Next Article in Journal
A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery
Previous Article in Journal
Multi-Image and Multi-Sensor Change Detection for Long-Term Monitoring of Arid Environments With Landsat Series
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(10), 14039-14054; doi:10.3390/rs71014039

Responses of Natural Vegetation to Different Stages of Extreme Drought during 2009–2010 in Southwestern China

1
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China
2
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
3
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
4
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Alfredo R. Huete, Clement Atzberger and Prasad S. Thenkabail
Received: 30 July 2015 / Revised: 12 October 2015 / Accepted: 13 October 2015 / Published: 26 October 2015
View Full-Text   |   Download PDF [1321 KB, uploaded 26 October 2015]   |  

Abstract

An extreme drought event is usually a long-term process with different stages. Although it is well known that extreme droughts that have occurred frequently in recent years can substantially affect vegetation growth, few studies have revealed the characteristics of vegetation responses for different stages of an extreme drought event. Especially, studies should address when the vegetation growth was disturbed and how it recovered through an extreme drought event. In this study, we used the Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) to evaluate the response of vegetation to different stages of a severe drought event during 2009–2010 throughout Southwestern China. The PDSI time series indicated that the drought can be divided into three stages, including an initial stage represented by moderate drought (S1), a middle stage represented by continual severe drought (S2), and a final recovery stage (S3). The results revealed that the drought during the initial stage inhibited the growth of grassland and woody savanna, however, forest growth did not decrease during the first stage of droughts, and there was even a trend towards higher NDVI values. The continual severe drought in the middle stage inhibited growth for all vegetation types, and the woody savanna was affected most severely. In the final stage, all vegetation types underwent recovery, including the grassland that had endured the most severe drought. This study provides observational evidence and reveals that the responses of forest to the extreme drought are different from grassland and woody savanna in the different drought stages. View Full-Text
Keywords: extreme drought; response of vegetation; southwestern China; remote sensing extreme drought; response of vegetation; southwestern China; remote sensing
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhao, X.; Wei, H.; Liang, S.; Zhou, T.; He, B.; Tang, B.; Wu, D. Responses of Natural Vegetation to Different Stages of Extreme Drought during 2009–2010 in Southwestern China. Remote Sens. 2015, 7, 14039-14054.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top