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dc.contributor.author Wessels, Konrad
dc.contributor.author Mathieu, Renaud
dc.contributor.author Knox, N
dc.contributor.author Main, Russel S
dc.contributor.author Naidoo, Laven
dc.contributor.author Steenkamp, Karen C
dc.date.accessioned 2020-06-24T10:25:17Z
dc.date.available 2020-06-24T10:25:17Z
dc.date.issued 2019-11
dc.identifier.citation Wessels, K. et al. 2019. Mapping and monitoring fractional woody vegetation cover in the arid savannas of Namibia using LiDAR training data, machine learning and ALOS PALSAR data. Remote Sensing, vol. 11(22): https://doi.org/10.3390/rs11222633 en_US
dc.identifier.issn 2072-4292
dc.identifier.uri https://www.mdpi.com/2072-4292/11/22/2633
dc.identifier.uri https://doi.org/10.3390/rs11222633
dc.identifier.uri http://hdl.handle.net/10204/11467
dc.description This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.description.abstract Namibia is a very arid country, but experienced significant bush encroachment which has decreased ivestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50m and 75m resolution) using SAR satellite data (ALOS PALSAR global mosaics, 2009, 2010, 2015, 2016) and ancillary variables (mean annual precipitation - MAP, elevation), with machine learning models that were trained with diverse airborne LiDAR data sets (244 032 ha, 2008-2014). When only the SAR variables were used, an average R2 of 0.65 (RSME = 0.16) was attained. Adding either elevation or MAP, or both ancillary variables, increased the mean R2 to 0.75 (RSME = 0.13), and 0.79 (RSME = 0.12). The inclusion of MAP addressed the overestimation of FWC in very arid areas, but resulted in some anomalies that were related to the geographic distribution and representativeness of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. FWC change maps provided insightful regional patterns and detailed local patterns related to debushing activities, wildfires and regrowth and can help inform national rangeland policies and debushing programs. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Worklist;23550
dc.subject Namibia en_US
dc.subject Bush encroachment en_US
dc.subject ALOS PALSAR en_US
dc.subject Woody covers en_US
dc.subject LiDAR en_US
dc.title Mapping and monitoring fractional woody vegetation cover in the arid savannas of Namibia using LiDAR training data, machine learning and ALOS PALSAR data en_US
dc.type Article en_US


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