Forest Sampling Desk Reference
About: This article is published in Forestry.The article was published on 2001-01-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Sampling (statistics) & Desk.
Citations
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TL;DR: In this article, an existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data.
Abstract: An existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data. Despite suboptimal calibration data, stand inventory variables such as top height, average height, basal area, gross total volume, gross merchantable volume, and above-ground biomass were estimated from 136 calibration plots and validated on 138 independent plots, with root mean square errors generally less than 20% of mean values. Stand densities (trees per ha) were estimated with less precision (30%). These relationships were used as regression estimators to predict the suite of variables for each 400 m2 tile on the 630 000-ha forest, with predictions capable of being aggregated in any user-defined manner—for a stand, block, or forest—with appropriate estimates of statistical precision. This pilot study demonstrated that LiDAR data may satisfy growing needs f...
141 citations
Book•
30 Nov 2004
TL;DR: The statistical theory of inventory and monitoring from a probabilistic point of view is presented, with the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population.
Abstract: We present the statistical theory of inventory and monitoring from a probabilistic point of view. We start with the basics and show the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population. For such applications, useful open source software is given in Appendix 4. Various sources of ancillary information are described and applications of the sampling strategies are discussed. Classical and bootstrap variance estimators are discussed also. Numerous problems with solutions are given, often based on the experiences of the authors. Key additional references are cited as needed or desired.
74 citations
Cites background from "Forest Sampling Desk Reference"
...The book by Johnson (2000) is very basic and gives extensive information....
[...]
TL;DR: In this article, the authors report biomass equations and sprouting productivity of two oak species (Quercus castanea and Quercus laeta) subject to traditional forms of woodfuel harvesting at Cuitzeo basin in central Mexico.
Abstract: Sustainable production systems for woodfuels in developing countries require basic information on tree productivity, and particularly on their coppicing productivity under current forms of management. We report biomass equations and sprouting productivity of two oak species (Quercus castanea and Quercus laeta) subject to traditional forms of woodfuel harvesting at Cuitzeo basin in central Mexico. Biomass components analyzed were total aboveground biomass (AGB), woody biomass suitable for charcoal making (WSC) and residues (foliage and small branches). The estimation of total aboveground biomass (AGB) and woody biomass suitable for charcoal making (WSC) of individual trees, when expressed as a function of DBH in the form y = a(DBH)b, resulted in values of pseudo-R2 higher than 92%. The Mean Annual Increment (MAI) of both species increased with site age. Significant differences were found in regrowth rates of these species. Maximum charcoal potential productivity in kg ha−1 year−1 is achieved between 30 and 50 years depending on the decay rate of coppicing-shoot density over time. This roughly doubles current harvest cycles of 10–15 years followed by charcoalers. Oaks in developing countries have the potential to be used as a mid-term rotation coppice species for energy purposes. We argue that the results shown in this study are an important input for designing appropriate management strategies for traditional oak charcoal production in developing countries
31 citations
Cites background from "Forest Sampling Desk Reference"
...Although the number of sites and their age distribution is sufficient to construct a regrowth curve, theymay not be sufficient to develop a best fit sigmoidal curve, as very young (<10 years) and old (>50 years) plots are missing from the chronosequence [65]....
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28 Feb 2011
TL;DR: To test for a spatial correlation in the residuals of a global form height model fitted over a large study area and to use this correlation in prediction of the same variable, using nested spherical and Bessel variograms were selected.
Abstract: Normal 0 21 false false false FI ZH-CN TH When a large-area model is utilized in smaller sub-areas, the results may be biased, even though the model is unbiased in general. One method for adjusting the large-area models for such bias is kriging, in which the predictions are corrected with the help of neighbouring observations. A variogram represents the spatial correlation between neighbouring observations as a function of distance. With the selected variogram and drift model that describes the general mean, the variable values for given objects are then predicted. The aim of this study was (1) to test for a spatial correlation in the residuals of a global form height model fitted over a large study area and (2) to use this correlation in prediction of the same variable. The dataset consisted of 19 175 Scots pines ( Pinus sylvestris L.) from the 9 th National Forest Inventory of Finland. Nested spherical and Bessel variograms were selected for the kriging calculations. In nested models the short-range intrastand correlation and long-range correlation are modelled separately. We used 10-fold cross-validation to evaluate the variogram models selected. We limited the number of neighbours from 20 to 100, i.e. at distances within an 8-17-km radius. At the global level, 30 neighbours were needed for stable estimates, and with 60 neighbours the RMSEs of kriging were lower than the globally fitted model. At the regional level, we obtained better estimates than with regionally re-fitted models when the number of neighbours was 60 for both variogram models. The biases at the regional level in the kriging were small (0.8% of the regional RMSE). In conclusion, there was an app. 6-km spatial correlation in the residuals, but the size of the kriging neighbourhood required for improving prediction was larger than the range. MCFNS 3(1):1-14.
13 citations
Cites methods from "Forest Sampling Desk Reference"
...Forest inventory is based on samples that are used to estimate the values of interest in the entire population (e.g. Shiver and Borders, 1996; Johnson, 2000)....
[...]
...Keywords: KED, Kriging, Localization, Regression, Semivariance, Variogram...
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TL;DR: In this paper, the authors compare the estimates of BEF, BEF-based biomass densities, and their uncertainties using different estimators (mean-of-ratios, ratio-ofmeans, and regression estimators) in double sampling.
Abstract: Frequently, biomass expansion factors (BEFs), the respective biomass densities, and their uncertainties are computed without taking into account the appropriate estimators. The objective of this study was to compare the estimates of BEF, BEF-based biomass densities, and their uncertainties using different estimators (mean-of-ratios, ratio-of-means, and regression estimators) in double sampling. Our results demonstrated that increased uncertainty is associated with regression-based biomass densities, and that the computation of BEF using merchantable timber volume should utilize regression estimators, not the usual ratio estimators, which preferably, should be avoided altogether, as they are found to be subjective and more susceptible to errors and personal judgment.
6 citations
Cites background from "Forest Sampling Desk Reference"
...BEFh) is the regression slope when the regression line passes through the origin [40]....
[...]
References
More filters
TL;DR: In this article, an existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data.
Abstract: An existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data. Despite suboptimal calibration data, stand inventory variables such as top height, average height, basal area, gross total volume, gross merchantable volume, and above-ground biomass were estimated from 136 calibration plots and validated on 138 independent plots, with root mean square errors generally less than 20% of mean values. Stand densities (trees per ha) were estimated with less precision (30%). These relationships were used as regression estimators to predict the suite of variables for each 400 m2 tile on the 630 000-ha forest, with predictions capable of being aggregated in any user-defined manner—for a stand, block, or forest—with appropriate estimates of statistical precision. This pilot study demonstrated that LiDAR data may satisfy growing needs f...
141 citations
Book•
30 Nov 2004
TL;DR: The statistical theory of inventory and monitoring from a probabilistic point of view is presented, with the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population.
Abstract: We present the statistical theory of inventory and monitoring from a probabilistic point of view. We start with the basics and show the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population. For such applications, useful open source software is given in Appendix 4. Various sources of ancillary information are described and applications of the sampling strategies are discussed. Classical and bootstrap variance estimators are discussed also. Numerous problems with solutions are given, often based on the experiences of the authors. Key additional references are cited as needed or desired.
74 citations
TL;DR: In this article, the authors report biomass equations and sprouting productivity of two oak species (Quercus castanea and Quercus laeta) subject to traditional forms of woodfuel harvesting at Cuitzeo basin in central Mexico.
Abstract: Sustainable production systems for woodfuels in developing countries require basic information on tree productivity, and particularly on their coppicing productivity under current forms of management. We report biomass equations and sprouting productivity of two oak species (Quercus castanea and Quercus laeta) subject to traditional forms of woodfuel harvesting at Cuitzeo basin in central Mexico. Biomass components analyzed were total aboveground biomass (AGB), woody biomass suitable for charcoal making (WSC) and residues (foliage and small branches). The estimation of total aboveground biomass (AGB) and woody biomass suitable for charcoal making (WSC) of individual trees, when expressed as a function of DBH in the form y = a(DBH)b, resulted in values of pseudo-R2 higher than 92%. The Mean Annual Increment (MAI) of both species increased with site age. Significant differences were found in regrowth rates of these species. Maximum charcoal potential productivity in kg ha−1 year−1 is achieved between 30 and 50 years depending on the decay rate of coppicing-shoot density over time. This roughly doubles current harvest cycles of 10–15 years followed by charcoalers. Oaks in developing countries have the potential to be used as a mid-term rotation coppice species for energy purposes. We argue that the results shown in this study are an important input for designing appropriate management strategies for traditional oak charcoal production in developing countries
31 citations
28 Feb 2011
TL;DR: To test for a spatial correlation in the residuals of a global form height model fitted over a large study area and to use this correlation in prediction of the same variable, using nested spherical and Bessel variograms were selected.
Abstract: Normal 0 21 false false false FI ZH-CN TH When a large-area model is utilized in smaller sub-areas, the results may be biased, even though the model is unbiased in general. One method for adjusting the large-area models for such bias is kriging, in which the predictions are corrected with the help of neighbouring observations. A variogram represents the spatial correlation between neighbouring observations as a function of distance. With the selected variogram and drift model that describes the general mean, the variable values for given objects are then predicted. The aim of this study was (1) to test for a spatial correlation in the residuals of a global form height model fitted over a large study area and (2) to use this correlation in prediction of the same variable. The dataset consisted of 19 175 Scots pines ( Pinus sylvestris L.) from the 9 th National Forest Inventory of Finland. Nested spherical and Bessel variograms were selected for the kriging calculations. In nested models the short-range intrastand correlation and long-range correlation are modelled separately. We used 10-fold cross-validation to evaluate the variogram models selected. We limited the number of neighbours from 20 to 100, i.e. at distances within an 8-17-km radius. At the global level, 30 neighbours were needed for stable estimates, and with 60 neighbours the RMSEs of kriging were lower than the globally fitted model. At the regional level, we obtained better estimates than with regionally re-fitted models when the number of neighbours was 60 for both variogram models. The biases at the regional level in the kriging were small (0.8% of the regional RMSE). In conclusion, there was an app. 6-km spatial correlation in the residuals, but the size of the kriging neighbourhood required for improving prediction was larger than the range. MCFNS 3(1):1-14.
13 citations
TL;DR: In this paper, the authors compare the estimates of BEF, BEF-based biomass densities, and their uncertainties using different estimators (mean-of-ratios, ratio-ofmeans, and regression estimators) in double sampling.
Abstract: Frequently, biomass expansion factors (BEFs), the respective biomass densities, and their uncertainties are computed without taking into account the appropriate estimators. The objective of this study was to compare the estimates of BEF, BEF-based biomass densities, and their uncertainties using different estimators (mean-of-ratios, ratio-of-means, and regression estimators) in double sampling. Our results demonstrated that increased uncertainty is associated with regression-based biomass densities, and that the computation of BEF using merchantable timber volume should utilize regression estimators, not the usual ratio estimators, which preferably, should be avoided altogether, as they are found to be subjective and more susceptible to errors and personal judgment.
6 citations