Analysis of spatial variability in factors contributing to vegetation restoration in Yan'an, China

Zhang, Daojun; Jia, Qiqi; Wang, Peng; Zhang, Jin‐Ting; Hou, Xianhui; Li, Xiandong; Li, Wangjun

doi:10.1016/j.ecolind.2020.106278

Cited by 24 publications

(12 citation statements)

References 45 publications

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“…Grassland ecological restoration and governance are related to the construction of ecological civilization, national unity, border stability, as well as sustainable and healthy social-economic development of pastoral areas ( The State Council, 2011 , Shi et al, 2018 ). Recently, climate change such as global warming and altered precipitation patterns exert great influence on the grassland growth ( Chen et al, 2019 , Liu et al, 2019 , Zhang et al, 2020 ), and some negative human disturbances, such as overgrazing, agricultural expansion, forest exploitation and land cover change, are also likely to cause grassland degradation ( Gang et al, 2018 , Zhao et al, 2020 ). Though the implementation of ecological restoration projects such as conversion of cropland to forest and grassland, management measures of forbid pasturing, fallow pasturing, forage and animal balance, and grassland eco-protection subsidy and incentive policy, the overall grassland ecosystem is still fragile, and grassland ecological security is still the weak link of national ecological security ( Han, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing restoration benefit of grassland ecosystem incorporating preference heterogeneity empirical data from Inner Mongolia Autonomous Region

Cai

Zhao

Shi

et al. 2020

Ecological Indicators

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Section: Introductionmentioning

confidence: 99%

Assessing restoration benefit of grassland ecosystem incorporating preference heterogeneity empirical data from Inner Mongolia Autonomous Region

Cai

Zhao

Shi

et al. 2020

Ecological Indicators

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“…The GWR model is constructed with the same variables as the OLS model in GWR4.0 software [65,66], and the optimal bandwidth of 102 is determined according to the minimum AIC value to obtain the coefficient distribution of each variable. To facilitate the software analysis, we divided the study area into a 10 km × 10 km grid using the fishnet tool, obtaining a total of about 5000 sampling points, and extracted NDVI trend slope (SNDVI), elevation, slope and aspect values for each sample point [66,67].…”

Section: Geographically Weighted Regression Modelmentioning

confidence: 99%

Multiple Effects of Topographic Factors on Spatio-temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Tibet

Wang¹,

Wang²,

Naudiyal³

et al. 2021

Preprint

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Topographic factors are recognized as one of the key factors influencing vegetation distribution patterns, and studying the interactions between them can contribute to enhancing our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000-2019), combined with Digital Elevation Model (DEM), and vegetation type data for trend analysis, and explored NDVI variation and its relationship with topographic factors through an integrated geographically-weighted model in the Three Parallel Rivers Region (TPRR) of southeastern Tibetan plateau in the past 20 years. Our results indicated that there was no significant increase of NDVI in the entire basin between 2000-2019, except for the Lancang River basin. In the year 2004, abrupt changes in NDVI were observed across the whole basin and each sub-basin. During 2000-2019, the mean NDVI value of the whole basin increased initially and then decreased with the increasing elevation. However, it changed marginally with changes in slope and aspect. We observed a distinct spatial heterogeneity in vegetation patterns with elevation, with vegetation in the southern regions showing higher NDVI than the north as a whole. Most of the vegetation cover was concentrated in the slope range of 8~35°, with no significant difference in distribution except flat-land. Furthermore, from 2000 to 2019, the vegetation cover in the TPRR showed an improving trend with the changes of various topographic factors, with the largest improvement area (36.10%) in the slightly improved category. The improved region was mainly distributed in the source area of the Jinsha River basin and the southern part of the whole basin. Geographically weighted regression (GWR) analysis showed that elevation was negatively correlated with NDVI trends in most areas, especially in the middle reaches of Nujiang River basin and Jinsha River basin, where the influence of slope and aspect on NDVI change was considerably much smaller than elevation.

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“…According to the research needs, LANDSAT 5 TM images in 2000 and LANDSAT 8 OLI_TIRS images in 2015 were selected as the base maps for interpretation. Then, the land use maps for 2000 and 2015 were obtained through image interpretation corresponding to the year, and the spatial database update from the land use map in 2010, which was obtained by Zhang et al (2020). The source of remote sensing images data was the same as that of the topography data, and the original spatial resolution was also 30 m. The GFG implementation intensity was defined as the area ratio between the cultivated land converted to forest or grassland, and the original cultivated land, which should be a dimensionless value ranging from 0 to 1 (Zhang, Jia, et al, 2019).…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

Quantifying the efficiency of soil conservation and optimized strategies: A case‐study in a hotspot of afforestation in the Loess Plateau

Ning

Zhang

2020

Land Degrad Dev

Self Cite

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Although it is generally believed the Grain for Green programme (GFG) implemented in China has attenuated soil erosion, the extent to which it is effective still needs verification. Taking Yan'an in the Loess Plateau as the study area, we analysed both total effect and efficiency differences during GFG implementation. Results showed that, while soil erosion on average decreased from 4,884.49 to 4,087.57 t km−2 yr−1, counties with higher GFG implementation intensity achieved a lower soil conservation effect. For example, Wuqi ranks third in the GFG implementation intensity among all counties in Yan'an, but its actual soil erosion reduction is the lowest, only 54.1% of Yan'an's average level. To analyse the reason for the efficiency difference, the concept of soil conservation potential was proposed. It is concluded that the soil conservation effect is controlled by the soil conservation potential. Ideally, regions with high soil conservation potential should get priority in the GFG application, yet there is a significant spatial mismatch between the GFG implementation intensity and the soil conservation potential because the correlation coefficient is only −0.05, which weakened the soil control effect. A dynamic implementation mechanism was put forward for the formulation and optimization of ecological programmes and projects in future: first, using the soil conservation potential to determine the implementation intensity in each region; second, adjusting the intensity to the changes of the soil conservation potential in the following implementation; third, repeating above steps to ensure high efficiency of soil erosion control, and achieving the sustainability and effectiveness of the ecological projects.

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Analysis of spatial variability in factors contributing to vegetation restoration in Yan'an, China

Cited by 24 publications

References 45 publications

Assessing restoration benefit of grassland ecosystem incorporating preference heterogeneity empirical data from Inner Mongolia Autonomous Region

Assessing restoration benefit of grassland ecosystem incorporating preference heterogeneity empirical data from Inner Mongolia Autonomous Region

Multiple Effects of Topographic Factors on Spatio-temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Tibet

Quantifying the efficiency of soil conservation and optimized strategies: A case‐study in a hotspot of afforestation in the Loess Plateau

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