Contribution of ALOS/PALSAR-1 multi-temporal data to map permanently and temporarily flooded coastal wetlands

LAURA SAN MARTIN; MORANDEIRA, NATALIA SOLEDAD; GRIMSON RAFAEL; RAJNGEWERC, MARIELA; GONZÁLEZ, ELIANA BELÉN; KANDUS, PATRICIA

INTERNATIONAL JOURNAL OF REMOTE SENSING

TAYLOR & FRANCIS LTD

Lugar: Londres; Año: 2020 vol. 41 p. 1582 - 1602

0143-1161

Although regional wetland mapping studies have mostly relied onoptical sensors, synthetic aperture radar (SAR) sensors are beingincreasingly applied. The aim of this study is to analyse the abilityof the Phased Array type L-band Synthetic Aperture Radar on boardof the Advanced Land Observing Satellite (ALOS/PALSAR-1) data toidentify, delineate and monitor wetlands, and to evaluate the importance of scene selection in a highly unpredictable wetland. Three SARscene sets (Year A, Year B and Inter-annual) were built for thispurpose, considering the intra-annual and inter-annual hydrologicvariability and the phenologic variability of the studied coastal wetland. Seven land cover types were defined, including three permanently flooded wetland classes, three temporarily flooded wetlandclasses and one non-wetland class. An object-based unsupervisedclassification approach was applied on each multi-temporal set. Theobtained clusters were characterized by a temporal signature andassigned to the seven land cover types using a decision tree withuser-defined thresholds. The accuracy assessment of each productwas performed using a set of 258 data sites, including field collecteddata and data retrieved from Landsat 8 Operational Land Imager(OLI) imagery acquired during the dates of the field campaign.The Year B set showed the best accuracy (83.4% overall, 75%Kappa coefficient (κ)) and the lowest omission and commissionmean errors (16.6% and 16.1% respectively). The classes that werebest differentiated are permanently flooded wetlands (PFW) andnon-wetlands (NW) in all sets