Date on Master's Thesis/Doctoral Dissertation

8-2015

Document Type

Master's Thesis

Degree Name

M.S.

Department

Geography and Geosciences

Degree Program

Geography (Applied), MS

Committee Chair

Mountain, Keith

Committee Member

Gaughan, Andrea

Committee Member

Croasdaile, Michael

Subject

Glaciers--Peru; Glaciers--Remote sensing; Glaciers--Measurement

Abstract

Accurate remote-sensing based inventories of glacial ice are often hindered by the presence of supraglacial debris cover. Attempts at automated mapping of debris-covered glacier areas from remotely-sensed multispectral data have met with limited success due to the spectral similarity of supraglacial debris to nearby bedrock, moraines, and fluvial deposition features. Data-fusion approaches leveraging terrain and/or thermal data with multispectral data have yielded improved results in certain geographic regions, but remain unproven in others. This research builds on the data-fusion approaches from the literature and explores the efficacy of object-based image analysis (OBIA) and tree-based machine learning classifiers using Landsat OLI imagery and SRTM elevation data, in effort to map debris-covered glaciers in the Cordillera Blanca range of Peru. Results suggest that the OBIA and machine learning methods render advantages over traditional methods given the unique morphological settings associated with debris-covered glaciers. Accurate inventories of glacial mass and debris-covered glaciers in the Cordillera Blanca are important for understanding the unique water resource, natural hazards, and climate change implications associated with these tropical mountain glaciers.

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Geography Commons

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