Geobia-Based Identification Of Alluvial Fans And Bajadas Through Geomorphometry, Image Analysis And Fuzzy Ontology

Alluvial fan and bajada mapping is important for practical and economic importance to society. In this study, advanced geomor-phometric, image processing and knowledge representation methods were investigated, enhanced and developed, towards the simultaneous identification of alluvial fans and bajadas. The developed method was based on the landform-pattern element approach. Data used included the 10 m spatial resolution National Elevation Dataset, provided by USGS and a pan-sharpened 15 m Landsat OLi imagery. The study area was located in the Death Valley, Nevada, USA. Digital Terrain Model (DTM) processing included noise-removal filtering and depression treatment. A two-phase GEographic Object-Based Image Analysis approach involving multi-scale segmentation and fuzzy ontology-based reasoning was designed. On the first phase, topographic forms were identified, by examining their morphometry and spatial relationships. On the second phase, the alluvial fans and bajadas were identified based on drainage pattern texture and topographic features such as landform shape. Due to a systematic omission on the fan toe outline, each fan and bajada spectral signature was taken into account to determine the omitted areas through region growing on the multispectral imagery. Accuracy assessment indicated satisfactory results, since a 90.3% completeness and 88.8% quality were achieved for both alluvial fans and bajadas.