Three-dimensional digital mapping of ecosystems: a new era in spatial ecology

Closing the gap between existing large-area imaging research and marine conservation needs

Emerging technology has immense potential to increase the scale and efficiency of marine conservation. One such technology is large-area imaging (LAI), which relies on structure-from-motion photogrammetry to create composite products, including 3-dimensional (3-D) environmental models, that are larger in spatial extent than the individual images used to create them. Use of LAI has become widespread in certain fields of marine science, primarily to measure the 3D structure of benthic ecosystems and track change over time. However, the use of LAI in the field of marine conservation appears limited. We conducted a review of the coral reef literature on the use of LAI to identify research themes and regional trends in applications of this technology. We also surveyed 135 coral reef scientists and conservation practitioners to determine community familiarity with LAI, evaluate barriers practitioners face in using LAI, and identify applications of LAI believed to be most exciting or relevant to coral conservation. Adoption of LAI was limited primarily to researchers at institutions based in advanced economies and was applied infrequently to conservation, although conservation practitioners and survey respondents from emerging economies indicated they expect to use LAI in the future. Our results revealed disconnect between current LAI research topics and conservation priorities identified by practitioners, highlighting the need for more diverse, conservation-relevant research using LAI. We provide recommendations for how early adopters of LAI (typically Global North scientists from well-resourced institutions) can facilitate access to this conservation technology. These recommendations include developing training resources, creating partnerships for data storage and analysis, publishing standard operating procedures for LAI workflows, standardizing methods, developing tools for efficient data extraction from LAI products, and conducting conservation-relevant research using LAI.

Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field

Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.

Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Methods for collecting animal behaviour data in natural environments, such as direct observation and biologging, are typically limited in spatiotemporal resolution, the number of animals that can be observed and information about animals' social and physical environments. Video imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals' locations, into geographical coordinates. We demonstrate a new system for studying behaviour in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals' body postures (poses) and extract environmental features, including topography of the landscape and animal trails. By quantifying animal movement and posture while reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments.

Measuring habitat complexity and spatial heterogeneity in ecology

Habitat complexity has been considered a key driver of biodiversity and other ecological phenomena for nearly a century. However, there is still no consensus over the definition of complexity or how to measure it. Up-to-date and clear guidance on measuring complexity is urgently needed, particularly given the rise of remote sensing and advent of technologies that allow environments to be scanned at unprecedented spatial extents and resolutions. Here we review how complexity is measured in ecology. We provide a framework for metrics of habitat complexity, and for the related concept of spatial heterogeneity. We focus on the two most commonly used complexity metrics in ecology: fractal dimension and rugosity. We discuss the pros and cons of these metrics using practical examples from our own empirical data and from simulations. Fractal dimension is particularly widely used, and we provide a critical examination of it drawing on research from other scientific fields. We also discuss informational metrics of complexity and their potential benefits. We chart a path forward for research on measuring habitat complexity by presenting, as a guide, sets of essential and desirable criteria that a metric of complexity should possess. Lastly, we discuss the applied significance of our review.

Vegetation and vantage point influence visibility across diverse ecosystems: Implications for animal ecology

Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear. In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.

Conceptualizing the 3D niche and vertical space use

Spatial partitioning in ecological communities has predominantly been described in two dimensions, yet habitat is complex and 3D. Complex space use mediates community structure and interaction strength by expanding spatial, temporal, and dietary dimensions. Vertical stratification of resources provides opportunities for novel specializations, creating a 3D niche. Competition and predation are mediated by 3D space use, as individuals use the vertical axis to access prey, flee predators, or avoid competitors. The 3D niche is important for long-term conservation strategies as species must navigate tradeoffs in habitat use between strata-specific threats and suboptimal habitat patches. Ultimately, elucidating the 3D niche has implications for protected area management and corridor design that directly influence species persistence and ecosystem function in a rapidly changing world.

Coral reef benthic community changes in the Anthropocene: Biogeographic heterogeneity, overlooked configurations, and methodology

Non-random community changes are becoming more frequent in many ecosystems. In coral reefs, changes towards communities dominated by other than hard corals are increasing in frequency, with severe impacts on ecosystem functioning and provision of ecosystem services. Although new research suggests that a variety of alternative communities (i.e. not dominated by hard corals) exist, knowledge on the global diversity and functioning of alternative coral reef benthic communities, especially those not dominated by algae, remains scattered. In this systematic review and meta-analysis of 523 articles, we analyse the different coral reef benthic community changes reported to date and discuss the advantages and limitations of the methods used to study these changes. Furthermore, we used field cover data (1116 reefs from the ReefCheck database) to explore the biogeographic and latitudinal patterns in dominant benthic organisms. We found a mismatch between literature focus on coral-algal changes (over half of the studies analysed) and observed global natural patterns. We identified strong biogeographic patterns, with the largest and most biodiverse biogeographic regions (Western and Central Indo-Pacific) presenting previously overlooked soft-coral-dominated communities as the most abundant alternative community. Finally, we discuss the potential biases associated with methods that overlook ecologically important cryptobenthic communities and the potential of new technological advances in improving monitoring efforts. As coral reef communities inevitably and swiftly change under changing ocean conditions, there is an urgent need to better understand the distribution, dynamics as well as the ecological and societal impacts of these new communities.

Seagrass restoration monitoring and shallow-water benthic habitat mapping through a photogrammetry-based protocol

Seagrasses rank among the most productive yet highly threatened ecosystems on Earth. Loss of seagrass habitat because of anthropogenic disturbances and evidence of their limited resilience have provided the impetus for investigating and monitoring habitat restoration through transplantation programmes. Although Structure from Motion (SfM) photogrammetry is becoming a more and more relevant technique for mapping underwater environments, no standardised methods currently exist to provide 3-dimensional high spatial resolution and accuracy cartographic products for monitoring seagrass transplantation areas. By synthesizing various remote sensing applications, we provide an underwater SfM-based protocol for monitoring large seagrass restoration areas. The data obtained from consumer-grade red-green-blue (RGB) imagery allowed the fine characterization of the seabed by using 3D dense point clouds and raster layers, including orthophoto mosaics and Digital Surface Models (DSM). The integration of high spatial resolution underwater imagery with object-based image classification (OBIA) technique provided a new tool to count transplanted Posidonia oceanica fragments and estimate the bottom coverage expressed as a percentage of seabed covered by such fragments. Finally, the resulting digital maps were integrated into Geographic Information Systems (GIS) to run topographic change detection analysis and evaluate the mean height of transplanted fragments and detect fine-scale changes in seabed vector ruggedness measure (VRM). Our study provides a guide for creating large-scale, replicable and ready-to-use products for a broad range of applications aimed at standardizing monitoring protocols in future seagrass restoration actions.

The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

Limitations with benchmark light detection and ranging (LiDAR) technologies in forestry have prompted the exploration of handheld or wearable low-cost 3D sensors (<2000 USD). These sensors are now being integrated into consumer devices, such as the Apple iPad Pro 2020. This study was aimed at determining future research recommendations to promote the adoption of terrestrial low-cost technologies within forest measurement tasks. We reviewed the current literature surrounding the application of low-cost 3D remote sensing (RS) technologies. We also surveyed forestry professionals to determine what inventory metrics were considered important and/or difficult to capture using conventional methods. The current research focus regarding inventory metrics captured by low-cost sensors aligns with the metrics identified as important by survey respondents. Based on the literature review and survey, a suite of research directions are proposed to democratise the access to and development of low-cost 3D for forestry: (1) the development of methods for integrating standalone colour and depth (RGB-D) sensors into handheld or wearable devices; (2) the development of a sensor-agnostic method for determining the optimal capture procedures with low-cost RS technologies in forestry settings; (3) the development of simultaneous localisation and mapping (SLAM) algorithms designed for forestry environments; and (4) the exploration of plot-scale forestry captures that utilise low-cost devices at both terrestrial and airborne scales.

Topographic Analysis of Intertidal Polychaete Reefs (Sabellaria alveolata) at a Very High Spatial Resolution

In temperate coastal regions of Western Europe, the polychaete Sabellaria alveolata (Linné) builds large intertidal reefs of several hectares on soft-bottom substrates. These reefs are protected by the European Habitat Directive EEC/92/43 under the status of biogenic structures hosting a high biodiversity and providing ecological functions such as protection against coastal erosion. As an alternative to time-consuming field campaigns, a UAV-based Structure-from-Motion photogrammetric survey was carried out in October 2020 over Noirmoutier Island (France) where the second-largest known European reef is located in a tidal delta. A DJI Phantom 4 Multispectral UAV provided a topographic dataset at very high resolutions of 5 cm/pixel for the Digital Surface Model (DSM) and 2.63 cm/pixel for the multispectral orthomosaic images. The reef footprint was mapped using a combination of two topographic indices: the Topographic Openness Index and the Topographic Position Index. The reef structures covered an area of 8.15 ha, with 89% corresponding to the main reef composed of connected and continuous biogenic structures, 7.6% of large isolated structures (<60 m2), and 4.4% of small isolated reef clumps (<2 m2). To further describe the topographic complexity of the reef, the Geomorphon landform classification was used. The spatial distribution of tabular platforms considered as a healthy stage of the reef in contrast to a degraded stage was mapped with a proxy that consists in comparing the reef volume to a theoretical tabular-shaped reef volume. Epibionts colonizing the reef (macroalgae, mussels, and oysters) were also mapped by combining multispectral indices such as the Normalised Difference Vegetation Index and simple band ratios with topographic indices. A confusion matrix showed that macroalgae and mussels were satisfactorily identified but that oysters could not be detected by an automated procedure due to their spectral complexity. The topographic indices used in this work should now be further exploited to propose a health index for these large intertidal reefs.

Integrating terrestrial laser scanning with functional-structural plant models to investigate ecological and evolutionary processes of forest communities

BACKGROUND

Woody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years functional-structural plant models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, terrestrial laser scanning (TLS), has emerged as an invaluable tool for capturing the 3-D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs.

SCOPE

The potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers.

CONCLUSIONS

We conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterizing FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3-D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.

Terrestrial laser scanning: a new standard of forest measuring and modelling?

BACKGROUND

Laser scanning technology has opened new horizons for the research of forest dynamics, because it provides a largely automated and non-destructive method to rapidly capture the structure of individual trees and entire forest stands at multiple spatial scales. The structural data themselves or in combination with additional remotely sensed data also provide information on the local physiological state of structures within trees. The capacity of new methods is facilitated by the ongoing development of automated processing tools that are designed to capture information from the point cloud data provided by the remote measurements.

SCOPE

Terrestrial laser scanning (TLS), performed from the ground or from unmanned aerial vehicles, in particular, has potential to become a unifying measurement standard for forest research questions, because the equipment is flexible to use in the field and has the capacity to capture branch-level structural information at the forestplot or even forest scale. This issue of Annals of Botany includes selected papers that exemplify the current and potential uses of TLS, such as for examination of crown interactions between trees, growth dynamics of mixed stands, non-destructive characterization of urban trees, and enhancement of ecological and evolutionary models. The papers also present current challenges in the applicability of TLS methods and report recent developments in methods facilitating the use of TLS data for research purposes, including automatic processing chains and quantifying branch and above-ground biomass. In this article, we provide an overview of the current and anticipated future capacity of TLS and related methods in solving questions that utilize measurements and models of forests.

CONCLUSIONS

Due to its measurement speed, TLS provides a method to effortlessly capture large amounts of detailed structural forest information, and consequent proxy data for tree and forest processes, at a far wider spatial scale than is feasible with manual measurements. Issues with measurement precision and occlusion of laser beams before they reach their target structures continue to reduce the accuracy of TLS data, but the limitations are counterweighted by the measurement speed that enables large sample sizes. The currently high time-cost of analysing TLS data, in turn, is likely to decrease through progress in automated processing methods. The developments point towards TLS becoming a new and widely accessible standard tool in forest measurement and modelling.

Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry

Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classes across three sites of ongoing restoration since linear disturbances in 2005, 2009, and 2014 in temperate and boreal coniferous forests in Alberta, Canada. The spatial arrangement of structure classes was assessed using land cover maps, mean patch size, and measures of local spatial association. We observed DAP heights of short-stature vegetation were consistently underestimated, but strong correlations (rs > 0.75) with field height were found for juvenile trees, shrubs, and perennials. Metrics of height and canopy complexity allowed for the extraction of relatively tall and complex vegetation structures, whereas canopy cover and height variability metrics enabled the classification of the shortest vegetation structures. We found that the boreal site disturbed in 2009 had the highest cover of classes associated with complex vegetation structures. This included early regenerative (22%) and taller (13.2%) wood-like structures as well as structures representative of tall graminoid and perennial vegetation (15.3%), which also showed the highest patchiness. The developed tools provide large-scale maps of the structure, enabling the identification and assessment of vegetational patterns, which is challenging based on traditional field sampling that requires pre-defined location-based hypotheses. The approach can serve as a basis for the evaluation of specialized restoration objectives as well as objectives tailored towards processes of ecological succession, and support prioritization of future inspections and mitigation measures.

Advancing Landscape and Seascape Ecology from a 2D to a 3D Science

Landscape ecology has fundamentally changed the way ecologists view the world through a greater understanding of the links between spatial patterns and ecological processes. Until recently, landscape ecology has been largely a two-dimensional (2D) science focused on the spatial patterning of 2D planar surfaces rather than three-dimensional (3D) structures. Advances in high-resolution remote sensing technologies, such as laser altimetry, acoustic sensors, and photogrammetry now provide the capability to map complex ecosystem structure in three dimensions, creating more structurally realistic models of the environment. In the present article, we focus on high-resolution 3D structure, using terrestrial and marine examples to illustrate how state-of-the-art advances in landscape ecology achieved through novel data fusion, spatial analysis, and geovisualization of environmental data can provide new ecological insights. These examples provide a look to the future in landscape and seascape ecology, where continued progress toward a multidimensional science will fundamentally shift the way we view, explore, and conceptualize the world.

3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf

The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.