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.

This dataset contains photographs of the plots where field vegetation sampling was conducted during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. Sampling occurred at the Jack and Laura Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve, which are located in Santa Barbara County, California, USA. Photographs were taken from 2022-02-23 to 2022-09-18. This project collected field data contemporaneously with weekly flights of Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over the study areas. Related SHIFT data packages contain additional biogeochemical, reflectance, and foliar data.

This dataset holds laboratory foliar chemical analyses results for field samples collected during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. Leaf samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve during the period of 2022-02-23 to 2022-09-27 and dried for later analysis. This project collected field data contemporaneously with weekly flights of the NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Sixteen chemical traits from two different lab analyses are provided. (a) Elemental analysis: foliar nitrogen (%), phosphorus (%), magnesium (%), potassium (%), calcium (%), sulfur (%), boron (ppm), iron (ppm), manganese (ppm), copper (ppm), zinc (ppm), aluminum (ppm), and sodium (ppm). (b) AnkomFiber analysis: foliar hemicellulose and bound protein (%), cellulose (%), and lignin (%). Related data packages contain additional plot-level characterization, biogeochemical, reflectance, and foliar data. These data are provided in comma separated values (CSV) format.

This dataset provides leaf images and measurements of leaf traits (area, wet weight, dry weight, leaf mass per area, leaf water content) and leaf pigments (chlorophyll) and species information as sampled from meadow, shrub, and tree from Santa Barbara California, USA. Samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt March Reserve during the period of February 23, 2022 to September 27, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign. The associated data package contains image scans used for the leaf area calculations as well as python processing code used to calculate the area. A comma-separated value (CSV) formatted file includes plot-level leaf area (cm2), wet weight (g), leaf mass area (LMA, g leaf dry mass per meter square), leaf water content (LWC, (wet weight - dry weight/wet weight, %)), chlorophyll fluorescence ratio (CFR), and chlorophyll content (CHL).

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.

This dataset contains data pertaining to ground surface cover in 30 meter plots around a random selection of points within chaparral from Santa Barbara county south to San Diego County in southern California, USA. These data were obtained from historical aerial imagery from 1943 to 1959 and current imagery from 2016 to 2018 and they were compared to quantify changes in cover type over time.

This dataset contains data pertaining to ground surface cover in 30 meter plots around a random selection of points within chaparral from Santa Barbara county south to San Diego County in southern California, USA. These data were obtained from historical aerial imagery from 1943 to 1959 and current imagery from 2016 to 2018 and they were compared to quantify changes in cover type over time.

This dataset holds laboratory foliar chemical analyses results for field samples collected during the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. Leaf samples were collected from plots within the Dangermond Preserve, Sedgwick Reserve, and Carpinteria Salt Marsh Reserve during the period of 2022-02-23 to 2022-09-27 and dried for later analysis. This project collected field data contemporaneously with weekly flights of the NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) facility instrument over the study areas. Sixteen chemical traits from two different lab analyses are provided. (a) Elemental analysis: foliar nitrogen (%), phosphorus (%), magnesium (%), potassium (%), calcium (%), sulfur (%), boron (ppm), iron (ppm), manganese (ppm), copper (ppm), zinc (ppm), aluminum (ppm), and sodium (ppm). (b) AnkomFiber analysis: foliar hemicellulose and bound protein (%), cellulose (%), and lignin (%). Related data packages contain additional plot-level characterization, biogeochemical, reflectance, and foliar data. These data are provided in comma separated values (CSV) format.

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.

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.