@inproceedings{bb8523dae43c4b548f351b3b4ff7710e,
title = "SAR-to-LiDAR mapping for tree volume prediction in the Kruger National Park",
abstract = "In this paper a neural network is used to perform a mapping between Synthetic Aperture Radar (SAR) backscatter information and LiDAR measurements, and the performance of the neural network model is evaluated against that of a multiple linear regression model. Our aim is to find a relationship between SAR backscatter information and the LiDAR tree volume measurements on a number of land uses in South Africa's Kruger National Park, using a linear as well as a non-linear model. We also seek to find the optimal grid cell size as well as the best combination of SAR polarisation- and decomposition parameters. Our findings suggest that there exists a linear or at least a near-linear relationship between the SAR backscatter information and the LiDAR measurements in South African savannas and that the addition of polarisation- and decomposition parameters to the input of the models aid in improving the Root Mean Squared Error (RMSE) performance.",
keywords = "LiDAR, Linear regression, Neural network, RMSE, Synthetic Aperture Radar, mapping",
author = "Myburgh, {H. C.} and Olivier, {J. C.} and R. Mathieu and K. Wessels and B. Leblon and G. Asner and J. Buckley",
year = "2011",
month = nov,
day = "16",
doi = "10.1109/IGARSS.2011.6049504",
language = "English (US)",
isbn = "9781457710056",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
pages = "1934--1937",
booktitle = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings",
note = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 ; Conference date: 24-07-2011 Through 29-07-2011",
}
