Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Insights into tree morphology and canopy space occupation under the influence of local neighbourhood interactions in mature temperate forests using laser scanning technology

Mounting evidence suggests that tree species richness promotes ecosystem functioning in forests. However, the mechanisms driving positive biodiversity ecosystem functioning relationships remain largely unclear. This also holds for the previously proposed key mechanisms of resource partitioning in canopy space. Until recently, surveying and hence the study of crown space was very time-consuming and the images low resolution. The application of high-resolution laser scanning, however, now enables a fast and precise recording of entire forests. This thesis presents how the abandonment of management strongly alters the individual tree structure from the wood distribution along the trunk to the crown, a tree species-rich neighbourhood can increase the wood volume and crown dimension of individual trees as well as the productivity of large-sized trees, mobile laser scanning in forests is suitable for the acquisition of high-quality point clouds and determination of relevant management parameters, and the direction and strength of the relationship between tree species richness and canopy occupation depends on the definition of both canopy and species richness. These results reinforce the influence of species richness on ecosystem functions in oldgrowth forests and underline the importance of laser scanning for forest ecology research. The findings of the comparative analyses further highlight the importance of underlying definitions for the results obtained

SENSITIVITY OF LIDAR DERIVED FUEL CELLS TO FIRE MODELING AT LABORATORY SCALE

Computational models of wildfires are an important tool for fire managers and scientists. However, fuel inputs to wildfire models can be difficult to represent with sufficient detail to be both computationally efficient and representative of observations. Recent advances in fuel mapping with airborne and terrestrial laser scanning (LIDAR) techniques present new opportunities to capture variation in fuels within a tree canopy and on a landscape. In this paper, we develop a technique for building 3D representations of vegetation from point clouds created by Terrestrial Laser Scans (TLS). Our voxel based approach can be extended to represent heterogeneous crown fuels as collections of fuel cells in modern 3D Computational Fluid Dynamics wildfire models such as FDS, QUIC-Fire, or FIRETEC. We evaluated the effectiveness of our technique at different fuel cell resolutions by using the DAKOTA optimization toolkit to compare simulated fire behavior in FDS with observed burn data collected during a series of experiments at the Missoula Fire Sciences Laboratory. The primary finding was that within the search space of point cloud derived fuel cells, we find accurate descriptions of observed fire behavior with the FDS model. We also find that within our search space, regions of global minima are consistent across fuel cells at different resolutions. This finding suggests that while new techniques are capable of characterizing fuel models with a high degree of fidelity, high resolution 3D fuel models do not improve parity with observed fire behavior in the FDS fire model. The results of this paper offer fire modelers guidelines for translating LIDAR data to 3D fire models, and what fuel cell resolution can best capture accurate fire behavior

Three-dimensional mapping reveals scale-dependent dynamics in biogenic reef habitat structure

Habitat structure influences a broad range of ecological interactions and ecosystem functions across biomes. To understand and effectively manage dynamic ecosystems, we need detailed information about habitat properties and how they vary across spatial and temporal scales. Measuring and monitoring variation in three-dimensional (3D) habitat structure has traditionally been challenging, despite recognition of its importance to ecological processes. Modern 3D mapping technologies present opportunities to characterize spatial and temporal variation in habitat structure at a range of ecologically relevant scales. Biogenic reefs are structurally complex and dynamic habitats, in which structure has a pivotal influence on ecosystem biodiversity, function and resilience. For the first time, we characterized spatial and temporal dynamics in the 3D structure of intertidal Sabellaria alveolata biogenic reef across scales. We used drone-derived structure-from-motion photogrammetry and terrestrial laser scanning to characterize reef structural variation at mm-to-cm resolutions at a habitat scale (~35 000 m2) over 1 year, and at a plot scale (2500 m2) over 5 years (2014–2019, 6-month intervals). We found that most of the variation in reef emergence above the substrate, accretion rate and erosion rate was explained by a combination of systematic trends with shore height and positive spatial autocorrelation up to the scale of colonies (1.5 m) or small patches (up to 4 m). We identified previously undocumented temporal patterns in intertidal S. alveolata reef accretion and erosion, specifically groups of rapidly accreting, short-lived colonies and slow-accreting, long-lived colonies. We showed that these highly dynamic colony-scale structural changes compensate for each other, resulting in seemingly stable reef habitat structure over larger spatial and temporal scales. These patterns could only be detected with the use of modern 3D mapping technologies, demonstrating their potential to enhance our understanding of ecosystem dynamics across scales

Mapping tree carbon with airborne remote sensing

Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales.We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density.We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect.An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.We thank Dr L. Frizzera for help with field-data collection. ALS data acquisition was supported by the European Commission (Alpine Space 2-3-2-FR NEWFOR). MD was supported by Trees4Future (European Union FP7 284181) and a NERC grant NE/K016377/1. DAC was also supported by a grant from BBSRC and DEFRA to study ash dieback.This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1111/2041-210X.1257