Photographic RS methods for capturing information about the environment, and then taking measurements from the images, has been used for many years (Evans and Coombe 1959). In recent times a comparatively cost effective RS methodology has developed with the capture of aerial imagery from high-quality, off-the-shelf digital cameras and readily accessible aerial platforms such as unmanned aerial vehicles (UAVs, also referred to as ‘drones’ particularly in nonprofessional terms). Leberl, Irschara et al. (2010) identify that from the development of the use of LiDAR based point clouds; there has been extensive sector wide discussion as to the efficacy, throughput and cost effectiveness of the use of photogrammetry, for vegetation investigations. However, Leberl, Irschara et al. (2010) also highlight that for “street- side” investigations, photogrammetric methodologies retain some advantages over LiDAR based approaches. Westoby, Brasington et al. (2012) describe modern photogrammetry techniques as providing the ability to capture high-resolution datasets from cheap, portable surveying platforms and the use of ground control points to enable 3D scene reconstruction, particularly advantageous for providing access to otherwise inaccessible or remote field sites. Furthermore, Westoby, Brasington et al. (2012) also state that there is great potential for photogrammetry methods to be used in many geoscientific or earth observation applications, in areas
with complex topography, and using image capture techniques that utilise multiple overlapping photographs. In terms of mathematical-geometric analysis, the content of an image is considered an idealised model, not a true representation of reality. This is due to unavoidable lens, camera and photographic errors that arise when using photography and photogrammetry equipment; therefore, these must be accounted for in order to enable the highest levels of accuracy to be achieved (Kraus 2011).
Liang, Jaakkola et al. (2014) identify that the use of un-calibrated hand-held digital cameras for individual tree investigations, can provide highly accurate photogrammetry derived data that is comparable to scanning individual trees with TLS. In addition, Liang, Jaakkola et al. (2014) report an 88% mapping accuracy (commission score) for image based point clouds of trees, when compared to GR tree maps. Similarly, for the comparison of leaf area index (LAI) assessment Lovell, Jupp et al. (2003) identified that there was a high commission rate between the modelled LAI from TLS acquired data, and the LAI values that were calculated directly from hemispherical imagery assessments, thereby suggesting that the use of hemispherical methods can be considered as accurate as laser quantified measurements.
2.6.1
Proximal Hemispheric Imagery
Not all photogrammetric investigations are conducted from an aerial perspective at distnace. Proximal RS with digital photography is a widely accepted “indirect optical” method for assessing and quantifying tree crown characteristics (Chianucci, Chiavetta et al. 2014, Chianucci 2016), particularly due to the ease and realtively low costs of which appropriate equipment such as digital single-lens reflex (dSLR) cameras, can be obtained. In order to reduce complications of technical problems and erroneous data collection for investigations as LAI, Jonckheere, Fleck et al. (2004) describe that the use of hemispherical imagery is a preferred solution. However, Jonckheere, Fleck
et al. (2004) also recognise that there needs to be improvements in the technique in order for hemispherical imagery to be used as the preferred tool for this type of investigation. Nonetheless, Jennings, Brown et al. (1999) state that the way to achieve the most thorough measure of the extent of canopy closure is be taking a photograph at a specified measurement point beneath the crown with a 180° fisheye lens adapted camera. The resultant image is to be thresholded to distinguish between tree crown structure and the sky. Furthermore, at the time of writing, Jennings, Brown et al. (1999) state that hemispherical imagery is the “most accurate method of estimating canopy closure” when operational issues such as adverse lighting conditions are overcome.
Chianucci and Cutini (2012), describe that hemispherical images from proximal photogrammetry are maps of canopy openings or closures depending on the requirements of the study, and provide rich insights into the assessment of heterogeneity within tree crowns. Chianucci and Cutini (2012) also state that proximal photogrammetry using a zenith angle range of 0°–15°, provides ideal oppertunities for the “management and monitoring” for tree canopies, particularly in applications of repeated routine canopy parameter assessment in tree inventories. Sanchez-Gonzalez, Cabrera et al. (2016) also used proximal imagery techniques to undertake data capture for forest inventories. Basal area, mean tree count and mean diameter were all assessed using hemispherical imagery, where structural parameters of the tree crowns were extracted from stereoscopic images.
2.6.2
Landscape Scale Photogrammetric Investigations
To improve the alignment of aerial imagery during photogrammetric investigations of forested landscapes is a difficult task due to the poor match of the spatial
distribution of grey-scale pixels in aerial images and the spatial distribution of the point cloud achieved with ALS LiDAR data. In the approach described by Lee, Biging et al. (2016) tree tops are extracted through image processing and an “extended- maxima transformation” with LiDAR data. This methodology was required due to the limited spatial information that is available from the aerial image, however, with the addition of the LIDAR data, enabled an improvement in accuracy and allowed the alignment of aerial images to single-tree level. Lee, Biging et al. (2016) believe that this combined approach can enable the use of aerial images to be used in fine resolution change detection investigations and enable the extraction of biophysical properties of trees due to the enhancement with LiDAR. Gobakken, Bollandsas et al. (2015) used a comparative method to investigate tree structure with both aerial imagery and small footprint LiDAR, by extracting a series of biophysical properties of forest trees, including: canopy height and density. The study showed that although forest tree structure can be assessed using an aerial photogrammetric approach, that the best overall results were actually obtained via LiDAR. Interestingly however, photogrammetric analysis performed better than the LiDAR when assessing sparse crowns and smaller or younger trees from within the wider dataset (Gobakken, Bollandsas et al. 2015).