Combining ALOS/PALSAR derived vegetation structure and inundation patterns to characterize major vegetation types in the Mamirauá Sustainable Development Reserve, Central Amazon floodplain, Brazil

Ferreira-Ferreira, Jefferson; Silva, Thiago Sanna Freire; Streher, Annia Susin; Affonso, Adriana Gomes; Furtado, Luiz Felipe de Almeida; Forsberg, Bruce R.; Valsecchi, João; Queiroz, Helder Lima de; Novo, Evlyn Márcia Leão de Moraes

doi:10.1007/s11273-014-9359-1

Cited by 54 publications

(46 citation statements)

References 52 publications

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“…, Ferreira‐Ferreira et al . ), thus supporting predictive modeling of forest diversity in a spatially continuous manner across the entire floodplain landscape. Nonetheless, flooding is related to a complex disturbance regime that includes different constraints for wetland plants ( e.g., river dynamics, sedimentation rates, substrate oxygenation, light intensities, and also droughts during low‐water stages), which must be studied in detail to better understand the diversity‐regulating mechanisms of várzea forest communities (Wittmann & Junk , Marinho et al .…”

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confidence: 53%

Effects of the Flooding Gradient on Tree Community Diversity in Várzea Forests of the Purus River, Central Amazon, Brazil

et al. 2015

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supporting

confidence: 53%

Effects of the Flooding Gradient on Tree Community Diversity in Várzea Forests of the Purus River, Central Amazon, Brazil

et al. 2015

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“…Major sources of error and uncertainty in the JERS-1-based mapping are likely to be reduced owing to sensor improvements such as PALSAR's dual-polarization mode and lower noise floor (Rosenqvist et al 2007). Examples of PALSAR-based mapping of Amazonian wetlands for small regions include Hawes et al (2012), Arnesen et al (2013), andFerreira-Ferreira et al (2014). Owing to a PALSAR acquisition strategy targeting global wetlands (Rosenqvist et al 2007;Lowry et al 2009), methods used in the present study have the potential for broader application to further the goal of quantifying wetland extent, vegetation type, and inundation patterns over large regions.…”

Section: Applications For Sar-based Wetlands Mappingmentioning

confidence: 99%

Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar

et al. 2015

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Wetland extent, vegetation cover, and inundation state were mapped for the first time at moderately high (100 m) resolution for the entire lowland Amazon basin, using mosaics of Japanese Earth Resources Satellite (JERS-1) imagery acquired during low-and high-water seasons in 1995-1996. A wetlands mask was created by segmentation of the mosaics and clustering of the resulting polygons; a rules set was then applied to classify wetland areas into five land cover classes and two flooding classes using dual-season backscattering values. The mapped wetland area of 8.4×10 5 km 2 is equivalent to 14 % of the total basin area (5.83×10 6 km 2 ) and 17 % of the lowland basin (5.06×10 6 km 2 ). During high-water season, open water surfaces accounted for 9 % of the wetland area, woody vegetation 77 %, and aquatic macrophytes 14 %. Producer's accuracy as assessed using high-resolution digital videography was better than 85 % for wetland extent. The mapped flooding extent is representative of average highand low-flood conditions for latitudes north of 6°S; flooding conditions were less well captured for the southern part of the basin. Global data sets derived from lowerresolution optical sensors capture less than 25 % of the wetland area mapped here.

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“…Radar data have been preferentially used in investigations focusing on vegetation mapping in wetlands, particularly in Amazonia (Saatchi et al 2000;Prigent et al 2001;Hess et al 2003;Costa 2005;Souza-Filho et al 2011;Ferreira-Ferreira et al 2015). Synthetic aperture radar (SAR) data are less influenced by cloud and smoke; thus, they can penetrate the vegetation at some wavelengths.…”

Section: Introductionmentioning

confidence: 99%

“…The automatic discrimination of vegetation patterns in the Amazonian lowland may require the use of multisensor techniques using the advantage of both optical and SAR images. The use of Lband combined with optical sensors has decreased classification errors in Amazonian areas (Sartori et al 2011;Arnesen et al 2013;Ferreira-Ferreira et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…It is efficient in predicting classes, even with the non-linear relationship often observed between classes and remote-sensing data; it does not require assumptions regarding the distribution of the input data or process of missing values; and it is also fast in execution. An increasing documentation has shown the application of OBIA for discriminating vegetation classes in different wetlands worldwide (Dronova et al 2012;Evans and Costa 2013;Moffett and Gorelick 2013;Ferreira-Ferreira et al 2015), including areas of the Brazilian territory. However, no previous tests have been performed concerning the application of OBIA and DM combining multisensors such as PALSAR, digital elevation model (DEM), and optical images aiming at the discrimination of vegetation classes over the megafan palaeoforms recently documented in the Amazonian wetlands.…”

Section: Introductionmentioning

confidence: 99%

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

Cordeiro

Rossetti

2015

International Journal of Remote Sensing

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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 (κ) 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, várzea, igapó, and alluvial), as well as Mauritia wetland classes in the inner and outer areas of the studied megafan.

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Combining ALOS/PALSAR derived vegetation structure and inundation patterns to characterize major vegetation types in the Mamirauá Sustainable Development Reserve, Central Amazon floodplain, Brazil

Cited by 54 publications

References 52 publications

Effects of the Flooding Gradient on Tree Community Diversity in Várzea Forests of the Purus River, Central Amazon, Brazil

Effects of the Flooding Gradient on Tree Community Diversity in Várzea Forests of the Purus River, Central Amazon, Brazil

Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar

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

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