scholarly journals Variation Trend Analysis of Runoff and Sediment Time Series Based on the R/S Analysis of Simulated Loess Tilled Slopes in the Loess Plateau, China

2017 ◽  
Vol 10 (2) ◽  
pp. 32 ◽  
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Loess Plateau ◽  
The Loess Plateau ◽  
Variation Trend

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>

scholarly journals Plant litter estimation and its correlation with sediment concentration in the Loess Plateau

2019 ◽  
Author(s):  
Qian Li ◽  
Ligang Ma ◽  
Suhong Liu ◽  
Adilai Wufu ◽  
Yinbo Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Loess Plateau ◽  
Predictive Models ◽  
Social Economic ◽  
Sediment Concentration ◽  
Plant Litter ◽  
Economic Factors ◽  
The Loess Plateau ◽  
Litter Cover

Background. Sediment concentration in the water of the loess Plateau region has dramatically decreased during the past two decades. Plant litter is considered to be one of the most important factors for this change. Existing remote sensing studies that focus on plant litter mainly use extraction methods based on vegetation indices or changes in the plant litter. Few studies have conducted time series analyses of plant litter or considered the correlation between plant litter and soil erosion. In addition, social factors are not given enough consideration in the remote sensing and soil community. Methods. This study performs time series estimation of plant litter by integrating three-scale remotely sensed data and a random forest (RF) modeling algorithm. Predictive models are used to estimate the spatially explicit plant litter cover for the entire Loess Plateau over the last two decades (2000–2018). Then, the sediment concentration in the water was classified into 9 grades based on environmental and social-economic factors. Results. Our results demonstrate the effectiveness of the proposed predictive models at the regional scale. The areas with increased plant litter cover accounted for 67% of the total area, while the areas with decreased plant litter cover accounted for 33% of the total area. In addition, plant litter is demonstrated to be one of the top three factors contributing to the decrease in the river sediment concentration. Social-economic factors were also important for the decrease of the sediment concentration in the water, for example, the population of the rural area.

scholarly journals Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1775 ◽  
Author(s):  
Zhihui Wang ◽  
Wenyi Yao ◽  
Qiuhong Tang ◽  
Liangyun Liu ◽  
Peiqing Xiao ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Loess Plateau ◽  
Vegetation Coverage ◽  
Landsat Data ◽  
Vegetation Types ◽  
Continuous Change ◽  
The Loess Plateau ◽  
Ndvi Time Series

Accurate identification of the spatiotemporal distribution of forest/grassland and cropland is necessary for studying hydro-ecological effects of vegetation change in the Loess Plateau, China. Currently, the accuracy of change detection of land cover using Landsat data in the loess hill and gully areas is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, we propose a method for continuous change detection for two types of land cover, mosaic forest/grassland and cropland, using all available Landsat 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. Consequently, the accuracy of change detection was improved by reducing temporal false detection and enhancing spatial classification accuracy.

scholarly journals Plant litter estimation and its correlation with sediment concentration in the Loess Plateau

2019 ◽  
Author(s):  
Qian Li ◽  
Ligang Ma ◽  
Suhong Liu ◽  
Adilai Wufu ◽  
Yinbo Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Loess Plateau ◽  
Predictive Models ◽  
Social Economic ◽  
Sediment Concentration ◽  
Plant Litter ◽  
Economic Factors ◽  
The Loess Plateau ◽  
Litter Cover

Background. Sediment concentration in the water of the loess Plateau region has dramatically decreased during the past two decades. Plant litter is considered to be one of the most important factors for this change. Existing remote sensing studies that focus on plant litter mainly use extraction methods based on vegetation indices or changes in the plant litter. Few studies have conducted time series analyses of plant litter or considered the correlation between plant litter and soil erosion. In addition, social factors are not given enough consideration in the remote sensing and soil community. Methods. This study performs time series estimation of plant litter by integrating three-scale remotely sensed data and a random forest (RF) modeling algorithm. Predictive models are used to estimate the spatially explicit plant litter cover for the entire Loess Plateau over the last two decades (2000–2018). Then, the sediment concentration in the water was classified into 9 grades based on environmental and social-economic factors. Results. Our results demonstrate the effectiveness of the proposed predictive models at the regional scale. The areas with increased plant litter cover accounted for 67% of the total area, while the areas with decreased plant litter cover accounted for 33% of the total area. In addition, plant litter is demonstrated to be one of the top three factors contributing to the decrease in the river sediment concentration. Social-economic factors were also important for the decrease of the sediment concentration in the water, for example, the population of the rural area.

scholarly journals Analyses of Rainfall Time Series in the Loess Plateau of China - Periodical Fluctuation and Links with Sea Surface Temperature -

2005 ◽  
Vol 60 (5) ◽  
pp. 617-620 ◽  
Author(s):  
Hiroshi YASUDA ◽  
Kang WANG ◽  
Mohamed Abd Elbasit MOHAMED AHMED ◽  
Hisao ANYOJI ◽  
Xingchang ZHANG
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Loess Plateau ◽  
Sea Surface ◽  
Rainfall Time Series ◽  
The Loess Plateau
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