Lidar Crop Classification with Data Fusion and Machine Learning

Crop type maps are frequently generated using remotely sensed data acquired by sensors mounted on satellites, manned aircraft or unmanned aerial vehicles (UAVs or'drones'), the most popular being multispectral sensors mounted on satellites. Aerial multispectral sensors are more frequently employed where imagery with very high spatial resolution is required. However, the use of Lidar data for crop type mapping is still uncommon. This article outlines research done on creating crop type maps using Lidar, Sentinel-2 and aerial data along with several machine learning classification algorithms for differentiating four crop types in an intensively cultivated area. Lidar data is becoming ever-more widely available as more aerial surveys are conducted, UAV-Lidar sensors are becoming more prevalent and Earth observation satellites are being fitted with Lidar sensors.