Vegetation phenology dynamics across ecoregions of Iberian Peninsula from MODIS NDVI time series: 2001-2017

2020 ◽  
Author(s):  
Jose Antonio Caparros-Santiago ◽  
Lorenzo Carlos Quesada-Ruiz ◽  
Miguel Angel Garcia-Perez ◽  
Victor Francisco Rodriguez-Galiano
Keyword(s):  
Time Series ◽  
Iberian Peninsula ◽  
Vegetation Phenology ◽  
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.

scholarly journals Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna

Fire ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Níckolas Santana
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Policy ◽  
Brazilian Savanna ◽  
Vegetation Phenology ◽  
Modis Ndvi ◽  
Fire Recurrence

Fire is one of the main modeling agents of savanna ecosystems, affecting their distribution, physiognomy and species diversity. Changes in the natural fire regime on savannas cause disturbances in the structural characteristics of vegetation. Theses disturbances can be effectively monitored by time series of remote sensing data in different terrestrial ecosystems such as savannas. This study used trend analysis in NDVI (Normalized Difference Vegetation Index)–MODIS (Moderate Resolution Imaging Spectroradiometer) time series to evaluate the influence of different fire recurrences on vegetation phenology of the Brazilian savanna in the period from 2001 to 2016. The trend analysis indicated several factors responsible for changes in vegetation: (a) The absence of fire in savanna phytophysiognomies causes a constant increase in MODIS–NDVI, ranging from 0.001 to 0.002 per year, the moderate presence of fire in these areas does not cause significant changes, while the high recurrence results in decreases of MODIS–NDVI, ranging from −0.002 to −0.008 per year; (b) Forest areas showed a high decrease in NDVI, reaching up to −0.009 MODIS–NDVI per year, but not related to fire recurrence, indicating the high degradation of these phytophysiognomies; (c) Changes in vegetation are highly connected to the protection status of the area, such as areas of integral protection or sustainable use, and consequently their conservation status. Areas with greater vegetation conservation had more than 70% of positive changes in pixels with significant tendencies. Absence or presence of fire are the main agents of vegetation change in areas with lower anthropic influence. These results reinforce the need for a suitable fire management policy for the different types of Cerrado phytophysiognomies, in addition to highlighting the efficiency of remote sensing time series for evaluation of vegetation phenology.

The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it

2014 ◽  
Vol 700 ◽  
pp. 394-399 ◽  
Author(s):  
Xin Ping Ma ◽  
Hong Ying Bai ◽  
Ying Na He ◽  
Shu Heng Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Phenology ◽  
Qinling Mountains ◽  
Threshold Method ◽  
Climate Change Research ◽  
Phenological Data ◽  
Growing Period ◽  
Macro Scale ◽  
Ndvi Time Series

The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .

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.

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