Time Series Variation in the Driving Factors Leading to Land Desertification in Yanchi County Over the Last 50 Years

2010 ◽  
Vol 26 (2) ◽  
pp. 249-254 ◽  
Author(s):  
Yong-huan MA ◽  
Li-hua ZHOU ◽  
Yan-ling ZHU ◽  
Bin LI
Keyword(s):  
Time Series ◽  
Driving Factors ◽  
Yanchi County ◽  
Land Desertification

The temporal change of driving factors during the course of land desertification in arid region of North China: the case of Minqin County

2006 ◽  
Vol 51 (6) ◽  
pp. 999-1008 ◽  
Author(s):  
Yonghuan Ma ◽  
Shengyue Fan ◽  
Lihua Zhou ◽  
Zhaoyang Dong ◽  
Kecun Zhang ◽  
...  
Keyword(s):  
Arid Region ◽  
North China ◽  
Temporal Change ◽  
Driving Factors ◽  
Land Desertification

Vegetation dynamics over 2000-2012 and its driving factors in Yanchi County, Ningxia Province

2015 ◽  
Vol 35 (22) ◽  
Author(s):  
宋乃平 SONG Naiping ◽  
杜灵通 DU Lingtong ◽  
王磊 WANG Lei
Keyword(s):  
Vegetation Dynamics ◽  
Driving Factors ◽  
Yanchi County

scholarly journals Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests

2020 ◽  
Vol 12 (9) ◽  
pp. 1499 ◽  
Author(s):  
Alba Viana-Soto ◽  
Inmaculada Aguado ◽  
Javier Salas ◽  
Mariano García
Keyword(s):  
Time Series ◽  
Fire Severity ◽  
Driving Factors ◽  
Pine Forests ◽  
Recovery Rates ◽  
Tasseled Cap ◽  
Successional Stages ◽  
Mediterranean Pine ◽  
Recovery Dynamics ◽  
In Fire

Wildfires constitute the most important natural disturbance of Mediterranean forests, driving vegetation dynamics. Although Mediterranean species have developed ecological post-fire recovery strategies, the impacts of climate change and changes in fire regimes may endanger their resilience capacity. This study aims at assessing post-fire recovery dynamics at different stages in two large fires that occurred in Mediterranean pine forests (Spain) using temporal segmentation of the Landsat time series (1994–2018). Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) was used to derive trajectory metrics from Tasseled Cap Wetness (TCW), sensitive to canopy moisture and structure, and Tasseled Cap Angle (TCA), related to vegetation cover gradients. Different groups of post-fire trajectories were identified through K-means clustering of the Recovery Ratios (RR) from fitted trajectories: continuous recovery, continuous recovery with slope changes, continuous recovery stabilized and non-continuous recovery. The influence of pre-fire conditions, fire severity, topographic variables and post-fire climate on recovery rates for each recovery category at successional stages was analyzed through Geographically Weighted Regression (GWR). The modeling results indicated that pine forest recovery rates were highly sensitive to post-fire climate in the mid and long-term and to fire severity in the short-term, but less influenced by topographic conditions (adjusted R-squared ranged from 0.58 to 0.88 and from 0.54 to 0.93 for TCA and TCW, respectively). Recovery estimation was assessed through orthophotos, showing a high accuracy (Dice Coefficient ranged from 0.81 to 0.97 and from 0.74 to 0.96 for TCA and TCW, respectively). This study provides new insights into the post-fire recovery dynamics at successional stages and driving factors. The proposed method could be an approach to model the recovery for the Mediterranean areas and help managers in determining which areas may not be able to recover naturally.

scholarly journals Analysis of Driving Factors of Innovation and Entrepreneurship Based on Time Series Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Feng
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Survey Methods ◽  
Original Data ◽  
Driving Factors ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Series Analysis ◽  
Entrepreneurial Attitude ◽  
Innovation And Entrepreneurship

In order to analyze the driving factors of innovation and entrepreneurship, based on the time series analysis algorithm, this paper combines the analysis requirements of innovation and entrepreneurship driving factors to improve the time series, uses decomposition methods to decompose the complex original data into relatively simple components and reconstruct them, and predicts the reconstructed components to integrate the final predicted value. Moreover, this paper introduces entrepreneurial attitude as an intermediary variable and verifies it through data collection and statistical analysis, so that entrepreneurial traits influence entrepreneurial propensity through entrepreneurial attitude. The test results show that entrepreneurial attitude can better explain the influence of entrepreneurial traits on entrepreneurial propensity. In addition, this paper constructs an analysis model of driving factors for innovation and entrepreneurship, obtains experimental data through questionnaire survey methods, and conducts experimental research in combination with mathematical statistics. From the statistical results, it can be seen that the innovative and entrepreneurial driving factor analysis model based on time series analysis proposed in this paper is effective.

Driving factors of the growth of US wind power generation - A decomposition of historical on-shore wind power generation data

2021 ◽  
Author(s):  
Peter Regner ◽  
Katharina Gruber ◽  
Sebastian Wehrle ◽  
Johannes Schmidt
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Wind Power ◽  
Wind Turbines ◽  
Time Series Data ◽  
Wind Power Generation ◽  
Driving Factors ◽  
Series Data ◽  
Data Set ◽  
Swept Area

<p>US Wind power generation has grown significantly over the last decades, driven by more and larger turbines being installed. However, less is known about how other factors affect the expansion of wind power. In this study, we use historical wind power generation time series, data on installed wind turbines and wind speed time series from the ERA5 data set to quantify driving factors of the growth of US wind power generation. By use of index-decomposition techniques and a regression analysis, we show how different factors affect the output of wind power generation in the US. These include changes in the number of installed turbines, average swept area, park efficiency, location choice, and hub height. Based on this, we discuss potential consequences for the future expansion of wind energy. As expected, the total rotor swept area is responsible for the largest part of the increase in generated power, due to a larger number of installed turbines and larger rotor sizes in particular. Unexpectedly, turbine efficiency slightly declined in the last decades. Wind speeds available to wind turbines have slightly increased. This is a result of larger hub heights, but also of new wind turbines being installed at windier locations.</p>

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