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Full-Text Articles in Physical Sciences and Mathematics
Assessing The Utility Of Imaging Radar For Identifying White Sand Vegetation Structure, Jessica Rosenqvist
Assessing The Utility Of Imaging Radar For Identifying White Sand Vegetation Structure, Jessica Rosenqvist
Dissertations and Theses
White sand vegetation communities are wide spread across South America; found in Peru, Venezuela, Brazilian Amazon and Guyana. They are distributed in patches ranging from <1 km2 to greater than tens of square kilometers and their origins and locations are still not well understood. The communities are related to a variety of factors (soil type, flooding, nutrient content and fire); hence a precise definition for the ecosystem is still not fully defined. Nevertheless, the result of these variations creates a unique environment for endemic plant and animal species to thrive. Furthermore, analysis of these areas has been very scattered and identification of local white sand areas (<1 km2) have not been accomplished. In addition, identification of these locations has currently only used optical satellite imagery (Landsat, MODIS). Hence, in this project, we have attempted to use synthetic aperture radar to create a classification system to locate the white sand vegetation systems. The goal is to be able to apply this method to identify white sand vegetation distribution across South America. The region of focus for this thesis has been in Aracá, a large white sand area located in Brazil in the State of Amazonas. Due to the lack of ground reference data, a classified map by Capurucho et al. (2013), generated using Landsat data, was used as a comparison and reference. JAXA’s ALOS-1 PALSAR (L-band), ESA’s Sentinel-1A (C-band) and NASA’s SRTM sensors were used for land classification. As microwave signals penetrate clouds and haze, the advantage of using sensors with this wavelength allows for an unobstructed coverage of the landscape all year round. Different combinations of polarizations and wavelengths were used during the analysis to try and separate the white sand vegetation from water and terra firme forest. The resulting classification images showed a 30% agreement with the classification map by Capurucho et al. It is important to note, that this number is in fact an agreement percentage as the map used was a classification image and coarse in resolution (due to the lack of reference data). Therefore, this value does not imply a bad classification. Future work will include time-series data, precise ground reference points and data from other sensors such as ALOS-2 PALSAR, to improve the classification accuracy.
Evaluation Of Alos Palsar Data For High-Resolution Mapping Of Vegetated Wetlands In Alaska, Daniel Clewley, Jane Whitcomb, Mahta Moghaddam, Kyle Macdonald, Bruce Chapman, Peter Bunting
Evaluation Of Alos Palsar Data For High-Resolution Mapping Of Vegetated Wetlands In Alaska, Daniel Clewley, Jane Whitcomb, Mahta Moghaddam, Kyle Macdonald, Bruce Chapman, Peter Bunting
Publications and Research
As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009) using Japanese Earth Resources Satellite (JERS-1) data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, …
Assessing The Performance Of A Northern Gulf Of Mexico Tidal Model Using Satellite Imagery, Stephen C. Medeiros, Scott C. Hagen, Naira Chaouch, Jesse Feyen, Marouane Temimi, John F. Weishampel, Yuji Funakoshi, Reza Khanbilvardi
Assessing The Performance Of A Northern Gulf Of Mexico Tidal Model Using Satellite Imagery, Stephen C. Medeiros, Scott C. Hagen, Naira Chaouch, Jesse Feyen, Marouane Temimi, John F. Weishampel, Yuji Funakoshi, Reza Khanbilvardi
Publications and Research
Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely sensed data (i.e., Radarsat-1, aerial imagery, LiDAR, Landsat 7 ETM+) was applied to assess the performance of the tidal model. The analysis demonstrates the applicability of the method and its agreement with traditional performance assessment techniques such as harmonic resynthesis and water level time series analysis. Based on the flooded/non-flooded coastal areas estimated by the integrated remotely …