Mapping soil texture classes using field textuing, particle size distribution and local knowledge by both conventional and geostatisical methods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90525/1/j.1365-2389.1999.00255.x.pd

Developing an index for forest productivity mapping - A case study for maritime pine production regulation in Portugal

Productivity is very dependent on the environmental and biotic factors present at the site where the forest species of interest is present. Forest site productivity is usually assessed using empirical models applied to inventory data providing discrete predictions. While the use of GIS-based models enables building a site productivity distribution map. Therefore, the aim of this study was to derive a productivity index using multivariate statistics and coupled GIS-geostatistics to obtain a forest productivity map. To that end, a study area vastly covered by naturally regenerated forests of maritime pine in central Portugal was used. First, a productivity index (PI) was built based on Factorial Correspondence Analysis (FCA) by incorporating a classical site index for the species and region (Sh25 - height index model) and GIS-derived environmental variables (slope and aspect). After, the PI map was obtained by multi-Gaussian kriging and used as a GIS layer to evaluate maritime pine areas by productivity class (e.g., low, intermediate and high). In the end, the area control method was applied to assess the size and the number of compartments to establish by productivity class. The management compartments of equal productivity were digitized as GIS layer and organized in a temporal progression of stands’ age regularly available for cutting each year during a 50-year schedule. The methodological approach developed in this study proved that can be used to build forest productivity maps which are crucial tools to support forest production regulation.A produtividade florestal depende dos fatores ambientais e bióticos da estação onde a espécie florestal se encontra. A produtividade da estação florestal é geralmente avaliada com modelos empíricos aplicados a dados de inventário fornecendo previsões discretas. Enquanto o uso de modelos num SIG permite a construção de mapas de distribuição de produtividade da estação. Assim, o objetivo deste estudo foi desenvolver um índice de produtividade utilizando estatística multivariada e geoestatística acopladas ao SIG para obter um mapa de produtividade florestal. Para o efeito, selecionou-se uma área de estudo com floresta de regeneração natural de pinheiro bravo no centro de Portugal. Primeiramente, o índice de produtividade (PI) foi construído usando a Análise Fatorial das Correspondências (FCA) com um índice de qualidade de estação para a espécie e região (Sh25- modelo de índice de altura) e variáveis ambientais (declive e exposição). Depois, o mapa PI foi obtido por Krigagem multi-Gaussiana e usado como camada SIG para avaliar as áreas da espécie por classe de produtividade (p.e., baixa, intermedia e alta). No final, o método de controle da área foi aplicado para determinar o tamanho e o número de parcelas a serem estabelecidas por classe de produtividade. As parcelas de igual produtividade foram digitalizadas em SIG e organizadas segundo uma progressão de idades disponíveis regularmente para abate anual ao longo de 50 anos. A abordagem metodológica desenvolvida neste estudo provou ser útil na construção de mapas de produtividade florestal que são instrumentos fundamentais para apoiar a regulação da produção florestal.info:eu-repo/semantics/publishedVersio

Highly efficient room temperature spin injection in a metal-insulator-semiconductor light emitting diode

We demonstrate highly efficient spin injection at low and room temperature in an AlGaAs/GaAs semiconductor heterostructure from a CoFe/AlOx tunnel spin injector. We use a double-step oxide deposition for the fabrication of a pinhole-free AlOx tunnel barrier. The measurements of the circular polarization of the electroluminescence in the Oblique Hanle Effect geometry reveal injected spin polarizations of at least 24% at 80K and 12% at room temperature

Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94929/1/wrcr10188.pd

Discriminative training for Convolved Multiple-Output Gaussian processes

Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued random fields in the context of pattern recognition. As a generative model, the multi-output GP is able to handle vector-valued functions with continuous inputs, as opposed, for example, to hidden Markov models. It also offers the ability to model multivariate random functions with high dimensional inputs. In this report, we use a discriminative training criteria known as Minimum Classification Error to fit the parameters of a multi-output Gaussian process. We compare the performance of generative training and discriminative training of MOGP in emotion recognition, activity recognition, and face recognition. We also compare the proposed methodology against hidden Markov models trained in a generative and in a discriminative way

A new generic open pit mine planning process with risk assessment ability

Conventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by the well-known geological uncertainty, i.e. the in situ grade and tonnage variability of the mineral deposit. Despite some recent attempts in developing stochastic mine planning models which have demonstrated promising results, the industry still remains sceptical about this innovative idea. With respect to unbiased linear estimation, kriging is the most popular and reliable deterministic interpolation technique for resource estimation and it appears to remain its popularity in the near future. This paper presents a new systematic framework to quantify the risk of kriging-based mining projects due to the geological uncertainties. Firstly, conditional simulation is implemented to generate a series of equally-probable orebody realisations and these realisations are then compared with the kriged resource model to analyse its geological uncertainty. Secondly, a production schedule over the life of mine is determined based on the kriged resource model. Finally, risk profiles of that production schedule, namely ore and waste tonnage production, blending grade and Net Present Value (NPV), are constructed using the orebody realisations. The proposed model was applied on a multi-element deposit and the result demonstrates that that the kriging-based mine plan is unlikely to meet the production targets. Especially, the kriging-based mine plan overestimated the expected NPV at a magnitude of 6.70% to 7.34% (135 M$to 151 M$). A new multivariate conditional simulation framework was also introduced in this paper to cope with the multivariate nature of the deposit. Although an iron ore deposit is used to prove the concepts, the method can easily be adapted to other kinds of mineral deposits, including surface coal mine

Modelação da produtividade de povoamentos de pinheiro bravo (Pinus pinaster Aiton) na região Centro de Portugal, através de técnicas de geoestatística e de ferramentas SIG

Poster apresentado no II Encontro de Sistemas de Informação Geográfica que decorreu, de 19 a 20 de Maio de 2011 na Escola Superior Agrária do Instituto Politéncio de Castelo Branco.O presente estudo permitiu desenvolver uma nova abordagem à quantificação da produtividade florestal, tradicionalmente avaliada de forma pontual ao nível dos povoamentos, para uma representação contínua obtida como uma superfície de krigagem, o que permitirá incorporar essa informação em SIG como um nível temático para a análise e planeamento espacial da gestão florestal

Ecological strategies in stable and disturbed environments depend on species specialisation

Ecological strategies are integral to understanding species survival in different environments. However, how habitat specialisation is involved in such strategies is not fully understood, particularly in heterogeneous and disturbed environments. Here, we studied the trait associations between specialisation, dispersal ability, competitiveness, reproductive investment and survival rate in a spatially explicit metacommunity model under various disturbance rates. Though no unique trait values were associated with specialisation, relationships were uncovered depending on environmental factors. We found strong trait associations mainly for generalist species, while specialist species exhibited a larger range of trait combinations. Trait associations were driven first by the disturbance rate and second by species’ dispersal ability and generation overlap. With disturbance, low dispersal ability was strongly selected against, for specialists as well as for generalists. Our results demonstrate that habitat specialisation is critical for the emergence of trait strategies in metacommunities and that disturbance in interaction with dispersal ability limits not only the range of trait values but also the type of possible trait associations