Document Type
Article
Publication Date
6-10-2016
Department
Michigan Tech Research Institute
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.
Publication Title
Remote Sensing
Recommended Citation
Bourgeau-Chavez, L.,
Lee, Y. M.,
Battaglia, M.,
Endres, S. L.,
Laubach, Z.,
&
Scarbrough, K.
(2016).
Identification of woodland vernal pools with seasonal change PALSAR data for habitat conservation.
Remote Sensing,
8(6).
http://doi.org/10.3390/rs8060490
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1881
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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Publisher's Statement
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). Publisher’s version of record: https://doi.org/10.3390/rs8060490