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[Dynamic Variation in Vegetation Cover and Its Influencing Factor Detection in the Yangtze River Basin from 2000 to 2020].
Xu, Yong; Zheng, Zhi-Wei; Guo, Zhen-Dong; Dou, Shi-Qing; Huang, Wen-Ting.
Afiliação
  • Xu Y; College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
  • Zheng ZW; College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
  • Guo ZD; College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
  • Dou SQ; College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
  • Huang WT; College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Huan Jing Ke Xue ; 43(7): 3730-3740, 2022 Jul 08.
Article em Zh | MEDLINE | ID: mdl-35791556
Studies on the dynamic variation in vegetation cover and detecting its influencing factors are highly valuable for monitoring regional ecological environment quality and evaluating forestry restoration project effects. In this study, on the basis of the MODIS normalized difference vegetation index (NDVI), in situ climate data, digital elevation model, population density, nighttime lights using Theil-Sen Median analysis, Mann-Kendall significance test, stability analysis, and geographical detector model, the spatiotemporal variation and stability of vegetation cover in the context of multi-spatiotemporal scales were analyzed, and the dominant influencing factors that affect the spatial differentiation of vegetation cover were further detected. The results showed that the vegetation cover showed a fluctuant increasing trend, and the changing trend exhibited obvious spatial heterogeneity with the increasing rate being higher in the middle and lower in the east and west portion of the Yangtze River basin from 2000 to 2020. At the sub-basin scale, except for that in the Taihu Lake basin, the vegetation cover in all sub-basin units exhibited an increasing trend during the study period. The areas with an increasing trend accounted for 84.09% of the study area, in which the areas with extremely significant increases and significant increases accounted for 53.67%, which were mainly distributed in the Wujiang River basin, Yibin-yichang, Jialing River basin, Han River basin, and Dongting Lake basin. The vegetation cover showed lower stability in the upper reaches of the Jinsha-shigu River basin and Taihu Lake basin and higher stability in other sub-basin units of the study area. Elevation was an important factor affecting the vegetation variation in all sub-basin areas. Climatic factors presented the highest impact on vegetation variation in the upper reaches of the Jinsha-shigu River basin, and human activities exhibited the greatest impact on vegetation variation in the Wujiang River basin, lower reaches of Hukou basin, and Taihu Lake basin. The interaction of the two influencing factors on vegetation variation showed mutual and non-linear enhancement, and the interaction between elevation and wind speed presented the highest value, with an explanatory power of 68%. The ecological exploration results showed that human activities combined with topographic factors and climate factors, except for slope and relative humidity, significantly differed in the explanatory power of vegetation variation in the Yangtze River basin. These results can provide a basis for formulating comprehensive vegetation resource management in the Yangtze River basin that takes into account regional climate, topography, and human activities.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Plantas / Lagos / Rios Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: Zh Revista: Huan Jing Ke Xue Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Plantas / Lagos / Rios Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: Zh Revista: Huan Jing Ke Xue Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China