Drought Analysis Using Satellite-Based Data
and Spectral Index in Upper Northeastern
Thailand

Uttaruk, Yannawut; Laosuwan, Teerawong

doi:10.15244/pjoes/94998

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…The SVI has the capacity to work in tandem with standard drought indices, as well as other weather and supplementary data, to help drought-response decisions [42].…”

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confidence: 99%

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

et al. 2021

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Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

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“…The SVI has the capacity to work in tandem with standard drought indices, as well as other weather and supplementary data, to help drought-response decisions [42].…”

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confidence: 99%

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

et al. 2021

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“…RST is currently used as the main tool for studying spatial phenomena and is extensively based on data from satellites that have been used as tools for recording data on area conditions efficiently and consecutively in surveys of natural resources such as water on and under the surface. It can cover wide areas and obtain continuously updated data [26][27][28][29][30][31][32][33][34][35][36][37][38][39]. In Thailand, the application of RST to chlorophylla research is at a low level.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Content and Distribution of Chlorophyll-a on the Sea Surface through Data from Aqua/MODIS Satellite

Laosuwan¹,

Uttaruk²,

Rotjanakusol³

2022

Pol. J. Environ. Stud.

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Phytoplankton are tiny creatures floating on water mass. Inside their cells, there is the pigment chlorophyll-a, a kind of substance used in photosynthesis. As a result, it is essential for the ecosystem of water sources because it is considered as the primary producer. This research aims to analyze the content and distribution of chlorophyll-a on the sea surface. It uses data from the Aqua/MODIS Satellite in a case study of the upper Gulf of Thailand in 2020. The SeaWiFS Data Analysis System (SeaDAS) program analyzed the obtained data. The results revealed that the content and distribution of chlorophyll-a on the sea surface in the area of the upper Gulf of Thailand were based on seasons and the influence of monsoons. It was found that the content of chlorophyll-a was highest in the southwest monsoon period (May-October) and lowest in the northeast monsoon period (November-April). The content of chlorophyll-a was highest in October with a mean of 3.148 mg/m 3 , followed by November, and it was lowest in February with a mean of 1.514 mg/m 3 . Moreover, it was also found that, if the sea surface temperature was higher than 30°C, phytoplankton exhibited proper growth and propagation. As a result, the content and distribution of chlorophyll-a were consistent with seasonal changes and weather variability.

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“…To evaluate burned areas caused by wildfires in Thailand, besides data from land surveying, it can be currently performed by using data from Remote Sensing Technology [7] because the application of Remote Sensing Technology like data from earth observation satellites, meteorological satellites, and Global Navigation Satellite System (GNSS), during these 4 decades has been developed rapidly with high efficiency [8]. Remote Sensing Technology is able to survey data in wild areas with lower expenses than those of land surveying therefore it has been applied to various fields, for example, forest fire, burned areas drought, flood, and Land Surface Temperatures, etc.…”

Section: Introductionmentioning

confidence: 99%

Burned Area Evaluation Method for Wildfires in Wildlife Sanctuaries Based on Data from Sentinel-2 Satellite

Uttaruk¹,

Rotjanakusol²,

Laosuwan³

2022

Pol. J. Environ. Stud.

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Wildfire is a kind of disaster that can damage living creatures and environment as well as cause small dust affecting to health of people. Wildfire can be naturally occurred and human's actions. This research aims to evaluate burned areas caused by wildfires in Omkoi Wildlife Sanctuary. The research was conducted by collecting data from Sentinel-2 Satellite for 4 years started from 2017 to 2020. Three formats of spectral indices, i.e., NBR, NDWI, and RBR were used for evaluating burned areas caused by wildfires. Validation was tested by using statistical methods. The results revealed that burned areas from 2017 to 2020 were 0.107 km 2 , 1.160 km 2 , 0.387 km 2 , and 1.031 km 2 , respectively. The larges burned area was found in

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Drought Analysis Using Satellite-Based Data and Spectral Index in Upper Northeastern Thailand

Cited by 13 publications

References 12 publications

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Analysis of Content and Distribution of Chlorophyll-a on the Sea Surface through Data from Aqua/MODIS Satellite

Burned Area Evaluation Method for Wildfires in Wildlife Sanctuaries Based on Data from Sentinel-2 Satellite

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