Deep learning for vegetation health forecasting: a case study in Kenya

Lees, Thomas; Tseng, Gabriel; Atzberger, Clement; Reece, Steven; Dadson, Simon ORCID: https://orcid.org/0000-0002-6144-4639. 2022 Deep learning for vegetation health forecasting: a case study in Kenya. Remote Sensing, 14 (3), 698. 20, pp. https://doi.org/10.3390/rs14030698

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Official URL: http://dx.doi.org/10.3390/rs14030698

Abstract/Summary

East Africa has experienced a number of devastating droughts in recent decades, including the 2010/2011 drought. The National Drought Management Authority in Kenya relies on real-time information from MODIS satellites to monitor and respond to emerging drought conditions in the arid and semi-arid lands of Kenya. Providing accurate and timely information on vegetation conditions and health—and its probable near-term future evolution—is essential for minimising the risk of drought conditions evolving into disasters as the country’s herders directly rely on the conditions of grasslands. Methods from the field of machine learning are increasingly being used in hydrology, meteorology, and climatology. One particular method that has shown promise for rainfall-runoff modelling is the Long Short Term Memory (LSTM) network. In this study, we seek to test two LSTM architectures for vegetation health forecasting. We find that these models provide sufficiently accurate forecasts to be useful for drought monitoring and forecasting purposes, showing competitive performances with lower resolution ensemble methods and improved performances over a shallow neural network and a persistence baseline.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/rs14030698
UKCEH and CEH Sections/Science Areas:	Hydro-climate Risks (Science Area 2017-)
ISSN:	2072-4292
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper- full text available via Official URL link.
Additional Keywords:	machine learning, deep learning, drought, vegetation health
NORA Subject Terms:	Ecology and Environment Computer Science
Date made live:	25 Mar 2022 11:30 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532319

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3390/rs14030698

UKCEH and CEH Sections/Science Areas:

Hydro-climate Risks (Science Area 2017-)

ISSN:

2072-4292

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper- full text available via Official URL link.

Additional Keywords:

machine learning, deep learning, drought, vegetation health