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首页> 外文期刊>Revista rvore >Mapeamento de fragmentos florestais com monodominancia de aroeira a partir da classifica??o supervisionada de imagens Rapideye
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Mapeamento de fragmentos florestais com monodominancia de aroeira a partir da classifica??o supervisionada de imagens Rapideye

机译：从监督图像的排名绑定森林碎片的划线与aroeira monodomance jailingye

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M. urundeuva (Fr. All.) is a tree currently listed as endangered species of Brazilian Flora since 1992. However, it manifests monodominance in some regions of Minas Gerais state, especially in middle Rio Doce. The objective of this work was to compare supervised methods of classification of Rapideye images for forest fragments monodominated by Myracrodruon urundeuva mapping, in Tumiritinga, MG. The maximum likelihood (Maxlike) and the artificial neural networks (ANN) classification were assessed.Nineteen combinations involving different bands, the principal components and the normalized difference vegetation index (NDVI) were tested for classification of Rapideye image. The neural network training was conducted by varying the learning rate, the number of interactions and the number of neurons in the hidden layer. The thematic maps were evaluated by Kappa and conditional Kappa index for "aroeira" soil use class and by the analysis of the Confusion Matrix. The best performance was achieved by the Maxever algorithm using all bands of Rapideye image, getting a Kappa index of 80 and conditional Kappa index of 90. The thematic map presented user's accuracy equal to 93% and producer's accuracy equal to 90%. The greatest errors of classification for "aroeira monominant" class were related to the "native forest" and "managed pasture" classes. The thematic map produced shows that 22% of the town is under occupation of M. urundeuva in monodominance and this specie is not endangered in Tumiritinga, Minas Gerais.
机译：M. Urundeuva（Fr. All.）是自1992年以来目前被列为濒临灭绝的巴西植物品种的树。然而，它在Minas Gerais国家的某些地区表现出单兆欧，特别是在中里约会。这项工作的目的是比较由MyracrodruonUrundeuva Mapping MyRACRodruon Urundeuva Maping Myodined的森林片段进行调节的受监管方法，在TumiritingA，MG中。评估最大可能性（最大值）和人工神经网络（ANN）分类。测试涉及不同条带的组合，主成分和归一化差异植被指数（NDVI）进行缩醛图像的分类。通过改变学习率，隐藏层中的神经元数和神经元数进行神经网络训练。通过Kappa和有条件的Kappa指数来评估主题地图，用于“Aroeira”土壤使用类以及杂乱矩阵的分析。最佳性能是通过使用Rapideye图像的所有频段的最大算法实现的，获得80的Kappa指数和90的条件Kappa指数。主题地图呈现用户的精度等于93％，生产者的准确性等于90％。 “Aroeira单数”课程的最大错误与“本土森林”和“管理牧场”课程有关。所制作的专题地图表明，该镇的22％是占领M. Urundeuva在单兆欧，而该物种在Minas Gerais的Tumiritinga没有濒危。

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