Individual-tree- and stand-based development following natural disturbance in a heterogeneously structured forest: A LiDAR-based approach
Introduction
Forests are complex dynamic systems that contribute several crucial ecosystem services. Future forest management and conservation require the development of appropriate target objectives and the evaluation of different management strategies. The scientific knowledge needed for this includes a holistic description of forest status and development (Koch et al., 2009).
The structural composition of forests in particular has become an important factor in the analysis and management of forest ecosystems (Franklin et al., 2002, McElhinny et al., 2005, Pommerening and Stoyan, 2006). Forest structure is basically defined as “the physical and temporal distribution of trees in a forest stand” (Oliver and Larson, 1996) and is the result of natural processes as well as human intervention (Gadow et al., 2012). Forest structure is both a product and driver of ecosystem processes (Spies, 1998). Spatial structure is an important factor affecting forest stand dynamics, growth, and yield, and it also controls a range of forest functions, including soil protection and recreation (Pretzsch, 2009). In addition, other important ecosystem values, such as habitat and species diversity, are also related to spatial stand structure (Bergen et al., 2009, MacArthur and MacArthur, 1961, Pommerening, 2002, Vierling et al., 2008). Several studies have revealed that changes in the forest structure also affect species diversity (Lehnert et al., 2013, Müller et al., 2008) and composition (Bässler et al., 2010b, Müller et al., 2010).
Natural disturbance events, e.g., fires, windthrow, and insect outbreaks, are among the most crucial drivers that alter the structure of forest stands (Franklin et al., 2002, Turner, 2010, Swanson et al., 2011). Throughout the 20th century, the number of such disturbance events in Europe increased (Schelhaas et al., 2003, Seidl et al., 2014). These events are particularly important for forest evolution as they alter forest landscape and enable regeneration. However, natural forest development in post-disturbed areas in Central Europe is only insufficiently documented; almost all forest areas are influenced by human intervention and the available long-term documentations often do not cover total regeneration cycles (Fischer, 2003).
Conventional forest succession models usually treat early-seral stages simply as a period of re-organization and re-establishment (Donato et al., 2012). These models assume that regenerating trees on post-disturbed sites tend to initially form a homogeneous stand structure, and that structural heterogeneity evolves in the later developmental stages after a phase of self-thinning. The increasing competition among the trees culminates in density-dependent tree mortality across the areas commonly dominated by pioneer species. Gaps and dead wood change the microclimatic conditions and enable the development of complex forest structures, including understory vegetation (Bormann and Likens, 1979, Franklin et al., 2002, Oliver and Larson, 1996, Spies and Franklin, 1996). However, under certain initial conditions, post-disturbed forest sites might be spatially heterogeneous, similar to old-growth forests, even in early-seral stages (Donato et al., 2012). Because traditional management measures primarily place emphasis on fast timber production and the development of late-seral heterogeneity, the ecological importance of early-seral stages has been mostly underappreciated (Swanson et al., 2011). Simulations of such immediate forest recoveries should be captured by numerous factors. For example, it has been stated that the changed biogeochemical fluxes and their relationship with the vegetation are of high importance as they highly affect the long-term successional trajectories by altering species assemblages, biomass, leaf area index (LAI) recovery, chemical properties, stand and flow of water, biological legacies, soil carbon, and nutrient sources (Scheller and Swanson, 2015).
Traditionally, forest stand information is collected during expensive and time-consuming field surveys (Hyyppä et al., 2000), where tree locations and relevant structural attributes are often not measured. Nowadays, remote sensing is being applied more often. The available methods based on airborne, active and passive data represent powerful tools for efficiently measuring environmental variables over a large spatial extent, either for direct use or complementary to ground observations. The 3-D light detection and ranging (LiDAR) measurements have garnered a great deal of scientific and operational attention. LiDAR uses a narrow beam of visible or near-infrared light to measure the distance to a target object by measuring the elapsed time between the sent and returned laser signals (Wehr and Lohr, 1999) with discrete-return and waveform-recording devices (Lefsky et al., 2002). Because of the high sampling density of the point clouds generated by post-processing of waveform data, a portion of laser pulses in closed-canopy forests reflect off the crown surface and the remainder penetrate through the canopy to the ground. LiDAR data provide a direct 3-D representation of terrain, surface, and vegetation, in contrast to 2-D spectral information, which can only be used to indirectly infer vertical forest information. LiDAR data have been proven to accurately capture forest structural information, such as height, basal area, and mean diameter of stands (Coops et al., 2007, Holmgren and Jonsson, 2004, Latifi et al., 2012, Næsset, 2002). LiDAR has also been used for obtaining information on wood volume (Næsset, 2002, Latifi et al., 2010), overstory and understory vegetation cover (Latifi et al., 2016), above- and below-ground biomass (González-Ferreiro et al., 2012, Næsset, 2004), carbon stocks (Stephens et al., 2012), successional stages (Falkowski et al., 2009), and habitat characteristics (Goetz et al., 2010, Müller and Brandl, 2009, Latifi et al., 2016). Terrestrial LiDAR applications are complementary to airborne LiDAR platforms and have shown great potential for the investigation of forest understory structure. Several studies have already investigated the use of terrestrial LiDAR for detailed reconstruction of tree information (Dassot et al., 2011, Bayer et al., 2013, Liang et al., 2016). The application of LiDAR data in forest inventory has been comprehensively reviewed by Hyyppä et al. (2008), Latifi (2012), and Wulder et al. (2012), to which the reader is referred for further reading.
Individual-tree growth models offer the possibility of modeling forest growth by incorporating individual trees and their spatial arrangement in the prediction of post-disturbance stand development and by using different combinations of species assemblage and stand structures, management regimes, and regeneration methods (Pretzsch, 1997, Pretzsch, 2009). Therefore, such models provide higher flexibility and are particularly suitable to respond to new management goals (Pretzsch et al., 2002). The present study was designed to combine aerial remote sensing and growth modeling based on individual trees to study the development of horizontal and vertical structural complexities on post-disturbed forest sites. In this study, we captured the current spatial patterns of rejuvenation using LiDAR data of individual trees, simulated post-disturbance forest development using a forest growth simulator based on individual trees, and analyzed both the initial and simulated tree arrangements based on a set of calculated structural metrics. We also applied point pattern analysis in combination with the individual tree locations to examine and compare the spatial tree arrangement formed by various ecological processes.
Section snippets
Study area
The study area is located in the Bavarian Forest National Park (BFNP) in Germany. The park is part of the Bohemian Forest ecosystem, a low mountain range located in southeastern Germany and southwestern Czech Republic. Together with the adjacent Czech National Park of Šumava, the two parks form one of the most extensive and contiguous forest landscapes in Central Europe. The BFNP (approximately 13,500 ha) was established in 1970, and was expanded in 1997 to its current extent of 24,218 ha. The
Individual-tree information extraction
We summarized the results of the individual-tree extraction by calculating the 2-D distance between each pair of tree locations, which yielded seven distance classes ranging between 0 and 7 m (Table 2). The sample-based validation of the matching algorithm (Section 2.4) showed that the majority of trees extracted from the CHM were matched to the nearest trees in the image within a range of 0–1 m distance. The highest rate of such close matching was observed for site 2 (89.31%), and the values
Discussion
The forest sites analyzed in this study represent a near-natural mountain forest ecosystem. A major concern in such forests is their resistance and resilience to windthrow events and insect outbreaks. Changes in disturbance dynamics and climate conditions require an improved understanding of forest dynamics for the development of better and more flexible management practices. Therefore, it is crucial to gain a better understanding of both the impacts of such disturbances and the subsequent
Conclusions
Our results confirmed the usefulness of airborne LiDAR supported by CIR aerial imagery and field data to derive multi-scale forest structural attributes. Especially in natural primeval forests with insufficient accessibility, traditional approaches based on sample plots are often limited, and field surveys are expensive, laborious, and logistically difficult. LiDAR offers a relatively cost-effective and barrier-free means of measuring the horizontal and vertical forest structure across broad
Acknowledgements
The authors are grateful to the BFNP administration for providing the necessary remote sensing, photogrammetry, and field data, as well as for providing the first author with technical infrastructure and field material during his research stay in the BFNP. We thank Dr. Martin Isenburg (RapidLasso GmbH) for his support with LiDAR data processing and Dr. Peter Biber (Technische Universität München) for providing us with the SILVA 2.2. growth simulator.
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