Global exacerbation of episodic local vegetation greenness decline since the 21st century.

Anomalous response Ecosystem response Global large-scale Increase trend NDVI anomaly Vegetation decline

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
20 Sep 2022
Historique:
received: 15 02 2022
revised: 29 05 2022
accepted: 30 05 2022
pubmed: 7 6 2022
medline: 29 6 2022
entrez: 6 6 2022
Statut: ppublish

Résumé

Extreme climate-induced vegetation greenness decline significantly affects the stability of ecosystem function. Extreme climate events have occurred frequently in the recent 20 years and the possibility of climate anomalies is forecasted to increase in the future. But currently, the spatial and temporal response of episodic local vegetation decline to climate extremes at a global scale are still unclear. In this study, the detrend NDVI data was utilized as the indicator of vegetation growth, and a spatiotemporally contiguous recognition method was proposed to identify episodic large-scale vegetation decline events globally, subsequently, the spatiotemporal characteristics of these vegetation decline events and their interannual variation trends during 2000-2019 were explored. The results showed that (1) the spatiotemporally contiguous recognition method proposed by this paper was proven to be accurate in identifying the hotspot regions of large-scale vegetation decline. A total of 243 large-scale vegetation decline events were recognized globally during 2000-2019 drived by the method. (2) The global hotspots of large-scale vegetation decline were mainly distributed in the low-elevation areas at middle and low latitudes, especially at 15°S ~ 35°S, 15°N and 35°N, where covered north-western Africa, the Sahel, the Middle East, Central Asia, western India, the border of north-eastern China and Mongolia, western and south-central United States, northern Mexico, southern Africa, Australia, and southern and north-eastern South America. (3) Recent global episodic local vegetation decline has increased significantly since 2000, at the rate of 180,000 km

Identifiants

pubmed: 35660428
pii: S0048-9697(22)03508-2
doi: 10.1016/j.scitotenv.2022.156411
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

156411

Informations de copyright

Copyright © 2022. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest All authors declare that No conflict of interest exists.

Auteurs

Ruohua Du (R)

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China.

Jianjun Wu (J)

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China. Electronic address: jjwu@bnu.edu.cn.

Jianhua Yang (J)

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China.

Feng Tian (F)

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China.

Meng Chen (M)

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China.

Ting Mao (T)

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China.

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