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Wetland Moist Soil Seed Productivity Maps for the Central Valley of California 2007 - 2017
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 the green chlorophyll index as an indicator of productivity (WGprod). The final maps show the productivity and extent of two dominant moist soil seed plants within managed wetlands in the Central Valley of California.
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Wetland Moist Soil Seed Productivity Maps for the Central Valley of California 2007 - 2017
공공데이터포털
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 the green chlorophyll index as an indicator of productivity (WGprod). The final maps show the productivity and extent of two dominant moist soil seed plants within managed wetlands in the Central Valley of California.
Wetland Moist Soil Seed Maps for the Central Valley of California 2007-2017
공공데이터포털
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 of moist soil seed plants within managed wetlands in the Central Valley of California.
Wetland Moist Soil Seed Maps for the Central Valley of California 2007-2017
공공데이터포털
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 of moist soil seed plants within managed wetlands in the Central Valley of California.
Wetland Moist Soil Seed Maps for the Central Valley of California 2007-2017 Training and Testing Data
공공데이터포털
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 accuracy of the classification model used to create the maps.
Wetland Moist Soil Seed Maps for the Central Valley of California 2007-2017 Training and Testing Data
공공데이터포털
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 accuracy of the classification model used to create the maps.
Wetland Habitat Structure Maps for the Central Valley of California 2013-2017
공공데이터포털
We produced a time series of maps of habitat structure within wetlands of the Central Valley of California. The structure of open water and tall emergent vegetation, such as Typha spp. and Schoenoplectus spp., is critical for migratory birds. Through field observation and digitization of high resolution imagery we identified the locations of tall emergent vegetation, water, and other land cover. Using a random forest classification, we classified multispectral Landsat 8 imagery 2013-2017. We used images from the fall when most wetlands are flooded and the summer to separate trees and tall emergent vegetation. The final maps show the distribution and extent of tall emergent vegetation within wetlands. Final time series has two products: the basic map which contains the tall emergent vegetation, water, and other, and the mixed map which the water and other classes are the same as the basic and the tall emergent class is broken into mixed (tall emergent 50-74%), tall emergent (>75%).
Spatial data of California riparian vegetation productivity trends over time (2000-2020) and environmental covariates
공공데이터포털
This data release contains a shapefile of riparian vegetation communities attributed with information on trends in satellite-estimates of vegetation productivity for the period from 2000-2020. Cloud-masked Landsat data were processed from 2000 to 2020 to generate a 21-year growing season (June, July, and August) time series combining data from Landsat 5 (2000-2011), Landsat 7 (2012), and Landsat 8 (2013-2020). We computed the near-infrared reflectance of vegetation (NIRv) which is strongly correlated to vegetation Gross Primary Productivity (GPP). We analyzed growing season time series trends in NIRv by riparian vegetation type at the polygon-level using the Theil-Sen estimator (aka Sen's slope). In addition to the vector dataset is a table containing climate, topographic and land use co-variates used to model the environmental drivers of riparian vegetation change.
i15 LandUse Calaveras2000
공공데이터포털
,The 2000 Calaveras County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Central District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and Central District. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using developed photoquads. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method were not collected for this survey. 5. Not all land use codes will be represented in the survey.,
Crop Specific Landsat Derived Reference Evapotranspiration, Evaporative Fraction, and Actual Evapotranspiration for 2016 in the California Central Valley
공공데이터포털
This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).
Crop Specific Landsat Derived Reference Evapotranspiration, Evaporative Fraction, and Actual Evapotranspiration for 2016 in the California Central Valley
공공데이터포털
This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).