Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires

Ba, Rui; Song, Weiguo; Lovallo, Michele; Zhang, Hui; Telesca, Luciano

doi:10.1016/j.physa.2022.127911

Cited by 11 publications

(9 citation statements)

References 26 publications

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“…Based on the formulation of NDVI, the EVI incorporates the visible blue spectral band and the adjustment coefficients, which is defined as follows:

where ρ blue represents the atmospherically corrected reflectance of the visible blue spectral domain, and the coefficients G, C 1 , C 2 , and L are set to 2.5, 6, 7.5, and 1, respectively, according to the research in [ 30 , 32 ], among which L is the canopy background adjustment to solve the transmission of nonlinear, differential NIR, and red radiant through a canopy, C 1 and C 2 are the coefficients of the aerosol resistance term, the blue band is used to correct the aerosol influences of the red band, and G is the gain factor. Since the atmospheric aerosol scattering in the visible blue band is higher than that in the visible red band, the reflectance difference between these two bands can be used to stabilize the index value at different aerosol concentrations [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires

Lovallo²,

Song

et al. 2022

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The MODIS Aqua and Terra Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series acquired during nearly two decades (2000 to 2020) covering the area burned by the Camp Fire (California) in 2018 is investigated in this study by using the multifractal detrended fluctuation analysis in relation to the recovery process of vegetation after fire. In 2008, the same area was partially burned by two wildfires, the BTU Lightning Complex Fire and the Humboldt Fire. Our results indicate that all vegetation index time series are featured by six- and twelve-month modulating periodicities, with a larger spectral content at longer periods for two-fire-affected sites. Furthermore, two fires cause an increase of the persistence of the NDVI and EVI time series and an increase of the complexity, suggesting that the recovery process of vegetation dynamics of fire-affected sites is characterized by positive feedback mechanisms, driving the growth-generating phenomena, which become even more effective in those sites affected by two fires.

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“…Based on the formulation of NDVI, the EVI incorporates the visible blue spectral band and the adjustment coefficients, which is defined as follows:

Section: Methodsmentioning

confidence: 99%

Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires

Lovallo²,

Song

et al. 2022

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“…2 2022 start of season (SOS) is defi ned as a sharp increase in the intensity of spectral characteristics of the vegetation layer directly after a long period of photosynthesis rest (Zeng et al, 2020). At present, the SOS is mainly determined using the methods of remote probing, most frequently by the vegetation indices NDVI or EVI (Enhanced Vegetation Index), calculated by the obtained satellite data (Ba et al, 2022). During the vegetation period, these data are presented in the form of a smoothed curve.…”

Section: Methodsmentioning

confidence: 99%

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

et al. 2022

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Aim. To study the effect of climate changes on the performance of cereals. Methods. Remote, statistical, and analytic methods. Climate changes were determined by the sum of radiation temperatures of the earth’s surface, calculated using the data of the infrared range (10.3–11.3; 11.4–12.4 μm) of the high precision radiometer AVHRR of the meteorological satellites of the Earth, NOAA. The effect of climate changes on the state of vegetation, including the phenological parameters, such as the beginning, the end, and the length of the vegetation peri- od, was determined by the Normalized Difference Vegetation Index (NDVI) obtained using the data of infrared (0.72–1.1 μm) and red (0.58–0.68 μm) ranges of the abovementioned radiometer. The satellite data are openly accessed on the website of STAR NESDIS NOAA – Satellite Applications and Research of NOAA’s National En- vironmental Satellite Data Information Services of the National Ocean and Atmosphere Research Department of the USA – http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/. The dynamics of СО2 concentration in the air was determined using the satellite data of AIRS/Aqua (https://disc.gsfc.nasa.gov/datasets/AIRS3C2M_005/summary), the precipitation dynamics – using the data of ERA5 ECMWF/Copernicus Climate Change Service (https://devel- opers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY#description). The association be- tween the performance of cereals and satellite data was found by the method of correlation and regression analysis. Results. Using the satellite data, it was found that in 1982–2021, the sum of radiation temperatures of the earth’s surface reliably increased in the vegetation period (April–September), and the amount of precipitation tended to decrease in the zones of Polissia, Forest-Steppe, and Steppe. The highest tempo of warming was observed in the territory of the Steppe. In general, the warming had a positive effect on the state of vegetation, according to NDVI, and on the performance of cereals. However, in the recent decade (2010–2020), the positive effect of the warming on the vegetation state decreased, and according to the NDVI trend, there has been a tendency towards the negative effect of the further increase in the temperature, which is especially remarkable for the Steppe and Forest-Steppe. On the contrary, the warming in the Polissia zone created the conditions for the increase in the performance of agri- cultural cereals. Conclusions. From 1982 till 2021, there was a strong tendency towards the increase in the sum of radiation temperatures of the earth’s surface in the vegetation period and the decrease in the precipitation amount in Polissia, Forest-Steppe, and Steppe. According to the NDVI data, due to the warming, the length of the vegeta- tion period increased by 14–21 days on average, which had a positive effect on the vegetation. Since 2010, the acceleration in the warming tempo has been observed. The average annual increase in the sum of the radiation tem- peratures of the earth’s surface in the vegetation period is 15–16 °С – for Polissia, 14–18 °С for the Forest-Steppe, and 18–20 °С for the Steppe. According to this tendency, if the current regularity in the temperature increase in 1982–2021 is preserved in the nearest future, the temperature of the vegetation period will additionally increase, and the average sum of temperatures of the earth’s surface will reach 4000 °С in Polissia, which was notable for the Forest-Steppe in 1982–2021, 4440–4600 °С – in the Forest-Steppe, and up to 5800–5900 °С in the Steppe. In 1982–2000, there was a strong regularity of the positive effect of the warming during the vegetation period both on the vegetation state according to the NDVI index, and the performance of cereals in all three natural climatic zones. However, after 2000, due to the negative effect of high summer temperatures on the crops, the effect of the warming on vegetation considerably decreased, which was especially notable for the Steppe where the increase in NDVI compared to the previous period was on average 9.41 % in 1992–2001, and in 2012–2021 – 1.78 %.

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“…The precipitation status index (GPMI) index is used for information on meteorological precipitation anomalies, and the temperature status index (TCI) demonstrates the effect of surface temperature on vegetation [34]. The enhanced vegetation index (EVI) indicates the growth status of vegetation, and the slope (Aspect), and slope direction and elevation (DEM) also have significant influence on regional drought.…”

Section: Principles Of Grassland Drought Monitoring Model Constructionmentioning

confidence: 99%

“…Therefore, in years when drought occurs, the surface temperature values are higher than in the same months of normal years. The LST is influenced by atmospheric and geographical factors, so it cannot be directly used for complete drought monitoring [34]. The temperature condition index (TCI) is described based on the principle that the surface temperature of the tree canopy or soil increases with increasing temperature [39].…”

Section: Temperature Condition Index (Tci)mentioning

confidence: 99%

A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data

et al. 2022

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The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.

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Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires

Cited by 11 publications

References 26 publications

Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires

Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data

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