Waldwachstumskundliche Softwaresysteme auf Basis von TreeGrOSS

Hansen, Jan Holmegaard; Nagel, Jürgen G.

doi:10.17875/gup2014-757

Cited by 21 publications

(19 citation statements)

References 6 publications

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“…For each of the BWI sample plots within our study area, we simulated one hundred synthetic forest stands with a circular area of 0.7 ha using the Waldplaner 2.0 [38] software. The Waldplaner software generates simulated forest stands based on forest inventory data using the single-tree-based forest growth model treeGROSS [39].…”

Section: Forest Stand Simulationmentioning

confidence: 99%

“…A tree crown is defined by the tree species-specific parameters b and c (see Table 2), the tree height h, crown base height cbh, and its crown radius r (all provided by the Waldplaner 2.0 software, [38]). The height above ground e i of a point with distance d i to the tree location less than or equal to r is calculated by Equation (2).…”

Section: Synthetic Tree Detectionsmentioning

confidence: 99%

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A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

et al. 2017

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Abstract:Determining the exact position of a forest inventory plot-and hence the position of the sampled trees-is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011(BWI, -2012 within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.

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Section: Forest Stand Simulationmentioning

confidence: 99%

Section: Synthetic Tree Detectionsmentioning

confidence: 99%

A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

et al. 2017

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“…Based on the information at each sampling point, i.e. for each occurring species age, mean diameter, mean height, site index, species proportion, stocking degree and volume per hectare, model stands of 0.2 ha in size with single trees are generated using the stand generation algorithm implemented in the WaldPlaner software (Hansen and Nagel 2014).…”

Section: Term Definitionmentioning

confidence: 99%

Assessing risks and uncertainties in forest dynamics under different management scenarios and climate change

et al. 2015

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Background: Forest management faces a climate induced shift in growth potential and increasing current and emerging new risks. Vulnerability analysis provides decision support based on projections of natural resources taking risks and uncertainties into account. In this paper we (1) characterize differences in forest dynamics under three management scenarios, (2) analyse the effects of the three scenarios on two risk factors, windthrow and drought stress, and (3) quantify the effects and the amount of uncertainty arising from climate projections on height increment and drought stress. Methods: In four regions in northern Germany, we apply three contrasting management scenarios and project forest development under climate change until 2070. Three climate runs (minimum, median, maximum) based on the emission scenario RCP 8.5 control the site-sensitive forest growth functions. The minimum and maximum climate run define the range of prospective climate development.

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“…Zentrales Prognosewerkzeug war das waldbauliche Entscheidungsunterstützungs-system WaldPlaner , ALBERT u. HANSEN 2007, HANSEN u. NAGEL 2011 der Nordwestdeutschen Forstlichen Versuchsanstalt. Es ist in der Lage, verschiedene, z. T. regelbasierte Teilmodelle zu einem Gesamtsimulationssystem zu kombinieren.…”

Section: Untersuchungsansätzeunclassified

Waldentwicklungsszenarien für das Hessische Ried

Versuchsanstalt¹

2013

Beiträge Aus Der Nordwestdeutschen Forstlichen Versuchsanstalt

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Waldwachstumskundliche Softwaresysteme auf Basis von TreeGrOSS

Cited by 21 publications

References 6 publications

A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

Assessing risks and uncertainties in forest dynamics under different management scenarios and climate change

Waldentwicklungsszenarien für das Hessische Ried

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