Mapping and monitoring fractional woody vegetation cover in the arid savannas of Namibia using LiDAR training data, machine learning, and ALOS PALSAR data

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dc.contributor.author Wessels, Konrad
dc.contributor.author Mathieu, Renaud
dc.contributor.author Knox, Nichola
dc.contributor.author Main, Russell
dc.contributor.author Naidoo, Laven
dc.contributor.author Steenkamp, Karen
dc.date.accessioned 2020-03-09T07:35:06Z
dc.date.available 2020-03-09T07:35:06Z
dc.date.issued 2019-11-11
dc.description Supplementary matgerial: Figure S1. SAR backscatter (gamma-naught) as a function of LiDAR derived fractional woody cover (FWC) for HH (left) and HV (right) polarizations of ALOS PALSAR data. Figure S2. Fractional woody cover (FWC) derived from LiDAR training data as a function of mean annual precipitation (MAP) in the training data set of northern Namibia. Figure S3. Changes in fractional woody cover between 2010 and 2009 for each vegetation structural class. Figure S4. Changes in fractional woody cover between 2016 and 2015 for each vegetation structural class. en_ZA
dc.description.abstract Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50 m and 75 m resolution) using Synthetic Aperture Radar (SAR) satellite data (Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (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 Light Detection and Ranging (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 anomalies in the form of sharp gradients in FWC along a MAP contour which were most likely caused by to the geographic distribution of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. This was the first attempt to produce SAR-derived FWC maps for Namibia and the maps contain substantially more detailed spatial information on woody vegetation structure than existing national maps. During the seven-year study period the Shrubland–Woodland Mosaic was the only vegetation structural class that exhibited a regional net gain in FWC of more than 0.2 across 9% (11,906 km2) of its area that may potentially be attributed to bush encroachment. FWC change maps provided regional insights and detailed local patterns related to debushing and regrowth that can inform national rangeland policies and debushing programs. en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.librarian am2020 en_ZA
dc.description.sponsorship CSIR and Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL). en_ZA
dc.description.uri http://www.mdpi.com/journal/remotesensing en_ZA
dc.identifier.citation Wessels, K., Mathieu, R., Knox, N. 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, no. 22, art. 2633, pp. 1-32. en_ZA
dc.identifier.issn 2072-4292 (online)
dc.identifier.other 10.3390/rs11222633
dc.identifier.uri http://hdl.handle.net/2263/73677
dc.language.iso en en_ZA
dc.publisher MDPI Publishing en_ZA
dc.rights © 2019 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_ZA
dc.subject Namibia en_ZA
dc.subject Bush encroachment en_ZA
dc.subject Debushing en_ZA
dc.subject Fractional woody cover (FWC) en_ZA
dc.subject Synthetic aperture radar (SAR) en_ZA
dc.subject Advanced land observing satellite (ALOS) en_ZA
dc.subject Phased arrayed L-band synthetic aperture radar (PALSAR) en_ZA
dc.subject Mean annual precipitation (MAP) en_ZA
dc.subject Light detection and ranging (LiDAR) en_ZA
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_ZA
dc.type Article en_ZA


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