Literature DB >> 30640086

Impacts of Chinese Grain for Green program and climate change on vegetation in the Loess Plateau during 1982-2015.

Gang Li1, Shaobo Sun2, Jichang Han1, Jianwu Yan3, Wenbin Liu4, Yang Wei1, Nan Lu1, Yingying Sun1.   

Abstract

Remote sensing based vegetation index provides a practical method for the monitoring of vegetation dynamics at regional and global scales. Here, using a long-term remotely sensed normalized difference vegetation index (NDVI) dataset, we quantified the vegetation changes in the Loess Plateau (LP) over the last three decades (1982-2015), which includes the period before the Chinese"Grain for Green Program"(GGP) was launched (1982-1999), and the period after the GGP (1999-2015). The correlations between the NDVI and four climate related variables, i.e., precipitation, temperature, root-soil moisture (RSM), and a drought proxy-standardized evapotranspiration deficit index (SEDI), were also examined. The results indicated that, (i) the GGP strongly changed the vegetation in the LP. The growing-season mean NDVI (GSM-NDVI) and the annual mean NDVI (AM-NDVI) decreased slightly before the GGP launched in 1999, with slopes of -3.38×10-3 and-8.00×10-4year-1, respectively. However, they showed slight and significant (p<0.05) increases after the GGP, with slopes of 4.75×10-3 and 2.32×10-3year-1, respectively. (ii) Climate change (i.e., warming and drying) resulted in adverse effects on vegetation in the LP during the period before the GGP. However, the observed changes (i.e., wetting and reduced drought) exerted positive effects on the vegetation during the period after the GGP. (iii) Inter-annual variations of spatially averaged NDVI over the LP were primarily determined by RSM rather than any other climate related variables. In the southeastern LP, the inter-annual variation of GSM-NDVI was mainly determined by precipitation and SEDI, while the inter-annual variation of AM-NDVI was mainly caused by SEDI and RSM. Inter-annual variations of both GSM-NDVI and AM-NDVI were mainly determined by SEDI and RSM in the northwestern LP, and by temperature in the southwestern LP.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Climate change; Drought; Grain for Green Program (GGP); Loess Plateau; Normalized difference vegetation index (NDVI); Remote sensing

Mesh:

Year:  2019        PMID: 30640086     DOI: 10.1016/j.scitotenv.2019.01.028

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  5 in total

1.  Quantifying the Impact of Grain for Green Program on Ecosystem Service Management: A Case Study of Exibei Region, China.

Authors:  Qianru Yu; Chen-Chieh Feng; NuanYin Xu; Luo Guo; Dan Wang
Journal:  Int J Environ Res Public Health       Date:  2019-06-29       Impact factor: 3.390

2.  Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China.

Authors:  Kongming Li; Mingming Feng; Asim Biswas; Haohai Su; Yalin Niu; Jianjun Cao
Journal:  Sensors (Basel)       Date:  2020-05-12       Impact factor: 3.576

3.  Temporal Stability of Vegetation Cover across the Loess Plateau Based on GIMMS during 1982-2013.

Authors:  Chunyan Zhang; Shan Guo; Yanning Guan; Danlu Cai; Xiaolin Bian
Journal:  Sensors (Basel)       Date:  2021-01-05       Impact factor: 3.576

4.  Evaluation of the Ecological Effects of Ecological Restoration Programs: A Case Study of the Sloping Land Conversion Program on the Loess Plateau, China.

Authors:  Yuanjie Deng; Lei Jia; Yajun Guo; Hua Li; Shunbo Yao; Liqi Chu; Weinan Lu; Mengyang Hou; Binbin Mo; Yameng Wang; Haiyu Yang; Tongyue Zhang
Journal:  Int J Environ Res Public Health       Date:  2022-06-26       Impact factor: 4.614

5.  Tempo-Spatial Variation of Vegetation Coverage and Influencing Factors of Large-Scale Mining Areas in Eastern Inner Mongolia, China.

Authors:  Aman Fang; Jihong Dong; Zhiguo Cao; Feng Zhang; Yongfeng Li
Journal:  Int J Environ Res Public Health       Date:  2019-12-19       Impact factor: 3.390

  5 in total

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