Structure from motion photogrammetry in forestry : a review

AbstractPurpose of ReviewThe adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers.Recent FindingsOur examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels.SummaryWe highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys

Using lightweight unmanned aerial vehicles to monitor tropical forest recovery

Large areas of tropical lands are being removed from agriculture and restored to address conservation goals. However, monitoring the ecological value of these efforts at the individual land-owner scale is rare, owing largely to issues of cost and accessibility. Traditional field-based measures for assessing forest recovery and habitat quality can be labour intensive and costly. Here we assess whether remote sensing measurements from lightweight unmanned aerial vehicles (UAV) are a cost-effective substitute for traditional field measures. An inexpensive UAV-based remote sensing methodology, "Ecosynth", was applied to measure forest canopy structure across field plots in a 7-9-yr tropical forest restoration study in southern Costa Rica. Ecosynth methods combine aerial images from consumer-grade digital cameras with computer vision software to generate 3D 'point cloud' models of vegetation at high spatial resolutions. Ecosynth canopy structure measurements were compared to field-based measures and their ability to predict the abundance of frugivorous birds; key seed dispersers that are sensitive to canopy structure. Ecosynth canopy height measurements were highly correlated with field-based measurements (R2≥0.85), a result comparable in precision to LiDAR-based remote sensing measurements. Ecosynth parameters were also strongly correlated with above-ground biomass (R2≥0.81) and percent canopy openness (R2=0.82). Correlations were weaker with proportion-based measures such as canopy roughness (R2=0.53). Several Ecosynth metrics (e.g., canopy openness and height) predicted frugivore presence and abundance at levels of accuracy similar to those of field-based measurements. Ecosynth UAV remote-sensing provides an effective alternate methodology to traditional field-based measures of evaluating forest structure and complexity across landscapes. Furthermore, given the volume of data that can be generated in a single flight plan, as well as the ability to use the technology in remote areas, these methods could expand the scope of studies on forest dynamics and recovery when combined with field-based calibration plots

Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: Results from 5 years of monthly drone data for a 50 ha plot

A mechanistic understanding of how tropical-tree mortality responds to climate variation is urgently needed to predict how tropical-forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used 5 years of approximately monthly drone-acquired RGB (red-green-blue) imagery for 50ĝ€¯ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure; temporal variation; and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or the collapse of standing dead trees. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though wind speeds were lower in the wet season. The strongest correlate of monthly variation in canopy disturbance rates was the frequency of extreme rainfall events. The size distribution of canopy disturbances was best fit by a Weibull function and was close to a power function for sizes above 25 m2. Treefalls accounted for 74 % of the total area and 52 % of the total number of canopy disturbances in treefalls and branchfalls combined. We hypothesize that extremely high rainfall is a good predictor because it is an indicator of storms having high wind speeds, as well as saturated soils that increase uprooting risk. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions over large areas, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers. Further insights could be gained by integrating these canopy observations with high-frequency measurements of wind speed and soil moisture in mechanistic models to better evaluate proximate drivers and with focal tree observations to quantify the links to tree mortality and woody turnover

High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology

The geometric features of agricultural trees such as canopy area, tree height and crown volume provide useful information about plantation status and crop production. However, these variables are mostly estimated after a time-consuming and hard field work and applying equations that treat the trees as geometric solids, which produce inconsistent results. As an alternative, this work presents an innovative procedure for computing the 3-dimensional geometric features of individual trees and tree-rows by applying two consecutive phases: 1) generation of Digital Surface Models with Unmanned Aerial Vehicle (UAV) technology and 2) use of object-based image analysis techniques. Our UAV-based procedure produced successful results both in single-tree and in tree-row plantations, reporting up to 97% accuracy on area quantification and minimal deviations compared to in-field estimations of tree heights and crown volumes. The maps generated could be used to understand the linkages between tree grown and field-related factors or to optimize crop management operations in the context of precision agriculture with relevant agro-environmental implications