Complex Forest Structure Research Articles

SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis.Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers.Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods.In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Topography-driven microclimate gradients shape forest structure, diversity, and composition in a temperate refugialforest.

Macroclimate drives vegetation distributions, but fine-scale topographic variation can generate microclimate refugia for plant persistence in unsuitable areas. However, we lack quantitative descriptions of topography-driven microclimatic variation and how it shapes forest structure, diversity, and composition. We hypothesized that topographic variation and the presence of the forest overstory cause spatiotemporal microclimate variation affecting tree performance, causing forest structure, diversity, and composition to vary with topography and microclimate, and topography and the overstory to buffer microclimate. In a 20.2-ha inventory plot in the North American Great Plains, we censused woody stems ≥1 cm in diameter and collected detailed topographic and microclimatic data. Across 59-m of elevation, microclimate covaried with topography to create a sharp desiccation gradient, and topography and the overstory buffered understory microclimate. The magnitude of microclimatic variation mirrored that of regional-scale variation: with increasing elevation, there was a decrease in soil moisture corresponding to the difference across ~2.1° of longitude along the east-to-west aridity gradient and an increase in air temperature corresponding to the difference across ~2.7° of latitude along the north-to-south gradient. More complex forest structure and higher diversity occurred in moister, less-exposed habitats, and species occupied distinct topographic niches. Our study demonstrates how topographic and microclimatic gradients structure forests in putative climate-change refugia, by revealing ecological processes enabling populations to be maintained during periods of unfavorable macroclimate.

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea.

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

Relating forest structural characteristics to bat and bird diversity in the Italian Alps

The global decline of biodiversity has affected European forests, involving many tree species and forest-dwelling threatened animals. An integrated approach linking forest structure and multi-taxon diversity is increasingly needed to maintain the multifunctionality of forest ecosystems. We investigated the relationship between forest structure, deadwood elements, canopy attributes, and tree-related microhabitats on bat and bird communities in the north-eastern Italian Alps. We collected forest attributes, bats, and bird data on 40 forest plots encompassing the diversity of forest types. To assess the different contributions of each forest attribute variables we performed a two-step statistical analysis using generalised and linear models, including bat and bird taxonomical and functional diversity indices as response variables. Our findings reveal that bats and birds respond differently to variation in forest structural characteristics. Specifically, bat species richness was higher in forests with both higher standing tree and lying deadwood volume. The Shannon diversity index for bird community was higher in forests with high volumes of coarse lying deadwood and stumps. Moreover, plots with mature trees, gaps, and heterogeneous diameter distribution fostered the presence of generalist species of bats and birds, while the abundance of tree-related microhabitats was not significant for these two taxa. This study demonstrates that the optimal habitat conditions for bats and birds in Alpine forests are multifaceted. Promoting distinctive elements within forest stands and a complex forest structure through adaptations in forest management interventions would enhance the conservation of multi-taxon forest biodiversity.

Terrestrial laser scanning vs. manual methods for assessing complex forest stand structure: a comparative analysis on plenter forests

In continuous cover forestry, plenter silviculture is regarded as an elaborated system for optimizing the sustainable production of high-quality timber maintaining a constant but heterogeneous canopy. Its complexity necessitates high silvicultural expertise and a detailed assessment of forest stand structural variables. Terrestrial laser scanning (TLS) can offer reliable techniques for long-term tree mapping, volume calculation, and stand variables assessment in complex forest structures. We conducted surveys using both automated TLS and conventional manual methods (CMM) on two plots with contrasting silvicultural regimes within the Black Forest, Germany. Variations in automated tree detection and stand variables were greater between different TLS surveys than with CMM. TLS detected an average of 523 tree stems per hectare, while CMM counted 516. Approximately 9.6% of trees identified with TLS were commission errors, with 6.5% of CMM trees being omitted using TLS. Basal area per hectare was slightly higher in TLS (38.9 m3) than in CMM (38.2 m3). However, CMM recorded a greater standing volume (492.7 m3) than TLS (440.5 m3). The discrepancy in stand volume between methods was primarily due to TLS underestimating tree height, especially for taller trees. DBH bias was minor at 1 cm between methods. Repeated TLS inventories successfully matched an average of 424 tree positions per hectare. While TLS adequately characterizes complex plenter forest structures, we propose enhancing this methodology with personal laser scanning to optimize crown coverage and efficiency and direct volume measurements for increased accuracy of wood volume estimations. Additionally, utilizing 3D point cloud data-derived metrics, such as structural complexity indices, can further enhance plenter forest management.

Drivers of spatial structure in thinned forests

BackgroundAs is widely known, an increasing number of forest areas were managed to preserve and enhance the health of forest ecosystems. However, previous research on forest management has often overlooked the importance of structure-based. AimsOur objectives were to define the direction of structure-based forest management. Subsequently, we investigated the relationships between forest structure and the regeneration, growth, and mortality of trees under different thinning treatments. Ultimately, the drivers of forest structural change were explored. MethodsOn the basis of 92 sites selected from northeastern China, with different recovery time (from 1 to 15 years) and different thinning intensities (0–59.9%) since the last thinning. Principal component analysis (PCA) identified relationships among factors determining forest spatial structure. The structural equation model (SEM) was used to analyze the driving factors behind the changes in forest spatial structure after thinning. ResultsLight thinning (0–20% trees removed) promoted forest regeneration, and heavy thinning (over 35% of trees removed) facilitated forest growth. However, only moderate thinning (20%–35% trees removed) created a reasonable spatial structure. While dead trees were clustered, and they were hardly affected by thinning intensity. Additionally, thinning intensity, recovery time, and altitude indirectly improve the spatial structure of the forest by influencing diameter at breast height (DBH) and canopy area. ConclusionCreating larger DBH and canopy area through thinning will promote the formation of complex forest structures, which cultivates healthy and stable forests.

Transboundary Cooperation in the Tumen River Basin Is the Key to Amur Leopard (Panthera pardus) Population Recovery in the Korean Peninsula.

The interconnected forest regions along the lower Tumen River, at the Sino-North Korean border, provide critical habitats and corridors for the critically endangered Amur Leopard (Panthera pardus orientalis). In this region, there are two promising corridors for leopard movement between China and North Korea: the Jingxin-Dapanling (JD) and Mijiang (MJ) corridors. Past studies have confirmed the functionality of the JD corridor, but leopards' utilization of the MJ corridor has not yet been established or confirmed. In this study, we assessed the functionality of the MJ corridor. The study area was monitored using camera traps between May 2019 and July 2021. We also analyzed 33 environmental and vegetation factors affecting leopard survival and analyzed leopard movement. In the Mijiang area, the Amur leopard was mainly active in the region adjacent to the Northeast China Tiger and Leopard National Park and did not venture into area near the North Korean border. The complex forest structure allowed leopards to move into the Mijiang area. However, the high intensity of human disturbance and manufactured physical barriers restricted further southward movement. Therefore, human-induced disturbances such as grazing, mining, farming, logging, and infrastructure development must be halted and reversed to make the Mijiang region a functional corridor for the Amur leopard to reach the North Korean forest. This necessitates inter-governmental and international cooperation and is essential for the long-term survival of the Amur leopard.

Practical LAI Estimation with DHP Images in Complex Forest Structure with Rugged Terrain

Leaf area index is a key structural parameter for biological and physical processes. Korea is planning to launch CAS500-4 in 2025, so in situ data is needed to validate the leaf area index. Unlike other networks (e.g., NEON and TERN), establishing an elementary sampling unit is difficult in Korea due to the complex forest structure and rugged terrain. Therefore, pixel-level correspondence between the satellite product and fisheye footprints is the best way to verify in complex terrain. In this study, we analyzed the spatial footprint of fisheye lenses in different forest types using terrestrial LiDAR data for the first time. The three-dimensional forest structure was analyzed at various viewing zenith angles, and the footprint radius was approximately 3 m at view zenith angle (VZA) 20° and approximately 10 m at VZA 90°. We also analyzed the Z-values from terrestrial laser data and the plant area index on leafless seasons to assess the impact of obstacles, such as tree trunks, under various viewing zenith angles. The analysis showed that the influence of woody components increases dramatically as the VZA exceeds 40°. Such factors influenced the increase in LAI and the decrease in the clumping index as the VZA increased. Overall, we concluded that narrowing VZA between 20° and 40° is appropriate for Korean forests with complex structures.

Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes

The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability.

Structural complexity biases vegetation greenness measures.

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Avian nutritional condition increases with forest structural complexity

Sylvicultural practices are increasingly directed at forest conservation, restoration and sustainable management. Forests with higher structural complexity are expected to be more resilient against climate change impacts and to have a higher diversity of ecological niches. The impact of forest structural complexity on individual organisms, however, is less well known. To bridge this knowledge gap, we here test if structurally more complex forests provide a more favourable nutritional environment for forests birds, using ptilochronology as an indicator for individual condition. We therefore measured the width of growth bars in tail feathers of great tits (Parus major) captured in 19 forest plots along a gradient of increasing structural complexity. After validating our assumption that series of growth bars reflect the outcome of an energetic income vs. expenditure balance with possible consequences for health and fitness, we show that birds in structurally more complex forests maintain a better nutritional status, probably due to a higher and more stable availability of food resources and more sheltered habitat. Dominant tree species, architecture and associated ecology also play an important role, as some of the observed relationships appear to be tree species-specific. Overall, we demonstrate the importance of a complex forest structure and herb layer, and argue for the need for individual research on the condition, performance and health of forest species to inform managers and policymakers on the impact of changing forestry practices on biodiversity and ecosystem functioning.

Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR

Forest landscape restoration is a global priority to mitigate negative effects of climate change, conserve biodiversity, and ensure future sustainability of forests, with international pledges concentrated in tropical forest regions. To hold restoration efforts accountable and monitor their outcomes, traditional strategies for monitoring tree cover increase by field surveys are falling short, because they are labor-intensive and costly. Meanwhile remote sensing approaches have not been able to distinguish different forest types that result from utilizing different restoration approaches (conservation versus production focus). Unoccupied Aerial Vehicles (UAV) with light detection and ranging (LiDAR) sensors can observe forests` vertical and horizontal structural variation, which has the potential to distinguish forest types. In this study, we explored this potential of UAV-borne LiDAR to distinguish forest types in landscapes under restoration in southeastern Brazil by using a supervised classification method. The study area encompassed 150 forest plots with six forest types divided in two forest groups: conservation (remnant forests, natural regrowth, and active restoration plantings) and production (monoculture, mixed, and abandoned plantations) forests. UAV-borne LiDAR data was used to extract several Canopy Height Model (CHM), voxel, and point cloud statistic based metrics at a high resolution for analysis. Using a random forest classification model we could successfully classify conservation and production forests (90% accuracy). Classification of the entire set of six types was less accurate (62%) and the confusion matrix showed a divide between conservation and production types. Understory Leaf Area Index (LAI) and the variation in vegetation density in the upper half of the canopy were the most important classification metrics. In particular, LAI understory showed the most variation, and may help advance ecological understanding in restoration. The difference in classification success underlines the difficulty of distinguishing individual forest types that are very similar in management, regeneration dynamics, and structure. In a restoration context, we showed the ability of UAV-borne LiDAR to identify complex forest structures at a plot scale and identify groups and types widely distributed across different restored landscapes with medium to high accuracy. Future research may explore a fusion of UAV-borne LiDAR with optical sensors, and include successional stages in the analyses to further characterize and distinguish forest types and their contributions to landscape restoration.

A Method for Forest Canopy Height Inversion Based on UAVSAR and Fourier–Legendre Polynomial—Performance in Different Forest Types

Mapping forest canopy height at large regional scales is of great importance for the global carbon cycle. Polarized interferometric synthetic aperture radar is an efficient and irreplaceable remote sensing tool. Developing an efficient and accurate method for forest canopy height estimation is an important issue that needs to be addressed urgently. In this paper, we propose a novel four-stage forest height inversion method based on a Fourier–Legendre polynomial (FLP) with reference to the RVoG three-stage method, using the multi-baseline UAVSAR data from the AfriSAR project as the data source. The third-order FLP is used as the vertical structure function, and a small amount of ground phase and LiDAR canopy height is used as the input to solve and fix the FLP coefficients to replace the exponential function in the RVoG three-stage method. The performance of this method was tested in different forest types (mangrove and inland tropical forests). The results show that: (1) in mangroves with homogeneous forest structure, the accuracy based on the four-stage FLP method is better than that of the RVoG three-stage method. For the four-stage FLP method, R2 is 0.82, RMSE is 6.42 m and BIAS is 0.92 m, while the R2 of the RVoG three-stage method is 0.77, RMSE is 7.33 m, and bias is −3.49 m. In inland tropical forests with complex forest structure, the inversion accuracy based on the four-stage FLP method is lower than that of the RVoG three-stage method. The R2 is 0.50, RMSE is 11.54 m, and BIAS is 6.53 m for the four-stage FLP method; the R2 of the RVoG three-stage method is 0.72, RMSE is 8.68 m, and BIAS is 1.67 m. (2) Compared to the RVoG three-stage method, the efficiency of the four-stage FLP method is improved by about tenfold, with the reduction of model parameters. The inversion time of the FLP method in a mangrove forest is 3 min, and that of the RVoG three-stage method is 33 min. In an inland tropical forest, the inversion time of the FLP method is 2.25 min, and that of the RVoG three-stage method is 21 min. With the application of large regional scale data in the future, the method proposed in this study is more efficient when conditions allow.

Derivation and assessment of forest-relevant polarimetric indices using RCM compact-pol data

ABSTRACT Three radar polarimetric indices, Radar Vegetation Index (RVI), Canopy Structure Index (CSI) and Radar Forest Degradation Index (RFDI)—normally associated with quad-pol (QP) synthetic aperture radar (SAR) sensors—were derived using compact polarimetry (CP) data from the Radarsat Constellation Mission (RCM). Indices were generated over a 10,000-hectare temperate mixedwood forest containing a range of complex forest structures. For comparative purposes, the same indices were generated using Radarsat-2 QP data. Agreement between CP and QP indices were assessed across broad vegetated land cover types, at the forest stand-level wherein ground plots and airborne LiDAR data were available, and at the pixel level within validation stands representing different forest types. Agreement was consistently strong for the RVI and weak for the CSI, with agreement stronger when generalized to the stand level. Indices were more informative on differences between vegetation types (e.g. forest and open wetland) than between forest types with different structures. With a radar nominal off-nadir incidence angle at 38°, RCM CP indices had narrower dynamic ranges compared to Radarsat-2 QP indices, especially the CSI, contributing to a lower level agreement for the CSI. CP data enables derivation of RVI, CSI and RFDI indices simultaneously, and provides large spatial coverage and capacity for dense time-series collection, features which are unavailable from current QP sensors.

Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar

Modeling forest attributes using lidar data has been a useful tool for forest management but the need to correlate lidar to ground-based measurements creates challenges to modeling in diverse forest landscapes. Many lidar models have been based on metrics derived from summarizations of individual lidar returns over sample plot areas, but more recently, metrics based on summarization by volumetric pixel (voxel) have shown promise to better characterize forest structure and distinguish between diverse forest types. Voxel-based metrics may improve characterization of leaf area distribution and horizontal forest structure, which could help create general models of forest attributes applicable in complex landscapes composed of many distinct forest types. We modeled wood volume in longleaf pine woodlands and associated forests to compare how area- and voxel- based lidar metrics predicted wood volume in forest type specific and general predictive models. We created four area-based and six voxel-based metrics to fit models of wood volume using a multiplicative power function. We selected models and compared metric importance using AIC and evaluated model performance using cross-validated mean prediction error. We found that one area-based metric and four voxel-based metrics consistently improved model predictions We suggest that area-based metrics alone may have limitations for characterizing complex forest structure. Area-based summarizes of lidar returns are more heavily influenced by upper canopy returns because lidar returns attenuate below the canopy. By contrast, summarizing lidar returns into a single value per voxel prior to summarization over plots homogenizes point density, giving added weight to sub-canopy returns. Thus voxel-based metrics may be more sensitive to structural variation that may not be adequately captured by area-based metrics alone. This study highlights the potential of voxel-based metrics for characterizing complex forest structure and model generalization capable of accurate forest attribute prediction across diverse forest types.

Dynamic stratification for vertical forest structure using aerial laser scanning over multiple spatial scales

• Develop a method to solve the problem of the overlapped tree crowns delineation. • Develop a new method for the multi-scale and dynamic forest stratification. • Quantitatively evaluate and compare the raw and the stratified canopy cover changes. The distinct vertical structure (i.e., “overstory-understory-grass”) in natural forest stand causes significant differences in species composition, carbon–water cycles, and phenology variations in different vertical layers. Due to the complex forest structure and apparent topographic variations, it remains challenging to stratify a forest stand with highly overlapped tree crowns on a large spatial scale. This work successfully developed a lidar-based method to stratify vertical forest structures with different canopy covers and tree species by separating understory using various stratification heights with the aerial laser scanning (ALS) data. Our results showed that: (1) The F-score values of forest overstory segmentation changed from 0.83 to 1 as forest density increased, and their values improved by 0.08 and 0.1 for coniferous and mixed forest stand by using our newly developed method, respectively. (2) The correlation coefficients (R 2 ) between the visual- and ALS-based forest canopy cover increased from 0.72 to 0.84 by masking forest understory layers. The values of the F-score ranged from 0.5 to 1 as the plot density changed in the understory. (3) Varied stratification heights showed apparent effects on vertical forest structure separation. Our work provides a solid foundation for accurately estimating the forest structural parameters (i.e., leaf area index and biomass) and precision forestry.

Forest microclimate and composition mediate long-term trends of breeding bird populations.

Climate change is contributing to biodiversity redistributions and species declines. However, cooler microclimate conditions provided by old-growth forest structures compared with surrounding open or younger forests have been hypothesized to provide thermal refugia for species that are sensitive to climate warming and dampen the negative effects of warming on population trends of animals (i.e., the microclimate buffering hypothesis). In addition to thermal refugia, the compositional and structural diversity of old-growth forest vegetation itself may provide resources to species that are less available in forests with simpler structure (i.e., the insurance hypothesis). We used 8 years of breeding bird abundance data from a forested watershed, accompanied with sub-canopy temperature data, and ground- and LiDAR-based vegetation data to test these hypotheses and identify factors influencing bird population changes from 2011 to 2018. After accounting for imperfect detection, we found that for 5 of 20 bird species analyzed, abundance trends tended to be less negative or neutral at sites with cooler microclimates, which supports the microclimate buffering hypothesis. Negative effects of warming on two species were also reduced in locations with greater forest compositional diversity supporting the insurance hypothesis. We provide the first empirical evidence that complex forest structure and vegetation diversity confer microclimatic advantages to some animal populations in the face of climate change. Conservation of old-growth forests, or their characteristics in managed forests, could help slow the negative effects of climate warming on some breeding bird populations via microclimate buffering and possibly insurance effects.

Assessing the effects of burn severity on post-fire tree structures using the fused drone and mobile laser scanning point clouds

Wildfires burn heterogeneously across the landscape and create complex forest structures. Quantifying the structural changes in post-fire forests is critical to evaluating wildfire impacts and providing insights into burn severities. To advance the understanding of burn severities at a fine scale, forest structural attributes at the individual tree level need to be examined. The advent of drone laser scanning (DLS) and mobile laser scanning (MLS) has enabled the acquisition of high-density point clouds to resolve fine structures of individual trees. Yet, few studies have used DLS and MLS data jointly to examine their combined capability to describe post-fire forest structures. To assess the impacts of the 2017 Elephant Hill wildfire in British Columbia, Canada, we scanned trees that experienced a range of burn severities 2 years post-fire using both DLS and MLS. After fusing the DLS and MLS data, we reconstructed quantitative structure models to compute 14 post-fire biometric, volumetric, and crown attributes. At the individual tree level, our data suggest that smaller pre-fire trees tend to experience higher levels of crown scorch than larger pre-fire trees. Among trees with similar pre-fire sizes, those within mature stands (age class: > 50 years) had lower levels of crown scorch than those within young stands (age class: 15—50 years). Among pre-fire small- and medium-diameter trees, those experiencing high crown scorch had smaller post-fire crowns with unevenly distributed branches compared to unburned trees. In contrast, pre-fire large-diameter trees were more resistant to crown scorch. At the plot level, low-severity fires had minor effects, moderate-severity fires mostly decreased tree height, and high-severity fires significantly reduced diameter at breast height, height, and biomass. Our exploratory factor analyses further revealed that stands dominated by trees with large crown sizes and relatively wide spacing could burn less severely than stands characterized by regenerating trees with high crown fuel density and continuity. Overall, our results demonstrate that fused DLS-MLS point clouds can be effective in quantifying post-fire tree structures, which facilitates foresters to develop site-specific management plans. The findings imply that the management of crown fuel abundance and configuration could be vital to controlling burn severities.

Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest

Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.