Reasoning on the Evaluation of Wildfires Risk Using the Receiver Operating Characteristic Curve and MODIS Images

被引:0
|
作者
Usero, L. [1 ]
Xose Rodriguez-Alvarez, M.
机构
[1] Univ Alcala de Henares, Dept Ciencias Computac, Alcala De Henares, Spain
关键词
FUEL MOISTURE-CONTENT; VEGETATION WATER; REFLECTANCE; LEAF; INDEX;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a method to evaluate the wildfires risk using the Receiver Operating Characteristic (ROC) curve and Terra moderate resolution imaging spectroradiometer (MODIS) images. To evaluate the wildfires risk fuel moisture content (FMC) was used, the relationship between satellite images and field collected FMC data was based on two methodologies; empirical relations and statistical models based on simulated reflectances derived from radiative transfer models (RTM). Both models were applied to the same validation data set to compare their performance. FMC of grassland and shrublands were estimated using a 5-year time series (2001-2005) of Terra moderate resolution imaging spectroradiometer (MODIS) images. The simulated reflectances were based on the leaf level PROSPECT coupled with the canopy level SAILH RTM. The simulated spectra were generated for grasslands and shrublands according to their biophysical parameters traits and FMC range. Both RTM-based models, empirical and statistical, offered similar accuracy with better determination coefficients for grasslands. In this work, we have evaluated the accuracy of (MODIS) images to discriminate between situations of high and low fire risk based on the FMC, by using the Receiver Operating Characteristic (ROC) curve. Our results show that none of the MODIS bands have a good discriminatory capacity (0.9984) when used separately, but the joint information provided by them offer very small misclassification errors.
引用
收藏
页码:476 / +
页数:3
相关论文
共 50 条
  • [21] Overestimation of the receiver operating characteristic curve for logistic regression
    Copas, JB
    Corbett, P
    BIOMETRIKA, 2002, 89 (02) : 315 - 331
  • [22] Interpreting area under the receiver operating characteristic curve
    de Hond, Anne A H
    Steyerberg, Ewout W
    van Calster, Ben
    The Lancet Digital Health, 2022, 4 (12):
  • [23] Receiver operating characteristic curve confidence intervals and regions
    Kerekes, John
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2008, 5 (02) : 251 - 255
  • [24] Receiver operating characteristic-curve limits of detection
    Wysoczanski, Artur
    Voigtman, Edward
    SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2014, 100 : 70 - 77
  • [25] Interpreting area under the receiver operating characteristic curve
    de Hond, Anne A. H.
    Steyerberg, Ewout W.
    van Calster, Ben
    LANCET DIGITAL HEALTH, 2022, 4 (12): : E853 - E855
  • [26] Receiver Operating Characteristic Curve in Diagnostic Test Assessment
    Mandrekar, Jayawant N.
    JOURNAL OF THORACIC ONCOLOGY, 2010, 5 (09) : 1315 - 1316
  • [27] Nonparametric and semiparametric estimation of the receiver operating characteristic curve
    Hsieh, FS
    Turnbull, BW
    ANNALS OF STATISTICS, 1996, 24 (01): : 25 - 40
  • [28] Receiver Operating Characteristic (ROC) Curve for Medical Researchers
    Kumar, Rajeev
    Indrayan, Abhaya
    INDIAN PEDIATRICS, 2011, 48 (04) : 277 - 287
  • [29] COMPARISON OF NEUTROPHIL HYPERSEGMENTATION INDICATORS USING RECEIVER OPERATING CHARACTERISTIC CURVE ANALYSIS
    CONNELLY, DP
    VOTAVA, H
    PARKIN, J
    PETERSON, L
    WARD, P
    JOHNSON, M
    AMERICAN JOURNAL OF CLINICAL PATHOLOGY, 1981, 75 (06) : 868 - 868
  • [30] Combining predictors for classification using the area under the receiver operating characteristic curve
    Pepe, MS
    Cai, TX
    Longton, G
    BIOMETRICS, 2006, 62 (01) : 221 - 229