Canopy Density Metrics Research Articles

Canopy Structural Changes in Black Pine Trees Affected by Pine Processionary Moth Using Drone-Derived Data

Pine species are a key social and economic component in Mediterranean ecosystems, where insect defoliations can have far-reaching consequences. This study aims to quantify the impact of pine processionary moth (PPM) on canopy structures, examining its evolution over time at the individual tree level using high-density drone LiDAR-derived point clouds. Focusing on 33 individuals of black pine (Pinus nigra)—a species highly susceptible to PPM defoliation in the Mediterranean environment—bitemporal LiDAR scans were conducted to capture the onset and end of the major PPM feeding period in winter. Canopy crown delineation performed manually was compared with LiDAR-based methods. Canopy metrics from point clouds were computed for trees exhibiting contrasting levels of defoliation. The structural differences between non-defoliated and defoliated trees were assessed by employing parametric statistical comparisons, including analysis of variance along with post hoc tests. Our analysis aimed to distinguish structural changes resulting from PPM defoliation during the winter feeding period. Outcomes revealed substantive alterations in canopy cover, with an average reduction of 22.92% in the leaf area index for defoliated trees, accompanied by a significant increase in the number of returns in lower tree crown branches. Evident variations in canopy density were observed throughout the feeding period, enabling the identification of two to three change classes using LiDAR-derived canopy density metrics. Manual and LiDAR-based crown delineations exhibited minimal differences in computed canopy LiDAR metrics, showcasing the potential of LiDAR delineations for broader applications. PPM infestations induced noteworthy modifications in canopy morphology, affecting key structural parameters. Drone LiDAR data emerged as a comprehensive tool for quantifying these transformations. This study underscores the significance of remote sensing approaches in monitoring insect disturbances and their impacts on forest ecosystems.

Estimating the Effects of Forest Structure Changes From Wildfire on Snow Water Resources Under Varying Meteorological Conditions

AbstractModeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.

Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands

Tree canopy density metrics (TCDM) derived from airborne lidar data are used in a range of crucial environmental monitoring, forestry and natural resource management applications. The derivation of spatially and temporally consistent TCDM, however, typically requires field calibration to account for differences in instrument/survey parameters. Lidar surveys with no coincident field measurements consequently will have an unknown error associated with TCDM limiting their application. In this study, we analysed an extensive set of lidar captures with coincident field data to determine the lidar TCDM that best match the canopy gap probability (Pgap), foliage projective cover (FPC) and crown projective cover (CPC). Furthermore, we developed and evaluated models designed to reduce the bias introduced by variations in lidar instrument and survey acquisition parameters. The dataset incorporated 148 field sites (100 m diameter circular plots) coincident with 13 different lidar surveys between 2008 and 2015, distributed across a range of Australian forests and woodlands. The best lidar metric for 1 − Pgap, achieving a root mean square error (RMSE) of 6.7% with 95% confidence intervals (CI) of 6.1–7.3%, was the proportion of all returns greater than a canopy height threshold (tcanopy) of 1.5 m above ground (dall). The best metric for FPC (RMSE = 6.0%, CI = 5.3–6.7%) used the proportion of returns, weighted as the fraction of the number of returns recorded from each pulse (dweighted), with tcanopy of 1.7 m. The best metric for CPC (RMSE = 7.0%, CI = 6.4–7.7%) was the proportion of 0.5 m pixels greater than 0.8 m above the ground, for an interpolated canopy height model (dinterp). Overall bias for these metrics was low (~1%), however, the bias for individual surveys varied significantly. For example, for one survey dall consistently underestimated 1 − Pgap with a bias of −8.3%, while a different survey consistently overestimated 1 − Pgap with a bias of 3.8%. Elastic net regression models, using instrument, survey and plot parameters as predictor variables, were unable to consistently remove the bias. No relationships could be discerned between lidar parameters and the bias between lidar metrics and field measurements, potentially due to complex interactions between parameters, the spatial scale of the field plots, and uncertainties in field measurements and lidar attributes. Although the bias could not be modelled, the results provide metrics to derive Pgap, FPC and CPC with less than 10% error from lidar surveys captured with similar parameters across Australia (>600,000 km2).

Modeling stand parameters for Pinus brutia (Ten.) using airborne LiDAR data: a case study in Bergama

Traditional field measurement methods are usually time-consuming and costly. With recent developments in remote sensing, precise estimation of some key forest parameters is becoming a close reality. One of the robust technologies being utilized for this aim is light detection and ranging (LiDAR). We used LiDAR to derive tree height and canopy density metrics using pixel values, texture features, and vegetation indices obtained from WorldView-3 imagery to estimate volume per hectare, mean height, dominant height, and number of trees per hectare. A total of 58 sample plots, predominantly composed of Pinus brutia , which is one of the most abundant tree species in Turkey, were analyzed. The adjusted R 2 values obtained by the best regression models, using LiDAR-derived metrics alone, were 0.66, 0.73, 0.83, and 0.83 for the volume per hectare, number of trees per hectare, and average and dominant heights, respectively. After integrating LiDAR-derived metrics with WorldView-3 imagery band values and subsequently with vegetation indices, higher adjusted R 2 values of 0.70 and 0.77, respectively, were obtained for the volume per hectare. In contrast, incorporating texture features besides other parameters had no positive effect on the accuracy of estimation.

Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach

In this study, we explored the consequences of using leaf-on and leaf-off airborne laser scanning (ALS) data on area-based model outcomes in a lodgepole pine (Pinus contorta var. latifolia Engelm.) dominated forest in the foothills of the Rocky Mountains in Alberta, Canada. We considered eight forest attributes: top height, mean height, Lorey’s mean height, basal area, quadratic mean diameter, merchantable volume, total volume, and total aboveground biomass. We used 787 ground plots for model development, stratified by ALS acquisition conditions (leaf-on or leaf-off) and dominant forest type (coniferous or deciduous). We also generated pooled models that combined leaf-on and leaf-off ALS data and generic models that combined plot data for all forest types. We evaluated differences in ALS metrics and leaf-on and leaf-off model outcomes, as well as the impacts of pooling leaf-on and leaf-off ALS data, creating generic models, and of applying leaf-on models to leaf-off data (and vice versa). In general, leaf-off and leaf-on ALS metrics were not significantly different (p < 0.05), except for the 5th percentile of height (coniferous) and canopy density metrics (deciduous). Overall, coniferous leaf-on and leaf-off models were comparable, with differences in relative root mean square error (RMSE) and bias of <2% for all attributes except volume, which differed by <4%. RMSE and bias for deciduous leaf-on and leaf-off models for height attributes and quadratic mean diameter differed by <2%, whereas models for volume and biomass differed by <7%. These results affirm that leaf-off data can be used in an area-based approach to estimate forest attributes for both coniferous and deciduous forest types. Relative RMSE and bias for pooled models (combining leaf-on and leaf-off ALS data) differed by <2% relative to leaf-on and leaf-off models, suggesting that in the forests studied herein, combining leaf-on and leaf-off data in an area-based approach does not adversely impact model outcomes. Generic models that did not account for forest type had large errors for volume and biomass (e.g., the relative RMSE for merchantable volume was twice as large as forest type specific models). Likewise, the mixing of leaf-on models with leaf-off data and vice versa resulted in large RMSE and bias for both forest types, and therefore mixing of models and data types should be avoided.

Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.

Comparisons between field- and LiDAR-based measures of stand structural complexity

Forest structure, as measured by the physical arrangement of trees and their crowns, is a fundamental attribute of forest ecosystems that changes as forests progress through suc;cessional stages. We examined whether LiDAR data could be used to directly assess the successional stage of forests by determining the degree to which the LiDAR data would show the same relative ranking of structural development among sites as would traditional field measurements. We sampled 94 primary and secondary sites (19–93, 223–350, and 600 years old) from three conifer forest zones in western Washington state, USA, in the field and with small-footprint, discrete return LiDAR. Seven sets of LiDAR metrics were tested to measure canopy structure. Ordinations using the of LiDAR 95th percentile height, rumple, and canopy density metrics had the strongest correlations with ordinations using two sets of field metrics (Procrustes R = 0.72 and 0.78) and a combined set of LiDAR and field metrics (Procrustes R = 0.95). These results suggest that LiDAR can accurately characterize forest successional stage where field measurements are not available. This has important implications for enabling basic and applied studies of forest structure at stand to landscape scales.

Examining the effects of sampling point densities on laser canopy height and density metrics

Forest resource managers rely on the information extracted from forest resource inventories to manage forests sustainably and efficiently, thereby supporting more precise decision-making. Light detection and ranging (LiDAR) is a relatively new technology that has proven to enhance forest resource inventories. However, the relationship between LiDAR sampling point density (which is directly related to acquisition and processing costs) and accuracy and precision of forest variable estimation has not yet been established across a range of forest ecosystems. In this study, 2 airborne LiDAR surveys using the same sensor, but configured with disparate parameters, were carried out over the York Regional Forest near Toronto, Canada producing 2 data sets characterized by different sampling point densities. The effects of 2 sampling point densities on 23 laser canopy height and density metrics typically used in forest studies at the plot level were examined with comparisons grouped by first and last return data. The minimum (hmin) and maximum (hmax) laser canopy heights were statistically different for first and last returns. The proportion of laser returns (i.e., canopy density) in the upper (d1) and lower (d10) range of laser canopy heights was statistically different for the first returns, whereas only a single canopy density metric was different for the last returns (d9). These results suggest that changes in sampling point density (due to changes in scan angle and altitude) only affect laser canopy height and density metrics that are characterized by the small percentage of returns from the very top (hmax; d1) and base of the canopy (hmin; d10) (i.e., those metrics that characterize the tail ends of the distributions of laser canopy heights). Consequently, higher sampling point densities may add little value to current LiDAR forest research or operations at the stand level, as metrics derived from the canopy profile can be implemented for biophysical variable estimation. Implications for forest management are in terms of identifying which aspects of LiDAR project design: a) impact the quality and cost-effectiveness of derived FRI information; b) should be specified within a LiDAR request for proposals; or c) scrutinized within LiDAR project tender documentation. Key words: LiDAR, airborne laser scanning, FRI, canopy profile, biophysical variables, sampling density

Measures of spatial forest structure derived from airborne laser data are associated with natural regeneration patterns in an uneven-aged spruce forest

The relationships between measures of forest structure as derived from airborne laser scanner data and the variation in quantity (Q) and vitality (V) of young trees in a size-diverse spruce forest were analyzed. A regeneration success rate (Q), leader length (V), relative leader length (V), and apical dominance ratio (V) were regressed against 27 different laser-derived explanatory variables representing three different spatial scales. The resulting 81 different models for each response variable were ranked according to their Akaike information criterion score and significance level. Each laser variable was then associated with four categories. These were scale, return, fraction, and type. Within the scale category, laser variables were grouped according to the spatial scale from which they originated. Similarly, within the return, fraction, and type categories, the variables were grouped according to if they originated from first or last return echoes; if they originated from lower, middle, or upper fraction of the range of laser heights or values derived from the full range of laser pulses, and if they were canopy height or canopy density metrics. The results show that the laser variables were strongest correlated with the quantity of small trees and that these variables could be attributed to large-scale, last return, lower fraction, and density metrics. The correlations with the vitality responses were weaker, but the results indicate that variables derived from a smaller scale than for the quantity were better in order to explain variation in leader length, relative leader length, and apical dominance ratio.

Weibull and percentile models for lidar-based estimation of basal area distribution

The aim of this study was to assess the accuracy of basal area distributions of sample plots in coniferous forest derived from small-footprint airborne laser scanner data, and to compare the accuracy of two methods for derivation of such distributions based on: (1) two percentiles of a two-parameter Weibull and parameter recovery, and (2) a system of 10 percentiles defined across the range of observed diameters. The 12 percentiles were derived from the empirical basal area distributions of 141 plots with size 300–400 m2. Regression analysis was used to relate the percentiles to various canopy height and canopy density metrics derived from the laser data. On average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 7–91% of the variability. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of −1.2 to 2.1% between predicted and ground-truth values of plot volume. The standard deviations of the differences between predicted and ground-truth values of plot volume were 15.1–16.4%. Neither bias nor standard deviation differed significantly between the two validated methods.

Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data

Mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 233 field sample plots were estimated from various canopy height and canopy density metrics— derived by means of a small-footprint laser scanner over young and mature forest stands— using ordinary least-squares (OLS) regression analysis, seemingly unrelated regression (SUR), and partial least-squares (PLS) regression. The sample plots were distributed systematically throughout two separate inventory areas with size 1000 and 6500 ha, respectively. The plots were divided into three predefined strata. Separate regression models were estimated for each inventory as well as common models utilizing the plots of both inventories simultaneously. In the models estimated by combining data from the two areas, the statistical effect of inventory was found to be significant ( p<0.05) in the mean height models only. A total of 115 test stands and plots with size 0.3–11.7 ha were used to validate the estimated regression models. The bias and standard deviations (parenthesized) of the differences between predicted and ground reference values of mean height, dominant height, mean diameter, stem number, basal area, and volume were −5.5% to 4.7% (3.1–7.3%), −6.0% to 0.4% (2.9–8.2%), −0.2% to 7.9% (5.5–15.8%), −21.3% to 12.5% (13.4–29.3%), −7.3% to 8.4% (7.1–13.6%), and −3.9% to 10.1% (8.3–14.9%), respectively. It was revealed that only minor discrepancies occurred between the three investigated estimation techniques. None of the techniques provided predicted values that were superior to the other techniques over all combinations of strata and variables.

Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data

Diameter and basal area distributions are used in many forest management planning packages for predicting stand volume and growth. The distribution parameters and the 24 and 93 percentiles for parameter recovery of a two-parameter Weibull were derived for empirical diameter and basal area distributions of 54 plots of 3740 m2 each. Regression analysis was used to relate the distribution parameters and percentiles to various canopy height and canopy density metrics derived from airborne laser scanner data over young and mature coniferous forest. On average, the distance between transmitted laser pulses was 1.0 m on the ground. Aerial photo-interpretation was used to divide the plots into three strata according to age class and site quality. The stratum-specific regressions explained 20–93% of the variability in the observed percentiles. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of −4.8 to 2.7% between predicted and ground-truth values of plot volume when the predicted frequencies of the diameter and basal area distributions were scaled to ground-truth stem number (N) and basal area (G), respectively. The standard deviations (SD) of the differences between predicted and ground-truth values of plot volume were 5.6–29.1%. However, when the scaling variables (N and G) were predicted from the laser data, the bias of plot volume determined by cross-validation was −4.7 to 6.6% and the SD was 11.4–24.2%.

Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m2. Regressions for coniferous forest explained 60–97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m2 each were divided into 232.9 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were −0.58 to −0.85 m (0.64–1.01 m), −0.60 to −0.99 m (0.67–0.84 m), 0.15–0.74 cm (1.33–2.42 cm), 34–108 ha−1 (97–466 ha−1), 0.43–2.51 m2 ha−1 (1.83–3.94 m2 ha−1) and 5.9–16.1 m3 ha−1 (15.1–35.1 m3 ha−1), respectively.

Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data

The mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 144 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 1000-ha study area, and the size of each plot was 200 m 2. On the average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 82–95%, 74–93%, 39–78%, 50–68%, 69–89%, and 80–93% of the variability in ground-truth mean height, dominant height, mean diameter, stem number, basal area, and volume, respectively. A proposed practical two-stage procedure for prediction of corresponding characteristics of entire forest stands was tested. Sixty-one stands within the study area, with an average size of 1.6 ha each, were divided into 200 m 2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data utilizing the estimated regression equations. Average values for each stand was computed. Most stand level predictions were unbiased ( P>.05). Standard deviations of the differences between predicted and ground-truth values of mean height, dominant height, mean diameter, stem number, basal area, and volume were 0.61–1.17 m, 0.70–1.33 m, 1.37–1.61 cm, 16.9–22.2% (128–400 ha −1), 8.6–11.7% (2.33–2.54 m 2 ha −1), and 11.4–14.2% (18.3–31.9 m 3 ha −1), respectively.