Wind profile within the forest canopy and in the transition layer above it

The forest as an underlying surface has to be considered in atmospheric models of different scales. Experimental evidence shows that there can be a significant variation of the wind profile within the forest and in the so-called transition layer above it. Experimentally observed wind speed is often found below as indicated by the wind speed profile obtained by: (a) the logarithmic relationship in the transition layer and (b) K theory within the forest. This situation can seriously disturb the real physical picture concerning the transfer of momentum, heat and water vapour from the surface into the atmosphere.In order to minimise the foregoing problems, we have suggested an empirical expression for the wind profile in the transition layer above the forest as well as the expressions for the wind profile and turbulent momentum transfer coefficient within the forest canopy layer. Additionally, for the proposed wind profiles, the expressions for the displacement height, roughness length and parameters are determined as functions of the forest structural characteristics using continuity conditions and a simple mass conservation hypothesis. The validity of the proposed expressions was checked using the micrometeorological measurements from the experimental sites in the Thetford Scots pine forest in Norfolk, United Kingdom and in the Ponderosa pine forest at the Shasta Experimental Forest, California, USA.     

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape

Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r²=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.     

Parameter estimation of surface layer turbulence from wind speed vertical profile

The present work presents an iterative method for estimating the characteristic parameters of surface layer turbulence, based exclusively on the knowledge of the roughness length z₀ and of the mean wind speed vertical profile. The method is a ‘specialised’ version of the well known Gauss–Newton non-linear optimisation method and its extreme simplicity allows the direct use of the latter in a real-time data acquisition system of limited potential. Comparison of the estimates derived from the proposed method with those obtained using the gradient method shows an excellent agreement between the two methods. In addition, numerical analysis has revealed excellent characteristics of speed and convergence.     

A threshold insensitive method for locating the forest canopy top with waveform lidar

Lidars have the unique ability to make direct, physical measurements of forest height and vertical structure in much denser canopies than is possible with passive optical or short wavelength radars. However the literature reports a consistent underestimate of tree height when using physically based methods, necessitating empirical corrections. This bias is a result of overestimating the range to the canopy top due to background noise and failing to correctly identify the ground.This paper introduces a method, referred to as “noise tracking”, to avoid biases when determining the range to the canopy top. Simulated waveforms, created with Monte-Carlo ray tracing over geometrically explicit forest models, are used to test noise tracking against simple thresholding over a range of forest and system characteristics. It was found that noise tracking almost completely removed the bias in all situations except for very high noise levels and very low (< 10%) canopy covers. In all cases noise tracking gave lower errors than simple thresholding and had a lower sensitivity to the initial noise threshold.Finite laser pulses spread out the measured signal, potentially overriding the benefit of noise tracking. In the past laser pulse length has been corrected by adding half that length to the signal start range. This investigation suggests that this is not always appropriate for simple thresholding and that the results for noise tracking were more directly related to pulse length than for simple thresholding. That this effect has not been commented on before may be due to the possible confounding impacts of instrument and survey characteristics inherent in field data. This method should help improve the accuracy of waveform lidar measurements of forests, whether using airborne or spaceborne instruments.     

Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health

Research was conducted in a forest adjacent to an abandoned acid mine tailings site to assess forest structural health using high spatial and spectral resolution digital camera imagery. Conventional approaches to this problem involve the use image spectral information, basic spectral transformations, or occasionally spatial transformations of image brightness. This research introduces fractional textures and semivariance analysis of image fractions. They were integrated with conventional image measures in stepwise multiple regression modelling of forest structure (canopy and crown closure, stem density, tree height, crown size) and health (a visual stress index). The goal was to conduct a relative comparison of the potential of the various image variable types in modelling of forest structure and health. Analysis was conducted for both canopy (crowns and shadows) and individual tree crown sample data sets extracted from 10 nm bandwidth spectral bands at three resolutions (0.25, 0.5, 1.0 m). Spatial transformations (texture, semivariogram range) of image brightness (DN) and image fractions (IF) were consistently the most significant and first entered variables in the best models of the forest parameters. At the canopy-scale, despite a limited number of available plots (6), stable models were produced that demonstrated the potential for spatially transformed variables. Semivariogram range explained 88% of the total variation of 9 of the 18 models and represented 56% of the variables used in all models while texture variables explained 51% of model variance in 8 of the 18 models and represented 40% of the variables used. At the tree crown scale (n=31), 88% of the total variation of six of eight models was explained by texture variables and 6% by semivariogram variables. DN and IF variables that were not spatially transformed contributed little to the models at both scales. They represented 4% and 6%, respectively, of the variables used in all models. Spatial information in image fractions and image brightness has proven to be more significant than spectral information in these analyses. Of the spatial resolutions evaluated, 0.5 m consistently produced similar or better models than those using the 0.25 or 1.0 m resolutions. These results demonstrate the potential for integration of spatial transforms of image fractions and raw brightness in high-resolution modelling of forest structure and health.     

Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method

In this article, a novel method is proposed for three-dimensional (3D) canopy surface reconstruction of trees using a region-based level set method. Both individual tree crowns and clusters of trees are first marked for further exploration. Multiple horizontal slices corresponding to different heights are obtained. The 3D structure of tree canopy is built using raw data from lidar point clouds. Also, new applications are proposed based on the new method for 3D forest reconstruction. The biomass parameters of the forest, including tree intersection area, tree equivalent crown radius, and canopy volume, can be calculated from stacking 2D slices of trees. Tree types are also identified and classified. The results indicate that this approach is effective for 3D surface reconstruction of forests including individual trees and clusters of trees, and that critical forest parameters (such as tree intersection area, tree position, and canopy volume) can be derived for the evaluation and measurement of biophysical parameters of forests.     

Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties

Imaging spectrometer data were acquired over conifer stands to retrieve spatially distributed information on canopy structure and foliage water content, which may be used to assess fire risk and to manage the impact of forest fires. The study relied on a comprehensive field campaign using stratified systematic unaligned sampling ranging from full spectroradiometric characterization of the canopy to conventional measurements of biochemical and biophysical variables. Airborne imaging spectrometer data (DAIS7915 and ROSIS) were acquired parallel to the ground measurements, describing the canopy reflectance of the observed forest. Coniferous canopies are highly heterogeneous and thus the transfer of incident radiation within the canopy is dominated by its structure. We demonstrated the viability of radiative transfer representation and compared the performance of two hybrid canopy reflectance models, GeoSAIL and FLIGHT, within this heterogeneous medium. Despite the different nature and canopy representation of these models, they yielded similar results. Subsequently, the inversion of a hyperspectral GeoSAIL version demonstrated the feasibility of estimating structure and foliage water content of a coniferous canopy based on radiative transfer modeling. Estimates of the canopy variables showed reasonably accurate results and were validated through ground measurements.     

Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7-7.0%, with an overestimation of 3.1-4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0-15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1-11.3%, bias -6.1-+4.6%). The dependency of LiDAR metrics and ACC at 0-75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America

Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R²=68% (adj.)) within the test-site is observed.     

Recovery of forest canopy characteristics through inversion of a complex 3D model

Radiative transfer models for vegetation serve as a basis for extracting vegetation variables using directional/spectral data from modern-borne sensors (e.g., MODIS, MISR, POLDER, SeaWiFS). Only recently have significant efforts been made to provide operational algorithms to invert these models. These efforts have exposed a need to significantly improve the efficiency and accuracy of traditional methods for inverting these physically based models. In an effort to overcome the limitations of traditional inversion methods, a neural network method was designed and tested. In this study, a complex 3D model (Discrete Anisotropic Radiative Transfer, DART) was inverted for a wide range of simulated forest canopies using POLDER-like data. The model was inverted to recover three forest canopy variables: forest cover, leaf area index, and a soil reflectance parameter. The ranges of these variables were 0.4–1.0, 0.8–9.3, and 0.0–1.0, respectively. Two inversion methods were used — a traditional inversion technique using a modified simplex method, and a neural network method in combination with an exhaustive variable selection technique. A comparison of the methods' efficiency, accuracy, and stability was made. The neural network method gave relatively accurate solutions to the inversion problem given a small subset of directional/spectral data using only one to five view angles. Using only nadir data, the root mean squared error (RMSE) for the forest cover, leaf area index, and the soil reflectance parameter were 0.025, 0.23, and 0.15, respectively, and using the “best” view directions (2–5) were 0.021, 0.21, and 0.11, respectively. In general, the neural network method was more accurate than the simplex method. The results from both methods showed that the addition of directional view angles, as opposed to only a nadir view, can significantly improve the accuracy of recovering forest canopy characteristics. The traditional simplex method is computationally intensive and may not be appropriate for many operational applications on a per-pixel basis for regional and global data. The neural network method was computationally efficient and can be applied on a per-pixel basis. In general, the neural network technique had significantly lower RMSE values at the low noise levels. However, at moderate noise levels, the simplex method was equal to the neural network method in RMSE values. At high noise levels, the simplex method had significantly lower RMSE values than the neural network method. The neural network approach can provide an accurate, efficient, and stable inversion method for radiative transfer models using directional/spectral data from modern-borne sensors.     

Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models

Field data describing the height growth of trees or stands over several decades are very scarce. Consequently, our capacity of analyzing forest dynamics over large areas and long periods of time is somewhat limited. This study proposes a new method for retrospectively reconstructing plot-wise average dominant tree height based on a time series of high-resolution canopy height maps, termed canopy height models (CHMs). The absolute elevation of the canopy surface, or digital surface model (DSM), was first reconstructed by applying image-matching techniques to stereo-pairs of aerial photographs acquired in 1945, 1965, 1983, and 2003. The historical CHMs were then created by subtracting the bare earth elevation provided from a recent lidar survey from the DSMs. A method for estimating average dominant tree height from these historical CHMs was developed and calibrated for each photographic year. The accuracy of the resulting remote sensing height estimates was compared to age-height data reconstructed based on dendrometric measurements. The height bias of the remote sensing estimates relative to the verification data ranged from 0.52 m to 1.55 m (1.16 m on average). The corresponding root-mean-square errors varied between 1.49 m and 2.88 m (2.03 m average). Despite being slightly less accurate than historical field data, the quality of the remote sensing estimates is sufficient for many types of forest dynamics studies. The procedures for implementing this method, with the exception of the calibration phase, are entirely automated such that forest height growth curves can be reconstructed and mapped over large areas for which recent lidar data and historical photographs exist.     

Effective medium approximation model of sea foam layer microwave emissivity of a vertical profile

A sea foam layer produced by wave breaking consists of seawater-coated air bubbles, fluid water, and air. The non-uniformity and microstructure of the air–water mixture in a foam layer can cause some important effects on microwave or optical properties. Considering the vertical non-uniformity of the air–water volume, the air-volume fraction is derived as a function of the foam depth variable, coated air bubble velocity, and foam temperature using the gas convection–diffusion equation. For a vertical graded profile of the air volume fraction, we discuss the effects of the coated air bubble velocity parameter and the air–sea temperature difference on the air volume fraction. The results show that the coated air bubble velocity is a key parameter that widely modulates the air volume fraction. Furthermore, an effective medium approximation (EMA) of spherical shell microstructures is proposed to investigate the graded foam effective permittivity and the emissivity of the sea surface covered with the foam layer of the graded air volume fraction in a vertical profile, and good agreement is obtained on comparing the EMA results with the experimental data of foam layer microwave emissivities at frequencies 1.4, 10.8, and 36.5 GHz. In our EMA model, the depth average of graded foam permittivity is adopted. This model can produce reasonable results by only tuning the air bubble velocity. It indicates that the EMA model can combine the effects of graded foam permittivity and foam microstructures into the coated air bubble velocity parameter. Meanwhile, we have qualitatively discussed the influence of air–sea temperature difference on the brightness temperature of the foam layer. It is shown that a negative (or positive) air–sea temperature difference enhances (or reduces) the sea surface brightness temperature, and the brightness temperature difference increases with an increase in microwave frequency.     

Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization

Forest dynamics are characterized by both continuous (i.e., growth) and discontinuous (i.e., disturbance) changes. Change detection techniques that use optical remotely sensed data to capture disturbance related changes are established and commonly applied; however, approaches for the capture of continuous forest changes are less mature. Optical remotely sensed imagery is well suited for capturing horizontally distributed conditions, structures, and changes, while Light Detection And Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to fully characterize forest canopy attributes and dynamics.The study described in this paper captures forest conditions along a corridor approximately 600 km long through the boreal forest of Canada. Two coincident LIDAR transects, representing 1997 and 2002 forest conditions respectively, are compared using image segments generated from Landsat ETM+ imagery. The image segments are used to provide a spatial framework within which the attributes and temporal dynamics of the forest canopy are estimated and compared. Segmented and classified Landsat imagery provides a context for the comparison of sufficiently spatially related LIDAR profiles and for the provision of categories to aid in the application of empirical models requiring knowledge of land cover.Global and local approaches were employed for characterizing changes in forest attributes over time. The global approach, emphasized the overall trend in forest change along the length of the entire transect, and indicated that key canopy attributes were stable, and transect characteristics, including forest canopy height, did not change significantly over the five-year period of this study (two sample t-test, p = 0.08). The local approach analyzed segment-based changes in canopy attributes, providing spatially explicit indications of forest growth and depletion. The local approach identified that 84% of the Landsat segments intercepted by both LIDAR transects either have no change, or have a small average increase in canopy height (0.7 m), while the other 16% of segments have an average decrease in canopy height of 1.6 m. As expected, the difference in the magnitude of the changes was markedly greater for depletions than it was for growth, but was less spatially extensive. Growth tends to occur incrementally over broad areas; whereas, depletions are dramatic and spatially constrained. The approach presented holds potential for investigating the impacts of climate change across a latitudinal gradient of boreal forest.     

Development of a forest canopy height estimation model using GLAS full waveform data over sloping terrain

ABSTRACT

The accurate estimation of forest canopy height is important because it leads to increased accuracy in the estimation of biomass, which is used in the study of the global carbon cycle, forest productivity, and climate change. However, there is no well-developed model that accurately estimates canopy height over undulating land. This paper describes the development of a back-propagation (BP) neural network model that estimates forest canopy height more accurately than other types of model. For modeling purposes, the land in the study area was classified as either plain (low relief areas) or hilly (high relief areas). Four different slope partition thresholds (5°, 10°, 15°, and 20°) were tested to determine the most suitable boundary value. ICESat-GLAS data provided by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud and Land Elevation Satellite (ICESat), field survey data, and digital elevation model (DEM) data were collected and refined, and various parameters, including waveform extent and topographic index, were calculated. A BP neural network model was created to estimate forest canopy height. Two other models were also developed, one using the topographic index and the other using multiple linear regression, for comparison with the BP neural network model. After calibration, the three models were tested to assess the accuracy of the estimates. The results showed that the BP model estimated canopy height more accurately than the other two models. The use of a 10° boundary to partition the topography into low relief areas and high relief areas improved the accuracy of each model; using the 10° slope boundary, the coefficient of correlation r between the estimates given by the BP neural network model and the field-measured data increased from 0.89 to 0.95 and the Root Mean Square Error (RMSE) decreased from 1.01 to 0.73 m.     

Inverting a forest canopy reflectance model to retrieve the overstorey and understorey leaf area index for forest stands

In this article, the Kuusk–Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstorey and understorey leaf area index (OU-LAI) of forest stands in the Longmenhe forest nature reserve in China. Data from detailed sample sites were collected in 30 forest stands representing the typical vegetation community in the study area. An uncertainty and sensitivity matrix (USM) was used to analyse the sensitivity of the FRT model parameters based on these data. The results indicated that overstorey LAI strongly influenced stand reflectance, whereas understorey LAI had a much lower impact. To predict OU-LAI in forest stands, FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. Various combinations of Hyperion bands were tested to evaluate the most effective wavelengths for the inversion of OU-LAI. The best estimates from 17 Hyperion bands (5 VIS, 8 NIR, 4 SWIR) by the FRT model inversion showed an R ²?=?0.41 and RMSE/mean?=?0.21 for overstorey LAI and R ²?=?0.49 and RMSE/mean?=?0.91 for understorey LAI. Advantages and disadvantages of FRT inversion for retrieval OU-LAI combined with Hyperion data are discussed.     

Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level

The use of lidar remote sensing for mapping the spatial distribution of canopy characteristics has the potential to allow an accurate and efficient estimation of tree dimensions and canopy structural properties from local to regional and continental scales. The overall goal of this paper was to compare biomass estimates and height metrics obtained by processing GLAS waveform data and spatially coincident discrete-return airborne lidar data over forest conditions in east Texas. Since biomass estimates are derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. More specific objectives were to compare the following parameters derived from GLAS and airborne lidar: (1) ground elevations; (2) maximum canopy height; (3) average canopy height; (4) percentiles of canopy height; and (5) above ground biomass. We used the elliptical shape of GLAS footprints to extract canopy height metrics and biomass estimates derived from airborne lidar. Results indicated a very strong correlation for terrain elevations between GLAS and airborne lidar, with an r value of 0.98 and a root mean square error of 0.78 m. GLAS height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models comparing GLAS and airborne lidar height metrics had R-square values above 0.9.     

LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management

Vegetation structure is an important parameter in fire risk assessment and fire behavior modeling. We present a new approach deriving the structure of the upper canopy by segmenting single trees from small footprint LIDAR data and deducing their geometric properties. The accuracy of the LIDAR data is evaluated using six geometric reference targets, with the standard deviation of the LIDAR returns on the targets being as low as 0.06 m. The segmentation is carried out by using cluster analysis on the LIDAR raw data in all three coordinate dimensions. From the segmented clusters, tree position, tree height, and crown diameter are derived and compared with field measurements. A robust linear regression of 917 tree height measurements yields a slope of 0.96 with an offset of 1 m and the adjusted R² resulting at 0.92. However, crown diameter is not well matched by the field measurements, with R² being as low as 0.2, which is most certainly due to random errors in the field measurements. Finally, a geometric reconstruction of the forest scene using a paraboloid model is carried out using values of tree position, tree height, crown diameter, and crown base height.     

Fusion of LiDAR and imagery for estimating forest canopy fuels

Due to increased fuel loading as a result of fire suppression, land managers in the American west are in need of precise information about the fuels they manage, including canopy fuels. Canopy fuel metrics such as canopy height (CH), canopy base height (CBH), canopy bulk density (CBD) and available canopy fuel (ACF) are specific inputs for wildfire behavior models such as FARSITE and emission models such as FOFEM. With finer spatial resolution data, accurate quantification of these metrics with detailed spatial heterogeneity can be accomplished. Light Detection and Ranging (LiDAR) and color near-infrared imagery are active and passive systems, respectively, that have been utilized for measuring a range of forest structure characteristics at high resolution. The objective of this research was to determine which remote sensing dataset can estimate canopy fuels more accurately and whether a fusion of these datasets produces more accurate estimates. Regression models were developed for ponderosa pine (Pinus ponderosa) stand representative of eastern Washington State using field data collected in the Ahtanum State Forest and metrics derived from LiDAR and imagery. Strong relationships were found with LiDAR alone and LiDAR was found to increase canopy fuel accuracy compared to imagery. Fusing LiDAR with imagery and/or LiDAR intensity led to small increases in estimation accuracy over LiDAR alone. By improving the ability to estimate canopy fuels at higher spatial resolutions, spatially explicit fuel layers can be created and used in wildfire behavior and smoke emission models leading to more accurate estimations of crown fire risk and smoke related emissions.