A signal-to-noise index to quantify the potential for peak detection in sediment-charcoal records

被引:168
|
作者
Kelly, Ryan F. [1 ]
Higuera, Philip E. [2 ]
Barrett, Carolyn M. [3 ]
Hu, Feng Sheng [1 ,3 ]
机构
[1] Univ Illinois, Dept Plant Biol, Urbana, IL 61801 USA
[2] Univ Idaho, Dept Forest Ecol & Biogeosci, Moscow, ID 83843 USA
[3] Univ Illinois, Program Ecol Evolut & Conservat Biol, Urbana, IL 61801 USA
基金
美国国家科学基金会;
关键词
Signal-to-noise index (SNI); Charcoal analysis; Fire history; Lake sediment; Paleoecology; YELLOWSTONE-NATIONAL-PARK; CLIMATE-CHANGE; FIRE REGIMES; BOREAL FORESTS; SOURCE AREA; HISTORY; DEPOSITION; DISPERSAL; HOLOCENE; RANGE;
D O I
10.1016/j.yqres.2010.07.011
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Charcoal peaks in lake-sediment records are commonly used to reconstruct fire histories spanning thousands of years, but quantitative methods for evaluating the suitability of records for peak detection are largely lacking. We present a signal-to-noise index (SNI) that quantifies the separation of charcoal peaks (signal) from other variability in a record (noise). We validate the SNI with simulated and empirical charcoal records and show that an SNI > 3 consistently identifies records appropriate for peak detection. The SNI thus offers a means to evaluate the suitability of sediment-charcoal records for reconstructing local fires. MATLAB and R functions for calculating SNI are provided. Published by Elsevier Inc. on behalf of University of Washington.
引用
收藏
页码:11 / 17
页数:7
相关论文
共 50 条
  • [21] High Signal-to-Noise Ratio MEMS Noise Listener for Ship Noise Detection
    Zhu, Shan
    Zhang, Guojun
    Wu, Daiyue
    Jia, Li
    Zhang, Yifan
    Geng, Yanan
    Liu, Yan
    Ren, Weirong
    Zhang, Wendong
    REMOTE SENSING, 2023, 15 (03)
  • [23] Signal-to-noise models for digital-holographic detection
    Spencer, Mark F.
    Thornton, Douglas E.
    LONG-RANGE IMAGING III, 2018, 10650
  • [24] New detection algorithm for poor signal-to-noise conditions
    Vihonen, J
    Ala-Kleemola, T
    Helander, E
    Tikkinen, J
    Visa, A
    PROCEEDINGS OF THE 2003 IEEE RADAR CONFERENCE, 2003, : 345 - 349
  • [25] Machine learning for low signal-to-noise ratio detection
    Lacy, Fred
    Ruiz-Reyes, Angel
    Brescia, Anthony
    PATTERN RECOGNITION LETTERS, 2024, 179 : 115 - 122
  • [26] DISPLAY SIGNAL-TO-NOISE RATIO REQUIREMENTS FOR IMAGE DETECTION
    ROSELL, FA
    WILLSON, RH
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA, 1972, 62 (11) : 1337 - 1337
  • [27] LoRa Signal Synchronization and Detection at Extremely Low Signal-to-Noise Ratios
    Ameloot, Thomas
    Rogier, Hendrik
    Moeneclaey, Marc
    Van Torre, Patrick
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (11) : 8869 - 8882
  • [28] Bayesian Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation
    Reinhard, Dominik
    Fauss, Michael
    Zoubir, Ahdelhak M.
    2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2019,
  • [29] LESION DETECTION AND SIGNAL-TO-NOISE RATIO IN CT IMAGES
    JUDY, PF
    SWENSSON, RG
    SZULC, M
    MEDICAL PHYSICS, 1981, 8 (01) : 13 - 23
  • [30] Detection of the number of sources at low signal-to-noise ratio
    Gu, J.-F.
    Wei, P.
    Tai, H.-M.
    IET SIGNAL PROCESSING, 2007, 1 (01) : 2 - 8