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Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa

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dc.contributor.author Xulu, Sifiso
dc.contributor.author Peerbhay, Kabir
dc.contributor.author Gebreslasi, Michael
dc.contributor.author Riyad, Ismail
dc.date.accessioned 2020-01-27T06:10:48Z
dc.date.available 2020-01-27T06:10:48Z
dc.date.issued 2019-06
dc.identifier.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. en_US
dc.identifier.issn 1999-4907
dc.identifier.uri https://doi.org/10.3390/f10070531
dc.identifier.uri http://hdl.handle.net/10530/1985
dc.description Peer reviewed article published under Forests Journal, Volume 10 Issue 7 en_US
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject Unsupervised Random Forest clustering en_US
dc.subject drought; plantation forests en_US
dc.subject normalized en_US
dc.subject difference water index; en_US
dc.subject Google Earth Engine en_US
dc.title Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa en_US
dc.type Article en_US

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