Forest Structure Metrics Research Articles

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

Assessing GEDI data fusions to map woodpecker distributions and biodiversity hotspots

Abstract In forested systems, woodpecker species richness has been linked with songbird diversity, and identifying woodpecker biodiversity hotspots may contribute important information for conservation planning. The availability of global forest structure data via the Global Ecosystem Dynamics Investigation (GEDI) instrument provides a new tool for examining broad extent relationships amongst environmental variables, forest structure, and woodpecker diversity hotspots. Within the Marine West Coast Forest ecoregion, USA, we used eBird data for 7 woodpecker species to model encounter rates based on bioclimatic variables, process data (e.g. duration and timing of survey), MODIS forest land cover data, and GEDI-fusion metrics. The GEDI-fusion metrics included foliage height diversity (fhd), rh98 (a representation of canopy height), and canopy cover, which were created by combining GEDI data with Landsat, Sentinel-1, topographic, and climatic information within a random forest modeling framework. AUCs for the species-specific models ranged from 0.77–0.98, where bioclimatic and process predictors were amongst the most important variables for all species. GEDI-fusion forest structure metrics were highly ranked for all species, with fhd included as a highly ranked predictor for all species. The structural metrics included as top predictors for each species were reflective of known species-specific habitat associations. Hotspots in this ecoregion tended to be inland and occurred most often on privately-owned lands. Identification of hotspots is the first step towards management plans focused on biodiversity, and understanding ownership patterns is important for future conservation efforts. The near-global extent of GEDI data, along with recent studies that recommend woodpeckers as indicators of biodiversity across multiple forest types at local and global scales, suggest that synthesis of GEDI-derived data applied to woodpecker detection information might be a powerful approach to identifying biodiversity hotspots.

Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Characterizing low‐lying coastal upland forests to predict future landward marsh expansion

AbstractSea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from light detection and ranging (lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS‐derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the plant area index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar‐derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated SLR conditions.

Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs

Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies have addressed their pros and cons in alpine temperate deciduous forests. In this study, different point clouds from UAV-mounted LiDAR and DAP under leaf-off conditions were first processed into individual tree point clouds, and then explicit 3D tree models of the forest were reconstructed using the TreeQSM and AdQSM methods. Structural metrics obtained from the two QSMs were evaluated based on terrestrial LiDAR (TLS)-based surveys. The results showed that ALS-based predictions of forest structure outperformed DAP-based predictions at both plot and tree levels. TreeQSM performed with comparable accuracy to AdQSM for estimating tree height, regardless of ALS (plot level: 0.93 vs. 0.94; tree level: 0.92 vs. 0.92) and DAP (plot level: 0.86 vs. 0.86; tree level: 0.89 vs. 0.90) point clouds. These results provide a robust and efficient workflow that takes advantage of UAV monitoring for estimating forest structural metrics and suggest the effectiveness of LiDAR in temperate deciduous forests.

Using airborne LiDAR to map forest microclimate temperature buffering or amplification

Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas.To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (Quercus petraea). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale) coefficient of the linear regression between microclimate and macroclimate, as a simple parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope) > 0) capacity of the habitat.An airborne LiDAR flight was conducted during summer 2021, matching the timing of our temperature measurements. From the resulting 3D point cloud, three complementary metrics of forest structure were derived: the maximum height, the Plant Area Index and the Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to 100 m) centred on each HOBO sensor.We found that the 5-m radius combining the three metrics into a single multivariate model explained the greatest proportion of variance in the microclimate effect of each stand (R2 = 0.91). We mapped the buffering or amplification effect of vegetation structure on understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4% of the surface of the forest was significantly buffered relative to macroclimate temperature, while 2.7% was amplified, especially in road verges, clear-cut and regeneration areas. Based on our simple linear model, we were able to derive understory air temperature maps for any temporal resolution (i.e. hourly, daily, or seasonal). The results highlight the great capacity of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps of the thermal environment.Applications for mapping the buffering or amplification of microclimate temperature are plentiful, especially in the context of climate change. They include improving the understanding of physiological processes such as thermoregulation or phenology, modelling the thermal connectivity of the landscape, improving species redistribution models, spotting microrefugia for conservation, informing forest management for tree regeneration or wildfire control, or even prioritising cooling recreational areas for humans to escape heatwaves in urban forests.

Contrasting impacts of climate change on protection forests of the Italian Alps

Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

Mapping spatial microclimate patterns in mountain forests from LiDAR

Forests create unique microclimates that have the potential to serve as microrefugia for species under climate change. Yet, our understanding of the heterogenous thermal patterns at the forest floor of complex landscapes (e.g., in mountain forests) remains incomplete. We here used Light Detection and Ranging (LiDAR) for predicting summer temperature offsets in a mountain forest landscape in the European Alps. We calibrated models on a network of 150 microclimate loggers that were combined with data from 15 meteorological stations to estimate the maximum, mean, and minimum temperature offsets, using LiDAR-derived metrics of forest structure and topography as predictors. Models predicted summer temperature offsets with an R²/RMSE of 0.50/3.15 °C for maximum temperature, 0.51/0.41 °C for mean temperature and 0.55/0.57 °C for minimum temperature. Forest canopy openness and elevation were most important for predicting temperature offsets. The mean offset ranged from – 1.9 °C to 2.7 °C (mean of - 0.3 °C), but both minimum and maximum offsets varied considerably, with some forests even having warmer maximum and colder minimum temperatures than open areas. This was particularly prominent in forests of the subalpine zone, which are characterized by open canopies and a considerable presence of coniferous shrubs. In contrast, submontane forests with largely closed canopies had mostly colder maximum and warmer minimum temperatures within forests compared to open areas. Analysing the development of temperature offsets with time since disturbance, we found that recently disturbed forests had higher maximum temperatures compared to open areas, but they recovered to closed forest conditions within two decades. We conclude that mountain forests exhibit complex microclimate patterns that vary strongly with forest type and canopy openness. We further highlight that disturbances are an important driver of spatiotemporal dynamics in forest microclimate. Finally, temperature offset maps such as the ones generated here have strong potential to improve the robustness of species distribution models and to assess climate risks for biodiversity.

Precise and unbiased biomass estimation from GEDI data and the US Forest Inventory

Atmospheric CO2 concentrations are dependent on land-atmosphere carbon fluxes resultant from forest dynamics and land-use changes. These fluxes are not well-constrained, in part because reliable baseline estimates of forest carbon stocks and the associated uncertainties are lacking. NASA's Global Ecosystem Dynamics Investigation (GEDI) produces estimates of aboveground biomass density (AGBD) that are unique because GEDI's hybrid estimation framework enables formal uncertainty calculations that accompany the biomass estimates. However, GEDI's estimates are not without issue; a recent validation using design-based AGBD estimates from the US Forest Inventory and Analysis (FIA) program revealed systematic differences between GEDI and FIA estimates within a hexagon tessellation of the continental United States. Here, we explored these differences and identified two issues impacting GEDI's estimation process: incomplete filtering of low quality GEDI observations and regional biases in GEDI's footprint-level biomass models. We developed a solution to each, in the form of improved data filtering and GEDI-FIA fusion AGBD models, developed in a scale-invariant small area estimation framework, that were compatible with hybrid estimation. We calibrated 10 regional Fay-Herriot models at the hexagon scale for application at the unit scale of GEDI footprints, for which we provide a mathematical justification and empirical testing of the models' scale-invariance. These models predicted realistic distributions of unit level AGBD, with equal or improved performance relative to GEDI's L4A models for all regions. We then produced GEDI-FIA fusion estimates that were more precise than the FIA estimates and resulted in a bias reduction of 86.7% relative to the original GEDI estimates: 19.3% due to improved data filtering and 67.5% due to the new AGBD models. Our findings indicate that (1) small area estimation models trained in a scale-invariant framework can produce realistic predictions of AGBD, and (2) there is substantial spatial variability in the relationship between GEDI forest structure metrics and AGBD. This work is a step toward achieving reliable baseline forest carbon stocks, provides a viable methodology for training remote sensing biomass models, and may serve as a reference for other investigations of GEDI AGBD estimates.

Using aerial LiDAR to assess regional availability of potential habitat for a conservation dependent forest bird

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 (ΔAICc = 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.

Estimating and mapping forest age across Canada's forested ecosystems

Forest age is an important variable for assessments of biodiversity and habitat, sustainable forest and land management, as well as forest carbon science and modeling. Tree and stand age are typically measured directly on site, or estimated through visual photo interpretation, with spatially explicit maps of forest age not often produced over large areas. Remote sensing enables the generation of wall-to wall, spatially explicit maps of disturbance events within the satellite record; however, as disturbance is relatively rare on the landscape in a given year, additional means of determining forest age are required. As reviewed herein, the estimation of forest age using optical Earth observation data is challenging due to the limited spectral link to the attribute of interest, especially as forests get older. The temporally dictated multi-method approach to forest age estimation outlined herein acknowledges these limitations, by applying the approach that is best suited to the quality of the information available, depending on the epoch of interest. In this research, we combine three approaches to estimate forest age at a 30-m spatial resolution using Landsat data. The first approach uses change detection protocols to detect disturbance from 1985 to 2019, with time since disturbance used as a proxy for forest age. The second approach uses Landsat surface reflectance composites to identify pixels exhibiting evidence of recovery from a disturbance that occurred within the twenty years prior to 1985, allowing for the extension of forest age estimates to 1965. Finally, given an understanding of the linkage between forest age and canopy height, inverted allometric equations are coupled with maps of forest structure and productivity metrics to model forest age for those pixels that show no evidence of disturbance or recovery to a maximum of 150 years, acknowledging that uncertainty in age estimate increases with increasing age. Combining these three approaches, forest age estimates are made for every treed pixel found within the 650 Mha forested ecosystems of Canada. Nationwide, mean estimated forest age for forests ≤150 years old (representing 94.1% of treed area) was 70 years (standard deviation = 32.1 years). For confidence building, forest age estimates were compared to reported forest age in the National Forest Inventory (NFI) both spatially and aspatially. Nationally, 5.9% of the forested area was estimated to be older than 150 years, while 9.5% of area within in the NFI sample was recorded as older than 150 years. The median estimated forest age for forested pixels ≤150 years old was 68 years while median forest age reported in the NFI was 73 years, with regional variability matching expectations related to disturbance regimes and productivity. Spatially explicit maps of forest age provide important information for understanding forest ecosystems and can be used to inform a wide range of policy, science, and management needs.

Application of point cloud data to assess edge effects on rainforest structural characteristics in tropical Sumatra, Indonesia

ContextIn forestry, edge zones created by forest degradation and fragmentation are more susceptible to disturbances and extreme weather events. The increase in light regime near the edge can greatly alter forest microclimate and forest structure in the long term. In this context, understanding edge effects and their impact on forest structure could help to identify risks, facilitate forest management decisions or prioritise areas for conservation.ObjectiveIn this paper, we focus on the application of airborne laser scanning (ALS) data to assess the impact of edge effects on forest structural metrics in degraded rainforests in Sumatra, Indonesia. Changes in structural heterogeneity with respect to distance from an edge were also quantified.MethodsWe used 22 ALS structural metrics extracted from 105 plots in secondary forests adjacent to oil palm plantations and analysed the change in canopy structure across edge-to-interior transects. In addition, 91 plots taken from less disturbed areas were used as reference for comparison with the near-to-edge plots.ResultsOur analysis found strong evidence of degradation in the secondary forests studied, with multiple edge interactions resulting in a non-diminishing effect even at long distances from the forest edge. On average, we observed a large decrease of about 40% in all metrics of canopy height and about 25% in some metrics of canopy structure across all distances from an edge when compared to the interior forest conditions. Thus, in our forests, canopy height and structure were more susceptible to edge effects than metrics related to canopy gaps. Finally, the degraded forest in our study exhibited lower structural complexity, both at patch and landscape levels, suggesting that disturbances can greatly alter structural complexity in tropical rainforests.ConclusionOur study confirms the potential of ALS-derived vegetation metrics to study and understand the effects of forest edges and the associated changes in structural complexity over large areas in tropical rainforests. The approach followed here is transferrable to similarly fragmented landscapes in the tropics.

Cover Picture and Issue Information

Methods in Ecology and EvolutionVolume 14, Issue 2 p. 303-305 COVER PICTURE AND ISSUE INFORMATIONOpen Access Cover Picture and Issue Information First published: 07 February 2023 https://doi.org/10.1111/2041-210X.13883AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract This months's cover image features an old growth eastern hemlock (Tsuga canadensis) forest on the property of the Huron Mountain Club, located in Marquette County, Upper Peninsula, Michigan, USA. This forest is one of many globally where light detection and ranging (lidar), a form of active remote sensing, has been used to quantify forest structure to address structure-function relationships. In this issue, Atkins et al. explore the scale-dependency of lidar-derived forest structural metrics, providing a statistical framing for the application of such metrics using the Representative Elementary Area concept. Photo credit: © J. W. Atkins, USDA Forest Service, Southern Research Station Volume14, Issue2Special feature: Leveraging Natural History Collections to Understand the Impacts of Global ChangeFebruary 2023Pages 303-305 RelatedInformation

Lidar and deep learning reveal forest structural controls on snowpack

Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling. We investigated how structural diversity and topography affect snow depth patterns across scales. The study site, Grand Mesa, Colorado, is representative of many areas worldwide where declining snowpack and its consequences for forest ecosystems are increasingly an environmental concern. On the basis of a convolution neural network model (R2 of 0.64; root mean squared error of 0.13 m), we found that forest structural and topographic metrics from airborne light detection and ranging (lidar) at fine scales significantly influence snow depth during the accumulation season. Moreover, complex vertically arranged foliage intercepts more snow and results in shallower snow depths below the canopy. Assessing forest structural controls on snow distribution and depth will aid efforts to improve understanding of the ecological and hydrological impacts of changing snow patterns.

Assessing protected area’s carbon stocks and ecological structure at regional-scale using GEDI lidar

Protected areas (PAs) serve as a critical strategy for protecting natural resources, conserving biodiversity, and mitigating climate change. While there is a critical need to guide area-based conservation efforts, a systematic assessment of PA effectiveness for storing carbon stocks has not been possible due to the lack of globally consistent forest biomass data. In this study, we present a new methodology utilizing forest structural information and aboveground biomass density (AGBD) obtained from the Global Ecosystem Dynamics Investigation (GEDI) mission. We compare PAs with similar, unprotected forests obtained through statistical matching to assess differences in carbon storage and forest structure. We also assess matching outcomes for a robust and minimally biased way to quantify PA efficacy. We find that all analyzed PAs in Tanzania possess higher biomass densities than their unprotected counterfactuals (24.4% higher on average). This is also true for other forest structure metrics, including tree height, canopy cover, and plant area index (PAI). We also find that community-governed PAs are the most effective category of PAs at preserving forest structure and AGBD – often outperforming those managed by international or national entities. In addition, PAs designated under more than one entity perform better than the PAs with a single designation, especially those with multiple international designations. Finally, our findings suggest that smaller PAs may be more effective for conservation, depending on levels of connectivity. Taken together, these findings support the designation of PAs as an effective means for forest management with considerable potential to protect forest ecosystems and achieve long-term climate goals.

Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE &gt; 30% and/or NBias &gt; 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were &gt;0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts.

Imageseg: An R package for deep learning‐based image segmentation

Abstract Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can, for example, be used to assess forest structural metrics. While CNN‐based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. Here, we present R package imageseg which implements a CNN‐based workflow for general purpose image segmentation using the U‐Net and U‐Net++ architectures in R. The workflow covers data (pre)processing, model training and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understorey vegetation density. We trained the models using large and diverse training datasets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understorey vegetation images. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understorey vegetation model (assessed with 821 and 367 images respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pretrained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on colour or grayscale images, for example, for applications in cell biology or for medical images. Our package is free, open source and available from CRAN. It will enable easier and faster implementation of deep learning‐based image segmentation within R for ecological applications and beyond.

Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics

LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score &gt; 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning.

Spaceborne LiDAR and animal-environment relationships: An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem

Animal conservation requires understanding animal-habitat relationships. The integration of novel remote sensing platforms such as Light Detection and Ranging (LiDAR) technology has dramatically improved the resolution of insight when evaluating animal-habitat relationships by characterizing forest structure. However, conventional LiDAR collection (e.g., airborne or terrestrial laser scanning) may be limited by small spatial extents and logistical constraints (e.g., budget) associated with sampling. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provides an alternative and complement to conventional LiDAR sampling with globally available waveform LiDAR, which is being collected to characterize vertical and horizontal structure of Earth’s forests. Forest carnivores are wide-ranging species occupying forested ecosystems, and are generally associated with vertical and horizontal forest structure for their survival and reproduction. We evaluated patterns in occurrence and habitat use of forest carnivores, which included Pacific martens (Martes caurina), Rocky Mountain red foxes (Vulpes vulpes macroura), and coyotes (Canis latrans) and patterns in occurrence of their prey; American red squirrels (Tamiasciurus hudsonicus) and snowshoe hares (Lepus americanus). Camera trap data were collected during the 2014–2017 winters in the Greater Yellowstone Ecosystem in Wyoming, USA. Our objectives were to (1) combine GEDI samples with multispectral satellite imagery from Landsat 8 to upscale vertical forest structure metrics; (2) assess the relative importance of environmental characteristics influencing occurrence and habitat use of forest-associated predators and prey; and (3) determine if GEDI-derived variables aided our efforts in characterizing animal-environment relationships. We used Random Forest regression models to upscale GEDI samples across our study area and implemented a multi-tiered approach using generalized linear mixed effect models to simultaneously evaluate animal-environment relationships and how GEDI-derived metrics improved the animal-habitat models. GEDI-derived metrics of relative height and foliage height diversity improved our animal-environment models and were among the strongest covariates (effect sizes were 1.3–1.8 times larger than the next closest) in the coyote, red squirrel, and snowshoe hare models. All five species were influenced to some degree by the frequency of rebaiting a camera trap and varying conditions of snow depth. Collectively, our work indicates forest canopy height and complexity variables significantly improved our ability to assess the importance of forest characteristics on forest carnivores and their prey. Indeed, there is an untapped opportunity to enhance animal ecology and conservation planning with continued integration of GEDI information with freely available satellite data to characterize attributes of forest structure across expansive areas.

Shifts in structural diversity of Amazonian forest edges detected using terrestrial laser scanning

Forest edges are an increasingly common feature of Amazonian landscapes due to human-induced forest fragmentation. Substantial evidence shows that edge effects cause profound changes in forest biodiversity and productivity. However, the broader impacts of edge effects on ecosystem functioning remain unclear. Assessing the three-dimensional arrangement of forest elements has the potential to unveil structural traits that are scalable and closely linked to important functional characteristics of the forest. Using over 600 high-resolution terrestrial laser scanning measurements, we present a detailed assessment of forest structural metrics linked to ecosystem processes such as energy harvesting and light use efficiency. Our results show a persistent change in forest structural characteristics along the edges of forest fragments, which resulted in a significantly lower structural diversity, in comparison with the interior of the forest fragments. These structural changes could be observed up to 35 m from the forest edges and are likely to reflect even deeper impacts on other ecosystem variables such as microclimate and biodiversity. Traits related to vertical plant material allocation were more affected than traits related to canopy height. We demonstrate a divergent response from the forest understory (higher vegetation density close to the edge) and the upper canopy (lower vegetation density close to the edge), indicating that assessing forest disturbances using vertically integrated metrics, such as total plant area index, can lead to an erroneous interpretation of no change. Our results demonstrate the strong potential of terrestrial laser scanning for benchmarking broader-scale (e.g. airborne and space-borne) remote sensing assessments of forest disturbances, as well as to provide a more robust interpretation of biophysical changes detected at coarser resolutions.