Mapping Spatio-Temporal Variations of Converting Farmland to Forest/Grassland on the Loess Plateau Using All Available Landsat Time-Series Images

2019 ◽  
Author(s):  
Zhihui Wang ◽  
Peiqing Xiao ◽  
Pan Zhang ◽  
Weiying Sun ◽  
Li Li ◽  
...  
Keyword(s):  
Time Series ◽  
Loess Plateau ◽  
Temporal Variations ◽  
The Loess Plateau ◽  
Spatio Temporal ◽  
Time Series Images

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

<p><strong>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% ± 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.</strong></p>

Spatial and temporal variations of soil moisture in three types of agroforestry boundaries in the Loess Plateau, China

2010 ◽  
Vol 21 (4) ◽  
pp. 415-422 ◽  
Author(s):  
Wen-zhong You ◽  
De-hui Zeng ◽  
Ming-guo Liu ◽  
Li-li Yun ◽  
Yan-hui Ye ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
The Loess Plateau

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 

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