Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events

被引:26
|
作者
Nam, Won-Ho [1 ,2 ,3 ,4 ]
Tadesse, Tsegaye [1 ,2 ]
Wardlow, Brian D. [2 ,5 ]
Hayes, Michael J. [2 ]
Svoboda, Mark D. [1 ,2 ]
Hong, Eun-Mi [6 ]
Pachepsky, Yakov A. [6 ]
Jang, Min-Won [7 ]
机构
[1] Univ Nebraska, Natl Drought Mitigat Ctr, Lincoln, NE USA
[2] Univ Nebraska, Sch Nat Resources, Lincoln, NE USA
[3] Hankyong Natl Univ, Dept Bioresources & Rural Syst Engn, Anseong, South Korea
[4] Hankyong Natl Univ, Inst Agr Environm Sci, Anseong, South Korea
[5] Univ Nebraska, Ctr Adv Land Management Informat Technol, Lincoln, NE USA
[6] USDA ARS, Beltsville Agr Res Ctr, Environm Microbial & Food Safety Lab, Beltsville, MD 20705 USA
[7] Gyeongsang Natl Univ, Inst Agr & Life Sci, Dept Agr Engn, Jinju, South Korea
基金
美国国家航空航天局;
关键词
REMOTE-SENSING DATA; SEVERITY INDEX; CLIMATE-CHANGE; GREAT-PLAINS; SATELLITE; EVAPOTRANSPIRATION; MANAGEMENT; STRESS; NDVI; VARIABILITY;
D O I
10.1080/01431161.2017.1407047
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country's drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. The VegDRI-SKorea was developed for South Korea, modifying the original VegDRI methodology (developed for the USA) by tailoring it to the available local data resources. The VegDRI-SKorea utilizes a classification and regression tree (CART) modelling approach that collectively analyses remote-sensing data (e.g. normalized difference vegetation index (NDVI)), climate-based drought indices (e.g. self-calibrated Palmer drought severity index (PDSI) and standardized precipitation index (SPI)), and biophysical variables (e.g. elevation and land cover) that influence the drought-related vegetation stress. This study evaluates the performance of the recently developed VegDRI-SKorea for severe and extreme drought events that occurred in South Korea in 2001, 2008, and 2012. The results demonstrated that the hybrid drought index improved the more spatially detailed drought patterns compared to the station-based drought indices and resulted in a better understanding of drought impacts on the vegetation conditions. The VegDRI-SKorea model is expected to contribute to the monitoring of drought conditions nationally. In addition, it will provide the necessary information on the spatial variations of those conditions to evaluate local and regional drought risk assessment across South Korea and assist local decision-makers in drought risk management.
引用
收藏
页码:1548 / 1574
页数:27
相关论文
共 50 条
  • [41] Assessment of Vegetation Response to Drought in Nebraska Using Terra-MODIS Land Surface Temperature and Normalized Difference Vegetation Index
    Swain, Sharmistha
    Wardlow, Brian D.
    Narumalani, Sunil
    Tadesse, Tsegaye
    Callahan, Karin
    GISCIENCE & REMOTE SENSING, 2011, 48 (03) : 432 - 455
  • [42] Determinations of the warm edge and cold edge of the Vegetation Temperature Condition Index's drought monitoring approach
    Wang Pengxin
    Sun Wei
    INNOVATION AND DEVELOPMENT OF URBAN AGRICULTURAL ENGINEERING, 2005, : 207 - 214
  • [43] Drought Impact Assessment Based on Spatial Up-scaling Methods of Vegetation Temperature Condition Index
    Bai X.
    Wang P.
    Zhang S.
    Li L.
    Wang L.
    Xie Y.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2017, 48 (02): : 172 - 178
  • [44] Long-term spatial and temporal variations of vegetative drought based on vegetation condition index in China
    Liang, Liang
    Sun, Qin
    Luo, Xiang
    Wang, Jiahui
    Zhang, Lianpeng
    Deng, Meixia
    Di, Liping
    Liu, Zhixiao
    ECOSPHERE, 2017, 8 (08):
  • [45] Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States
    Jiao, Wenzhe
    Zhang, Lifu
    Chang, Qing
    Fu, Dongjie
    Cen, Yi
    Tong, Qingxi
    REMOTE SENSING, 2016, 8 (03)
  • [46] Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest
    Semeraro, Teodoro
    Luvisi, Andrea
    Lillo, Antonio O.
    Aretano, Roberta
    Buccolieri, Riccardo
    Marwan, Norbert
    REMOTE SENSING, 2020, 12 (06)
  • [47] Assessing the response of vegetation change to drought during 2009–2018 in Yunnan Province, China
    Yuanhe Yu
    Yuzhen Shen
    Jinliang Wang
    Yuchun Wei
    Lanping Nong
    Huan Deng
    Environmental Science and Pollution Research, 2021, 28 : 47066 - 47082
  • [48] Loss and recovery of vegetation productivity in response to extreme drought during 2022 across China
    Li, Meng
    Cui, Rui
    Bu, Lingjia
    Yang, Yuting
    ECOLOGICAL INDICATORS, 2024, 166
  • [49] Vegetation response to grazing and drought (13 yr) in a conservation area in the Succulent Karoo, South Africa
    Geldenhuys, Conrad
    van der Merwe, Helga
    van Rooyen, Margaretha W.
    JOURNAL OF ARID ENVIRONMENTS, 2023, 219
  • [50] Monitoring drought condition through detecting the vegetation condition index at Barry City in Wales, UK using temporal Landsat imageries
    Kareem H.H.
    International Journal of Water, 2024, 16 (01) : 23 - 41