Identification of Woodland Vernal Pools with Seasonal Change PALSAR Data for Habitat ConservationReport as inadecuate




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Michigan Tech Research Institute, Michigan Technological University, 3600 Green Ct. Suite 100, Ann Arbor, MI 48105, USA

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Michigan Natural Features Inventory, Michigan State University Extension, P.O. Box 13036, Lansing, MI 48901, USA





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Author to whom correspondence should be addressed.



Academic Editors: Javier Bustamante, Alfredo R. Huete, Patricia Kandus, Ricardo Díaz-Delgado, Josef Kellndorfer and Prasad S. Thenkabail

Abstract Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, cost-effective, repeatable and accurate. Satellite-based L-band radar data from the high 10 m resolution Japanese ALOS PALSAR sensor were evaluated for suitability in vernal pool detection beneath forest canopies. In a two phase study, potential vernal pool PVP detection was first assessed with unsupervised PALSAR LHH two season change detection spring when flooded—summer when dry and validated with 268, 1 ha field-sampled test cells. This resulted in low false negatives 14%–22%, overall map accuracy of 48% to 62% and high commission error 66%. These results make this blind two-season PALSAR approach for cryptic PVP detection of use for locating areas of high vernal pool likelihood. In a second phase of the research, PALSAR was integrated with 10 m USGS DEM derivatives in a machine learning classifier, which greatly improved overall PVP map accuracies 91% to 93%. This supervised approach with PALSAR was found to produce better mapping results than using LiDAR intensity or C-band SAR data in a fusion with the USGS DEM-derivatives. View Full-Text

Keywords: synthetic aperture radar; DEM; vernal pools; LiDAR synthetic aperture radar; DEM; vernal pools; LiDAR





Author: Laura L. Bourgeau-Chavez 1,* , Yu Man Lee 2, Michael Battaglia 1, Sarah L. Endres 1, Zachary M. Laubach 1 and Kirk Scarbrough 1

Source: http://mdpi.com/



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