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Tesis sobre el tema "Forestry inventories"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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1

Fransson, Johan. "Analysis of synthetic aperture radar images for forestry applications /". Umeå : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1999. http://www.resgeom.slu.se/fjarr/personal/jf/.

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2

Barth, Andreas. "Spatially comprehensive data for forestry scenario analysis : consequences of errors and methods to enhance usability /". Umeå : Dept. of Forest Resource Management, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/2007101.pdf.

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3

Yang, Sheng-I. "Efficient Sampling Methods for Forest Inventories and Growth Projections". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101496.

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For operational forest management, a forest inventory is commonly conducted to determine the timber stocking and the value of standing trees in a stand. With time and costs constraints, appropriate sampling designs and models are required to perform the inventory efficiently, as well as to obtain reliable estimates for the variables needed to make projections. In this dissertation research, a simulation study was conducted to extensively explore four important topics in forest inventories: selection of measurement trees in point samples, projection from plot- and stand-level aggregations, subsampling height for volume estimation, and updating stand projections using periodic inventories. A series of simulated loblolly pine plantations with varying degrees of spatial heterogeneity were generated at different stages in stand development. Repeated sampling was used to examine various sampling schemes and growth projection methods. Highlights for the four topics follow: 1. Stand total volume can be reliably estimated using measurement trees tallied by Big BAF, point-double sampling, or random selection of a specified number of trees. However, number of trees per unit area in small-size classes were overestimated across the three tree-selection methods when sample data were aggregated into diameter classes. 2. Plot-level and stand-level projections produced similar estimates for dominant height, basal area, and stems per unit area. As spatial heterogeneity increased, stand-level projections indicated a significant bias of predicted total volume compared with the plot-level projections. 3. Sampling intensity, stand age and spatial heterogeneity have greater influence on the reliability for total volume estimation compared to subsampling intensity and measurement error for height measurements. 4.The variability of total volume estimates increases with increasing projection length (i.e., longer time intervals between inventory entry points). However, the estimates of stand total volume can be greatly improved by updating the models with information obtained in periodic forest inventories, especially when the original models are not well calibrated. The results of this study provide useful guidance and insights for forest practitioners to design forest inventories and improve growth projection systems in operational forest management.
Doctor of Philosophy

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4

Ringvall, Anna. "Assessment of sparse populations in forest inventory : development and evaluation of probability sampling methods /". Umeå : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2000. http://epsilon.slu.se/avh/2000/91-576-5885-4.pdf.

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5

Mandallaz, Daniel. "Geostatistical methods for double sampling schemes : application to combined forest inventories /". Zürich : Chair of Forest Inventory and Planning, Swiss Federal Institute of Technology (ETH), 1993. http://e-collection.ethbib.ethz.ch/show?type=habil&nr=19.

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6

Green, Patrick Corey. "Decision Support for Operational Plantation Forest Inventories through Auxiliary Information and Simulation". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/103054.

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Informed forest management requires accurate, up-to-date information. Ground-based forest inventory is commonly conducted to generate estimates of forest characteristics with a predetermined level of statistical confidence. As the importance of monitoring forest resources has increased, budgetary and logistical constraints often limit the resources needed for precise estimates. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study using synthetic populations provided the basis for investigating the effects of plot and stand-level inventory aggregations on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after plantation establishment. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting using two computer vision approaches. The imagery was found to be unreliable for consistent detection in the conditions evaluated. Following establishment, conditions are assessed throughout the lifespan of forest plantations. Using small area estimation (SAE) methods, the incorporation of light detection and ranging (lidar) and thinning status improved the precision of inventory estimates compared with ground data alone. Further investigation found that reduced density lidar point clouds and lower resolution elevation models could be used to generate estimates with similar increases in precision. Individual tree detection estimates of stand density were found to provide minimal improvements in estimation precision when incorporated into the SAE models. Plot and stand level inventory aggregations were found to provide similar estimates of future conditions in simulated stands without high levels of spatial heterogeneity. Significant differences were noted when spatial heterogeneity was high. Model form was found to have a more significant effect on the observed differences than plot size or thinning status. The results of this research are of interest to forest managers who regularly conduct forest inventories and generate estimates of future stand conditions. The incorporation of auxiliary data in mid-rotation stands using SAE techniques improved estimate precision in most cases. Further, guidance on strategies for using this information for predicting future conditions is provided.
Doctor of Philosophy
Informed forest management requires accurate, up-to-date information. Groundbased sampling (inventory) is commonly used to generate estimates of forest characteristics such as total wood volume, stem density per unit area, heights, and regeneration survival. As the importance of assessing forest resources has increased, resources are often not available to conduct proper assessments. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study investigated the effects of two forest inventory data aggregation methods on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after tree planting. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting. The imagery was found to be unreliable for use in accurately detecting seedlings in the conditions evaluated. Following establishment, forest conditions are assessed at additional points in forest development. Using a class of statistical estimators known as small-area estimation, a combination of ground and light detection and ranging data generated more confident estimates of forest conditions. Further investigation found that more coarse ancillary information can be used with similar confidence in the conditions evaluated. Forest inventory data are used to generate estimates of future conditions needed for management decisions. The final component of this research found that there are significant differences between two inventory data aggregation strategies when forest conditions are highly spatially variable. The results of this research are of interest to forest managers who regularly assess forest resources with inventories and models. The incorporation of ancillary information has potential to enhance forest resource assessments. Further, managers have guidance on strategies for using this information for estimating future conditions.

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7

Bienert, Anne, Louis Georgi, Matthias Kunz, Hans-Gerd Maas y Oheimb Goddert von. "Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories". Molecular Diversity Preservation International MDPI, 2018. https://tud.qucosa.de/id/qucosa%3A31826.

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Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D data capture, thus providing a much larger coverage within shorter acquisition time. This paper compares and discusses advantages and disadvantages of multi-temporal MLS data acquisition compared to established TLS data recording schemes. In this pilot study on integrated TLS and MLS data processing in a forest, it could be shown that existing TLS data evaluation routines can be used for MLS data processing. Methods of automatic laser scanner data processing for forest inventory parameter determination and quantitative structure model (QSM) generation were tested in two sample plots using data from both scanning methods and from different seasons. TLS in a multi-scan configuration delivers very high-density 3D point clouds, which form a valuable basis for generating high-quality QSMs. The pilot study shows that MLS is able to provide high-quality data for an equivalent determination of relevant forest inventory parameters compared to TLS. Parameters such as tree position, diameter at breast height (DBH) or tree height can be determined from MLS data with an accuracy similar to the accuracy of the parameter derived from TLS data. Results for instance in DBH determination by cylinder fitting yielded a standard deviation of 1.1 cm for trees in TLS data and 3.7 cm in MLS data. However, the resolution of MLS scans was found insufficient for successful QSM generation. The registration of MLS data in forests furthermore requires additional effort in considering effects caused by poor GNSS signal.

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8

Duvemo, Karl. "The influence of data uncertainty on planning and decision processes in forest management /". Umeå : Dept. of Forest Resource Management, Swedish University of Agricultural Sciences, 2009. http://epsilon.slu.se/200906.pdf.

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9

Walter, Fredrik. "Extraction of forest stand parameters from CARABAS VHF SAR images /". Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1999. http://epsilon.slu.se/avh/1999/91-576-5649-5.pdf.

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10

Holmgren, Johan. "Estimation of forest variables using airborne laser scanning /". Umeå : Dept. of Forest Resource Management and Geomatics, Swedish Univ. of Agricultural Sciences, 2003. http://epsilon.slu.se/s278.pdf.

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11

Moisen, Gretchen Gengenbach. "Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA". DigitalCommons@USU, 2000. https://digitalcommons.usu.edu/etd/7108.

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Recent emphasis has been placed on merging regional forest inventory data with satellite-based information both to improve the efficiency of estimates of population totals, and to produce regional maps of forest variables. There are numerous ways in which forest class and structure variables may be modeled as functions of remotely sensed variables, yet surprisingly little work has been directed at surveying modem statistical techniques to determine which tools are best suited to the tasks given multiple objectives and logistical constraints. Here, a series of analyses to compare nonlinear and nonparametric modeling techniques for mapping a variety of forest variables, and for stratification of field plots, was conducted using data in the Interior Western United States. The analyses compared four statistical modeling techniques for predicting two discrete and four continuous forest inventory variables. The modeling techniques include generalized additive models (GAMs), classification and regression trees (CARTs), multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs). Alternative stratification schemes were also compared for estimating population totals. The analyses were conducted within six ecologically different regions using a variety of satellite-based predictor variables. The work resulted in the development of an objective modeling box that automatically models spatial response variables as functions of any assortment of predictor variables through the four nonlinear or nonparametric modeling techniques. In comparing the different modeling techniques, all proved themselves workable in an automated environment, though ANNs were more problematic. When their potential mapping ability was explored through a simple simulation, tremendous advantages were seen in use of MARS and ANN for prediction over GAMs, CART, and a simple linear model. However, much smaller differences were seen when using real data. In some instances, a simple linear approach worked virtually as well as the more complex models, while small gains were seen using more complex models in other instances. In real data runs, MARS performed (marginally) best most often for binary variables, while GAMs performed (marginally) best most often for continuous variables. After considering a subjective "ease of use" measure, computing time and other predictive performance measures, it was determined that MARS had many advantages over other modeling techniques. In addition, stratification tests illustrated cost-effective means to improve precision of estimates of forest population totals. Finally, the general effect of map accuracy on the relative precision of estimates of population totals obtained under simple random sampling compared to that obtained under stratified random sampling was established and graphically illustrated as a tool for management decisions.

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12

Galatsidas, Spyridon. "Development of an inventory system for non-timber functions of forests in the frame of management inventories the case of Greece /". [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963719904.

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13

Wallerman, Jörgen. "Remote sensing aided spatial prediction of forest stem volume /". Umeå : Dept. of Forest Resource Management and Geomatics, 2003. http://epsilon.slu.se/s271.pdf.

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14

Magnusson, Mattias. "Evaluation of remote sensing techniques for estimation of forest variables at stand level /". Umeå : Dept. of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, 2006. http://epsilon.slu.se/200685.pdf.

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15

Brandtberg, Tomas. "Automatic individual tree-based analysis of high spatial resolution remotely sensed data /". Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1999. http://epsilon.slu.se/avh/1999/91-576-5852-8.pdf.

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16

Lüpke, Nikolas von [Verfasser], Joachim [Akademischer Betreuer] Saborowski y Jürgen [Akademischer Betreuer] Nagel. "Approaches for the optimisation of double sampling for stratification in repeated forest inventories / Nikolas von Lüpke. Gutachter: Joachim Saborowski ; Jürgen Nagel. Betreuer: Joachim Saborowski". Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2013. http://d-nb.info/1044361735/34.

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17

Mundhenk, Philip Henrich Verfasser], Christoph [Akademischer Betreuer] Kleinn, Thomas [Akademischer Betreuer] [Kneib y STEEN [Akademischer Betreuer] MAGNUSSEN. "Integrating remotely sensed data into forest resource inventories : The impact of model and variable selection on estimates of precision / Philip Henrich Mundhenk. Gutachter: Christoph Kleinn ; Thomas Kneib ; Steen Magnussen. Betreuer: Christoph Kleinn". Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2015. http://d-nb.info/1071713205/34.

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18

Fischer, Christoph Verfasser], Christoph [Akademischer Betreuer] Kleinn, Dirk [Akademischer Betreuer] [Hölscher y Alexander [Akademischer Betreuer] Knohl. "Further development and adaptation of large area forest inventories and remote sensing applications to comprehensive data providers for international processes / Christoph Fischer. Gutachter: Christoph Kleinn ; Dirk Hölscher ; Alexander Knohl. Betreuer: Christoph Kleinn". Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2011. http://d-nb.info/1043995382/34.

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19

Bloniarz, David Vincent. "The use of volunteer initiatives in conducting urban forest resource inventories". 1996. https://scholarworks.umass.edu/dissertations/AAI9638930.

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Results indicate that the urban forest resource inventory data collected in this study by trained volunteers is valid, and its accuracy compares favorably to levels found among a control group of Certified Arborists. In addition to successfully collecting urban forest field data, both the community and the individual volunteers received substantial indirect benefits. They developed greater environmental awareness, became more informed about "urban" forests, and they recognized their own abilities to improve the quality of urban life through participation in a satisfying project that provided the basis for a more active and informed political voice in promoting urban forest initiatives.

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20

Perugia, Barbara Del. "Lidar remote sensing for forestry applications". Doctoral thesis, 2019. http://hdl.handle.net/2158/1150981.

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21

Giannetti, Francesca. "3D Remote Sensing technologies for Precision Forestry". Doctoral thesis, 2018. http://hdl.handle.net/2158/1131942.

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Precision forestry is a new approach for more sustainable forest management. Modern technologies are important to the development of new tools and applications to conduct site-specific management practices. 3D remote sensing technologies are new tools and have new applications useful for improving the data collection, work efficiency and quality of forest information that can be used to take better management decisions. This thesis is aimed at assessing the use of 3D data to develop new tools and procedures useful for forest inventories and for the estimation of soil disturbances caused by forest operations. In so doing, this study attempts to close the gaps underlined by previous studies. The thesis is divided into two main sections. The first one comprises the studies I, II, and III related to forest inventory optimization, while the second section comprises the studies IV and V related to estimation of soil disturbances caused by forest operations. Study I demonstrates how a 3D point cloud acquired by a Terrestrial Laser Scanner (TLS) and a Hand-Held Mobile Laser Scanner (HMLS) can be used to automatically derive forest single tree variables such as diameter at breast height (DBH) and tree position (TP). Moreover, the study underlines how the integration of TLS with Airborne Laser Scanner (ALS) point clouds improves the estimation of tree top height (H) and crown base projection (CPA). In study II a novel approach is presented for the extraction of explanatory variables from unmanned aerial vehicle (UAV) 3D photogrammetric data for predicting forest biophysical properties without relying on a digital terrain model. This study assesses the use of DTM-independent variables to predict forest biophysical proprieties using as a benchmark two more traditional sets of variables: (i) height and density variables from UAV photogrammetric data normalized using a DTM acquired using airborne laser scanning (ALS) (Image-DTMALS variables), and (ii) height and density variables extracted from normalized ALS data (ALS variables). We obtained comparable results between the models developed with DTM-independent models and the ones obtained with the other two types of variables (i.e. Image-DTMALS and ALS) to predict: Growing Stock Volume (V), Basal Area (G), Number of trees (N), Dominant Height (Hdom) and Lory’s height (Hl). Study III used the new set of DTM-independent variables developed in study II to predict area-based (ABA) forest structure variables (e.g. V, G, Mean Diameter (DBHmean), Gini coefficient of DBH (Gini), standard deviation of DBH(σdbh), Hdom, Hl and standard deviation of H (σh)) using as benchmarks the variables from ALS. The results underline comparable results between the two types of metrics in the estimation of forest structure variables. Moreover, the models developed with DTM-independent metrics were used to create two maps of two forest structure indices. In study IV and V we tested the utility of multi-temporal high resolution DTM derived by Personal Laser Scanner (PLS) (IV) and by close range photogrammetry (V) to measure and quantify soil disturbances caused by forest operation. These studies underline how multi-temporal high resolution (DTM) can be used to quantify rut deep, bulges, and soil volume changes. In conclusion, 3D RS data appears useful in the development of new methods to collect and measure forest ecosystem components such as vegetation and soils

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22

Rockhill, Aime ̌P. "Imputation of missing observations in forest inventories". 2008. http://www.lib.ncsu.edu/theses/available/etd-03262008-174733/unrestricted/etd.pdf.

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23

Mundhenk, Philip Henrich. "Integrating remotely sensed data into forest resource inventories". Doctoral thesis, 2014. http://hdl.handle.net/11858/00-1735-0000-0022-5FE6-3.

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Die letzten zwanzig Jahre haben gezeigt, dass die Integration luftgestützter Lasertechnologien (Light Detection and Ranging; LiDAR) in die Erfassung von Waldressourcen dazu beitragen kann, die Genauigkeit von Schätzungen zu erhöhen. Um diese zu ermöglichen, müssen Feldaten mit LiDAR-Daten kombiniert werden. Diverse Techniken der Modellierung bieten die Möglichkeit, diese Verbindung statistisch zu beschreiben. Während die Wahl der Methode in der Regel nur geringen Einfluss auf Punktschätzer hat, liefert sie unterschiedliche Schätzungen der Genauigkeit. In der vorliegenden Studie wurde der Einfluss verschiedener Modellierungstechniken und Variablenauswahl auf die Genauigkeit von Schätzungen untersucht. Der Schwerpunkt der Arbeit liegt hierbei auf LiDAR Anwendungen im Rahmen von Waldinventuren. Die Methoden der Variablenauswahl, welche in dieser Studie berücksichtigt wurden, waren das Akaike Informationskriterium (AIC), das korrigierte Akaike Informationskriterium (AICc), und das bayesianische (oder Schwarz) Informationskriterium. Zudem wurden Variablen anhand der Konditionsnummer und des Varianzinflationsfaktors ausgewählt. Weitere Methoden, die in dieser Studie Berücksichtigung fanden, umfassen Ridge Regression, der least absolute shrinkage and selection operator (Lasso), und der Random Forest Algorithmus. Die Methoden der schrittweisen Variablenauswahl wurden sowohl im Rahmen der Modell-assistierten als auch der Modell-basierten Inferenz untersucht. Die übrigen Methoden wurden nur im Rahmen der Modell-assistierten Inferenz untersucht. In einer umfangreichen Simulationsstudie wurden die Einflüsse der Art der Modellierungsmethode und Art der Variablenauswahl auf die Genauigkeit der Schätzung von Populationsparametern (oberirdische Biomasse in Megagramm pro Hektar) ermittelt. Hierzu wurden fünf unterschiedliche Populationen genutzt. Drei künstliche Populationen wurden simuliert, zwei weitere basierten auf in Kanada und Norwegen erhobenen Waldinveturdaten. Canonical vine copulas wurden genutzt um synthetische Populationen aus diesen Waldinventurdaten zu generieren. Aus den Populationen wurden wiederholt einfache Zufallsstichproben gezogen und für jede Stichprobe wurden der Mittelwert und die Genauigkeit der Mittelwertschätzung geschäzt. Während für das Modell-basierte Verfahren nur ein Varianzschätzer untersucht wurde, wurden für den Modell-assistierten Ansatz drei unterschiedliche Schätzer untersucht. Die Ergebnisse der Simulationsstudie zeigten, dass das einfache Anwenden von schrittweisen Methoden zur Variablenauswahl generell zur Überschätzung der Genauigkeiten in LiDAR unterstützten Waldinventuren führt. Die verzerrte Schätzung der Genauigkeiten war vor allem für kleine Stichproben (n = 40 und n = 50) von Bedeutung. Für Stichproben von größerem Umfang (n = 400), war die Überschätzung der Genauigkeit vernachlässigbar. Gute Ergebnisse, im Hinblick auf Deckungsraten und empirischem Standardfehler, zeigten Ridge Regression, Lasso und der Random Forest Algorithmus. Aus den Ergebnissen dieser Studie kann abgeleitet werden, dass die zuletzt genannten Methoden in zukünftige LiDAR unterstützten Waldinventuren Berücksichtigung finden sollten.

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24

Nieschulze, Jens. "Regionalization of Variables of Sample Based Forest Inventories at the District Level". Doctoral thesis, 2003. http://hdl.handle.net/11858/00-1735-0000-0006-B163-2.

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25

von, Lüpke Nikolas. "Approaches for the optimisation of double sampling for stratification in repeated forest inventories". Doctoral thesis, 2013. http://hdl.handle.net/11858/00-1735-0000-001E-F78E-8.

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Die zweiphasige Stichprobe zur Stratifizierung ist ein effizientes Inventurverfahren, das seine Praxistauglichkeit in verschiedenen Waldinventuren unter Beweis stellen konnte. Dennoch sind weitere Effizienzsteigerungen wünschenswert. In der vorliegenden Arbeit werden verschiedene Ansätze die Effektivität dieses Verfahrens zu steigern separat vorgestellt, in Fallstudien mit Daten der Niedersächsischen Betriebsinventur getestet und diskutiert. Der erste Ansatz (Kapitel 2) beschäftigt sich mit der Anwendung der zweiphasigen Stichprobe zur Stratifizierung in Wiederholungsinventuren. In einem Zusammengesetzten Schätzer werden Daten eines aktuellen mit Simulationsergebnissen des vorhergehenden Inventurdurchgangs kombiniert. Dabei kann der Stichprobenumfang der aktuellen Inventur verringert werden, während die Daten aller Inventurpunkte des vorherigen Durchgangs für Simulationen genutzt werden. Zwar kann ein solcher Schätzer konstruiert werden, jedoch lässt die Fallstudie darauf schließen, dass keine, oder zumindest keine ausreichende, Effizienzsteigerung erzielt werden kann. Erklärt werden kann dies durch die großen Unterschiede zwischen den aktuellen Inventurergebnissen aus den reduzierten Inventuren und den prognostizierten Volumina aus den Simulationen. Eine Erhöhung der Effizienz dieses Verfahrens könnte nur durch Weiterentwicklungen der Waldwachstumsmodelle möglich werden. In Wiederholungsinventuren kann jedoch eine höhere Effizienzsteigerung mit einem dreiphasigen Verfahren erreicht werden, das die zweiphasige Stichprobe mit der zwei\-phasigen Regressionsstichprobe kombiniert (Kapitel 3). Mittelwert- und Varianzschätzer, die auf dem sogenannten infinite population approach in der ersten Phase beruhen, werden präsentiert. Genutzt werden dabei die Korrelationen zwischen den aktuellen Inventurergebnissen und den Wachstumssimulationen auf der Basis des vorherigen Inventurdurchgangs. Statt der Simulationsergebnisse können auch einfach die Ergebnisse des vorherigen Inventurdurchgangs zur Berechnung der Korrelationen genutzt werden. Allerdings führt die Nutzung der Simulationsergebnisse als Regressor in den meisten Fällen zu besseren Ergebnissen. Bei verringertem Stichprobenumfang der Folgeinventur und damit einhergehendem Präzisionsverlust, ist die Effizienz des dreiphasigen Verfahrens höher als die des klassischen zweiphasigen Verfahrens. Die Nutzung der Vorinventur in Form eines stratenweisen Regressionsschätzers hat sich damit als erfolgreich und gegenüber dem zusammengesetzten Schätzer als deutlich überlegen gezeigt. Als weiterer Ansatz wird die Erweiterung der zweisphasigen Stichprobe zur Stratifizierung um eine geclusterte Unterstichprobe zu einem dreiphasigen Design vorgestellt (Kapitel 4). Sowohl für den Ratio-to-Size- als auch für den unverzerrten Ansatz werden entsprechende Mittelwert- und Varianzschätzer präsentiert. Verglichen mit dem zweiphasigen Verfahren, führt dieses dreiphasige Design in der Fallstudie zu keiner Effizienzsteigerung. Gründe hierfür können in der vergleichsweise kleinen Größe der Forstämter und der hohen Stichprobendichte der Niedersächsischen Betriebsinventur gesehen werden. Sinnvolle Anwendungen dieses Verfahrens sind aber möglicherweise unter anderen Erschließungsbedingungen in Großgebieten denkbar. In einer weiteren Fallstudie wird versucht existierende Probepunkte in Clustern von homogener Größe zusammenzufassen (Kapitel 5). Eine solche Zusammenfassung soll der Optimierung der Wegzeiten bei der Aufnahme von Inventurpunkten dienen. Dazu werden sieben verschiedene Methoden getestet und deren Ergebnisse miteinander verglichen. Durch einen Vergleich mit optimierten Richtwert-Lösungen wird zudem die Qualität dieser Lösungen evaluiert. Es zeigt sich, dass drei Algorithmen des Vehicle Routing Problems gut dazu geeignet sind, Cluster von homogener Größe zu erstellen. Nicht empfohlen werden kann dagegen die Verwendung von drei anderen Cluster-Algorithmen, sowie die Nutzung von Bewirtschaftungseinheiten als Cluster, da diese Methoden zu Clustern von sehr heterogener Größe führen.

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26

White, Molly Kinane. "Life-cycle inventories of roundwood production in Wisconsin inputs into an industrial forest carbon budget /". 2005. http://catalog.hathitrust.org/api/volumes/oclc/61348382.html.

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27

Nieschulze, Jens [Verfasser]. "Regionalization of variables of sample based forest inventories at the district level / vorgelegt von Jens Nieschulze". 2003. http://d-nb.info/968519695/34.

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28

Fischer, Christoph. "Further development and adaptation of large area forest inventories and remote sensing applications to comprehensive data providers for international processes". Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-B14D-5.

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29

Galatsidas, Spyridon [Verfasser]. "Development of an inventory system for non-timber functions of forests in the frame of management inventories : the case of Greece / vorgelegt von Spyridon Galatsidas". 2001. http://d-nb.info/963719904/34.

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