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This paper is intended to perform a comparative and qualitative review among eight tools to measure energy sustainability. Therefore, it was necessary to create a theoretical and conceptual framework based on four criterias of selection and six categories of comparison. In this work, the conceptual bases that supported the research and the methodology created to carry out the comparative review will be presented. This analysis was based on the intrinsic concepts of energy sustainability of each of the reviewed tools with a critical qualitative analysis. Some conclusions shown through the conceptual framework developed that it was possible to apply an innovative methodology to qualitatively compare different tools to measure sustainability. The importance of this reflects the difficulty of conceptualizing the subjectivity of sustainable development, as shown throughout the paper, where it is often not possible to obtain a measurable result since the measured phenomenon is too complex to reduce it to a numerical value.

O presente estudo objetivou a classificação de tipologias florestais por meio de redes neurais artificiais utilizando dados provenientes de um inventário florestal, fornecido pelo Instituto de Desenvolvimento Florestal e da Biodiversidade do Estado do Pará (IDEFLOR-BIO), e das bandas 3, 4 e 5 do TM do satélite Landsat 5. As informações provenientes das imagens de satélite foram extraídas por meio do aplicativo QGIS 2.8.1 Wien e utilizadas no banco de dados para o treinamento das redes neurais pertencentes às ferramentas do software MATLAB® R2011b. Foram treinadas redes neurais como classificadores de dois tipos florestais: Floresta Ombrófila Densa de Terras baixas Dossel emergente (Dbe) e Floresta Ombrófila Densa Terras baixas Dossel emergente mais Aberta com palmeiras (Dbe + Abp) no conjunto de glebas estaduais Mamuru Arapiuns, Pará, e avaliadas usando os indicadores matriz de confusão, cálculo de acurácia global, coeficiente Kappa e o gráfico de características do receptor operacional (ROC). O melhor resultado de classificação foi obtido por meio da rede neural probabilística de função de base radial (RBF) “newpnn”, com uma acurácia global de 88%, e coeficiente Kappa de 76%, sendo avaliado como um classificador muito bom, evidenciando a aplicação dessa metodologia na análise de áreas com potencial para prestar serviços ecossistêmicos e, principalmente, na prestação de serviços ambientais em áreas antrópicas que adotam sistema de produção agropecuária com baixa emissão de carbono na Amazônia.

This study presents a method developed for lightning forecasting in eastern Amazonia, based on the estimates of the hourly evolution of the convective available potential energy (CAPE). The CAPE is a computed index of the air stability situation over a given area of the Earth. This parameter is determined from vertical profiles of temperature and humidity of the atmosphere, obtained through radiosondes. The CAPE values may also be estimated during the period between soundings, by using the meteorological variables observed continuously at surface weather stations. Two data mining techniques were used for the forecasts: k-Nearest Neighbor and Decision Tree. For the calculation of the CAPE and its estimated hourly evolution, we used radio soundings data made available by a site of the University of Wyoming, in addition to surface temperature data provided by the METAR code, both collected at the Belém-Brazil airport, during 2009. The CAPE index levels, indicative of strong convection in the area were compared to data of actual lightning activity, provided by the STARNET detection system, in a circular area of 100 km radius, centered at that airport. The angular coefficient of the adjusted line equation to the hourly evolution values of the CAPE and the average value of the CAPE were the predicting attributes, while the number of lightning flashes detected by the STARNET was the classification attribute. The results indicated that it is possible to predict the lightning class of occurrences with an accuracy of the 70%, in this research area.