INTEGRATING AHP ANALYSIS TECHNIQUES AND GIS TECHNOLOGY TO ESTABLISH THE DRY MAP OF HA TINH PROVINCE

Drought is a complex natural phenomenon that develops slowly and causes serious impacts on people’s living environment and production activities. Researching technologies that can assess the current situation while also providing early warnings or predicting drought risks is essential. To date, numerous methods and approaches for assessing, monitoring, and forecasting drought have been researched and applied. Notably, research has focused on developing and calculating drought indices derived from remote sensing images, which offer many advantages in assessing drought levels over large areas. In this study, five component indices (TCI, SAVI, VCI, WSVI, TVDI) are classified into different levels of importance, extracted from Landsat 8 data, and assigned weights based on their level of influence according to the direction of the index using the Analytical Hierarchy Method (AHP). The results have established a drought risk map for Ha Tinh province with five levels: No drought, low drought, moderate drought, high drought and very high drought. The findings indicate that the majority of ​​Ha Tinh province is at non-drought and low drought levels (accounting for 88.59 % of the province’s natural area). Moderate, high and very high drought levels make up 11.41 % of the province's natural area. Specifically, the area at moderate drought level accounts for 8.82 %, concentrated in Huong Son, Huong Khe, Cam Xuyen districts, as well as in the coastal communes of Thach Ha, Can Loc and Ky Anh districts. Areas affected by high and very high drought levels account for about 2.59 % of the province’s natural area, appearing locally at some parts of Ky Anh, Nghi Xuan and Cam Xuyen districts.

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Publication Details

Journal
Tạp chí Khoa học Tài nguyên và Môi trường
Published
2024-12-25
DOI
https://doi.org/10.63064/khtnmt.2024.649
Primary Topic
Image and Object Detection Techniques
Type
article
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article

INTEGRATING AHP ANALYSIS TECHNIQUES AND GIS TECHNOLOGY TO ESTABLISH THE DRY MAP OF HA TINH PROVINCE

Blami Dao, Ninh Thi Kim Anh
Tạp chí Khoa học Tài nguyên và Môi trường
Image and Object Detection Techniques
article

INTEGRATING AHP ANALYSIS TECHNIQUES AND GIS TECHNOLOGY TO ESTABLISH THE DRY MAP OF HA TINH PROVINCE

Blami Dao, Ninh Thi Kim Anh
article en

Abstract

Drought is a complex natural phenomenon that develops slowly and causes serious impacts on people’s living environment and production activities. Researching technologies that can assess the current situation while also providing early warnings or predicting drought risks is essential. To date, numerous methods and approaches for assessing, monitoring, and forecasting drought have been researched and applied. Notably, research has focused on developing and calculating drought indices derived from remote sensing images, which offer many advantages in assessing drought levels over large areas. In this study, five component indices (TCI, SAVI, VCI, WSVI, TVDI) are classified into different levels of importance, extracted from Landsat 8 data, and assigned weights based on their level of influence according to the direction of the index using the Analytical Hierarchy Method (AHP). The results have established a drought risk map for Ha Tinh province with five levels: No drought, low drought, moderate drought, high drought and very high drought. The findings indicate that the majority of ​​Ha Tinh province is at non-drought and low drought levels (accounting for 88.59 % of the province’s natural area). Moderate, high and very high drought levels make up 11.41 % of the province's natural area. Specifically, the area at moderate drought level accounts for 8.82 %, concentrated in Huong Son, Huong Khe, Cam Xuyen districts, as well as in the coastal communes of Thach Ha, Can Loc and Ky Anh districts. Areas affected by high and very high drought levels account for about 2.59 % of the province’s natural area, appearing locally at some parts of Ky Anh, Nghi Xuan and Cam Xuyen districts.

Tạp chí Khoa học Tài nguyên và Môi trường(54)
Responsible consumption and production
Openalex Percentile: Top 76%
Image and Object Detection Techniques
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