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Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term monitoring of ground-dwelling arthropods in the greater Phoenix metropolitan area of central Arizona, USA (1998-2025)
The Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program has been monitoring ground-dwelling arthropods (e.g., insects, ararchnids) at locations throughout the greater Phoenix metropolitan area (GPMA) and surrounding Sonoran desert region since 1998. Monitoring locations span a diversity of habitat types, including mesic and xeric residential yards, commercial areas, agricultural fields, desert locations within the GPMA (desert remnant), and undisturbed desert locations. Organisms are collected quarterly using unbaited pitfall traps, typically ten per location but with some variation, exposed for approximately seventy-two hours. Organisms are identified to the lowest practical taxonomic level and enumerated. Many of the sampling locations established at the beginning of the monitoring project were relocated in 2001-2002 to overlap with the CAP LTER's Ecological Survey of Central Arizona (ESCA; formerly named Survey200) long-term monitoring sites, although within the same general landscape categories.
Long-term composited Normalized Difference Vegetation Index (NDVI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Bald cypress radiocarbon and dendrochronological data from trees located in the Altamaha Wildlife Management Area and on Sapelo Island, Georgia, USA
Ancient bald cypress trees buried under anoxic mud on the Altamaha Wildlife Management Area islands were sampled, prepared, radiocarbon dated, and the ringwidths measured for crossdating. Modern bald cypress samples were also obtained from Sapelo Island. Tree rings were measured using a Velmex and Measure J2X software and/or the ObjectJ extension of ImageJ. Measured radii were crossdated using visual dendrochronological methods and Cofecha software. Tree rings anchored to the present date back to 3161 B.C.E. and extend to 2016 C.E. In addition, the oldest two trees provide another 529 years of ringwidth data. This work was conducted under the National Science Foundation Doctoral Dissertation Improvement Award: Human Adaptation to Long Term Environmental Change (Award #1834682). All samples are part of the accessioned collections of the University of Georgia Laboratory's of Archaeology.
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Kearney_Outdoor_Learning_Area for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this pr
Raw microclimate data from plots at burned areas from the 2020 Holiday Farm fire in the Andrews Experimental Forest and Hagan Block, 2022-2024
This dataset includes a suite of microclimate sensor data from areas burned by the 2020 Holiday Farm fire within the McKenzie River basin. A total of 42 microclimate sensor suites were installed in July and August of 2022 distributed across RS01, RS08, RS15, WS02, WS09, WS01, HGBK. All sensor locations are within the Holiday Farm Fire footprint and within Permanent Sample Plots (PSPs). An additional sensor was placed within the Primary meteorological station (PRIMET) of the HJ Andrews Experimental Forest for comparison and calibration between open-air measurements. Sites are stratified across three treatment variables including 1) fire severity: high or low; 2) management: managed or unmanaged; 3) water balance: moister or drier topographic positions. This resulted in eight treatment blocks, each with 5 sensor suite replicates. Each microclimate site includes a suite of measurements: Hobo temperature and relative humidity sensor installed at 1.5m within a gill shield (recording at 30 minute interval), one TOMST TMS-4 temperature and soil moisture sensor was located within 1 m of plot center (recording at 15 minute interval). The TOMST sensors include air temperature sensors at 15cm, 2cm, and soil temperature at -6cm in soil near-surface. Soil moisture is measured across the ~10cm near surface zone.
Above ground plant biomass and leaf area of moist acidic tussock tundra 1981 experimental site (MAT81), Arctic LTER, Toolik Lake, Alaska.1995.
Above ground plant biomass and leaf area were measured in a tussock tundra experimental site. The plots were set up in 1981 and have been harvested in previous years (See Shaver and Chapin Ecological Monographs, 61, 1991 pp.1-31).
Long-term monitoring of stormwater runoff and water quality in urbanized watersheds of the greater Phoenix metropolitan area, ongoing since 2008
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Stormwater sampling is conducted at numerous locations. The longest running sampling location is near the outflow of the IBW ~0.6 km above its confluence with the Salt River. The sampling location coincides with a permanent USGS gauging sta
Stormwater Nitrogen in Arizona (SNAZ): runoff and stormwater-mediated export from urbanized catchments within the greater Phoenix metropolitan area, Arizona, USA (2010-2012)
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Data and expertise garnered by stormwater monitoring near the outflow of the IBW helped pave the way for a more expansive stormwater research effort facilitated by a leveraged grant from the National Science Foundation (DEB-0918457, NSF Eco
Long-term composited land surface temperature for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of land surface temperature (LST) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). We derived LST values based on the thermal band from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations: - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022
This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Tree Canopy Leaf Area Index in CRUI Land Use Project at Harvard Forest 1997
Numerous variables related to land use disturbance and recovery processes can influence forest composition and structure. We’ve measured differences in forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill. None of the sites had noticeable canopy gap disturbance at the time of the measurements. Leaf area index (LAI) was measured with an LAI-2000 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE) at all 77 edge and interior intersection points in the 30 m x 50 m permanent plot (7 columns x 11 rows) in 5 of our 6 land use sites. Under-canopy measurements were made in each site over 25-30 minutes during midday hours (11:00-2:30 EST) on overcast days near solstice (June 13, 18). The under-canopy readings were contrasted with an open-sky measurement taken in an open field near the Harvard Forest headquarters just before beginning data collection in each site. LAI averaged 3.98 and ranged from 2.28 to 5.93 across all sites. W1 had the highest site-level mean (4.62) and maximum (5.93) LAI while S2 had the lowest values (mean = 3.41, max = 4.55). The woodlot also showed the greatest spatial variation as measured by C.V., while plow #1 showed the least variation.
Leaf Area Index at Harvard Forest HEM and LPH Towers since 1998
Leaf area index (LAI) measurements are made to detect changes in forest leaf area that are important in understanding forests’ carbon dioxide (CO2) uptake and water use. Measurements of LAI in different forest types are useful understanding differences between forests in CO2 uptake and water use, while variation in LAI in the same forest type over time helps to explain interannual variation and long-term trends in carbon storage and water use. Within a single year, especially for deciduous forests, seasonal changes in LAI are very important in determining the forest’s cycle of CO2 uptake and water use. LAI measurements at Harvard Forest were begun in the old-growth hemlock stand in 1998 in order to understand and develop a predictive model for its carbon exchange, and were begun at the Little Prospect Hill site in 2002 to better understand CO2 uptake and carbon storage at this site, which were measured by the eddy-covariance method beginning in 2002. Due to a lack of personnel, multiple annual measurements of LAI to examine seasonal change in leaf area did not begin until 2007.
Leaf Area in the Clearcut Site at Harvard Forest 2010-2012
This dataset contributed to an estimate of leaf area by species at the Prospect Hill clearcut regeneration site, used in a publication to attribute measured changes in gross primary productivity over time to either changes in species composition and associated traits versus changes in total leaf area. The file reports data on leaf area by species from destructive harvest in select plots. It also reports leaf area and leaf weight from destructive harvest of the foliage of select individual species to characterize the vertical distribution of foliage for those species. Corresponding measurements of total plant area recorded with a LAI-2000 before and after harvest of this foliage is reported as well, useful for inferring the leaf versus stem/branch portions of light interception as needed for improved estimates of LAI with the light-interception method. Lastly, below we report the result of an August 2012 site-level survey of plant area index measured with the LAI-2000 and converted to leaf area index.
Specific Leaf Area in the Clearcut Site at Harvard Forest 2012
Clearcutting a forest ecosystem can result in a drastic reduction of the stand’s productivity. Despite the severity of this disturbance type, past studies have found that the productivity of young regenerating stands can quickly rebound, approaching that of mature undisturbed stands within a few years. One of the obvious reasons is increased leaf area with each year of recovery. However, a less obvious reason may be the variability in species composition and distribution during the natural regeneration process. The purpose of this study was to investigate to what extent the increase in GEP, observed during the first four years of recovery, in a naturally regenerating clearcut stand was due to 1) an overall expansion of leaf area, and 2) an increase in the canopy’s photosynthetic capacity stemming from either species compositional shifts or drift in physiological traits within species. We found that the multi-year rise in GEP following harvest was clearly attributed to the expansion of leaf area rather than a change in vegetation composition. Sizeable changes in relative abundance of species were masked by remarkably similar leaf physiological attributes for a range of vegetation types present in this early successional environment. Comparison of upscaled leaf-chamber to eddy-covariance-based light-response curves revealed broad consistency in both maximum photosynthetic capacity and quantum yield efficiency. The approaches presented here illustrate how chamber- and ecosystem-scale measurements of gas exchange can be blended with species-level leaf area data to draw conclusive inferences about changes in ecosystem processes over time in a highly dynamic environment.
Minneapolis-St. Paul Metro Area Residential Bee Lawn Survey, 2022
We surveyed Minneapolis-St. Paul (MSP) metropolitan region residents who have an interest in pollinators and pollinator habitat (e.g., bee lawns). Potential respondents were recruited from MSP pollinator-friendly email listservs. The survey was open from May 24, 2022 to June 30, 2022, and distributed to 494 individuals. Our final N, accounting for broken email addresses, non-responses, and less than 50% survey completion, is 256. The survey contains six categories of questions along with a set of panel data. Question categories include: home, yard, and lawn characteristics and observations; lawn care responsibilities and opinions; bee and bee lawn knowledge and opinions; formal and informal rules shaping lawn decisions; and lawn information sources, trust, authority, and advocacy. Survey respondents were primarily white, older, wealthy, and well-educated women. Our findings show that values influencing lawn management are primarily environmentally focused (e.g., pollinators, climate) rather than aesthetically focused (e.g., increases property values, fits neighborhood look). Generally, individuals reported that there are no—or they are unsure-- whether there are formal (e.g., ordinances), and informal (e.g., neighbor expectations) rules about lawn management. Respondents overwhelmingly report that University of Minnesota Extension is a highly trusted source of lawn management information. Finally, the majority of respondents promote or encourage alternative lawn management practices (e.g., bee lawns, pollinator gardens), primarily through informal activities (e.g., block parties, word of mouth).
WSC - Leaf area index (LAI) at various points within Wibu field site, 2012-2014
Leaf area index (LAI) measurements collected at various points within the Wibu field site between 2012-2014. Measurements were collected approximately weekly from plant emergence until appr. 1 month past the onset of senescence. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons; therefore, these are all LAI values for corn. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site and use of the LAI data.
Mid-winter habitat suitability indices for centrarchids in contiguous lentic areas of the Upper Mississippi River System: 1994-2018
This dataset includes raw measurements and calculated bluegill winter habitat suitability indices for depth (HSID), dissolved oxygen (HSIDO), temperature (HSIT), and flow (HSIF), as well as an overall bluegill winter habitat suitability index (HSIO), for 2915 mid-winter, lentic sampling locations across 208 contiguous lentic areas throughout the Upper Mississippi River System (Upper Mississippi and Illinois Rivers) from 1994-2018. This dataset also includes several spatial and temporal climatic and hydrogeomorphic parameters that were used to assess potential drivers of winter habitat suitability.
Long-term monitoring of herpetofauna along the Salt and Gila Rivers in and near the greater Phoenix metropolitan area, ongoing since 2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/627/5. The abstract below was extracted from the Level 0 data package and is included for context:
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.