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spanner\n\u003cbr/\u003e1 (chiefly British): WRENCH\n\u003cbr/\u003e2: a wrench that has a hole, projection, or hook at one or both ends of the head for engaging with a corresponding device on the object that is to be turned\n\u003cbr/\u003e3: implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021), and hierarchical patch delineation (Girvetz \u0026 Greco, 2007). \n\n\u003cimg src=\"https://github.com/bi0m3trics/spanner/blob/master/img/tshirt3.png\" width=\"100%\"  height=\"auto\" align=\"center\"/\u003e\n\n# Install `spanner`\n\nGet the latest released version of spanner from github.\n\n```r\nremotes::install_github('bi0m3trics/spanner')\n```\n\n# Workflows\n## Terrestrial Lidar Tree Attributes and Segmentation\n\n\u003cimg align=\"right\" height=\"240\" src=\"./img/output.gif\"\u003e\n\nThis package implements the tree detection and segmentation algorithm described in \u003ca href=\"https://doi.org/10.3390/rs13122297\"\u003eDonager et al. (2021)\u003c/a\u003e. The algorithm estimates tree locations and DBH by rasterizing individual point cloud values of relative neighborhood density and verticality within a slice of the normalized point cloud around breast height \n(1.37 m), then performs individual tree (instance) segmentation following ecological principles for \"growing\" trees based on input locations in a graph-theory approach. Implementation relies on work of \u003ca href = \"https://doi.org/10.1016/j.rse.2020.112061\"\u003eRoussel et al. (2020)\u003c/a\u003e, \u003ca href=\"https://doi.org/10.1016/j.isprsjprs.2015.10.007\"\u003eTao et al. (2015)\u003c/a\u003e, and \u003ca href=\"https://doi.org/10.1016/j.compag.2017.10.019\"\u003ede Conto et al. (2017)\u003c/a\u003e.\u003cbr/\u003e\u003cbr/\u003e\n\nCitation: Donager, Jonathon J., Andrew J. Sánchez Meador, and Ryan C. Blackburn 2021. Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare? Remote Sensing 13, no. 12: 2297. https://doi.org/10.3390/rs13122297\n\n## PatchMorph: Patch Delineation Algorithm\n\n\u003cimg align=\"right\" height=\"240\" src=\"./img/pm_output.png\"\u003e\n\nThe patchwoRk function implements a patch delineation algorithm [at present it only implements \u003ca href=\"https://doi.org/10.1007/s10980-007-9104-8\"\u003e'PatchMorph' (Girvetz \u0026 Greco 2007)\u003c/a\u003e, which can delineate patches across a range of spatial scales based on three organism-specific thresholds - (1) land cover density threshold, (2) habitat gap maximum thickness (gap threshold), and (3) habitat patch minimum thickness (spur threshold)].\n\nCitation: Girvetz EH, and Greco SE. 2007. How to define a patch: a spatial model for hierarchically delineating organism-specific habitat patches. 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