scholarly journals Spatial and temporal variations in fractional vegetation cover and its driving factors in the Hulun Lake region

2022 ◽  
Vol 135 ◽  
pp. 108490
Author(s):  
Pingping Mao ◽  
Jing Zhang ◽  
Ming Li ◽  
Yiliang Liu ◽  
Xu Wang ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Temporal Variations ◽  
Driving Factors ◽  
Spatial And Temporal Variations ◽  
Fractional Vegetation Cover ◽  
Lake Region ◽  
Hulun Lake

scholarly journals A Semi-Parametric Geographically Weighted Regression Approach to Exploring Driving Factors of Fractional Vegetation Cover: A Case Study of Guangdong

2020 ◽  
Vol 12 (18) ◽  
pp. 7512
Author(s):  
Yuhao Jin ◽  
Han Zhang ◽  
Yuchao Yan ◽  
Peitong Cong
Keyword(s):  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Southern China ◽  
Ordinary Least Squares ◽  
Driving Factors ◽  
Weighted Regression ◽  
Impact Factors ◽  
Negative Effects ◽  
Vegetation Growth ◽  
Fractional Vegetation Cover

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

Spatial and temporal variations in vegetation cover in an eroded region of substropical red soil and its relationship with the impact of human activity

2016 ◽  
Vol 36 (21) ◽  
Author(s):  
徐凯健 XU Kaijian ◽  
曾宏达 ZENG Hongda ◽  
任婕 REN Jie ◽  
谢锦升 XIE Jinsheng ◽  
杨玉盛 YANG Yusheng
Keyword(s):  
Human Activity ◽  
Vegetation Cover ◽  
Temporal Variations ◽  
Red Soil ◽  
Spatial And Temporal Variations ◽  
The Impact

scholarly journals Spatio-temporal Analysis of Sediment Yield Estimation Using Integrated RUSLE and GIS Technique: Case of Nashe Dam Reservoir, Abay Basin, Ethiopia

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Land Cover ◽  
Sediment Yield ◽  
Temporal Variations ◽  
Driving Factors ◽  
Spatial And Temporal Variations ◽  
Dam Reservoir ◽  
Rusle Model ◽  
Gis Technique ◽  
Dam Reservoirs ◽  
Annual Sediment

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

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