Forest Vertical Structure Research Articles

Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method

Forest resource management and ecological assessment have been recently supported by emerging technologies. Terrestrial laser scanning (TLS) is one that can be quickly and accurately used to obtain three-dimensional forest information, and create good representations of forest vertical structure. TLS data can be exploited for highly significant tasks, particularly the segmentation and information extraction for individual trees. However, the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness, and hence do not lead to satisfactory results for natural forests in complex environments. In this paper, we propose a trunk-growth (TG) method for single-tree point-cloud segmentation, and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan, China. First, the point normal vector and its Z-axis component are used as trunk-growth constraints. Then, the points surrounding the trunk are searched to account for regrowth. Finally, the nearest distributed branch and leaf points are used to complete the individual tree segmentation. The results show that the TG method can effectively segment individual trees with an average F-score of 0.96. The proposed method applies to many types of trees with various growth shapes, and can effectively identify shrubs and herbs in complex scenes of natural forests. The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

The impact of land-use legacies and recent management on natural disturbance susceptibility in mountain forests

Mountain forests provide a wide range of ecosystem services, including carbon sequestration and protection from natural hazards. Forest cover in the European Alps has increased over the last century, but in recent years, these forests have experienced an increasing rate of natural disturbances by agents such as windthrow, bark beetle outbreaks, and forest fires. These disturbances pose a challenge for forest management, making it important to understand how site and stand characteristics, land use legacies and recent management influence disturbance probability. We combined a dataset of forest disturbances detected from space with in-situ forest management records, allowing us to differentiate between different types of disturbances for the Canton of Graubünden, Switzerland, in the years 2005–2018. The resulting dataset of over 28′000 attributed disturbance patches (corresponding to a disturbed forest area of ca. 23′600 ha) was combined with information on topography, forest structure, and historical forest cover. A machine-learning approach was used to investigate the non-linear and interacting relationships between potential drivers and disturbance occurrence. Natural disturbances (especially windthrow and bark beetle outbreaks) were most common at lower elevations, on shallow and south-facing slopes, and in even-aged, spruce-dominated stands with a closed canopy. Forests established in the 20th century were significantly more susceptible to natural disturbances than forests that were already present before 1880, which may be due to the uniform age and vertical structure of secondary forests, as well as legacy effects of former agricultural use. On the other hand, forest management more often took place in forests present before 1880. Management interventions (such as thinning) in turn increased the susceptibility to natural disturbances in the short term. This finding emphasizes the need to balance short-term increases in disturbance susceptibility with long-term benefits in forest resilience when planning management interventions in mountain forests. Our findings highlight the importance of considering multiple interactive drivers, including management and land-use history, for understanding forest disturbance regimes.

Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

Processing and analysis of airborne full-waveform laser scanning data for the characterization of forest structure and fuel properties

<p class="Bodytext">This PhD thesis addresses the development of full-waveform airborne laser scanning (ALS<sub>FW</sub>) processing and analysis methods to characterize the vertical forest structure, in particular the understory vegetation. In this sense, the influence of several factors such as pulse density, voxel parameters (voxel size and assignation value), scan angle at acquisition, radiometric correction and regression methods is analyzed on the extraction of ALS<sub>FW</sub> metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALS<sub>FW</sub> data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALS<sub>FW</sub>, discrete airborne laser scanning (ALS<sub>D</sub>) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALS<sub>FW</sub> data, as well as characterized by using the new proposed metrics.</p>

Time of night and moonlight structure vertical space use by insectivorous bats in a Neotropical rainforest: an acoustic monitoring study.

BackgroundPrevious research has shown diverse vertical space use by various taxa, highlighting the importance of forest vertical structure. Yet, we know little about vertical space use of tropical forests, and we often fail to explore how this three-dimensional space use changes over time.MethodsHere we use canopy tower systems in French Guiana and passive acoustic monitoring to measure Neotropical bat activity above and below the forest canopy throughout nine nights. We use a Bayesian generalized linear mixed effect model and kernel density estimates to demonstrate patterns in space-use over time.ResultsWe found that different bats use both canopy and understory space differently and that these patterns change throughout the night. Overall, bats were more active above the canopy (including Cormura brevirostris, Molossus molossus, Peropteryx kappleri and Peropteryx macrotis), but multiple species or acoustic complexes (when species identification was impossible) were more active in the understory (such as Centronycteris maximiliani, Myotis riparius, Pteronotus alitonus and Pteronotus rubiginosus). We also found that most bats showed temporally-changing preferences in hourly activity. Some species were less active (e.g., P. kappleri and P. macrotis), whereas others were more active (Pteronotus gymnonotus, C. brevirostris, and M. molossus) on nights with higher moon illuminance.DiscussionHere we show that Neotropical bats use habitat above the forest canopy and within the forest understory differently throughout the night. While bats generally were more active above the forest canopy, we show that individual groups of bats use space differently over the course of a night, and some prefer the understory. This work highlights the need to consider diel cycles in studies of space use, as animals use different habitats during different periods of the day.

Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco

Anthropogenic activity leading to forest structural and functional changes needs specific ecological indicators and monitoring techniques. Since decades, forest structure, composition, biomass, and functioning have been studied with ground-based forest inventories. Nowadays, satellites survey the earth, producing imagery at different spatial and temporal resolutions. However, measuring the ecological state of large extensions of forest is still challenging. To reconstruct the three-dimensional forest structure, the structure from motion (SfM) algorithm was applied to imagery taken by an unmanned aerial vehicle (UAV). Structural indicators from UAV-SfM products are then compared to forest inventory indicators of 64 circular plots of 1000 m2 in a subtropical dry forest. Our data indicate that the UAV-SfM indicators provide a valuable alternative for ground-based forest inventory’ indicators of the upper canopy structure. Based on the correlation between ground-based measures and UAV-SfM derived indicators, we can state that the UAV-SfM technique provides reliable estimates of the mean and maximum height of the upper canopy. The performance of UAV-SfM techniques to characterize the undergrowth forest structure is low, as UAV-SfM indicators derived from the point cloud in the lower forest strata are not suited to provide correct estimates of the vegetation density in the lower strata. Besides structural information, UAV-SfM derived indicators, such as canopy cover, can provide relevant ecological information as the indicators are related to structural, functional, and/or compositional aspects, such as biomass or compositional dominance. Although UAV-SfM techniques cannot replace the wealth of data collected during ground-based forest inventories, its strength lies in the three-dimensional (3D) monitoring of the tree canopy at cm-scale resolution, and the versatility of the technique to provide multi-temporal datasets of the horizontal and vertical forest structure.

Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

Discovering forest height changes based on spaceborne lidar data of ICESat-1 in 2005 and ICESat-2 in 2019: a case study in the Beijing-Tianjin-Hebei region of China

BackgroundThe assessment of change in forest ecosystems, especially the change of canopy heights, is essential for improving global carbon estimates and understanding effects of climate change. Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests. NASA’s Ice, Cloud and Land Elevation Satellites, ICESat-1 for the period 2003 to 2009, and ICESat-2 (available since 2018), have collected elevation data over the Earth’s surface with a time interval of 10 years. In this study, we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005 (complete global coverage with 1 km resolution) derived from ICESat-1 data and the ATL08 land and vegetation products of 2019 (sampling footprints with 17 m diameter) from ICESat-2.ResultsOur study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region. Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level (− 8.0 to 6.2 m) to site-level (− 1.5 to 1.1 m), while RMSE values are still relatively high (6.1 to 15.2 m, 10.2 to 12.0 m). Additionally, 58% of ATL08 data are located in ‘0 m’ pixels with an average height of 7.9 m, which are likely to reflect the ambitious tree planting programs in China.ConclusionsOur study shows that it is possible, with proper calibrations, to use ICESat-1 and -2 products to detect forest canopy height changes in a regional context. We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.

Pol-InSAR sensitivity to hemi-boreal forest structure at L- and P-bands

A dual-frequency approach is proposed to discriminate hemi-boreal forest species using L- and P-band data. First, it is shown that L-band is less sensitive to the forest structure than P-band. Second, the underlying ground topography is estimated using polarimetric sub-space decomposition at P-band to minimize any potential bias. Then, the Gaussian backscatter model is employed to extract the forest vertical backscatter at P-band. The inverted profiles differ significantly over the various forest stands and in the polarimetric channels, indicating important P-band sensitivity to the forest vertical structure. According to these measurements, it is shown that tree species, as pines and spruces, were presenting different signatures related to the vertical distribution of their structural elements. On the contrary, sensitivity to the forest vertical heterogeneity is low at L-band, allowing to accurately invert the forest height from an empirical structure function. A general workflow taking advantage of both frequencies is finally proposed to discriminate the forest species.

Performance of terrestrial laser scanning to characterize managed Scots pine (Pinus sylvestris L.) stands is dependent on forest structural variation

There is a limited understanding of how forest structure affects the performance of methods based on terrestrial laser scanning (TLS) in characterizing trees and forest environments. We aim to improve this understanding by studying how different forest management activities that shape tree size distributions affect the TLS-based forest characterization accuracy in managed Scots pine (Pinus sylvestris L.) stands. For that purpose, we investigated 27 sample plots consisting of three different thinning types, two thinning intensities as well as control plots without any treatments. Multi-scan TLS point clouds were collected from the sample plots, and a point cloud processing algorithm was used to segment individual trees and classify the segmented point clouds into stem and crown points. The classified point clouds were further used to estimate tree and forest structural attributes. With the TLS-based forest characterization, almost 100% completeness in tree detection, 0.7 cm (3.4%) root-mean-square-error (RMSE) in diameter-at-breast-height measurements, 0.9–1.4 m (4.5–7.3%) RMSE in tree height measurements, and <6% relative RMSE in the estimates of forest structural attributes (i.e. mean basal area, number of trees per hectare, mean volume, basal area-weighted mean diameter and height) were obtained depending on the applied thinning type. Thinnings decreased variation in horizontal and vertical forest structure, which especially favoured the TLS-based tree detection and tree height measurements, enabling reliable estimates for forest structural attributes. A considerably lower performance was recorded for the control plots. Thinning intensity was noticed to affect more on the accuracy of TLS-based forest characterization than thinning type. The number of trees per hectare and the proportion of suppressed trees were recognized as the main factors affecting the accuracy of TLS-based forest characterization. The more variation there was in the tree size distribution, the more challenging it was for the TLS-based method to capture all the trees and derive the tree and forest structural attributes. In general, consistent accuracy and reliability in the estimates of tree and forest attributes can be expected when using TLS for characterizing managed boreal forests.

Soundscape mapping for spatial-temporal estimate on bird activities in urban forests

Soundscape mapping provides a unique perspective to explain the complicated spatial-temporal change of bird activities in urban forests. Most studies of soundscapes have used multi-point distribution to record sounds, which is challenging for explaining complex interferences factors in urban areas. In this study, we used soundscape mapping to explore the habitat selection of bird communities in the context of spatial-temporal structural changes. We selected the transition area from city to forest in Shenzhen, China, as the study area, set up 30 recording points which arranged in grid patterns (5 × 6), and used synchronic recording methodology to collect sounds. Aural species identification, power spectral density (PSD), and normalized-difference sound index (NDSI) were used to quantify sounds. Passerine birds (92.11 %) were found to comprise the first acoustic communities. Changes in bird species populations among seasons were noted, with spring having the most abundant bird species number (n = 59) and the most abundant ecoacoustic events. Bird communities with different frequency band clusters having different preferences for vegetation characteristics were detected. A significant two-way interaction between the accessibility of recording points and seasons on bird activities was found in this study. Urban forest spatial structure had a great effect on bird activities, and specifically through the shape of forest vertical structure complexity and forest edge effect. Our study shows the broader application potential of soundscape mapping in urban forest ecosystem research.

Vertical Structure Classification of a Forest Sample Plot Based on Point Cloud Data

The vertical structure of forests affects energy transfer and material exchange within forest ecosystems and is of great significance to scientific forestry and ecology research. In this paper, the Shangri-La forest plot in northwestern Yunnan Province of China was the study area. The forest sample plot point cloud data were obtained by terrestrial laser scanning technology. A new method for classifying the vertical structure of forest sample plots based on point cloud data is proposed. The method comprehensively utilizes morphological filtering and comparative shortest-path (CSP) algorithm point cloud segmentation technology. Additionally, the method proposes the concept of secondary CSP segmentation that precisely classifies three types of vertical features in forest point cloud data: trees, shrubs and the ground. Finally, an accuracy analysis showed that the error rate of the tree results was 1.87%, and the error rate of the shrub results was 16.23%.

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

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

Stratifying Forest Overstory for Improving Effective LAI Estimation Based on Aerial Imagery and Discrete Laser Scanning Data

Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.

Persistent changes in the horizontal and vertical canopy structure of fire-tolerant forests after severe fire as quantified using multi-temporal airborne lidar data

Wildfires can influence the canopy structure of resprouter forests in multiple ways including crown scorching and consumption, tree damage and mortality, and changes in resources available for recovery. Few studies have quantified post-fire changes in the canopy structure of fire-tolerant eucalypt forests, which limits understanding of the extent and persistence of fire effects on ecosystem processes including interactions with environment. In this study, we assess the impacts of a landscape-scale wildfire on the canopy structure of fire-tolerant eucalypt forests in temperate Australia using metrics derived from airborne lidar data. Our assessment, made seven years after the wildfire, encompassed four fire severities (unburnt, low, moderate, high) and involved 1084 lidar plots (0.06 ha, including validation using 51 field plots) across an area of ~30,000 ha in a single forest type. We used Random Forest models to examine the relative importance of fire severity, pre-fire canopy metrics, climate and topography to the prediction of seven post-fire metrics representing the horizontal and vertical structure of the full canopy, the dominant and sub-dominant strata. Canopy cover at seven years post-fire was significantly reduced in moderate- and particularly high-severity plots (mean decrease of 30%). High-severity fire also decreased mean canopy height and led to changes in the canopy relief ratio and rumple index consistent with a dominant stratum that was more heterogeneous and fragmented. In addition to fire severity, the pre-fire values of each metric were important to their post-fire prediction, suggesting that canopy structural recovery after this fire could have been influenced by any lingering effects of previous fires. Topographic and climatic variables were of comparatively minor importance to the prediction of post-fire canopy metrics, although relationships with the forest drought stress index indicated that canopy recovery was slowest at the driest sites. Our study provides quantitative evidence of significant changes in the canopy structure of fire-tolerant eucalypt forests that persisted for at least seven years after high-severity wildfire. Recent trends in southern Australia of shortened wildfire intervals and a drier climate will likely narrow the windows for canopy recovery of fire-tolerant forests, realizing predictions of a gradual opening and fragmentation of forest canopies in temperate Australia and associated changes in multiple ecosystem processes.

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

Forest canopy height co‐determines taxonomic and functional richness, but not functional dispersion of mammals and birds globally

AbstractAimsTaller forest canopies may harbour higher biodiversity by providing more and varied resources. No previous studies have assessed whether forest canopy height shapes the taxonomic and functional diversity of terrestrial vertebrates at global and regional scales. Here, we examine the roles of forest canopy height and other environmental variables in shaping global and regional patterns of species richness and functional diversity of mammals and birds.LocationGlobal.Time periodPresent day.Major taxa studiedTerrestrial mammals and birds.MethodsGlobal forest canopy height data at 1 km spatial resolution were used to measure forest vertical structure. Species richness, functional richness and functional dispersion of mammals and birds were calculated using range maps and trait data. Spatial simultaneous autoregressive error models were used to evaluate associations between species richness and functional diversity and their predictors, including mean canopy height, standard deviation of canopy height, net primary productivity, current climate and historical climate stability, topography and human activities.ResultsMean canopy height emerged as one of two predictors most associated with species richness of mammals and birds as well as mammal functional richness. However, mean canopy height had little explanatory power for functional dispersion. Mean annual temperature and net primary productivity contributed most to explain global‐scale mammal and bird functional dispersion. At the regional scale, mean canopy height, net primary productivity and mean annual temperature were the variables most associated with the species richness and functional diversity of mammals and birds.Main conclusionsForest canopy height is an important predictor of species richness and functional diversity of terrestrial vertebrates at both global and regional scales, at a similar overall level to productivity and temperature. Our study highlights the crucial role of the complex vertical structure in shaping the global and regional patterns of vertebrate diversity.

Detection of sub-canopy forest structure using airborne LiDAR

Knowledge on forest structure is vital for sustainable forest management decisions. Currently, Airborne Laser Scanning (ALS) has been well established as an effective tool to delineate and characterize the structure of canopies across a range of forested biomes. However, the use of ALS to provide information on sub-canopy structure is less well developed. Sub-canopy structure consists of suppressed mature trees, regenerating tree saplings, shrubs, herbs, snags and coarse-woody-debris. With the increasing density of ALS point clouds, new opportunities exist to describe these sub-canopy structural components in forests that were previously difficult to detect using passive remote sensing technologies. In this research we use discrete return ALS data acquired at a density of 23 points × m2 to estimate sub-canopy forest structure for 48,000 ha of conifer dominated forest in central British Columbia, Canada. We first segmented the forest vertical structure into canopy and sub-canopy based on Lorey's mean height (HL). HL, which favours larger trees as a baseline for canopy height, provides separation between the taller trees that dominate the canopy and smaller trees that represent the sub-canopy. We defined sub-canopy trees as those <70% of HL. Both ground-truthed forest inventory data and the ALS point cloud were then segmented into canopy and sub-canopy components. A mixture of standard height-based and density-based ALS metrics were then computed to develop predictive models of sub-canopy component of the stands. Models were calibrated with 28 ground plots and developed using stepwise regression with the strongest predictors being a combination of height, structure and cover-based metrics. Two model sets were developed, one for the entire point cloud, and another for an isolated sub-canopy point cloud defined by HL. The isolated sub-canopy set of models resulted in stronger cross-validated R-squared values of 0.88, 0.68, and 0.55 for tree volume, basal area and number of sub-canopy trees, respectively. We then applied these models over the entire study area to characterize the sub-canopy structure, resulting inventory can be used by land managers for a number of purposes including selecting candidate locations for selective logging to preserve mid-term timber opportunities, fire susceptibility and carbon sequestration modelling, and wildlife habitat values.

Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability.

A Multibaseline PolInSAR Forest Height Inversion Model Based on Fourier–Legendre Polynomials

In this letter, we propose a forest height inversion model based on three-order Fourier-Legendre (FL) polynomials from the multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. The proposed model expresses the vertical structure of the volume layer as three-order FL polynomials. Meanwhile, the forest height is treated as an unknown parameter, rather than a priori information, as adopted in polarization coherence tomography technology. On the other hand, to be more realistic, the proposed model uses multipolarization PolInSAR data and considers that the synthetic aperture radar (SAR) signals in different polarizations describe the forest vertical structure in different ways so that we can obtain a more comprehensive forest vertical structure. Airborne P-band PolInSAR data acquired over the boreal and tropical forest areas were selected for testing the forest height inversion method. The results show that, compared to random volume over ground (RVoG) model-based inversion, the accuracy of the proposed model is improved by 28.20% and 17.30%, respectively, for the boreal and tropical forest scenes.