PolyMorph-2D: An open-source GIS plug-in for morphometric analysis of vector-based 2D polygon features

Güler, Cüneyt; Beyhan, Burak; Tağa, Hidayet

doi:10.1016/j.geomorph.2021.107755

Cited by 4 publications

(12 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the case for polygon area, perimeter of polygon (PPOL) and radius of the minimum circumscribed circle (RMCC). Additionally, a positive correlation was found for some derived morphometric variables, namely: length-to-width ratio (LTWR) [102], circularity ratio (CIRR) [105], compactness factor (COMF) [86], dispersion measure (DISM) [105], complexity index (COMI) [86], lemniscate ratio (LEMR) [109], regularity factor (REGF) [104], and concavity (CONC) [85]. Conversely, other morphometric variables showed a decreasing trend with increasing S L i .…”

Section: Context Detection and Tree Assemblagementioning

confidence: 94%

“…The 20 basic morphometric variables are simple parameters obtained by fitting elemental geometric shapes to each tree assemblage polygon (e.g., area of maximum inscribed circle), and basic positional parameters (e.g., XPOL, which is the X coordinate of the centroid of the tree assemblage polygon). The 20 derived parameters are adimensional metrics (except for concavity [85], measured in m) computed from the 20 basic morphometric variables, as explained in [86] (a full description of the 40 morphometric parameters is given in Annex I). The morphometric analysis of tree assemblages was conducted using PolyMorph-2D algorithm [86], which is a toolbox for the morphometric analysis of vector-based polygon objects, available as a plug-in for the open source JumpGIS software [87].…”

Section: Tree Assemblages: Definition and Morphometrymentioning

confidence: 99%

“…The 20 derived parameters are adimensional metrics (except for concavity [85], measured in m) computed from the 20 basic morphometric variables, as explained in [86] (a full description of the 40 morphometric parameters is given in Annex I). The morphometric analysis of tree assemblages was conducted using PolyMorph-2D algorithm [86], which is a toolbox for the morphometric analysis of vector-based polygon objects, available as a plug-in for the open source JumpGIS software [87].…”

Section: Tree Assemblages: Definition and Morphometrymentioning

confidence: 99%

“…compactness factor [86]; MCIR: Miller's circularity ratio [107]; DISM: dispersion measure [105]; COMI: complexity index [86];…”

Section: Context Detection and Tree Assemblagementioning

confidence: 99%

See 3 more Smart Citations

Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Jaime¹,

Oehmcke²,

Gharun³

et al. 2024

Preprint

View full text Add to dashboard Cite

Forest structure analyses and biomass prediction systems are key tools for advancing forest trait-based ecology and ecosystem stewardship. The combination of near-field remote sensing techniques---e.g., Unmanned Aerial Vehicles (UAV) and Light Detection and Ranging (LiDAR) systems---with machine-learning methods enhances the accuracy of forest structure analyses and above ground-biomass~(AGB) estimates. In this study, we utilized a UAV-LiDAR system to map the 3D architecture of a monoculture Norway spruce forest in Davos, Switzerland, where a field-based inventory served as ground truth data. The objectives of this effort were (i) to gain insights into variation and gradients of structural traits (i.e., tree height) and (ii) to evaluate whether this knowledge of community structure may prove useful as contextual information to improve predictions of AGB at the individual tree level. To investigate the local association of structural traits, we segmented the point cloud data scene into individual trees and treated tree height as the morphological variable of interest. We then used local indicators of spatial association to determine the extent of significant local context, and defined tree neighborhoods within the forest. For the task of AGB regression, we obtained results of several feature-based regression methods (i.e., AdaBoost, Lasso and Random Forest) and evaluated these based on nested cross-validation.We applied this approach to two separate tree data sets within the same site, one being clustered and continuous, the other discontinuous and scattered in separate sampling plots. In both cases, we found evidence of enhanced AGB prediction performance in context-aware regressions, indicating that gradients in morphological tree traits across the ecosystem proxy for unveiled ecological information that influence tree growth, which can be leveraged to enhance predictions of AGB.

show abstract

Section: Context Detection and Tree Assemblagementioning

confidence: 94%

Section: Tree Assemblages: Definition and Morphometrymentioning

confidence: 99%

Section: Tree Assemblages: Definition and Morphometrymentioning

confidence: 99%

“…compactness factor [86]; MCIR: Miller's circularity ratio [107]; DISM: dispersion measure [105]; COMI: complexity index [86];…”

Section: Context Detection and Tree Assemblagementioning

confidence: 99%

See 2 more Smart Citations

Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Jaime¹,

Oehmcke²,

Gharun³

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A special feature of vector data consists of the object-based structure, which entails points, lines, or polygons formed by a variety of curves or points with coordinates in a spatial reference format. Thus, vector objects indicate the boundaries or depict the contours of geographic objects varying in geometry, form, size, shape, curvature, connectivity, and spatial resolution, which depend on the map scale [46].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts

Lemenkova

2022

Data

View full text Add to dashboard Cite

In this paper, an integrated mapping of the georeferenced data is presented using the QGIS and GMT scripting tool set. The study area encompasses the Bolivian Andes, South America, notable for complex geophysical and geological parameters and high seismicity. A data integration was performed for a detailed analysis of the geophysical and geological setting. The data included the raster and vector datasets captured from the open sources: the IRIS seismic data (2015 to 2021), geophysical data from satellite-derived gravity grids based on CryoSat, topographic GEBCO data, geoid undulation data from EGM-2008, and geological georeferences’ vector data from the USGS. The techniques of data processing included quantitative and qualitative evaluation of the seismicity and geophysical setting in Bolivia. The result includes a series of thematic maps on the Bolivian Andes. Based on the data analysis, the western region was identified as the most seismically endangered area in Bolivia with a high risk of earthquake hazards in Cordillera Occidental, followed by Altiplano and Cordillera Real. The earthquake magnitude here ranges from 1.8 to 7.6. The data analysis shows a tight correlation between the gravity, geophysics, and topography in the Bolivian Andes. The cartographic scripts used for processing data in GMT are available in the author’s public GitHub repository in open-access with the provided link. The utility of scripting cartographic techniques for geophysical and topographic data processing combined with GIS spatial evaluation of the geological data supported automated mapping, which has applicability for risk assessment and geological hazard mapping of the Bolivian Andes, South America.

show abstract

Morphometric analysis of ice scour lakes in Iceland: A proxy for ice sheet dynamics

Mastro,

Principato,

Sobel

et al. 2023

Earth Surf Processes Landf

View full text Add to dashboard Cite

Glacial erosion rates on the Iceland landscape throughout the Pleistocene are not well quantified. Ice scour lakes provide an opportunity to investigate glacial erosive activity and relative intensity because they commonly form in areas beneath ice sheets due to intense quarrying. This study evaluates ice scour lake morphology and density as a potential proxy for paleo‐ice flow direction and basal thermal regime for parts of the Iceland Ice Sheet. Using GIS analysis, properties of ice scour lakes are examined in regions that experienced different rates of ice flow during and following the Last Glacial Maximum (LGM), as interpreted from streamlined subglacial landforms. The primary input datasets were topographic, hydrologic and bedrock data. Lake distribution and morphology were quantified for all natural lakes of Iceland (n = 30 169), with close examination of three 400 km2 sub‐regions in Vestfirðir (n = 904), Húnaflói (n = 69) and Bakkaheiði (n = 232). Lake distribution parameters include density, packing and centroid elevation. PolyMorph‐2D was used to quantify lake morphology including orientation, area and elongation ratio. Ice scour lake density, packing and elevation were all greatest on Vestfirðir. The high density and packing of lakes across Vestfirðir suggests unique ice dynamics relative to the other two sub‐regions, and multidirectional flow of lake axes supports the presence of an independent ice cap covering Vestfirðir. Differential intensity of glacial erosion interpreted from regions of high and low lake density on Bakkaheiði supports a proposed ice divide in Northeast Iceland. The orientation of lakes align with the flow directions of proposed LGM ice streams in Northern and Northeast Iceland. Ice scour lakes were most elongate on average on Bakkaheiði, which supports fast ice flow. Improving understanding of former ice sheet basal thermal regime and paleo‐ice flow velocity serves to inform modern controls on ice sheet stability and ice sheet basal processes.

show abstract

PolyMorph-2D: An open-source GIS plug-in for morphometric analysis of vector-based 2D polygon features

Cited by 4 publications

References 62 publications

Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts

Morphometric analysis of ice scour lakes in Iceland: A proxy for ice sheet dynamics

Contact Info

Product

Resources

About