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.     

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

A rapid canopy reflectance model inversion experiment was performed using multi-angle reflectance data from the NASA Multi-angle Imaging Spectro-Radiometer (MISR) on the Earth Observing System Terra satellite, with the goal of obtaining measures of forest fractional crown cover, mean canopy height, and aboveground woody biomass for large parts of south-eastern Arizona and southern New Mexico (> 200,000 km²). MISR red band bidirectional reflectance estimates in nine views mapped to a 250 m grid were used to adjust the Simple Geometric-optical Model (SGM). The soil-understory background signal was partly decoupled a priori by developing regression relationships with the nadir camera blue, green, and near-infrared reflectance data and the isotropic, geometric, and volume scattering kernel weights of the LiSparse–RossThin kernel-driven bidirectional reflectance distribution function (BRDF) model adjusted against MISR red band data. The SGM's mean crown radius and crown shape parameters were adjusted using the Praxis optimization algorithm, allowing retrieval of fractional crown cover and mean canopy height, and estimation of aboveground woody biomass by linear rescaling of the dot product of cover and height. Retrieved distributions of crown cover, mean canopy height, and aboveground woody biomass for forested areas showed good matches with maps from the United States Department of Agriculture (USDA) Forest Service, with R² values of 0.78, 0.69, and 0.81, and absolute mean errors of 0.10, 2.2 m, and 4.5 tons acre^- 1 (10.1 Mg ha^- 1), respectively, after filtering for high root mean square error (RMSE) on model fitting, the effects of topographic shading, and the removal of a small number of outliers. This is the first use of data from the MISR instrument to produce maps of crown cover, canopy height, and woody biomass over a large area by seeking to exploit the structural effects of canopies reflected in the observed anisotropy patterns in these explicitly multiangle data.     

Allometric constraints on sources of variability in multi-angle reflectance measurements

Bidirectional reflectance signatures of vegetation are strongly shaped by the shadows cast between objects in a scene, such as tree crowns or leaves. Differences in the shape and spatial density of these objects result in distinct bidirectional reflectance distribution functions (BRDFs) in different biomes. We examined how allometry may constrain the variability of canopy architectural parameters in BRDF models, and consequently alter the attribution of variation in the simulated bidirectional reflectance factor (BRF). Allometry is the covariation between the size or number of organisms and their component parts.To test the importance of realistic variation and covariation of canopy architecture on BRDF, we incorporated the 3-D radiative transfer model DISORD (which uses the geometric optics (GO) model of Li and Strahler) into a Monte Carlo (MC) algorithm. The MC algorithm generated an ensemble of tree canopies whose parameters fulfilled the allometry of a set of measured forest plots from Russian forest inventory. The role of view geometry was directly considered using perturbations of the parameters to evaluate the sensitivity of the BRF itself, evaluated at different view angles, and the difference in BRF (ΔBRF) as measured at two view angles representing paired satellite observations.The allometrically constrained forest plots had reduced variation in ΔBRF compared to the uncorrelated plots, but the variation of the BRF itself is dramatically increased by allometry. The variation of the BRF is relatively constant among the view angles examined, whereas the variation in ΔBRF increases dramatically with larger phase angles. The BRF was most sensitive to canopy attributes that were important in radiative transfer, such as LAI and stem area index (SAI), but there were also large (∼ 40% of variance) contributions of geometric components such as tree number, crown size, and ground cover. By contrast, sensitivity of ΔBRF was dominated by ground cover, crown size and tree number, which all play a role in the GO calculations. The mix of sensitive parameters was not dramatically different between gymnosperms and angiosperms, nor between allometric and correlated runs. Together these results indicate that forest structure and leaf area could be usefully inverted together using paired observations with different viewing geometries. Ideal pairs of observations are those with large difference in phase angle, and along the gradient of the BRF peak, which most commonly occur with sequential MODIS/Terra overpasses.     

Multiresolution foliage for forest rendering

Plants are important objects in virtual environments. High complexity of shape structure is found in plant communities. Level of detail (LOD) of plant geometric models becomes important for interactive forest rendering. We emphasize three major problems in current research: the time consumption in LOD model construction and extraction, the balance between visual effect and data compression, and the time consumption in the communication between Central Processing Unit (CPU) and Graphics Processing Unit (GPU). We present a new foliage simplification framework for LOD model and forest rendering. By an uneven subdivision of the tree crown volume, the cost for LOD model construction is drastically reduced. With a GPU‐oriented design of LOD storage structure for foliage, the costly hierarchical traversal of a binary tree is replaced by a sequential lookup of an array. The structure also decreases the communication between the CPU and the GPU in rendering. In addition, Leaf density is introduced to adapt compression to the local distribution of leaves, so that more visually relevant details are kept. According to foliage nature (broad leaves or needles), higher compression are finally reached using mixed polygon/line models. This framework is implemented on virtual scenes of simulated trees with high detail. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Integration of multisensor data provides the opportunity to explore benefits emanating from different data sources. A fusion between fraction images derived from spectral mixture analysis of Landsat-7 ETM+ and phased array L-band synthetic aperture radar (PALSAR) is introduced. The aim of this fusion is to improve the estimation accuracy of above-ground biomass (AGB) in lowland mixed dipterocarp forest. Spectral mixture analysis was applied to decompose a mixture of spectral components of Landsat-7 ETM+ into vegetation, soil, and shade fractions. These fraction images were integrated with PALSAR data using the discrete wavelet transform (DWT) and Brovey transform. As a comparison, spectral reflectance of Landsat-7 ETM+ was fused directly with PALSAR data. Backscatter of horizontal–horizontal and horizontal–vertical polarizations was also used to estimate AGB. Forest inventory was carried out in 77 randomly distributed plots, the data being used for either model development or validation. A local allometric equation was applied to calculate AGB per plot. Regression models were developed by integrating field measurements of 50 sample plots with remotely sensed data, e.g. fraction images, reflectance of Landsat-7 ETM+, and PALSAR data. The models developed were validated using 27 independent sample plots. The results showed that not all fused images significantly improved the accuracy of AGB estimation. The model based on Brovey transform using the reflectance of Landsat-7ETM+ and PALSAR produced an R² of only 0.03–0.10. By contrast, fusion between PALSAR data and fraction images using Brovey transform improved the accuracy of R² to 0.33–0.46. Further improvement in the accuracy of estimating AGB was observed when DWT was applied to integrate PALSAR with the reflectance of Landsat-7ETM+ (R² = 0.69–0.72) and PALSAR with fraction images (R² = 0.70–0.75).     

Classification of Australian forest communities using aerial photography, CASI and HyMap data

Within Australia, the discrimination and mapping of forest communities has traditionally been undertaken at the stand scale using stereo aerial photography. Focusing on mixed species forests in central south-east Queensland, this paper outlines an approach for the generation of tree species maps at the tree crown/cluster level using 1 m spatial resolution Compact Airborne Spectrographic Imager (CASI; 445.8 nm–837.7 nm wavelength) and the use of these to generate stand-level assessments of community composition. Following automated delineation of tree crowns/crown clusters, spectral reflectance from pixels representing maxima or mean-lit averages of channel reflectance or band ratios were extracted for a range of species including Acacia, Angophora, Callitris and Eucalyptus. Based on stepwise discriminant analysis, classification accuracies of dominant species were greatest (87% and 76% for training and testing datasets; n = 398) when the mean-lit spectra associated with a ratio of the reflectance (ρ) at 742 nm (ρ₇₄₂) and 714 nm (ρ₇₁₄) were used. The integration of 2.6 m HyMap (446.1 nm–2477.8 nm) spectra increased the accuracy of classification for some species, largely because of the inclusion of shortwave infrared wavebands. Similar increases in accuracy were achieved when classifications of field spectra resampled to CASI and HyMap wavebands were compared. The discriminant functions were applied subsequently to classify crowns within each image and produce maps of tree species distributions which were equivalent or better than those generated through aerial photograph interpretation. The research provides a new approach to tree species mapping, although some a priori knowledge of the occurrence of broad species groups is required. The tree maps have application to biodiversity assessment in Australian forests.     

Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data

Airborne laser scanner systems provide detailed forest information that can be used for important improvements in forest management decisions. Planning systems under development use plot-survey data to represent forest stands in large forest holdings which enables new flexible methods to model the forest and optimize selection of silvicultural treatments. In Sweden today, only averages of forest stand variables are used, and the survey methods used do not provide plot-survey data for all stands in large forest holdings. This is a task possibly solved using airborne laser scanner data. Various measures can be derived from laser data, each describing different forest variables, such as tree height distribution, vegetation density and vertical tree crown structure. Here, imputation of field plot (10 m radius) data using measures derived from airborne laser scanner data (TopEye) and optical image data (SPOT 5 HRG satellite sensor) were evaluated as a method to provide data for new long-term management planning systems. In addition to commonly applied measures, the semivariogram of laser measurements was evaluated as a new measure to extract spatial characteristics of the forest. The study used data from 870, 10 m radius field plots (0 to 812 m³ ha^− 1) surveyed for a 1200 ha large forest estate in the south of Sweden. At the best, combining measures derived from laser scanner data and SPOT 5 data, stand mean volume was estimated with a root mean square error (RMSE) of 20% of the sample mean and stem density with 22% RMSE. Bias of stem density estimates was 5%, and stand stem volume 4%. Although these accuracies are sufficient for operational application, estimates of tree species proportions and within-stand variation were clearly not.     

Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain

The amount and spatial distribution of aboveground forest biomass (AGB) are required inputs to forest carbon budgets and ecosystem productivity models. Satellite remote sensing offers distinct advantages for large area and multi-temporal applications, however, conventional empirical methods for estimating forest canopy structure and AGB can be difficult in areas of high relief and variable terrain. This paper introduces a new method for obtaining AGB from forest structure estimates using a physically-based canopy reflectance (CR) model inversion approach. A geometric-optical CR model was run in multiple forward mode (MFM) using SPOT-5 imagery to derive forest structure and biomass at Kananaskis, Alberta in the Canadian Rocky Mountains. The approach first estimates tree crown dimensions and stem density for satellite image pixels which are then related to tree biomass and AGB using a crown spheroid surface area approach. MFM estimates of AGB were evaluated for 36 deciduous (trembling aspen) and conifer (lodgepole pine) field validation sites and compared against spectral mixture analysis (SMA) and normalised difference vegetation index (NDVI) biomass predictions from atmospherically and topographically corrected (SCS+C) imagery. MFM provided the lowest error for all validation plots of 31.7 tonnes/hectare (t/ha) versus SMA (32.6 t/ha error) and NDVI (34.7 t/ha) as well as for conifer plots (MFM: 23.0 t/ha; SMA 27.9 t/ha; NDVI 29.7 t/ha) but had higher error than SMA and NDVI for deciduous plots (by 4.5 t/ha and 2.1 t/ha, respectively). The MFM approach was considerably more stable over the full range of biomass values (67 to 243 t/ha) measured in the field. Field plots with biomass > 1 standard deviation from the field mean (over 30% of plots) had biomass estimation errors of 37.9 t/ha using MFM compared with 65.5 t/ha and 67.5 t/ha error from SMA and NDVI, respectively. In addition to providing more accurate overall results and greater stability over the range of biomass values, the MFM approach also provides a suite of other biophysical structural outputs such as density, crown dimensions, LAI, height and sub-pixel scale fractions. Its explicit physical-basis and minimal ground data requirements are also more appropriate for larger area, multi-scene, multi-date applications with variable scene geometry and in high relief terrain. MFM thus warrants consideration for applications in mountainous and other, less complex terrain for purposes such as forest inventory updates, ecological modeling and terrestrial biomass and carbon monitoring studies.     

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.     

Reflectance factor dynamics of boreal forest clear-cut communities during early secondary succession

Abstract

Spectral reflectance factors in the visible and near-infrared spectral bands were measured over five years on permanent sample plots in forest clear-cut communities in Estonia. The spectral-temporal profiles of vegetation indices Greenness and Brightness were calculated from discrete measurements of reflectance factors. The following hypotheses concerning successional reflectance dynamics in boreal forest communities following clear-cutting and understory destruction were tested. 1. Changes in the Greenness seasonal profile peak value (PV) during secondary succession are mainly directional and related to time after clear-cutting rather than to environmental fluctuations. 2, The rate of succession, i.e., difference between seasonal PVs of consecutive years, declines over time. 3. The seasonal PVs of forest communities from different site types become more similar (converge) during secondary succession.

The results show that the secondary succession of forests in reflectance terms is directed towards the enhancement of contrast between visible to near-infrared reflectance, towards obtaining maximum spectral vegetation index Greenness value at a given Brightness level. The PV changes during secondary succession were explained by the age of the communities. The seasonal PVs of different site type clear-cuts converge during early secondary succession, resulting from different speed of clear-cut recolonization on fertile and poor sites.     

The spectral reflectance of stands of norway spruce and scotch pine, measured from a helicopter

Radiometer measurements were made from a helicopter over selected stands of Norway spruce (Picea abies) and Scotch Pine (Pinus sylvestris) near Stockholm, Sweden. Continuous reflectance spectra in the wavelength range 0.4–1.7 μm were measured over forest stands having different species, ages, crown densities, and understories. The irradiance was measured continuously with a cosine receptor on board the helicopter. The average reflectances of pine stands with an age over 40 years were higher in all bands than the reflectances for comparable spruce stands. In rank order, bands centered at 0.67, 1.6, and 0.48 μm offered the best separation possibilities. The reflectance changes through the summer for the two species were small. The reflectance of the pine stands varied less with deviations in look angle away from nadir than the reflectance of the spruce stands.     

Effect of thinning cutting on boreal forest reflectance: a comparison of simulations and Landsat TM estimates

A forest reflectance model was used to simulate the reflectance changes due to thinning in some stands of boreal forest in Sweden. The simulated reflectance changes were compared with empirical reflectance changes, measured by multi-temporal analysis of Landsat TM data. The stand data from a field survey served as the main inputs for the reflectance model. The mean absolute difference between stimulated and measured reflectance change was about one third of the average measured change.     

Automated estimation of localized forest volume from large-scale aerial photographs and ancillary cartographic information in a boreal forest

The development of an automated method for obtaining locally reliable estimates of forest volume is demonstrated for a mixed-species boreal forest of the Lac St. Jean region of Quebec. The method relies on the ability of an algorithm based on local maxima to identify individual stems from a scanned aerial photograph under the assumption that the points of maximum light reflectance will be the highest points on individual trees. This information is linked via regression analysis to mean heights of dominant and co-dominant trees and ground-based forest inventory data to provide a statistical relationship with forest volume. It was demonstrated that, by using the method, the local uncertainty of volume estimates could be decreased by 61% relative to standard forest inventory procedures. The method is not applicable to young or disturbed stands. The greatest difficulty with the method is that sample plots used for validation must be locatable with absolute accuracy on the scanned aerial photographs something that is likely to be problematic in many forest conditions.     

Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass

Regression has been widely applied in Light Detection And Ranging (LiDAR) remote sensing to spatially extend predictions of total aboveground biomass (TAGB) and other biophysical properties over large forested areas. Sample (field) plot size has long been considered a key sampling design parameter and focal point for optimization in forest surveys, because of its impact on sampling effort and the estimation accuracy of forest inventory attributes. In this study, we demonstrate how plot size and co-registration error interact to influence the estimation of LiDAR canopy height and density metrics, regression model coefficients, and the prediction accuracy of least-squares estimators of TAGB. We made use of simulated forest canopies and synthetic LiDAR point clouds, so that we could maintain strict control over the spatial scale and complexity of forest scenes, as well as the magnitude and type of planimetric error inherent in ground-reference and LiDAR datasets. Our results showed that predictions of TAGB improved markedly as plot size increased from 314 (10 m radius) to 1964 m² (25 m radius). The co-registration error (spatial overlap) between ground-reference and LiDAR samples negatively impacted the estimation of LiDAR metrics, regression model fit, and the prediction accuracy of TAGB. We found that larger plots maintained a higher degree of spatial overlap between ground-reference and LiDAR datasets for any given GPS error, and were therefore more resilient to the ill effects of co-registration error compared to small plots. The impact of co-registration error was more pronounced in tall, spatially heterogeneous stands than short, homogeneous stands. We identify and briefly discuss three possible ways that LiDAR data could be used to optimize plot size, sample selection, and the deployment of GPS resources in forest biomass surveys.     

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R² = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R² = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.     

A multi-angle spectrometer for automatic measurement of plant canopy reflectance spectra

The paper describes the design and operation of a multi-angle spectrometer (MAS) for automatic measurement of near-field spectral reflectances of plant canopies at hourly intervals. A novel feature of the instrument is a rotating periscope connected to a spectrometer via a fiber optic cable. Canopy reflectances are calculated for multiple view azimuths, at a single zenith angle from measurements of spectrometer dark current, incoming solar irradiance and reflected radiances. Spectral measurements are made between 300 and 1150 nm wavelength at a band-to-band spacing of 3 nm, and a bandwidth (full-width, half maximum) of 10 nm. Preliminary data analysis showed that the canopy reflectance model of Kuusk [Kuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment 51, 342-350] reproduced the observed large differences in visible and near-infrared (NIR) reflectances, but the model was unable to predict quantitatively the observed variations in the measured reflectance spectra with azimuth, particularly in the NIR. Discrepancies between model and measurements are likely due to the inhomogeneous nature of the forest canopy in contrast to the assumption of a uniformly absorbing turbid medium in the model. Measurements using the MAS can be used to investigate directional dependences of reflectance indices and for testing BRDF models used to separate geometrical and plant physiological contributions to the reflectance signals. The MAS provides continuous sampling of reflectance indices which can be compared with canopy properties such as chlorophyll content and photosynthetic capacity.     

Estimating forest canopy fuel parameters using LIDAR data

Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R²=0.86; ln(crown bulk density): R²=0.84; canopy base height: R²=0.77; canopy height: R²=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.     

The calibration of digitized aerial photographs for forest stratification

The high spatial resolution of digitized aerial photographs may offer an accurate and effective means of mapping, inventorying, and monitoring forests. Due to the presence of bi-directional reflectance, however, the pixel values are affected by their location within the photo. Two similar sample plots or vegetation types in different parts of the photo may thus have quite dissimilar pixel values and texture features. It is consequently necessary to correct, or calibrate, pixel values when they are used in numerical interpretation. The effect of location of a window of pixels on various colour-infrared (CIR) aerial photographs corresponding to the field sample plots was analysed. Two calibration methods, regression calibration and ratioing, were derived and tested. Linear regression calibration to the principal-point level of the photos was shown to be the most effective, in which the mean pixel value of the window was modelled as a function of solar and sensor direction at the time of exposure. The results indicated that the effect of location on the window mean values was considerable. Calibration also increased the spectral separability of forest stand-classes.     

Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling

Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30-0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.