmonized estimates of forest biodiversity indicators using NFI data. Four related factors motivated the investigations. Firstly, the importance of forest biodiversity for the economic, environmental, and social well-being of earth's civilizations is gaining wide international acceptance. Secondly, this acceptance has led to numerous international forest sustainability and biodiversity agreements that require periodic reports of estimates of indicators. Thirdly, the ability to report comparable estimates is impeded by the variety of sampling designs, plot configurations, selected variables, and measurement protocols used by the NFIs of different countries. Fourthly, the features of individual NFIs have evolved in response to unique ecological, economic, topographic, and climatic characteristics, and desire of the individual countries to retain the features. The general conclusion of these motivating factors is that apart from substantial standardization of NFIs, the best method for facilitating comparable reporting is to develop harmonization methods. Working Group 3 undertook a four-phase approach to developing methods for harmonizing estimates of biodiversity indicators using NFI data. The first phase entailed evaluating the importance of biodiversity variables and the feasibility of assessing them using NFI data. The conclusion of this phase was the selection of 17 biodiversity variables that were both important and feasible, grouping of them into seven essential features, and construction of common reference definitions for the variables. The second phase entailed evaluation of the agreement among NFIs with respect to the common definitions and measurement practices. The third phase entailed development of bridges (Stahl et al submitted) for converting estimates of forest biodiversity indicators obtained using national definitions to estimates consistent with the reference definitions. The fourth phase entailed construction of a common database of NFI data contributed by NFIs participating in COST Action E43 and testing of reference definitions and bridges developed by Working Group 3. The following chapters provide details and specific results for the four phases.COST - European Cooperation in Science and Technolog
Comparison of estimators of variance for forest inventories with systematic sampling-results from artificial populations
Full text linkBackground: Large area forest inventories often use regular grids (with a single random start) of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations. A design-unbiased estimator of variance does not exist for this design. Oftentimes, a quasi-default estimator applicable to simple random sampling (SRS) is used, even if it carries with it the likely risk of overestimating the variance by a practically important margin. To better exploit the precision of systematic sampling we assess the performance of five estimators of variance, including the quasi default. In this study, simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends. We compared the results obtained with the SRS, Matérn’s, successive difference replication, Ripley’s, and D’Orazio’s variance estimators. Results: The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated, and in all study settings consistently closer to the target design variance than the estimator for SRS. The latter always produced the greatest overestimation. In populations with a near zero spatial autocorrelation, all estimators, performed equally, and delivered estimates close to the actual design variance. Conclusion: Without a linear trend, the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark; yet in terms of the least average absolute deviation, Matérn’s estimator held a narrow lead. With a strong or moderate linear trend, Matérn’s estimator is choice. In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar. Keywords: Spatial autocorrelation, Linear trend, Model based, Design biased, Matérn variance, Successive difference replication variance, Geary contiguity coefficient, Random site effect
Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework
No full textAmong the forestry-related applications for which airborne laser scanning (ALS) data have been shown to be beneficial, forest inventory has been investigated as much if not more than other applications. Metrics extracted from ALS data for spatial units such as plots and grid cells are typically of two forms: echo-based metrics derived directly from the three-dimensional distribution of the point cloud data and metrics derived from a canopy height model (CHM). For both cases, a large number of metrics can be calculated and used to construct parametric and non-parametric models to predict forest variables.We compared model-assisted estimates of total forest aboveground biomass (AGB) obtained using echo-based and CHM-based height metrics with two prediction methods: (i) a parametric linear model, and (ii) the non-parametric k-Nearest Neighbors (k-NN) technique. Model-assisted (MA) estimators were used with sample data obtained using a two-phase, tessellation stratified sampling (TSS) framework to estimate population parameters. The study was conducted in Molise Region in central Italy.For the four combinations of metrics and prediction techniques, estimates of total biomass were similar, in the range 1.96-2.1 million t, with standard error estimates that were also similar, in the range 0.20-0.21 t. Thus, the CHM-based metrics produced AGB estimates that were similar to and as accurate as those for the echo-based metrics, regardless of whether the parametric or the non-parametric prediction method was used. Additionally, the proposed MA estimator was more accurate than the estimator that did not use auxiliary data
Methods for variable selection in LiDAR-assisted forest inventories
No full textEstimation of wood volume and biomass is an important assignment of any National Forest Inventory. However, the estimation process is often expensive, laborious and sometimes imprecise because of small sample sizes relative to populationvariability. Remote sensing techniques are an option to assist in surveying large areas by providing data that can be related to the forest attribute of interest through mathematical models of relationships. Light Detection and Ranging (LiDAR) is a technology that can provide data that are closely related to forest wood volume and biomass. With these data, linear regression is often used to estimate forest attributes. If the relationship provides evidence of nonlinearity, a transformation in the variables can be considered. However, modern computation allows fitting nonlinear regression models without transformations of the variables. Nonlinear least squares (NLS) techniques also give more freedom to assure satisfaction of natural conditions such as non-negativity and/or lower and upper asymptotes. Like any estimation technique, NLS is subject to overfitting when using a large number of predictor variables. Because NLS is more computationally intensive than linear regression, stepwise selection techniques may require considerable programming effort. We compared three methods to select predictor variables for nonlinear models of relationships between forest attributes and LiDAR metrics, two of them based on genetic algorithms (GAs) and one based on random forest (RM). GAs were implemented to optimize a cost function that yields root mean square error or the Akaike Information Criterion (AIC), while RM was based on variable importance in decision trees. A model with the predictor variable most correlated with the response variable was also considered. We compared the results of overall estimation for two datasets using the model-assisted, generalized regression estimator and concluded that the combination of GAs and AIC was the most efficient and stable procedure for selection of variables.We attribute this result to the penalty that AIC applies to models with large numbers of variables,which leads to a more efficient model with a minimum loss of information
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
