Monitoring drought vulnerability using multispectral indices observed from sequential remote sensing (Case Study: Tuy Phong, Binh Thuan, Vietnam)

Tran, Hoa Thi; Campbell, James B.; Tran, Tri Dinh; Tran, Ha Thanh

doi:10.1080/15481603.2017.1287838

Cited by 56 publications

(36 citation statements)

References 17 publications

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“…Upon local reports, during the 2014-2015 dry season, there was a significant decline of water in the main reservoirs; in 2015, Da Bac Lake lost 90% of its fresh water [16]. Our recent study of agricultural drought severity in Tuy Phong district under a scenario of rainfall shortage indicated that those coastal communes are sensitive subjects to drought at moderate to mild levels [17], which are shown in light green to yellow in Figure 4. The analysis of that study was based on an idea of using the Vegetation Health Index (VHI) extracted from satellite images [18], which are Landsat time series data to access spatial and temporal agricultural drought patterns in Tuy Phong.…”

Section: Study Area Backgroundmentioning

confidence: 99%

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Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

et al. 2019

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Drought is a dry-weather event characterized by a deficit of water resources in a period of year due to less rainfall than normal or overexploitation. This insidious hazard tends to occur frequently and more intensively in sub-humid regions resulting in changes in the landscape, transitions in agricultural practices and other environmental-social issues. The study area is in the sub-humid region of the northern coastal zone of Binh Thuan province, Vietnam—Tuy Phong district. This area is indicated as a subject of prolonged droughts during 6-month dry seasons, which have occurred more frequently in recent years. Associated with economic transitions in agricultural practicing, urbanization, and industrialization, prolonged droughts have resulted in rapid changes in land use and land cover (LULC) in Tuy Phong, especially in three coastal communes: Binh Thanh, Lien Huong, and Phuoc The. A bi-temporal analysis using high-resolution data, the 2011 WorldView2 and the 2016 GeoEye1, was examined to assess LULC changes from observed severe droughts in those three communes. Results showed a dramatic reduction in the extent of hydrological systems (about 20%), and significant increases of tree canopies in urban areas and near the coastal areas (approximately 76.8%). Paddy fields declined by 51% in 2016; such areas transitioned to inactive status or were alternated for growing drought-tolerant plants. Shrimp farming experienced a recognizable decrease by approximately 44%. The 2014 map and field observations during summer 2016 provide references for object-based classification and validation. Overall agreement of results is about 85%.

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Section: Study Area Backgroundmentioning

confidence: 99%

“…The study area was indicated under moderate to mild impact of drought. The analysis was based on assessing spatial and temporal Vegetation Health Index distribution via Landsat images over 27 years [17].…”

Section: Study Area Backgroundmentioning

confidence: 99%

Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

et al. 2019

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“…Under these conditions in which precipitation decreases and the temperature increases, semi-arid zones can face large water losses due to high evapotranspiration and aggravate scarcity [16]. Remote-sensing products such as NDVI and LST have proven to be valuable proxies for monitoring vegetation dynamics [17], land surface moisture conditions [18], yield estimates [19], soil evaporation rates [20], and drought vulnerability [21]. In the context of generating early alert systems, various spectral indexes have been developed for monitoring droughts, such as the Vegetation Temperature Condition Index-VTCI [22,23], the Vegetation Supply Water Index-VSWI [24] or the Soil Moisture Agricultural Drought Index-SMADI [25] among others.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between LST and NDVI has been generally described as having a negative correlation (e.g., Refs. [21,26]) with steeper slopes for dryer conditions [25]. However, the relationship between LST and NDVI may exhibit notable spatial [27] and temporal [26] alterations, and new research is needed in order to assess the sensitivity and resilience of ecosystems to climate variability [28].…”

Section: Introductionmentioning

confidence: 99%

Assessing Water Availability in Mediterranean Regions Affected by Water Conflicts through MODIS Data Time Series Analysis

et al. 2019

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Water scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.

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“…Meteorological drought occurs due to a deficit of precipitation, which brings about a shortage of available soil water for plant growth [4][5][6]. Agricultural drought is caused by the shortage of soil water, which results in significant damage to agricultural ecosystems (e.g., crop yield) [7][8][9]. Hydrological drought refers to groundwater depletion and the lack of surface water, which affects water resources for allocation [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia

et al. 2018

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Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.

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Monitoring drought vulnerability using multispectral indices observed from sequential remote sensing (Case Study: Tuy Phong, Binh Thuan, Vietnam)

Cited by 56 publications

References 17 publications

Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

Assessing Water Availability in Mediterranean Regions Affected by Water Conflicts through MODIS Data Time Series Analysis

Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia

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