Supporting Decisions: Understanding natural resource management assessment techniques

Report to the Land and Water Resources Research and Development Corporation. This document presents a review of NRM decision support techniques. It draws upon previous studies in the fields of management science, operations research, environmental economics and natural resource management. The objectives of the document are to: Explain the workings of the more significant (representative) methods of NRM decision support (including the latest developments); Discuss how these decision support methods may influence the outcome of NRM decisions; and Provide practicing NRM decision makers with guidance for choosing which methods to apply.Australia;natural resource management;assessment;decision support;

Evaluating Crowd Density Estimators via Their Uncertainty Bounds

In this work, we use the Belief Function Theory which extends the probabilistic framework in order to provide uncertainty bounds to different categories of crowd density estimators. Our method allows us to compare the multi-scale performance of the estimators, and also to characterize their reliability for crowd monitoring applications requiring varying degrees of prudence

Fusion Framework for Moving-Object Classification

International audiencePerceiving the environment is a fundamental task for Advance Driver Assistant Systems. While simultaneous localization and mapping represents the static part of the environment, detection and tracking of moving objects aims at identifying the dynamic part. Knowing the class of the moving objects surrounding the vehicle is a very useful information to correctly reason, decide and act according to each class of object, e.g. car, truck, pedestrian, bike, etc. Active and passive sensors provide useful information to classify certain kind of objects, but perform poorly for others. In this paper we present a generic fusion framework based on Dempster-Shafer theory to represent and combine evidence from several sources. We apply the proposed method to the problem of moving object classification. The method combines information from several lists of moving objects provided by different sensor-based object detectors. The fusion approach includes uncertainty from the reliability of the sensors and their precision to classify specific types of objects. The proposed approach takes into account the instantaneous information at current time and combines it with fused information from previous times. Several experiments were conducted in highway and urban scenarios using a vehicle demonstrator from the interactIVe European project. The obtained results show improvements in the combined classification compared with individual class hypothesis from the individual detector modules

Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images.

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology

The total assessment profile, volume 2

Appendices are presented which include discussions of interest formulas, factors in regionalization, parametric modeling of discounted benefit-sacrifice streams, engineering economic calculations, and product innovation. For Volume 1, see

Brazilian National Forest Inventory: a landscape scale approach to monitoring and assessing forested landscapes.

A importância estratégica dos recursos florestais, tanto em escala nacional quanto global, assim como a falta de informações qualitativas e quantitativas confiáveis acerca das florestas brasileiras, está entre as motivações que levaram à realização de um novo Inventário Florestal Nacional do Brasil (IFN-BR). Além do tradicional levantamento de campo por meio de amostragem por conglomerados, o IFN-BR incorporou um componente geoespacial, as unidades amostrais de paisagem. A partir da análise do uso e cobertura da terra nessas unidades amostrais, são gerados indicadores e índices de paisagem, capazes de apresentar informações a respeito da sua composição, morfologia, padrão de mosaico, similaridade de habitats adjacentes, conectividade, fragmentação e situação das zonas ripárias. No presente trabalho são descritos os indicadores selecionados para avaliar a paisagem de amostras piloto no estado do Paraná, bem como sua forma de cálculo e composição de índices e scores.Artigo de revisão

The Gains from Pension Reform

TBATBA

The Gains from Pension Reform

We characterize pension systems along three dimensions: 1) actuarial vs. non-actuarial, 2) funded vs. pay-as-you-go, 3) defined-contribution vs. defined-benefit. Increasing the degree of actuarial fairness, by strengthening the linkage between contributions and benefits, reduces labor market distortions and may increase welfare in a Pareto-efficiency sense. Increasing the degree of funding implies mainly a redistribution of income among generations, although a partial shift to funding also provides better risk-return combinations for individuals. Shifting from defined-benefit to defined-contribution schemes (with fixed contribution rates) shifts the income risk from workers and taxpayers to pensioners.Social security; Funding