GENERAL ENQUIRIES: Tel: + 27 12 841 2911 | Email:

Show simple item record Naidoo, L Mathieu, Renaud SA Main, R Kleynhans, W Wessels, Konrad J Asner, G Leblon, B 2015-12-18T12:50:54Z 2015-12-18T12:50:54Z 2015-07
dc.identifier.citation Naidoo, L., Mathieu, R.S.A., Main, R., Kleynhans, W., Wessels, K.J., Asner, G. and Leblon, B. 2015. Savannah woody structure modelling and mapping using multifrequency (X-, C- and L-band) synthetic aperture radar data. ISPRS Journal of Photogrammetry and Remote Sensing, vol 105, pp. 234-250 en_US
dc.identifier.issn 0924-2716
dc.description 12. Description: Copyright: 2015 Elsevier. Due to copyright restrictions, the attached PDF file only contains the post-print version of the full text item. For access to the full text item, please consult the publisher's website. The definitive version of the work is published in ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 1055, pp 234–250 en_US
dc.description.abstract Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterized by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar - SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R(sup2) of 0.77), TCV (R(sup2) of 0.79) and AGB (R(sup2) of 0.78) metrics in Southern African savannahs than the shorter wavelengths (Xand C-band) both as individual and combined (X+C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X+C+Lband) yielded the best overall results for all three metrics (R(sup2)=0.83 for CC and AGB and R(sup2)=0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Workflow;15464
dc.subject Woody structures en_US
dc.subject Savannahs en_US
dc.subject Synthetic Aperture Radar en_US
dc.subject SAR en_US
dc.subject Multi-frequency en_US
dc.subject Light Detection and Ranging en_US
dc.subject LiDAR en_US
dc.subject Random forests en_US
dc.title Savannah woody structure modelling and mapping using multifrequency (X-, C- and L-band) synthetic aperture radar data en_US
dc.type Article en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ResearchSpace

Advanced Search


My Account