Indicadores de floresta urbana a partir de imagens aéreas multiespectrais de alta resolução
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Data
2005-04-01
Autores
Da Silva Filho, Demóstenes Ferreira
Pivetta, Kathia Fernandas Lopes [UNESP]
Do Couto, Hilton Thadeu Zarate
Polizel, Jefferson Lordello
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Resumo
The present work evaluated urban forest indicators, acquired through airborne high-resolution multiespectral images, on the quality of the urban design and its vegetative fraction, in special its trees, in nine neighborhoods of Piracicaba, SP. There were made supervised classifications for characterization of intra-urban elements and the proportions obtained, as exposed soil, tree cover, lawns, asphalt, concrete pavements and roofs. They were studied for the measurement of the urban forest in each place. These variables were related to each other, as well as with the independent variables: population density, people with more than fifteen years of study and family heads with income above twenty minimum wages, obtained through population census. Through the analysis of linear regression variables were identified for intra-urban areas evaluation. Correlations were made and linear regressions among the data obtained from the image and among the proposed indicators. Negative correlations were obtained among population density and arboreal covering and the evaluated indices, in accordance with the predicted in the literature. Composite indicators are proposed, as: the proportion between arboreous space on waterproof space (PAW) and the proportion between arboreous space on building space (PAB). It is concluded by the possibility of the use of those indicators for evaluation of the urban forest and definition of priorities in the execution of ordinances to the improvement of the urban forestry, being prioritized the application of resources in the most lacking neighborhoods.
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Palavras-chave
High-resolution multispectral videography images, Urban forest indices, Urban trees, Population density, Image analysis, Regression analysis, Soils, Urban planning, Waterproofing, Forestry, Cities, Image Analysis, Regression Analysis, Soil, Water Proofing
Como citar
Scientia Forestalis/Forest Sciences, n. 67, p. 88-100, 2005.