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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Mapping vegetation in a late Quaternary landform of the Amazonian wetlands using object-based image analysis and decision tree classification

Texto completo
Autor(es):
de Oliveira Cordeiro, Carlos Leandro [1] ; Rossetti, Dilce de Fatima [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Brazilian Natl Inst Space Res INPE, Remote Sensing Dept, BR-12227010 Sao Jose Dos Campos - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: International Journal of Remote Sensing; v. 36, n. 13, p. 3397-3422, 2015.
Citações Web of Science: 18
Resumo

Fan-shaped morphologies related to late Quaternary residual megafan depositional systems are common features over wide areas in northern Amazonia. These features were formed by ancient distributary drainage systems that are in great contrast to tributary drainage networks that typify the modern Amazon basin. The surfaces of the Amazonian megafans constitute vegetacional mosaic wetlands with different campinarana types. A fine-scale-resolution investigation is required to provide detailed classification maps for the various campinarana and surrounding forest types associated with the Amazonian megafans. This approach remains to be presented, despite its relevance for analysing the relationship between stages of plant succession and sedimentary dynamics associated with the evolution of megafans. In this work, we develop a methodology for classifying vegetation over a fan-shaped megafan palaeoform from a northern Amazonian wetland. The approach included object-based image analysis (OBIA) and data-mining (DM) techniques combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, land-cover fractions derived by the linear spectral mixing model, synthetic aperture radar (SAR) images, and the digital elevation model (DEM) acquired during the Shuttle Radar Topography Mission (SRTM). The DEM, vegetation fraction, and ASTER band 3 were the most useful parameters for defining the forest classes. The normalized difference vegetation index (NDVI), ASTER band 1, vegetation fraction, and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) transmitting and receiving horizontal polarization (HH) and transmitting horizontal and receiving vertical polarization (HV) were all effective in distinguishing the wetland classes campinarana and Mauritia. Tests of statistical significance indicated the overall accuracies and kappa coefficients (kappa) of 88% and 0.86 for the final map, respectively. The allocation disagreement coefficient of 5% and a quantity disagreement value of 7% further attested the statistical significance of the classification results. Hence, in addition to water, exposed soil, and deforestation areas, OBIA and DM were successful for differentiating a large number of open (forest, wood, shrub, and grass campinaranas), forest (terra firme, varzea, igapo, and alluvial), as well as Mauritia wetland classes in the inner and outer areas of the studied megafan. (AU)

Processo FAPESP: 13/50475-5 - Mapping Amazonian biodiversity at multiple scales by integrating geology and ecology
Beneficiário:Dilce de Fátima Rossetti
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 10/09484-2 - Megaleques quaternários no estado de Roraima, norte da Amazônia: implicações sedimentológicas, climáticas e tectônicas
Beneficiário:Dilce de Fátima Rossetti
Linha de fomento: Auxílio à Pesquisa - Regular