Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value = 0.31, r2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value = 0.004, r2 = 0.51) and tree density (p-value = 0.002, r2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value = 0.003, r2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties

Increasing Sorption Isotherms Accuracy: Weibull Modelling and Linear Regression

http://sherpa.ac.uk/romeo/search.php?issn=0144-5987Relying on an adequate mathematical approach, two different mathematical procedures can be applied to the huge database produced during gas sorption isotherm experiments in order to obtain accurate data to be used in the industrial practice. To treat data determined from gas sorption isotherms without a careful mathematical support will produce inaccurate results, because all the determinations will be dependent on human decision. The minimum error reported since the first stage of a sorption isotherm determination, which corresponds to volume calibrations of reference and sample cells performed through the use of helium, will produce enormous inaccuracies on sorption isotherm behavior. These inaccurate behaviors may sometimes invalidate any Coalbed Methane recovery and CO2 injection programs. The study consisted on investigating gas sorption isotherm accuracies determined during the first part of the sorption process, which is mainly conducted by monitoring the pressure decline with time, in the reference and the sample cells (when both cells are not in contact), until the stabilization stage is achieved. Three samples from two different coals were selected in order to study their gas sorption behavior, in terms of a clear mathematical approach, when submitted to three different gas compositions, viz. 99.999% methane (CH4); 99.999% carbon dioxide (CO2); and a gas mixture containing 74.99% CH4 + 19.99% CO2 + 5.02% nitrogen (N2). Sorption experiments allow to conclude that the three samples present the same mathematical response during the first part of the sorption process. However, all gas sorption data (adsorption and desorption) collected from reference cell have a better fitting to a Modified Weibull Model, and all gas sorption data (adsorption and desorption) collected from sample cell respond in a trustworthy way to a Linear Regression Model. Confidence bands and prediction intervals (or bands) were also computed.info:eu-repo/semantics/publishedVersio

A framework development to predict remaining useful life of a gas turbine mechanical component

Power-by-the-hour is a performance based offering for delivering outstanding service to operators of civil aviation aircraft. Operators need to guarantee to minimise downtime, reduce service cost and ensure value for money which requires an innovative advanced technology for predictive maintenance. Predictability, availability and reliability of the engine offers better service for operators, and the need to estimate the expected component failure prior to failure occurrence requires a proactive approach to predict the remaining useful life of components within an assembly. This research offers a framework for component remaining useful life prediction using assembly level data. The thesis presents a critical analysis on literature identifying the Weibull method, statistical technique and data-driven methodology relating to remaining useful life prediction, which are used in this research. The AS-IS practice captures relevant information based on the investigation conducted in the aerospace industry. The analysis of maintenance cycles relates to the examination of high-level events for engine availability, whereby more communications with industry showcase a through-life performance timeline visualisation. Overhaul sequence and activities are presented to gain insights of the timeline visualisation. The thesis covers the framework development and application to gas turbine single stage assembly, repair and replacement of components in single stage assembly, and multiple stage assembly. The framework is demonstrated in aerospace engines and power generation engines. The framework developed enables and supports domain experts to quickly respond to, and prepare for maintenance and on-time delivery of spare parts. The results of the framework show the probability of failure based on a pair of error values using the corresponding Scale and Shape parameters. The probability of failure is transformed into the remaining useful life depicting a typical Weibull distribution. The resulting Weibull curves developed with three scenarios of the case shows there are components renewals, therefore, the remaining useful life of the components are established. The framework is validated and verified through a case study with three scenarios and also through expert judgement

USES OF CONTINUOUS-TIME MARKOV CHAIN TO DESCRIBE LONGITUDINAL PATIENT-REPORTED OUTCOMES FOR SURVIVAL PREDICTION AND DIMENSION REDUCTION

A patient-reported outcome (PRO) is a type of outcome reported directly from patients, and it has been widely used in medical research and clinical trials to measure a patient’s symptoms, health-related quality of life, physical functioning, and health status. Previous studies have linked PROs to survival outcomes, but most of them only used the PRO information at baseline or at a specific clinical time point [1, 2]. Even though some of these studies collected longitudinal PROs, only few of them evaluated the association between the longitudinal PROs and a survival outcome. One of the major challenges in longitudinal PRO studies is to address the individual heterogeneity in PRO repeated measurements. Due to the fact that PRO is reported directly from patients, and different patients may have different experiences, longitudinal PROs have been often observed with individual heterogeneity, yet current methods [3-5] are not able to account for the individual heterogeneity. Therefore, in this research, we developed three methods using two-state Continuous-Time Markov Chain (CTMC) to summarize longitudinal PRO. The primary summary used is the estimated state transition rates, which serve as summary statistics to depict longitudinal PRO patterns at the individual level. These transition rates can also be incorporated into survival models as predictors or into factor analysis as observed variables. Specifically, in the first two papers, we developed prognostic models that contained baseline covariates and a longitudinal process in two survival models, Weibull Regression and Cox Proportional Hazard Regression, with different estimation approaches. Simulation studies were conducted to validate the proposed methods, and the proposed models were then applied to two PRO studies separately, with both using repeated PRO measurements during the treatment period in cancer patients to predict the survival outcomes that happened after the treatment. In the third paper, we then integrated two-state CTMC with factor analysis to evaluate the usage of CTMC in PRO symptom clustering. This study showed that CTMC could well summarize the longitudinal PRO information during the treatment period of cancer patients. The underlying construct of patient-reported symptoms had also met our expectations from clinical experience

Accurate forest projections require long-term wood decay experiments because plant trait effects change through time.

Whether global change will drive changing forests from net carbon (C) sinks to sources relates to how quickly deadwood decomposes. Because complete wood mineralization takes years, most experiments focus on how traits, environments and decomposer communities interact as wood decay begins. Few experiments last long enough to test whether drivers change with decay rates through time, with unknown consequences for scaling short-term results up to long-term forest ecosystem projections. Using a 7 year experiment that captured complete mineralization among 21 temperate tree species, we demonstrate that trait effects fade with advancing decay. However, wood density and vessel diameter, which may influence permeability, control how decay rates change through time. Denser wood loses mass more slowly at first but more quickly with advancing decay, which resolves ambiguity about the after-life consequences of this key plant functional trait by demonstrating that its effect on decay depends on experiment duration and sampling frequency. Only long-term data and a time-varying model yielded accurate predictions of both mass loss in a concurrent experiment and naturally recruited deadwood structure in a 32-year-old forest plot. Given the importance of forests in the carbon cycle, and the pivotal role for wood decay, accurate ecosystem projections are critical and they require experiments that go beyond enumerating potential mechanisms by identifying the temporal scale for their effects

Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier