Folders: ROOT > ScienceBase Catalog > USGS Data Release Products > Wetland Moist Soil Seed Maps for the Central Valley of California 2007-2017 ( Show all descendants )
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We produced a series of maps of moist soil seed plants within managed wetlands in the Central Valley of California from 2007-2011 & 2013-2017. Moist soil seed plants, such as swamp timothy (Crypsis schoenoides) and watergrass (Echinochloa crusgallim), are a critical food source for migratory waterfowl. Through field observation and digitization of high resolution imagery we identified the locations of moist soil seed plants, tall emergent vegetation, water, and other land cover. Using a Support Vector Machine classification, we classified multispectral Landsat imagery from 2007-2011 and 2013-2017. We used images from May through August to create phenology metrics. The final datasets were used to train and test the...
We produced a series of maps of moist soil seed plants within managed wetlands in the Central Valley of California from 2007-2011 & 2013-2017. Moist soil seed plants, such as swamp timothy (Crypsis schoenoides) and watergrass (Echinochloa crusgallim), are a critical food source for migratory birds. For each of the Moist Soil Seed maps from 2007 to 2017, we mapped productivity of swamp timothy where swamp timothy was mapped according to a multiple regression of the average log seed head weight per Landsat pixel to Landsat derived values for green chlorophyll index (NIR/green - 1), swir1 reflectance, red green simple ratio (red/green) and SSURGO derived percent clay (STprod). For areas mapped as watergrass, we mapped...
We produced a series of maps of moist soil seed plants within managed wetlands in the Central Valley of California from 2007-2011 & 2013-2017. Moist soil seed plants, such as swamp timothy (Crypsis schoenoides) and watergrass (Echinochloa crusgallim), are a critical food source for migratory birds. Through field observation and digitization of high resolution imagery we identified the locations of moist soil seed plants, tall emergent vegetation, water, and other land cover. Using a Support Vector Machine classification, we classified multispectral Landsat imagery from 2007-2011 and 2013-2017. We used images from April through September to create phenology metrics. The final maps show the distribution and extent...
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