Search

Highlights • Landsat greenness and brightness were correlated with canopy structure complexity. • NDVI could estimate six metrics of structural complexity from terrestrial LiDAR. • This improves the spatial understanding of 3D forest structure in Landsat metrics. • Coupling LiDAR and Landsat could lead to improved ecosystem function predictions. Abstract Vegetation metrics derived from satellite imagery provide continuous and large spatial-scale measurements that are critical for interpreting and predicting ecosystem function. However, uncertainty still remains as to the precise structural information that could be estimated from these metrics. Landsat-derived metrics provide pixel measurements of vegetation across the landscape, whereas Light Detection and Ranging (LiDAR) provides multidimensional data on the vertical arrangement of forests. Terrestrial LiDAR metrics of structural complexity describe the arrangement of vegetation in the canopy, and could be coupled with Landsat-derived metrics through their influence on energy and light. Linking Landsat to terrestrial LiDAR estimates of canopy structure could expand the interpretation of Landsat-derived metrics and broaden the spatial scale at which structural complexity can be evaluated. Here, we examined associations between Landsat-derived metrics and terrestrial LiDAR measurements of structural complexity. Structural complexity measurements were obtained with terrestrial LiDAR from plots within eight forested NEON sites across eastern North America. Vegetation metrics (NDVI, EVI, tasseled cap metrics) were calculated for corresponding locations from Landsat 8 satellite imagery. Results showed that canopy reflectance, greenness and brightness, were linked with several measures of canopy structure. Higher levels of greenness were associated with stands having a taller canopy, greater leaf area density and variability, and a less open and porous canopy. Among greenness metrics, NDVI was most strongly correlated with structural complexity metrics (adj. R2 = 0.52 – 0.62 for six metrics). Additionally, we found that a brighter canopy was associated with greater leaf area density and variability, canopy cover, porosity, and lower leaf clumping. Our results demonstrated the potential for large-spatial extent estimates of structural complexity using satellite imagery, and may lead to improved predictions of forest ecosystem functioning such as those predicted in “big leaf” ecosystem models. [ABSTRACT FROM AUTHOR]

Since biodiversity often increases ecosystem functioning, changes in tree species diversity could substantially influence terrestrial carbon cycling. Yet much less is known about the relationships between forest structural diversity (i.e., the number and physical arrangement of vegetation elements in a forest) and carbon cycling, and the factors that mediate these relationships. We capitalize on spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) and on-the-ground forest inventory and analysis (FIA) data from 1796 plots across the contiguous United States to assess relationships among the structural and species diversity of live trees and aboveground carbon storage. We found that carbon storage was more strongly correlated with structural diversity than with species diversity, for both forest inventory-based metrics of structural diversity (e.g., height and DBH diversity) and GEDI-based canopy metrics (i.e., foliage height diversity (FHD)). However, the strength of diversity‑carbon storage relationships was mediated by forest origin and forest types. For both plot-based and GEDI-based metrics, the relationship between structural diversity (i.e., height diversity, DBH diversity, and FHD) and carbon storage was positive in natural forests for all forest types (broadleaf, mixed, conifer). For planted forests, structural diversity showed positive relationships in planted conifer forests but not in planted mixed forests. Species diversity did not show strong associations with carbon storage in natural forests but showed a positive relationship in mixed coniferous-broadleaf planted forests. Although plot-based structural diversity metrics refine our understanding of drivers of forest carbon balances at the plot scale, remotely sensed metrics such as those from GEDI can help extend that understanding to regional/national scales in a spatially continuous manner. Carbon storage showed stronger associations with plot-based structural diversity than with stand age, soil variables, or climate variables. Incorporating structural diversity into management and restoration strategies could help guide efforts to increase carbon storage and mitigate climate change as nature-based solutions. • Structural explained carbon storage better than did species diversity. • Structural diversity outperformed stand age, soil, and climate variables. • Influences of diversity on carbon storage vary across natural and planted forests. • Plot-based structural diversity showed stronger correlations than GEDI-based metrics. • GEDI-based metrics of forest structure will help enable large scale carbon mapping. [ABSTRACT FROM AUTHOR]

• Structural and composition change following stem-girdling varied by disturbance severity. • Removal of large trees affected soil temperature, soil water content, and canopy light absorption. • Removal of small to medium trees primarily affected only soil temperature. Forest structural diversity and community composition are key in regulating forest microclimates. When disturbance affects structural diversity or composition, forest microclimates may be altered due to changes in soil temperature, soil water content, and light availability. It is unclear however which structural or compositional components, when changed or to what extent, result in microclimatic change. To address this question, we used data from a large scale, manipulative stem-girdling experiment in northern, lower Michigan—the Forest Resilience and Threshold Experiment (FoRTE). FoRTE follows a factorial design with multiple levels of disturbance severity (0, 45, 65, 85%) based on targeted reductions in gross leaf area index via stem-girdling induced mortality. These disturbance severity treatments are applied in two ways: either as top-down (largest trees are killed) or bottom-up (small to medium trees killed) treatments. We examined how multiple components of structural diversity and community composition changed as a product of disturbance severity and type, and then tested for resulting effects on forest microclimates (light availability, soil temperature, and soil water), using a multivariate, Random Forest framework. We found that measures of community composition (species richness, species evenness, and Shannon-Wiener Diversity Index) and stand structure (basal area, standard deviation of DBH, tree size diversity) declined more following disturbance than did measures of canopy cover, heterogeneity, arrangement, or height. However, when changes in each variable from pre- to post-disturbance, measured as log change, were employed in a multivariate, Random Forest regression framework, structural diversity measures of heterogeneity (rugosity, top rugosity), cover (canopy cover), and arrangement (porosity) were the most influential variables, but with differences among bottom-up and top-down treatments We found that the death of large trees from disturbance impacts soil temperature, water, and light environments more substantially and uniformly across disturbance gradients than does the death of smaller trees. Our results have implications for both statistical and process-based modeling of forest disturbance. [ABSTRACT FROM AUTHOR]

• Remotely sensed data on forest structure are critical for wildlife conservation. • Light Detection and Ranging (LiDAR) holds promise for predicting wildlife habitat. • LiDAR predicted occupancy for Golden-winged Warblers better than field data. • LiDAR data can describe the distribution of habitat for forest-dependent wildlife. Remotely-sensed data can inform conservation efforts that target forest wildlife, however, few spatial data products are able to quantify fine-scale aspects of structural variation within forests. Increased availability of Light Detection and Ranging (LiDAR) datasets that cover broad spatial extents and ownership types (e.g., entire states) provide useful information regarding canopy and understory structure within forested landscapes. The fusion of LiDAR data with field-based species surveys can advance our understanding of species-habitat relationships and improve the effectiveness of conservation programs to recover habitat-limited species. The Golden-winged Warbler (Vermivora chrysoptera) is a forest-dependent songbird that nests in structurally-complex young forest across eastern North America. As with many early-successional obligates, this species has been declining for decades due, in part, to the steady loss of young forest/shrubland nesting habitat. Although conservation programs have begun restoring Golden-winged Warbler habitat, these efforts are currently limited by the inability to identify existing habitat across large spatial extents and diverse ownership patterns. Recent availability of state-wide LiDAR data for Pennsylvania provides an opportunity to overcome this limitation. From 2019 to 20, we surveyed for Golden-winged Warblers and structural vegetation at 837 sites across six forest blocks in eastern Pennsylvania. We combined these data with LiDAR derived forest structural metrics to develop statistical models to predict patterns of occupancy. Golden-winged Warbler occupancy probability was explained by models containing several LiDAR-derived structural metrics (e.g., percentage of first returns between 1 and 5 m in height, structural complexity, etc.). Moreover, models fit with LiDAR-derived covariates predicted occupancy much better than those using only field-measured vegetation covariates (ΔAIC c = 53.27). Mapped predictions of Golden-winged Warbler occupancy revealed potential habitat (especially regenerating timber harvests) on both private and public lands. These results demonstrate the efficacy of LiDAR for modeling forest bird habitat associations, and how such data sources can provide a valuable tool for conservation planning. [ABSTRACT FROM AUTHOR]

• Honeysuckle removal caused immediate declines in canopy leaf area and changes in spatial leaf distribution that resulted in increased canopy structural complexity that continued to accrue more than a decade after removal. • The long-term structural changes were dependent on initial site conditions prior to disturbance, whereby increased structural complexity only occurred in areas with initially large honeysuckle abundance and low native tree density. • Visual assessments of honeysuckle cover may be used to rapidly quantify areas that would benefit most from honeysuckle removal due to the strong correlation with native tree canopy structure, while honeysuckle stem count and basal area are poor candidates, due to a lack of correlation with canopy structural metrics and the time consuming and labor-intensive nature of collecting this data. • In application, our results suggest that honeysuckle removal has the potential to increase the long-term canopy structural complexity and increase understory openness; but initial stand conditions should be considered during forest management aimed at restoring structural complexity of forests. Riparian forests across the continental United States are heavily invaded by Amur honeysuckle (Lonicera maackii (Rupr.)), an invasive shrub which suppresses native plants, homogenizes community structure and composition, and alters ecosystem processes. However, no studies have quantified the impacts of honeysuckle removal on forest canopy structure across the first decade of restoration. In this study we used a portable canopy LiDAR (PCL) to characterize the immediate (<1 year), short-term (1–2 years) and long-term (>10 years) impacts of honeysuckle removal on the horizontal and vertical complexity of canopy structure in 5 heavily invaded riparian forests in Ohio. Within two years of removal, forest canopies had a 40% reduction in canopy leaf volume, a greater average height of maximum canopy density, and increased aggregation of the remaining leaf area around trees than pre-removal conditions. Honeysuckle removal also prompted long-term (>10 years) increases in canopy structural complexity, but only in areas with initially high honeysuckle abundance and low native tree density. Honeysuckle cover had a much stronger influence on canopy structure than either its basal area or stem density. Our results suggest that removing honeysuckle from heavily-invaded stands can promote complex canopy structure over the long-term that is beyond the short-term accrual immediately following disturbance, but might depend on initial stand conditions. [ABSTRACT FROM AUTHOR]