TERRESTRIAL 3D MAPPING OF FORESTS: GEOREFERENCING CHALLENGES AND SENSORS COMPARISONS

Terrestrial 3D reconstruction is a research topic that has recently received significant attention in the forestry sector. This practice enables the acquisition of high-quality 3D data, which can be used not only to derive physical forest criteria such as tree positions and diameters, but also more detailed analyses related to ecological parameters such as habitat availability and biomass. However, several challenges must be addressed before fully integrating this technology into forestry practices. The primary challenge is accurately georeferencing surveyed 3D data acquired in the same location and placing them into a national projection reference system. Unfortunately, due to the forest canopy, the GNSS signal is often obstructed, and it cannot guarantee sub-meter accuracy. In this paper, we have implemented an indirect georeferencing methodology based on spheres with known coordinates placed at the forest’s edge where GNSS reception was more reliable and accurate than under the canopy. We evaluated its performance through three analyses that confirmed the validity of our approach. Indeed, the accuracy of the TLS point cloud, georeferenced using our method, is within a centimetre level (4.7 cm), whereas mobile scanning methods demonstrate accuracy within the decimetre range but still less than a metre. Additionally, we have initiated the analysis of a potential future application for mixed reality headsets, which could enable real-time acquisition and visualisation of 3D data

COMPARISON OF FOREST STRUCTURE METRICS DERIVED FROM UAV LIDAR AND ALS DATA

Point clouds derived from airborne laser scanning (ALS) and from LiDAR sensors mounted on unmanned aerial vehicles (ULS) reveal differences caused by the different sensor systems and acquisition geometries. These differences in the system characteristics are reflected in forest structure metrics that are derived from the respective point clouds. In our study, we investigate the completeness of scene coverage between the two systems and address differences between structure metrics derived from ULS and ALS, namely in point height quantiles, fractional cover (fc), the vertical complexity index (VCI) and the number of canopy layers (nLayers). The metrics are evaluated for raster cell sizes of 1–10 m in order to investigate the spatial scale on which the sensor systems provide comparable metrics. We found highest correspondences between ALS and ULS in the VCI- and the nLayers-metrics, while fc revealed large differences. For the height quantiles, the absolute differences were larger for the 10%- (h10) and the 50%- (h50) than for the 90%- (h90) height quantile. Furthermore, we found differences between ALS- and ULS-metrics to decrease for larger cell sizes, except for fc, for which the differences increased, and h50 and h90, respectively, for which the differences were relatively stable for all cell sizes

UAV-based LiDAR acquisition for the derivation of high-resolution forest and ground information

Laser scanning of forested areas helps in analyzing and understanding various aspects of forest conditions, including distribution of plants and trees, height distribution of trees, tree density, size and volume of wood, as well as ground surface properties. However, laser scanning of forest areas is also very challenging for many reasons. The best time for scanning is before trees leaf out in the spring or after trees cast their leaves in autumn before snowfall so an unmanned aerial vehicle (UAV) laser scanner can penetrate the forest from the tops of the trees down to the ground surface. To receive highly accurate laser data and high point density, the flight planning must be adjusted judiciously. Flight planning will be even more complex in steep terrain where the UAV cannot operate at a constant altitude. This paper discusses a UAV-based 3D laser data recording — LiDAR scanning — of a forestry area with high accuracy and point cloud resolution. In addition, the point cloud of airborne laser scanning (ALS) is compared with local terrestrial laser-scanning (TLS) results. The forest area consists of mixed forests containing varying tree sizes and branch deformation. This paper summarizes our latest results in UAVbased LiDAR acquisition over a forest area to extract detailed forest and ground information and finds that UAV-based laser scanning (UAV-LS) is well suited for provision of both high-quality forest structural and terrain elevation information

Modelling of three-dimensional, diurnal light extinction in two contrasting forests

The three-dimensional (3D) distribution of light within forest ecosystems is a major driver for species competition, coexistence, forest ecosystem functioning, productivity, and diversity. However, accurate knowledge about the 3D distribution of light within the canopy is difficult to obtain. Recent advances in 3D forest reconstruction as well as the use of radiative transfer modelling provide new insights into spatio-temporal variations of light distribution within a forest canopy. We used high resolution laser scanning data coupled with in-situ leaf optical properties (LOP) measurements to parameterize the DART radiative transfer model for a temperate deciduous forest on the Laegern mountain, Switzerland, and for a tropical rain forest located in the Lambir Hills national park, Borneo, Malaysia. Combining terrestrial and unmanned aerial vehicle (UAV) laser scanning acquisitions allowed a high detailed, 3D reconstruction of forest canopies. We analyse the impact of the two contrasting forest canopies, both in terms of structure as well as optical properties, on the 3D extinction of photosynthetic active radiation (PAR, 400 nm - 700 nm) for a whole diurnal cycle. We show that PAR extinction is mainly driven by the canopy structure, resulting in an exponential light extinction profile for the temperate and a more linear extinction profile in the tropical site. The larger 3D heterogeneity in canopy structure for the tropical site also resulted in larger variability in light extinction throughout the whole canopy. We found that contrasting LOPs between the two forests had a minor influence on light extinction. However, approximating light extinction profiles with layered Beer-Lambert or Big-Leaf models only poorly represented the 3D heterogeneity of light extinction within the canopy, illustrating the need for more detailed 3D modelling of light distribution within forest ecosystems. This can give us important insights into light-related mechanisms driving species coexistence, competition and diversity in complex forest ecosystems

Voxel based occlusion mapping and plant area index estimation from airborne laser scanning data

We introduce a ray-tracing based approach for mapping occluded areas in airborne laser scanning (ALS) data for a temperate mixed forest in Switzerland. Furthermore, the approach showed promising results towards a three-dimensional retrieval of plant area index (PAI) from ALS data

Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm

Accurate three-dimensional information on canopy structure contributes to better understanding of radiation fluxes within the canopy and the physiological processes associated with them. Small-footprint airborne laser scanning (ALS) data proved valuable for characterising the three-dimensional structure of forest canopies and the retrieval of biophysical parameters such as plant and leaf area index (PAI and LAI), fractional cover or canopy layering. Nevertheless, few studies analysed combined occluded and observed canopy elements in dense vegetation as a result of airborne laser scanning geometries. The occluded space contains a substantial amount of vegetation elements (i.e. leaf, needle and wood material), which are missing in the analysis of the three-dimensional canopy structure. Consequently, this will lead to erroneous retrieval of biophysical parameters. In this study, we introduce a voxel traversal algorithm to characterize ALS observation patterns inside a voxel grid. We analyse the dependence of occluded and unobserved canopy volume on pulse density, flight strip overlap and season of overflight in a temperate mixed forest. ALS measurements under leaf-on and leaf-off conditions were used. For cross-comparison purposes, terrestrial laser scanning (TLS) measurements on a 50×50 m2 subplot under leaf-on conditions were used. TLS acquisitions were able to depict the three-dimensional structure of the forest plot in high detail, ranging up to the top-most canopy layer. Our results at 1 m voxel size show that even with the highest average pulse density of 11 pulses/m2, at least 25% of the forest canopy volume remains occluded in the ALS acquisition under leaf-on conditions. Comparison with TLS acquisitions further showed that roughly 28% of the vegetation elements detected by the TLS acquisitions were not detected by the ALS system due to occlusion effects. By combining leaf-on and leaf-off acquisitions, we were able to recover roughly 7% of the occluded vegetation elements from the leaf-on acquisition. We find that larger flight strip overlap can significantly increase the amount of observed canopy volume due to the added observation angles and increased pulse density

Quantifying 3D structure and occlusion in dense tropical and temperate forests using close-range LiDAR

Terrestrial laser scanning (TLS) has emerged as a reference for three-dimensional measurements of forest structure as well as forest reconstruction and modeling. Ground-based measurements can be complemented by new light-weight sensors on unmanned aerial vehicles (UAVs) or laser scans from canopy cranes or towers. However, it is still largely unknown how much of the forest canopy volume can be sampled and how occlusion is spatially distributed. We present an approach for highly detailed 3D structure measurements based on TLS on the ground and above canopy measurements from a canopy crane or UAV platform, and assess their spatial sampling in terms of occlusion. Comparing the application in a dense tropical and temperate forest, we demonstrate the ability to sample the complete canopy volume with <2% occlusion at very high spatial resolution when combining ground and above canopy measurements. This is necessary for a full canopy reconstruction. Ground-based TLS can provide sufficient coverage when no sampling of leaves and branches at top of canopy is required, whereas UAV or tower-based measurements show considerable occlusion in the mid- and understory. We therefore recommend to perform above canopy measurements under leaf off conditions, in sparse forests, or as an addition to ground measurements if a full representation of the whole canopy is required at very high spatial resolution. The latter can pave the way for studies on light availability, micrometeorology, sensor simulations and algorithm testing and development

Close-range laser scanning in forests: towards physically based semantics across scales

Laser scanning with its unique measurement concept holds the potential to revolutionize the way we assess and quantify three-dimensional vegetation structure. Modern laser systems used at close range, be it on terrestrial, mobile or unmanned aerial platforms, provide dense and accurate three-dimensional data whose information just waits to be harvested. However, the transformation of such data to information is not as straightforward as for airborne and space-borne approaches, where typically empirical models are built using ground truth of target variables. Simpler variables, such as diameter at breast height, can be readily derived and validated. More complex variables, e.g. leaf area index, need a thorough understanding and consideration of the physical particularities of the measurement process and semantic labelling of the point cloud. Quantified structural models provide a framework for such labelling by deriving stem and branch architecture, a basis for many of the more complex structural variables. The physical information of the laser scanning process is still underused and we show how it could play a vital role in conjunction with three-dimensional radiative transfer models to shape the information retrieval methods of the future. Using such a combined forward and physically based approach will make methods robust and transferable. In addition, it avoids replacing observer bias from field inventories with instrument bias from different laser instruments. Still, an intensive dialogue with the users of the derived information is mandatory to potentially re design structural concepts and variables so that they profit most of the rich data that close range laser scanning provides

Mapping the irradiance field of a single tree: quantifying vegetation-induced adjacency effects

Imaging spectroscopy is frequently used to assess traits and functioning of vegetated ecosystems. Applied reflectance- and radiance-based approaches critically rely on accurate estimates of surface irradiance. Accurate retrievals of surface irradiance are, however, nontrivial and often error-prone, thus causing inaccurate estimates of vegetation information. We analyze the irradiance field surrounding an isolated tree using the 3-D radiative transfer model DART in high spatial (25 cm) and spectral (1 nm, 350-2500 nm) resolution. We validate modeled irradiance with in situ measurements and quantify the impact of erroneous surface irradiance estimates on the retrieval of vegetation indices. We observe the irradiance gradients in the cast shadows of <;560% in the blue spectral range, while this gradient decreases with increasing wavelength and becomes negligible in the near infrared (NIR). Furthermore, we quantify a vegetation-induced decrease in the irradiance of <;6% in the visible spectral region and an increase of <;7% in the NIR outside the cast shadow. Commonly employed vegetation indices are also affected by such brightening or darkening effects. Outside the cast shadow, indices sensitive to the relative content of chlorophyll (CHL) and carotenoids (CAR) show an overestimation of <;14%. The photochemical reflectance index shows an underestimation of <;5%. This paper provides first quantitative insight in high spatial and spectral resolution, on the impact of vegetation on its surrounding irradiance field. Findings highlight important implications for vegetation assessments and provide the fundamental base to advance retrievals of vegetation traits and functioning from imaging spectroscopy data