Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling

Information on individual tree attributes is important for sustainable management of forest stands. Airborne Laser Scanning (ALS) point clouds are an excellent source of information for predicting a range of forest stand attributes, with plot and single tree volume being among the most important. Two approaches exist for estimating volume: area-based approach (ABA) and individual tree detection (ITD). The ABA is now routinely applied in operational forestry applications, and results in generalized plot- or stand-level attribute predictions. Alternatively, ITD-based estimates provide detailed information for individual trees, but are typically biased due to challenges associated with individual tree detection. In this study, we applied an ABA to estimate tree counts and individual tree volumes by downscaling plot-level predictions of total volume derived using ALS data in a highly productive and complex coastal temperate forest environment in British Columbia, Canada, characterized by large volumes and multi-species and multi-age stand structures. To do so, a two-parameter Weibull probability density function (PDF) was used to describe the within-plot tree volume distribution. The ABA approach was then used to model the total plot volume and the two Weibull PDF parameters. Next, the parameters were used to calculate mean tree volume and derive the number of trees and the individual tree volume distribution. Tree count estimates were minimally biased with RMSE of 149 trees·ha−1 or 24.4%. The volume distributions showed good agreement with reference data (mean Reynold’s error index = 71.7). We conclude that the approach was suitable for enriching ABA-derived forest stand attributes in the majority of the studied forest stands; however the accuracy was lower in multi-layered stands that had a multimodal individual tree volume distribution.

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abstract: Laser scanners of small footprint diameter and high sampling density provide possibility to obtain accurate height information on the forest canopy. When applying tree crown segmentation methods, individual single trees can be recognised and tree height as well as crown area can be detected. Detection of suppressed trees from a height model based on laser scanning is difficult; however, it is possible to predict these trees by using theoretical distribution functions. In this study, two different methods are used to predict small trees. In the first method, the parameter prediction method is utilised with the complete Weibull distribution, the parameters of which are predicted with separate parameter prediction models; thus, small trees are determined from the predicted tree height distribution. In the second method, the two-parameter left-truncated Weibull distribution is fitted to the detected tree height distribution. The results are presented by using timber volume and stem density as predicted stand characteristics. The results showed that the root mean square error (RMSE) for the timber volume is about 25% when using only information obtained from laser scanning, whereas the RMSE for the number of stems per ha is about 75%. Predictions for both characteristics are also highly biased and the underestimates are 24% and 62%, respectively. The use of the parameter prediction method to describe small trees improved the accuracy considerably; the RMSE figures for estimates of timber volume and number of stems are 16.0% and 49.2%, respectively. The bias for the estimates is also decreased to 6.3% for timber volume and 8.2% for the number of stems. When a left-truncated height distribution is used to predict the heights of the missing small trees, the RMSEs for the estimates of timber volume and number of stems are 22.5% and 72.7%, respectively. In the case of the timber volume, the reliability figures for both the original laser scanning-based estimates and for the estimates that also contain small trees are comparable to those obtained by conventional compartment-wise Finnish field inventories.

title: Estimating Tree Height Distribution Using Low-Density ALS Data With and Without Training Data
abstract: This study applies an approach based on stochastic geometry for retrieval of forest characteristics from airborne laser scanning (ALS) in two situations: 1) without ground-measured training data and 2) with training data. The applied model treats the ALS echo heights as an outcome of a random process, expressing the observed heights of canopy envelope as a function of stand density, the parameters of the tree height distribution, and the shape of the individual tree crown. The model was applied to a eucalyptus plantation dataset with known spacing, where the main interest was to estimate the plot-specific tree height distribution. Estimation without training data resulted in RMSEs of 2.9 and 0.9 m for mean and dominant heights, respectively. Estimation using training data resulted in RMSE's of 1.4 and 0.8 m, respectively. In both cases, the estimates of dominant height were more accurate than with the reference method, but the estimates of mean height were less accurate (area-based approach; RMSEs 1.1 and 0.9 m, respectively). The model-based method was robust to substantial decrease in echo density from 1.4 echoes/m2 to 0.14 echoes/m2.

title: Improving k-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data
abstract: Airborne laser scanning (ALS) acquisitions are frequently carried out in long (several kilometers) but narrow (several hundred meters) flight strips forming a block of overlapping and parallel strips. Small inaccuracies in the height measurements result from the interaction among the different parts of the ALS system and differ within each strip. Therefore, some additional strips are flown perpendicular to the main flight direction and across the first set of strips. Strip adjustment is carried out by using information from the additional flight strips. Even if low density data are acquired for the main block, due to the double coverage, the return density under the additional strips flown may be high enough for the use of individual tree crown (ITC) approaches. The study analyzed how this kind of high and low density ALS data can be combined in order to improve k-nearest neighbor (kNN) predictions. An ITC approach was followed to predict tree attributes for automatically segmented tree crowns on more than 580 artificially created sample plots under high density flight strips. No field-measured trees were available for these artificial plots. The predicted tree attributes of the artificial plots and the measured tree attributes of 154 sample plots were then aggregated to the plot level. For all sample plots, plot-level metrics were derived from low density ALS data. The metrics were used as explanatory variables in a kNN model to predict above-ground biomass. The kNN model was validated on external data. The combined use of artificial plots and sample plots resulted in smaller relative root mean squared differences (RRMSD) (26.7%) compared to the use of the sample plots alone (28.5%). It can be concluded that the use of artificial plots improves the accuracy of kNN predictions. This is especially the case if the data range of sample plots for which predictions are sought is not adequately covered by the sample plots used to fit the kNN model. To illustrate this, approximately 30% of the sample plots with the largest and smallest biomass values were omitted. In this case, the combined use of artificial plots and sample plots resulted in a RRMSD of 29.7%, whereas the use of the sample plots alone resulted in a RRMSD of 37.4%.

title: Status and prospects for LiDAR remote sensing of forested ecosystems
abstract: The science associated with the use of airborne and satellite Light Detection and Ranging (LiDAR) to remotely sense forest structure has rapidly progressed over the past decade. LiDAR has evolved from being a poorly understood, potentially useful tool to an operational technology in a little over a decade, and these instruments have become a major success story in terms of their application to the measurement, mapping, or monitoring of forests worldwide. Invented in 1960, the laser and, a short time later, LiDAR, were found in research and military laboratories. Since the early 2000s, commercial technological developments coupled with an improved understanding of how to manipulate and analyze large amounts of collected data enabled notable scientific and application developments. A diversity of rapidly developing fields especially benefit from communications offered through conferences such as SilviLaser, and LiDAR has been no different. In 2002 the SilviLaser conference series was initiated to bring together those interested in the development and application of LiDAR for forested environments. Now, a little over a decade later, commercial use of LiDAR is common. In this paper – using the deliberations of SilviLaser 2012 as a source of information – we aim to capture aspects of importance to LiDAR users in the forest ecosystems community and to also point to key emerging issues as well as some remaining challenges.

title: Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources
abstract: ContextEvaluations of forest inventories usually end when accuracy and precision have been quantified.AimsWe aim to value the accuracy of information derived from different remote sensing sensors (airborne laser scanning, aerial multispectral and hyperspectral imagery) and four alternative forest inventory approaches.MethodsThe approaches were (1) mean values or (2) diameter distributions both obtained by the area-based approach (ABA), (3) individual tree crown (ITC) segmentation and (4) an approach called semi-individual tree crown (SITC) segmentation. The estimated tree information was assessed and used to evaluate how erroneous inventory data affect economic value and loss due to suboptimal harvesting decisions. Field measured data used as reference come from 23 field plots collected in a study area in south-eastern Norway typical of managed boreal forests in Norway.ResultsThe accuracy of the forest inventory was generally in line with previous studies. Our results show that using mean values from the area-based approach may yield large economic losses, while adding a diameter distribution to the area-based approach yielded less loss than the individual tree crown methods. Adding information from imagery had little effect on the results.ConclusionsTaking inventory costs into account, diameter distributions from the area-based approach without additional information seems favourable.

title: Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods
abstract: Individual tree crown segmentation from airborne laser scanning (ALS) data often fails to detect all trees depending on the forest structure. This paper presents a new method to produce tree lists consistent with unbiased estimates at area level. First, a tree list with height and diameter at breast height (DBH) was estimated from individual tree crown segmentation. Second, estimates at plot level were used to create a target distribution by using a k-nearest neighbour (k-NN) approach. The number of trees per field plot was rescaled with the estimated stem volume for the field plot. Finally, the initial tree list was calibrated using the estimated target distribution. The calibration improved the estimates of the distributions of tree height (error index (EI) from 109 to 96) and DBH (EI from 99 to 93) in the tree list. Thus, the new method could be used to estimate tree lists that are consistent with unbiased estimates from regression models at field plot level.

title: Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data
abstract: The two main approaches to deriving forest variables from laser-scanning data are the statistical area-based approach (ABA) and individual tree detection (ITD). With ITD it is feasible to acquire single tree information, as in field measurements. Here, ITD was used for measuring training data for the ABA. In addition to automatic ITD (ITDauto), we tested a combination of ITDauto and visual interpretation (ITDvisual). ITDvisual had two stages: in the first, ITDauto was carried out and in the second, the results of the ITDauto were visually corrected by interpreting three-dimensional laser point clouds. The field data comprised 509 circular plots (r = 10 m) that were divided equally for testing and training. ITD-derived forest variables were used for training the ABA and the accuracies of the k-most similar neighbor (k-MSN) imputations were evaluated and compared with the ABA trained with traditional measurements. The root-mean-squared error (RMSE) in the mean volume was 24.8%, 25.9%, and 27.2% with the ABA trained with field measurements, ITDauto, and ITDvisual, respectively. When ITD methods were applied in acquiring training data, the mean volume, basal area, and basal area-weighted mean diameter were underestimated in the ABA by 2.7−9.2%. This project constituted a pilot study for using ITD measurements as training data for the ABA. Further studies are needed to reduce the bias and to determine the accuracy obtained in imputation of species-specific variables. The method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized.

title: Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning
abstract: Diameter distribution is essential for calculating stem volume and timber assortments of forest stands. A new method was proposed in this study to improve the estimation of stem volume and timber assortments, by means of combining the Area-based approach (ABA) and individual tree detection (ITD), the two main approaches to deriving forest attributes from airborne laser scanning (ALS) data. Two methods, replacement, and histogram matching were employed to calibrate ABA-derived diameter distributions with ITD-derived diameter estimates at plot level. The results showed that more accurate estimates were obtained when calibrations were applied. In view of the highest accuracy between ABA and ITD, calibrated diameter distributions decreased its relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points at best, respectively. Calibration improved pulpwood fraction significantly, which contributed to the negligible bias of the estimated entire growing stock.

title: Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data
abstract: 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%.

title: LiDAR and Weibull modeling of diameter and basal area
abstract: This study investigates the ability to predict forest diameter distributions from light detection and ranging (LiDAR) data using Weibull modelling for forest stands in central Ontario. Results suggest that the unimodal 2-parameter Weibull model is a promising technique for the prediction of diameter class distributions, with strong relationships evident for several subgroups (at 95% confidence, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83, and 0.65, with validation RMSE of 4.09 m 2 /ha, 0.61 stems/ha, 6.05, 0.64, 4.73, and 0.09 for basal area, stem density, and the Weibull a and b parameters for basal area and stem density, respectively). The unimodal models were found to be least effective for the irregularly shaped diameter distributions, particularly for low-density coniferous plots that have undergone shelterwood treatment. A significant improvement in results for these irregular plots was found with a finite mixture modelling approach, suggesting that finite mixture models may extend our ability to predict diameter distributions over large portions of the landscape.

title: Comparison of basal area and stem frequency diameter distribution modelling using airborne laser scanner data and calibration estimation
abstract: The diameter distribution of a forest stand is of primary importance for deriving various stand characteristics, but measuring the diameters of a tree stock is rather time-consuming, so theoretical diameter distribution models are usually employed. We examine here the prediction of diameter distribution using information obtained from airborne laser scanning (ALS). The aim is to compare the accuracy of ALS-based stem frequency and basal area diameter distribution models and the usability of calibration to adjust the predicted distributions to be compatible with the ALS based estimated stand volume. A network of 472 sample plots of 9 m radius was established in a typical managed boreal forest area in eastern Finland. ALS data were acquired from an altitude of 1500 m above ground level, which produced a sampling density of about 0.7 pulses per square metre. A two-parameter Weibull function was employed to determine field-measured stem frequency and basal area diameter distributions using maximum likelihood estimation, and these parameters together with the number of stems, basal area and stand volume were then regressed simultaneously using ALS-based characteristics as explanatory variables. Predicted basal area diameter distribution from each plot was scaled using the ALS-based estimate for the basal area, and the stem frequency diameter distribution was scaled using the ALS-based estimate for the stem number. Finally, the distributions were calibrated against volume, basal area and number of stems which were ALS data based model estimates. Without calibration estimation the accuracy of the volume prediction was almost equal for both distribution types (basal area and stem frequency). However, calibration estimation improved the accuracy of the volume estimates of both distribution types and also of the stem number estimates in the case of the basal area distribution.

title: Modeling Forest Trees and Stands
abstract: 1. Introduction.- 2. Tree Form and Stem Taper.- 3. Tree-stem Volume Equations.- 4. Tree Weight and Biomass Estimation.- 5. Quantifying Tree Crowns.- 6. Growth Functions.- 7. Evaluating Site  Indices of Individual-tree Competition.- 10. Modeling Forest Stand Development.- 11. Whole-stand Models for Even-aged Stands.- 12. Diameter-distribution Models for Even-aged Stands.- 13. Size-class Models for Even-aged Stands.- 14. Individual-tree Models for Even-aged Stands.- 15. Growth and Yield Models for Uneven-aged Stands.- 16. Modeling Response to Silvicultural Treatments.- 17. Modeling Wood Characteristics.- 18. Model Implementation and

title: Predicting Tree Diameter Distributions
abstract: Diameter distribution of trees is an important stand attribute that describes stand structure in terms of volume, biomass, value, growth and biodiversity factors. Diameter distribution can be characterized using different approaches such as probability density functions, percentile-based distributions or nearest neighbour applications. We review the research related to airborne laser scanning (ALS)-based predictions of diameter distributions. This includes the above-mentioned plot level approaches, as well as predicting the diameter of individual trees and combinations of different approaches. Although ALS does not directly measure tree diameter, there is a strong statistical relationship between ALS metrics and the characteristics of a diameter distribution. The capability of ALS to reproduce different shapes of diameter distribution is the most notable feature of these applications.

title: Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest
abstract: This study used two different approaches to model diameter distributions on data from 201 field plots in a boreal conifer forest in south eastern Norway using airborne laser scanning. These two methods were a non-parametric most similar neighbour (MSN) approach and a parametric seemingly unrelated regression (SUR) approach to predict diameter percentiles, and their accuracies were compared by validation with an independent dataset. Based on calculated differences between predicted and observed number of stems on the entire validation dataset, we found that SUR gave unbiased results and that MSN slightly underestimated total number of stems. However, both methods overpredicted the number of stems per hectare in the range of 15.6‐61.5 stems in the smallest diameter classes (between 4 and 12 cm). If the predicted diameter distributions were converted into basal area per hectare (G), both methods gave unbiased results. The average difference for G was 1.9 per cent of the observed value for the MSN approach. The corresponding number for the SUR model was 12.4 per cent. Neither of these differences were statistically significant (P . 0.05). We concluded that the even though both methods overall yielded accurate results, the MSN approach was more reliable in terms of predicting the number of large trees.

title: RECOVERING PLOT-SPECIFIC DIAMETER DISTRIBUTION AND HEIGHT- DIAMETER CURVE USING ALS BASED STAND CHARACTERISTICS
abstract: Density, diameter distribution and height-diameter (H-D) pattern of a forest stand are of primary importance in deriving various stand characteristics, but measuring diameters and heights of a tree stock is rather time-consuming. That is why theoretical diameter distribution and H-D models are usually employed. We examine the prediction of them for Scots pine sample plots using information obtained with airborne laser scanning (ALS). We propose a parameter recovery approach, where such values for the parameters of assumed diameter distribution and H-D models are determined, that satisfy the mathematical relationships between the predicted plot-specific characteristics. If the solution for the formulated system of equations exists, it is always compatible with the predictions of stand characteristics. The method is developed and tested with a dataset of 213 Scots pine stands. A solution was found for all but 2 plots. The proposed method appears to be a reasonable alternative for predicting stand structure from ALS data.

title: Weibull and percentile models for lidar-based estimation of basal area distribution
abstract: 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.

title: Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs
abstract: The use of diameter distributions originates from a need for tree-level description of forest stands, which is required, for example, in growth simulators and bucking. Diameter distribution models are usually applied, since measuring empirical diameter distributions in practical forest inventories is too laborious. This study investigated the ability of remote sensing information to predict species-specific diameter distributions. The study was carried out in Finland in a typical managed boreal forest area. The tree species considered were Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and deciduous trees as a group. Growing stock was estimated using the k-MSN method using airborne laser scanning data and aerial photographs. Two approaches were compared: first, the nearest neighbour approach based on field measured trees was used as such to predict diameter distribution, and second, a theoretical diameter distribution approach in which the parameters of the Weibull distribution...

title: Carbon dioxide fluxes in coastal Douglas-fir stands at different stages of development after clearcut harvesting
abstract: Forests play a significant role in the global carbon (C) cycle. Variability in weather, species, stand age, and current and past disturbances are some of the factors that control stand-level C dynamics. This study examines the relative roles of stand age and associated structural characteristics and weather variability on the exchange of carbon dioxide between the atmosphere and three different coastal Douglas-fir stands at different stages of development after clearcut harvesting. The eddy covariance technique was used to measure carbon dioxide fluxes and a portable soil chamber system was used to measure soil respiration in the three stands located within 50 km of each other on the east coast of Vancouver Island, British Columbia, Canada. In 2002, the recently clearcut harvested stand (HDF00) was a large C source, the pole/sapling aged stand (HDF88) was a moderate C source, and the rotation-aged stand (DF49) was a moderate C sink (net ecosystem production of −606, −133, and 254 g C m −2  year −1 , respectively). Annual gross ecosystem production and ecosystem respiration also increased with increasing stand age. Differences in stand structural characteristics such as species composition and phenology were important in determining the timing and magnitude of maximum gross ecosystem production and net ecosystem production through the year. Both soil and ecosystem respiration were exponentially related to soil temperature in each stand with total ecosystem respiration differing more among stands than soil respiration. Between 1998 and 2003, annual net ecosystem production ranged from 254 to 424 g C m −2  year −1 over 6 years for DF49, from −623 to −564 g C m −2  year −1 over 3 years for HDF00, and from −154 to −133 g C m −2  year −1 over 2 years for HDF88. Interannual variations in C exchange of the oldest, most structurally stable stand (DF49) were related to variations in spring weather while the rapid growth of understory and pioneer species influenced variations in HDF00. The differences in net ecosystem production among stands (maximum of 1000 g C m −2  year −1 between the oldest and youngest stands) were an order of magnitude greater than the differences among years within a stand and emphasized the importance of age-related differences in stand structure on C exchange processes.

title: Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest
abstract: Height measurements from small-footprint discrete-return LiDAR and backscatter coefficients from C- and L-band radar were used independently and in combination to estimate above-ground component and total biomass for a coniferous temperate forest, located on Vancouver Island, British Columbia, Canada. Reference biomass data were obtained from plot-level data and used for comparison against the LiDAR and radar-based biomass models. For the LiDAR-only model, height metrics such as mean first return height and percentiles (e.g., 10th and 90th) of first returns correlated best to total above-ground and stem biomass. While percent of first returns above 2 m and percentiles (75th and 90th) of first returns height metrics correlated best to crown biomass. A comparison between above-ground components and total biomass indicate that stem biomass displayed the highest relationship with the LiDAR measurements while crown biomass showed the lowest relationship with relative root mean squared error ranging from 16% to 22%, respectively. Alternatively, the radar-only models indicated that for C-band radar, a combination of HH and VV backscatter demonstrated the most significant correlation with forest biomass compared to coherence based models with a relative root mean squared error of 53%. For L-band radar, a combination of HH and HV backscatter showed the most significant correlation compared to coherence based models with a relative root mean squared error of 44%. Exploring a mixture of C- and L-band backscatter and coherence based models revealed that a combination of C-HV and L-HV coherence magnitudes provided the best radar relationship with forest biomass with a relative root mean squared error of 35%. Also for all radar-based models, L- and C-band backscatter and coherence magnitudes were poorly correlated with individual biomass components when compared to total above-ground biomass. The addition of C- and L-band backscatter and coherence variables to the LiDAR-only biomass model was also investigated. The results showed that the integration of C-band HH backscatter to the LiDAR-only model significantly improved the relationship with forest biomass by explaining an additional 8.9% and 6.5% of the variability in total aboveground and stem biomass respectively, while C-band polarimetric entropy explained an additional 17.9% of the variability in crown biomass. Improvements in the relative root mean squared errors were also observed ranging from 7.1% to 11.7%. The study suggests that for a temperate forest dominated by coniferous stands, the addition of C-band radar variables to a best LiDAR-only linear model provides improved estimates of above-ground component and total biomass.

title: Canadian national biomass equations: new parameter estimates that include British Columbia data
abstract: National allometric equations covering the most common tree species of Canada’s forests were produced based on tree mass data acquired in the early 1980s during the ENergy from the FORest (ENFOR) program. The equations allow us to calculate the mass estimate of four tree components (foliage, branches, stem bark, and stem wood) using either diameter at breast height or a combination of diameter at breast height and height. Missing from that data set, however, were the data from British Columbia. A usable British Columbia data set was finally found and has now been incorporated into the national data set. Here, we present revised allometric equations for six species covered in the previous work and also found in the British Columbia data set as well as for the “hardwoods”, “softwoods”, and “all species” equations. New equations are also provided for eight species specific to the British Columbia data.

title: Update of forest inventory data with lidar and high spatial resolution satellite imagery
abstract: Most countries with significant forest resources have designed and implemented monitoring systems to inventory, at regular intervals, a range of forest stand attributes such as species composition, age, volume, biomass, and disturbance. These inventory systems are typically based upon the interpretation of air photos supplemented by ground measurements, with digital remotely sensed data often used to capture changes within inventory cycles. Light detection and ranging (lidar) and high spatial resolution digital satellite imagery (e.g., QuickBird) offer additional capacity and complementary data sources for inventory assessment, as demonstrated by this study over a 400 ha area on Vancouver Island, British Columbia, Canada. A range of lidar survey parameters were applied to update an existing forest inventory. Results indicate a strong relationship between the small-footprint lidar-derived heights and stand height as derived from aerial photographic interpretation (API) (R = 0.79, p < 0.05). In addition, there was no statistical difference (p < 0.05) between stand height as predicted from a complete lidar coverage or when sampled as a single 400 m wide transect (R = 0.89, p < 0.001). These results demonstrate the utility of lidar data, as a full coverage or sample, in combination with high spatial resolution imagery, as useful data sources for capturing forest inventory stand height and cover information.

title: Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data
abstract: This study proposed modifying the conceptual approach that is commonly used to model development of stand attribute estimates using airborne LiDAR data. New models were developed using an area-based approach to predict wood volume, stem volume, aboveground biomass, and basal-area across a wide range of canopy structures, sites and LiDAR characteristics. This new modeling approach does not adopt standard approaches of stepwise regression using a series of height metrics derived from airborne LiDAR. Rather, it used four metrics describing complementary 3D structural aspects of the stand canopy. The first three metrics were related to mean canopy height, height heterogeneity, and horizontal canopy distribution. A fourth metric was calculated as the coefficient of variation of the leaf area density profile. This fourth metric provided information on understory vegetation. The models that were developed with the four structural metrics provided higher estimation accuracy on stand attributes than models using height metrics alone, while also avoiding data over-fitting. Overall, the models provided prediction error levels ranging from 12.4% to 24.2%, depending upon forest type and stand attribute. The more homogeneous coniferous stand provided the highest estimation accuracy. Estimation errors were significantly reduced in mixed forest when separate models were developed for individual stand types (coniferous, mixed and deciduous stands) instead of a general model for all stand types. Model robustness was also evaluated in leaf-off and leaf-on conditions where both conditions provided similar estimation errors.

title: Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment
abstract: Airborne laser scanning (ALS) is an active remote sensing technique that uses the time-of-flight measurement principle to capture the three-dimensional structure of the earth's surface with pulsed lasers that transmit nanosecond-long laser pulses with a high pulse repetition frequency. Over forested areas most of the laser pulses are reflected by the leaves and branches of the trees, but a certain fraction of the laser pulses reaches the forest floor through small gaps in the canopy. Thus it is possible to reconstruct both the three-dimensional structure of the forest canopy and the terrain surface. For the retrieval of quantitative forest parameters such as stem volume or biomass it is necessary to use models that combine ALS with inventory data. One approach is to use multiplicative regression models that are trained with local inventory data. This method has been widely applied over boreal forest regions, but so far little experience exists with applying this method for mapping alpine forest. In this study the transferability of this approach to a 128 km2 large mountainous region in Vorarlberg, Austria, was evaluated. For the calibration of the model, inventory data as operationally collected by Austrian foresters were used. Despite these inventory data are based on variable sample plot sizes, they could be used for mapping stem volume for the entire alpine study area. The coefficient of determination R2 was 0.85 and the root mean square error (RMSE) 90.9 m3ha−1 (relative error of 21.4%) which is comparable to results of ALS studies conducted over topographically less complex environments. Due to the increasing availability, ALS data could become an operational part of Austrian's forest inventories.

title: Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors
abstract: In a mixed temperate forest landscape in southwestern Germany, multiple remote sensing variables from aerial orthoimages, Thematic Mapper data and small footprint light detection and ranging (LiDAR) were used for plot-level nonparametric predictions of the total volume and biomass using three distance measures of Euclidean, Mahalanobis and Most Similar Neighbour as well as a regression tree-based classifier (Random Forest). The performances of nearest neighbour (NN) approaches were examined by means of relative bias and root mean squared error. The original highdimensional dataset was pruned using an evolutionary genetic algorithm search with a NN classification scenario, as well as by a stepwise selection. The genetic algorithm (GA)-selected variables showed improved performance when applying Euclidean and Mahalanobis distances for predictions, whereas the Most Similar Neighbour and Random Forests worked more precise with the full dataset. The GA search proved to be unstable in multiple runs because of intercorrelations among the high-dimensional predictors. The selected datasets are dominated by LiDAR height metrics. Furthermore, The LiDAR-based metrics showed major relevance in predicting both response variables examined here. The Random Forest proved to be superior to the other examined NN methods, which was eventually used for a wallto-wall mapping of predictions on a grid of 20 × 20 m spatial resolution.

title: Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data
abstract: LiDAR measurements of canopy structure can be used to classify forest stands into structural stages to study spatial patterns of canopy structure, identify habitat, or plan management actions. A key assumption in this process is that differences in canopy structure based on forest age and elevation are consistent with predictions from models of stand de- velopment. Three LiDAR metrics (95th percentile height, rumple, and canopy density) were computed for 59 secondary and 35 primary forest plots in the Pacific Northwest, USA. Hierarchical clustering identified two precanopy closure classes, two low-complexity postcanopy closure classes, and four high-complexity postcanopy closure classes. Forest de- velopment models suggest that secondary plots should be characterized by low-complexity classes and primary plots char- acterized by high-complexity classes. While the most and least complex classes largely confirmed this relationship, intermediate-complexity classes were unexpectedly composed of both secondary and primary forest types. Complexity classes were not associated with elevation, except that primary Tsuga mertensiana (Bong.) Carriere (mountain hemlock) plots were complex. These results suggest that canopy structure does not develop in a linear fashion and emphasize the im- portance of measuring structural conditions rather than relying on development models to estimate structural complexity across forested landscapes.

title: Comparisons between field- and LiDAR-based measures of stand structural complexity
abstract: 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 s...

title: Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon
abstract: A scanning lidar, a relatively new type of sensor which explicitly measures canopy height, was used to measure structure of conifer forests in the Pacific Northwest. SLICER (Scanning Lidar Imager of Canopies by Echo Recovery), an airborne pulsed laser developed by NASA which scans a swath of five 10-m diameter footprints along the aircraft’s flightpath, captures the power of the reflected laser pulse as a function of height from the top of the canopy to the ground. Ground measurements of forest stand structure were collected on 26 plots with coincident SLICER data. Height, basal area, total biomass, and leaf biomass as estimated from field data could be predicted from SLICER-derived metrics with r 2 values of 0.95, 0.96, 0.96, and 0.84, respectively. These relationships were strong up to a height of 52 m, basal area of 132 m 2 /ha and total biomass of 1300 Mg/ha. In light of these strong relationships, large-footprint, airborne scanning lidar shows promise for characterizing stand structure for management and research purposes.

title: Correcting for Bias in Log‐Transformed Allometric Equations
abstract: mals: their productivity and population dynamics, IBP. Volume 5.  Alary polymorphism in the small brown planthopper Laodelphax striatellus (Homoptera: Delphacidae). Entomologia Experimentalis et Applicata 28: 47-53. McCoy, E. D., and J. R. Rey. 1981. Alary polymorphism among the salt marsh Delphacidae (Homoptera: Fulgoroidea) of northwest Florida. Ecological Entomology 6:285291. Rey,  Ecological biogeography of arthropods on Spartina islands in northwest Florida. Ecological Monographs 51:237-265. Stiling, P. D., and D. R. Strong. 1982. The parasitoids of the planthopper Prokelisia marginate (Homoptera: Delphacidae).  Insect population ecology. Blackwell, Oxford, England. Watt, A. D., and A. F. G. Dixon. 1981. The role of cereal growth stages and crowding in the induction of alatae in Sitobion a'enae and its consequences for population growth. Ecological Entomology 6:441-447.

title: Calibration Estimators in Survey Sampling
abstract: This article investigates estimation of finite population totals in the presence of univariate or multivariate auxiliary information. Estimation is equivalent to attaching weights to the survey data. We focus attention on the several weighting systems that can be associated with a given amount of auxiliary information and derive a weighting system with the aid of a distance measure and a set of calibration equations. We briefly mention an application to the case in which the information consists of known marginal counts in a two- or multi-way table, known as generalized raking. The general regression estimator (GREG) was conceived with multivariate auxiliary information in mind. Ordinarily, this estimator is justified by a regression relationship between the study variable y and the auxiliary vector x. But we note that the GREG can be derived by a different route by focusing instead on the weights. The ordinary sampling weights of the kth observation is 1/πk , where πk is the inclusion probabilit...

title: The Dip Test of Unimodality
abstract: On determine la distribution de la statistique de test, asymptotiquement et empiriquement, pour un echantillonnage a partir de la distribution uniforme

title: Retrieval of forest structural parameters using LiDAR remote sensing
abstract: In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which the retrieval of structural parameters at the respective scales has been attained. The paper further elaborates on the potential of LiDAR (Light Detection and Ranging) data to be fused with other types of remote sensing data and it concludes with recommendations for future research and potential gains in the application of LiDAR for the characterization of forests.

title: Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest
abstract: This study compares methods to estimate stem volume, stem number and basalarea from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, sprucedominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with fivedifferent regression models: one model based on height and density metrics from the ALSdata derived from the whole field plot, two models based on similar combinations derivedfrom 0.5 m raster cells, and two models based on canopy volumes from the ALS data. Thebest result was achieved with a model based on height and density metrics derived from0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a modelbased on height and density metrics derived from the whole field plot (RMSE 41.9%). Thestem number and the basal area were estimated with: (i) area-based regression modelsusing height and density metrics from the ALS data; and (ii) single tree-based informationderived from local maxima in a normalized digital surface model (nDSM) mean filteredwith different conditions. The estimates from the regression model were more accurate(RMSE 52.7% for stem number and 21.5% for basal area) than those derived from thenDSM (RMSE 63.4%-91.9% and 57.0%-175.5%, respectively). The accuracy of theestimates from the nDSM varied depending on the filter size and the conditions of theapplied filter. This suggests that conditional filtering is useful but sensitive tothe conditions.

title: Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs : a distribution-based approach
abstract: The low-density airborne laser scanning (ALS) data based estimation methods have been shown to produce accurate estimates of mean forest characteristics and diameter distributions, according to several studies. The used estimation methods have been based on the laser canopy height distribution approach, where various laser pulse height distribution -derived predictors are related to the stand characteristics of interest. This approach requires very delicate selection methods for selecting the suitable predictor variables. In this study, we introduce a new nearest neighbor search method that requires no complicated selection algorithm for choosing the predictor variables and can be utilized in multipurpose situations. The proposed search method is based on Minkowski distances between the distributions extracted from low density ALS data and aerial photographs. Apart from the introduction of a new search method, the aims of this study were: 1) to produce accurate species-specific diameter distributions and 2) to estimate factual saw log recovery, using the estimated height-diameter distributions and a stem data bank. The results indicate that the proposed method is suitable for producing species-specific diameter distributions and volumes at the stand level. However, it is proposed, that the utilization of more extensive and locally emphasized reference data and auxiliary variables could yield more accurate saw log recoveries.

title: Non-parametric prediction of diameter distributions using airborne laser scanner data
abstract: The aim of this study was to apply the non-parametric k-most similar neighbour (MSN) method and airborne laser scanner data to predict stand diameter distributions in a 960 km2 forest district in south-eastern Norway. The specific objectives of the study were (1) to examine the use of different dependent and independent variables in the canonical correlation analysis of MSN, and (2) to examine the influence of reduced number of training data plots by means of simulations. The reliability of the constructed diameter distributions was analysed using error indices and the accuracy of stand attributes derived from predicted diameter distributions. The study material included a total of 201 plots and they were reduced to 181, 161, … , 41 plots in the simulations. The results indicated that when selecting dependent variables in the canonical correlation analysis it is sufficient to have variables reflecting stand means and aggregated variables (sums) to obtain accurate predictions of diameter distributions. Furthermore, the prediction models should not to be too detailed, i.e. they should not include a great number of independent variables since cross-validation always tends to give too optimistic results. Validation on independent data will often show considerably poorer reliability figures. Finally, the results indicated that even such a low number of training plots as about 100 can produce accurate enough predictions of stand attributes and diameter distributions.