Assessing spatio‐temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia

2006 ◽  
Vol 27 (16) ◽  
pp. 3455-3471 ◽  
Author(s):  
H. Wagenseil ◽  
C. Samimi
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Temporal Variations ◽  
Plant Phenology ◽  
Ndvi Time Series ◽  
Spatio Temporal

scholarly journals Spatio-temporal variations of HNO<sub>3</sub> total column from 9 years of IASI measurements – A driver study

2017 ◽  
Author(s):  
Gaétane Ronsmans ◽  
Catherine Wespes ◽  
Daniel Hurtmans ◽  
Cathy Clerbaux ◽  
Pierre-François Coheur
Keyword(s):  
Time Series ◽  
Polar Vortex ◽  
Temporal Variations ◽  
P Value ◽  
Spatial And Temporal Variability ◽  
Quasi Biennial Oscillation ◽  
Explanatory Variables ◽  
Multivariate Enso Index ◽  
Spatio Temporal ◽  
Stratospheric Clouds

Abstract. This study aims at understanding the spatial and temporal variability of HNO3 total columns in terms of explanatory variables. To achieve this, multiple linear regressions are used to fit satellite-derived time series of HNO3 daily averaged total columns. First, an analysis of the IASI 9-year time series (2008–2016) is conducted based on various equivalent latitude bands. The strong and systematic denitrification of the southern polar stratosphere is observed very clearly. It is also possible to distinguish, within the polar vortex, three regions wich are differently affected by the denitrification. Three exceptional denitrification episodes in 2011, 2014 and 2016 are also observed in the northern hemisphere, due to unusually low arctic temperatures. The time series are then fitted by multivariate regressions to identify what variables are responsible for HNO3 variability in global distributions and time series, and to quantify their respective influence. Out of an ensemble of proxies (annual cycle, solar flux, quasi-biennial oscillation, multivariate ENSO index, Arctic and Antarctic oscillations and volume of polar stratospheric clouds), only the ones defined as significant (p-value 

Spatio-temporal detection of converting from cropland to forest/grassland on the Loess Plateau using all available Landsat time-series images

2021 ◽  
Author(s):  
Zhihui Wang ◽  
Peiqing Xiao
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Loess Plateau ◽  
Water Conservation ◽  
Series Data ◽  
Vegetation Types ◽  
Continuous Change ◽  
The Loess Plateau ◽  
Ndvi Time Series ◽  
Spatio Temporal

&lt;p&gt;&lt;strong&gt;Conversion of cropland to forest/grassland has become a key ecological restoration measure on the Loess Plateau since 1999. Accurate mapping of the spatio-temporal dynamic information of conversion from cropland into forest/grassland is necessary for studying the effects of vegetation change on hydro-ecological process and soil and water conservation on the Loess Plateau, China. Currently, the accuracy of change detection of farmland and forest/grassland at 30-m scale in this area is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, an innovative method for continuous change detection of cropland and forest/grassland using all available Landsat time-series data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% &amp;#177; 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation.&lt;/strong&gt;&lt;/p&gt;

scholarly journals A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

2017 ◽  
Vol 9 (11) ◽  
pp. 1125 ◽  
Author(s):  
Chunhua Liao ◽  
Jinfei Wang ◽  
Ian Pritchard ◽  
Jiangui Liu ◽  
Jiali Shang
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Temporal Data ◽  
Fusion Model ◽  
Ndvi Time Series ◽  
Spatio Temporal

scholarly journals A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction

2020 ◽  
Vol 9 (11) ◽  
pp. 665
Author(s):  
Yangnan Guo ◽  
Cangjiao Wang ◽  
Shaogang Lei ◽  
Junzhe Yang ◽  
Yibo Zhao
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Linear Relationship ◽  
Vegetation Index ◽  
Input Image ◽  
Fusion Algorithm ◽  
Algorithm Selection ◽  
Temporal Variance ◽  
Ndvi Time Series ◽  
Spatio Temporal

Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.

scholarly journals Magnetic field variations associated with umbral flashes and penumbral waves

2018 ◽  
Vol 619 ◽  
pp. A63 ◽  
Author(s):  
Jayant Joshi ◽  
Jaime de la Cruz Rodríguez
Keyword(s):  
Magnetic Field ◽  
Time Series ◽  
Field Strength ◽  
Magnetic Field Strength ◽  
Temporal Variations ◽  
Physical Parameters ◽  
Thermodynamical Equilibrium ◽  
Spatio Temporal ◽  
Two Phases ◽  
The Magnetic Field

Context. Umbral flashes (UF) and running penumbral waves (RPWs) in sunspot chromospheres leave a dramatic imprint in the intensity profile of the Ca II 8542 Å line. Recent studies have focussed on also explaining the observed polarization profiles, which show even more dramatic variations during the passage of these shock fronts. While most of these variations can be explained with an almost constant magnetic field as a function of time, several studies have reported changes in the inferred magnetic field strength during UF phases. These changes could be explained by opacity effects or by intrinsic changes in the magnetic field strength. Aims. In this study we investigate the origin of these periodic variations of the magnetic field strength by analyzing a time-series of high-temporal-cadence observations acquired in the Ca II 8542 Å line with the CRISP instrument at the Swedish 1-m Solar Telescope. In particular, we analyze how the inferred geometrical height scale changes between quiescent and UF phases, and whether those changes are enough to explain the observed changes in the magnetic field, B. Methods. We have performed non local thermodynamical equilibrium (non-LTE) data inversions with the NICOLE code of a time-series of very high spatio-temporal-resolution observations in the Ca II 8542 Å, Fe I 6301.5, and Fe I 6302.5 Å lines. We analyze in detail the variations of the different physical parameters of the model as a function of time. Results. Our results indicate that the Ca II 8542 Å line in sunspots is greatly sensitive to magnetic fields at log τ500 = −5 (hereafter log τ = −5) during UFs and quiescence. However this optical depth value does not correspond to the same geometrical height during the two phases. Our results indicate that during UFs and RPWs the log τ = −5 is located at a higher geometrical height than during quiescence. Additionally, the inferred magnetic field values are higher in UFs (up to ∼270 G) and in RPWs (∼100 G). Conclusions. Our results suggest that opacity changes caused by UFs and RPWs cannot explain the observed temporal variations in the magnetic field, as the line seems to form at higher geometrical heights where the field is expected to be lower.

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