Monitoring the Distribution and Dynamics of an Invasive Grass in Tropical Savanna Using Airborne LiDAR

Levick, Shaun R.; Setterfield, Samantha A.; Rossiter‐Rachor, Natalie A.; Hutley, Lindsay B.; McMaster, Damien; Hacker, Jörg

doi:10.3390/rs70505117

Cited by 22 publications

(20 citation statements)

References 32 publications

(42 reference statements)

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“…(b) Establishment ability, which allows immigrants to rapidly establish populations and become ecologically dominant (e.g. Levick, Setterfield, Rossiter-Rachor et al, 2015). (c) Phenotypic plasticity and evolutionary lability, which enable grasses to occupy a diversity of environments and maintain viable populations.…”

Section: Introductionmentioning

confidence: 99%

Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation

et al. 2017

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Poaceae (the grasses) is arguably the most successful plant family, in terms of its global occurrence in (almost) all ecosystems with angiosperms, its ecological dominance in many ecosystems, and high species richness. We suggest that the success of grasses is best understood in context of their capacity to colonize, persist, and transform environments (the "Viking syndrome"). This results from combining effective long-distance dispersal, efficacious establishment biology, ecological flexibility, resilience to disturbance and the capacity to modify environments by changing the nature of fire and mammalian herbivory. We identify a diverse set of functional traits linked to dispersal, establishment and competitive abilities. Enhanced long-distance dispersal is determined by anemochory, epizoochory and endozoochory and is facilitated via the spikelet (and especially the awned lemma) which functions as the dispersal unit. Establishment success could be a consequence of the precocious embryo and large starch reserves, which may underpin the extremely short generation times in grasses. Post-establishment genetic bottlenecks may be mitigated by wind pollination and the widespread occurrence of polyploidy, in combination with gametic self-incompatibility. The ecological competitiveness of grasses is corroborated by their dominance across the range of environmental extremes tolerated by angiosperms, facilitated by both C and C photosynthesis, well-developed frost tolerance in several clades, and a sympodial growth form that enabled the evolution of both annual and long-lived life forms. Finally, absence of investment in wood (except in bamboos), and the presence of persistent buds at or below ground level, provides tolerance of repeated defoliation (whether by fire, frost, drought or herbivores). Biotic modification of environments via feedbacks with herbivory or fire reinforce grass dominance leading to open ecosystems. Grasses can be both palatable and productive, fostering high biomass and diversity of mammalian herbivores. Many grasses have a suite of architectural and functional traits that facilitate frequent fire, including a tufted growth form, and tannin-like substances in leaves which slow decomposition. We mapped these traits over the phylogeny of the Poales, spanning the grasses and their relatives, and demonstrated the accumulation of traits since monocots originated in the mid-Cretaceous. Although the sympodial growth form is a monocot trait, tillering resulting in the tufted growth form most likely evolved within the grasses. Similarly, although an ovary apparently constructed of a single carpel evolved in the most recent grass ancestor, spikelets and the awned lemma dispersal units evolved within the grasses. Frost tolerance and C photosynthesis evolved relatively late (late Palaeogene), and the last significant trait to evolve was probably the production of tannins, associated with pyrophytic savannas. This fits palaeobotanical data, suggesting several phases in the grass success story: from a late Cretac...

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Section: Introductionmentioning

confidence: 99%

Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation

et al. 2017

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“…Mandlburger et al have coupled airborne laser bathymetry [25] with a hydrodynamic model at the micro-habitat and meso-habitat scale to predict habitat quality for nase (Chondrostoma nasus) and how this is changed by flood events. Levick et al applied high-density airborne laser scanning to map invasion of savannah ecosystems by an alien grass species (Andropogon gayanus) [26] at sub-regional to regional scale. Monitoring grassland management at large scales is also a challenge: MODIS data time series were successfully processed by Halabuk et al to infer meadow cutting regimes at the scale of a whole country [27].…”

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confidence: 99%

Remote Sensing and GIS for Habitat Quality Monitoring: New Approaches and Future Research

et al. 2015

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Habitat quality is the ability of the environment to provide conditions appropriate for individual and species persistence. Measuring or monitoring habitat quality requires complex integration of many properties of the ecosystem, where traditional terrestrial data collection methods have proven extremely time-demanding. Remote sensing has known potential to map various ecosystem properties, also allowing rigorous checking of accuracy and supporting standardized processing. Our Special Issue presents examples where remote sensing has been successfully used for habitat mapping, quantification of habitat quality parameters, or multi-parameter modelling of habitat quality itself. New frontiers such as bathymetric scanning, grassland vegetation classification and operational use were explored, various new ecological verification methods were introduced and integration with ongoing OPEN ACCESS

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“…Fish and Wildlife Service. 1 North Carolina State University, Marine, Earth, and Atmospheric Sciences, 2800 Faucette Drive, Raleigh, NC 27695, USA Full list of author information is available at the end of the article Many existing methods for 3D point cloud analyses are limited to 2D or 2.5D [7,10,11], have been implemented in a specialized lidar-processing software [5,12,13], or use custom low-level code [14]. To make advanced analysis of point clouds more general and accessible, we use 3D rasters and associated 3D raster algebra as the basis for developing new methods for lidar data analysis.…”

Section: Introductionmentioning

confidence: 99%

Generalized 3D fragmentation index derived from lidar point clouds

Petras

Newcomb

Mitášová

2017

Open geospatial data, softw. stand.

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Background: Point clouds with increased point densities create new opportunities for analyzing landscape structure in 3D space. Taking advantage of these dense point clouds we have extended a 2D forest fragmentation index developed for regional scale analyses into a 3D index for analyzing vegetation structure at a much finer scale. Methods: Based on the presence or absence of points in a 3D raster (voxel model) the 3D fragmentation index is used to evaluate the configuration of a cell's 3D neighborhood resulting in fragmentation classes such as interior, edge, or patch. In order to incorporate 3D fragmentation into subsequent conventional 2D analyses, we developed a transformation of this 3D fragmentation index into a series of 2D rasters based on index classes. Results: We applied this method to a point cloud obtained by airborne lidar capturing a suburban area with mixed forest cover. All processing and visualization was done in GRASS GIS, an open source, geospatial processing and remote sensing tool. The newly developed code is also publicly available and open source. The entire processing chain is available and executable through Docker for maximum reproducibility. Conclusions: We demonstrated that this proposed index can be used to describe different types of vegetation structure making it a promising tool for remote sensing and landscape ecology. Finally, we suggest that processing point clouds using 3D raster methods including 3D raster algebra is as straightforward as using well-established 2D raster and image processing methods.

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Monitoring the Distribution and Dynamics of an Invasive Grass in Tropical Savanna Using Airborne LiDAR

Cited by 22 publications

References 32 publications

Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation

Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation

Remote Sensing and GIS for Habitat Quality Monitoring: New Approaches and Future Research

Generalized 3D fragmentation index derived from lidar point clouds

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