Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China
Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China
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DOI：
10.3390/rs6064998
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发表时间：
2014-06-01
期刊：
影响因子：
5
通讯作者：
Xu, Jianchu
中科院分区：
文献类型：
--
作者：
Abbas, Sawaid;Nichol, Janet E.;Xu, Jianchu
这项研究评估了遥感数据在提取关键干旱指标方面的适用性，这些指标包括不同土地覆盖类型内的水分不足程度、干旱持续时间和面积干旱程度。设计了归一化植被供水指数(NVSWI)，结合遥感气候数据，反演不同植被覆盖类型的关键干旱指标，并基于前期降雨量建立滞后时间关系。结果表明，在2010年春季主要干旱事件期间，常绿林(EF)经历严重干旱的天数少于农田(CL)和灌丛(SL)。最后一次降雨以来不同滞后时间段的植被对干旱的响应测试表明，CL和SL与第4滞后期(即天数)的相关性最高，而EF与第5滞后期(即80天)的相关性最大。包括乔木作物在内的常绿森林似乎是一个绿色的蓄水池，由于其具有较深的根部来获取地下水的保水能力，因此比CL和SL更耐干旱。利用基于遥感的综合干旱指数确定不同土地覆盖类型之间的降雨滞后关系的差异，能够更准确地预测干旱，从而有助于制定更具体的干旱适应战略。
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.