2014
DOI: 10.3390/rs61111444
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An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

Abstract: Effective conservation of woodland vernal pools-important components of regional amphibian diversity and ecosystem services-depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis wit… Show more

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Cited by 47 publications
(25 citation statements)
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“…NDVI is well-recognized and commonly-used in the remote sensing-based characterization of vegetation because of its sensitivity to photosynthetically active biomass and phenological dynamics in vegetation [50,51]. For this reason, we elected to include this popular index as one of our optical model inputs.…”
Section: Optical Datamentioning
confidence: 99%
“…NDVI is well-recognized and commonly-used in the remote sensing-based characterization of vegetation because of its sensitivity to photosynthetically active biomass and phenological dynamics in vegetation [50,51]. For this reason, we elected to include this popular index as one of our optical model inputs.…”
Section: Optical Datamentioning
confidence: 99%
“…Wu and colleagues [80] used lidar-derived elevation data and ancillary high spatial resolution information on land use, land cover, and hydrography to identify locations and boundaries of potential vernal pools in a fine-grained landscape in Massachusetts, USA, that included many woody wetlands. Their approach identified numerous small wetlands that had previously not been included in a statewide database.…”
Section: Purpose Of This Special Issuementioning
confidence: 99%
“…Before inundation mapping, we applied a median filter to smooth the lidar intensity image. The median filter is considered as an edge-preserving filter that can effectively remove data noise while preserving boundaries between image objects (Wu et al, 2014). Subsequently, a simple thresholding method was used to separate inundated and noninundated classes.…”
Section: Wetland Inundation Mappingmentioning
confidence: 99%