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Journal articles on the topic 'Ground-based data'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Xiaofeng Li, Xiaofeng Li, Jun Xu Jun Xu, Jijun Luo Jijun Luo, Lijia Cao Lijia Cao, and Shengxiu Zhang Shengxiu Zhang. "Ground target recognition based on imaging LADAR point cloud data." Chinese Optics Letters 10, s1 (2012): S11002–311005. http://dx.doi.org/10.3788/col201210.s11002.

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Дашкевич, Жанна, Zhanna Dashkevich, Владимир Иванов, and Vladimir Ivanov. "Estimated nitric oxide density in auroras from ground-based photometric data." Solar-Terrestrial Physics 5, no. 1 (2019): 58–61. http://dx.doi.org/10.12737/stp-51201908.

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In this paper, we numerically estimate the nitric oxide density in auroras, using photometric data on 427.8, 557.7, and 630.0 nm emission intensities. The data were obtained at midnight at observatories of the Polar Geophysical Institute. These estimates were made using a numerical modeling procedure with a time-dependent model of the auroral ionosphere [Dashkevich et al., 2017]. It is shown that the NO density in the maximum of the altitude profile is between (1÷3.3)∙10^8 cm–3. The obtained estimates indicate the absence of a correlation between the [NO]max values and 427.8 nm emission intens

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3

Kepler, S. O. "Session1A: DATA − Ground-based observations." Communications in Asteroseismology 157 (2009): 23–62. http://dx.doi.org/10.1553/cia157s23.

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4

Priya, R., and Dr R. Mallika. "Ground Water Quality Modelling Using Data Mining Techniques and Artificial Neural Network Based Approach." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1001–7. http://dx.doi.org/10.5373/jardcs/v11sp10/20192897.

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5

Kleshchenko, A. D., V. V. Asmus, A. I. Strashnaya, et al. "Drought Monitoring Based on Ground and Satellite Data." Russian Meteorology and Hydrology 44, no. 11 (2019): 772–81. http://dx.doi.org/10.3103/s1068373919110074.

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Eresmaa, R., H. Järvinen, S. Niemelä, and K. Salonen. "Asymmetricity of ground-based GPS slant delay data." Atmospheric Chemistry and Physics 7, no. 12 (2007): 3143–51. http://dx.doi.org/10.5194/acp-7-3143-2007.

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Abstract. The ground-based measurements of the Global Positioning System (GPS) allow estimation of the tropospheric delay along the slanted signal paths through the atmosphere. The meteorological exploitation of such slant delay (SD) observations relies on the hypothesis of azimuthal asymmetry of the information content. This article addresses the validity of the hypothesis. A new concept of asymmetricity is introduced for studying the SD observations and their model counterparts. The asymmetricity is defined as the ratio of the absolute asymmetric delay component to total SD. The model counte

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Andersen, Kristoffer R., Casper Kirkegaard, Esben Auken, and Anders V. Christiansen. "Towards 3D inversion of ground based TEM data." ASEG Extended Abstracts 2016, no. 1 (2016): 1–5. http://dx.doi.org/10.1071/aseg2016ab243.

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8

Sharma, Niranjan Prasad. "Validation of Satellite Estimated Solar Ultraviolet Radiation Data with Ground Based data in Kathmandu, Nepal." Journal of the Institute of Engineering 11, no. 1 (2016): 101–7. http://dx.doi.org/10.3126/jie.v11i1.14701.

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The main objective of this study is to validate the satellite estimated solar Ultraviolet radiation data with ground based data in Kathmandu (27.72 N, 85.32 E), located at an elevation of 1350m, from the sea level. The ground based measurement and the satellite estimation were performed by NILU-UV irradiance meter and EOS Aura OMI satellite respectively. The NILU-UV irradiance meter is a six channel 0 0 radiometer designed to measure hemispherical irradiances on a fat surface. Meanwhile the Ozone Monitoring Instrument (OMI) on board, the NASA EOS Aura space craft is a nadir viewing spectromete

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Denisenko, Valery, and Andrey Lyakhov. "Comparison of ground-based and satellite data on spatiotemporal distribution of lightning discharges under solar minimum." Solar-Terrestrial Physics 7, no. 4 (2021): 104–12. http://dx.doi.org/10.12737/stp-74202112.

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Worldwide maps of lightning activity have been obtained from the ground-based World Wide Lightning Location Network (WWLLN) for 2007–2009. We have compiled these maps separately for different seasons and UT periods, using WWLLN data on the time and coordinates of each of the recorded lightning. The total number of flashes of lightning in WWLLN data is by an order of magnitude smaller than in satellite data from Optical Transient Detector and the Lightning Imaging Sensor satellites. However, the key features of the spatial distribution and seasonal trends coincide well. The main difference obse

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10

Tavakol, Ameneh, Kelsey R. McDonough, Vahid Rahmani, Stacy L. Hutchinson, and J. M. Shawn Hutchinson. "The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data." Remote Sensing Applications: Society and Environment 24 (November 2021): 100649. http://dx.doi.org/10.1016/j.rsase.2021.100649.

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11

Qi Baiyu, 齐白玉, 陈思颖 Chen Siying, 张寅超 Zhang Yinchao, 陈和 Chen He, and 郭磐 Guo Pan. "Geometric Form Factor Retrieval Method for Ground-Based Lidar Based on Ground-Based and Space-Borne Synchronous Observation Data." Chinese Journal of Lasers 44, no. 9 (2017): 0910003. http://dx.doi.org/10.3788/cjl201744.0910003.

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12

Tan, Weixian, Xiaohong Li, Pingping Huang, Wei Xu, and Wen Hong. "Ground-based radar data processing based on pseudo-polar coordinate system." Journal of Engineering 2019, no. 20 (2019): 6429–33. http://dx.doi.org/10.1049/joe.2019.0453.

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13

Kashani, Alireza G., and Andrew J. Graettinger. "Cluster-Based Roof Covering Damage Detection in Ground-Based Lidar Data." Automation in Construction 58 (October 2015): 19–27. http://dx.doi.org/10.1016/j.autcon.2015.07.007.

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14

Shen, A., W. Zhang, and H. Shi. "CSF BASED NON-GROUND POINTS EXTRACTION FROM LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 291–95. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-291-2017.

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Region growing is a classical method of point cloud segmentation. Based on the idea of collecting the pixels with similar properties to form regions, region growing is widely used in many fields such as medicine, forestry and remote sensing. In this algorithm, there are two core problems. One is the selection of seed points, the other is the setting of the growth constraints, in which the selection of the seed points is the foundation. In this paper, we propose a CSF (Cloth Simulation Filtering) based method to extract the non-ground seed points effectively. The experiments have shown that thi

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15

AHMAD, Iftikhar, Hiroyuki MABUCHI, Manabu KANO, Shinji HASEBE, Yoshikazu INOUE, and Hiroaki UEGAKI. "Data-Based Ground Fault Diagnosis of Power Cable Systems." SICE Journal of Control, Measurement, and System Integration 6, no. 4 (2013): 290–97. http://dx.doi.org/10.9746/jcmsi.6.290.

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16

Rangappa, Nehul, Y. Raja Vara Prasad, and Shiv Ram Dubey. "LEDNet: Deep Learning-Based Ground Sensor Data Monitoring System." IEEE Sensors Journal 22, no. 1 (2022): 842–50. http://dx.doi.org/10.1109/jsen.2021.3129173.

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17

Zhbankov, G. A., P. F. Denisenko, and V. V. Sotsky. "Correction of Ionosphere Models Based on Ground Ionosonde Data." Geomagnetism and Aeronomy 59, no. 6 (2019): 704–12. http://dx.doi.org/10.1134/s001679321906015x.

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18

MARJORAM, A. R., P. J. VICKERY, and D. C. McKENZIE. "The acquisition and analysis of ground-based reflectance data." International Journal of Remote Sensing 6, no. 1 (1985): 187–94. http://dx.doi.org/10.1080/01431168508948434.

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19

Hutchinson, M. F. "Stochastic space-time weather models from ground-based data." Agricultural and Forest Meteorology 73, no. 3-4 (1995): 237–64. http://dx.doi.org/10.1016/0168-1923(94)05077-j.

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20

Kadygrov, E. N., E. V. Ganshin, E. A. Miller, and T. A. Tochilkina. "Ground-based microwave temperature profilers: Potential and experimental data." Atmospheric and Oceanic Optics 28, no. 6 (2015): 598–605. http://dx.doi.org/10.1134/s102485601506007x.

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21

Rohlf, Detlef, and Holger Friehmelt. "High fidelity ground effect model based on DGPS data." Aerospace Science and Technology 9, no. 6 (2005): 543–52. http://dx.doi.org/10.1016/j.ast.2005.05.003.

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22

Chen, Gaoxiang, Liyun Fu, Kanfu Chen, Cyril D. Boateng, and Shuangcheng Ge. "Adaptive Ground Clutter Reduction in Ground-Penetrating Radar Data Based on Principal Component Analysis." IEEE Transactions on Geoscience and Remote Sensing 57, no. 6 (2019): 3271–82. http://dx.doi.org/10.1109/tgrs.2018.2882912.

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23

Brunt, Kelly M., Robert L. Hawley, Eric R. Lutz, et al. "Assessment of NASA airborne laser altimetry data using ground-based GPS data near Summit Station, Greenland." Cryosphere 11, no. 2 (2017): 681–92. http://dx.doi.org/10.5194/tc-11-681-2017.

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Abstract. A series of NASA airborne lidars have been used in support of satellite laser altimetry missions. These airborne laser altimeters have been deployed for satellite instrument development, for spaceborne data validation, and to bridge the data gap between satellite missions. We used data from ground-based Global Positioning System (GPS) surveys of an 11 km long track near Summit Station, Greenland, to assess the surface–elevation bias and measurement precision of three airborne laser altimeters including the Airborne Topographic Mapper (ATM), the Land, Vegetation, and Ice Sensor (LVIS)

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24

Odumuyiwa, Victor T., Anurika Umeanozie, Oladipupo Sennaike, Olubukola Adekola, Babatunde Sawyerr, and Ebun Fasina. "Clustering Based Approach for Ground Truth Inference in Crowdsourced Data." FUOYE Journal of Engineering and Technology 7, no. 2 (2022): 141–47. http://dx.doi.org/10.46792/fuoyejet.v7i2.800.

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Crowdsourcing provides a means of gathering data from the public in order to infer what the ground truth label of an unfamiliar entity is. Such data are not used for decision making in their raw form until further processing is done to infer ground truth from the crowdsourced data. This paper presents a detailed comparative analysis of the ground truth inference ability of three clustering algorithms on crowd sourced datasets with different experimental scenarios (Initializing centroids and extracting class labels). The algorithms include, the self-organizing maps, the k-means and the expectat

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25

Kataev, M. Yu, and A. A. Skugarev. "Intelligent situational center based on the aggregation space and ground-based data." Proceedings of Tomsk State University of Control Systems and Radioelectronics 19, no. 3 (2016): 61–64. http://dx.doi.org/10.21293/1818-0442-2016-19-3-61-64.

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26

Ding, M., W. Hu, X. Jin, and L. Yu. "A new ZTD model based on permanent ground-based GNSS-ZTD data." Survey Review 48, no. 351 (2016): 385–91. http://dx.doi.org/10.1179/1752270615y.0000000034.

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27

Stepanov, Alexander, Sargylana Kobyakova, and Viktor Khalipov. "FAST SUBAURORAL DRIFTS OF IONOSPHERE PLASMA ACCORDING TO DATA FROM YAKUT MERIDIONAL CHAIN OF STATIONS." Solar-Terrestrial Physics 5, no. 4 (2019): 60–65. http://dx.doi.org/10.12737/stp-54201908.

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Using long-term data from Yakut meridional chain of Yakutsk — Zhigansk — Batagay — Tixie ionospheric stations, we study ionospheric signatures of fast subauroral ion drift. Sharp drops or “falls” of critical frequencies (FCF) of the ionospheric F layer are shown to be one of the main signatures of the development of fast subauroral ion drifts near or at the zenith of the observation station. Comparison between long-term ground-based and satellite measurements indicates that there is good agreement between seasonal variation in the probability of occurrence of FCF derived from ground-based data

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28

Ural, Serkan, and Jie Shan. "A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 395–401. http://dx.doi.org/10.5194/isprs-archives-xli-b3-395-2016.

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LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the poi

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Ural, Serkan, and Jie Shan. "A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 395–401. http://dx.doi.org/10.5194/isprsarchives-xli-b3-395-2016.

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LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the poi

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30

Xu, Yongfang, Yan Shen, Xiaowei Jiang, Fengyun Tian, Lei Cao, and Nan Wang. "Quality Control Technique for Ground-Based Lightning Detection Data Based on Multi-Source Data over China." Remote Sensing 17, no. 11 (2025): 1928. https://doi.org/10.3390/rs17111928.

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Lightning is one of the most severe natural disasters, characterized by its sudden onset, short duration, and significant damage. Existing quality control (QC) schemes for millisecond-level lightning observation data from a single source are primarily limited by the instrument and equipment, leading to inadequate monitoring, forecasting, and early warning accuracy in severe convective weather. This study proposes a comprehensive QC scheme for lightning location data from the China Meteorological Administration ground-based National Lightning Detection Network (CMA-LDN). The scheme integrates r

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31

Maeda, Takahiro, and Hiroyuki Fujiwara. "Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data." Journal of Disaster Research 12, no. 2 (2017): 233–40. http://dx.doi.org/10.20965/jdr.2017.p0233.

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This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on t

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32

Zhang, Taiping, Paul W. Stackhouse, Stephen J. Cox, J. Colleen Mikovitz, and Charles N. Long. "Clear-sky shortwave downward flux at the Earth's surface: Ground-based data vs. satellite-based data." Journal of Quantitative Spectroscopy and Radiative Transfer 224 (February 2019): 247–60. http://dx.doi.org/10.1016/j.jqsrt.2018.11.015.

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33

Cheng, Dongyang, Dangjun Zhao, Junchao Zhang, Caisheng Wei, and Di Tian. "PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data." Sensors 21, no. 11 (2021): 3703. http://dx.doi.org/10.3390/s21113703.

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Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to o

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34

Botvich, I. Yu, N. A. Kononova, D. V. Emelyanov, and T. I. Pisman. "Grassland Monitoring Based on Geobotanical, Ground, Spectrometric and Satellite Data." Исследования Земли из космоса 2023, no. 2 (2023): 43–53. http://dx.doi.org/10.31857/s0205961423010037.

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The study assessed the possibility of grassland monitoring based on various spectral vegetation indices (NDVI, ClGreen, NDRE, NDMI) calculated according to Sentinel-2 satellite data during the 2018 growing season. Geobotanical studies and collection of ground-based spectrophotometry data were carried out simultaneously, at the same time of day, and were used as an additional stage of haymaking monitoring. It was possible to identify grasslands and determine the date of mowing based on ground and satellite spectrometric data. A drop in the indices (NDVI, clGreen, NDRE, NDMI) was observed on the

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35

Chang, Yongjun. "Ground-Truth Data Modeling for Gait Analysis based on Kinect V2 Sensor Data." Journal of the Institute of Electronics and Information Engineers 59, no. 8 (2022): 99–103. http://dx.doi.org/10.5573/ieie.2022.59.8.99.

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36

VEDEL, Henrik, and Xiang-Yu HUANG. "Impact of Ground Based GPS Data on Numerical Weather Prediction." Journal of the Meteorological Society of Japan 82, no. 1B (2004): 459–72. http://dx.doi.org/10.2151/jmsj.2004.459.

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37

Maiti, Manabendra, Kausik Bhattacharyya, Salil Kumar Biswas, Md Anoarul Islam, Ayan Pradhan, and Judhajit Sanyal. "Determination of sky status by ground based radiometric data analysis." Indian Journal of Science and Technology 14, no. 27 (2021): 2250–56. http://dx.doi.org/10.17485/ijst/v14i27.447.

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38

Tazibt, Celia Yasmine, Nadjib Achir, and Tounsia Djamah. "Online drone-based data gathering strategies for ground sensor networks." International Journal of Sensor Networks 38, no. 3 (2022): 177. http://dx.doi.org/10.1504/ijsnet.2022.121702.

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39

Lucas, Célia, Silvan Leinss, Yves Bühler, Armando Marino, and Irena Hajnsek. "Multipath Interferences in Ground-Based Radar Data: A Case Study." Remote Sensing 9, no. 12 (2017): 1260. http://dx.doi.org/10.3390/rs9121260.

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40

Noferini, L., T. Takayama, M. Pieraccini, et al. "Analysis of Ground-Based SAR Data With Diverse Temporal Baselines." IEEE Transactions on Geoscience and Remote Sensing 46, no. 6 (2008): 1614–23. http://dx.doi.org/10.1109/tgrs.2008.916216.

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41

Cheng, Jie, Shunlin Liang, Qinhuo Liu, and Xiaowen Li. "Temperature and Emissivity Separation From Ground-Based MIR Hyperspectral Data." IEEE Transactions on Geoscience and Remote Sensing 49, no. 4 (2011): 1473–84. http://dx.doi.org/10.1109/tgrs.2010.2076818.

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42

Maturi, Matteo, and Julian Merten. "Weak-lensing detection of intracluster filaments with ground-based data." Astronomy & Astrophysics 559 (November 2013): A112. http://dx.doi.org/10.1051/0004-6361/201322007.

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43

Liu, Tao, Yi Su, and Chunlin Huang. "Inversion of Ground Penetrating Radar Data Based on Neural Networks." Remote Sensing 10, no. 5 (2018): 730. http://dx.doi.org/10.3390/rs10050730.

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44

Ahn, B. H., A. D. Richmond, Y. Kamide, et al. "An ionospheric conductance model based on ground magnetic disturbance data." Journal of Geophysical Research: Space Physics 103, A7 (1998): 14769–80. http://dx.doi.org/10.1029/97ja03088.

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45

Manjunath, K. R., Shibendu Shankar Ray, and Sushma Panigrahy. "Discrimination of Spectrally-Close Crops Using Ground-Based Hyperspectral Data." Journal of the Indian Society of Remote Sensing 39, no. 4 (2011): 599–602. http://dx.doi.org/10.1007/s12524-011-0099-x.

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46

Orr, L., S. C. Chapman, and J. W. Gjerloev. "Directed Network of Substorms Using SuperMAG Ground‐Based Magnetometer Data." Geophysical Research Letters 46, no. 12 (2019): 6268–78. http://dx.doi.org/10.1029/2019gl082824.

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47

Allroggen, Niklas, and Jens Tronicke. "Attribute-based analysis of time-lapse ground-penetrating radar data." GEOPHYSICS 81, no. 1 (2016): H1—H8. http://dx.doi.org/10.1190/geo2015-0171.1.

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Analysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tes

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48

Hildebrandt, H., C. Wolf, and N. Benítez. "A blind test of photometric redshifts on ground-based data." Astronomy & Astrophysics 480, no. 3 (2008): 703–14. http://dx.doi.org/10.1051/0004-6361:20077107.

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49

Marjoram, A. R., P. J. Vickery, and D. C. McKenzie. "A microcomputer data-acquisition system for ground-based reflectance measurements." Computers & Geosciences 11, no. 5 (1985): 595–604. http://dx.doi.org/10.1016/0098-3004(85)90088-3.

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50

Pott, Luan P., Telmo JC Amado, Raí A. Schwalbert, Elodio Sebem, Mithila Jugulam, and Ignacio A. Ciampitti. "Pre‐planting weed detection based on ground field spectral data." Pest Management Science 76, no. 3 (2019): 1173–82. http://dx.doi.org/10.1002/ps.5630.

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