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Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data
摘要： The Bamboo species accounts for almost 1% of the Earth’s forested area with an exceptionally fast growth peaking up to 7.5–100 cm per day during the growing period, making it an unique species with respect to measuring and monitoring using conventional forest inventory tools. In addition their widespread coverage and quick growth make them a critical component of the terrestrial carbon cycle and for mitigating the impacts of climate change. In this study, the capability of using airborne Light Detection and Ranging (LiDAR) data for estimating canopy structure and biomass of Moso bamboo (Phyllostachys pubescens) was assessed, which is one of the most valuable and widely distributed bamboo species in the subtropical forests of south China. To do so, we first evaluated the accuracy of using LiDAR data to interpolate the underlying ground terrain under bamboo forests and developed uncertainty surfaces using both LiDAR-derived vegetation and topographic metrics and a Random Forest (RF) classifier. Second, we utilized Principal Component Analysis (PCA) to quantify the variation of the vertical distribution of LiDAR-derived effective Leaf Area Index (LAI) of bamboo stands, and fitted regression models between selected LiDAR metrics and the field-measured attributes such mean height, DBH and biomass components (i.e., culm, branch, foliage and aboveground biomass (AGB)) across a range of management strategies. Once models were developed, the results were spatially extrapolated and compared across the bamboo stands. Results indicated that the LiDAR interpolated DTMs were accurate even under the dense intensively managed bamboo stands (RMSE = 0.117–0.126 m) as well as under secondary stands (RMSE = 0.102 m) with rugged terrain and near-ground dense vegetation. The development of uncertainty maps of terrain was valuable when examining the magnitude and spatial distribution of potential errors in the DTMs. The middle height intervals (i.e., HI4 and HI5) within the bamboo cumulative effective LAI profiles explained more variances by PCA analysis in the bamboo stands. Moso bamboo AGB was well predicted by the LiDAR metrics (R2 = 0.59–0.87, rRMSE = 11.92–21.11%) with percentile heights (h25-h95) and the coefficient of variation of height (hcv) having the highest relative importances for estimating AGB and culm biomass. The hcv explained the most variance in branch and foliage biomass. According to the spatial extrapolation results, areas of relatively low biomass were found on secondary stands (AGB = 49.42 ± 14.16 Mg ha?1), whereas the intensively managed stands (AGB = 173.47 ± 34.16 Mg ha?1) have much higher AGB and biomass components, followed by the extensively managed bamboo stands (AGB = 67.61 ± 13.10 Mg ha?1). This study demonstrated the potential benefits of using airborne LiDAR to accurately derive high resolution DTMs, characterize vertical structure of canopy and estimate the magnitude and distribution of biomass within Moso bamboo forests, providing key data for regional ecological, environmental and global carbon cycle models.
关键词： Biomass,Bamboo,Leaf area index,LiDAR,Canopy structure
更新于2025-09-23 15:23:52
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Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest
摘要： Leaf inclination angle and leaf angle distribution (LAD) are important plant structural traits, influencing the flux of radiation, carbon and water. Although leaf angle distribution may vary spatially and temporally, its variation is often neglected in ecological models, due to difficulty in quantification. In this study, terrestrial LiDAR (TLS) was used to quantify the LAD variation in natural European beech (Fagus Sylvatica) forests. After extracting leaf points and reconstructing leaf surface, leaf inclination angle was calculated automatically. The mapping accuracy when discriminating between leaves and woody material was very high across all beech stands (overall accuracy = 87.59%). The calculation accuracy of leaf angles was evaluated using simulated point cloud and proved accurate generally (R2 = 0.88, p < 0.001; RMSE = 8.37°; nRMSE = 0.16). Then the mean ( mean), mode ( mode), and skewness of LAD were calculated to quantify LAD variation. Moderate variation of LAD was found in different successional status stands ( mean [36.91°, 46.14°], mode [14°, 64°], skewness [?0.30, 0.71]). Rather than the previously assumed spherical distribution or reported planophile distribution, here we find that LAD tended towards a uniform distribution in young and medium stands, and a planophile distribution in mature stands. A strong negative correlation was also found between plot mean and plot median canopy height, making it possible to estimate plot specific LAD from canopy height data. Larger variation of LAD was found on different canopy layers ( mean [33.64°, 52.97°], mode [0.07, 0.48], skewness [?0.30, 0.71]). Beech leaves grow more vertically in the top layer, while more obliquely or horizontally in the middle and bottom layer. LAD variation quantified by TLS can be used to improve leaf area index mapping and canopy photosynthesis modelling.
关键词： Variation,Leaf angle distribution,Terrestrial laser scanning,European beech,Canopy structure,Leaf inclination distribution function,Leaf inclination
更新于2025-09-19 17:15:36
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Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis
摘要： Light is a key driver of forest biodiversity and functioning. Light regimes beneath tree canopies are mainly driven by the solar angle, topography, and vegetation structure, whose three‐dimensional complexity creates heterogeneous light conditions that are challenging to quantify, especially across large areas. Remotely sensed canopy structure data from airborne laser scanning (ALS) provide outstanding opportunities for advancement in this respect. We used ALS point clouds and a digital terrain model to produce hemispherical photographs from which we derived indices of nondirectional diffuse skylight and direct sunlight reaching the understory. We validated our approach by comparing the performance of these indices, as well as canopy closure (CCl) and canopy cover (CCo), for explaining the light conditions experienced by forest plant communities, as indicated by the Landolt indicator values for light (Llight) from 43 vegetation surveys along an elevational gradient. We applied variation partitioning to analyze how the independent and joint statistical effects of light, macro‐climate, and soil on the spatial variation in plant species composition (i.e., turnover, Simpson dissimilarity, βSIM) depend on light approximation methodology. Diffuse light explained Llight best, followed by direct light, CCl and CCo (R2 = .31, .23, .22, and .22, respectively). The combination of diffuse and direct light improved the model performance for βSIM compared with CCl and CCo (R2 = .30, .27 and .24, respectively). The independent effect of macroclimate on βSIM dropped from an R2 of .15 to .10 when diffuse light and direct light were included. The ALS methods presented here outperform conventional approximations of below‐canopy light conditions, which can now efficiently be quantified along entire horizontal and vertical forest gradients, even in topographically complex environments such as mountains. The effect of macroclimate on forest plant communities is prone to be overestimated if local light regimes and associated microclimates are not accurately accounted for.
关键词： canopy structure,forest biodiversity,remote sensing,light availability,microclimate,Ellenberg indicator value,beta diversity,hemispherical photography,biodiversity,airborne light detection and ranging LiDAR
更新于2025-09-11 14:15:04
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Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence
摘要： The growing availability of global measurements of sun-induced chlorophyll fluorescence (SIF) can help in improving crop monitoring, especially the monitoring of photosynthetic activity. However, variations in top-of-canopy (TOC) SIF cannot be directly interpreted as physiological changes because of the confounding effects of vegetation biochemistry (i.e. pigments, dry matter and water) and structure. In this study, we propose an approach of using radiative transfer models (RTMs) and TOC reflectance to estimate the biochemical and structural effects on TOC SIF, as a necessary step in retrieving physiological information from TOC SIF. The approach was assessed by using airborne (HyPlant) reflectance and SIF data acquired over an agricultural experimental farm in Germany on two days, before and during a heat event in summer 2015 with maximum temperatures of 27°C and 34°C, respectively. The results show that over 76% variation among different crops in SIF observations was explained by variation in vegetation biochemistry and structure. In addition, the changes of vegetation biochemistry and structure explained as much as 73% variation between the two days in far-red SIF, and 40% variation in red SIF. The remaining unexplained variation was mostly attributed to the variability in physiological status. We conclude that reflectance provides valuable information to account for biochemical and structural effects on SIF and to advance analysis of SIF observations. The combination of RTMs, reflectance and SIF opens new pathways to detect vegetation biochemical, structural and physiological changes.
关键词： Canopy structure,Radiative transfer models,Chlorophyll fluorescence,HyPlant,Airborne,Reflectance
更新于2025-09-09 09:28:46
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Comprehensive Remote Sensing || Vegetation Structure (LiDAR)
摘要： Advances in remote sensing technology since the mid-2000s have drastically increased the potential to acquire traditional forest inventory variables as well as information about the effects of forest canopy structure on radiative transfer, and its implications for tree and ecosystem physiology. Light detection and ranging (LiDAR) has provided means to record in high-resolution (cm-scale), the three-dimensional (3D) structure of the canopy, including height and leaf area density and in certain cases even information related to the branching architecture of trees (Wulder et al., 2012b; Dubayah and Drake, 2000). LiDAR data are currently being acquired from airborne and ground-based platforms with pulse repetition frequencies over 500,000 pulses per second. The many billions of returned laser pulses that reflect off the ground surface and other objects, such as trees, shrubs, and rocks, captured by the detector and for which the exact 3D positions are determined based on accurate measurements of the instrument’s position and orientation, are accessible in easy-to-use tabular data files, and can be easily displayed on modern graphics hardware through a handful of command line expressions or even user-friendly software packages, often available free of charge to academic and noncommercial users (e.g. SPDlib.org, see Bunting et al., 2013a,b; cloudcompare.org; rapidlasso.com). Over the past decades, the combined effort of a broad community of academic researchers and corporations has delivered a range of algorithms for the processing of these data, such as the extraction of terrain models and surface shapes, including stems and tree crowns, or wall-to-wall forest inventory attributes, including but not limited to basal area, merchantable timber volume, biomass, tree stocking density, and tree height. An overview of these algorithms and some of the accuracies by which forest attributes can be extracted from the data can be found in review articles and in the introductions of many research papers. A query on the Scopus search engine for academic research papers shows over 2000 hits on the topic of “LiDAR” and “forestry” alone (Disney, 2016), indicating both the great success of the technology in acquiring forest inventory information and the perceived value of the technology to the field.
关键词： 3D modeling,vegetation structure,forest canopy structure,LiDAR,remote sensing
更新于2025-09-04 15:30:14