Literature DB >> 29100687

Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau.

Xiang Zhou1, Yasushi Yamaguchi2, Sanjiwana Arjasakusuma2.   

Abstract

Distinguishing the vegetation dynamics induced by anthropogenic factors and identifying the major drivers can provide crucial information for designing actionable and practical countermeasures to restore degraded grassland ecosystems. Based on the residual trend (RESTREND) method, this study distinguished the vegetation dynamics induced by anthropogenic factors from the effects of climate variability on the Mongolian Plateau during 1993-2012 using vegetation optical depth (VOD) and normalized difference vegetation index (NDVI), which measure vegetation water content in aboveground biomass and chlorophyll abundance in canopy cover respectively; afterwards, the major drivers within different agricultural zones and socio-institutional periods were identified by integrating agricultural statistics with statistical analysis techniques. The results showed that grasslands in Mongolia and the grazing zone of Inner Mongolia Autonomous Region (IMAR), China underwent a significant human-induced decrease in aboveground biomass during 1993-2012 and 1993-2000 respectively, which was attributable to the rapid growth of livestock densities stimulated by livestock privatization and market factors; by contrast, grasslands in these two regions did not experience a concurrent human-induced reduction in canopy greenness. Besides, the results indicated that grasslands in the grazing zone of IMAR underwent a significant human-induced increase in aboveground biomass since 2000, which was attributable to the reduced grazing pressure induced by China's ecological restoration programs; concurrently, grasslands in this region also experienced a remarkable increase in canopy greenness, however, this increase was found not directly caused by the decreased stocking densities. Furthermore, the results revealed that the farming and semi-grazing/farming zone of IMAR underwent a significant human-induced increase in both aboveground biomass and canopy greenness since 2000, which was attributable to the intensified grain production stimulated by market factors, open grazing regulation and confined feeding popularization. These findings suggest that China's grassland restoration practice has important implications for Mongolia to reverse the severe and continuous grassland degradation in the future.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Ecological restoration programs; Grain yields; Livestock populations; RESTREND; The Mongolian Plateau; Vegetation dynamics

Mesh:

Year:  2017        PMID: 29100687     DOI: 10.1016/j.scitotenv.2017.10.253

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


  4 in total

1.  Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform.

Authors:  Jiaqiang Du; Zhanjun Quan; Shifeng Fang; Chengcheng Liu; Jinhua Wu; Qing Fu
Journal:  Environ Sci Pollut Res Int       Date:  2019-12-09       Impact factor: 4.223

2.  Vegetation change in response to climate factors and human activities on the Mongolian Plateau.

Authors:  Meng Meng; Ni Huang; Mingquan Wu; Jie Pei; Jian Wang; Zheng Niu
Journal:  PeerJ       Date:  2019-09-30       Impact factor: 2.984

3.  Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation.

Authors:  Hao Chen; Xiangnan Liu; Chao Ding; Fang Huang
Journal:  Sensors (Basel)       Date:  2018-10-29       Impact factor: 3.576

4.  Vegetation Dynamic Assessment by NDVI and Field Observations for Sustainability of China's Wulagai River Basin.

Authors:  Panpan Chen; Huamin Liu; Zongming Wang; Dehua Mao; Cunzhu Liang; Lu Wen; Zhiyong Li; Jinghui Zhang; Dongwei Liu; Yi Zhuo; Lixin Wang
Journal:  Int J Environ Res Public Health       Date:  2021-03-04       Impact factor: 3.390

  4 in total

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