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Spatial data of California riparian vegetation productivity trends over time (2000-2020) and environmental covariates
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.
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연관 데이터
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.
Current and Future Vegetation Refugia in California from 2010-2099
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This dataset contains rasters of vegetation refugia and habitat exposure variables for the state of California. Two potential future climate scenarios were used: warmer and wetter (CNRM-CM5), and hotter and drier (MIROC-ESM) & 2 emission scenarios: a higher level one that represents our current trajectory (RCP 8.5) and a lower level one that represents a more optimistic scenario (RCP 4.5). The vegetation exposure models used aims to help in assessing potential climatic stress to vegetation communities and this dataset contains the statewide data for use in assessing the potential risk to each of the California Allotments. Current and future vegetation stress was determined by integrating the hydroclimate data with a detailed 2015 map of the spatial patterns of California’s vegetation community types, and examining how climate conditions will change at those locations using 9 hydroclimatic variables (30-year averages) from the Basin Characterization Model. The main habitat exposure outputs contain rasters all of the climate exposure results: 1 historic run: 1981-2010 and 12 future runs: 3 time periods (2010-2039, 2040-2069, 2070-2099) under 2 emission scenarios and 2 climate scenarios as well as reclassified rasters where the outputs were binned into 5 groups. To distinguish refugia areas from high-stress areas in the climate exposure results above, the team classified the climate frequency distribution for each vegetation type, which are labeled as CA refugia combined 45 and 85 for the respective RCP. Finally, the team looked at the spatial patterns of just refugia for the 2 climate models to identify areas where they align, defined as CA refugia concensus.
SHIFT: Vegetation Plot Characterization, Santa Barbara County, CA, 2022
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This dataset contains vegetation plot locations, descriptions, fractional cover, and sample identifier information from surveys conducted as part of the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Surveys took place from 2022-02-23 to 2022-09-27 at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA. This project collected field data contemporaneously with weekly flights of the NASA Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Plot information includes: plot tree subform, species lists, plot description, plot samples characterization, and plot location and contextual information. Related data packages contain additional biogeochemical, reflectance, and foliar data. Survey data and metadata are presented in comma-separated values (*.csv) format along with survey plot polygons in GeoJSON (*.geojson) format.
i15 LandUse Calaveras2000
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,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.,
Wetland Moist Soil Seed Productivity Maps for the Central Valley of California 2007 - 2017
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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 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 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.
i15 LandUse Colusa1993
공공데이터포털
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i15 LandUse Ventura2000
공공데이터포털
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