DOI: 10.5061/dryad.v41ns1s4t
title: Mean NDVI values from July 2017 to December 2023 for selected beaver complex sites and control sites along the Salinas River

description: This study used remotely sensed mean normalized difference vegetation
 index (NDVI) data, which is a measure of vegetative greenness, to serve as
 a primary indicator of resilience. Mean NDVI for riparian vegetation was
 collected in 3 beaver complexes and 5 non-beaver developed sections along
 the Salinas River in California over a time span of July 2017 until
 December 2023.
description: Landsat Imagery was collected using the United States Geological
 Survey’s (USGS) web tool Earth Explorer (“EarthExplorer,”). Earth Explorer
 is an open-source tool that only requires a USGS account, which is a free
 process. In the search criteria tab, the date range was refined for the
 study’s desired time frame, which was 2017-06-30 to 2023-12-31. Cloud
 coverage was also modified so that only images with 10% or less cloud
 cover would be generated. In the data sets tab, Landsat C2 U.S. Analysis
 Ready Data (ARD) was selected. Using the spacecraft identifier dropdown
 menu under the additional criteria tab, I selected Landsat 8 and 9, which
 include the most recently launched satellites producing imagery with a
 resolution of 30 meters. The satellites have global coverage and revisit
 the same spot every 8 days. To get Landsat imagery that encompassed the
 entirety of the study area, the tile grid horizontal and vertical were set
 to 2 and 10, respectively, under the additional criteria tab. The fill (no
 data) dropdown menu from the additional criteria is also enabled for the
 specification of results with less than 10% of no data. Sometimes data is
 missing from captured images, so this option ensures that my search only
 produces images where less than 10% of the tiles have no data values. The
 results tab rendered an image with the desired parameters. Band 4 (red)
 and Band 5 (near infrared) .tif files were downloaded for each image, as
 these are the necessary bands for normalized difference vegetation index
 computation. To calculate NDVI from the downloaded Landsat imagery, a
 Python notebook within ArcGIS Pro was created. Images taken from the same
 date had the same prefix; thus, a script was needed to loop through the
 files and conduct the NDVI computation on bands 4 and 5 from the same date
 of collection. The output of this script was NDVI .tif files for each date
 of collection.  NDVI files were also re-projected to a consistent
 coordinate reference system. Another Python notebook was used to alter the
 NDVI raster set so that any negative values were considered as no data or
 null. Using “Create Random Points” in geoprocessing tools for ArcGIS Pro,
 a feature class of 100 points was randomly placed within each site
 polygon. Next, the tool “Extract Multi Values to Points” was used to
 calculate the daily NDVI for each point. Finally, using the “Summary
 Statistics” tool in the geoprocessing toolkit for ArcGIS Pro, the average
 daily NDVI was extracted for each site from the initial point sample
 values. For each site and time frame, there were on average 98.9 points of
 the 100 used to derive the mean NDVI. This tool skipped null values in the
 computation of the mean. The exclusion of null values ensured that open
 water, which can have negative values, was not used in the computation of
 the mean NDVI. Each image has a distinct date of collection; thus, the
 date was employed to distinguish data headings. This process was run using
 another Python notebook. The join field tool, as a batch, was used to join
 the mean NDVI data to the “Sites” feature class attribute table. The input
 table was the “Sites” feature class, and the join field was the “ObjectID”
 column. The summary statistic table served as the join table, and the join
 table field was “CID.” Other parameters were kept on the default settings,
 and the tool ran successfully. The delete field tool was then used to
 clean up the “Sites” attribute table and remove irrelevant columns. Fields
 which were kept included ObjectID, Type, Location, UniqueID, AREA, and the
 statistic that followed the format MEAN_{date}.
description: # Mean NDVI values from July 2017 to December 2023 for selected beaver
 complex sites and control sites along the Salinas River
 [https://doi.org/10.5061/dryad.v41ns1s4t](https://doi.org/10.5061/dryad.v41ns1s4t) This dataset contains remotely sensed mean normalized difference vegetation index (NDVI) data, which is a measure of vegetative greenness on a scale of -1 to 1. Mean NDVI for riparian vegetation was collected in 3 beaver complexes and 5 non-beaver developed sections along the Salinas River in California over a time span of July 2017 until December 2023. Mean NDVI for each site at each available date was derived from Landsat C2 U.S. Analysis Ready Data (ARD) on the USGS EarthExplorer website.  ## Description of the data and file structure Each row in the dataset represents a study site. Provided below is a list of the columns and their descriptions. OBJECTID: A unique numeric value automatically assigned to the site polygons that were created in ArcGIS Pro. Type: Specifies whether a site is beaver influenced (complex) or has no beaver influence (control\_ UniqueID: Special codes were user-assigned to each site where beaver complexes had the same prefix “BW” followed by a unique number, and the control sites had the same prefix “C” followed by a unique numeric value. AREA: Calculated area in square meters of the site polygon. Shape_Length: Default column in ArcGIS Pro containing polygon perimeter measurement in meters. Shape_Area: Default column created in ArcGIS Pro containing polygon area in square meters.  CID: When I used the geoprocessing tool in ArcGIS Pro to create 100 random sample points within polygons, the CID column was created to link the points to the polygon they were bound in.  FREQUENCY: The number of sample points within each polygon.  mean_{date}: Rest of the columns follow this format with "mean_" as the prefix, followed by a unique numeric suffix representing a date. The {date} is an 8-digit integer, where the first four digits represent the year, the next two digits represent the month, and the last two digits represent the day of the month. For each date listed in the suffix of the column, there is a mean NDVI value associated with a site.  For clarification, OBJECTID and CID are the same. OBJECTID was created first when I made polygon features. CID was linked to this OBJECTID in the creation of random sample points. I could then join information from one table to another based on this shared field, despite the different nomenclature. There is no significance to being named differently other than the distinction of which table it comes from. Values are nonconsecutive due to human error. There were times when polygons were deleted so I could create more accurate outlines. Also, in two cases, I merged polygons representing beaver complexes due to proximity, where it would have been difficult to discern the influences of each complex. Thus, the numbers are not sequential.