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Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data

1
Earth Observation Research Group, Natural Resources and Environment Unit, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa
2
Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
3
Department of Geography and GeoInformation Science (GGS), George Mason University, Fairfax, VA 22030, USA
4
Remote Sensing Research Unit, Meraka Institute, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa
5
Global Change Institute (GCI), University of Witwatersrand, Johannesburg 2050, South Africa
6
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1537; https://doi.org/10.3390/rs10101537
Received: 5 July 2018 / Revised: 21 August 2018 / Accepted: 28 August 2018 / Published: 25 September 2018
(This article belongs to the Special Issue MISR)
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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%), for Hmean 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. View Full-Text
Keywords: vegetation structure; LiDAR; multi-spectral and multi-angular measurements; MISR; MISR-HR; Random Forest vegetation structure; LiDAR; multi-spectral and multi-angular measurements; MISR; MISR-HR; Random Forest
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Mahlangu, P.; Mathieu, R.; Wessels, K.; Naidoo, L.; Verstraete, M.; Asner, G.; Main, R. Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data. Remote Sens. 2018, 10, 1537.

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