Informed forest management requires accurate, up-to-date information. Ground-based forest inventory is commonly conducted to generate estimates of forest characteristics with a predetermined level of statistical confidence. As the importance of monitoring forest resources has increased, budgetary and logistical constraints often limit the resources needed for precise estimates. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study using synthetic populations provided the basis for investigating the effects of plot and stand-level inventory aggregations on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after plantation establishment. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting using two computer vision approaches. The imagery was found to be unreliable for consistent detection in the conditions evaluated. Following establishment, conditions are assessed throughout the lifespan of forest plantations. Using small area estimation (SAE) methods, the incorporation of light detection and ranging (lidar) and thinning status improved the precision of inventory estimates compared with ground data alone. Further investigation found that reduced density lidar point clouds and lower resolution elevation models could be used to generate estimates with similar increases in precision. Individual tree detection estimates of stand density were found to provide minimal improvements in estimation precision when incorporated into the SAE models. Plot and stand level inventory aggregations were found to provide similar estimates of future conditions in simulated stands without high levels of spatial heterogeneity. Significant differences were noted when spatial heterogeneity was high. Model form was found to have a more significant effect on the observed differences than plot size or thinning status. The results of this research are of interest to forest managers who regularly conduct forest inventories and generate estimates of future stand conditions. The incorporation of auxiliary data in mid-rotation stands using SAE techniques improved estimate precision in most cases. Further, guidance on strategies for using this information for predicting future conditions is provided. / Doctor of Philosophy / Informed forest management requires accurate, up-to-date information. Groundbased sampling (inventory) is commonly used to generate estimates of forest characteristics such as total wood volume, stem density per unit area, heights, and regeneration survival. As the importance of assessing forest resources has increased, resources are often not available to conduct proper assessments. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study investigated the effects of two forest inventory data aggregation methods on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after tree planting. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting. The imagery was found to be unreliable for use in accurately detecting seedlings in the conditions evaluated. Following establishment, forest conditions are assessed at additional points in forest development. Using a class of statistical estimators known as small-area estimation, a combination of ground and light detection and ranging data generated more confident estimates of forest conditions. Further investigation found that more coarse ancillary information can be used with similar confidence in the conditions evaluated. Forest inventory data are used to generate estimates of future conditions needed for management decisions. The final component of this research found that there are significant differences between two inventory data aggregation strategies when forest conditions are highly spatially variable. The results of this research are of interest to forest managers who regularly assess forest resources with inventories and models. The incorporation of ancillary information has potential to enhance forest resource assessments. Further, managers have guidance on strategies for using this information for estimating future conditions.

Nesta tese investiga-se uma nova abordagem visando implementar os conceitos propostos na Teoria da Evidencia para fins de classificação de imagens digitais em Sensoriamento Remoto. Propõe-se aqui a utilização de variáveis auxiliares, estruturadas na forma de Planos de Informação (P.I.s) como em um SIG para gerar dados de confiança e de plausibilidade. São então aplicados limiares aos dados de confiança e de plausibilidade, com a finalidade de detectar erros de inclusão e de omissão, respectivamente, na imagem temática. Propõe-se nesta tese que estes dois limiares sejam estimados em função das acurácias do usuário e do produtor. A metodologia proposta nesta tese foi testada em uma área teste, coberta pela classe Mata Nativa com Araucária. O experimento mostrou que a metodologia aqui proposta atinge seus objetivos. / In this thesis we investigate a new approach to implement concepts developed by the Theory of Evidence to Remote Sensing digital image classification. In the proposed approach auxiliary variables are structured as layers in a GIS-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user’s and producer’s accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.

Nesta tese investiga-se uma nova abordagem visando implementar os conceitos propostos na Teoria da Evidencia para fins de classificação de imagens digitais em Sensoriamento Remoto. Propõe-se aqui a utilização de variáveis auxiliares, estruturadas na forma de Planos de Informação (P.I.s) como em um SIG para gerar dados de confiança e de plausibilidade. São então aplicados limiares aos dados de confiança e de plausibilidade, com a finalidade de detectar erros de inclusão e de omissão, respectivamente, na imagem temática. Propõe-se nesta tese que estes dois limiares sejam estimados em função das acurácias do usuário e do produtor. A metodologia proposta nesta tese foi testada em uma área teste, coberta pela classe Mata Nativa com Araucária. O experimento mostrou que a metodologia aqui proposta atinge seus objetivos. / In this thesis we investigate a new approach to implement concepts developed by the Theory of Evidence to Remote Sensing digital image classification. In the proposed approach auxiliary variables are structured as layers in a GIS-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user’s and producer’s accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.

Nesta tese investiga-se uma nova abordagem visando implementar os conceitos propostos na Teoria da Evidencia para fins de classificação de imagens digitais em Sensoriamento Remoto. Propõe-se aqui a utilização de variáveis auxiliares, estruturadas na forma de Planos de Informação (P.I.s) como em um SIG para gerar dados de confiança e de plausibilidade. São então aplicados limiares aos dados de confiança e de plausibilidade, com a finalidade de detectar erros de inclusão e de omissão, respectivamente, na imagem temática. Propõe-se nesta tese que estes dois limiares sejam estimados em função das acurácias do usuário e do produtor. A metodologia proposta nesta tese foi testada em uma área teste, coberta pela classe Mata Nativa com Araucária. O experimento mostrou que a metodologia aqui proposta atinge seus objetivos. / In this thesis we investigate a new approach to implement concepts developed by the Theory of Evidence to Remote Sensing digital image classification. In the proposed approach auxiliary variables are structured as layers in a GIS-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user’s and producer’s accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.

[Truncated abstract] The use of uncertainty analysis is gaining considerable attention in catchment hydrological modeling. In particular, the choice of an appropriate model structure, the identifiability of parameter values, and the reduction of model predictive uncertainty are deemed as essential elements of hydrological modelling. The chosen model structure must be parsimonious, and the parameters used must either be derivable from field measured data or inferred unambiguously from analysis of catchment response data. In this thesis, a long-term water balance model for the Susannah Brook catchment in Western Australia has been pursued using the ?downward approach?, which is a systematic approach to determine the model with the minimum level of complexity, with parameter values that in theory are derivable from existing physiographic data relating to the catchment. Through the analysis of the rainfall-runoff response at different timescales, and the exploration of the climate, soil and vegetation controls on the water balance response, an initial model structure was formulated, and a priori model parameter values estimated. Further investigation with the use of auxiliary data such as deuterium concentration in the stream and groundwater level data exposed inadequacies in the model structure. Two more model structures were then proposed and investigated through formulating alternative hypotheses regarding the underlying causes of observed variability, including those associated with the absence of a contribution of deep groundwater flow to the streamflow, which was indicated by deuterium concentration and internal dynamics characterized by the observed groundwater levels. ... These differences are due to differences in the time delay between rainfall and recharge between upland and riparian regions. The ages of water recharging the groundwater and discharging from the catchment were estimated by assuming a piston flow mechanism. In the deeper, upland soils, the age of recharging water was considerably larger than the unsaturated zone delay would suggest; a recharge response 16 days after an infiltration event may involve water as much as 160 days old. On the other hand, the delay and the age of recharging water were much lower in the shallow riparian zone. Where the upland zone contributes significantly to discharge, the predicted difference between the rainfall-discharge response time and the average age of discharging water can be significant.

This study delves into the application of Small Area Estimation (SAE) techniques to enhance the accuracy of predicting global needs for assistive spectacles. By leveraging the power of SAE, the research undertakes a comprehensive exploration, employing arange of predictive models including Linear Regression (LR), Empirical Best Linear Unbiased Prediction (EBLUP), hglm (from R package) with Conditional Autoregressive (CAR), and Generalized Linear Mixed Models (GLMM). At last phase,the global spectacle needs’ prediction includes various essential steps such as random effects simulation, coefficient extraction from GLMM estimates, and log-linear modeling. The investigation develops a multi-faceted approach, incorporating area-level modeling, spatial correlation analysis, and relative standard error, to assess their impact on predictive accuracy. The GLMM consistently displays the lowest Relative Standard Error (RSE) values, almost close to zero, indicating precise but potentially overfit results. Conversely, the hglm with CAR model presents a narrower RSE range, typically below 25%, reflecting greater accuracy; however, it is worth noting that it contains a higher number of outliers. LR illustrates a performance similar to EBLUP, with RSE values reaching around 50% in certain scenarios and displaying slight variations across different contexts. These findings underscore the trade-offs between precision and robustness across these models, especially for finer geographical levels and countries not included in the initial sample.