Purpose of Review The 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 Findings Our 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. Summary We 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.

Data from the Geoscience Laser Altimeter System (GLAS) aboard the Ice Cloud and land Elevation Satellite (ICESat) offer an unprecedented opportunity for canopy height retrieval at a regional to global scale. The data also provide useful information for forest stand level assessment at coincident locations. In this study height indices from light detection and ranging (LiDAR) waveforms were explored as a means of extracting canopy height; these were examined with reference to a mixed temperate forest in Gloucestershire, UK, containing planted stands with a mean age of 51 years and mean maximum height of 26.6 m. A method based on using a terrain index (TI; maximum minus minimum elevations from a 7×7 subset 10‐m resolution digital terrain model (DTM)) to adjust the waveform extent (WE; signal begin minus signal end) produced an R ² value of 0.89 when regressed against field measurements of maximum canopy height at footprint locations (field height = 0.91(WE−TI)+4.86; root mean squared error (RMSE) = 2.99 m, coefficient significance p0.01). Multiple regression performed on both WE and TI with field measurements produced an R ² of 0.90 and an RMSE of 2.86 m (field height = 1.0208WE−0.7310TI; coefficient significance p<0.001, intercept not significant). Maximum canopy height estimates using an automated approach to ground return identification based on iterative fitting of Gaussian peaks (GP1_2_MAXAMP) to the waveform explained 74% of variance when compared to field measurements (field height = 1.05(GP1_2_MAXAMP); RMSE = 4.53 m, coefficient significance p<0.001, intercept not significant). The ability of satellite LiDAR to retrieve data for such a complex and diverse area further indicates the potential of this technique for both carbon accounting and forest management.

We present new coarse resolution (0.5° × 0.5°) vegetation height and vegetation-cover fraction data sets between 60° S and 60° N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals for each 0.5° × 0.5°. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from r = 0.33 to r = 0.78, decreases the root-mean-square error by a factor 3 to about 6 m (RMSE) or 4.5 m (68% error distribution) and decreases the bias from 5.7 m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6 m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (r = 0.67); the GLAS tree-cover fraction is compared with the MODIS tree-cover fraction (r = 0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface.

We present a method and results for a model of the interaction of waveform Light Detection And Ranging (LiDAR) with a three-dimensional vegetation canopy. The model is developed from the FLIGHT radiative transfer model based on Monte Carlo simulations of photon transport. Foliage is represented by structural properties of leaf area, leaf-angle distribution, crown dimensions and fractional cover, and the optical properties of leaves, branch, shoot and ground components. The model represents multiple scattering of light within the canopy and with the ground surface, simulates the return signal efficiently at multiple wavebands and includes the effects of topography. LiDAR-emitted pulse and spatial and temporal sampling characteristics of the instrument are explicitly modelled. Agreement is found between the integrated waveform energy and directly derived bidirectional reflectance factors from FLIGHT (root mean square error < 0.01), and between simulated and observed Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) waveforms for a complex forest site. A sensitivity analysis gives expected effects of canopy parameters on the waveform, and indicates potential for retrieval of the canopy properties of fractional cover and leaf area, in addition to height. Where canopy and ground pulses can be separated, an index derived from the waveform shows theoretical retrieval of vertically projected plant area index with correlation coefficient R ² = 0.87.