Forest Structural Heterogeneity Research Articles

Analysing far-red SIF directional anisotropy of three structurally contrasting forest canopies towards improved GPP estimation

Solar-induced chlorophyll fluorescence (SIF) is used to estimate terrestrial gross primary production (GPP) due to its physiological link to photosynthesis. However, strong angular variations in satellite-observed SIF (SIFobs), especially for forest canopies with a high structural heterogeneity, hampers robust estimation of GPP. Here, we use directional SIFobs datasets from OCO-2 and TROPOMI satellite sensors and the 3D discrete anisotropic radiative transfer model to investigate the directional properties of far-red SIFobs and their relationships with two GPP products obtained from MODIS and FluxCom datasets for three European forest types with contrasting canopy structures, i.e., boreal, temperate and Mediterranean forests. We found bowl-like angular distribution patterns of SIFobs in both observed and simulated boreal and temperate forests, but no generic distribution pattern of SIFobs was found for the Mediterranean forest. In our GPP estimation of boreal forest, oblique SIFobs performed better than the widely used nadir SIFobs. However, we did not find an optimal view angle suitable for all three forest types. Further, the angular dependencies and forest structure impacts present in SIFobs were found to still propagate into the estimation of total emitted SIF (SIFtotal), which generally regarded as free of these confounding effects. Finally, our results demonstrate that two commonly used variables, i.e., SIFobs with a constant viewing angle and the approximated SIFtotal, both fail to produce a robust relationship with GPP for the Mediterranean forest. These findings highlight the importance of forest canopy structural heterogeneity, including forest floor, on interpretation of directional satellite SIF observations, even when corrected and expressed as a total canopy SIF emission.

Changes in canopy cover and forest structure following dormant season and early growing season prescribed burns in the Southern Appalachians, USA

BackgroundFire seasonality is important for forest managers to consider when restoring historical disturbance regimes and recovering native ecosystem structure and composition, but it is less understood and less frequently studied than other aspects of fire ecology. In the Southern Appalachians, historical fires likely occurred most often in late spring and early summer when fuels were dry and canopy conditions were conducive to fire; however, most prescribed fires today occur during the dormant season (January–March). Because fire behavior can vary seasonally, it is important for forest managers to understand the practical applications of fire season in order to burn at a time that meets management objectives. Therefore, we investigated the effect of fire seasonality on forest structure and land cover diversity in the Southern Appalachians.ResultsUsing a complete randomized block design, we analyzed leaf-on canopy cover imagery with ArcGIS Pro to compare canopy cover and forest structure between growing and dormant season burns. We compared imagery between three blocks, each with an unburned control unit, dormant season burn, and growing season burn, and found an average of 8.84% (SE = ± 1.46) reduction in canopy cover in growing season treatment units from pre-burn (2017) to post-burn (2019) compared to 5.21% (SE = ± 1.51) reduction in dormant season treatment units and 0.01% (SE = ± 0.009) reduction in unburned controls. Canopy cover reductions corresponded with substantial increases in early- and mid-successional habitat, edge length, and land cover diversity — especially in growing season burn treatment units.ConclusionsOur results indicate that early growing season burns are more effective than dormant season burns at enhancing forest structural heterogeneity. Early growing season burns, therefore, may be a viable option for forest managers looking to expand their burn season and achieve restoration and management goals faster than traditional dormant season burns.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Do different indices of forest structural heterogeneity yield consistent results?

Forest management with a focus on high structural heterogeneity is a major goal in modern forestry to increase multifunctionality. The assessment and quantification of forest structures has, therefore, gained much attention in recent years. However, there is no standardized approach to surveying forest heterogeneity; instead, a variety of structural indices, which have been developed over past decades, are used. This makes it difficult to interpret the results of different studies and to base management decisions on such data. In this study, we compared six structural indices that differ in terms of their complexity and the method of data acquisition. These included the Gini coefficient of the diameter at breast height and of tree height, the Shannon index of tree species diversity, two complex indices of structural heterogeneity, one based on conventional inventory data and one on terrestrial laser scanning (TLS) data, and a simple-holistic TLS-based stand structural complexity index. For the comparison of these six indices, we used data from 84 plots in 12 different forest stand types in two study areas in Germany. The stand types consisted of different dominant tree species and included different age classes. The degree of correlations among the different indices was highly variable. In addition, we did not find a clear age-dependency of the indices. We conclude that the choice of a specific index plays an important role in the evaluation and interpretation of forest structural heterogeneity. Because TLS data offer multiple benefits in terms of precision, reproducibility and comprehensiveness, we recommend to use TLS-based indices of structural heterogeneity.

A novel algorithm of individual tree crowns segmentation considering three-dimensional canopy attributes using UAV oblique photos

Extracting the individual tree crowns (ITCs) information is significant to fine forest resource investigation and carbon storage estimation. UAV oblique photogrammetry (UOP) can obtain point cloud data with high density due to higher presence of overlap, which has potential for tree crown information extraction. Many ITCs segmentation methods have been proposed to extract individual tree information. However, accurate ITCs segmentation using UOP data remains a challenge due to the uncertainties in environments with high heterogeneity of forest canopy vertical structure. Here, we proposed a novel approach, adaptive-kernel bandwidth mean-shift algorithm (AMS) considering three-dimensional canopy attributes, to segment ITCs using UOP data in complex forest environment. First, we developed a kernel bandwidth model with automatic adaptive parameter assignment using tree height derived from UOP data and applied it to the mean-shift algorithm. We demonstrated the generality of our algorithm in different tree species plots of a subtropical forest in China with overall precision f ≥ 0.72 and crown width rRMSE ≤ 0.13. Compared with the fixed-kernel bandwidth mean-shift algorithm (FMS) and seed region growth algorithm (SRG), the average f and rRMSE of the AMS algorithm were improved by 0.04 and 0.12, 0.16 and 0.11 respectively. Then, we evaluated the segmentation effect of AMS algorithm with point cloud densities of 25%, 50%, 75% and 100% respectively. We found that the segmentation accuracy decreases with decreasing point cloud density, but 75% of the point cloud density can satisfy most ITCs segmentation needs. In addition, we used LiDAR data (∼45 pts/m2) obtained by UAV to validate generalization ability of our approach, and the average r, p and f reached 0.97, 0.73 and 0.83. These results showed that the AMS algorithm can solve the ITCs segmentation problem in forests with complex structures using UOP and LiDAR point cloud data, which can support the accurate survey and scientific management of forest resources and provide basic data for the accurate estimation of forest carbon sinks.

Canopy Temperature Is Regulated by Ecosystem Structural Traits and Captures the Ecohydrologic Dynamics of a Semiarid Mixed Conifer Forest Site

AbstractPlant canopy temperature (Tc) is partly regulated by evaporation and transpiration from the canopy surface and can be used to infer changes in stomatal regulation and vegetation water stress. In this study, we used a thermal Unmanned Aircraft Systems in conjunction with eddy covariance, sap flow, and spectral reflectance data to assess the diurnal characteristics of Tc and water stress status over a semiarid mixed conifer forest in Arizona, USA. Diurnal Tc dynamics were closely related to tree sap flow and changes in spectral reflectance associated with stomatal regulation. Consistent with previously reported deviations, we found that on average Tc was 1.8°C lower than the above canopy air temperature (Ta). However, the relationship between Tc and Ta varied significantly according to tree density and tree height classes, with taller and denser trees exhibiting relatively low |Tc‐Ta| (2.4 and 2.1°C cooler canopies, respectively) compared to shorter and less‐dense tree stands (1.7 and 1.5°C cooler canopies, respectively). We used these data to evaluate space‐borne diurnal measurements of Tc and water stress from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. We found that ECOSTRESS observations of Tc accurately tracked seasonal shifts in diurnal surface temperatures and vegetation water stress, and that site‐level observations of heterogeneity in forest composition and structure could be applied to separate the processes of canopy transpiration and soil evaporation within the ECOSTRESS footprint. This study demonstrates how proximal and satellite remote sensing approaches can be combined to reveal the diurnal and seasonally dynamic nature of Tc and water stress.

A new index of forest structural heterogeneity using tree architectural attributes measured by terrestrial laser scanning

The maintenance or enhancement of structural heterogeneity (SH) of forest stands is considered a key element of sustainable forest management in central Europe, making the monitoring of stand structure an important task. However, existing approaches that enable the quantification of SH usually neglect the three-dimensional (3D) representation of forest structures. To overcome this limitation, terrestrial laser scanning (TLS) can be applied to provide an efficient, non-destructive and high resolution record of 3D structures. In this study, we present a new structural heterogeneity index (SHITLS) at the plot level which is based on 3D point clouds of individual trees that were derived from multiple scans. Because tree crowns are of particular importance for the 3D SH of forests, we focus particularly on outer and inner crown attributes. To test the robustness and the explanatory power of our index we sampled data on 84 plots across 12 different forest stand types in two study areas of Germany. We analyzed whether our index is able to explain differences between the different stand types. In addition, we compared our SHITLS to an existing stand structural complexity index (SSCI) that is based purely on single TLS scans. We found different trends of SHITLS between stand types dominated by Fagus sylvatica compared to those dominated by coniferous tree species (mainly Picea abies and Pinus sylvestris). Correlation analyses showed a significant, but weak, correlation with SSCI. The advantage of the SHITLS is the use of multiple scans from several scan positions. Thus, a better and very high resolution coverage of the forest structures is achieved. Due to its TLS-based nature this method provides many benefits in terms of reproducibility, objectivity and simplicity of data evaluation.

Decomposition and stabilization of organic matter in an old-growth tropical riparian forest: effects of soil properties and vegetation structure

BackgroundNutrient cycling in tropical forests has a large importance for primary productivity, and decomposition of litterfall is a major process influencing nutrient balance in forest soils. Although large-scale factors strongly influence decomposition patterns, small-scale factors can have major influences, especially in old-growth forests that have high structural complexity and strong plant-soil correlations. Here we evaluated the effects of forest structure and soil properties on decomposition rates and stabilization of soil organic matter using the Tea Bag Index (TBI) in an old-growth riparian forest in southeastern Brazil. These data sets were described separately using Principal Components Analysis (PCA). The main axes for each analysis, together with soil physical properties (clay content and soil moisture), were used to construct structural equations models that evaluated the different parameters of the TBI, decomposition rates and stabilization factor. The best model was selected using Akaike’s criterion.ResultsForest structure and soil physical and chemical properties presented large variation among plots within the studied forest. Clay content was strongly correlated with soil moisture and the first PCA axis of soil chemical properties, and model selection indicated that clay content was a better predictor than this axis. Decomposition rates presented a large variation among tea bags (0.009 and 0.098 g·g− 1·d− 1) and were positively related with forest structure, as characterized by higher basal area, tree density and larger trees. The stabilization factor varied between 0.211–0.426 and was related to forest stratification and soil clay content.ConclusionsThe old-growth forest studied presented high heterogeneity in both forest structure and soil properties at small spatial scales, that influenced decomposition processes and probably contributed to small-scale variation in nutrient cycling. Decomposition rates were only influenced by forest structure, whereas the stabilization factor was influenced by both forest structure and soil properties. Heterogeneity in ecological processes can contribute to the resilience of old-growth forests, highlighting the importance of restoration strategies that consider the spatial variation of ecosystem processes.

Conservation of saproxylic beetles in the Carpathians

Romanian Carpathians are considered a biodiversity hotspot in Europe, with large forested areas, including old-growth forests. Past forestry practices, such as selective logging resulting in forest high grading and removal of ancient or decaying trees, reduced the heterogeneity of forest structure and composition. These practices led to forest habitats with few veteran trees and a small amount of deadwood, which protected saproxylic beetles rely on for completing their complex life cycles. Moreover, saproxylic species are considered pest species under traditional forestry practices, as they reduce the value of timber. As such, Romanian forestry practices have actively sought to reduce the amount of deadwood in an effort to decrease the presence of saproxylic species, thus effectively isolating stands with a high diversity of saproxylic beetles. The European Commission will finance the project LIFE19 NAT/RO/000023 Conservation of saproxylic beetles in the Carpathians LIFE ROsalia. The project aims to stop and reverse the loss of protected saproxylic beetles (Rosalia longicorn Rosalia alpina, the hermit beetle Osmoderma eremita, great capricorn beetle Cerambyx cerdo, grey beetle Morimus funereus, and stag beetle Lucanus cervus) in the Carpathians by demonstrating conservation actions for increasing the connectivity of favorable habitats in the ROSCI0208 Putna-Vrancea (Eastern Carpathians, Romania), and transferring and replicating best management practices in other Romanian Natura 2000 sites. LIFE ROsalia will be implemented between 2020 and 2025 by the Environmental Protection Agency Vrancea, the Center for Environmental Research at the University of Bucharest, the Putna-Vrancea Natural Park Administration, and the Association for Biodiversity Conservation.

Pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA.

Tree spatial patterns in dry coniferous forests of the western United States, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre‐existing forest structure shapes tree spatial patterns is limited. In this study, we (a) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed‐conifer forest, (b) estimated tree mortality, and (c) examined tree spatial patterns of live trees before and after fire, and of fire‐killed trees. Tree mortality in the historical period was clustered and density‐dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, postfire tree patterns were more uniform and devoid of moderately sized tree groups. Postfire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western United States. This divergence suggests that decades of forest dynamics without significant disturbances have altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.

Natural dynamics of temperate mountain beech-dominated primary forests in Central Europe

Natural disturbances are key factors in the formation of forest ecosystem structure. Evaluation of the spatial and temporal extent of disturbance regimes is critical for understanding forest dynamics, forest structural heterogeneity, and biodiversity habitats. Quantifying disturbance regimes is therefore imperative for appropriate management of forests and protected areas. However, natural disturbance regimes have rarely been assessed using dendrochronological methods on a regional scale across primary mixed beech-fir forest stands - one of the dominant forest vegetation types in Europe. To study the natural disturbance regimes of beech-dominated mixed-forest stands, we established 42 permanent study plots with an area of 0.1 ha across three primary forest stands in the Western Carpathians, a region that still contains large areas of primary forest. We reconstructed each stand-level disturbance history using a tree-ring based approach. The temporal synchronicity of disturbance events was then evaluated by delineating stand-level disturbance events using a kernel density function, and through the detection of plot-level disturbances with severities greater than 10 percent. The results obtained from the chronologies showed substantial variability in time and space, especially in the mid-19th century. Low- and moderate-severity plot-level disturbance events were most common, but high- and extremely high-severity plot-level disturbance events also occurred. The observed spatial and temporal variability suggests that the beech-dominated forests were primarily driven by mixed-severity disturbance regimes, with windstorms as the main disturbance agent.This reconstruction of the disturbance regime provided unique insight into the scale of mortality processes in these beech-dominated mixed forests. This information can help guide ecological forestry in areas where both wood production and biodiversity preservation are simultaneous goals, such as by employing more spatiotemporally-complex silvicultural systems that resemble natural disturbance patterns and facilitate heterogeneous forest structures.

Simulating spatial complexity in dry conifer forest restoration: implications for conservation prioritization and scenario evaluation

ContextSeveral initiatives seek to increase the pace and scale of dry forest restoration and fuels reduction to enhance forest resilience to wildfire and other stressors while improving the quality and reliability of key ecosystem services. Ecological effects models are increasingly used to prioritize these efforts at the landscape-scale based on simulated treatment outcomes.ObjectivesTreatments are often simulated using uniform post-treatment target conditions or proportional changes to baseline forest structure variables, but do not account for the common objective of restoration to mimic the complex forest structure that was present historically which is thought to provide an example of structural conditions that contributed to ecosystem diversity and resilience.MethodsWe simulate spatially homogenous fire hazard reduction treatments along with heterogeneous restoration treatments in dry conifer forests to investigate how spatial complexity affects ecological indicators of (1) forest structural heterogeneity, (2) forest and watershed vulnerability to high-severity fire, and (3) feasibility of future prescribed fire use.ResultsOur results suggest that spatially explicit restoration treatments should produce similar wildfire and prescribed fire outcomes as homogeneous fuels reduction treatments, but with greater forest structural heterogeneity. The lack of strong tradeoffs between ecological objectives suggests the primary benefit of spatially complex treatments is to increase forest structural heterogeneity which may promote biodiversity.ConclusionsWe show that landscape-scale prioritization to maximize ecological benefits can change when spatially complex restoration treatments are modeled. Coupling landscape-scale management simulations and ecological effects models offers flexible decision support for conservation assessment, prioritization, and planning.

Effects of forest structure on the interaction between avian hosts, dipteran vectors and haemosporidian parasites

BackgroundForest habitats are important biodiversity refuges for a wide variety of bird species. Parasitism may modulate host species presence and abundance, and parasite effects can change according to forest management practices. Such processes are not well studied in vector-borne avian haemosporidians. We analyzed the effects of forest management on bird-dipteran-haemosporidian interactions, using seven common bird species in managed and unmanaged beech forest habitats in northeastern Germany. We assumed that forest structural heterogeneity affects parasite population parameters in avian hosts (i.e., prevalence and parasitemia), through its effect on the condition of the avian host but also through varying vector abundances.ResultsParasite prevalence was high (about 80%) and homogeneous across different beech forest categories (i.e., young, old, unmanaged) and for all bird species, except Erithacus rubecula (35%). Parasitemia varied across bird species but not across forest categories within each avian species (lowest parasitemia were found in E. rubecula, Turdus merula, and Turdus philomelos). In our study system, we found that vector abundance was not the main driver of parasite dynamics. We found that forest structure affects parasite infection probability directly and potentially host condition via available resources that have to be used either to combat infections (i.e., high parasitemia) or to maintain a good body condition.ConclusionsThe effects of each of the predictors were bird species-specific, and we found that Diptera vectors were not the foremost influence in our host-vector-parasite system. Effects of forest habitat variables indicated that for most bird species in this study, habitat regulation of infection probability was more likely (i.e., E. rubecula, Fringilla coelebs, Sylvia atricapilla), whereas for Parus major habitat characteristics impacted first individuals' body condition and subsequently the probability of infection. Our findings emphasize the need of species-specific analyses and to use continuous forest structural parameters (e.g., the proportion of gap, south facing aspect) to better understand habitat and land use effects on host-vector-parasite dynamics.

Heterogeneity-diversity relationships differ between and within trophic levels in temperate forests.

The habitat heterogeneity hypothesis predicts that biodiversity increases with increasing habitat heterogeneity due to greater niche dimensionality. However, recent studies have reported that richness can decrease with high heterogeneity due to stochastic extinctions, creating trade-offs between area and heterogeneity. This suggests that greater complexity in heterogeneity-diversity relationships (HDRs) may exist, with potential for group-specific responses to different facets of heterogeneity that may only be partitioned out by a simultaneous test of HDRs of several species groups and several facets of heterogeneity. Here, we systematically decompose habitat heterogeneity into six major facets on ~500 temperate forest plots across Germany and quantify biodiversity of 12 different species groups, including bats, birds, arthropods, fungi, lichens and plants, representing 2,600 species. Heterogeneity in horizontal and vertical forest structure underpinned most HDRs, followed by plant diversity, deadwood and topographic heterogeneity, but the relative importance varied even within the same trophic level. Among substantial HDRs, 53% increased monotonically, consistent with the classical habitat heterogeneity hypothesis but 21% were hump-shaped, 25% had a monotonically decreasing slope and 1% showed no clear pattern. Overall, we found no evidence of a single generalizable mechanism determining HDR patterns.

Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Within the realms of both wildland and prescribed fire, an understanding of how fire severity and forest structure interact is critical for improving fuels treatment effectiveness, quantifying the ramifications of wildfires, and improving fire behavior modeling. We integrated high resolution estimates of fire severity with multi-temporal airborne laser scanning data to examine the role that various fuel loading, canopy shape, and other variables had on predicting fire severity for a complex of prescribed fires and one wildfire and how three-dimensional fuels changed as a result of these fires. Fuel loading characteristics were widely variable, and fires were ignited using a several techniques (heading, flanking, and backing), leading to a large amount of variability in fire behavior and subsequent fire effects. Through our analysis, we found that fire severity was linked explicitly to pre-fire fuel loading and structure, particularly in the three-dimensional distribution of fuels. Fire severity was also correlated with post-fire fuel loading, forest structural heterogeneity, and shifted the diversity and abundance of canopy classes within the landscape. This work demonstrates that the vertical distribution of fuel is an important factor and that subtle difference has defined effects on fire behavior and severity.

Home-range, habitat use and diet of the Tasmanian Masked Owl Tyto novaehollandiae castanops

Estimates of home-range are reported for the Tasmanian Masked Owl Tyto novaehollandiae castanops using radio-telemetry in a modified agricultural land–forest landscape in south-eastern Tasmania. Home-range sizes for two female Owls were 1991 ha and 1896 ha using the Minimum Convex Polygon method, and 2507 ha and 2320 ha, respectively, using the Kernel Estimator method. The 50% isopleth Kernel estimated a core area of 174 ha and 309 ha for each Owl, respectively. Both Owls used two core areas within their home-range reflecting frequent foraging and roosting sites. The location and configuration of the home-ranges within the landscape, and areas of core use, suggest that Masked Owls are responding to geomorphology, heterogeneity of forest patches, forest structure and abundance and accessibility of prey. Both Owls preferentially foraged at forest edges, riparian zones and small forest patches. Frequently used foraging locations were in open forest with minimal understorey vegetation and a pasture groundcover, which probably reflects increased prey abundance, diversity and accessibility in this habitat type. The diet of both Owls was dominated by locally abundant native and introduced ground-dwelling eutherian mammals and marsupials. The strong association with riparian zones of both Owls in this study highlights the potential importance of retaining streamside vegetation to the conservation of the Tasmanian Masked Owl.

Using heterogeneity indices to adjust basal area – Leaf area index relationship in managed coniferous stands

The structure of contemporary managed forests is complex and deviates from experimental forests which are usually even-aged monocultures and single-storied. To apply theoretical growth and yield functions on managed forests, adjustments are required, especially for leaf area index (LAI) which is a key biophysical variable in process-based growth models. To asses this, the performance of canopy LAI in modelling the basal area (BA) of managed boreal forests dominated by Norway spruce (Picea abies (L). Karst) and Scots pine (Pinus sylvestris L.) was investigated by heterogeneity analysis. The study was based on the assumption that canopy LAI and BA are strongly related and are vital for estimating stand productivity and growth. Managed forests were represented by field data from the 2016 and 2017 Swedish National Forest Inventory (NFI) campaigns. Species-specific LAI conversion parameters were applied on the general plant area index (PAI) values from hemispheric fish-eye photos taken from the permanent and temporary NFI sample plots. The heterogeneity analysis was studied in two parts by: (a) ground-based stand structural heterogeneity (SSH) described by species composition, coefficient of tree diameter variation, tree social status and height-diameter ratio, and (b) spectral heterogeneity (SPH) by vegetation and textural indices developed from Sentinel-2. Species-specific final (with heterogeneity metrics) and base (without heterogeneity metrics) models were fitted for BA-LAI and BA-PAI relationships by nonlinear least squares and generalised additive regression functions, respectively. The performance of models was assessed by the root-mean-squared error (RMSE, m2 ha−1) and the relative root-mean-squared error (RelRMSE, %) metrics. For both species, BA-LAI final models (FMs) accounting for heterogeneity resulted in larger explained variance than the base models (BMs). Compared with the BMs, FMs with SSH reduced the variance by 55% in Norway spruce (RMSE = 3.33, RelRMSE = 15.39) and 43% in Scots pine (RMSE = 3.70, RelRMSE = 17.38). The fit between BA-LAI with SPH also showed an improvement for Norway spruce (RMSE = 5.56) and Scots pine (RMSE = 5.66) over the BMs, suggesting the potential use of Sentinel-2 in future growth models. The results of the study suggest that in growth models when extrapolating theoretical growth functions to managed forests, there is a need to calibrate the models with the forest structural heterogeneity. This is important for drawing realistic conclusions from growth and yield modelling of managed stands of Norway spruce and Scots pine.

Mapping forest structural heterogeneity of tropical montane forest remnants from airborne laser scanning and Landsat time series

Abstract Tropical montane forests are important reservoirs of carbon and biodiversity and have a central role in the hydrological cycle. They are, however, very fragmented and degraded, leaving isolated remnants across the landscape. These montane forest remnants have considerable differences in forest structure, depending on factors such as tree species composition and degree of forest degradation. Our objectives were (1) to analyse the reliability of airborne laser scanning (ALS) in modelling forest structural heterogeneity, as described by the Gini coefficient (GC) of tree size inequality; (2) to determine whether models are improved by including tree species-sensitive spectral-temporal metrics from the Landsat time series (LTS); and (3) to evaluate differences between three forest remnants and different forest types using the resulting maps of predicted GC. The study area was situated in Taita Hills, Kenya, where indigenous montane forests have been partly replaced by single-species plantations. The data included field measurements from 85 sample plots and two ALS data sets with different pulse densities (9.6 and 3.1 pulses m−2). GC was modeled using beta regression. We found that GC was predicted more accurately by the ALS data set with a higher point density (a cross-validated relative root mean squared error (rRMSECV) 13.9%) compared to ALS data set with lower point density (rRMSECV 15.1%). Furthermore, important synergies exist between ALS and LTS metrics. When combining ALS and LTS metrics, rRMSECV was improved to 12.5% and 13.0%, respectively. Therefore, if the LTS metrics are included in models, ALS data with lower pulse density are sufficient to yield similar accuracy to more expensive, higher pulse density data acquired from the lower altitude. In Ngangao and Yale, forest canopy has multiple layers of variable tree sizes, whereas elfin forests in Vuria are of more equal tree size, and the GC value ranges of the indigenous forests are 0.42–0.71, 0.20–0.74, and 0.17–0.76, respectively. The single-species plantations of cypress and pine showed lower values of GC than indigenous forests located in the same remnants in Yale, whereas Eucalyptus plantations showed GC values more similar to the indigenous forests. These results show the usefulness of GC maps for identifying and separating forest types as well as for assessing their distinctive ecologies.

A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR.

There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks.

Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness

The structural heterogeneity of vegetation is a key factor for explaining animal diversity patterns at a local scale. Improvements in airborne light detection and ranging (lidar) technologies have enabled researchers to study forest 3D structure with increasing accuracy. Most structure–animal diversity work has focused on structural metrics derived from lidar returns from canopy and terrain features. Here, we built new lidar structural metrics based on the Leaf Area Density (LAD) at each vegetation height layer, and used these metrics to study how different aspects of forest structural heterogeneity explain variation in bird species richness. Our goals were to test: (1) whether LAD-based metrics better explained bird species richness compared to metrics based on the top of the canopy; and (2) if different aspects of structural heterogeneity had diverse effects on bird richness. We used discrete lidar data together with 61 breeding landbird points provided by the National Ecological Observatory Network at five forest sites of the eastern US. We used the lidar metrics as predictors of bird species richness and analyzed the shape of the response curves against each predictor. Metrics based on LAD measurements had better explanatory power (43% of variance explained) than those based on the variation of canopy heights (32% of variance explained). Dividing the forest plots into smaller grids allowed us to study the within-plot horizontal variation of the vertical heterogeneity, as well as to analyze how the vegetation density is horizontally distributed at each height layer. Bird species richness increased with horizontal heterogeneity, while vertical heterogeneity had negative effects, contrary to previous research. The increasing capabilities of lidar will allow researchers to characterize forest structure with higher detail. Our findings highlight the need for structure–animal diversity studies to incorporate metrics that are able to capture different aspects of forest 3D heterogeneity.