Forest Vertical Structure Research Articles

Near-Complete Sampling of Forest Structure from High-Density Drone Lidar Demonstrated by Ray Tracing

Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar.

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

ABSTRACT Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests.

Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning

Vertical forest structure is closely linked to multiple ecosystem characteristics, such as biodiversity, habitat, and productivity. Mixing tree species in planted forests has the potential to create diverse vertical forest structures due to the different physiological and morphological traits of the composing tree species. However, the relative importance of species richness, species identity and species interactions for the variation in vertical forest structure remains unclear, mainly because traditional forest inventories do not observe vertical stand structure in detail. Terrestrial laser scanning (TLS), however, allows to study vertical forest structure in an unprecedented way. Therefore, we used TLS single scan data from 126 plots across three experimental planted forests of a large-scale tree diversity experiment in Belgium to study the drivers of vertical forest structure. These plots were 9–11 years old young pure and mixed forests, characterized by four levels of tree species richness ranging from monocultures to four-species mixtures, across twenty composition levels. We generated vertical plant profiles from the TLS data and derived six stand structural variables. Linear mixed models were used to test the effect of species richness on structural variables. Employing a hierarchical diversity interaction modelling framework, we further assessed species identity effect and various species interaction effects on the six stand structural variables. Our results showed that species richness did not significantly influence most of the stand structure variables, except for canopy height and foliage height diversity. Species identity on the other hand exhibited a significant impact on vertical forest structure across all sites. Species interaction effects were observed to be site-dependent due to varying site conditions and species pools, and rapidly growing tree species tend to dominate these interactions. Overall, our results highlighted the importance of considering both species identity and interaction effects in choosing suitable species combinations for forest management practices aimed at enhancing vertical forest structure.

Plant community differentiation of desertification region in northwest Liaoning Province, China

Understanding plant communities in desertification area is the scientific basis of evaluating the local eco-environmental quality and carrying out desertification control. According to longitude, we divided the desertification area in northwest Liaoning Province into three regions: the eastern region (122°50'37″ -123°49'40″ E), the middle region (121°16'41″-122°35'00″ E), and the western region (119°20'03″ -120°02'41″ E), and investigated the plant communities in each region. The results showed that the proportion of forest and canopy density of tree layer increased from the west to the east. Ass. Pinus sylvestris var. mongolica and Ass. Populus sp. in the eastern, Ass. Pinus tabuliformis and Ass. Populus sp. in the middle, as well as Ass. P. tabuliformis and Ass. Prunus sibirica in the western were dominant communities, respectively. The proportion of brush community in the eastern, middle and western was 0, 22.2%, and 28.0%, respectively. Grasslands formed mainly by human disturbance in the eas-tern and middle regions. The total species numbers were 110 in the middle, 88 in the western and 75 in the eastern, respectively. Therophytes were dominant in the eastern and middle with proportions of 68.2% and 66.7%, respectively. Hemicryptophytes were the dominant type (36.3%) in the western region. The proportion of microphanerophyt, nanophanerophyte, chamaephyte, and geophyte increased from the eastern to the western. The species number, Shannon index, and Simpson diversity index of the middle were the highest among the three regions. Pielou evenness index increased gradually from the eastern to the western. Community similarity between the eastern and the western was the lowest, as shown by the β-biodiversity, and the similarity between the middle and the western was the highest. The community type, species number, characteristics of species composition and species biodiversity of the middle region had the characteristics of ecological transition zone. In general, vegetation status in the desertification area of northwest Liaoning Province was in good condition. There were still some problems including the monotonous vertical structure of forest and tree species as well as the serious human interference.

Impact of GEDI-Derived Forest Vertical Structure Characteristics on the Accuracy Gains in Regional Dominant Tree Species Mapping

Impact of GEDI-Derived Forest Vertical Structure Characteristics on the Accuracy Gains in Regional Dominant Tree Species Mapping

Exploring the influence of tree species richness on vertical structure variability in young plantations using terrestrial laser scanning

Forest structure spatially varies at the landscape-scale (between-plot) but also local-scale (within-plot). Both scales of structural variability have significant impacts on forest ecosystem functioning and stability. However, we still have a limited understanding of how tree species richness affects the spatial structural variability. In this study, we disentangled within- and between-plot variability in stand structural variables depending on tree species richness level. Terrestrial laser scanning (TLS) technology, capable of developing forest vertical structure in 3D detail, was applied in the FORBIO tree diversity experiment at three sites in Belgium. One site was 9-year old (Hechtel-Eksel) and other two sites were 11-year old (Zedelgem and Gedinne). Each site included five different sets of one, two, three, and four tree species mixtures with one replica. At each plot, four single scan locations were used. Six stand structural variables were quantified based on TLS vertical plant profiles. We hypothesized that within-plot structural variability would increase with species richness, but that the between-plot structural variability would decrease with species richness. Our study revealed notable differences in structural variability between monocultures and mixtures across all sites examined. However, the relationship between tree species richness and structural variability patterns is site-dependent. Specifically, at the Zedelgem site, we observed increased within-plot variability and decreased between-plot variability with species richness, supporting the hypothesis that species richness drives stand structural variability. However, the hypothesized pattern was not as strong at the Hechtel-Eksel and Gedinne sites. Moreover, the various structural variables displayed contrasting responses to changes in species richness. These findings indicated the potential influence of tree species richness on within- and between-plot structural variability in young plantations, particularly in favorable site conditions with well-adapted species pools. Hence, afforestation measures would benefit from planting multiple tree species to initiate structurally complex stands by considering species richness level.

Cohabitation Study of Tricolour Langur (Presbytis chrysomelas ssp. cruciger) and Proboscis Monkey (Nasalis larvatus) in Bukit Semujan Danau Sentarum National Park

The tricolour langur and proboscis monkey in Bukit Semujan cohabited and utilized the same resources. Cohabitation, mainly due to resource constraints, needed substantial attention to ensure the survival of these primates. This research aimed to map the utilization of canopy strata vertical forest structure and vegetation as food sources and identify the size and overlap of ecological niches for both primates. This research occurred in Danau Sentarurn National Park (DSNP) from July to December 2021. The data collection employed the ad libitum method. The results indicated that cohabitation occurred in the canopy strata vertical forest structure utilization, particularly stratum B and C, and was related to food availability for both primates. The Jaccard Index of the stratum utilization association was 0.6 for swamp forests and 0.8 for hillside forests, while the food plant association was 0.2. In addition, the ecological niche of the tricolour langur overlapped 48% against the proboscis monkey and 34% on the contrary. The tricolour langur's niche size (FT) was 0.32278 ≤ 0.5960 ≥ 0.81253, while the proboscis monkey was 0.20866 ≤ 0.52837 ≥ 0.78529 at 95% confidence interval.

Total and component forest aboveground biomass inversion via LiDAR-derived features and machine learning algorithms

Forest aboveground biomass (AGB) and its biomass components are key indicators for assessing forest ecosystem health, productivity, and carbon stocks. Light Detection and Ranging (LiDAR) technology has great advantages in acquiring the vertical structure of forests and the spatial distribution characteristics of vegetation. In this study, the 56 features extracted from airborne LiDAR point cloud data were used to estimate forest total and component AGB. Variable importance–in–projection values calculated through a partial least squares regression algorithm were utilized for LiDAR-derived feature ranking and optimization. Both leave-one-out cross-validation (LOOCV) and cross-validation methods were applied for validation of the estimated results. The results showed that four cumulative height percentiles (AIH30,AIH40, AIH20, and AIH25), two height percentiles (H8 and H6), and four height-related variables (Hmean, Hsqrt, Hmad, and Hcurt) are ranked more frequently in the top 10 sensitive features for total and component forest AGB retrievals. Best performance was acquired by random forest (RF) algorithm, with R2 = 0.75, root mean square error (RMSE) = 22.93 Mg/ha, relative RMSE (rRMSE) = 25.30%, and mean absolute error (MAE) = 19.26 Mg/ha validated by the LOOCV method. For cross-validation results, R2 is 0.67, RMSE is 24.56 Mg/ha, and rRMSE is 25.67%. The performance of support vector regression (SVR) for total AGB estimation is R2 = 0.66, RMSE = 26.75 Mg/ha, rRMSE = 28.62%, and MAE = 22.00 Mg/ha using LOOCV validation and R2 = 0.56, RMSE = 30.88 Mg/ha, and rRMSE = 31.41% by cross-validation. For the component AGB estimation, the accuracy from both RF and SVR algorithms was arranged as stem > bark > branch > leaf. The results confirmed the sensitivity of LiDAR-derived features to forest total and component AGBs. They also demonstrated the worse performance of these features for retrieval of leaf component AGB. RF outperformed SVR for both total and component AGB estimation, the validation difference from LOOCV and cross-validation is less than 5% for both total and component AGB estimated results.

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.

Vertical and Horizontal Crown Fuel Continuity Influences Group-Scale Ignition and Fuel Consumption

A deeper understanding of the influence of fine-scale fuel patterns on fire behavior is essential to the design of forest treatments that aim to reduce fire hazard, enhance structural complexity, and increase ecosystem function and resilience. Of particular relevance is the impact of horizontal and vertical forest structure on potential tree torching and large-tree mortality. It may be the case that fire behavior in spatially complex stands differs from predictions based on stand-level descriptors of the fuel distribution and structure. In this work, we used a spatially explicit fire behavior model to evaluate how the vertical and horizontal distribution of fuels influences the potential for fire to travel from the surface into overstory tree crowns. Our results support the understanding that crown fuels (e.g., needles and small-diameter branchwood) close to the surface can aid in this transition; however, we add important nuance by showing the interactive effect of overstory horizontal fuel connectivity. The influence of fuels low in the canopy space was overridden by the effect of horizontal connectivity at surface fire-line intensities greater than 1415 kW/m. For example, tree groups with vertically continuous fuels and limited horizontal connectivity sustained less large-tree consumption than tree groups with a significant vertical gap between the surface and canopy but high-canopy horizontal connectivity. This effect was likely the result of reduced net vertical heat transfer as well as decreased horizontal heat transfer, or crown-to-crown spread, in the upper canopy. These results suggest that the crown fire hazard represented by vertically complex tree groups is strongly mediated by the density, or horizontal connectivity, of the tree crowns within the group, and therefore, managers may be able to mitigate some of the torching hazard associated with vertically heterogenous tree groups.

Forest restoration thinning accelerates development of old‐growth characteristics in the coastal Pacific Northwest,USA

AbstractA century of industrial‐scale management has transformed vast swaths of forest land across the Pacific Northwest (PNW), USA, from ancient forests with complex structure and diverse habitats to young forests with simple structure and dominated by few species. Consequently, there have been calls to restore ecosystem integrity and resilience. Here, we apply data from a watershed‐scale experiment to determine if restoration treatments have achieved our management goal of accelerating the development of old‐growth forest characteristics. We provide empirical evidence of how restoration treatments have affected key old‐growth forest indicators resulting in larger trees, more complex vertical and horizontal forest structure, reduced stand density, and increased understory plant richness. Our study also demonstrates that some restoration indicators responded in counter‐intuitive ways contingent on interactions between stand age and restoration treatment. Through this work, we learned two important lessons: (1) more time and monitoring may be needed to fully understand the effects of restoration treatments and (2) a “one and done” approach of implementing restoration treatments may not achieve a full suite of old‐growth characteristics. Moreover, long‐term management for wildlife habitat and climate resilience will likely require an adaptive approach, with ongoing monitoring continually informing and adjusting management practices.

Can models for forest attributes based on airborne laser scanning be generalized for different silvicultural management systems?

In Finland, interest in continuous cover forestry (CCF) has increased rapidly in recent years. During those years CCF has been examined from various viewpoints but not from the perspective of forest inventories. This holds especially true for applications based on remote sensing. Conversely, airborne laser scanning (ALS) data have been widely used to predict forest characteristics such as size distribution and vertical forest structure, which are closely related to the forest information needs of CCF.In this study we used the area-based approach to predict a set of stand attributes from ALS data (5 pulses per m2) in a CCF forest management experiment in Katajamäki, eastern Finland. In addition to the CCF stands, the experiment included shelterwood stands and untreated stands. The predicted attributes included volume, biomass, basal area, number of stems, mean diameter, Lorey’s height, dominant height, standing dead wood volume, parameters of the theoretical stem diameter distribution model, understory height and number of understory stems. Our main aim was to test whether the same model could be used across different management systems. The accuracy of the attributes predicted for the CCF stands was compared with the predictions for the other management systems in the same experiment. We also compared and discussed our results in relation to the even-aged stand attribute predictions that were conducted by using separate operational forest data collected from sites surrounding Katajamäki.The results showed that forest data from the different management systems could be combined into a single model of a stand attribute, i.e., ALS metrics were found to be suitable for comparing different management systems in regard to differences in forest structure. The accuracy of the predicted attributes in the CCF plots was comparable to that of the other management alternatives in the experiment. The accuracy was also comparable to that of even-aged forests.The results of this study were promising; the stand attributes of CCF-managed forests could be predicted analogously to those of other management systems. This indicates that for the purposes of forest inventories there may not be a need to stratify forest lands by management system. It should be noted, however, that the study area was relatively small, that the forest stands were harvested in the 1980 s, and that the attributes may not have been completely exhaustive for CCF.

Mapping the vertical forest structure in a large subtropical region using airborne LiDAR data

Vertical forest structure (VFS) refers to the vertical stratification or layering of forest communities in space, which is a fundamental characteristic of a plant community. It plays a vital role in forest vitality and facilitates various ecological activities and processes. The mapping of VFS is of significant value for both ecological and forestry purposes. In this paper, we presented a novel approach for the automated mapping of VFS in a large subtropical region based on discrete airborne LiDAR data. Firstly, the LiDAR point clouds of a stand (plot or grid cell) were segmented into 100 height bins from the top to the ground, and a height-frequency histogram was obtained by calculating the proportion of the number of returns in the bins to the total number of returns, which objectively represented the vertical distribution of canopy material. Secondly, a univariate ten-order polynomial was used to fit the height-frequency histogram, enabling the generation of a continuous vertical canopy profile (pseudo-waveform) of the stand. Thirdly, a comprehensive set of vertical structure parameters was defined and extracted based on the pseudo-waveforms, which effectively characterized the vertical profile layer and the canopy layer. Fourthly, to construct a comprehensive framework, 43 model profiles were summarized from the field plots, taking into account the number of effective peaks in the pseudo-waveforms and other vertical structure parameters. Finally, 43 classification rules were developed and 18 judgment criteria were established using the vertical structure parameters of the mode profiles. They classified vertical forest structures into 24 classes with explicit spatial definitions. The classification of 1147 field plots resulted in an overall accuracy of 94.7% and a kappa coefficient of 0.937. The VFS mapping over a large area demonstrated an effective execution rate of 99.8% for both rules and criteria. The proposed approach exhibits high accuracy and excellent generalization ability across different forest types, species, and study sites, highlighting its ecological and forestry significance. It represents a significant advance in the automated classification and mapping of VFS in large subtropical regions using airborne LiDAR data. However, the proposed approach needs to be validated in other vegetation zones to assess its generalizability and extend its applicability.

Carbon Storage Estimation of Quercus aquifolioides Based on GEDI Spaceborne LiDAR Data and Landsat 9 Images in Shangri-La

The assessment of forest carbon storage plays a crucial role in forest management and ecosystem exploration, enabling the evaluation of forest quality, resources, carbon cycle and management. The Global Ecosystem Dynamics Investigation (GEDI) satellite provides a means to accurately measure these various forest vertical structure parameters by penetrating the forest canopy. However, the distribution of the footprint along the orbit track is heterogeneous and discontinuous, preventing the acquisition of spatially distributed carbon storage formation at the county level. Consequently, this study integrated GEDI and Landsat 9 data to estimate Quercus aquifolioides carbon storage in Shangri-La. By applying the Kriging interpolation to previously pretreated footprints, surface information from the GEDI L2B footprints was obtained. At the same time, Landsat 9 vegetation indices and band reflectance were extracted to analyze the correlation with the carbon storage of Quercus aquifolioides samples. Then, three methods (support vector machine, bagging, and random forest) were used to create a carbon storage estimation model for Shangri-La. The research results showed that (1) among the models for the selection of GEDI footprint parameters based on semi-variance, the optimal model of the digital_elevation_model was the spherical model, while the best model of percentage tree cover from the MODIS data (modis_treecover) and the foliage height diversity index (fhd_normal) was the exponential model. (2) Analyzing the thirty-three extracted independent variable factors correlated with the carbon storage of Quercus aquifolioides showed that the top five variables with the highest correlation were digital_elevation_model, modis_treecover, fhd_normal, DEM, and band 1 (B1). (3) After variable selection, the R2 = 0.82 and RMSE = 11.92 t/hm2 values of the Quercus aquifolioides carbon storage estimation model established via random forest were obtained, and its evaluation precision was superior to that of the support vector machine method and bagging regression. The carbon storage of Quercus aquifolioides was primarily in the range of 8.22~94.63 t/hm2, and the mean value was 42.44 t/hm2, while the total carbon storage was about 5,374,137.62 t. The findings from this paper illustrated the feasibility of obtaining carbon storage data on a county scale by combining GEDI LiDAR data with Landsat 9 optical data. The results also suggested a new perspective for combining GEDI L2B data with other remote sensing images to estimate other forest structure parameters.

Taxonomic, functional, and phylogenetic diversity of primate communities in Ecuador

One of the fundamental objectives of ecology is to study the relative importance of ecological, evolutionary, and stochastic processes in determining local community structure. Many studies have focused on taxonomic diversity, paying relatively little attention to other dimensions of biological diversity, such as phylogenetic and functional diversity. Little is known about how these dimensions relate to each other, and the ecological processes that influence their variation. In this study, we characterize these three dimensions of biodiversity in 14 primate communities from different ecosystems in Ecuador to understand possible mechanisms responsible for their assembly. Results show that coastal communities are taxonomically less diverse than those from the Amazon and present different functional groups. On the other hand, phylogenetic diversity is higher in Amazonian communities, showing a tendency for overdispersion (high values of MPDPD = mean phylogenetic distance per pair and low PSC = degree to which coexisting species are related). This indicates a possible influence of biological factors, such as competition, on community assembly. For all three dimensions, climatic variables were the most significant predictors of community structure, while vertical forest structure contributed significantly to variation in the phylogenetic dimension. The high functional diversity reported in this study highlights the importance and vulnerability of this group and the ecosystems they inhabit. Macroecological studies, such as the one presented here, allow a better understanding of community structure and provide important information for the development of conservation strategies.

Forest Vertical Structure Mapping Using Multi-Seasonal UAV Images and Lidar Data via Modified U-Net Approaches

With the acceleration of global warming, research on forests has become important. Vertical forest structure is an indicator of forest vitality and diversity. Therefore, further studies are essential. The investigation of forest structures has traditionally been conducted through in situ surveys, which require substantial time and money. To overcome these drawbacks, in our previous study, vertical forest structure was mapped through machine learning techniques and multi-seasonal remote sensing data, and the classification performance was improved to a 0.92 F1-score. However, the use of multi-seasonal images includes tree location errors owing to changes in the timing and location of acquisition between images. This error can be reduced by using a modified U-Net model that generates a low-resolution output map from high-resolution input data. Therefore, we mapped vertical forest structures from a multi-seasonal unmanned aerial vehicle (UAV) optic and LiDAR data using three modified U-Net models to improve mapping performance. Spectral index maps related to forests were calculated as optic images, and canopy height maps were produced using the LiDAR-derived digital surface model (DSM) and digital terrain model (DTM). Spectral index maps and filtered canopy height maps were then used as input data and applied to the following three models: (1) a model that modified only the structure of the decoder, (2) a model that modified both the structure of the encoder and decoder, and (3) a model that modified the encoder, decoder, and the part that concatenated the encoder and decoder. Model 1 had the best performance with an F1-score of 0.97. The F1-score value was higher than 0.9 for both Model 2 and Model 3. Model 1 improved the performance by 5%, compared to our previous research. This implies that the model performance is enhanced by reducing the influence of position error.

An Individual Tree Segmentation Method That Combines LiDAR Data and Spectral Imagery

The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications.

Spaceborne LiDAR reveals the effectiveness of European Protected Areas in conserving forest height and vertical structure

The effectiveness of Protected Areas in conserving forest ecosystems has been examined at the continental scale using area-based habitat parameters, but knowledge of the three-dimensional structure of forest habitats is still lacking. Here, we assess the effectiveness of European Protected Areas in conserving the vertical structure of forests by analysing more than 30 million records from the Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR (Light Detection And Ranging) mission. We compare a suite of indicators of the vertical structure of forests inside and outside nearly 10,000 protected areas. We find that European forests are on average 2 m taller and vertically more complex in protected areas than in nearby unprotected areas, albeit with some regional differences. At the same time, forests outside protected areas show greater variations in canopy height than inside, probably as a result of past and current forest management operations. Our findings highlight the positive imprint of environmental policies on forest structure across Europe and underscore how spaceborne LiDAR enables the large-scale monitoring of forest vertical structural attributes that are key to conservation and restoration policies.

Using simulated GEDI waveforms to evaluate the effects of beam sensitivity and terrain slope on GEDI L2A relative height metrics over the Brazilian Amazon Forest

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by lidar sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor collects data systematically over the Earth's surface from the International Space Station. Since GEDI became operational, it has collected billions of ∼25-m diameter footprints. This massive dataset has been used to create higher level gridded and non-grided products. However, GEDI's ∼25-m footprints can be subject to errors associated with effects of geolocation, terrain slope, and beam sensitivity, among others, which are likely transferred to the downstream products. This study aims to (1) evaluate the effect of beam sensitivity and terrain slope on the accuracy of relative heights (RH) of GEDI product L2A version 2 through comparison with discrete-return airborne lidar data collected over transects in the Brazilian Amazon Forest biome, (2) assess GEDI's geolocation uncertainty and investigate its combined effects with beam sensitivity and terrain slope, and (3) re-evaluate beam sensitivity and terrain slope effects on the GEDI L2A RHs using footprints that were geolocation-adjusted through a simple novel approach. The analysis separates GEDI footprints by acquisition time, i.e., daytime, nighttime, and combined (all-data). The discrete-return airborne lidar point clouds are used to derive terrain slope within the GEDI footprints and to simulate GEDI waveforms and derive RHs comparable to the GEDI L2A product. Results indicate that terrain slope only causes significant effects on GEDI data collected during daytime because solar radiation affects waveform signal-to-noise ratio. Beam sensitivity causes significant effects on nighttime and all-data GEDI L2A RHs. If geolocation uncertainty is considered, the effects of beam sensitivity and terrain slope have just minor changes. Geolocation-adjusted data continue showing significant effects on nighttime and all-data RH differences caused by beam sensitivity but produce unbiased results. This study improves the understanding of how beam sensitivity, terrain slope, and their combined effect with geolocation uncertainty may affect the GEDI L2A RH collected over the Brazilian Amazon forest during daytime and nighttime.