Annighöfer, Peter J.

[["dc.bibliographiccitation.firstpage","107699"],["dc.bibliographiccitation.journal","Agricultural and Forest Meteorology"],["dc.bibliographiccitation.volume","278"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Ehbrecht, Martin"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Ammer, Christian"],["dc.date.accessioned","2020-12-10T14:22:17Z"],["dc.date.available","2020-12-10T14:22:17Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.agrformet.2019.107699"],["dc.identifier.issn","0168-1923"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71566"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","From tree to stand-level structural complexity — Which properties make a forest stand complex?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","616"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Forestry (Oxford)"],["dc.bibliographiccitation.lastpage","626"],["dc.bibliographiccitation.volume","92"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Ammer, Christian"],["dc.contributor.author","Stephens, Scott L"],["dc.contributor.author","York, Robert A"],["dc.date.accessioned","2020-12-10T18:19:12Z"],["dc.date.available","2020-12-10T18:19:12Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1093/forestry/cpz038"],["dc.identifier.eissn","1464-3626"],["dc.identifier.issn","0015-752X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/75161"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Silvicultural implications from analyzing light induced height growth development of eight North American juvenile tree species in mixed-conifer forests"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","690"],["dc.bibliographiccitation.journal","Frontiers in Plant Science"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Mölder, Andreas"],["dc.contributor.author","Ammer, Christian"],["dc.date.accessioned","2019-07-09T11:51:57Z"],["dc.date.available","2019-07-09T11:51:57Z"],["dc.date.issued","2019"],["dc.description.abstract","Tree saplings are exposed to a competitive growth environment in which resources are limited and the ability to adapt determines general vitality and specific growth performance. In this study we analyzed the aboveground spatial neighborhood of oak [Quercus petraea (Matt.) Liebl.] and beech (Fagus sylvatica L.) saplings growing in Germany, by using hemispherical photography and terrestrial laser scanning as proxy for the competitive pressure saplings were exposed to. The hemispherical images were used to analyze the light availability and the three-dimensional (3D) point clouds from the laser scanning were used to assess the space and forest structure around the saplings. The aim was to increase the precision with which the biomass allocation, growth, and morphology of the saplings could be predicted by including more detailed information of their environment. The predictive strength of the models was especially increased through direct neighborhood variables (e.g., relative space filling), next to the light availability being the most important predictor variable. The biomass allocation patterns within the more light demanding oak were strongly driven by the space availability around the saplings. Diameter and height growth variables of both species reacted significantly to changes in light availability, and partly also to the neighborhood variables. The leaf morphology [as leaf-area ratio (LAR)] was also driven by light availability and decreased with increasing light availability. However, the branch morphology (as mean branch weight) could not be explained for oak and the model outcome for beech was hard to interpret. The results could show that individuals of the same species perform differently under constant light conditions but differing neighborhoods. Assessing the neighborhood of trees with highly precise measurement devices, like terrestrial laser scanners, proved to be useful. However, the primary response to a dense neighborhood seemed to be coping with a reduction of the lateral light availability aboveground, rather than responding to an increase of competition belowground. The results suggest continuing efforts to increase the precision with which plant environments can be described through innovative and efficient methods, like terrestrial laser scanning."],["dc.identifier.doi","10.3389/fpls.2019.00690"],["dc.identifier.pmid","31191589"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16245"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60048"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","570"],["dc.title","Advanced Aboveground Spatial Analysis as Proxy for the Competitive Environment Affecting Sapling Development"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","76"],["dc.bibliographiccitation.journal","Applied Geography"],["dc.bibliographiccitation.lastpage","80"],["dc.bibliographiccitation.volume","93"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Hähn, Nalise"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Benten, Anke"],["dc.contributor.author","Vor, Torsten"],["dc.contributor.author","Ammer, Christian"],["dc.date.accessioned","2020-12-10T14:22:25Z"],["dc.date.available","2020-12-10T14:22:25Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1016/j.apgeog.2018.02.015"],["dc.identifier.issn","0143-6228"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71602"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Assessment of roe deer (Capreolus capreolus L.) – vehicle accident hotspots with respect to the location of \\‘trees outside forest' along roadsides"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0378112721010355"],["dc.bibliographiccitation.firstpage","119942"],["dc.bibliographiccitation.journal","Forest Ecology and Management"],["dc.bibliographiccitation.volume","505"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Mund, Martina"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Ammer, Christian"],["dc.contributor.author","Ameztegui, Aitor"],["dc.contributor.author","Balandier, Philippe"],["dc.contributor.author","Bebre, Ieva"],["dc.contributor.author","Coll, Lluís"],["dc.contributor.author","Collet, Catherine"],["dc.contributor.author","Hamm, Tobias"],["dc.contributor.author","Jürgen, Bauhus"],["dc.date.accessioned","2022-04-01T10:02:25Z"],["dc.date.available","2022-04-01T10:02:25Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.foreco.2021.119942"],["dc.identifier.pii","S0378112721010355"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105906"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.issn","0378-1127"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Examination of aboveground attributes to predict belowground biomass of young trees"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1603"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Canadian Journal of Forest Research"],["dc.bibliographiccitation.lastpage","1613"],["dc.bibliographiccitation.volume","47"],["dc.contributor.author","Höwler, Kirsten"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Ammer, Christian"],["dc.contributor.author","Seidel, Dominik"],["dc.date.accessioned","2017-12-19T15:34:10Z"],["dc.date.available","2017-12-19T15:34:10Z"],["dc.date.issued","2017"],["dc.description.abstract","Accurate information on the timber quality of hardwoods is often lacking, in particular for standing trees. In situ measurements of timber quality have the potential to improve the economic yield of a stand and may contribute to the optimal timing of a harvest and, in general, to improving forest management. Here, we used terrestrial laser scanning (TLS) to assess external timber quality metrics nondestructively. We investigated how competition intensity affected the metrics of 118 European beech (Fagus sylvatica L.) trees. We found that two newly developed TLS-based measures of external stem characteristics (number of bark anomalies per metre and stem non-circularity) were affected by competition intensity, suggesting that regulating competition levels may improve timber quality. Our study confirms empirical findings indicating a positive relationship between competition intensity and timber quality of European beech and offers a new methodology to assess external timber quality measures in the field objectively and nondestructively."],["dc.identifier.doi","10.1139/cjfr-2017-0262"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/11508"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.title","Competition improves quality-related external stem characteristics of Fagus sylvatica"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1723"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Trees"],["dc.bibliographiccitation.lastpage","1735"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Juchheim, Julia"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Ammer, Christian"],["dc.contributor.author","Calders, Kim"],["dc.contributor.author","Raumonen, Pasi"],["dc.contributor.author","Seidel, Dominik"],["dc.date.accessioned","2017-12-19T16:07:34Z"],["dc.date.available","2017-12-19T16:07:34Z"],["dc.date.issued","2017"],["dc.description.abstract","The intensity of silvicultural interventions and the composition of tree species are important forest management decisions. Both determine tree shape and thus influence the value of a tree, be it in terms of economy (trunk form, branchiness), or in terms of ecology (microhabitats). However, our knowledge on the distinct changes in tree architecture due to silvicultural management intensity or different neighborhood diversities is still limited, especially if the focus is on single tree attributes, e.g., branching patterns or crown shapes. We used terrestrial laser scanner data to calculate 25 structural measures for 55 European beech (Fagus sylvatica L.) trees that grew either in pure stands along a gradient of management intensity or in intra or interspecific neighborhoods in unmanaged stands. We found a lower height of maximal horizontal crown extension, a higher crown surface area, and straighter trunks with increasing management intensity. Moreover, our study revealed that beech trees surrounded by valuable hardwoods showed a lower height of maximal horizontal crown extension, a lower height–diameter ratio, and longer branches with flatter branch angles than beech trees surrounded by conspecific neighbors. Our findings provide evidence of phenotypic plasticity of European beech to diverse environmental conditions. The differences in tree structure indicate an increasing crown competition with decreasing management intensity and stronger competitive pressure for beech surrounded by conspecific neighbors in comparison to alien neighbors."],["dc.identifier.doi","10.1007/s00468-017-1581-z"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/11511"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.title","How management intensity and neighborhood composition affect the structure of beech (Fagus sylvatica L.) trees"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1854"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Annighöfer, Peter J."],["dc.contributor.author","Ehbrecht, Martin"],["dc.contributor.author","Magdon, Paul"],["dc.contributor.author","Wöllauer, Stephan"],["dc.contributor.author","Ammer, Christian"],["dc.date.accessioned","2020-08-17T05:33:08Z"],["dc.date.available","2020-08-17T05:33:08Z"],["dc.date.issued","2020"],["dc.description.abstract","The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from fractal analysis, a measure of structural complexity, can be obtained from airborne laser scanning data. Based on 66 plots across different forest types in Germany, each 1 ha in size, we tested the performance of the Db by evaluating it against conventional ground-based measures of forest structure and commonly used stand characteristics. We found that the Db was related (0.34 < R < 0.51) to stand age, management intensity, microclimatic stability, and several measures characterizing the overall stand structural complexity. For the basal area, we could not find a significant relationship, indicating that structural complexity is not tied to the basal area of a forest. We also showed that Db derived from airborne data holds the potential to distinguish forest types, management types, and the developmental phases of forests. We conclude that the box-dimension is a promising measure to describe the structural complexity of forests in an ecologically meaningful way."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft"],["dc.identifier.doi","10.3390/rs12111854"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/67577"],["dc.language.iso","en"],["dc.notes.intern","DeepGreen Import"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.relation.issn","2072-4292"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Plant Science"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Annighöfer, Peter"],["dc.contributor.author","Thielman, Anton"],["dc.contributor.author","Seifert, Quentin Edward"],["dc.contributor.author","Thauer, Jan-Henrik"],["dc.contributor.author","Glatthorn, Jonas"],["dc.contributor.author","Ehbrecht, Martin"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Ammer, Christian"],["dc.contributor.orcid","0000-0001-8674-1309"],["dc.creator.author","Seifert, Quentin Edward"],["dc.date.accessioned","2021-04-14T08:27:58Z"],["dc.date.available","2021-04-14T08:27:58Z"],["dc.date.issued","2021"],["dc.description.abstract","Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3389/fpls.2021.635440"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82461"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","Frontiers Media"],["dc.relation.eissn","1664-462X"],["dc.relation.orgunit","Fakultät für Forstwissenschaften und Waldökologie"],["dc.rights","CC BY 4.0"],["dc.title","Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

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