Header Image Title: OPTIMISING VEGETATION-INPUT FOR DROUGHT ASSESSMENT WITH SENTINEL-2A DATA
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Title: OPTIMISING VEGETATION-INPUT FOR DROUGHT ASSESSMENT WITH SENTINEL-2A DATA
OPTIMISING VEGETATION-INPUT FOR DROUGHT ASSESSMENT WITH SENTINEL-2A DATA
Joachim Vercruysse; Greet Deruyter
10.5593/sgem2022/2.1/s10.40
10.5593/sgem2022/2.1
1314-2704
978-619-7603-40-8
English
22
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Photogrammetry and Remote Sensing
As a consequence of climate change, in some regions, more intense rain showers go hand in hand with longer dry periods. The subsequent more and more severe droughts can have devastating effects on many economic and social sectors. Therefore, it is necessary to be able to predict and assess the consequences of these droughts on a local scale, in order to develop policies to cope.
Drought assessment needs a lot of detailed and accurate input-data, such as land use, land cover, soil moisture, vegetation, evapotranspiration, etc., often obtained by continuous earth monitoring by satellites. Satellite images are generally converted into indices, of which the Normalized Difference Vegetation Index (NDVI) is one of the most widely used. It was developed for use with Landsat imagery and allows for the classification of satellite images for land use and the assessment of the vegetation’s vitality.
In this research, a new composite index is presented and compared to the NDVI to be used with Sentinel-2A imagery, having higher resolution and more spectral bands than Landsat. This new composite index can be used to detect water and vegetation.
Test results show that this newly developed composite index achieves a better accuracy through Support Vector Machine (SVM) classification than the widely used NDVI. Although further validation is necessary, the results promise a possible amelioration of vegetation related input data for drought assessment and management.
NDVI, Drought assessment, Remote sensing, Sentinel-2A, Composite index
[1] L. Boelens, G. Allaert, and C. Walot, Adapt for life: Cooperatie In Planning UA, 2017.
[2] IPCC, Climate Change 2014: Synthesis Report, Geneva, Switzerland, 2014.
[3] C. Murthy, B. Laxman, and M. S. Sai, “Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity,” International journal of disaster risk reduction, vol. 12, pp. 163-171, 2015.
[4] A.-A. Belal, H. R. El-Ramady, E. S. Mohamed et al., “Drought risk assessment using remote sensing and GIS techniques,” Arabian Journal of Geosciences, vol. 7, no. 1, pp. 35-53, 2014.
[5] P. Han, P. X. Wang, and S. Y. Zhang, “Drought forecasting based on the remote sensing data using ARIMA models,” Mathematical and computer modelling, vol. 51, no. 11-12, pp. 1398-1403, 2010.
[6] J. Drisya, S. K. D, and T. Roshni, "Chapter 27 - Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model," Integrating Disaster Science and Management, P. Samui, D. Kim and C. Ghosh, eds., pp. 451-460: Elsevier, 2018.
[7] European Space Agency. "Resolution and Swath - Spatial and Spectral Resolutions," https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/instrumentpayload/resolution-and-swath.
[8] USGS. "What are the band designations for the Landsat satellites?," https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites.
[9] H. K. Zhang, D. P. Roy, L. Yan et al., “Characterization of Sentinel-2A and Landsat- 8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences,” Remote sensing of environment, vol. 215, pp. 482-494, 2018.
[10] P. Ettehadi Osgouei, S. Kaya, E. Sertel et al., “Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery,” Remote Sensing, vol. 11, no. 3, pp. 345, 2019.
[11] H. G. Jones, and R. A. Vaughan, Remote sensing of vegetation: principles, techniques, and applications: Oxford university press, 2010.
[12] P. Ceccato, S. Flasse, S. Tarantola et al., “Detecting vegetation leaf water content using reflectance in the optical domain,” Remote sensing of environment, vol. 77, no. 1, pp. 22-33, 2001.
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https://doi.org/10.5593/sgem2022/2.1/s10.40
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Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 04 - 10 July, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
339-346
04 - 10 July, 2022
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