Contributions of Climatic Factors to Interannual Variability of the Vegetation Index in Northern China Grasslands

Zhao, Wei; Hu, Zhongmin; Guo, Qun; Wu, Guojiang; Chen, Ruru; Li, Shenggong

doi:10.1175/jcli-d-18-0587.1

Cited by 49 publications

(31 citation statements)

References 45 publications

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“…Zhou et al [44] similarly showed an absence of a lag effect induced by agriculture on vegetation drought on a monthly scale in most parts of China; they found a significant lag in forests, whereas there tended to be no lag or a lag of less than a month in grassland and agriculture. Zhao et al [45] highlighted the importance of the role of atmospheric aridity to vegetation activities in grasslands, and the drought scale of six months used in the present study also increased uncertainty in the results.…”

Section: Effects Of Drought On Ndvimentioning

confidence: 83%

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

Wang

Cao

et al. 2021

Land

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Temporal and spatial changes in vegetation and their influencing factors are of great significance for the assessment of climate change and sustainable development of ecosystems. This study applied the Asymmetric Gaussians (AG) fitting method, Mann-Kendall test, and correlation analysis to the Global Inventory Monitoring and Modeling System (GIMMS) third-generation Normalized Difference Vegetation Index and gridded climate and drought data for 1982–2015. The temporal and spatial changes to NDVI for natural grassland and forest during the growing season were analyzed. Relationships among NDVI, climate change, and droughts were also analyzed to reveal the influence of vegetation change. The results showed that: (1) Land use/cover change (LUCC) in China was mainly represented by increases in agricultural land (Agrl) and urban and rural land (Uril), and decreases in unutilized land (Bald), grassland, forest, and permanent glacier and snow (Snga). The increase in agricultural land was mainly distributed in the western northwest arid area (WNW) and northern North China (NNC), whereas regions with severe human activities such as southern South China (SNC), western South China (WSC), and eastern South China (ESC) showed significant decreases in agricultural land due to conversion to urban and rural land. (2) The start of the growing season (SOS) was advanced in WNW, SNC, WSC, and ESC, and the end of growing season (EOS) was delayed in WNW, NNC, and SNC. The growing season length (GSL) of natural vegetation in China has been extended by eight days over the last 34 years. However, the phenology of the Qinghai-Tibet Plateau (TP) was opposite to that of the other regions and the GSL showed an insignificant decreasing trend. (3) The NDVI increased significantly, particularly in the SNC, WSC, ESC, and the grassland of the WNW. Precipitation was found to mainly control the growth of vegetation in the arid and semi-arid regions of northwest China (WNW and ENW), and precipitation had a much greater impact on grassland than on forests. Temperature had an impact on the growth of vegetation throughout China, particularly in SNC, ESC, and WSC. (4) The Standardized Precipitation Evapotranspiration Index (SPEI) showed a downward trend, indicating an aridification trend in China, particularly in ENW, NNC, and WNW. Similar to precipitation, the main areas affected by drought were WNW and ENW and grassland was found to be more sensitive to drought than forest. The results of this study are of great significance for predicting the response of ecosystem productivity to climate change under future climate change scenarios.

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Section: Effects Of Drought On Ndvimentioning

confidence: 83%

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

Wang

Cao

et al. 2021

Land

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“…The geographical detector model (GDM) proposed by Wang [24] can estimate the linear, nonlinear, and interactive influence of explanatory variables on the target variable based on the coherence of their spatial distribution pattern. The GDM has been widely used in soil science [25][26][27][28], ecology [29][30][31][32], meteorology [33][34][35], public health [36][37][38][39][40] and other fields. In this study, the GDM was used to identify the primary factors influencing SOM in the black soil zone of northeast China.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

Gao

et al. 2021

IJGI

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Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

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“…The larger the value is, the stronger the explanatory power of factor X on the spatial heterogeneity of Y is, and vice versa. 49,50 Results and analysis…”

Section: Methodsmentioning

confidence: 99%

Long-term changes of forest biomass and its driving factors in karst area, Guizhou, China

Qian

Qiang

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

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The spatiotemporal dynamic changes of forest biomass can provide scientific reference and scheme for improving the quality of forest resources and the ecological environment in karst areas. In this article, the China’s National Forest Continuous Inventory data (from 1984 to 2015) was used to analyze the dynamic changes of forest biomass with the univariate linear slope k, barycenter trajectory, improved hot spots detection which was applied in the analysis of forest biomass dynamic change, and geospatial detector method in Guizhou in the first time. The results showed that the total forest biomass had a steady upward trend, 29.3% unit biomass of the forest had significantly increased, while 1.4% decreased dramatically. The forest biomass gravity center shifted from Qiandongnan to Qiannan, with a total distance of 54.1 km. Thus, the following conclusions were drawn: (1) benefiting from the effective implementation of forestry-related policies, the forest biomass had significant increased in a long time series, especially for the artificial shelter forest; (2) the gravity center shifted to the northwest and the number of level 1 forest biomass hot spots increased year by year, which showed a generalized symmetric pattern along the Wujiang River mainstream; and (3) the results of geographical survey showed that the change of forest biomass was greatly affected by topography, climate and human activities, among which terrain factors had the greatest impact.

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Contributions of Climatic Factors to Interannual Variability of the Vegetation Index in Northern China Grasslands

Cited by 49 publications

References 45 publications

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

Long-term changes of forest biomass and its driving factors in karst area, Guizhou, China

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