Dataset Information

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests.

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.

SUBMITTER: Krishna Moorthy SM

PROVIDER: S-EPMC6686632 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests.

Krishna Moorthy Sruthi M SM Bao Yunfei Y Calders Kim K Schnitzer Stefan A SA Verbeeck Hans H

ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) 20190801

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 o ...[more]

PMID: 31417229

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Project description:Closed-canopy forests are being rapidly fragmented across much of the tropical world. Determining the impacts of fragmentation on ecological processes enables better forest management and improves species-conservation outcomes. Lianas are an integral part of tropical forests but can have detrimental and potentially complex interactions with their host trees. These effects can include reduced tree growth and fecundity, elevated tree mortality, alterations in tree-species composition, degradation of forest succession, and a substantial decline in forest carbon storage. We examined the individual impacts of fragmentation and edge effects (0-100-m transect from edge to forest interior) on the liana community and liana-host tree interactions in rainforests of the Atherton Tableland in north Queensland, Australia. We compared the liana and tree community, the traits of liana-infested trees, and determinants of the rates of tree infestation within five forest fragments (23-58 ha in area) and five nearby intact-forest sites. Fragmented forests experienced considerable disturbance-induced degradation at their edges, resulting in a significant increase in liana abundance. This effect penetrated to significantly greater depths in forest fragments than in intact forests. The composition of the liana community in terms of climbing guilds was significantly different between fragmented and intact forests, likely because forest edges had more small-sized trees favoring particular liana guilds which preferentially use these for climbing trellises. Sites that had higher liana abundances also exhibited higher infestation rates of trees, as did sites with the largest lianas. However, large lianas were associated with low-disturbance forest sites. Our study shows that edge disturbance of forest fragments significantly altered the abundance and community composition of lianas and their ecological relationships with trees, with liana impacts on trees being elevated in fragments relative to intact forests. Consequently, effective control of lianas in forest fragments requires management practices which directly focus on minimizing forest edge disturbance.

Dataset Information

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests.

Publications

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests.

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