Scholarly record
OPTIMISING VEGETATION-INPUT FOR DROUGHT ASSESSMENT WITH SENTINEL-2A DATA
Abstract
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.
Publication Impact Profile
- Captures
- Mendeley - Readers: 9
Publication details
References11
L. Boelens, G. Allaert, and C. Walot, Adapt for life: Cooperatie In Planning UA, 2017.
IPCC, Climate Change 2014: Synthesis Report, Geneva, Switzerland, 2014.
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. DOI: 10.1016/j.ijdrr.2015.01.004
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. DOI: 10.1007/s12517-012-0707-2
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. DOI: 10.1016/j.mcm.2009.10.031
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. DOI: 10.1016/b978-0-12-812056-9.00027-0
European Space Agency. "Resolution and Swath - Spatial and Spectral Resolutions," https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/instrumentpayload/resolution-and-swath.
USGS. "What are the band designations for the Landsat satellites?," https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites.
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. DOI: 10.1016/j.rse.2018.04.031
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. DOI: 10.3390/rs11030345
H. G. Jones, and R. A. Vaughan, Remote sensing of vegetation: principles, techniques, and applications: Oxford university press, 2010.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.