Classificador de máxima verossimilhança aplicado à identificação de espécies nativas na Floresta Amazônica.

Among a variety of digital classification methods based on remote sensing images, the Maximum Likelihood (ML) is widely used in environmental studies, mainly for land cover and vegetation analysis. This study aimed to evaluate the effectiveness of supervised classification by ML technique in a forest management area of dense ombrophilous forest, using one RapidEye image. With this purpose, it was conducted the census of species over 30 cm in diameter at breast height and calculated the Cover Value Index (CVI), and selected the 20 species with the highest CVI as a parameter for classification in a Geographic Information System. 13 of the 20 species selected in the study area were not identified by the classification method, and among the seven identified species, two were underestimated and the others were overestimated. Both the maximum likelihood technique and the spatial resolution of the image used were not suitable for supervised classification of native vegetation, with Kappa index of 0.05 and global accuracy of 5.53%. Studies using spectral characterization in leaf level supported by higher or hyper spectral and spatial resolution images are recommended to increase the accuracy of classification

Reduced-impact logging by allocating log-decks using multiobjective evolutionary algorithm in Western Amazon.

To reduce the damage caused by logging in the Amazon rainforest, new metaheuristics have been implemented and tested to ensure the sustainability of this economic segment. Therefore, this study aimed to compare alternatives for road sizing and log deck allocation. In a forest management unit, the skidding to log decks was evaluated in two diff erent areas. To determine the skidding/log deck relation, georeferenced points were generated equally spaced every 50 m. In area 1, the Integer Linear Programming (ILP) model and the Multi-Objective Evolutionary Algorithm (MOEA) were compared. In area 2, only the MOEA was considered. In both areas, these models were also compared to the current planning used in the forest management unit. Solutions were then generated to identify the best management alternative. In both areas, the MOEA showed greater efficiency regarding the processing time, as well as the reduction of log decks number and the road sizing. The multi-objective evolutionary approach assists the decision-making process, due to the presentation of alternatives based on Pareto-optimal solutions, making the choice more flexible and well supported. Com o objetivo de reduzir os danos causados pela exploração madeireira na floresta amazônica, novas meta-heurísticas vem sendo implementadas e testadas para garantir a sustentabilidade desse segmento econômico. Assim, este trabalho tem como objetivo comparar alternativas para o dimensionamento de estradas e para alocação de pátios de estocagem de madeira. Em uma unidade de manejo florestal, o deslocamento da exploração de árvores para os pátios de estocagem de madeira foi avaliado em duas diferentes áreas. Para determinar a relação exploração de árvores / pátios de estocagem de madeira, pontos georreferenciados foram gerados igualmente espaçados a cada 50 m. Na área 1, o modelo de Programação Linear Inteira (ILP) e o Algoritmo Evolutivo Multiobjetivo (MOEA) foram comparados. Na área 2, apenas o MOEA foi considerado. Em ambas as áreas, esses modelos também foram comparados com o planejamento executado na unidade de manejo florestal. Soluções foram geradas para identificar a melhor alternativa de manejo. Em ambas as áreas, o MOEA apresentou maior eficiência quanto ao tempo de processamento, bem como na redução do número de pátios de estocagem de madeira e no dimensionamento das estradas. A abordagem evolutiva multiobjetivo auxilia o processo de tomada de decisão, devido à apresentação de alternativas baseadas em soluções Paretoótimas, tornando a escolha mais flexível e bem fundamentada

Public Use and Landscape Analysis in the Serra da Canastra National Park, Brazil: A Geospatial Approach.

Abstract The Conservation Units (CU) were created to protect natural environments from growing degradation and to impede the expansion of urbanization and agricultural crops. The Serra da Canastra National Park, established to protect the headwaters of the São Francisco River and other places of scenic and ecological interest, is extensively visited due to its many tourist attractions, such as waterfalls, fauna and flora. An analysis of the park?s geography is needed to assess the risk involved in and its suitability for public use due to its territorial extension and environmental complexities. Thus, this study aimed to investigate the background of issues of interest to CU management. Additionally, we used high-resolution RapidEye imagery, altimetry and database of park infrastructure to build geospatial database and estimate classes of suitability for and risk in public use through GIS tools. The resulting cartographic data can support the planning of policies concerning the landscape and park?s territorial management.201