Indirect estimation of structural parameters in South African forests using MISR-HR and LiDAR remote sensing data
dc.contributor.author | Mathieu, Renaud | |
dc.contributor.author | Wessels, K.J. (Konrad) | |
dc.contributor.author | Naidoo, Laven | |
dc.contributor.author | Verstraete, Michel | |
dc.contributor.author | Asner, Gregory | |
dc.contributor.author | Main, Russell | |
dc.date.accessioned | 2019-01-15T08:29:49Z | |
dc.date.available | 2019-01-15T08:29:49Z | |
dc.date.issued | 2018-09 | |
dc.description.abstract | Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This study sought to determine the performance of a new MISR-High Resolution (HR) dataset, recently produced at a full 275 m spatial resolution, and consisting of 36 Bidirectional Reflectance Factors (BRF) and 12 Rahman–Pinty–Verstraete (RPV) parameters, to estimate the mean tree height (Hmean) and canopy cover (CC) across structurally diverse, heterogeneous, and fragmented forest types in South Africa. Airborne LiDAR data were used to train and validate Random Forest models which were tested across various MISR-HR scenarios. The combination of MISR multi-angular and multispectral data was consistently effective in improving the estimation of structural parameters, and produced the lowest relative root mean square error (rRMSE) (33.14% and 38.58%), forHmean and CC respectively. The combined RPV parameters for all four bands yielded the best results in comparison to the models of the RPV parameters separately: Hmean (R2 = 0.71, rRMSE = 34.84%) and CC (R2 = 0.60, rRMSE = 40.96%). However, the combined RPV parameters for all four bands in comparison to the MISR-HR BRF 36 band model it performed poorer (rRMSE of 5.1% and 6.2% higher for Hmean and CC, respectively). When considered separately, savanna forest type had greater improvement when adding multi-angular data, with the highest accuracies obtained for the Hmean parameter (R2 of 0.67, rRMSE of 31.28%). The findings demonstrate the potential of the optical multi-spectral and multi-directional newly processed data (MISR-HR) for estimating forest structure across Southern African forest types. | en_ZA |
dc.description.department | Geography, Geoinformatics and Meteorology | en_ZA |
dc.description.sponsorship | The authors acknowledge the Council for Scientific and Industrial Research (CSIR) and the German Federal Ministry of Education and Research (BMBF, Task 205 of the Southern African Science Service Climate Change and Adaptive Land Management, SASSCAL, project) which contributed to the funding of this study. The first author is funded through a Professional Development Grant provided by the National Research Foundation (NRF), South Africa. The Carnegie Airborne Observatory is supported by the Avatar Alliance Foundation, Margaret A. Cargill Foundation, John D. and Catherine T. MacArthur Foundation, Grantham Foundation for the Protection of the Environment, W.M. Keck Foundation, Gordon and Betty Moore Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., andWilliam R. Hearst III. The application of the CAO data in South Africa is made possible by the Andrew Mellon Foundation, Grantham Foundation for the Protection of the Environment, and the endowment of the Carnegie Institution for Science. The MISR data were made available by NASA Atmospheric Science Data Center ASDC while the MISR-HR products were obtained from the Global Change Institute (GCI) at the University of the Witwatersrand in Johannesburg, South Africa. The authors acknowledge Frans van den Bergh of the Meraka Institute, CSIR, South Africa, who provided the script to reformat and re-project the MISR-HR products into a form compatible with the LiDAR data. The iSimangalisoWetland Park and AAM Group are both acknowledged for supplying the LiDAR subset data of the iSimangaliso Saint-Lucia. | en_ZA |
dc.description.uri | http://www.mdpi.com/journal/remotesensing | en_ZA |
dc.identifier.citation | Mahlangu, P., Mathieu, R., Wessels, K. 2018, 'Indirect estimation of structural parameters in South African forests using MISR-HR and LiDAR remote sensing data', Remote Sensing, vol. 10, no. 10, art. 1537, pp. 1-31. | en_ZA |
dc.identifier.issn | 2072-4292 (online) | |
dc.identifier.other | 10.3390/rs10101537 | |
dc.identifier.uri | http://hdl.handle.net/2263/68141 | |
dc.language.iso | en | en_ZA |
dc.publisher | MDPI Publishing | en_ZA |
dc.rights | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. 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 | Vegetation structure | en_ZA |
dc.subject | LiDAR | en_ZA |
dc.subject | Multi-spectral and multi-angular measurements | en_ZA |
dc.subject | MISR-HR | en_ZA |
dc.subject | Random forest | en_ZA |
dc.subject | Multi-angle imaging spectroradiometer (MISR) | en_ZA |
dc.title | Indirect estimation of structural parameters in South African forests using MISR-HR and LiDAR remote sensing data | en_ZA |
dc.type | Article | en_ZA |