Impacts of climate change and anthropogenic activities on vegetation change : Evidence from typical areas in China
(2021) In Ecological Indicators 126.- Abstract
Understanding the interactions of climate-vegetation and human-vegetation has been a critical issue and increasingly attracting attention from scientific community in the field of global change research. This study investigated the heterogeneous impacts of climate change and anthropogenic activities on vegetation change by applying the trend analysis and Geodetector approach. The spatial and temporal patterns of MODIS NDVI and LAI during 2003–2017 were firstly examined in China. We then quantified the contribution and interactions effects of climatic factors (temperature and precipitation) and anthropogenic factors (population, gross domestic product and other four categories of forestry investment) on vegetation change in five typical... (More)
Understanding the interactions of climate-vegetation and human-vegetation has been a critical issue and increasingly attracting attention from scientific community in the field of global change research. This study investigated the heterogeneous impacts of climate change and anthropogenic activities on vegetation change by applying the trend analysis and Geodetector approach. The spatial and temporal patterns of MODIS NDVI and LAI during 2003–2017 were firstly examined in China. We then quantified the contribution and interactions effects of climatic factors (temperature and precipitation) and anthropogenic factors (population, gross domestic product and other four categories of forestry investment) on vegetation change in five typical areas of China. Both NDVI and LAI across China demonstrated a significant increasing trend over the past two decades. However, the eastern developmental regions such as Beijing-Tianjin-Hebei Region and Yangtze River Delta exhibited a decreasing trend due to fast urbanization. Socio-economical inputs (the explanatory power of forestry investment > 40%, the range of explanatory power is 0 to 100%) were identified as the dominant driving forces of vegetation change for the most of study areas. Precipitation was the most important natural influencing factor of vegetation change. We also found that the interactions between forestry investment and other factors presented much greater explanatory power on vegetation change than a single factor. Our research highlights that the afforestation program in China during the past several decades plays an important role in contributing to vegetation greening across the country. Greening and degradation, however, are largely related to landscape context, which could be due to natural change and anthropogenic impact. To maintain high levels of forests, conserving the vegetation is more important than increasing the economic development.
(Less)
- author
- Zheng, Kaiyuan ; Tan, Linshan ; Sun, Yanwei ; Wu, Yanjuan ; Duan, Zheng LU ; Xu, Yu and Gao, Chao
- organization
- publishing date
- 2021-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Anthropogenic activities, China, Climate change, Geodetector, Greening, Vegetation change
- in
- Ecological Indicators
- volume
- 126
- article number
- 107648
- publisher
- Elsevier
- external identifiers
-
- scopus:85103689200
- ISSN
- 1470-160X
- DOI
- 10.1016/j.ecolind.2021.107648
- language
- English
- LU publication?
- yes
- id
- 4e245d0e-aa84-4d99-82b4-4afe5e16a2df
- date added to LUP
- 2021-12-20 11:37:03
- date last changed
- 2022-04-27 06:44:18
@article{4e245d0e-aa84-4d99-82b4-4afe5e16a2df,
abstract = {{<p>Understanding the interactions of climate-vegetation and human-vegetation has been a critical issue and increasingly attracting attention from scientific community in the field of global change research. This study investigated the heterogeneous impacts of climate change and anthropogenic activities on vegetation change by applying the trend analysis and Geodetector approach. The spatial and temporal patterns of MODIS NDVI and LAI during 2003–2017 were firstly examined in China. We then quantified the contribution and interactions effects of climatic factors (temperature and precipitation) and anthropogenic factors (population, gross domestic product and other four categories of forestry investment) on vegetation change in five typical areas of China. Both NDVI and LAI across China demonstrated a significant increasing trend over the past two decades. However, the eastern developmental regions such as Beijing-Tianjin-Hebei Region and Yangtze River Delta exhibited a decreasing trend due to fast urbanization. Socio-economical inputs (the explanatory power of forestry investment > 40%, the range of explanatory power is 0 to 100%) were identified as the dominant driving forces of vegetation change for the most of study areas. Precipitation was the most important natural influencing factor of vegetation change. We also found that the interactions between forestry investment and other factors presented much greater explanatory power on vegetation change than a single factor. Our research highlights that the afforestation program in China during the past several decades plays an important role in contributing to vegetation greening across the country. Greening and degradation, however, are largely related to landscape context, which could be due to natural change and anthropogenic impact. To maintain high levels of forests, conserving the vegetation is more important than increasing the economic development.</p>}},
author = {{Zheng, Kaiyuan and Tan, Linshan and Sun, Yanwei and Wu, Yanjuan and Duan, Zheng and Xu, Yu and Gao, Chao}},
issn = {{1470-160X}},
keywords = {{Anthropogenic activities; China; Climate change; Geodetector; Greening; Vegetation change}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Ecological Indicators}},
title = {{Impacts of climate change and anthropogenic activities on vegetation change : Evidence from typical areas in China}},
url = {{http://dx.doi.org/10.1016/j.ecolind.2021.107648}},
doi = {{10.1016/j.ecolind.2021.107648}},
volume = {{126}},
year = {{2021}},
}