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  • Rural Digital Europe
  • Research data
  • National Science Foundation
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  • Authors: John Gardner; Tamlin Pavelsky; Xiao Yang; Simon Topp; +1 Authors

    The River Sediment Database (RiverSed) database contains Total Suspended Sediment (TSS) concentrations derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. TSS concentrations represent reach integrated medians concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected ithin each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). Files: 1) Metadata (RiverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (riverSed_usa_v1.0.txt). Table of TSS concentration and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv). 6) Matchup database with extended metadata on locations and in-situ data (Aquasat_TSS_v1.0.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_v1.csv) 8) The xgboost model that can make TSS predictions over inland waters in USA with 9 Landsat bands/band combinations (final_model_xgbLinear_v1.rds). The model can be loaded in R. Future version will have the xgb object to be compatible across languages.

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    ZENODO
    Dataset . 2023
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      Data sources: ZENODO
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    Authors: Dakota S. Dale; Lu Liang; Liheng Zhong; Michele L. Reba; +1 Authors

    This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. Both file configurations are processed by the code linked in the paper. Supported in Part by NASA Water Resources Award 80NSSC22K0923 and U.S. Geological Survey under Cooperative Agreement G20AC00448 and G21AC10729. {"references": ["DS Dale Et al., 2023"]}

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO
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      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO
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    Authors: John Gardner; Tamlin Pavelsky; Xiao Yang; Simon Topp; +1 Authors

    The River Sediment Database (RivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). The paper associated with RivSed: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 Files: 1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp). 5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths. 6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv) 8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      Dataset . 2023
      License: CC BY
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    Authors: Sion, Brad; Samburova, Vera; Berli, Markus; Baish, Christopher; +2 Authors

    This dataset includes both raw and processed data associated with the publication entitled "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA", published in MDPI Fire (doi: 10.3390/fire6020066). Raw files include exported .xlsx files from Meter Group HYPROP analyses, .csv files from 10 replicate measurements of saturated hydraulic conductivity for each analyzed sample using the Meter Group KSAT device, and raw .dat files from measurement of bulk thermal properties. A single additional file also documents the laboratory results from particle size and loss on ignition analyses. Processed data includes curve fitting parameters associated with fitting the soil water retention curves (SWRC) and thermal conductivity functions (TCFs) for each sample, as described in Sion et al. (2023). Additional requests associated with data from Sion et al. (2023) should be directed to the lead author.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Frost Mitchell; Aniqua Baset; Sneha Kumar Kasera; Aditya Bhaskara;

    Dataset Description This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters. The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus. Dataset Description Sample Count Receiver Count No-Tx Samples 46 10 to 25 1-Tx Samples 4822 10 to 25 2-Tx Samples 346 11 to 12 The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows: \(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \) Measurement Parameters Description Frequency 462.7 MHz Radio Gain 35 dB Receiver Sample Rate 2 MHz Sample Length N=10,000 Band-pass Filter 6 kHz Transmitters 0 to 2 Transmission Power 1 W Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device. Usage Instructions Data is provided in .json format, both as one file and as split files. import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys: rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name. tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude. metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords File Separations and Train/Test Splits In the separated_data.zip folder there are several train/test separations of the data. all_data contains all the data in the main JSON file, separated by the number of transmitters. stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers. train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json. The random split is a random 80/20 split of the data. special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus. The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split. The seasonal split contains data separated by the month of collection, in April or July. The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data. campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json. Digital Surface Model The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models. To read the data in python: import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product. DSM DOI: https://doi.org/10.5069/G9TH8JNQ

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Haoxiang Yu; Jie Hua; Christine Julien;

    This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices" With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule. For more detail about this dataset, please refer to the paper listed above. {"references": ["Haoxiang Yu, Jie Hua, and Christine Julien. 2021. Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys '21). Association for Computing Machinery, New York, NY, USA, 537\u2013541. DOI:https://doi.org/10.1145/3485730.3494115"]} This work was funded in part by the National Science Foundation under grants CNS-1813263 and CNS-1909221. Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of the NSF.

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    Authors: Phil Wilkes; Matheus Boni Vicari; Mathias Disney;

    Terrestrial LiDAR data collected by the team at University College London.This is Version 2 containing data processed into 10 m x 10 m tiles, this has also been filtered to remove high "deviation" points.Data is in .ply format containing xyz fields as well as reflectance, deviation, range, return number and scan position<\p>UCL project name: 2017-07-18.001.riprojectPlot ID: STPState or region: CamdenDate project started: 7/18/2017Area scanned: 25,392 m2Instrument: UCL RIEGL VZ-400Scan pattern: 19 positionsAngular resolution: 0.04Images captured: NoLinks to media: Number of scans: 38Google Maps URL: https://www.google.com/maps/place/The+Hardy+Tree/@51.5348275,-0.1302261,19.07z/data=!4m5!3m4!1s0x48761b22ae4a50ff:0x5ee5e6d9819cb888!8m2!3d51.5351276!4d-0.1297699Publications: https://doi.org/10.1186/s13021-018-0098-0, https://doi.org/10.1016/j.rse.2020.112102For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017Please acknowldege the producers of this data set if using this data for publication.

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    ZENODO
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    ZENODO
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    ZENODO
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    Authors: Simon Topp; Tamlin Pavelsky; Xiao Yang; John Gardner; +1 Authors

    LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows: HydroLakes_DP.shp: A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015). LakeExport.py: Python code to extract reflectance values for U.S. lakes from Google Earth Engine. GEE_pull_functions.py: Functions called within LakeExport.py 01_LakeExtractor.Rmd: An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database. SceneMetadata.csv: A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID. srCorrected_us_hydrolakes_dp_20200628: The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes. {"references": ["Shen, Z., Yu, X., Sheng, Y., Li, J., & Luo, J. (2015). A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration. PLOS ONE, 10(12), e0144700. https://doi.org/10.1371/journal.pone.0144700", "Wang, S., Li, J., Shen, Q., Zhang, B., Zhang, F., & Lu, Z. (2015). MODIS-Based radiometric color extraction and classification of inland water with the forel-ule scale: A case study of lake Taihu. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 907\u2013918. https://doi.org/10.1109/JSTARS.2014.2360564"]}

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    ZENODO
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    Authors: John Gardner; Xiao Yang; Simon Topp; Matthew Ross; +1 Authors

    riverSR database (River Surface Reflectance) v1.0.0 This database contains Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. The surface reflectance values across bands (red, green, blue, nir, swir1, swir1) represent the median reflectance of pixels detected as water within each Landsat scene that are within the boundaries of each reach represented by NHDPlusV2 centerlines. Surface reflectance is therefore geo-referenced to river center lines with network topology (NHDPlusV2) for quick geospatial analysis. Files: 1) Metadata (riverSR_v1.0_metadata.docx): Description of all data files associated with this repository. 2) Surface reflectance database (riverSR_usa_v1.0,feather). Readable in R or Python using the feather package and is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv).

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    Authors: Ben Weinstein; Sergio Marconi; Alina Zare; Stephanie Bohlman; +3 Authors

    Abstract The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. How can I see the data? A web server to look through predictions is available through idtrees.org Dataset Organization The shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map. The predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available: Height: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001) Area: The crown area in m2 of the rectangular bounding box. Label: All data in this release are "Tree". Score: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence) How were predictions made? The DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed. How were predictions validated? Please see Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics, 56, 101061. Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv. Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing 11.11 (2019): 1309. Were any sites removed? Several sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request. # Contact We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. Gordon and Betty Moore Foundation: GBMF4563

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    ZENODO
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  • Authors: John Gardner; Tamlin Pavelsky; Xiao Yang; Simon Topp; +1 Authors

    The River Sediment Database (RiverSed) database contains Total Suspended Sediment (TSS) concentrations derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. TSS concentrations represent reach integrated medians concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected ithin each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). Files: 1) Metadata (RiverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (riverSed_usa_v1.0.txt). Table of TSS concentration and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv). 6) Matchup database with extended metadata on locations and in-situ data (Aquasat_TSS_v1.0.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_v1.csv) 8) The xgboost model that can make TSS predictions over inland waters in USA with 9 Landsat bands/band combinations (final_model_xgbLinear_v1.rds). The model can be loaded in R. Future version will have the xgb object to be compatible across languages.

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    ZENODO
    Dataset . 2023
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      ZENODO
      Dataset . 2023
      Data sources: ZENODO
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    Authors: Dakota S. Dale; Lu Liang; Liheng Zhong; Michele L. Reba; +1 Authors

    This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. Both file configurations are processed by the code linked in the paper. Supported in Part by NASA Water Resources Award 80NSSC22K0923 and U.S. Geological Survey under Cooperative Agreement G20AC00448 and G21AC10729. {"references": ["DS Dale Et al., 2023"]}

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY NC
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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      Dataset . 2023
      License: CC BY NC
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    Authors: John Gardner; Tamlin Pavelsky; Xiao Yang; Simon Topp; +1 Authors

    The River Sediment Database (RivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). The paper associated with RivSed: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 Files: 1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp). 5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths. 6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv) 8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Sion, Brad; Samburova, Vera; Berli, Markus; Baish, Christopher; +2 Authors

    This dataset includes both raw and processed data associated with the publication entitled "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA", published in MDPI Fire (doi: 10.3390/fire6020066). Raw files include exported .xlsx files from Meter Group HYPROP analyses, .csv files from 10 replicate measurements of saturated hydraulic conductivity for each analyzed sample using the Meter Group KSAT device, and raw .dat files from measurement of bulk thermal properties. A single additional file also documents the laboratory results from particle size and loss on ignition analyses. Processed data includes curve fitting parameters associated with fitting the soil water retention curves (SWRC) and thermal conductivity functions (TCFs) for each sample, as described in Sion et al. (2023). Additional requests associated with data from Sion et al. (2023) should be directed to the lead author.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Frost Mitchell; Aniqua Baset; Sneha Kumar Kasera; Aditya Bhaskara;

    Dataset Description This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters. The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus. Dataset Description Sample Count Receiver Count No-Tx Samples 46 10 to 25 1-Tx Samples 4822 10 to 25 2-Tx Samples 346 11 to 12 The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows: \(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \) Measurement Parameters Description Frequency 462.7 MHz Radio Gain 35 dB Receiver Sample Rate 2 MHz Sample Length N=10,000 Band-pass Filter 6 kHz Transmitters 0 to 2 Transmission Power 1 W Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device. Usage Instructions Data is provided in .json format, both as one file and as split files. import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys: rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name. tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude. metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords File Separations and Train/Test Splits In the separated_data.zip folder there are several train/test separations of the data. all_data contains all the data in the main JSON file, separated by the number of transmitters. stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers. train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json. The random split is a random 80/20 split of the data. special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus. The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split. The seasonal split contains data separated by the month of collection, in April or July. The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data. campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json. Digital Surface Model The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models. To read the data in python: import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product. DSM DOI: https://doi.org/10.5069/G9TH8JNQ

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
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      ZENODO
      Dataset . 2022
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      Data sources: Datacite
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    Authors: Haoxiang Yu; Jie Hua; Christine Julien;

    This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices" With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule. For more detail about this dataset, please refer to the paper listed above. {"references": ["Haoxiang Yu, Jie Hua, and Christine Julien. 2021. Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys '21). Association for Computing Machinery, New York, NY, USA, 537\u2013541. DOI:https://doi.org/10.1145/3485730.3494115"]} This work was funded in part by the National Science Foundation under grants CNS-1813263 and CNS-1909221. Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of the NSF.

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    Authors: Phil Wilkes; Matheus Boni Vicari; Mathias Disney;

    Terrestrial LiDAR data collected by the team at University College London.This is Version 2 containing data processed into 10 m x 10 m tiles, this has also been filtered to remove high "deviation" points.Data is in .ply format containing xyz fields as well as reflectance, deviation, range, return number and scan position<\p>UCL project name: 2017-07-18.001.riprojectPlot ID: STPState or region: CamdenDate project started: 7/18/2017Area scanned: 25,392 m2Instrument: UCL RIEGL VZ-400Scan pattern: 19 positionsAngular resolution: 0.04Images captured: NoLinks to media: Number of scans: 38Google Maps URL: https://www.google.com/maps/place/The+Hardy+Tree/@51.5348275,-0.1302261,19.07z/data=!4m5!3m4!1s0x48761b22ae4a50ff:0x5ee5e6d9819cb888!8m2!3d51.5351276!4d-0.1297699Publications: https://doi.org/10.1186/s13021-018-0098-0, https://doi.org/10.1016/j.rse.2020.112102For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017Please acknowldege the producers of this data set if using this data for publication.

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    ZENODO
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    Authors: Simon Topp; Tamlin Pavelsky; Xiao Yang; John Gardner; +1 Authors

    LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows: HydroLakes_DP.shp: A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015). LakeExport.py: Python code to extract reflectance values for U.S. lakes from Google Earth Engine. GEE_pull_functions.py: Functions called within LakeExport.py 01_LakeExtractor.Rmd: An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database. SceneMetadata.csv: A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID. srCorrected_us_hydrolakes_dp_20200628: The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes. {"references": ["Shen, Z., Yu, X., Sheng, Y., Li, J., & Luo, J. (2015). A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration. PLOS ONE, 10(12), e0144700. https://doi.org/10.1371/journal.pone.0144700", "Wang, S., Li, J., Shen, Q., Zhang, B., Zhang, F., & Lu, Z. (2015). MODIS-Based radiometric color extraction and classification of inland water with the forel-ule scale: A case study of lake Taihu. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 907\u2013918. https://doi.org/10.1109/JSTARS.2014.2360564"]}

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    Authors: John Gardner; Xiao Yang; Simon Topp; Matthew Ross; +1 Authors

    riverSR database (River Surface Reflectance) v1.0.0 This database contains Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. The surface reflectance values across bands (red, green, blue, nir, swir1, swir1) represent the median reflectance of pixels detected as water within each Landsat scene that are within the boundaries of each reach represented by NHDPlusV2 centerlines. Surface reflectance is therefore geo-referenced to river center lines with network topology (NHDPlusV2) for quick geospatial analysis. Files: 1) Metadata (riverSR_v1.0_metadata.docx): Description of all data files associated with this repository. 2) Surface reflectance database (riverSR_usa_v1.0,feather). Readable in R or Python using the feather package and is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv).

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    Authors: Ben Weinstein; Sergio Marconi; Alina Zare; Stephanie Bohlman; +3 Authors

    Abstract The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. How can I see the data? A web server to look through predictions is available through idtrees.org Dataset Organization The shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map. The predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available: Height: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001) Area: The crown area in m2 of the rectangular bounding box. Label: All data in this release are "Tree". Score: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence) How were predictions made? The DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed. How were predictions validated? Please see Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics, 56, 101061. Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv. Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing 11.11 (2019): 1309. Were any sites removed? Several sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request. # Contact We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. Gordon and Betty Moore Foundation: GBMF4563

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