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.     

Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data

Biomass fractions (total aboveground, branches and foliage) were estimated from a small footprint discrete-return LiDAR system in an unmanaged Mediterranean forest in central Spain. Several biomass estimation models based on LiDAR height, intensity or height combined with intensity data were explored. Raw intensity data were normalized to a standard range in order to remove the range dependence of the intensity signal. In general terms, intensity-based models provided more accurate predictions of the biomass fractions. Height models selected were mainly based on a percentile of the height distribution. Intensity models selected included variables that consider the percentage of the intensity accumulated at different height percentiles, which implicitly take into account the height distribution. The general models derived considering all species together were based on height combined with intensity data. These models yielded R² values greater than 0.58 for the different biomass fractions considered and RMSE values of 28.89, 18.28 and 1.51 Mg ha⁻¹ for aboveground, branch and foliage biomass, respectively. Results greatly improved for species-specific models using the main species present in each plot, with R² values greater than 0.85, 0.70 and 0.90 for black pine, Spanish juniper and Holm oak, respectively, and with lower RMSE for the biomass fractions. Reductions in LiDAR point density had only a small effect on the results obtained, except for those models based on a variation of the Canopy Reflection Sum, which was weighted by the mean point density. Based on the species-specific equations derived, Holm oak dominated plots showed the highest average carbon contained by aboveground biomass and branch biomass 44.66 and 31.42 Mg ha^− 1 respectively, while for foliage biomass carbon, Spanish juniper showed the highest average value (3.04 Mg ha^− 1).     

Development of a simulation model to predict LiDAR interception in forested environments

Airborne scanning LiDAR systems are used to predict a range of forest attributes. However, the accuracy with which this can be achieved is highly dependent on the sensor configuration and the structural characteristics of the forest examined. As a result, there is a need to understand laser light interactions with forest canopies so that LiDAR sensor configurations can be optimised to assess particular forest types. Such optimisation will not only ensure the targeted forest attributes can be accurately and consistently quantified, but may also minimise the cost of data acquisition and indicate when a survey configuration will not deliver information needs.In this paper, we detail the development and application of a model to simulate laser interactions within forested environments. The developed model, known as the LiDAR Interception and Tree Environment (LITE) model, utilises a range of structural configurations to simulate trees with variable heights, crown dimensions and foliage clumping. We developed and validated the LITE model using field data obtained from three forested sites covering a range of structural classes. Model simulations were then compared to coincident airborne LiDAR data collected over the same sites. Results indicate that the LITE model can be used to produce comparable estimates of maximum height of trees within plots (differences < 2.42 m), mean heights of first return data (differences < 2.27 m), and canopy height percentiles (r² = 0.94, p < 0.001) when compared to airborne LiDAR data. In addition, the distribution of airborne LiDAR hits through the canopy profile was closely matched by model predictions across the range of sites. Importantly, this demonstrates that the structural differences between forest stands can be characterised by LITE. Models that are capable of interpreting the response of small-footprint LiDAR waveforms can facilitate algorithm development, the generation of corrections for actual LiDAR data, and the optimisation of sensor configurations for differing forest types, benefiting a range of experimental and commercial LiDAR applications. As a result, we also performed a scenario analysis to demonstrate how differences in forest structure, terrain, and sensor configuration can influence the interception of LiDAR beams.     

Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process

Due to the complexity and uncertainty in the process, the soft computing methods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic and multi-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. Therefore, this paper proposes a modified-MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated and only the high performing genes are combined. In this work, the M-MGGP method is applied in modelling the surface roughness in the turning of hardened AISI H11 steel. The results show that the M-MGGP model produces better performance than those of MGGP, SVR and ANN. In addition, when compared to that of MGGP method, the models formed from the M-MGGP method are of smaller size. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the turning phenomenon of AISI H11 steel by unveiling dominant input process parameters and the hidden non-linear relationships.     

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.     

Development of a hybrid method for estimating land surface shortwave net radiation from MODIS data

Accurate estimation of shortwave net radiation (S_n) at a high spatial resolution is critical for regional and global land surface models. Current surface radiation budget products have fine temporal resolutions, but only coarse spatial resolutions that are not suitable for land applications. A hybrid algorithm was developed in this study to estimate S_n from MODIS data under both clear and cloudy sky conditions without requiring coarser resolution ancillary data. This algorithm was validated using ground measurements at seven sites of the SURFace RADiation budget observing network (SURFRAD) in the United States. Instantaneous S_n estimated by this method was also compared with GEWEX/SRB and ISCCP data, and other methods. The results indicate that our algorithm can produce S_n at 1-km resolution with improved accuracy and is easily implemented to generate operational global products. Daily integrated S_n is estimated at 1-km resolution using instantaneous S_n. These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface. Therefore, they support recent high resolution land surface models.     

Singularity formation in a model for the vortex sheet with surface tension

In a recent analytical study, the author has proved well-posedness of the vortex sheet with surface tension. This work included using a formulation of the problem introduced by Hou, Lowengrub, and Shelley for a numerical study of the same problem. The analytical study required identification of a term in the evolution equations which can be viewed as being responsible for the Kelvin–Helmholtz instability; this term is of lower order than the surface tension term. In the present work, the author introduces a simple model for the vortex sheet with surface tension which maintains the same dispersion relation and the same destabilizing force as in the vortex sheet with surface tension. For the model problem, it is found that finite-time singularities can form when the initial data is taken from a certain class. For the vortex sheet with surface tension, the only observed singularities thus far in numerical work have coincided with self-intersection of the fluid interface. There is no analogue of self-intersection in the model problem, and thus the singularities observed in the present work may well be related to a previously unobserved singularity for the full vortex sheet problem.     

Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating

This paper presents an empirical method for deriving fuel moisture content (FMC) for Mediterranean grasslands and shrub species based on multitemporal analysis of NOAA-AVHRR data. The results are based on 6 years of field measurements of FMC. The empirical function was derived from a 4-year series and includes multitemporal composites of AVHRR's normalized difference vegetation index (NDVI) and surface temperature (ST) values, as well as a function of the day of the year. It was tested using data from 2 other years on the same site as well as other sites with similar species but very distant from each other and with different elevation ranges. The results show that the model provides a consistent estimation of FMC, with high accuracies for all study sites and species considered, with r² values over 0.8 for both grasslands and shrub species. This performance enables the model to be used to derive spatial estimator of FMC, which is a key factor in operational fire danger management in Mediterranean conditions.     

Proposal and investigation of a method for estimating surface energy balance in regional forests using TM derived vegetation index and observatory routine data

Taking forest areas specified according lo information on the National digital land data, the vapour pressure deficit at leaf temperature and the degree of stomatal opening were presumed using the vegetation index of forest areas derived from Landsat TM data. Assuming the principle of Monin-Obukhov similarity is correct, a new method for calculating the latent and sensible heat fluxes of regional forests was proposed taking into account the difference in Leaf Area Index. The relation of the increase of latent heat flux with the vegetation index was obtained, and the plane distribution of latent and sensible heat fluxes in large areas is calculated.     

Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data

After an earthquake, every damaged building needs to be properly evaluated in order to determine its capacity to withstand aftershocks as well as to assess safety for occupants to return. These evaluations are time-sensitive as the quicker they are completed, the less costly the disaster will be in terms of lives and dollars lost. In this direction, there is often not sufficient time or resources to acquire all information regarding the structure to do a high-level structural analysis. The post-earthquake damage survey data may be incomplete and contain missing values, which delays the analytical procedure or even makes structural evaluation impossible. This paper proposes a novel multiple imputation (MI) approach to address the missing data problem by filling in each missing value with multiple realistic, valid candidates, accounting for the uncertainty of missing data. The proposed method, called sequential regression-based predictive mean matching (SRB-PMM), utilizes Bayesian parameter estimation to consecutively infer the model parameters for variables with missing values, conditional based on the fully observed and imputed variables. Given the model parameters, a hybrid approach integrating PMM with a cross-validation algorithm is developed to obtain the most plausible imputed data set. Two examples are carried out to validate the usefulness of the SRB-PMM approach based on a database including 262 reinforced concrete (RC) column specimens subjected to earthquake loads. The results from both examples suggest that the proposed SRB-PMM approach is an effective means to handle missing data problems prominent in post-earthquake structural evaluations.     

Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest

This study compared aspatial and spatial methods of using remote sensing and field data to predict maximum growing season leaf area index (LAI) maps in a boreal forest in Manitoba, Canada. The methods tested were orthogonal regression analysis (reduced major axis, RMA) and two geostatistical techniques: kriging with an external drift (KED) and sequential Gaussian conditional simulation (SGCS). Deterministic methods such as RMA and KED provide a single predicted map with either aspatial (e.g., standard error, in regression techniques) or limited spatial (e.g., KED variance) assessments of errors, respectively. In contrast, SGCS takes a probabilistic approach, where simulated values are conditional on the sample values and preserve the sample statistics. In this application, canonical indices were used to maximize the ability of Landsat ETM+ spectral data to account for LAI variability measured in the field through a spatially nested sampling design. As expected based on theory, SGCS did the best job preserving the distribution of measured LAI values. In terms of spatial pattern, SGCS preserved the anisotropy observed in semivariograms of measured LAI, while KED reduced anisotropy and lowered global variance (i.e., lower sill), also consistent with theory. The conditional variance of multiple SGCS realizations provided a useful visual and quantitative measure of spatial uncertainty. For applications requiring spatial prediction methods, we concluded KED is more useful if local accuracy is important, but SGCS is better for indicating global pattern. Predicting LAI from satellite data using geostatistical methods requires a distribution and density of primary, reference LAI measurements that are impractical to obtain. For regional NPP modeling with coarse resolution inputs, the aspatial RMA regression method is the most practical option.     

Development of a linear predictive model for carbon dioxide sequestration in deep saline carbonate aquifers

CO₂ injection into deep saline aquifers is a preferred method for mitigating CO₂ emission. Although deep saline aquifers are found in many sedimentary basins and provide very large storage capacities, several numerical simulations are needed before injection to determine the storage capacity of an aquifer. Since numerical simulations are expensive and time-consuming, using a predictive model enables quick estimation of CO₂ storage capacity of a deep saline aquifer. In order to create a predictive model, the ranges of variables that affect the CO₂ storage capacity were determined from published literature data. Correlations found in literature were used for other important parameters such as pore volume compressibility and density of brine. Latin hypercube space filling design was used to construct 100 simulation cases prepared using CMG STARS. The simulation period covered a total of 300 years of CO₂ storage. By using a least-squares method, linear and nonlinear predictive models were developed to estimate CO₂ storage capacity of deep saline carbonate aquifers. Numerical dispersion effects were considered by decreasing the grid dimensions. It was observed that a dimensionless linear predictive model is better than the nonlinear. The sensitivity analyses showed that the most important parameter that affects CO₂ storage capacity is depth. Most of the (up to 90%) injected gas dissolved into the formation water and a negligible amount of CO₂ reacted with carbonate.     

Development of a digital human model generation method for ergonomic design in virtual environment

A group of digital human models (DHMs) representing the target population under consideration is used to design products and workplaces in virtual environment. The present study proposes a two-step method which generates a group of DHMs in various sizes to properly accommodate the designated level of the human size variability of the target population. In the first step, a designated number of pairs of stature and weight within a specified accommodation range are generated from the bivariate normal distribution of stature and weight of the target population. In the second step, for each pair of stature and weight, the sizes of the DHM body segments are determined using hierarchical regression models and corresponding prediction distributions of individual values. The proposed generation method was applied to the 1988 US Army anthropometric survey data and then implemented to a web-based system for passenger car interior design. This web-based generation system is capable of generating a group of DHMs as nationality, gender, accommodation percentage, and the number of DHMs required is specified.

Relevance to industry

A digital human simulation system has been used as an effective tool for ergonomic design and evaluation of products and workplaces in virtual environment. The human model generation method proposed in the present study is of use to efficiently generate a group of human models representing the target population.     

Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.     

Development of a model for the estimation of photosynthetically active radiation from geostationary satellite data in a tropical environment

This paper presents a model for the estimation of photosynthetically active radiation (PAR) from geostationary satellite data. The model is aimed to estimate the monthly average hourly PAR in a tropical environment. This model represents a physical relation of PAR incident on the earth's surface and satellite-derived earth-atmospheric albedo together with the absorption and scattering coefficients of various atmospheric constituents. The earth-atmospheric albedo was obtained from the Multifunctional Transport Satellite-1R (MTSAT-1R). The absorption of PAR by water vapor, an important process for the tropics, was computed from the ambient temperature and relative humidity. The absorption of PAR by aerosols was estimated by using the visibility data and aerosol optical properties obtained from the Aerosol Robotic Network (AERONET) of NASA in this region. The total column ozone from the Ozone Monitoring Instrument onboard of AURA satellite (OMI/AURA) was used for the estimation of the absorption of PAR by ozone. The model was validated against the monthly average hourly PAR from measurements at four solar radiation measuring stations situated in the tropical environment of Thailand. The values of the monthly average hourly PAR estimated from the model and those obtained from the measurement were in good agreement, with the root mean square error (RMSE) and mean bias error (MBE) of 9.8% and 0.6%, respectively. After the validation, the model was employed to estimate the monthly average hourly PAR over Thailand using a 4-year period of data from MTSAT-1R and other ancillary surface data. Values of the monthly average hourly PAR were presented as maps showing the geographical distribution of PAR. These maps reveal the diurnal and seasonal variation of PAR over the country.     

Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area,Australia

Land use regression models are an established method for estimating spatial variability in gaseous pollutant levels across urban areas. Existing LUR models have been developed to predict annual average concentrations of airborne pollutants. None of those models have been developed to predict daily average concentrations, which are useful in health studies focused on the acute impacts of air pollution. In this study, we developed LUR models to predict daily NO₂ and NO_x concentrations during 2009–2012 in the Brisbane Metropolitan Area (BMA), Australia's third-largest city. The final models explained 64% and 70% of spatial variability in NO₂ and NO_x, respectively, with leave-one-out-cross-validation R² of 3–49% and 2–51%. Distance to major road and industrial area were the common predictor variables for both NO₂ and NO_x, suggesting an important role for road traffic and industrial emissions. The novel modeling approach adopted here can be applied in other urban locations in epidemiological studies.     

Development of a model for part reorientation in vibratory bowl feeders with active air jet tooling

Vibratory bowl feeders (VBFs) are widely used in industry for feeding and reorienting small parts in high volume production. Standard VBF tooling consists of various mechanical barriers inserted in the bowl path which are prone to jamming and limit the feeder to only one type of part. Programmable feeders have been developed to improve the flexibility of these devices, however feed rates are often low. This research describes the development of a model of part behavior required for reorienting a part with an air-jet-based computer controlled orienting system. This system can be used to eliminate jamming and improve feed rates in VBFs. The control algorithm accepts the part's weight, geometry, and its orientation. Sensors then compare the present with the desired orientation and the algorithm determines the appropriate pulse of air to produce the desired orientation.     

Development and evaluation of a compartmental picture archiving and communications system model for integration and visualization of multidisciplinary biomedical data to facilitate student learning in an integrative health clinic

Information technology (IT) has the potential to improve the clinical learning environment. The extent to which IT enhances or detracts from healthcare professionals’ role performance can be expected to affect both student learning and patient outcomes. This study evaluated nursing students’ satisfaction with a novel compartmental Picture Archiving and Communications System (PACS) for the automatic object-oriented integration and visualization of heterogeneous biomedical data. The compartmental PACS was specially designed to support client assessment and clinical education in the integrative health clinic of a university, which is run by a multidisciplinary service team. The sample was 63 nursing students, who were asked to complete a series of realistic tasks using the compartmental PACS. Upon completing the tasks, the Computer System Usability Questionnaire (CSUQ) was administered to assess their satisfaction with the system. Results from data analysis showed that nursing students who completed the evaluation had a satisfactory experience with the system. The Information Quality subscale mean was the highest mean of the CSUQ subscales. This is an important finding as the multidisciplinary data visualization feature of the system provides a technology-enhanced learning environment that can support nursing students’ efforts to both organize and represent knowledge. Through the compartmental PACS, students are assisted in connecting relevant knowledge via various representations of medical data for the clinical conditions under study.