Regional Mapping of Vineyards Using Machine Learning and LiDAR Data

Abstract

This study evaluates the use of LiDAR data and machine learning algorithms for mapping vineyards. Vineyards are planted in rows spaced at various distances, which can cause spectral mixing within individual pixels and complicate image classification. Four resolution where used for generating normalized digital surface model and intensity derivatives from the LiDAR data. In addition, texture measures with window sizes of 3x3 and 5x5 were generated from the LiDAR derivatives. The different combinations of the resolutions and window sizes resulted in eight data sets that were used as input to 11 machine learning algorithms. A larger window size was found to improve the overall accuracy for all the classifier–resolution combinations. The results showed that random forest with texture measures generated at a 5x5 window size outperformed the other experiments, regardless of the resolution used. The authors conclude that the random forest algorithm used on LiDAR derivatives with a resolution of 1.5m and a window size of 5x5 is the recommend configuration for vineyard mapping using LiDAR data.