Abstract
Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest.
Highlights
Tropical forests are undergoing large-scale structural changes including an increase in liana abundance and biomass (Schnitzer and Bongers, 2011)
We presented a method for semi-automated liana extraction from plot-level terrestrial laser scanning (TLS) data with a high precision of 88%
Random Forest (RF) models had a low recall of 54% and 65% in Nouragues and Gigante, which was increased to 90% and 70% by the post-processing steps respectively
Summary
Tropical forests are undergoing large-scale structural changes including an increase in liana abundance and biomass (Schnitzer and Bongers, 2011). Lianas are woody climbing plants that use trees as structural support for ascending to the canopy (Schnitzer and Bongers, 2002) and compete with trees for both aboveground and belowground resources (Toledo-Aceves, 2015; Rodríguez-Ronderos et al, 2016). Lianas have shown to reduce the net above-carbon uptake of the forest by 76% in three years mostly by reducing tree growth (van der Heijden et al, 2015). Increase in liana abundance results in reduced tree growth, survival, reproduction, biomass and increased tree mortality thereby playing an important role in the global carbon cycle (Schnitzer, 2018; García et al, 2018; van der Heijden et al, 2015). A recent study has shown that liana load on trees can alter the allometry of trees by decreasing tree slenderness resulting in shorter and thicker stems (Dias et al, 2017)
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