Back

SimpleForest - a comprehensive tool for 3d reconstruction of trees from forest plot point clouds

Hackenberg, J.; Calders, K.; Demol, M.; Raumonen, P.; Piboule, A.; Disney, M.

2021-07-30 plant biology
10.1101/2021.07.29.454344 bioRxiv
Show abstract

The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC [Formula] of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling "The sum of cross sectional areas after a branch junction equals the one before the branch junction" (Pipe Model Theory) and "Twigs are self-similar" (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B0 * RPRB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC [Formula] of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript. Upload statementSelf-uploaded pre-print for peer-review submitted manuscript. The manuscript was submitted on 26th of July 2021 to Plos Computational Biology: I, Jan Hackenberg uploaded this manuscript because the automated journal upload was rejected for the following reason: Thank you for considering posting your manuscript "SimpleForest - a comprehensive tool for 3d reconstruction of tree from forest plot point clouds." as a preprint. Your manuscript does not meet bioRxivs criteria and therefore we will not be sending it for posting as a preprint. For more information about our checks, see link. We have noted that it contains material that is potentially subject to copyright. In particular, screenshot in Figure 1. Preprints posted to bioRxiv following submission to PLOS journals are done so under the CC BY license. To avoid a potential breach of the copyright that applies to the material listed above, we are unable to make the manuscript publicly available. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/454344v1_fig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@1094b17org.highwire.dtl.DTLVardef@120d7a3org.highwire.dtl.DTLVardef@12d2fbcorg.highwire.dtl.DTLVardef@1990a4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO Submission system screenshot. C_FIG Please note that this decision does not affect the editorial process at PLOS Computational Biology. Your manuscript is being separately assessed with regards to sending for peer review. From section Abstract on, the pdf you see is same as submitted one.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.