Applications of multi- and hyperspectral image decomposition in visible near infrared remote sensing

 Presented by Joseph D. Ortiz | Professor, Kent State University Department of Earth Sciences

Talk Abstract:

The mixed pixel problem – the averaging of sensor information over the sensor’s ground sampling distance and instantaneous field of view – provides a fundamental challenge for optical remote sensing which limits the quality of classification results. A traditional approach to dealing with this problem has been to reduce the pixel size, which has been beneficial given the coarse resolution of early sensors and the large inherent spatial scales of terrestrial scenes, which are dominated by macroscopic targets, such as crops and tree canopy. This approach has been less helpful in aquatic systems, where the remote sensing targets (algae and suspended sediment) are microscopic in size. Over the past few decades, optical sensors have made great technological leaps forward resulting in smaller pixel size and rapidly increasing band count. Those technological changes have also resulted in rapid increases in the size of remote data sets. The transition from low band count multispectral to high band count hyperspectral sensors however carried with it the introduction of significant quantities of redundant information. Multicollinearity – the intercorrelation of visible and near infrared bands – is one of the primary sources of uncertainty in optical remote sensing. The focus of my research is to explore how numerical methods can be used to address the mixed pixel problem and multicollinearity in VNIR remote sensing. The result of that work has been the development of varimax rotated principal component analysis (VPCA) in conjunction with visible derivative spectroscopy to address the mixed-pixel and multicollinearity problems. An added benefit to VPCA is that it also serves as a data-adaptive filtering method, reducing redundant data volume to a smaller number of independent components. I will present applications of VPCA by myself and my students to address harmful algal blooms, map seagrass distribution, quantify soil organic matter in agricultural settings and explore the impact of coal mine drainage on algal diversity.

Biography:

With training in Aquatic Biology from Brown University (B.S with honors, 1988) and Oceanography from Oregon State Univ. (Ph.D.,1995), Ortiz is a broadly trained Environmental Scientist who views environmental and societal problems from an earth system science perspective. He works at the interface between sciences unraveling climate mysteries, exploring the relationship between sedimentary strata, helping to improve water quality using electromagnetic sensing techniques, and exploring renewable energy and sustainability solutions to address anthropogenic climate change.

He makes use of research methods that span disciplines. He studies sedimentary records to extract climate-related information on seasonal to glacial-interglacial time scales. He employes diverse methods ranging from marine micropaleontology to light isotope geochemistry and core and well logging to decipher Earth's climate record. Most recently, he makes use of environmental remote sensing to study harmful algal blooms and agricultural remote sensing methods to monitor soil carbon content and evaluate the impacts of various farming practices.