Application of Remote Sensing Imagery and Algorithms in Google Earth Engine platform for Drought Assessment

  • Affiliations:

    Hanoi University of Mining and Geology, Hanoi, Vietnam

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  • Received: 26th-Dec-2020
  • Revised: 14th-Apr-2021
  • Accepted: 22nd-May-2021
  • Online: 30th-June-2021
Pages: 53 - 67
Views: 1965
Downloads: 1074
Rating: 5.0, Total rating: 107
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Abstract:

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.

How to Cite
Pham, H.Thanh Thi and Tran, H.Thanh 2021. Application of Remote Sensing Imagery and Algorithms in Google Earth Engine platform for Drought Assessment. Journal of Mining and Earth Sciences. 62, 3 (Jun, 2021), 53-67. DOI:https://doi.org/10.46326/JMES.2021.62(3).07.
References

Aksoy, S., Gorucu, O.,Sertel, E., (2019). Drought Monitoring using MODIS derived indices and Google Earth Engine Platform. Paper presented at the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). 1-6. doi: 10.1109/Agro-Geoinformatics.2019.8820209.

Alley, W., (1984). The Palmer Drought Severity Index: Limitations and Assumptions. Journal of Climate and Applied Meteorology, 23, 1100-1109. doi: 10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2.

Alshaikh, A. Y., (2015). Space applications for drought assessment in Wadi-Dama (West Tabouk), KSA. The Egyptian Journal of Remote Sensing and Space Science, 18(1, Supplement 1), S43-S53. doi: https://doi.org/10.1016/j.ejrs.2015.07.001.

Bento, V., Gouveia, C., Dacamara, C.,Trigo, I., (2018). A climatological assessment of drought impact on vegetation health index. Agricultural and Forest Meteorology, 259, 286-295. doi: 10.1016/j.agrformet.2018.05.014.

Chander, G., Markham, B. L.,Helder, D. L., (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893-903. doi: https://doi.org/10.1016/j.rse.2009.01.007.

Department of Natural Resources and Environment of Phu Yen Province, (2019). Climate assessment reports of Phu Yen province (in Vietnamese). 

DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J.,Lang, M., (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240. doi: 10.1016/j.rse.2020.111664.

Ferrelli, F., Huamantinco Cisneros, M., Delgado, A.,Piccolo, M., (2018). Spatial and temporal analysis of the LST-NDVI relationship for the study of land cover changes and their contribution to urban planning in Monte Hermoso, Argentina. Documents d'Anàlisi Geogràfica, 64, 25. doi: 10.5565/rev/dag.355.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D.,Moore, R., (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. doi: https://doi.org/10.1016/j.rse.2017.06.031.

Gorgani, S., Panahi, M.,Rezaie, F., (2013). The Relationship between NDVI and LST in the urban area of Mashhad, Iran. Paper presented at the International Conference on Civil Engineering Architecture and Urban Sustainable Development. At Tabriz , Iran.

Khan, R., Gilani, H., Iqbal, N.,Shahid, I., (2019). Satellite-based (2000–2015) drought hazard assessment with indices, mapping, and monitoring of Potohar plateau, Punjab, Pakistan. Environmental Earth Sciences, 79(1), 23. doi: 10.1007/s12665-019-8751-9.

Kogan, F. N., (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 

91-100. doi: https://doi.org/10.1016/0273-1177(95)00079-T.

Kogan, F. N., (2000). Satellite-Observed Sensitivity of World Land Ecosystems to El Niño/La Niña. Remote Sensing of Environment, 74, 445-462. doi: 10.1016/S0034-4257(00)00137-1.

Long, V. H., Giang, N. V., Hoanh, T. P.,Hoa, P. V., (2019). Applying Google Earth Engine in river bank erosion monitoring – a case study in lower Mekong river (in Vietnamese). Journal of science- Ho Chi Minh city University of Education

Masitoh, F.,Rusydi, A. N., (2019). Vegetation Health Index (VHI) analysis during drought season in Brantas Watershed. IOP Conference Series: Earth and Environmental Science, 389, 012033. doi: 10.1088/1755-1315/389/1/012033.

Midekisa, A., Holl, F., Savory, D. J., Andrade-Pacheco, R., Gething, P. W., Bennett, A.,Sturrock, H. J. W., (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLOS ONE, 12(9), e0184926. doi: 10.1371/journal.pone.0184926.

Mutanga, O.,Kumar, L., (2019). Google Earth Engine Applications. Remote Sensing, 11, 591. doi: 10.3390/rs11050591.

Nguyen Trong Nhan,Cuong, V. X., (2018). Google Earth Engine was applied to forest land monitoring in Lam Dong province from 2010 - 2016 (in Vietnamese). The fourth Scientific Conference - SEMREGG 2018, 258-265. 

Nguyen Viet Lanh, Dung, N. V., Duong, T. H.,Tam, T. T., (2018). Using satellite precipitation data to assess meteorological drought based on SPI index for Thanh Hoa province. Vietnam Journal of Hydro - Meteorology, 696, 1-9. 

Nhut, H. S., Hoa, P. V., Binh, N. A., An, N. N., Phuong, T. A.,Thao, G. T. P., (2018). Using Google Earth Engine for assessing mangrove forest change in Ngoc Hien district, Ca Mau province in the period 2000-2015 (in Vietnamese). Journal of science- Ho Chi Minh city University of Education, 15(11b), 101-107. 

Online Vietnam Agriculture Newspaper. (2019). https://nongnghiep.vn/phu-yen-han-han-khoc-liet-mot-xa-thieu-nuoc-nghiem-trong-d247764.html.

Palmer, W. C., (1965). Meteorological Drought. Research Paper No. 45, US Weather Bureau, Washington, DC. 

Sazib, N., Mladenova, I.,Bolten, J., (2018). Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. Remote Sensing, 10, 1265. doi: 10.3390/rs10081265.

Schirmbeck, L. W., Fontana, D. C., Schirmbeck, J.,Mengue, V. P., (2017). Understanding TVDI as an index that expresses soil moisture. Journal of Hyperspectral Remote Sensing, 7, 82-90. 

Sholihah, R., Trisasongko, B., Shiddiq, D., Iman, L. O., Kusdaryanto, S., Manijo,Panuju, D., (2016). Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia. Procedia Environmental Sciences, 33, 14-20. doi: 10.1016/j.proenv.2016.03.051

Sidhu, N., Pebesma, E.,Câmara, G., (2018). Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 51, 486-500. doi: 10.1080/22797254.2018.1451782

Sobrino, J. A., Jiménez-Muñoz, J. C.,Paolini, L., (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434-440. doi: https://doi.org/10.1016/j.rse.2004.02.003.

Sreekesh, S., Kaur, N.,Sreerama Naik, S. R., (2019). Agricultural drought and soil moisture analysis using satellite image based indices. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 507-514. doi: 10.5194/isprs-archives-XLII-3-W6-507-2019.

Sruthi, S.,Aslam, M. A. M., (2015). Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. Aquatic Procedia, 4, 1258-1264. doi: https://doi.org/10.1016/j.aqpro.2015.02.164

Sunar, F., Yağmur, N.,Dervisoglu, A., (2019). Flood Analysis with Remote Sensing Data - a Case Study: Maritsa River, Edirne. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W8, 497-502. doi: 10.5194/isprs-archives-XLII-3-W8-497-2019.

Tran, H. T., Campbell, J. B., Tran, T. D.,Tran, H. T., (2017). Monitoring drought vulnerability using multispectral indices observed from sequential remote sensing (Case Study: Tuy Phong, Binh Thuan, Vietnam). GIScience and Remote Sensing, 54(2), 167-184. doi: 10.1080/15481603.2017.1287838.

Tuan, V. Q., Khai, D. H., Nhan, H. T. K.,Hoa, N. T., (2018). Development of flood monitoring algorithms in the Mekong Delta based on Google Earth Engine platform (in Vietnamese). Can Tho University Journal of Science, 54(9A), 29-36. 

Tucker, C. J., (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. doi: https://doi.org/10.1016/0034-4257(79)90013-0.

Vermote, E., Justice, C., Claverie, M.,Franch, B., (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, Volume 185(Iss 2), 46-56. doi: 10.1016/j.rse.2016.04.008.

Weng, Q., Lu, D.,Schubring, J., (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467-483. doi: https://doi.org/10.1016/j.rse.2003.11.005.

Wilhite, D., (2000). Drought as a Natural Hazard: Concepts and Definitions. Drought, a Global Assessment, 1.