Spatial pattern analysis of forest trees based on the vectorial mark
Analysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most studies of spatial patterns have concentrated on tree location and tree sizes rathe...
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| Abstract | Analysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees, and thus cannot reveal the cause of the distributions of tree locations and quantitative marks. To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics, we extracted vertical and horizontal marks (tree height and crown projection area) characterizing tree size, and a vectorial mark (crown displacement vector characterizing the crown asymmetry) using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire, UK. Quantitatively and vectorially marked spatial patterns were developed, with corresponding null models established for a significance test. We analyzed eight types of univariate and bivariate spatial patterns, after first proposing four types. The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud. The algorithm-segmented point cloud managed to detect 70–86% of patterns correctly. The eight types of spatial patterns analyzed the spatial distribution of trees, the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species. These four types of univariate patterns jointly showed that, at smaller scales, the trees tend to be clustered, and taller, with larger crowns due to the detected facilitations among trees in the study area. The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species, while crown size is mostly homogeneous across scales. These results indicate that interspecific facilitation and competition mainly affect tree height in the study area. This work further confirms the connection of tree size with individual facilitation and competition, revealing the potential spatial structure that previously was hard to detect. |
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| AbstractList | Analysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees, and thus cannot reveal the cause of the distributions of tree locations and quantitative marks. To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics, we extracted vertical and horizontal marks (tree height and crown projection area) characterizing tree size, and a vectorial mark (crown displacement vector characterizing the crown asymmetry) using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire, UK. Quantitatively and vectorially marked spatial patterns were developed, with corresponding null models established for a significance test. We analyzed eight types of univariate and bivariate spatial patterns, after first proposing four types. The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud. The algorithm-segmented point cloud managed to detect 70–86% of patterns correctly. The eight types of spatial patterns analyzed the spatial distribution of trees, the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species. These four types of univariate patterns jointly showed that, at smaller scales, the trees tend to be clustered, and taller, with larger crowns due to the detected facilitations among trees in the study area. The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species, while crown size is mostly homogeneous across scales. These results indicate that interspecific facilitation and competition mainly affect tree height in the study area. This work further confirms the connection of tree size with individual facilitation and competition, revealing the potential spatial structure that previously was hard to detect. Analysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most stud-ies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees, and thus cannot reveal the cause of the distributions of tree locations and quantitative marks. To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynam-ics, we extracted vertical and horizontal marks (tree height and crown projection area) characterizing tree size, and a vectorial mark (crown displacement vector characterizing the crown asymmetry) using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire, UK. Quantitatively and vectorially marked spatial patterns were developed, with corresponding null models established for a significance test. We analyzed eight types of univari-ate and bivariate spatial patterns, after first proposing four types. The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud. The algorithm-segmented point cloud managed to detect 70-86％ of patterns correctly. The eight types of spatial patterns analyzed the spatial dis-tribution of trees, the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species. These four types of univariate patterns jointly showed that, at smaller scales, the trees tend to be clustered, and taller, with larger crowns due to the detected facilitations among trees in the study area. The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species, while crown size is mostly homogeneous across scales. These results indicate that interspecific facilitation and competi-tion mainly affect tree height in the study area. This work further confirms the connection of tree size with individual facilitation and competition, revealing the potential spatial structure that previously was hard to detect. |
| Audience | Academic |
| Author | Zhang, Huanyuan Jackson, Toby Shenkin, Alexander Lin, Yi Xin, Honglu Cao, Yujie |
| AuthorAffiliation | Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Science,Peking University,Beijing 100871,People's Republic of China;Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK%Department of Plant Sciences,University of Cambridge, Downing Street,Cambridge CB23EA,UK%College of Surveying and Geo-Informatics,Tongji University,Shanghai 200092,People's Republic of China%Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK%Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Science,Peking University,Beijing 100871,People's Republic of China%Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK;SICCS,Northern Arizona University,Flagstaff,AZ 86001, USA |
| AuthorAffiliation_xml | – name: Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Science,Peking University,Beijing 100871,People's Republic of China;Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK%Department of Plant Sciences,University of Cambridge, Downing Street,Cambridge CB23EA,UK%College of Surveying and Geo-Informatics,Tongji University,Shanghai 200092,People's Republic of China%Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK%Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Science,Peking University,Beijing 100871,People's Republic of China%Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK;SICCS,Northern Arizona University,Flagstaff,AZ 86001, USA |
| Author_xml | – sequence: 1 givenname: Honglu surname: Xin fullname: Xin, Honglu organization: Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Science, Peking University, Environmental Change Institute, School of Geography and the Environment, University of Oxford – sequence: 2 givenname: Toby surname: Jackson fullname: Jackson, Toby organization: Department of Plant Sciences, University of Cambridge – sequence: 3 givenname: Yujie surname: Cao fullname: Cao, Yujie organization: College of Surveying and Geo-Informatics, Tongji University – sequence: 4 givenname: Huanyuan surname: Zhang fullname: Zhang, Huanyuan organization: Environmental Change Institute, School of Geography and the Environment, University of Oxford – sequence: 5 givenname: Yi surname: Lin fullname: Lin, Yi organization: Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Science, Peking University – sequence: 6 givenname: Alexander surname: Shenkin fullname: Shenkin, Alexander email: alexander.shenkin@ouce.ox.ac.uk organization: Environmental Change Institute, School of Geography and the Environment, University of Oxford, SICCS, Northern Arizona University |
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| Keywords | Quantitative mark Spatial pattern Vectorial mark Spatial correlation Summary statistics |
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| PublicationTitle | Journal of forestry research |
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| Publisher | Springer Nature Singapore Springer Springer Nature B.V Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Science,Peking University,Beijing 100871,People's Republic of China Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK%Department of Plant Sciences,University of Cambridge, Downing Street,Cambridge CB23EA,UK%College of Surveying and Geo-Informatics,Tongji University,Shanghai 200092,People's Republic of China%Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK%Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Science,Peking University,Beijing 100871,People's Republic of China%Environmental Change Institute,School of Geography and the Environment,University of Oxford,South Parks Road,Oxford OX13QY,UK SICCS,Northern Arizona University,Flagstaff,AZ 86001, USA |
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| SubjectTerms | Airborne lasers Algorithms Analysis Asymmetry Biomedical and Life Sciences Bivariate analysis Competition data collection Forestry forests Life Sciences Original Paper Pattern analysis Population biology Population dynamics Spatial analysis Spatial distribution Symbiosis tree height Trees |
| Title | Spatial pattern analysis of forest trees based on the vectorial mark |
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