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501 results for “Remote Sensing”
Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey data.</p>
Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.</p>
Multi-type Aircraft of Remote Sensing Images: MTARSI 2
<p>Multi-Type Aircraft of Remote Sensing Images (MTARSI 2) dataset of aircraft on runways. The dataset has had some reclassification into 42 classifications, and extra data augmentation in those classifications. It is an example of an unbalanced dataset, with challenges of different light and viewing angles. Originated from https://zenodo.org/record/3464319#.YNwk3-hKiUk. (MTARSI)</p>
Data Archive for: Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods
<p>This archive corresponds to the data described in Brauer et al. (2021) to be published in <em>Journal of Geophysical Research: Atmospheres.</em> Please see the included readme.txt file for details about each data file.</p>
A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach
<p>This file is the .csv database compiling surge-type glaciers automatically identified in Guillet et al (2022).</p> <p>File format is compliant with the Randolph Glacier Inventory (RGI) V6.0.</p> <p>If you have questions about the dataset - please refer to the following reference or contact Dr. Guillet.</p> <table> <tbody> <tr> <td>Guillet, G., King, O., Lv, M., Ghuffar, S., Benn, D., Quincey, D., & Bolch, T. (2022). A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach. <em>The Cryosphere</em>, <em>16</em>(2), 603-623.</td> </tr> <tr> <td> </td> </tr> </tbody> </table> <p> </p>
Remote Sensing of Radiation Belt Energetic Electrons Using Lightning Triggered Upper Band Chorus
<p>We report very low frequency (VLF) observation of interaction of waves from atmospheric lightning with radiation belt particles. The observation provides a unique opportunity to remotely sense the dynamics of the energetic particle population in the Earth’s radiation belts and how this population leads to amplification and generation of waves.</p>
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
<p>The changes of urban built-up areas can reflect the process of urbanization, and it can reflect the population, economy, and cultural development of the city. Therefore, accurate and timely extraction of urban built-up areas plays an important role in the dynamic management of the city. In the existing research, single-source remote sensing data is used to extract urban built-up areas, and there is a problem that the spectrum of urban areas and non-urban areas is easily confused. Multi-source remote sensing data, including luojia-1 remote sensing data, Landsat 8 OLI remote sensing data, etc., can make up for the spectrum confusing issues.</p> <p>We fuse the time series information of night light remote sensing data, neighborhood information and point of interest (POI) data in spatial dimension, and propose a built-up area extraction method that integrates night light time and space information and POI information.</p>
TerraSenseTK - Towards Reproducible Machine-Learning and Remote Sensing Research
<p>Dataset used in TerraSenseTK - Towards Reproducible Machine-Learning and<br> Remote Sensing Research.</p> <p>Nutrient Estimation in Common wheat Case Study available in the notebook</p> <p>Documentation is available in <a href="https://terrasensetk.readthedocs.io/en/latest/">here</a>.</p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) in China Seas (2003-2019)
<p>This dataset is sea surface partial pressure of CO2 (pCO2) in China seas (0-42°N, 105-132°E) over 2003-2019 with a spatial resolution of 1km and temporal resolution of a month. This is our second version of pCO2 in China seas. The first version was published on the SatCO2 website (http://www.satco2.com/index.php?m=content&c=index&a=show&catid=317&id=188).</p> <p>We produce this dataset by creating a boost machine learning algorithm (XGBoost) based on the gradient boost decision tree (GBDT). The input parameters used in this are sea surface temperature (SST), chlorophyll-a concentration (Chl-a), remote sensing reflectance of three bands (Rrs412, 443, 488 nm), the temperature difference in longitude direction (SST_DIF), and the theoretical background <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>_therm) under corresponding SST. SST_DIF is derived from SST by subtracting the mean value at the same latitude. To calculate <em>p</em>CO<sub>2</sub>_therm, we first assume that the seawater background <em>p</em>CO<sub>2</sub> equals the annual average atmospheric <em>p</em>CO<sub>2</sub> (sea-air <em>p</em>CO<sub>2</sub> balanced under ideal conditions), and the equilibrium temperature is assumed to be the yearly average SST; Then, the background <em>p</em>CO<sub>2</sub> is corrected according to SST to obtain <em>p</em>CO<sub>2</sub>_therm. Air pressure at sea level (SLP) and mole fraction of CO<sub>2</sub> in the air (xCO<sub>2</sub>) are employed when calculating atmospheric <em>p</em>CO<sub>2</sub>. </p> <p>The underway <em>p</em>CO<sub>2</sub> is first gridded to monthly 1 km and then divided into the training and validation sets with the volumes 151009 and 32584 samples. The validation set shows coefficients of determination (R<sup>2</sup>) between the XGBoost model-predicted and in-situ <em>p</em>CO<sub>2</sub> were 0.86, and the root means squared errors (RMSE) for the <em>p</em>CO<sub>2</sub> were 21.1 μatm.</p>
Reconstructed Aneto glacier surfaces from historic aerial image photogrammetry (1981) and remote sensing techniques (2020, 2021, 2022)
<p>The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time. </p>
High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula
<p><strong>Abstract:</strong></p> <p>Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017.</p> <p><strong>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</strong></p> <p>This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below:</p> <p><strong>Raster value Land cover/use category</strong></p> <p>1 Forest</p> <p>2 Marsh</p> <p>3 Salt patch</p> <p>4 Built</p> <p>5 Open water</p> <p>6 Farmland</p> <p>7 Bare soil</p> <p>8 Other vegetation</p> <p> </p> <p><strong>Input Data:</strong></p> <p>These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE.</p> <p><strong>Methods:</strong></p> <p>NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel.</p> <p>NDVI = (Near-infrared – Red) / (Near-infrared + Red)</p> <p>NDWI = (Green – Near-infrared) / (Green + Near-infrared)</p> <p>SI = (256 – Blue) * (256 + Blue)</p> <p>In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel.</p> <p>EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1)</p> <p>A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands.</p> <p>Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits.</p> <p><strong>Accuracy assessment:</strong></p> <p>A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below.</p> <table> <tbody> <tr> <td> <p><strong>Delaware 2013</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.46%</p> </td> <td> <p>90.89%</p> </td> <td> <p>0.90</p> </td> <td> <p>86.37%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>84.03%</p> </td> <td> <p>80.13%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.02%</p> </td> <td> <p>71.18%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>94.56%</p> </td> <td> <p>95.06%</p> </td> <td> <p>0.95</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>91.01%</p> </td> <td> <p>96.63%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.16%</p> </td> <td> <p>86.19%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.70%</p> </td> <td> <p>87.30%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>82.46%</p> </td> <td> <p>84.20%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Delaware 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>95.07%</p> </td> <td> <p>90.30%</p> </td> <td> <p>0.93</p> </td> <td> <p>91.37%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>88.86%</p> </td> <td> <p>92.44%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>91.82%</p> </td> <td> <p>85.59%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>87.58%</p> </td> <td> <p>93.54%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.68%</p> </td> <td> <p>90.48%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>91.61%</p> </td> <td> <p>93.61%</p> </td> <td> <p>0.93</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>95.67%</p> </td> <td> <p>87.67%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>91.16%</p> </td> <td> <p>90.24%</p> </td> <td> <p>0.91</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2011</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.32%</p> </td> <td> <p>90.97%</p> </td> <td> <p>0.90</p> </td> <td> <p>87.20%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.14%</p> </td> <td> <p>82.08%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>96.74%</p> </td> <td> <p>78.76%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>89.02%</p> </td> <td> <p>88.50%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.74%</p> </td> <td> <p>96.10%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>84.31%</p> </td> <td> <p>89.83%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.53%</p> </td> <td> <p>90.05%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.11%</p> </td> <td> <p>80.57%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.66%</p> </td> <td> <p>88.66%</p> </td> <td> <p>0.89</p> </td> <td> <p>87.34%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.65%</p> </td> <td> <p>88.35%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>93.29%</p> </td> <td> <p>68.30%</p> </td> <td> <p>0.79</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>93.44%</p> </td> <td> <p>86.92%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.36%</p> </td> <td> <p>92.36%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.84%</p> </td> <td> <p>94.55%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>92.86%</p> </td> <td> <p>82.61%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.56%</p> </td> <td> <p>76.00%</p> </td> <td> <p>0.81</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2012</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>91.18%</p> </td> <td> <p>0.88</p> </td> <td> <p>86.88%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.03%</p> </td> <td> <p>84.39%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.67%</p> </td> <td> <p>72.41%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>90.97%</p> </td> <td> <p>86.24%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>94.17%</p> </td> <td> <p>87.39%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>86.87%</p> </td> <td> <p>91.81%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>85.82%</p> </td> <td> <p>83.04%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>83.60%</p> </td> <td> <p>80.59%</p> </td> <td> <p>0.82</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2016</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>86.00%</p> </td> <td> <p>0.85</p> </td> <td> <p>85.83%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>86.25%</p> </td> <td> <p>90.72%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>90.61%</p> </td> <td> <p>62.12%</p> </td> <td> <p>0.74</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>88.70%</p> </td> <td> <p>77.72%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.12%</p> </td> <td> <p>84.41%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>85.17%</p> </td> <td> <p>90.36%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>83.30%</p> </td> <td> <p>88.54%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>84.49%</p> </td> <td> <p>84.17%</p> </td> <td> <p>0.84</p> </td> </tr> </tbody> </table> <p>While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.</p> <p> </p> <p><strong>Data format:</strong></p> <p>The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Code:</strong> Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes.</p> <p><strong>Datasets for download:</strong></p> <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> <p>These data layers cover 3 counties: Kent, New Castle, Sussex.</p> <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> <p>These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester.</p> <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> <p>These data layers cover 2 counties: Accomack, Northampton.</p> <p>We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files. </p> <p><strong>Data citation:</strong></p> <p>Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- Derived data 2020 & 2021
<p>This is derived data from a remote sensing experiment performed in 2020 and 2021. This repository contains .csv and .R files that can be used to replicate the analysis presented here:</p> <p><a href="https://zenodo.org/record/6859791#.Y-K6ky-B1z8">https://zenodo.org/record/6859791#.Y-K6ky-B1z8</a></p> <p>If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2020 Data
<p>This is a remote sensing dataset collected in 2020 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 5 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2020.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2020_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p>
Benign effects of logging on aerial insectivorous bats in Southeast Asia revealed by remote sensing technologies
<b>Description: </b><p>Number of bat calls recorded by SongMeter bat 2 detectors set to record continuously on a trigger. Counts are classified into 21 acoustic call types, including 13 species.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/101"><b>Impacts of forest modification on bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK Natural Environment Research Council (NERC) (Human Modified Tropical Forests programme & a PhD scholarship jointly funded by University of Kent & NERC & EnvEast DTP scholarship, NE/L002582/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Economic Planning Unit of the Malaysian Government and the Sabah Biodiversity Council (Research licence UPE: 40/200/19/2723)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740421">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_data_archive_Yoh2.xlsx</p><p><b>SAFE_data_archive_Yoh2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>All data </b> (described in worksheet MasterData)</p><p>Description: All auto and manual identifications for bat passes across a disturbance gradient, identified to functional group or species when possible</p><p>Number of fields: 13</p><p>Number of data rows: 134920</p><p>Fields: </p><ul><li><b>LOCATION</b>: Where the data was collected (Field type: location)</li><li><b>DATE</b>: Date surveyed (Field type: date)</li><li><b>TIME</b>: Time of recording (Field type: time)</li><li><b>AUTO_ID</b>: Taxa as identified using the automatic classifier (Field type: taxa)</li><li><b>ACCURACY</b>: Confidence value for auto identification results (Field type: numeric)</li><li><b>THRESLEVEL</b>: Whether the data met the desired auto-identification confidence value (Field type: categorical)</li><li><b>MANUAL_ID_CLEAN</b>: Taxa as identified manually (Field type: taxa)</li><li><b>FINAL_ID</b>: Final taxa label considering both the auto and manual ID (Field type: taxa)</li><li><b>TREATMENT</b>: Habitat type (Field type: categorical)</li><li><b>fc_100m</b>: Forest extent within 100m buffer of the survey location (Field type: numeric)</li><li><b>chm_100m</b>: Average canopy height within 100m buffer from survey location (Field type: numeric)</li><li><b>shape_100m</b>: Forest shape within 100m buffer of survey location (Field type: numeric)</li><li><b>TRI_100m</b>: Topographic ruggedness within 100m buffer of survey location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2011-04-01 to 2012-06-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  [CF_CROB] <br> -  -  -  -  -  [CF_H140] <br> -  -  -  -  -  [FMQCF1] <br> -  -  -  -  -  [FMQCF2] <br> -  -  -  -  -  [FMQCF3] <br> -  -  -  -  -  [FMQCF4] <br> -  -  -  -  -  [FMQCF5] <br> -  -  -  -  -  [FMQCF6] <br> -  -  -  -  -  [QCF] <br> -  -  -  -  -  [FM] <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus affinis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus creaghi</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus luctus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus philippinensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ater</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros galeritus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br></div><p></p>
Workflow for Remote Sensing for Forest Dynamics and Its Implications for Tree Outside Forest over Maryland, U.S.A.
<p>The workflow shows the process of using the data to plot the figures in the paper "Remote Sensing for Forest Dynamics and Its Implications for Tree Outside Forest over Maryland, U.S.A."</p>
Theory of Maximum Entropy Production (MEP) and Its Application to Microwave Remote Sensing - Simultaneous Retrieval of Soil Moisture and Vegetation Water Content
<p>A theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L-band microwave brightness temperature (TB). One representation of the MEP principle states that a non-equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof-of-concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g. VWC > 5 kg m-2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to just microwave observations.</p><p>Note: We would appreciate if users contact us for the use of the data.</p>
Multi-topography dataset for wind turbine detection from remote sensing image
<p>The land remote sensing wind turbine dataset has 1270 remote sensing images and contains 4459 individual wind turbines. The images are taken over a large time span and contain remote sensing images of the same wind farm at different times. The dataset has both YOLO and VOC tagging formats. The dataset can be divided into five categories based on the land background: sandy land, forest land, grassland, snow land, and wasteland. Rich land background can improve the robustness of the model, and different marker formats and a large number of wind turbine individuals can meet the object detection of different models. Affected by the size of different power wind turbines, the multi-angle imaging characteristics of remote sensing satellites, the different solar radiation angles in different seasons and vegetation shading, wind turbines show large differences in the images. The image features of the wind turbine shadow are more obvious than those of the wind turbine body, so in order for the detection model to better identify the wind turbine, we label the wind turbine body and the wind turbine shadow as a whole when using the labelImg tool for labeling the wind turbine target.</p>
Satellite remote sensing dataset of Sentinel-2 for phenology metrics extraction from sites in Bulgaria and France
<p><strong>Site Description:</strong></p> <p>In this dataset, there are seventeen production crop fields in Bulgaria where winter rapeseed and wheat were grown and two research fields in France where winter wheat – rapeseed – barley – sunflower and winter wheat – irrigated maize crop rotation is used. The full description of those fields is in the database "In-situ crop phenology dataset from sites in Bulgaria and France" (doi.org/10.5281/zenodo.7875440).</p> <p> </p> <p><strong>Methodology and Data Description:</strong></p> <p>Remote sensing data is extracted from Sentinel-2 tiles 35TNJ for Bulgarian sites and 31TCJ for French sites on the day of the overpass since September 2015 for Sentinel-2 derived vegetation indices and since October 2016 for HR-VPP products. To suppress spectral mixing effects at the parcel boundaries, as highlighted by Meier et al., 2020, the values from all datasets were subgrouped per field and then aggregated to a single median value for further analysis.</p> <p>Sentinel-2 data was downloaded for all test sites from CREODIAS (https://creodias.eu/) in L2A processing level using a maximum scene-wide cloudy cover threshold of 75%. Scenes before 2017 were available in L1C processing level only. Scenes in L1C processing level were corrected for atmospheric effects after downloading using Sen2Cor (v2.9) with default settings. This was the same version used for the L2A scenes obtained intermediately from CREODIAS. </p> <p>Next, the data was extracted from the Sentinel-2 scenes for each field parcel where only SCL classes 4 (vegetation) and 5 (bare soil) pixels were kept. We resampled the 20m band B8A to match the spatial resolution of the green and red band (10m) using nearest neighbor interpolation. The entire image processing chain was carried out using the open-source Python Earth Observation Data Analysis Library (EOdal) (Graf et al., 2022).</p> <p>Apart from the widely used Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we included two recently proposed indices that were reported to have a higher correlation with photosynthesis and drought response of vegetation: These were the Near-Infrared Reflection of Vegetation (NIRv) (Badgley et al., 2017) and Kernel NDVI (kNDVI) (Camps-Valls et al., 2021). We calculated the vegetation indices in two different ways: </p> <p>First, we used <strong>B08</strong> as near-infrared (NIR) band which comes in a native spatial resolution of 10 m. <strong>B08</strong> (central wavelength 833 nm) has a relatively coarse spectral resolution with a bandwidth of 106 nm.</p> <p>Second, we used <strong>B8A</strong> which is available at 20 m spatial resolution. <strong>B8A</strong> differs from B08 in its central wavelength (864 nm) and has a narrower bandwidth (21 nm or 22 nm in the case of Sentinel-2A and 2B, respectively) compared to B08.</p> <p> </p> <p>The High Resolution Vegetation Phenology and Productivity (<strong>HR-VPP</strong>) dataset from Copernicus Land Monitoring Service (CLMS) has three 10-m set products of Sentinel-2: vegetation indices, vegetation phenology and productivity parameters and seasonal trajectories (Tian et al., 2021). Both vegetation indices, Normalized Vegetation Index (NDVI) and Plant Phenology (PPI) and plant parameters, Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and Leaf Area Index (LAI) were computed for the time of Sentinel-2 overpass by the data provider. </p> <p>NDVI is computed directly from B04 and B08 and PPI is computed using Difference Vegetation Index (DVI = B08 - B04) and its seasonal maximum value per pixel. FAPAR and LAI are retrieved from B03 and B04 and B08 with neural network training on PROSAIL model simulations. The dataset has a quality flag product (QFLAG2) which is a 16-bit that extends the scene classification band (SCL) of the Sentinel-2 Level-2 products. A “medium” filter was used to mask out QFLAG2 values from 2 to 1022, leaving land pixels (bit 1) within or outside cloud proximity (bits 11 and 13) or cloud shadow proximity (bits 12 and 14). </p> <p>The <strong>HR-VPP</strong> daily raw vegetation indices products are described in detail in the user manual (Smets et al., 2022) and the computations details of PPI are given by Jin and Eklundh (2014). Seasonal trajectories refer to the 10-daily smoothed time-series of PPI used for vegetation phenology and productivity parameters retrieval with TIMESAT (Jönsson and Eklundh 2002, 2004).</p> <p>HR-VPP data was downloaded through the WEkEO Copernicus Data and Information Access Services (DIAS) system with a Python 3.8.10 harmonized data access (HDA) API 0.2.1. Zonal statistics [’min’, ’max’, ’mean’, ’median’, ’count’, ’std’, ’majority’] were computed on non-masked pixel values within field boundaries with rasterstats Python package 0.17.00.</p> <p> </p> <p>The Start of season date (SOSD), end of season date (EOSD) and length of seasons (LENGTH) were extracted from the annual Vegetation Phenology and Productivity Parameters (<strong>VPP</strong>) dataset as an additional source for comparison. These data are a product of the Vegetation Phenology and Productivity Parameters, see (https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity/vegetation-phenology-and-productivity) for detailed information.</p> <p> </p> <p><strong>File Description:</strong></p> <p>4 datasets:</p> <p>1_senseco_data_S2_B08_Bulgaria_France; 1_senseco_data_S2_B8A_Bulgaria_France; 1_senseco_data_HR_VPP_Bulgaria_France; 1_senseco_data_phenology_VPP_Bulgaria_France</p> <p>3 metadata:</p> <p>2_senseco_metadata_S2_B08_B8A_Bulgaria_France; 2_senseco_metadata_HR_VPP_Bulgaria_France; 2_senseco_metadata_phenology_VPP_Bulgaria_France</p> <p> </p> <p>The dataset files “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” concerns all vegetation indices (EVI, NDVI, kNDVI, NIRv) data values and related information, and metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France” describes all the existing variables. Both “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” have the same column variable names and for that reason, they share the same metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France”.</p> <p>The dataset file “1_senseco_data_HR_VPP_Bulgaria_France” concerns vegetation indices (NDVI, PPI) and plant parameters (LAI, FAPAR) data values and related information, and metadata file “2_senseco_metadata_HRVPP_Bulgaria_France” describes all the existing variables. </p> <p>The dataset file “1_senseco_data_phenology_VPP_Bulgaria_France” concerns the vegetation phenology and productivity parameters (LENGTH, SOSD, EOSD) values and related information, and metadata file “2_senseco_metadata_VPP_Bulgaria_France” describes all the existing variables.</p> <p> </p> <p><strong>Bibliography</strong></p> <p>G. Badgley, C.B. Field, J.A. Berry, Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv. 3 (2017) e1602244. https://doi.org/10.1126/sciadv.1602244.</p> <p>G. Camps-Valls, M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M.D. Mahecha, J. Muñoz-Marí, F.J. García-Haro, L. Guanter, M. Jung, J.A. Gamon, M. Reichstein, S.W. Running, A unified vegetation index for quantifying the terrestrial biosphere, Sci. Adv. 7 (2021) eabc7447. https://doi.org/10.1126/sciadv.abc7447.</p> <p>L.V. Graf, G. Perich, H. Aasen, EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data, Comput. Electron. Agric. 203 (2022) 107487. https://doi.org/10.1016/j.compag.2022.107487.</p> <p>H. Jin, L. Eklundh, A physically based vegetation index for improved monitoring of plant phenology, Remote Sens. Environ. 152 (2014) 512–525. https://doi.org/10.1016/j.rse.2014.07.010.</p> <p>P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens. 40 (2002) 1824–1832. https://doi.org/10.1109/TGRS.2002.802519.</p> <p>P. Jönsson, L. Eklundh, TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci. 30 (2004) 833–845. https://doi.org/10.1016/j.cageo.2004.05.006.</p> <p>J. Meier, W. Mauser, T. Hank, H. Bach, Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions, Comput. Electron. Agric. 169 (2020) 105205. https://doi.org/10.1016/j.compag.2019.105205.</p> <p>B. Smets, Z. Cai, L. Eklund, F. Tian, K. Bonte, R. Van Hoost, R. Van De Kerchove, S. Adriaensen, B. De Roo, T. Jacobs, F. Camacho, J. Sánchez-Zapero, S. Else, H. Scheifinger, K. Hufkens, P. Jönsson, HR-VPP Product User Manual Vegetation Indices, 2022.</p> <p>F. Tian, Z. Cai, H. Jin, K. Hufkens, H. Scheifinger, T. Tagesson, B. Smets, R. Van Hoolst, K. Bonte, E. Ivits, X. Tong, J. Ardö, L. Eklundh, Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ. 260 (2021) 112456. https://doi.org/10.1016/j.rse.2021.112456.</p>
Remote sensing and ecological variables related to Influenza A prevalence and subtype diversity in wild birds in the Lluta wetland of northern Chile
<p>Supplemental material for manuscript, tables 1 and 2</p>
Characterisation of night-time outdoor lighting in small urban centres using cluster analysis of remotely sensed light emissions (Dataset)
<p>Data used for the paper "Characterisation of night-time outdoor lighting in small urban centres using cluster analysis of remotely sensed light emissions". </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.