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Remote Sens. 2014, 6(6), 4998-5018; doi:10.3390/rs6064998

Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China

1
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
2
International Center for Integrated Mountain Development (ICIMOD), Kathmandu 44700, Nepal
3
World Agroforestry Center, East and Central Asia, Kunming 650201, China
*
Author to whom correspondence should be addressed.
Received: 18 February 2014 / Revised: 19 May 2014 / Accepted: 19 May 2014 / Published: 30 May 2014
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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Abstract

This study assesses the applicability of remote sensing data for retrieval of key drought indicators including the degree of moisture deficiency, drought duration and areal extent of drought within different land cover types across the landscape. A Normalized Vegetation Supply Water Index (NVSWI) is devised, combining remotely sensed climate data to retrieve key drought indicators over different vegetation cover types and a lag-time relationship is established based on preceding rainfall. The results indicate that during the major drought event of spring 2010, Evergreen Forest (EF) experienced severe dry conditions for 48 days fewer than Cropland (CL) and Shrubland (SL). Testing of vegetation response to drought conditions with different lag-time periods since the last rainfall indicated a highest correlation for CL and SL with the 4th lag period (i.e., 64 days) whereas EF exhibited maximum correlation with the 5th lag period (i.e., 80 days). Evergreen Forest, which includes tree crops, appears to act as a green reservoir of water, and is more resistant than CL and SL to drought due to its water retention capacity with deeper roots to tap sub-surface water. Identifying differences in rainfall lag-time relationships among land cover types using a remote sensing-based integrated drought index enables more accurate drought prediction, and can thus assist in the development of more specific drought adaptation strategies. View Full-Text
Keywords: NDVI; NVSWI; drought index; LST; TRMM NDVI; NVSWI; drought index; LST; TRMM
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Abbas, S.; Nichol, J.E.; Qamer, F.M.; Xu, J. Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China. Remote Sens. 2014, 6, 4998-5018.

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