Drought is one of the most frequent hazards that has serious impacts on both agriculture and peoples livelihood in the Central Highlands. In this study, Normalized Difference Drought Index (NDDI) retrieved from multi Landsat imageries in March from 1989 to 2017 has been used to recorded drought dynamics in the Central Highlands. The results show that areas of droughtsevere area account for 8% to 22% in 1991 and 2005, respectively where mainly distribute in Kon Tum, Dak To (Kon Tum), Ea Sup, Buon Don (Dak Lak), Pleiku, Chu Se, Chu Prong (Gia Lai), Cu Jut (Dak Nong), Lam Ha (Lam Dong) in the study area. Besides historical drought zones between the years 1989 and 2009, the regions including Tu Mo Rong (Kon Tum), La Grai (Gia Lai), Dak Song, Tuy Duc, Dak Glong, Krong No (Dak Nong), Dam Rong (Lam Dong) are recognized to be extendedly droughtimpacted area to the present day. Additionally, it is estimated that the droughtimpacted area increases moderately from 27% to 42% of total study regions via the period 19891999 to 20102017, of which the period 20012009 witnessed 31% of total study areas was influenced by drought. Therefore, drought mitigation and sustainable management should be paid attention, especially in annual droughtprone areas.
30 YEARS MONITORING SPATIAL - TEMPORAL DYNAMICS OF AGRICULTURAL DROUGHT IN THE CENTRAL HIGHLANDS USING LANDSAT DATA Ngo Thi Dinha, Nguyen Thi Thu Haa*, Nguyen Thien Phuong Thaoa, Nguyen Thuy Linha a VNU University of Science, 334 Nguyen Trai Street, Thanh Xuan, Hanoi, Vietnam * Corresponding author: hantt_kdc@vnu.edu.vn Abstract: Drought is one of the most frequent hazards that has serious impacts on both agriculture and people's livelihood in the Central Highlands In this study, Normalized Difference Drought Index (NDDI) retrieved from multi Landsat imageries in March from 1989 to 2017 has been used to recorded drought dynamics in the Central Highlands The results show that areas of drought-severe area account for 8% to 22% in 1991 and 2005, respectively where mainly distribute in Kon Tum, Dak To (Kon Tum), Ea Sup, Buon Don (Dak Lak), Pleiku, Chu Se, Chu Prong (Gia Lai), Cu Jut (Dak Nong), Lam Ha (Lam Dong) in the study area Besides historical drought zones between the years 1989 and 2009, the regions including Tu Mo Rong (Kon Tum), La Grai (Gia Lai), Dak Song, Tuy Duc, Dak Glong, Krong No (Dak Nong), Dam Rong (Lam Dong) are recognized to be extendedly drought-impacted area to the present day Additionally, it is estimated that the droughtimpacted area increases moderately from 27% to 42% of total study regions via the period 1989-1999 to 2010-2017, of which the period 2001-2009 witnessed 31% of total study areas was influenced by drought Therefore, drought mitigation and sustainable management should be paid attention, especially in annual drought-prone areas Key words: Drought, Central Highlands, NDDI, Landsat Imagery INTRODUCTION Drought is a global phenomenon that can and does occur in virtually all landscapes, resulting in significant economic, social, and environmental costs and losses (Willhite, 2000) Generally, droughts are classified as either a meteorological drought (lack of precipitation over a region for a period of time), hydrological drought (deficiencies in surface and subsurface water supplies), agricultural drought (deficiency in water availability for crop or plant growth) or socioeconomic drought (failure of water resources systems to meet water demands, which impacts human activities both directly and indirectly) (Wilhite, 2000; Son et al., 2012) These costs and losses have risen dramatically in recent decades The consequences of drought in Central Highlands caused very severe: make thousands of lakes, rivers are depleted, many regions lack of potable water, and drought also led to high wildfire risk, particularly drought reduces down crop yields or inability arable of many farm-land Light drought make often to reduce crop yields from 20 to 30%, heavy drought makes to reduce to 50%, extremely heavy drought makes to loss crops (Quyen et al., 2016) In 2015, the Vietnamese government has provided 5,221 tons of food and allocated 1008 billion VND (45 million USD) worth of relief and disaster support services for people in the Central Highlands’s drought-affected regions MARD recognizes that this crisis and its subsequent effects (e.g inundation after the drought) will recur in the future, and that there is a need to prepare and plan for necessary response measures (CGIAR Research Centers in Southeast Asia, 2016) Drought characterization enables operations such as drought early warning and drought risk analysis, which allow improved preparation and contingency planning (Zargar et al., 2011) Traditionally, drought monitoring has been based on weather station observations, which lack the continuous spatial coverage needed to characterize and monitor the detailed spatial pattern of drought conditions (Gu et al., 2007; Hazaymeh et al., 2016) Drought severity, too, is difficult to determine It is dependent not only the duration, intensity, and geographical extent of a specific drought episode but also on the demands made by human activities and by the vegetation on a region’s water supplies (Wilhite et al., 1985) The adaptation of a simplified method by drought indices has facilitated drought characterization for various users and entities More than 100 drought indices have so far been proposed, some of which are operationally used to characterize drought using gridded maps at regional and national levels These indices correspond to different types of drought, including meteorological, agricultural, and hydrological drought (Zargar et al., 2011) Drought has been the subject of a great deal of systematic study, particularly reconstructions of drought history, computations of drought frequency, and to a lesser extent, investigations of first-, second-, and even third-order impacts of drought on society (Wilhite et al., 1985) Therefore, drought dynamics and its impacts can be rapidly assessed by using this technique The Normalized Difference Drought Index (NDDI), which combines both vegetation greenness (NDVI) (Tucker, 1979; Rouse et al., 1974) and wetness conditions (NDWI) (Gao, 1996) can be suitable for long-term drought monitoring, particularly for agricultural drought (Hazaymeh et al., 2016) Landsat imageries is one of tools that help deeply understand drought as well as drought impacted areas in the Central Highland with more than 40 years history Significant droughts mainly happen from January to March in the whole Central Vietnam (Hang et al., 2010) This study aims to explore the drought dynamics in the Central Highlands of Vietnam in March (common last month of local dry season) of years: 1989, 1991, 1993, 1995, 1997, 1998, 1999, 2001, 2003, 2005, 2007, 2009, 2010, 2011, 2014, 2015, 2016, and 2017 using multigenerational Landsat images Furthermore, the area annual drought as well as expand drought impacted was record in this article DATASETS AND METHODS 2.1 Datasets The Central Highlands is one of eight agro-ecological regions of Vietnam (Fig 1a) The region consists of various plateaus surrounded by mountain ranges The elevations of plateaus range from 500-1500 meters above sea level The Central Highlands has a total land area of 5,454,500 (17% of the national area), covering five provinces: Kon Tum, Gia Lai, Dak Lak, Dak Nong and Lam Dong Reported from Pleiku Hydro-climatological Station, local climate factors were assembled and analysis such as average temperature and rainfall for the years 1989, 1991, 1993, 1995, 1997, 1998, 1999, 2001, 20003, 2005, 2007, 2009, 2010, 2011, 2014, 2015, 2016 and first four months of 2017 published by National Center for Meteorological and Climatology (Table 1) According to climate data, the 1993’s dry season had the lowest rainfall with average value is only 0.6 mm, and dry season in 2004 had the highest average rainfall value (9.5 mm) Seasonal average temperature and length of dry season values have not much varied, from 20 to 23oC and within months (November to April) In dry season, average humid fluctuates from 73.9 to 83.2% and sunny hours change around 60 hours during the period 19892017 (approximately 30 years) b) Fig a) Location of the Central Highlands in Vietnam (Source: http://www.nchmf.gov.vn) b) Mosaic images scenes following path 124 row 50, 51, 52 and path 125 row 50 Table Descriptive statistics of local climate factors Climate factors Unit Minimum Maximum Mean Standard Deviation Seasonal average rainfall mm 0.62 9.48 3.78 2.43 C 20.39 22.97 21.35 0.69 Seasonal average humid % 73.92 83.22 77.00 2.46 Seasonal average sunny hours hours 205.33 265.38 242.02 18.17 Seasonal average temperature o 2.2 Methods Landsat satellites acquire images over the Central Highlands from 9:40 to 10:12 am due to local time, every 16 days following path 124 row 50, 51, 52 and path 125 row 50 This study used Landsat TM and Landsat Oli images that acquired in March of the years 1989, 1991, 1993, 1995, 1997, 1998, 1999, 2001, 2003, 2005, 2007, 2009, 2010, 2011, 2014, 2015, 2016 and 2017 Selected images are clear viewing with maximum cloudcoverage smaller 10% that gives optimal data to distinguish objects All pre-processing of the Landsat images, including radiometric calibration, atmospheric correction was completed using ENVI 5.3 image processing software All used Landsat images were first radiometric calibration using designed tool to convert image DNs into top-of-atmosphere (TOA) reflectances Accordingly, the pixel TOA-reflectance was computed as a equation (1) applied Landsat from the Landsat Users Handbook and a equation (2) applied Landsat OLi from Landsat (L8) Data Users Handbook: ρp = π Lλ d2 ESUNλ cosθS (1) where ρp is unit-less planetary reflectance, Lλ is radiance in units of W/(m2∙sr∙μm); d is Earth-sun distance in astronomical units; ESUNλ is solar exo-atmospheric irradiance in W/(m2∙μm); θS is solar zenith angle in degrees ρλ = Mρ ∗ Qcal + Aρ sinθSE (2) where Mρ is reflectance multiplicative scaling factor for the band in (REFLECTANCEW_MULT_BAND_n from the metadata); Qcal is L1 pixel value in DN; Aρ is reflectance additive scaling factor for the band (REFLECTANCE_ADD_BAND_n from the metada); θSE is solar elevation angle These images then were atmospheric corrected using dark-object subtraction method (Chavez, 1996) to transfer TOA-reflectances into surface reflectances NDVI and NDWI proposed by Rouse et al (1974) (3) and Gao et al (1996) (4), respectively: NDVI = NDWI = 𝑅(NIR) − 𝑅(red) 𝑅(NIR) + 𝑅(red) (3) R(NIR) − R(SWIR) R(NIR) + R(SWIR) (4) where R(NIR), R(Red), and R(SWIR) are the reflectance at 666 nm and 655 nm, 830 nm and 865 nm, 2215 nm and 2200 nm, for Landsat TM and Landsat OLI imageries, respectively A new vegetation drought indicator, the NDDI combines information from both the NDVI and NDWI following Gu et al (2007) (5): (5) According to Drought Categories proposed by Gu et al (2007), “non-drought” state was detected when NDDI value is smaller than 0.1; “abnormal dry” sate and “moderate drought” sate was detected by area within NDDI range from 0.1 to 0.3; “severe drought” occurred in area with NDDI larger than 0.3 RESULT AND DISCUSSION Fig Maps of drought in the Central Highlands in Marches of period 1989-2017 The inter-annual variation in the percentage of drought impacted areas (NDDI>0.1) in the Central Highlands from 1989 - 2017 are shown in Fig 1b Relative large drought areas recorded in the middle of the 1990s, 2000s and most recent years that is bigger than 1,911 thousand ha, fluctuate in the interval from 35% to 46% of the highland’s total area The largest drought impacted area of 46% occurred in 2015 and represented approximately 2,486 thousand which is conformable to reports on drought by Ha et al., 2016 However, the smallest drought impacted area account for 20% in 1991 with 1,081 thousand via 30 years In spite of the large inter-annual variability, the long-term trends in the drought-impacted areas in the Central Highlands as a whole increased dramatically over the past 30 years, especially in the El Niño years (period 1993-1995, 1997-1998 and 20142016) The largest of severe drought is 1,201 thousand in 2005 (NDDI larger than 0.3) which represented nearly 22.2% of the total highland area This value was followed by 1,181 thousand in 1995, 1,127 thousand in 2015 and 1,093 thousand in 2014, corresponding to 21.7%, 20.7%, and 20.1% of total study areas, respectively Therefore, it is easily notice trend that sharply raised severe drought in the Central Highlands after about 10 years in the period 1989 – 2017 Severe drought area recorded in 2005 is the same in 2007 around 16,4% with approximately 897 thousand However, total drought impacted area in 2005 is 2,192 thousand corresponding to 40% of the highland’s total land area In addition, the 2007’s drought impacted area was covered 1,390 thousand which is 25% of total study area Thus, it shows annual drought affected area Fig Variation maps of drought- severe area and drought-impacted areas using Landsat based NDDI via three periods 1989 – 1999, 2001 – 2009, and 2010 – 2017 Highlands have been changed noticeably in the last three decades Moreover, it is remarkably growth of drought impacted area in the 2010s, especially in 2015 Generally, the average drought impacted area account for 30% of the highland’s total area with approximately 1,913 thousand over the past 30 years Meanwhile, drought severe has proportion about 13% of total study area with 807 thousand The results indicate that abnormal dry area (0.1