Airborne Laser Scanning Data Research Articles

Repeat airborne laser scanning to assess canopy height changes in tropical montane forests

Abstract. Tropical montane forests are vital ecosystems globally, preserving biodiversity, carbon stocks, and capturing moisture. We employed two airborne laser scanning (ALS) data sets to study changes in montane forest canopy heights in the Taita Hills, Kenya between 2014/2015 and 2022. We studied two forests, Ngangao (129 ha) and Yale (57 ha), which encompassed both indigenous montane forest and exotic plantations. First, forest types were mapped using field observations and aerial imagery, and then, canopy height changes were analysed using canopy height models at spatial resolution (i.e. the cell size) ranging from 1 m to 20 m. The results revealed overall increase in canopy height in the studied forests, with considerable spatial variation between the forest segments and main tree species. Planted exotic tree species, particularly eucalyptus but also pine and cypress, exhibited faster growth rates than native tree species. Point density differences between the ALS data sets can cause bias to estimation of canopy height changes. However, we observed that reducing the cell size of the canopy height models from 1 m to 10 m and 20 m, decreased the positive trend between point density difference and overestimation of canopy height change due to higher point density of more recent ALS data set. These findings contribute to our understanding on spatial complexity of montane forest ecosystems dynamics and help informing forest monitoring and development of management strategies for fragmented forests in montane regions.

Improving airborne laser scanning-based species-specific forest volume estimation using sentinel-2 time series

ABSTRACT Species-specific timber volume estimates are required to support forest planning and conservation. We evaluated whether additional predictors from a Sentinel-2 time series can improve airborne laser scanning (ALS)-based estimation of species-specific timber volumes. Furthermore, we determined the satellite image dates that provided the greatest improvement in accuracy. The Sentinel-2 time series was constructed to cover 1 March–30 November time-period, with a focus on late spring and early summer. The estimation was done using the k Most Similar Neighbor method and predictors extracted from the ALS data and Sentinel-2 images. Our best model included both ALS and Sentinel-2 time-series predictors, and the relative root mean square error (RMSE) values for pine, spruce and deciduous timber volumes were 40.8%, 57.0% and 51.3%, respectively (mean 49.7%). All deciduous trees were treated as one species. When bands from an individual image were used instead of the time series, the best result was obtained with an image from September where the respective relative RMSE values were 42.2% (deciduous), 58.4% (pine) and 60.6% (spruce) with a mean value of 53.7%. A fusion of a Sentinel-2 time-series and ALS data can improve species-specific estimation results compared to the use of individual Sentinel-2 images or ALS only.

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

Determination of forest canopy cover of different sized stands with diverse structure using ALS data: case study of the Białowieża Forest (Poland)

ABSTRACT The need for objective methods to determine tree canopy cover (CC) across large numbers of stands has led to the development of techniques that utilise airborne laser scanning (ALS) data, which provides a reproducible and detailed representation of canopy geometry. We developed a method for determining CC area and evaluated the estimation accuracy for stands of different sizes, structure and composition. This method is based on tree crown geometries obtained from ALS data, and verified with field measurements using data for 3245 stands of the Białowieża Forest District in Poland. In relatively large stands (3–5 ha), the theoretical error of prediction decreased from 0.13 to 0.10 with increasing stand area. In stands larger than 10 ha, however, the error in estimating CC was less than ±0.10. Although every estimation method comes with its own assumptions and errors, the presented method eliminates the subjectivity in observer bias prevalent in traditional field-based ocular assessments and provides a more transparent and methodologically uniform approach for estimating CC in forests.

Quantifying changes in forest structural complexity using bi-temporal airborne laser scanning

ABSTRACT Structural complexity is an important forest characteristic for habitat assessment, forest management and planning. However, monitoring how forest structural complexity evolves over time in various forest types has not been widely explored. In this study we investigate the feasibility of bi-temporal low-density airborne laser scanning (ALS) for the assessment of changes in light availability conditions within forest canopy, considered to imply changes in forest structural complexity. We used ALS data acquired in 2012 and 2019 to generate canopy vertical profiles by slicing the point clouds into 4 × 4 ×1 m voxels which were then rasterized and reclassified into four light availability categories. To understand structural development over time in different forest types we used field-measured tree heights and tree species information to stratify sample plots in different stand complexity categories. Stands with higher structural complexity represented increased proportions of space occupied by vegetation as well as decreased proportions of empty space below the canopy. The experiments showed the ability of low-density ALS to characterize the dynamics in canopy layering structure, implying changes in forest structural complexity. The presented methodology could potentially be upscaled and applied in the landscape-level monitoring of the development of boreal forest structural characteristics.

Tracking tree demography and forest dynamics at scale using remote sensing.

Capturing how tree growth and survival vary through space and time is critical to understanding the structure and dynamics of tree-dominated ecosystems. However, characterising demographic processes at scale is inherently challenging, as trees are slow-growing, long-lived and cover vast expanses of land. We used repeat airborne laser scanning data acquired across 25 km2 of semi-arid, old-growth temperate woodland in Western Australia to track the height growth, crown expansion and mortality of 42 213 individual trees over 9 yr. We found that demographic rates are constrained by a combination of tree size, competition and topography. After initially investing in height growth, trees progressively shifted to crown expansion as they grew larger, while mortality risk decreased considerably with size. Across the landscape, both tree growth and survival increased with topographic wetness, resulting in vegetation patterns that are strongly spatially structured. Moreover, biomass gains from woody growth generally outpaced losses from mortality, suggesting these old-growth woodlands remain a net carbon sink in the absence of wildfires. Our study sheds new light on the processes that shape the dynamics and spatial structure of semi-arid woody ecosystems and provides a roadmap for using emerging remote sensing technologies to track tree demography at scale.

A low-cost alternative to LiDAR for site index models: applying repeated digital aerial photogrammetry data in the modelling of forest top height growth

Abstract Environmental and forest structural information derived from remote sensing data has been found suitable for modelling forest height growth and site index and therefore forest productivity assessment, with the advances in airborne laser scanning (ALS) playing a major role in this development. While there is growing interest in the use of ALS-derived point clouds, point clouds from high-resolution digital aerial photography (DAP) are also often used for mapping and estimating forest ecosystem properties due to their lower acquisition costs. In this study, we document the applicability of bi-temporal DAP data for developing top height (TH) growth models for Scots pine stands. Our results indicate that DAP data can function as an alternative to traditional TH measurements used in growth modelling when corrected based on a limited sample of field-measured reference TH values. As the correction cannot be constant for each DAP dataset due to the different parameters during data acquisition, we propose a straightforward method for the bias correction of DAP-derived TH estimates. By undertaking iterative random sampling, we were able to find the minimum number of reference measurements needed to calculate the TH correction in order to achieve the desired accuracy of the TH estimations based on DAP. Here, we used ALS data as the reference data; however, the ALS measurements can be replaced by any other reliable source of TH values. The presented method for determining TH can be used not only for site index and forest growth modelling but also in forest inventories.

Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed

AbstractGlobal mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy height maps have been released, the global forest canopy height map (GFCH), the high‐resolution canopy height model of the Earth (HRCH), and the global map of tree canopy height (GMTCH). Here, we assessed their accuracy and usability for biodiversity modeling. We examined their accuracy by comparing them with the reference canopy height models derived from airborne laser scanning (ALS). Our results show considerable differences between the evaluated maps. The root mean square error ranged between 10 and 18 m for GFCH, 9–11 m for HRCH, and 10–17 m for GMTCH, respectively. GFCH and GMTCH consistently underestimated the height of all canopies regardless of their height, while HRCH tended to overestimate the height of low canopies and underestimate tall canopies. Biodiversity models using predicted global canopy height maps as input data are sufficient for estimating simple relationships between species occurrence and canopy height, but their use leads to a considerable decrease in the discrimination ability of the models and to mischaracterization of species niches where derived indices (e.g., canopy height heterogeneity) are concerned. We showed that canopy height heterogeneity is considerably underestimated in the evaluated global canopy height maps. We urge that for temperate areas rich in ALS data, activities should concentrate on harmonizing ALS canopy height maps rather than relying on modeled global products.

AIRBORNE LASER SCANNER AS A DATA SOURCE FOR BUILDING SELECTED ELEMENTS OF AN INTELLIGENT DATABASE FOR TRANSPORTATION

In this study, the main objective was to detect the road network and key road infrastructure elements based on airborne laser scanning data. The study included identification of the road network and determination of its axes using three independent methods, as well as detection of horizontal signs such as pedestrian crossings. The analysis process was based mainly on digital image processing methods, based solely on lidar data, without using information from other sources. The results of the analysis showed that the use of lidar data provides a fast and effective method for continuously updating information on road infrastructure and expanding the transportation database. This potentially opens the door to effectively updating relevant data in the area of transportation infrastructure.

Estimation of within-gap regeneration height growth in managed temperate deciduous forests using bi-temporal airborne laser scanning data

Key messageMulti-temporal airborne laser scanning (ALS) data were used to estimate regeneration stem height growth within gaps in uneven-aged deciduous forests. The height and height growth measured in the field were used to calibrate and validate ALS estimates. This method provided highly precise estimates of height and unbiased height increment estimates of regeneration at stem level.ContextAssessing regeneration height growth is essential for evaluating forest dynamics and optimizing silvicultural operations. However, regeneration description at high spatiotemporal resolution has remained limited to restricted areas by the limiting cost constraints of field measurements. Highly precise airborne laser scanning (ALS) data are currently acquired over wide areas. Such datasets are promising for characterizing regeneration dynamics.AimsWe aimed to estimate height and height growth within regenerating areas at the stem level using multi-temporal ALS data.MethodsALS data were acquired from 56,150 ha of uneven-aged deciduous forest in Belgium in 2014 and 2021. Stem tops were detected using local maxima (LM) within regenerating areas in both ALS datasets and matched. Field data were collected in 2021 and used to calibrate the ALS-estimated heights using linear and non-linear models at stem level. Height growth estimation was then validated using field-measured increments.ResultsWithout height calibration, the 2021 ALS-estimated height had a − 1.06 m bias and 1.39 m root-mean-squared error (RMSE). Likewise, the 2014 ALS-estimated height had a − 0.58 m bias and 1.14 m RMSE. The non-linear calibration seemed more appropriate for small regeneration stems (height < 4 m). Using height calibration, the 2021 ALS-estimated height had a − 0.01 m bias and 0.84 m RMSE. In 2014, the bias and RMSE were 0.02 and 0.91 m, respectively. ALS-estimated height growth was unbiased and had an RMSE of 0.10 m·year−1.ConclusionsThis original method is based on the bi-temporal ALS datasets calibrated by limited field measurements. The proposed method is the first to provide unbiased regeneration height growth of regeneration stems in uneven-aged forests and new perspectives for studying and managing forest regeneration.

Validation and Error Minimization of Global Ecosystem Dynamics Investigation (GEDI) Relative Height Metrics in the Amazon

Global Ecosystem Dynamics Investigation (GEDI) is a relatively new technology for global forest research, acquiring LiDAR measurements of vertical vegetation structure across Earth’s tropical, sub-tropical, and temperate forests. Previous GEDI validation efforts have largely focused on top of canopy accuracy, and findings vary by geographic region and forest type. Despite this, many applications utilize measurements of vertical vegetation distribution from the lower canopy, with a wide diversity of uses for GEDI data appearing in the literature. Given the variability in data requirements across research applications and ecosystems, and the regional variability in GEDI data quality, it is imperative to understand GEDI error to draw strong inferences. Here, we quantify the accuracy of GEDI relative height metrics through canopy layers for the Brazilian Amazon. To assess the accuracy of on-orbit GEDI L2A relative height metrics, we utilize the GEDI waveform simulator to compare detailed airborne laser scanning (ALS) data from the Sustainable Landscapes Brazil project to GEDI data collected by the International Space Station. We also assess the impacts of data filtering based on biophysical and GEDI sensor conditions and geolocation correction on GEDI error metrics (RMSE, MAE, and Bias) through canopy levels. GEDI data accuracy attenuates through the lower percentiles in the relative height (RH) curve. While top of canopy (RH98) measurements have relatively high accuracy (R2 = 0.76, RMSE = 5.33 m), the accuracy of data decreases lower in the canopy (RH50: R2 = 0.54, RMSE = 5.59 m). While simulated geolocation correction yielded marginal improvements, this decrease in accuracy remained constant despite all error reduction measures. Some error rates for the Amazon are double those reported in studies from other regions. These findings have broad implications for the application of GEDI data, especially in studies where forest understory measurements are particularly challenging to acquire (e.g., dense tropical forests) and where understory accuracy is highly important.

Assessing the impact of the 2021 flood event on the archaeological heritage of the Rhineland (Germany)

BackgroundArchaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage.ResultsDuring fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage.ConclusionsThe study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography

ABSTRACT This study presents a new approach for predicting forest aboveground biomass (AGB) from airborne laser scanning (ALS) data: AGB is predicted from sequences of images depicting vertical cross-sections through the ALS point clouds. A 3D version of the VGG16 convolutional neural network (CNN) with initial weights transferred from pre-training on the ImageNet dataset was used. The approach was tested on datasets from Canada, Poland, and the Czech Republic. To analyse the effect of training sample size on model performance, different-sized samples ranging from 10 to 375 ground plots were used. The CNNs were compared with random forest models (RFs) trained on point cloud metrics. At the maximum number of training samples, the difference in RMSE between observed and predicted AGB of CNNs and RFs ranged from −2 t/ha to 5 t/ha, and the difference in squared Pearson correlation coefficient ranged from −0.05 to 0.06. Additional pre-training on synthetic data derived from virtual laser scanning of simulated forest stands could only improve the prediction performance of the CNNs when only a few real training samples (10–40) were available. While 3D CNNs trained on cross-section images derived from real data showed promising results, RFs remain a competitive alternative.

Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m3 (38.8 %) for Norway spruce, 0.228 m3 (52.4 %) for Scots pine and 0.246 m3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m3 (38.8 %), 0.228 m3 (52.4 %), and 0.246 m3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods.

Travertine and calcareous tufa occurrences as an indicator of the ongoing tectonic activity of the Central Slovak Fault System inferred from airborne laser scanning data, geomorphometric, and structural analysis (Northern Slovakia)

The travertine formation process, active over the Quaternary, has enabled the origin of one of the largest travertine accumulations in the Western Carpathians. Travertines and calcareous tufas are associated with the tectonic activity of the Central Slovak Fault System, a 45 km wide and 140 km long N-S oriented fault zone. As the geological mapping, structural analysis, geomorphometric and isobase surface analysis revealed, the development of faults was polyphase and the travertine/tufa precipitation was not a continuous process. In addition, numerous travertine/tufa morphofacial varieties have been recognized within the study area, such as travertine/tufa terraces and tufa cascades, hillslope paludal tufa crust, phytoherm framework tufa barrages, lacustrine microdetrital tufas, bryophyte-dominated self-built channel, and fluvial tufa crust, reflecting its local geomorphology and precipitation factors. In the Revúcka dolina Valley, the travertine/tufa geobodies thus represent a reliable indicator of the tectonic activity over the Neogene up to Quaternary. The structural data provided relevant evidence confirming the character of orogen-parallel extension for the current tectonic/neotectonic regime in the Inner Western Carpathians. High-resolution topography derived from airborne laser scanning and the subsequent application of various geomorphometry techniques allowed quick identification of the edges of the terrace cascade systems and morpholineaments. Based on our observations, in this zone, the significant vertical movements of normal faults took place with a maximum estimated offset of 800–1000 m. The achieved results also support the use of the presented multifaceted methodology to understand the interplay between geomorphogenesis, hydrology, and tectonics.

Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations

While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m3/ha/year) in operational Pinus radiata plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAIALS). We found that ALS accurately estimated LAI across 121 plots (R2 = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAIALS with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R2 of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R2 = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R2 = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.

Validation of the vertical canopy cover profile products derived from GEDI over selected forest sites

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ρv/ρg values.

Testing treecbh in Central European forests: an R package for crown base height detection using high-resolution aerial laser-scanned data

Abstract Accurate information regarding tree canopy characteristics is crucial for forest management, but it is often difficult to assess. This study presents an innovative framework designed for crown base height (CBH) detection using high-resolution laser-scanned data, with a specific focus on individual trees within forests. The framework comprises three key steps: (i) segmenting the input tree point cloud to identify the tree trunk and its branches using the treesio software; (ii) applying vertical cross-sectional K-means clustering to cluster the identified tree and to define the elevation threshold for removing low-lying understory vegetation; (iii) employing a novel 2D kernel method for detecting CBH after eliminating low-lying understory vegetation. The 2D kernel method, developed for broadleaf forests using leaf-off airborne laser scanning (ALS) data, underpins the treecbh tool. This tool features a visual CBH adjustment component that shows a 2D profile plot of the tree point cloud, and suggests a CBH value for user approval or adjustment. To evaluate accuracy, in situ measured CBH data from five forest plots in Germany and Hungary with varied species compositions were used. ALS data were collected during leaf-off conditions for the two Hungarian plots and during leaf-on conditions for the three German plots. Leaf-off terrestrial laser-scanned data from individual trees were also used in the accuracy assessment. A sensitivity analysis using random point decimation was conducted on the terrestrial laser-scanned data to assess treecbh’s sensitivity to point density. The initial results exhibited matching rates of 45% and 60% for leaf-off ALS plots, which significantly improved to 71% and 77%, respectively, when using the visual CBH adjustment feature of the tool. The leaf-on ALS results demonstrated matching rates between 24% and 33%, whereas the CBHs of individual terrestrial laser-scanned trees could be detected with 93% accuracy in visual mode. It was observed that treecbh operates effectively when the input ALS data have a minimum point density of 20 pts/${\text{m}}^2$, with its optimal performance achieved at 110 pts/${\text{m}}^2$. These findings indicated treecbh’s sensitivity to ALS data quality, scanning season (leaf-on and leaf-off), and point density. This sensitivity can be effectively mitigated in the case of leaf-off ALS data by utilizing the visual CBH adjustment feature of the tool.

Characterizing forest structural changes in response to non-stand replacing disturbances using bitemporal airborne laser scanning data

Characterizing the extent, severity, and persistence of natural disturbances in forests is crucial in areas as large and heterogeneous as the Canadian boreal forest. Non-stand replacing (NSR) disturbances, in particular, can produce subtle and lagged impacts to forest canopy and structure with mechanisms that remain elusive, and they are challenging to discern using typical remote sensing approaches including aerial photointerpretation and spectral analysis of satellite imagery. Consequently, there is a need for timely and accurate information on the structural modifications due to NSR disturbances to inform proactive forest management practices. To address these needs, we leveraged a unique bitemporal airborne laser scanning (ALS) dataset to characterize changes in the forest structure caused by eastern spruce budworm (ESB, Choristoneura fumiferana (Clem.)), responsible for one of the greatest tree mortality in Canada. A range of infestation severity with varying impacts to forest structure are examined in a mixedwood boreal forest in Lac-Saint Jean, Quebec, Canada. We derived 14 ALS structural change metrics at 10 m spatial resolution, including height, cover, and gappiness 7 years apart (2014–2020). Six distinct structural responses to cumulative ESB infestations severity were identified using cluster analysis from the combination of the 14 change metrics, with canopy cover, the 75th and 25th height percentiles (p75-25) driving cluster separability. Canopy cover and p25 consistently decreased as cumulative infestation severity increased, whereas p75 showed greater variability across the landscape. Photointerpretation of aerial imagery over the same period confirmed the validity of the structural characterization. Further, we studied the role of initial forest structures in modulating the severity of the infestation and found that sparser canopies with cover <65% and shorter trees (p75 < 7.5 m, p25 < 2.5 m) were associated with less severe ESB infestations after 7 years, and controlling for underlying environmental factors. These findings showed the potential of bitemporal ALS data in characterizing structural changes due to ESB infestations at fine scale based on canopy cover and height, relevant for forest management strategies to better target current and future infestations.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.