Remote Sensing, Vol. 15, Pages 2833: Forest Vertical Structure Mapping Using Multi-Seasonal UAV Images and Lidar Data via Modified U-Net Approaches

Remote Sensing, Vol. 15, Pages 2833: Forest Vertical Structure Mapping Using Multi-Seasonal UAV Images and Lidar Data via Modified U-Net Approaches

Remote Sensing doi: 10.3390/rs15112833

Authors: Yu Jung

With the acceleration of global warming, research on forests has become important. Vertical forest structure is an indicator of forest vitality and diversity. Therefore, further studies are essential. The investigation of forest structures has traditionally been conducted through in situ surveys, which require substantial time and money. To overcome these drawbacks, in our previous study, vertical forest structure was mapped through machine learning techniques and multi-seasonal remote sensing data, and the classification performance was improved to a 0.92 F1-score. However, the use of multi-seasonal images includes tree location errors owing to changes in the timing and location of acquisition between images. This error can be reduced by using a modified U-Net model that generates a low-resolution output map from high-resolution input data. Therefore, we mapped vertical forest structures from a multi-seasonal unmanned aerial vehicle (UAV) optic and LiDAR data using three modified U-Net models to improve mapping performance. Spectral index maps related to forests were calculated as optic images, and canopy height maps were produced using the LiDAR-derived digital surface model (DSM) and digital terrain model (DTM). Spectral index maps and filtered canopy height maps were then used as input data and applied to the following three models: (1) a model that modified only the structure of the decoder, (2) a model that modified both the structure of the encoder and decoder, and (3) a model that modified the encoder, decoder, and the part that concatenated the encoder and decoder. Model 1 had the best performance with an F1-score of 0.97. The F1-score value was higher than 0.9 for both Model 2 and Model 3. Model 1 improved the performance by 5%, compared to our previous research. This implies that the model performance is enhanced by reducing the influence of position error.