Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa
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Date
2019-06
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Drought limits the production of plantation forests, notably in the drought-prone Zululand
region of South Africa. During the last 40 years, the country has faced a series of severe droughts,
however that of 2015 stands out as the most extreme and prolonged. The 2015 drought impaired
forest productivity and led to widespread tree mortality in this region, but the identification of
tree response to drought stress remains uncertain because of its spatial variability. To address this
problem, a method that can capture drought patterns and identify trees with similar reactions to
drought stress is desired. This could improve the accuracy of detecting trees su ering from drought
stress which is key for forest management planning. In this study, we aimed to evaluate the utility
of unsupervised mapping approaches in compartments of Eucalyptus trees with similar drought
characteristics based on the Normalized Di erence Water Index (NDWI) and to demonstrate the
value of cloud-based Google Earth Engine (GEE) resources for rapid landscape drought monitoring.
Our results showed that calculating distances between pixels using three di erent matrices (Random
Forest (RF) proximity, Euclidean and Manhattan) can accurately detect similarities within a dataset.
The RF proximity matrix produced the best measures, which were clustered using Wards hierarchical
clustering to detect drought with the highest overall accuracy of 87.7%, followed by Manhattan
(85.9%) and Euclidean similarity measures (79.9%), with user and producer results between 84.2% to
91.2%, 42.8% to 98.2% and 37.2% to 94.7%, respectively. These results confirm the value of the RF
proximity matrix and underscore the capability of automatic unsupervised mapping approaches for
monitoring drought stress in tree plantations, as well as the value of using GEE for providing cost
effective datasets to resource stricken countries.
Description
Peer reviewed article published under Forests Journal, Volume 10 Issue 7
Keywords
Unsupervised Random Forest clustering, drought; plantation forests, normalized, difference water index;, Google Earth Engine
Citation
Xulu, S., Peerbhay, K., Gebreslasie, M. and Ismail, R., 2019. Unsupervised clustering of forest response to drought stress in Zululand region, South Africa. Forests, 10(7), p.531.