Analysis of Multifractal and Organization/Order Structure in Suomi-NPP VIIRS Normalized Difference Vegetation Index Series of Wildfire Affected and Unaffected Sites by Using the Multifractal Detrended Fluctuation Analysis and the Fisher–Shannon Analysis

Abstract

The analysis of vegetation dynamics affected by wildfires contributes to the understanding of ecological changes under disturbances. The use of the Normalized Difference Vegetation Index (NDVI) of satellite time series can effectively contribute to this investigation. In this paper, we employed the methods of multifractal detrended fluctuation analysis (MFDFA) and Fisher-Shannon (FS) analysis to investigate the NDVI series acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar-Orbiting Partnership (Suomi-NPP). Four study sites that were covered by two different types of vegetation were analyzed, among them two sites were affected by a wildfire (the Camp Fire, 2018). Our findings reveal that the wildfire increases the heterogeneity of the NDVI time series along with their organization structure. Furthermore, the fire-affected and fire-unaffected pixels are quite well separated through the range of the generalized Hurst exponents and the FS information plane. The analysis could provide deeper insights on the temporal dynamics of vegetation that are induced by wildfire. Entropy 2020, 22, 415 2 of 16Under the influence of wildfires, the vegetation dynamics can be more complicated because of their interactions with biomass, soil, atmosphere, climate, and so on [8]. Deeper investigations need to be conducted at different spatial-temporal scales in conjunction with the local, regional, and global levels in order to assess the impact of fires on the vegetation communities [9]. Fortunately, the development of satellite remote sensing provides an excellent means of the continuous observation for the natural biomass [10,11]. Massive long-term data records are captured via multiple satellite sensors, of which the Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellites were launched on October 28, 2011 as the new generation system to undertake the mission of the previous Moderate Resolution Imaging Spectroradiometer (MODIS) of Earth Observing System (EOS) satellites, which can further provide continuous data records and observations. For the Suomi-NPP sensors, the sensor of Visible Infrared Imaging Radiometer Suite (VIIRS) is designed to collect the measurements of spectral domain from 0.3µm to 14µm for the Earth, which advances its increasing applications in wildfires [12,13].Various vegetation indices (VI) have been developed to characterize the status of surface biomass, among which the Normalized Difference Vegetation Index (NDVI) [14] is generally used to quantify the status of vegetation. It is defined as the ratio between the reflectance in the spectral domain of visible red and near-infrared; this definition minimizes the noise that is related to different bands and diminishes the effects induced by clouds, shadows, sun, atmospheric attenuation, etc. [15]. The primary land product of VIIRS, the VNP13A1, records the NDVI series since 2012 all over the world, providing abundant data of the long-term satellite time series of the vegetation in the region of interest (ROI)....

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

“…The scaling property of these NDVI original series agrees with earlier works in which the persistence condition for crops is also reported through H(q = 2) or Hurst index using R/S method [21,26]. However, the results have shown that this series could have a multifractal nature as investigated by Li et al [28] and Ba et al [29] over the vegetation dynamic of areas affected by the fire through multifractal analysis achieving the same conclusion. Several parameters can be calculated, as the ones showed in Table 7, given more possibilities to classify the NDVI series studied.…”

“…GSF focuses on the absolute values of the differences that occur at different time scales, and it represents an excellent tool to study the dynamic of a series from a multiscaling point of view, as explained in the materials and methods section of this study. Recently, several works have approached the study of NDVI series using multiscaling analysis [28,29].…”

Fisher Shannon analysis of drought/wetness episodes along a rainfall gradient in Northeast Brazil

“…The concept of Fisher Information (FI) was introduced in the statistical theory estimation (Fisher, 1925), while the special case of FI of a location parameter of a parametric distribution was termed FIM (Frieden, 1990). Subsequently, FIM has been used to describe physical systems (Frieden, 1990;Boumali and Labidi, 2018), and has been applied for time series analysis in physiology (Martin et al, 1999), geophysics (Telesca et al, 2011;Balasis et al, 2016), ecology (Ba et al, 2020), meteorology (Pierini et al, 2016;Guignard et al, 2019b), hydrology (Pierini et al, 2011(Pierini et al, , 2015Lovallo et al, 2013), and social behaviour (Li et al, 2020). For continuous one dimensional variable X with probability density function (pdf) f(x) the FIM I X is defined as (Vignat and Bercher, 2003)…”

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

“…The GSF has been used to study vegetation [ 34 ] and other geophysical data [ 35 ]. More recently, MF-DFA has been used to study the long-term ecosystem dynamics at a large scale [ 36 , 37 , 38 , 39 ] and compare the dynamics of areas affected and unaffected by fire [ 40 ]. MF-DFA allows the study of multiscaling on vegetation and the detection of whether it is related to long-term correlations or broad probability density functions.…”