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Globally, many ecosystems are being challenged and transformed by anthropogenic climate change. Future ecosystem configurations will be heavily influenced by the critical ecological functions that affect resilience. Robust measures of these functions will thus be essential for understanding and responding to ecological change. Coral reefs are experiencing unprecedented ecological change due to global mass coral bleaching. After bleaching events and other disturbances, herbivorous fishes provide functions that are critical for reef resilience by controlling harmful proliferation of algae. Identifying functional diversity amongst herbivorous fishes has been a mainstay of reef fish research, but it has remained unclear how, and to what extent, functional diversity translates to functional impacts on reefs. Rather than assessing the functional potential of the herbivorous fish community, we explicitly considered the delivery of herbivory to the reef by quantifying, in unprecedented detail, the spatial extent and overlap of feeding areas across different functional groups. Core feeding areas were highly concentrated and consistently covered just 14% of available reef space. Overlap across functional groups was limited, showing high spatial complementarity as functional groups tended to feed next to one another. Thus, the delivery of critical ecosystem processes was patchy, effectively reducing functional redundancy, even in the presence of a diverse fish assemblage. Our findings caution against assumptions of spatial homogeneity in the delivery of critical ecosystem functions. The functional impact of local herbivorous fish assemblages in current approaches may be overestimated, potentially leading to skewed assessments of reef resilience. Our results highlight the need to incorporate collective animal behaviour and spatio‐temporal scales into future assessments of ecosystem functions and ultimately ecological resilience. A plain language summary is available for this article. Plain Language Summary

In unmanned aerial vehicle (UAV) photogrammetric surveys, the cameracan be pre-calibrated or can be calibrated \"on-the-job\" using structure-from-motion anda self-calibrating bundle adjustment. This study investigates the impact on mapping accuracyof UAV photogrammetric survey blocks, the bundle adjustment and the 3D reconstructionprocess under a range of typical operating scenarios for centimetre-scale natural landformmapping (in this case, a coastal cliff). We demonstrate the sensitivity of the process tocalibration procedures and the need for careful accuracy assessment. For this investigation, vertical (nadir or near-nadir) and oblique photography were collected with 80%–90%overlap and with accurately-surveyed (σ ≤ 2 mm) and densely-distributed ground control.This allowed various scenarios to be tested and the impact on mapping accuracy to beassessed. This paper presents the results of that investigation and provides guidelines thatwill assist with operational decisions regarding camera calibration and ground control forUAV photogrammetry. The results indicate that the use of either a robust pre-calibration ora robust self-calibration results in accurate model creation from vertical-only photography,and additional oblique photography may improve the results. The results indicate thatif a dense array of high accuracy ground control points are deployed and the UAVphotography includes both vertical and oblique images, then either a pre-calibration or anon-the-job self-calibration will yield reliable models (pre-calibration RMSEXY = 7.1 mmand on-the-job self-calibration RMSEXY = 3.2 mm). When oblique photography was Remote Sens. 2015, 7 11934 excluded from the on-the-job self-calibration solution, the accuracy of the model deteriorated(by 3.3 mm horizontally and 4.7 mm vertically). When the accuracy of the ground controlwas then degraded to replicate typical operational practice (σ = 22 mm), the accuracyof the model further deteriorated (e.g., on-the-job self-calibration RMSEXY went from3.2–7.0 mm). Additionally, when the density of the ground control was reduced, the modelaccuracy also further deteriorated (e.g., on-the-job self-calibration RMSEXY went from7.0–7.3 mm). However, our results do indicate that loss of accuracy due to sparse groundcontrol can be mitigated by including oblique imagery.

Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants’ shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches.

It is important to grasp the number and location of trees, and measure tree structure attributes, such as tree trunk diameter and height. The accurate measurement of these parameters will lead to efficient forest resource utilization, maintenance of trees in urban cities, and feasible afforestation planning in the future. Recently, light detection and ranging (LiDAR) has been receiving considerable attention, compared with conventional manual measurement techniques. However, it is difficult to use LiDAR for widespread applications, mainly because of the costs. We propose a method for tree measurement using 360° spherical cameras, which takes omnidirectional images. For the structural measurement, the three-dimensional (3D) images were reconstructed using a photogrammetric approach called structure from motion. Moreover, an automatic tree detection method from the 3D images was presented. First, the trees included in the 360° spherical images were detected using YOLO v2. Then, these trees were detected with the tree information obtained from the 3D images reconstructed using structure from motion algorithm. As a result, the trunk diameter and height could be accurately estimated from the 3D images. The tree detection model had an F-measure value of 0.94. This method could automatically estimate some of the structural parameters of trees and contribute to more efficient tree measurement.

This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many environmental applications like landslide research. The methodology starts with the execution of UAV flights to acquire very high resolution images, which are oriented and georeferenced by means of aerial triangulation, bundle block adjustment and Structure from Motion (SfM) techniques, using ground control points (GCPs) as well as points transferred between flights. After Digital Surface Models (DSMs) and orthophotographs were obtained, both differential models and displacements at DSM check points between campaigns were calculated. Vertical and horizontal displacements in the range of a few decimeters to several meters were respectively measured. Finally, as the landslide occurred in an olive grove which presents a regular pattern, a semi-automatic approach to identifying and determining horizontal displacements between olive tree centroids was also developed. In conclusion, the study shows that landslide monitoring can be carried out with the required accuracy—in the order of 0.10 to 0.15 m—by means of the combination of non-invasive techniques such as UAV, photogrammetry and geographic information system (GIS).

1. Grasslands deliver a range of ecosystem services, including the provision of food and biodiversity, and regulation of soil carbon storage and hydrology. Monitoring schemes are needed to quantify spatial changes in these multiple functions alongside ecosystem degradation. Sward height is widely recognised as a key spatial variable in the provision of these services. Current manual monitoring approaches are labour intensive, and often fail to capture spatial patterns of important features, including sward height. 2. Proximal sensing from small aerial drones carrying lightweight cameras can be transformed into surface height models using image-based structure-from-motion and Multi-View Stereo-based approaches; this presents a new opportunity for monitoring the spatial structure of grassland sward height. We combined aerial photographs with field survey data and an open-source image-based modelling-processing workflow to generate sward height measurements for a field comprising mainly Loìium perenne (perennial ryegrass) and Trifolium pratense (red clover). We compared the derived measurements with in situ data captured on the same day using traditional agronomic sward height techniques to determine the quality of the drone-derived surface model product for sward characterisation. 3. The SfM and Multi-View Stereo-based surface model had a mean absolute sward height measurement error of between 3.7 and 4.2 cm. To produce field observations with equivalent quality would require up to 550 sward height measurements for the study site (area: 8,059 m²), which is not feasible over larger extents required for conservation of key species or agronomic purposes. 4. Synthesis and applications. We demonstrate how the collection of precise and detailed information on the spatial structure of grasslands can be made over management-relevant extents. Aerial digital photographs can be transformed into surface models using an image-based modelling approach: structure-from-motion and Multi-View Stereo techniques. Image-based measurements of sward heights were compared with manual sward height data captured on the same day. This novel source of vegetation spatial information could improve sward management for conservation and agronomy applications. The approach supports frequent surveys, at user-controlled revisit times, and delivers data for spatial monitoring of key grassland functions and services.

Demographic data are essential to construct mechanistic models to understand how populations change over time and in response to global threats like climate change. Existing demographic data are either lacking or insufficient for many species, particularly those that are challenging to obtain direct measurements from that can be used to estimate demographic rates, like marine mammals. A method for collecting accurate demographic data to construct robust demographic models at scale would fill this knowledge gap for difficult‐to‐access species. We introduce a novel, non‐invasive method to estimate the 3D body size (volume) of pinnipeds (seals, sea lions and walruses) that will allow monitoring at high spatial and temporal scales. Our method integrates 3D structure‐from‐motion photogrammetry data collected via planned flight missions using off‐the‐shelf, multirotor unmanned aerial vehicles (UAVs). We apply and validate this method on the grey seal Halichoerus grypus, a pinniped species that spends much of its time at sea but is predictably observable during its annual breeding season. We investigate the optimal ground sampling distance (GSD) for surveys by calculating the success rates and accuracy of volume estimates of individuals at different altitudes. Based on current technology, we establish an optimal GSD of at least 0.8 cm px−1 for animals similar in size to UK grey seals (~1.2–2.5 m length), making our method reproducible and applicable to other species. We found volume estimates were accurate and could be successfully estimated for up to 68% of hauled‐out seals in study areas. Our method accurately estimates individual body volume of pinnipeds in a time‐ and cost‐effective manner while minimising disturbance. While the approach is applied to pinnipeds here, the method could be adapted to further taxa that are otherwise challenging to obtain direct measurements from. Our proposed approach therefore has the potential to fill demographic research gaps, which will improve our ability to protect and conserve species into the future.

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows. We use high resolution UAV multispectral images and Structure‐from‐Motion to detect individual trees and characterize forest stands before and after a restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter are most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV‐derived data, one that considers varying density conditions and spatial scales.

Many man-made objects have intrinsic symmetries and often Manhattan structure. By assuming an orthographic or a weak perspective projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, for the two cases when the input is a single image or multiple images from the same category, e.g. multiple different cars from various viewpoints. More specifically, analysis on the single image case shows that Manhattan alone is sufficient to recover the camera projection and then the 3D structure can be reconstructed uniquely by exploiting symmetry. But Manhattan structure can be hard to observe from a single image due to occlusion. Hence, we extend to the multiple-image case which can also exploit symmetry but does not require Manhattan structure. We propose novel structure from motion methods for both rigid and non-rigid object deformations, which exploit symmetry and use multiple images from the same object category as input. We perform experiments on the Pascal3D+ dataset with either human labeled 2D keypoints or with 2D keypoints localized from a convolutional neural network. The results show that our methods which exploit symmetry significantly outperform the baseline methods.