Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms

by Atman Prins

The second experiment used the methods developed for the first experiment to perform a five-class classification. The five classes consisted of maize, cotton, groundnuts, orchards and non-agriculture. Sentinel-2 and aerial imagery data were added to the analysis and were compared to LiDAR data. The LiDAR data was obtained from a 2016 survey of the Vaalharts irrigation scheme. Furthermore, the three datasets (Sentinel-2, aerial imagery and LiDAR data) were combined in order to evaluate which combination of datasets produces the highest OA. The results showed that the performance of LiDAR data was similar to that of Sentinel-2 imagery, with LiDAR data obtaining a mean OA of 84.3%, while Sentinel-2 obtained a mean OA of 83.6%. The difference between the OAs of LiDAR and Sentinel-2 were statistically insignificant. The highest OA (94.6%) was obtained with RF when the LiDAR, Sentinel-2 and aerial datasets were combined. However, a combination of LiDAR data and Sentinel-2 imagery obtained similar results to when all three datasets were used in combination, with the difference in OA being statistically insignificant.

Generally, LiDAR data are suitable for classifying different crop types, with RF obtaining the highest OAs in both experiments. The combination of multispectral and LiDAR data produced the highest OA.