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Publications [#337363] of Jennifer J. Swenson

Papers Published

  1. Gray, PC; Ridge, JT; Poulin, SK; Seymour, AC; Schwantes, AM; Swenson, JJ; Johnston, DW, Integrating drone imagery into high resolution satellite remote sensing assessments of estuarine environments, Remote Sensing, vol. 10 no. 8 (August, 2018), pp. 1257-1257, MDPI AG [doi]
    (last updated on 2019/06/19)

    Abstract:
    © 2018 by the authors. Very high-resolution satellite imagery (≤5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution ( < 5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field forWorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff beingWorldView's fine spatial resolution versus RapidEye's temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.


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