Wavelet approach applied to EVI/MODIS time series and meteorological data

Moreira, Andreise; Fontana, Denise Cybis; Kuplich, Tatiana Mora

doi:10.1016/j.isprsjprs.2018.11.024

Cited by 23 publications

(14 citation statements)

References 30 publications

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“…Unlike for agricultural lands, where the spectral response to the influencing factor is quick, in the case of forests this process might be very extended in time, so a significant time lag in correlation between NDVI and meteorological element can occur. The lags in the forests' response to the negative changes in meteorological elements and hence the lags in the maximum values of correlation coefficients are also within the scope of researchers' interests (Mao et al, 2012; Moreira et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Influence of climatic conditions on Normalized Difference Vegetation Index variability in forest in Poland (2002–2021)

Kulesza,

Hościło

2023

Meteorological Applications

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The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders.

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Section: Discussionmentioning

confidence: 99%

Influence of climatic conditions on Normalized Difference Vegetation Index variability in forest in Poland (2002–2021)

Kulesza,

Hościło

2023

Meteorological Applications

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“…rain, temperature, frost, etc.) are extracted from the Météo France SAFRAN-ISBA dataset since weather has a strong effect on S1 signals and grasslands (Moreira et al, 2019). These variables are daily provided and computed for each parcel from the nearest weather station.…”

Section: Datamentioning

confidence: 99%

On the Joint Exploitation of Optical and Sar Satellite Imagery for Grassland Monitoring

Garioud

Valero

Giordano

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R2 = 0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution.

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“…Similar attempts for EVI-based forecasting of vegetation cover changes were successfully made by other scientific groups using other techniques and inputs for the models [Gurung et al, 2009]. In order to support the results of current study, it should be mentioned that Moreira et al have also proven strong correlation between climate indices, viz., air temperature and rainfall, and EVI [Moreira et al, 2019]. They also discovered that EVI-temperature and EVI-rainfall correlation is dependent on the El Nino and La Nina events.…”

Section: Discussionmentioning

confidence: 93%

Study of Climate Impact on Vegetation Cover in Kherson Oblast (Ukraine) Using Normalized Difference and Enhanced Vegetation Indices

Lykhovyd¹

2021

J. Ecol. Eng.

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Remote sensing is a convenient tool for the study of vegetation cover conditions and dynamics using normalized difference and enhanced vegetation indices. Determination of the connection between weather and vegetation indices plays an important role in better understanding peculiarities of ecosystems reaction to changing climate conditions. The study devoted to the evaluation of annual and long-term dynamics under vegetation cover conditions, and its reaction to the climate factor, was performed through the establishment of the link between remote sensing information (smoothed time series data on normalized and enhanced vegetation indices) and results of on-land hydrometeorological observations for air temperature and precipitation amounts in Kherson oblast of Ukraine during the period from 2012 to 2019 by the means of linear regression analysis of the data. The values of the studied vegetation indices (Terrain MODIS NDVI and MODIS EVI 250 m smoothed time series) were calculated and generalized by the means of GDAL raster analysis toolkit in QGIS 3.10. Statistical data processing was performed using BioStat v7 software. It was found that there is a strong tendency towards the enhancement of vegetation in the region year by year. Climate has strong effect on the vegetation, and the main input belongs to air temperature, while precipitation amounts cannot be considered as a driving force of changes in the growth of vegetation. Enhanced vegetation index seems to be more reliable for the estimation of vegetation cover conditions in comparison to normalized difference vegetation index.

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Wavelet approach applied to EVI/MODIS time series and meteorological data

Cited by 23 publications

References 30 publications

Influence of climatic conditions on Normalized Difference Vegetation Index variability in forest in Poland (2002–2021)

Influence of climatic conditions on Normalized Difference Vegetation Index variability in forest in Poland (2002–2021)

On the Joint Exploitation of Optical and Sar Satellite Imagery for Grassland Monitoring

Study of Climate Impact on Vegetation Cover in Kherson Oblast (Ukraine) Using Normalized Difference and Enhanced Vegetation Indices

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