Application of Remote Sensing Imagery and Algorithms in Google Earth Engine platform for Drought Assessment
Hanoi University of Mining and Geology, Hanoi, Vietnam
- Received: 26th-Dec-2020
- Revised: 14th-Apr-2021
- Accepted: 22nd-May-2021
- Online: 30th-June-2021
- Section: Geomatics and Land Administration
In Vietnam, drought is one of the natural disasters caused by high temperatures and lack of precipitation, especially with El Nino and the global warming phenomenon. It affects directly environmental, economical, social issueproblems, and the lives of humans. Many methods have been used to assess drought, andin which remote sensing indices are considered the most commonly used tool today. They are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. Especially with the launch of Google Earth Engine (GEE) - a cloud-based platform for geospatial analysis, it is easy to access high-performance computing resources for processing multi-temporal satellite data online. With the GEE platform, we focus on writing and running scripts with the indicators suitable for evaluating drought phenomenon, instead of calculating on software and downloading remote sensing imagery with large size. In this study, we collected 26 Landsat 8 images in the dry season in 2019 (from April to July) in Tay Hoa district, Phu Yen – a region in the South Central Coast of Vietnam where agricultural drought occurs frequently. wWe assessed the distribution of drought conditions in the dry season in 2019 in Tay Hoa district, Phu Yen – a region in the South Central Coast of Vietnam where agricultural drought occurs frequently by using a drought index (VHI index – Vegetation Health Index) produced from Landsat satellite data in the GEE platform. The study results indicated that the drought (from mild to severe) concentrated in the North of the region, corresponding to high surface temperature and NDVI low or NDVI moderate values. VHI maps were visually compared with the drought map of the South Central Coast and the Central Highlands. In general, the results also reflect the the method’s reliability and can be used to support the managers to plan policies, making long-term plans to cope with climate change in the future at Tay Hoa in particular and other regions in general.
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