This dataset contains data used in the associated publication in the International Journal of Remote Sensing.Wilson, Natalie R., and Laura M. Norman. 2018. “Analysis of Vegetation Recovery Surrounding a Restored Wetland Using the Normalized Difference Infrared Index (NDII) and Normalized Difference Vegetation Index (NDVI).” International Journal of Remote Sensing 39 (10): 3243–74. https://doi.org/10.1080/01431161.2018.1437297.The geodatabase contains four feature classes: AOI, MajorZone, MinorZone, and Green2007.Publication abstract: Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

This dataset contains vegetation data collected at a variety of watershed restoration sites across southeastern Arizona over 5 years. The semiarid habitats in the Madrean Archipelago Ecoregion, which extends from southern Arizona into northern Mexico, are facing many challenges from climate change to land use change which threaten the ecological and cultural values of the region. Watershed restoration practitioners use a variety of techniques such as gabions, check dams, and cross vanes to reduce the effects of these threats and improve or maintain watershed function. Since vegetation dynamics in the area are driven by water availability, these restoration techniques appear to have secondary effects on the vegetation of a watershed. To evaluate and quantify these effects, vegetation data was collected at 5 restoration sites across southeastern Arizona. Three of these sites, Barboot (BB), Silver Creek (SC), and Vaughn Canyon (VC), were restored just before sampling occurred allowing for an "After-Control-Impact" design to assess the change in vegetation. Data was collected at SC and VC for 5 years while only 4 years of data was collected at BB due to a lack of continued change. At these sites treatment plots were established at structures within stream channels while control plots were established at locations in reaches without structures within the same drainages. At Deep Dirt (DD), the project drainage was fairly short and there was no area within it or in a similar drainage that did not have restoration structures. This prevented the installation of control plots, instead data was collected at two different types of structures for 5 years. At Cienega Ranch (CR), restoration structures had not been installed when data was collected. Plots were distributed in the project reaches and in a nearby control reach. One year of baseline data has been collected at CR which will allow for a “Before-After-Control-Impact” study design. At all project sites, each plot was stratified in zones based on hydrological position relative to the restoration structure and proximity to the structure. Nested quadrats were used to quantify abundance as well as basal and foliar cover. Subplots were used to quantify species composition. Data was collected for perennial species and non-native species during the summer monsoon season from August – September, sometimes extending into early October. This vegetation data is provided in 5 CSV files which form a relational database which is explained in further detail in "RelationalDatabase_VegetationResponseWatershedRestoration.xml". "Relationships.pdf" is an illustration of the relationships in the database. "StudySampleDesign.pdf" contains figures illustrating plot design and nested quadrat design to clarify the data collection process. "mtbl_Plants.csv" is the master plant list that includes taxonomic and other information for Arizona species; the development of this dataset is explained in further detail in "mtbl_Plants.xml". Every plot and nested quadrat was photodocumented; photos can be found in the "Photos_VegetationResponseWatershedRestoration" directory and process explained in the associated metadata file "Photos_VegetationResponseWatershedRestoration.xml". This data release can be downloaded with or without the photos. "VegetationResponseWatershedRestoration.zip" includes the photos; "VegetationResponseWatershedRestoration_noPhotos.zip" does not include the photos for faster download times.

This dataset, created in support of USGS Scientific Investigations Report 2020-5075, Estimates of Groundwater Discharge by Evapotranspiration, Stump Spring and Hiko Springs, Clark County, Nevada, 2016-18, represents a Normalized Difference Vegetation Index calculated for vegetated areas in the Stump Spring groundwater discharge area (GDA) and Area of Critical Environmental Concern (ACEC). Vegetated areas within the GDA are composed of phreatophytic shrubs interspersed with xeric vegetation and bare soil. The GDA was delineated by visual interpretation of 1-meter National Agriculture Imagery Program (NAIP) aerial imagery acquired in May of 2015. The NDVI was calculated from a June 2017 WorldView 2 image resampled to 1-meter cell size and masked to remove bare ground areas identified from a supervised classification based on the same 2015 NAIP image used to define the GDA.

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developing using satellite imagery, including the Normalized Difference Vegetation Index (NDVI) and the Tasseled Cap (TC) Transformation. The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. The TC transformation is an approached used to transform satellite imagery into a collection of spectral metrics that can quantify various aspects of the vegetation and soil surfaces (Kauth and Thomas, 1976). Specifically, the TC transformation develops 6 separate metrics, though we only assess the three primary metrics: (i) brightness (transformation 1), (ii) greenness (transformation 2), and (iii) wetness (transformation 3). The TC transformation metrics are calculated using a series of coefficients multiplied across reflectance values for the suite of Landsat bands, then summed across each metric. No specific range is identified for the TC transformation metrics, though for both brightness and greenness, positive values represent brighter and greener conditions, respectively, while negative values represent wetter conditions for wetness. Because bandwidths differ slightly between Landsat 4, 5, 7 and Landsat 8, we use two sets of coefficients and complete the calculation separately before combining the collections into a single series of images (DeVries et al., 2016; Zhai et al., 2022). All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a two multi-band raster stacks. The raster stacks are identified by the vegetation index they represent (i.e., NDVI, TC greenness). Each band within the separate raster stacks represents a month from January 2014 through December 2020 (i.e., band 1 is January 2014 and band 84 is December 2020). Though the full study extends until December 2021, we chose to remove data from 2021 from the phenological analysis due to increased fire activity during 2021. Additionally, we chose not to include the TC transformation metrics of brightness and wetness to focus on metrics to simply measure vegetation greenness.

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developing using satellite imagery, including the Normalized Difference Vegetation Index (NDVI) and the Tasseled Cap (TC) Transformation. The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. The TC transformation is an approached used to transform satellite imagery into a collection of spectral metrics that can quantify various aspects of the vegetation and soil surfaces (Kauth and Thomas, 1976). Specifically, the TC transformation develops 6 separate metrics, though we only assess the three primary metrics: (i) brightness (transformation 1), (ii) greenness (transformation 2), and (iii) wetness (transformation 3). The TC transformation metrics are calculated using a series of coefficients multiplied across reflectance values for the suite of Landsat bands, then summed across each metric. No specific range is identified for the TC transformation metrics, though for both brightness and greenness, positive values represent brighter and greener conditions, respectively, while negative values represent wetter conditions for wetness. Because bandwidths differ slightly between Landsat 4, 5, 7 and Landsat 8, we use two sets of coefficients and complete the calculation separately before combining the collections into a single series of images (DeVries et al., 2016; Zhai et al., 2022). All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a two multi-band raster stacks. The raster stacks are identified by the vegetation index they represent (i.e., NDVI, TC greenness). Each band within the separate raster stacks represents a month from January 2014 through December 2020 (i.e., band 1 is January 2014 and band 84 is December 2020). Though the full study extends until December 2021, we chose to remove data from 2021 from the phenological analysis due to increased fire activity during 2021. Additionally, we chose not to include the TC transformation metrics of brightness and wetness to focus on metrics to simply measure vegetation greenness.

These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Missouri National Recreational River. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Missouri National Recreational River. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developed using satellite imagery, including the Normalized Difference Vegetation Index (NDVI). The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a multi-band raster stack of monthly NDVI images from January 2014 through July 2022 covering the area of the Bylas Fire. We included data from 2022, contrasting the full study which only includes data through 2021, to include additional data regarding our post-fire vegetation response analysis. Each band within the raster stack represents a month from 2014 through 2022 (i.e., band 1 is January 2014 and band 103 is July 2022).

Data is based on overlap of topographic, soil drainage, and national wetland inventory areas. This dataset is associated with the following publication: Horvath, E., J. Christensen, M. Mehaffey, and A. Neale. Building a Potential Wetland Restoration Indicator for the Contiguous United States.. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 83: 462-473, (2017).

This data set consists of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) data, derived from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery.