Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia

被引：1
作者：
Gidey E. [1 ,2 ,3 ]
Dikinya O. [1 ]
Sebego R. [1 ]
Segosebe E. [1 ]
Zenebe A. [2 ,3 ]
机构：
[1] Department of Environmental Science, University of Botswana, Gaborone
[2] Land Resource Management and Environmental Protection, Mekelle University, Mekelle
[3] Institute of Climate and Society, Mekelle University, Mekelle
来源：
Environmental Systems Research | 2018年 / 7卷 / 01期
基金：
美国国家航空航天局;
关键词：
Agricultural drought; Ethiopia; GIS; LST; NDVI; Rainfall; Raya; Remote sensing; TCI; VCI; VHI;
D O I：
10.1186/s40068-018-0115-z
中图分类号：
学科分类号：
摘要：
Background: Droughts cause serious effects on the agricultural and agro-pastoral sector due to its heavy dependence on rainfall. Several studies on agricultural drought monitoring have been conducted in Africa in general and Ethiopia in particular. However, these studies were carried out using the limited capacity of drought indices such as Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Deviation of Normalized Difference Vegetation Index (DevNDVI) only. To overcome this challenge, the present study aims to analyze the long-term agricultural drought onset, cessation, duration, frequency, severity and its spatial extents based on remote sensing data using the Vegetation Health Index (VHI) 3-month time-scale in Raya and its surrounding area, Northern Ethiopia. Both the MOD11A2 Terra Land Surface Temperature (LST) and eMODIS NDVI at 250 by 250 m spatial resolution and hybrid TAMSAT monthly rainfall data were used. A simple linear regression model was also applied to examine how the agricultural drought responds to the rainfall variability. Results: Extremely low mean NDVI value ranged from 0.23 to 0.27 was observed in the lowland area than mid and highlands. NDVI coverage during the main rainy season decreased by 3–4% in all districts of the study area, while LST shows a significant increase by 0.52–1.08 °C. VHI and rainfall value was significantly decreased during the main rainy season. Agricultural drought responded positively to seasonal rainfall (R2 = 0.357 to R2 = 0.651) at p < 0.01 and p < 0.05 significance level. This relationship revealed that when rainfall increases, VHI also tends to increase. As a result, the event of agricultural drought diminished. Conclusions: Remote sensing and GIS-based agricultural drought can be better monitored by VHI composed of LST, NDVI, VCI, and TCI drought indices. Agricultural drought occurs once in every 1.36–7.5 years during the main rainy season, but the frequency, duration and severity are higher (10–11 times) in the lowland area than the mid and highlands area (2–6 times) during the last 15 years. This study suggests that the effect of drought could be reduced through involving the smallholder farmers in a wide range of on and off-farm practices. This study may help to improve the existing agricultural drought monitoring systems carried out in Africa in general and Ethiopia in particular. It also supports the formulation and implementation of drought coping and mitigation measures in the study area. © The Author(s) 2018.
引用
收藏
相关论文
共 52 条
[1]  
Alemaw B.F., Simalenga T., Climate change impacts and adaptation in rainfed farming systems: a modeling framework for scaling-out climate smart agriculture in Sub-Saharan Africa, Am J Clim Change, 4, 4, (2015)
[2]  
Ayenew T., GebreEgziabher M., Kebede S., Mamo S., Integrated assessment of hydrogeology and water quality for groundwater-based irrigation development in the Raya Valley, northern Ethiopia, Water Int, 38, 4, pp. 480-492, (2013)
[3]  
Barbosa H.A., Huete A.R., Baethgen W.E., A 20-year study of NDVI variability over the Northeast Region of Brazil, J Arid Environ, 67, 2, pp. 288-307, (2006)
[4]  
Bhuiyan C., Various drought indices for monitoring drought condition in Aravalli terrain of India, Proceedings of the Xxth ISPRS Congress, Istanbul, Turkey, pp. 12-23, (2004)
[5]  
Bhuiyan C., Desert vegetation during droughts: response and sensitivity, Int Arch Photogramm Remote Sens Spat Inf Sci, 21, pp. 907-912, (2008)
[6]  
Bhuiyan C., Singh R.P., Kogan F.N., Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data, Int J Appl Earth Obs Geoinf, 8, 4, pp. 289-302, (2006)
[7]  
Brown J.F., Howard D., Wylie B., Frieze A., Ji L., Gacke C., Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition, Remote Sens, 7, 12, pp. 16226-16240, (2015)
[8]  
Choi M., Jacobs J.M., Anderson M.C., Bosch D.D., Evaluation of drought indices via remotely sensed data with hydrological variables, J Hydrol, 476, pp. 265-273, (2013)
[9]  
Dutta D., Kundu A., Patel N.R., Saha S.K., Siddiqui A.R., Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI), Egyptian J Remote Sens Space Sci, 18, 1, pp. 53-63, (2015)
[10]  
Frey C.M., Kuenzer C., Dech S., Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005, Int J Remote Sens, 33, 22, pp. 7165-7183, (2012)
123456