Analysis of MODIS NDVI Time Series to Calculate Indicators of Mediterranean Forest Fire Susceptibility

2011 ◽  
Vol 48 (2) ◽  
pp. 171-194 ◽  
Author(s):  
V. Chéret ◽  
J.-P. Denux
Keyword(s):  
Time Series ◽  
Forest Fire ◽  
Mediterranean Forest ◽  
Modis Ndvi ◽  
Ndvi Time Series

Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia

2022 ◽  
Vol 114 ◽  
pp. 103804
Author(s):  
Issam Touhami ◽  
Hassane Moutahir ◽  
Dorsaf Assoul ◽  
Kaouther Bergaoui ◽  
Hamdi Aouinti ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Climatic Variables ◽  
Mediterranean Forest ◽  
Land Surface Phenology ◽  
Modis Ndvi ◽  
Ndvi Time Series

Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

2019 ◽  
Vol 35 (13) ◽  
pp. 1400-1414 ◽  
Author(s):  
Miriam Rodrigues da Silva ◽  
Osmar Abílio de Carvalho ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes ◽  
Cristiano Rosa Silva
Keyword(s):  
Time Series ◽  
Euclidean Distance ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Nearest Neighbour ◽  
Time Series Classification ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Cosine Similarity Measures

scholarly journals Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

CERNE ◽  
2010 ◽  
Vol 16 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Thomaz Chaves de Andrade Oliveira ◽  
Luis Marcelo Tavares de Carvalho ◽  
Luciano Teixeira de Oliveira ◽  
Adriana Zanella Martinhago ◽  
Fausto Weimar Acerbi Júnior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Vegetation Indices ◽  
Deciduous Forests ◽  
Modis Ndvi ◽  
Minas Gerais State ◽  
Ndvi Time Series ◽  
Multi Temporal ◽  
Comparison Of The Results ◽  
Filtering Techniques

Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.

SURE-based optimum-length S-G filter to reconstruct NDVI time series iteratively with outliers removal

2019 ◽  
Vol 18 (02) ◽  
pp. 2050001
Author(s):  
Liang Tang ◽  
Zhongming Zhao ◽  
Ping Tang ◽  
Haijun Yang
Keyword(s):  
Time Series ◽  
Polynomial Regression ◽  
Random Noise ◽  
Simulated Data ◽  
Optimum Length ◽  
Modis Ndvi ◽  
Local Polynomial ◽  
Ndvi Time Series ◽  
Iterative Filtering ◽  
Unbiased Risk

Savitzky–Golay (S-G) filter is a method of local polynomial regression, and iterative filtering with S-G filter can be used to smooth out random noise and outliers of cloud noise in NDVI time series. It involves a continuous approximation to the upper envelope of NDVI time series. In this paper, the optimum-length of S-G filter was estimated based on Steinc’s unbiased risk estimator theory when S-G filtering was conducted iteratively, and the reconstruction result was presented. Reconstruction experiments on the simulated data and MODIS NDVI time series of the year 2010–2014 showed that the optimum-length S-G filter can outperform the fixed bandwidth S-G filter.

Effects of Long-Term Fire Exclusion in the Modis NDVI Time Series in the Águas Emendadas Ecological Station, Brazil

2019 ◽  
Author(s):  
Nickolas Castro Santana ◽  
Osmar Abilio de Carvalho Junior ◽  
Roberto Arnaldo Trancoso Gomes ◽  
Renato Fontes Guimaraes
Keyword(s):  
Time Series ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Fire Exclusion
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