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

Abstract

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.

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

“…OBIA is well known to suit high spatial resolution UAS imagery, which has frequently been utilized in urban/urban-rural feature extraction (Aguilar, Saldaña, & Aguilar, 2013;Chen, Shi, Fung, Wang, & Li, 2007;Johnson, 2013;Li, Bijker, & Stein, 2015;Tehrany, Pradhan, & Jebur, 2013). Along with being applied to extract features in urban and rural landscapes, OBIA techniques helped in providing detailed classification of natural plant communities as represented in wetland areas (Cordeiro & Rossetti, 2015;Dronova, Gong, & Wang, 2011;Gao, Trettin, & Ghoshal, 2012;Moffett & Gorelick, 2013). Nevertheless, the high spatial resolution of the images taken by UAS creates a challenge, especially in natural land cover classification.…”

Geochemical Signature of Amazon Tropical Rainforest Soils

“…Vegetation in the study area is marked by great contrast from one geomorphic location to another (Cordeiro and Rossetti, 2015): a) dense rainforest (Terra Firme Forest) covers 65 % of the Amazon rainforest over Ferralsols, Acrisols, and Cambisols on remnants of ferrallitic plateaus and convex hills which are not flooded; b) submontane rainforest over …”

Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

“…This procedure repeats on each subset until all objects in this subset belongs to one class. The J48 algorithm has also been proven to be applicable to object-based classification (Vieira et al 2012;Cordeiro and Rossetti 2015), and the entire process can be conducted in the Waikato Environment for Knowledge Analysis (WEKA) software (Hall et al 2009). In this study, we selected segmented objects as samples and potential features, and then input them into the WEKA software to train the CTs.…”