Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Abstract

ABSTRACT:In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground-and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75[m 2 ] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45°were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55[m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14 − 42[mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9[ha] test area, 102 individual trees above 7[cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under-or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50[cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

Inventory of Small Forest Areas Using an Unmanned Aerial System

“…The first examples of applying advanced UAS-SfM methods are represented by Wang et al [23] and Gini et al [24], who described the systems used, the data acquisition process, and the algorithms implemented. Fritz et al [25] used oblique UAS imagery in SfM pipelines to reconstruct tree stems in open stands. A first attempt to model forest biophysical properties by means of UAS-SfM data was presented by Dandois and Ellis [9], who used a hobbyist-grade UAS integrated with a digital camera for multi-temporal 3D and spectral data acquisition over a deciduous forest.…”

Estimating tree heights with images from an unmanned aerial vehicle

“…Forestry work with UAV platforms started in the 2000s with model planes and helicopters (Hongoh et al 2001;Hunt et al 2005;Fritz et al 2013). Recently they have been used with consumer-grade cameras and low-cost systems in order to make surveys more efficient.…”

“…Several methodologies like structure-from-notion (SfM) and multiviewstereo must be performed to correct for these issues (Strecha et al 2008;K€ ung et al 2011;Remondino et al 2011;James & Robson 2012;Vallet et al 2012;Fritz et al 2013;Zarco-Tejada et al 2014). Traditionally, airborne photogrammetric acquisition of images has been used to obtain canopy heights (Waser et al 2008;Wallerman et al 2012) with on-board digital mapping cameras.…”

Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour

“…The detection rate evaluated and illustrated the decrease of matching between the two point clouds, in regard to increasing height, forming a linear behaviour. In their work, Fritz et al (2013) used a similar approach, except they used an unmanned aerial vehicle platform instead, mounted with an optical sensor instead of a hand-held camera and compared that data with TLS (FARO ® Focus3D S120 laser scanner). They came to the same conclusion, namely with the decrease of point matching with increasing height.…”