3036794
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.
87275068
A review of methods for mapping and prediction of inventory attributes for operational forest management
Forest inventory attributes are an important source of information for a variety of strategic and tactical forest management purposes. However, it is not possible or feasible for field inventories to be conducted contiguously across large areas, especially at a resolution fine enough to be useful for operational management. Therefore, a large number of quantitative modeling and prediction methods have been and are being developed and applied to predict and map forest attributes, with the goal of providing an accurate, spatially continuous, and detailed information base for practitioners of forestry and ecosystem management. This article reviews the most commonly used prediction techniques in the context of a comprehensive modeling framework that includes a discussion of methods, data sources, variable selection, and model validation. The methods discussed include regression, nearest neighbor, artificial neural networks, decision trees, and ensembles such as random forest. No single technique is revealed as universally superior for predicting forest inventory attributes; the ideal approach depends on goals, available training and ancillary data, and the modeler’s interest in tradeoffs between realism and statistical considerations. Useful ancillary data included in the models tend to include climate and topographic variables as well as vegetation indices derived from optical remote sensing systems such as Landsat. However, the use of airborne LiDAR in modeling of forest inventory attributes is increasing rapidly and shows promise for operational forest management applications. Different considerations are encapsulated within a generalized model development framework that provides a structure against which tradeoffs can be evaluated.
55417485
Integration of forest mapping and inventory to support forest management
Abstract: Forest inventory and forest mapping can be considered as monitoring and assessment applications that respond to different demands. However, the integration of mapping and inventory provides an effective framework for the support of forest management from multiple perspectives: (i) use of thematic maps for stratifying the inventory sample for the purpose of improving the precision of inventory estimates; (ii) coupling remotely sensed and sample inventory data for the purpose of constructing maps of inventoried forest attributes; (iii) coupling remotely sensed data and sample inventory data for the purpose of improving the precision of the inventory estimates; (iv) using inventory data as prior information to support thematic mapping; and (v) using inventory data to correct map areal estimates. This paper aims to provide general considerations on this integration issue in the form of a scientific review and commentary discussion.
129540929
An introduction to Canada’s boreal zone: ecosystem processes, health, sustainability, and environmental issues
The boreal zone and its ecosystems provide numerous provisioning, regulating, cultural, and supporting services. Because of its resources and its hydroelectric potential, Canada’s boreal zone is important to the country’s resource-based economy. The region presently occupied by Canada’s boreal zone has experienced dramatic changes during the past 3 million years as the climate cooled and repeated glaciations affected both the biota and the landscape. For about the past 7000 years, climate, fire, insects, diseases, and their interactions have been the most important natural drivers of boreal ecosystem dynamics, including rejuvenation, biogeochemical cycling, maintenance of productivity, and landscape variability. Layered upon natural drivers are changes increasingly caused by people and development and those related to human-caused climate change. Effects of these agents vary spatially and temporally, and, as global population increases, the demands and impacts on ecosystems will likely increase. Understanding how humans directly affect terrestrial and aquatic ecosystems in Canada’s boreal zone and how these effects and actions interact with natural disturbance agents is a prerequisite for informed and adaptive decisions about management of natural resources, while maintaining the economy and environment upon which humans depend. This paper reports on the genesis and present condition of the boreal zone and its ecosystems and sets the context for a detailed scientific investigation in subsequent papers published in this journal on several key aspects: carbon in boreal forests; climate change consequences, adaptation, and mitigation; nutrient and elemental cycling; protected areas; status, impacts, and risks of non-native species; factors affecting sustainable timber harvest levels; terrestrial and aquatic biodiversity; and water and wetland resources.
17975078
Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR
Variations in vertical and horizontal forest structure are often difficult to quantify as field-based methods are labour intensive and passive optical remote sensing techniques are limited in their capacity to distinguish structural changes occurring below the top of the canopy. In this study the capacity of small footprint (0.19 cm), discrete return, densely spaced (0.7 hits/m−2), multiple return, Light Detection and Ranging (LiDAR) technology, to measure foliage height and to estimate several stand and canopy structure attributes is investigated. The study focused on six Douglas-fir [Pseudotsuga menziesii spp. menziesii (Mirb.) Franco] and western hemlock [Tsuga heterophylla (Raf.) Sarg.] stands located on the east coast of Vancouver Island, British Columbia, Canada, with each stand representing a different structural stage of stand development for forests within this biogeoclimatic zone. Tree height, crown dimensions, cover, and vertical foliage distributions were measured in 20 m × 20 m plots and correlated to the LiDAR data. Foliage profiles were then fitted, using the Weibull probability density function, to the field measured crown dimensions, vertical foliage density distributions and the LiDAR data at each plot. A modified canopy volume approach, based on methods developed for full waveform LiDAR observations, was developed and used to examine the vertical and horizontal variation in stand structure. Results indicate that measured stand attributes such as mean stand height, and basal area were significantly correlated with LiDAR estimates (r2 = 0.85, P < 0.001, SE = 1.8 m and r2 = 0.65, P < 0.05, SE = 14.8 m2 ha−1, respectively). Significant relationships were also found between the LiDAR data and the field estimated vertical foliage profiles indicating that models of vertical foliage distribution may be robust and transferable between both field and LiDAR datasets. This study demonstrates that small footprint, discrete return, LiDAR observations can provide quantitative information on stand and tree height, as well as information on foliage profiles, which can be successfully modelled, providing detailed descriptions of canopy structure.
55188432
Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability
Abstract The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for mapping the presence/absence of understory shrub species and different snag diameter classes important for birds (i.e. ≥ 15 cm, ≥ 25 cm and ≥ 30 cm) in a 30,000 ha mixed-conifer forest in Northern Idaho (USA). We used forest inventory plots, LiDAR-derived metrics, and the Random Forest algorithm to achieve classification accuracies of 83% for the understory shrubs and 86% to 88% for the different snag diameter classes. Second, we evaluated the use of LiDAR data for mapping wildlife habitat suitability using four avian species (one flycatcher and three woodpeckers) as case studies. For this, we integrated LiDAR-derived products of forest structure with available models of habitat suitability to derive a variety of species-habitat associations (and therefore habitat suitability patterns) across the study area. We found that the value of LiDAR resided in the ability to quantify 1) ecological variables that are known to influence the distribution of understory vegetation and snags, such as canopy cover, topography, and forest succession, and 2) direct structural metrics that indicate or suggest the presence of shrubs and snags, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs) and the vertical heterogeneity of the forest canopy (for the snags). When applied to wildlife habitat assessment, these new LiDAR-based maps refined habitat predictions in ways not previously attainable using other remote sensing technologies. This study highlights new value of LiDAR in characterizing key forest structure components important for wildlife, and warrants further applications to other forested environments and wildlife species.
19991347
Potential contributions of remote sensing to ecosystem service assessments
Ecological and conservation research has provided a strong scientific underpinning to the modeling of ecosystem services (ESs) over space and time, by identifying the ecological processes and components of biodiversity (ecosystem service providers, functional traits) that drive ES supply. Despite this knowledge, efforts to map the distribution of ESs often rely on simple spatial surrogates that provide incomplete and non-mechanistic representations of the biophysical variables they are intended to proxy. However, alternative data sets are available that allow for more direct, spatially nuanced inputs to ES mapping efforts. Many spatially explicit, quantitative estimates of biophysical parameters are currently supported by remote sensing, with great relevance to ES mapping. Additional parameters that are not amenable to direct detection by remote sensing may be indirectly modeled with spatial environmental data layers. We review the capabilities of modern remote sensing for describing biodiversity, plant traits, vegetation condition, ecological processes, soil properties, and hydrological variables and highlight how these products may contribute to ES assessments. Because these products often provide more direct estimates of the ecological properties controlling ESs than the spatial proxies currently in use, they can support greater mechanistic realism in models of ESs. By drawing on the increasing range of remote sensing instruments and measurements, data sets appropriate to the estimation of a given ES can be selected or developed. In so doing, we anticipate rapid progress to the spatial characterization of ecosystem services, in turn supporting ecological conservation, management, and integrated land use planning.
95474652
An evaluation of greenhouse gas mitigation options for coal-fired power plants in the US Great Lakes States
Abstract We assessed options for mitigating greenhouse gas emissions from electricity generation in the US Great Lakes States, a region heavily dependent on coal-fired power plants. A proposed 600 MW power plant in northern Lower Michigan, USA provided context for our evaluation. Options to offset fossil CO2 emissions by 20% included biomass fuel substitution from (1) forest residuals, (2) short-rotation woody crops, or (3) switchgrass; (4) biologic sequestration in forest plantations; and (5) geologic sequestration using CO2 capture. Review of timber product output data, land cover data, and expected energy crop productivity on idle agriculture land within 120 km of the plant revealed that biomass from forestry residuals has the potential to offset 6% and from energy crops 27% of the annual fossil fuel requirement. Furthermore, annual forest harvest in the region is only 26% of growth and the surplus represents a large opportunity for forest products and bioenergy applications. We used Life Cycle Assessment (LCA) to compare mitigation options, using fossil energy demand and greenhouse gas emissions per unit electricity generation as criteria. LCA results revealed that co-firing with forestry residuals is the most attractive option and geologic sequestration is the least attractive option, based on the two criteria. Biologic sequestration is intermediate but likely infeasible because of very large land area requirements. Our study revealed that biomass feedstock potentials from land and forest resources are not limiting mitigation activities, but the most practical approach is likely a combination of options that optimize additional social, environmental and economic criteria.
85773061
Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations
This paper presents a review of recent developments in the assessment and monitoring of health in Australian eucalypt forests and plantations of pine and eucalypt species, with an emphasis on damage caused by herbivorous insects. The diverse range of interests and priorities amongst Australian stakeholders of native forests and plantations influences the scale, resolution and accuracy of results sought, and this in turn influences how the assessment data are collected, analysed and reported. The authors discuss sampling systems that include extensive ground-based surveys, permanent plots and airborne technologies being developed in Australia. In all cases, there is an appreciation that the assessment protocols should be objective, repeatable and cost effective. Significant progress has been made in the application of digital, remotely sensed imagery to detect and classify damaged forest canopies. The success of this approach depends, in part, on a sound understanding of the progression of symptoms at the leaf, tree crown and stand scale, especially those symptoms that influence spectral reflectance behaviour.
51998375
The role of LiDAR in sustainable forest management
Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing.
33222846
Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests
21 of biomass and an LAI of 12, with 90% and like conventional microwave and optical sensors, lidar 75% of variance explained, respectively. Furthermore, we sensors directly measure the distribution of vegetation were able to make accurate estimates of other stand material along the vertical axis and can be used to pro- structure attributes, including the mean and standard devide three-dimensional, or volumetric, characterizations viation of diameter at breast height, the number of stems of vegetation structure. Ecological applications of scan- greater than 100 cm in diameter, and independent estining lidar have hitherto used one-dimensional indices to mates of the basal area of Douglas-fir and western hemcharacterize canopy height. A novel three-dimensional lock. These measurements can be directly related to indianalysis of lidar waveforms was developed to character- ces of forest stand structural complexity, such as those ize the total volume and spatial organization of vegeta- developed for old-growth forest characterization. Indices tion material and empty space within the forest canopy. of canopy structure developed using the novel, threeThese aspects of the physical structure of canopies have dimensional analysis accounted for most of the variables been infrequently measured, from either field or remote used in predictive equations generated by the stepwise methods. We applied this analysis to 22 plots in Douglas- multiple regression. Published by Elsevier Science Inc. fir/western hemlock stands on the west slope of the Cascades Range in Oregon. Each plot had coincident lidar data and field measurements of stand structure. We com- INTRODUCTION pared results from the novel analysis to two earlier methCharacterization of structure in moderate to high bioods of canopy description. Using the indices of canopy mass forests is a major challenge in remote sensing. structure from all three methods of description as inde
84281723
Validating estimates of merchantable volume from airborne laser scanning (ALS) data using weight scale data
There is increasing interest in the use of airborne laser scanning (ALS; also referred to as airborne Light Detection and Ranging or LiDAR) for forest inventory purposes in Canada. Timber volume is one of the key inventory attributes that is commonly estimated using ALS data, and estimates of volume can be validated using post-harvest measures. ALS data and the area-based approach were used to develop an enhanced forest inventory for the Hinton Forest Management Area (FMA) in central Alberta. Weight scale measures of coniferous merchantable volume from 272 stands harvested between 2008 and 2010 were used as validation data for both conventional and ALS-based estimates. Overall, conventional estimates of coniferous merchantable volume derived from cover type adjusted volume tables were found to underestimate weight scale volumes by 19.8%. Conversely, estimates generated from the ALS data overestimated weight scale volumes by 0.6%. ALS-based estimates provide wall-to-wall, spatially explicit estimates of me...
129236111
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.
84156380
Imputing Forest Structure Attributes from Stand Inventory and Remotely Sensed Data in Western Oregon, USA
Imputation is commonly used to assign reference stand observations to target stands based on covariate relationships to remotely sensed data to assign inventory attributes across the entire landscape. However, most remotely sensed data are collected at higher resolution than the stand inventory data often used by operational foresters. Our primary goal was to compare various aggregation strategies for modeling and mapping forest attributes summarized from stand inventory data, using predictor variables derived from either light detection and ranging (LiDAR) or Landsat and a US Geological Survey (USGS) digital terrain model (DTM). We found that LiDAR metrics produced more accurate models than models using Landsat/USGS-DTM predictors. Calculating stand-level means of all predictors or all responses proved most accurate for developing imputation models or validating imputed maps, respectively. Developing models or validating maps at the unaggregated scale of individual stand subplots proved to be very inaccurate, presumably due to poor geolocation accuracies. However, using a sample of pixels within stands proved only slightly less accurate than using all available pixels. Furthermore, bootstrap tests of similarity between imputations and observations showed no evidence of bias regardless of aggregation strategy. We conclude that pixels sampled from within stands provide sufficient information for modeling or validating stand attributes of interest to foresters.
128640282
Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve
Tree height, the height from the ground surface to the tree crown, and the crown length as a proportion of tree height of individual trees were derived from various canopy height metrics measured by a small-footprint airborne laser scanner flown over a boreal forest reserve. The average spacing on the ground of the laser pulses ranged from 0.66 to 1.29 m. Ground-truth values were regressed against laser-derived canopy height metrics. The regressions explained 75%, 53%, and 51% of the variability in ground-truth tree height, height to the crown, and relative crown length, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 3.15 m (17.6%), 2.19 m (39.1%), and 10.48% (14.9% of ground-truth mean), respectively. On 10 plots with size 50 m2 in the boreal forest reserve and on 27 plots with size 200 m2 in a managed spruce forest, mean tree height, average height from the ground surface to the crown, and average relative crown length were regressed against laser canopy height metrics. The coefficients of determination (R2) ranged from .47 to .91. Cross-validation revealed a precision of 1.49 m (7.6%), 1.24–1.52 m (20.9–23.3%), and 6.32–7.11% (8.8–10.9% of ground-truth mean) for mean tree height, average height to the crown, and average relative crown length, respectively. At least, mean tree height can be determined more accurately from laser data than by current methods.
207986423
DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER
This paper demonstrates extensively for the first time that high-pulse-rate laser scanners are capable to detect single trees in boreal forest zone, and characteristics of single trees, such as height, location, and crown dimensions can be determined providing accurate standwise estimates for stem volume and mean height. In the boreal forest zone and in many forest areas, there exists gaps between the forest crowns. For example, in Finland, more than 30 % of the first pulse data reflect directly from the ground without any interaction with the canopy. By increasing the number of pulses, it is possible to have samples from each individual tree and also from the gaps between the trees. Basically this means that several laser pulses can be recorded per m 2 . This allows detailed investigation of forest areas and the creation of a 3dimensional tree height model. Tree height model can be calculated from the digital terrain and crown models both obtained with the laser scanner data. By analysing the 3-dimensional tree height model by using image vision methods, e.g. segmentation, it is possible to locate individual trees, estimate individual tree heights, crown area and, by using that data, to derive the stem diameter, number of stems, basal area and stem volume. The advantage of the method is the capability to measure directly physical dimensions from the trees and use that information to calculate the needed stand attributes. It is shown that tree heights of individual trees in the dominating storey can be obtained with less than 1 m standard error. In addition, the following standard errors were obtained for mean height, basal area and stem volume at stand level: 2.3 m (13.6 %), 1.9 m 2 /ha (9.6 %), and 16.5 m 3 /ha (9.5 %), respectively. The accuracy was better than the accuracy of conventional standwise field inventory.
140576510
Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data
Abstract 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.
128763286
Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data
Abstract While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called “semi-ITC” that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context.
15924228
Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations
Abstract: The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees ( e td ), error in tree height prediction ( e h ) and error in tree diameter prediction ( e d ). The effects of errors were analyzed with Monte Carlo simulations. e td was modeled empirically based on a tree’s relative size. A total of five different tree detection scenarios were tested. Effect of e h was investigated using 5% and 0% and effect of e d using 20%, 15%, 10%, 5%, 0% error levels, respectively. The research material comprised 15 forest stands located in Southern Finland. Measurements of 5,300 trees and their timber assortments were utilized as a starting point for the Monte Carlo simulated ITD inventories. ITD carried out for the same study area provided a starting point (Scenario 1) for e td . In Scenario 1, 60.2% from stem number and 75.9% from total volume (V
84034651
Matching remotely sensed and field-measured tree size distributions
Undetected trees and inaccuracies in the predicted allometric relationships of tree stem attributes seriously constrain single-tree remote sensing of seminatural forests. A new approach to compensate for these error sources was developed by applying a histogram matching technique to map the transformation between the cumulative distribution functions of crown radii extracted from airborne laser scanning (ALS) data and field-measured stem diameters (dbh, outside bark measured at 1.3 m aboveground). The ALS-based crown data were corrected for the censoring effect caused by overlapping tree crowns, assuming that the forest is an outcome of a homogeneous, marked Poisson process with independent marks of the crown radii. The transformation between the cumulative distribution functions was described by a polynomial regression model. The approach was tested for the prediction of plot-level stem number (N), quadratic mean diameter (DQM), and basal area (G) in a managed boreal forest. Of the 40 plots studied, a to...
122910057
3D segmentation of single trees exploiting full waveform LIDAR data
Abstract This paper highlights a novel segmentation approach for single trees from LIDAR data and compares the results acquired both from first/last pulse and full waveform data. In a first step, a conventional watershed-based segmentation procedure is set up, which robustly interpolates the canopy height model from the LIDAR data and identifies possible stem positions of the tallest trees in the segments calculated from the local maxima of the canopy height model. Secondly, this segmentation approach is combined with a special stem detection method. Stem positions in the segments of the watershed segmentation are detected by hierarchically clustering points below the crown base height and reconstructing the stems with a robust RANSAC-based estimation of the stem points. Finally, a new three-dimensional (3D) segmentation of single trees is implemented using normalized cut segmentation. This tackles the problem of segmenting small trees below the canopy height model. The key idea is to subdivide the tree area in a voxel space and to set up a bipartite graph which is formed by the voxels and similarity measures between the voxels. Normalized cut segmentation divides the graph hierarchically into segments which have a minimum similarity with each other and whose members (= voxels) have a maximum similarity. The solution is found by solving a corresponding generalized eigenvalue problem and an appropriate binarization of the solution vector. Experiments were conducted in the Bavarian Forest National Park with conventional first/last pulse data and full waveform LIDAR data. The first/last pulse data were collected in a flight with the Falcon II system from TopoSys in a leaf-on situation at a point density of 10 points/m 2 . Full waveform data were captured with the Riegl LMS-Q560 scanner at a point density of 25 points/m 2 (leaf-off and leaf-on) and at a point density of 10 points/m 2 (leaf-on). The study results prove that the new 3D segmentation approach is capable of detecting small trees in the lower forest layer. So far, this has been practically impossible if tree segmentation techniques based on the canopy height model were applied to LIDAR data. Compared to a standard watershed segmentation procedure, the combination of the stem detection method and normalized cut segmentation leads to the best segmentation results and is superior in the best case by 12%. Moreover, the experiments show clearly that using full waveform data is superior to using first/last pulse data.
11974889
Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height
The main study objective was to develop robust processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating plot-level tree height by measuring individual trees identifiable on the three-dimensional lidar surface. Lidar processing techniques included data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The lidar system used for this study produced an average footprint of 0.65 m and an average distance between laser shots of 0.7 m. The lidar data set was acquired over deciduous and coniferous stands with settings typical of the southeastern United States. The lidar-derived tree measurements were used with regression models and cross-validation to estimate tree height on 0.017-ha plots. For the pine plots, lidar measurements explained 97 percent of the variance associated with the mean height of dominant trees. For deciduous plots, regression models explained 79 percent of the mean height variance for dominant trees. Filtering for local maximum with circular windows gave better fitting models for pines, while for deciduous trees, filtering with square windows provided a slightly better model fit. Using lidar and optical data fusion to differentiate between forest types provided better results for estimating average plot height for pines. Estimating tree height for deciduous plots gave superior results without calibrating the search window size based on forest type.
131008643
Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest
In this study, we present a new method for single tree segmentation and characterization from a canopy surface model (CSM), and its corresponding point cloud, based on airborne laser scanning. The method comprises new algorithms for controlling the shape of crown segments, and for residual adjustment of the canopy surface model (CSM). We present a new criterion that measures the success of locating trees, and demonstrate how this criterion can be used for optimizing the degree of CSM smoothing. From the adjusted CSM segments, we derived tree height and crown diameter, and based on all first laser pulse measurements within the segments we derived crown-base height. The method was applied and validated in a Norway spruce dominated forest reserve having a heterogeneous structure. The number of trees automatically detected varied with social status of the trees, from 93 percent of the dominant trees to 19 percent of the suppressed trees. The RMSE values for tree height, crown diameter, and crown-base height were around 1.2 m, 1.1 m, and 3.5 m, respectively. The method overestimated crown diameter (0.8 m) and crown base height (3.0 m).
129180664
Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions
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.
19561291
Estimating Tree Height Distribution Using Low-Density ALS Data With and Without Training Data
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.
129253866
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%.
34672721
Status and prospects for LiDAR remote sensing of forested ecosystems
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.
16650368
Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources
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.
53601319
Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods
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.
120272131
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.
123406296
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.
85009419
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%.
84901707
LiDAR and Weibull modeling of diameter and basal area
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.
84473363
Comparison of basal area and stem frequency diameter distribution modelling using airborne laser scanner data and calibration estimation
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.
12716425
Modeling Forest Trees and Stands
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 Quality.- 8. Quantifying Stand Density.- 9. 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 Evaluation.-
117064962
Predicting Tree Diameter Distributions
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.
85866658
Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest
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.
18875433
RECOVERING PLOT-SPECIFIC DIAMETER DISTRIBUTION AND HEIGHT- DIAMETER CURVE USING ALS BASED STAND CHARACTERISTICS
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.
85384839
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.
85384039
Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs
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...
83905130
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.
121175254
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.
84687774
Canadian national biomass equations: new parameter estimates that include British Columbia data
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.
130401325
Update of forest inventory data with lidar and high spatial resolution satellite imagery
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.
140563727
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.
34849665
Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment
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.
73632754
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors
Summary 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.
52088938
Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data
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.
34012174
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 s...
8114485
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.
83951914
Correcting for Bias in Log‐Transformed Allometric Equations
mals: their productivity and population dynamics, IBP. Volume 5. Cambridge University Press, London, England. Mahmud, F. S. 1980. 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, J. R. 1981. 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). Florida Entomologist 65:191-192. Varley, G. C., G. R. Gradwell, and M. P. Hassell. 1973. 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.
122542260
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...
119847480
The Dip Test of Unimodality
On determine la distribution de la statistique de test, asymptotiquement et empiriquement, pour un echantillonnage a partir de la distribution uniforme
13116266
Retrieval of forest structural parameters using LiDAR remote sensing
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.
16469299
Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest
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.
52063795
Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs : a distribution-based approach
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.
84721875
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.