Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis

Gago, Xurxo; Bučas, Martynas; Estrany, Joan; Fortesa, Josep; Manfreda, Salvatore; Michez, Adrien; Mokroš, Martin; Paulus, Gernot; Tiškus, Edvinas; Tsiafouli, Maria A.; Kent, Rafi

doi:10.1016/j.ecolind.2021.108156

Cited by 26 publications

(14 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Importantly, despite the dissimilarities between field‐ and remote sensing‐based measures of vegetation complexity, multiple studies have found strong congruence between estimates of VSC using different techniques (e.g. Bergen et al, 2009; Ishii et al, 2004; Kane et al, 2010; Müllerová et al, 2021), including field‐ and remote sensing‐based approaches. Collectively, these advantages suggest that remote sensing holds the promise to allow ecologists to measure VSC in ways directly comparable to previous field‐based methods, but at unprecedented spatial scales and resolutions; the recent proliferation of studies using remote sensing to investigate the effects of VSC on ecological processes is one indication of this potential (Table 1).…”

Section: Remote Sensing and Vscmentioning

confidence: 99%

“…To date, a variety of LiDAR and RADAR platforms have been used to investigate VSC. The technical constraints, capabilities, and application of these technologies for ecology, conservation, and management have been previously reviewed (Atkins, Bohrer, et al, 2018; Bergen et al, 2009; Davies & Asner, 2014; Leite et al, 2022; Lenoir et al, 2022; Müllerová et al, 2021; Wang et al, 2010), so here we briefly highlight constraints and capabilities of different remote sensing approaches as they pertain to the estimation of VSC. Generally, LiDAR has proven to be especially useful for fine‐scale quantification of VSC due to the ability to parse laser waveforms to gather additional information about leaf traits (e.g.…”

Section: Remote Sensing and Vscmentioning

confidence: 99%

“…Coops et al, 2018) scales, and that plant community traits underly many of these differences. Dozens of quantitative metrics have been developed to describe vegetation structural complexity (VSC; reviewed by Atkins, Fahey, et al, 2018; Bergen et al, 2009; McElhinny et al, 2005; Müllerová et al, 2021) though, to date, these have been primarily restricted to forest ecosystems (Davies & Asner, 2014). A small number of studies have investigated vegetation structure in non‐forested habitats (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unravelling the relationship between plant diversity and vegetation structural complexity: A review and theoretical framework

Coverdale

Davies

2023

Journal of Ecology

View full text Add to dashboard Cite

Vegetation structural complexity (VSC)—the three‐dimensional distribution of plants within an ecosystem—is an important ecological trait. To date, research has focused primarily on the effects of VSC on ecological patterns and processes, but comparatively little is known about what drives variation in VSC. Recent advances in active remote sensing technology, particularly light detection and ranging and radio detection and ranging, have allowed the measurement of VSC at unprecedented spatial scales and resolutions. Out of this and earlier work has emerged evidence that VSC is typically associated with greater ecosystem functioning (especially microclimate regulation, productivity, faunal diversity and habitat provisioning), making restoration of vegetation complexity a potentially powerful restoration tool. Recent studies of VSC across natural and experimental gradients of plant diversity have also revealed that more diverse plant communities tend to be more structurally complex. However, the shape and generality of this relationship—and the mechanism(s) by which phytodiversity might contribute to structural complexity—remain poorly understood. Here, we review how active remote sensing has facilitated recent VSC research and shaped our understanding of the relationship between vegetation complexity and ecosystem function. We then present a theoretical framework for the relationship between phytodiversity and VSC based on classic biodiversity‐ecosystem functioning principles. Finally, we evaluate the evidence for the notion that diverse plant assemblages tend to be more structurally complex and explore the shape of the relationship between phytodiversity and VSC using data from 13 recent remote sensing studies. Synthesis. The relationship between phytodiversity and VSC appears to be almost universally positive. Preliminary evidence further suggests that the most common relationships between phytodiversity and VSC are linear or saturating, indicating that the extent of functional redundancy between species varies across plant communities and ecosystems. In contrast, we find little evidence for exponential or negative relationships between plant diversity and VSC, suggesting that even modest increases in plant diversity could markedly increase structural complexity. Additional investigations of phytodiversity‐VSC relationships are necessary to establish whether the observed positive relationships are causal (and, if so, in which direction) and to clarify the potential impact of plant community restoration on structural complexity and broader ecosystem function.

show abstract

Section: Remote Sensing and Vscmentioning

confidence: 99%

Section: Remote Sensing and Vscmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unravelling the relationship between plant diversity and vegetation structural complexity: A review and theoretical framework

Coverdale

Davies

2023

Journal of Ecology

View full text Add to dashboard Cite

show abstract

“…For tasks like irrigation scheduling, disease detection, soil texture mapping, weed detection, residue cover, tillage mapping, crops management, cultivations analysis, and other applications in precision agriculture, rotarywing UAVs are created and brought to use [50,51]. UAV applications in agriculture clearly outperform conventional farming methods using people and ground machinery.…”

Section: Applications In Agriculturementioning

confidence: 99%

Design Configuration and Technical Application of Rotary-Wing Unmanned Aerial Vehicles

Zhao¹,

Li²

2022

MITS

View full text Add to dashboard Cite

Due to their advantages in hovering, takeoff and landing adaptability, maneuverability, and other factors, rotary-wing unmanned aerial vehicles (UAVs) are widely applied across many different fields. The UAVs' design and configuration can be quite flexible to fit diverse operation conditions. The major goal of innovations in rotary-wing UAVs is to lower operating risk and expense by optimizing payload and structure layout. This study examines three aspects of rotary-wing UAV design and evolution: the number and arrangement of rotors, hybrid-wing-based UAVs, and configuration and loading structures. The most current advancements of UAV applications in crucial industries, including agriculture, fire rescue, inspection and monitoring, and aerial logistics, are then thoroughly examined. Finally, the authors discussed the prospective uses for rotary-wing UAV design in the future.

show abstract

“…Sensors onboard UAS can collect a variety of different data sets that are used to calculate vegetation indices for monitoring the vitality and the geometric and morphological characteristics of the objects under study. Müllerová et al [10] presented a comprehensive framework where common rules in the domain of UAS-based monitoring of ecosystems are identified. By analyzing studies, the researchers found similarities in workflows according to the character of the vegetative properties under investigation.…”

Section: Introductionmentioning

confidence: 99%

Automated Derivation of Vine Objects and Ecosystem Structures Using UAS-Based Data Acquisition, 3D Point Cloud Analysis, and OBIA

Ruess,

Paulus,

Lang

2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R2 = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis.

show abstract

Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis

Cited by 26 publications

References 84 publications

Unravelling the relationship between plant diversity and vegetation structural complexity: A review and theoretical framework

Unravelling the relationship between plant diversity and vegetation structural complexity: A review and theoretical framework

Design Configuration and Technical Application of Rotary-Wing Unmanned Aerial Vehicles

Automated Derivation of Vine Objects and Ecosystem Structures Using UAS-Based Data Acquisition, 3D Point Cloud Analysis, and OBIA

Contact Info

Product

Resources

About