| Abstrakt: |
Over the last 150–200 y, urbanization and agricultural development have contributed to the loss of >99% of coastal prairie that once spanned >1 million ha of land in Louisiana. Given the extent of loss, fragmented nature of extant coastal prairie, and current threats (e.g., incompatible grazing practices, fire suppression, human disturbance, invasive species), identifying locations of unknown coastal prairie is necessary to preserve this critically imperiled ecosystem. We used remotely sensed data to identify potential locations of coastal prairie in southwestern Louisiana, USA. Given similarities between coastal prairie and other land cover types (e.g., pasture) in the region, the small number of locations available for use as training data, and the likelihood that any previously undiscovered remnants are quite small, we created two separate unsupervised classification models for our study area—one based on a Normalized Difference Vegetation Index that we calculated from 2019 NAIP imagery and one based on an Enhanced Vegetation Index that we calculated from 2019 Sentinel-2 imagery. We examined both models separately and overlapped models to look at areas of agreement, or congruence, in the potential locations of coastal prairie. The total area of model congruence was 3733 ha within our ∼330,000 ha study area, with 53% of model congruence within Calcasieu Parish and 38% in Cameron Parish. In addition, we primarily found concentrations of model congruence in north-central Cameron Parish. The methods we outline here could help inform locations of future surveys for coastal prairie, which is critical for protection and restoration of this unique ecosystem. [ABSTRACT FROM AUTHOR] |
