Performance evaluation of convolution neural networks in canopy height estimation using sentinel 2 data, application to Thailand

Electric shorting induced by tall vegetation is one of the major hazards affecting power transmission lines extending through rural regions and rough terrain for tens of kilometres. This raises the need for an accurate, reliable, and cost-effective approach for continuous monitoring of canopy heights. This paper proposes and evaluates two deep convolution neural network (CNN) variants based on Seg-Net and Res-Net architectures, characterized by their small number of trainable weights (nearly 800,000) while maintaining high estimation accuracy. The proposed models utilize the freely available data from Sentinel-2, and a digital surface model to estimate forest canopy heights with high accuracy and a spatial resolution of 10 metres. Various factors affect canopy height estimation, including topography signature, dataset diversity, input layers, and model structure. The proposed models are applied separately to two powerline regions located in the northern and southern parts of Thailand. The application results show that the proposed Encoder-Decoder CNN Seg-Net model presents an average mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (Formula presented.) of 1.38 m, 1.85 m, and 0.87, respectively, and is nearly 4.8 times faster than the CNN Res-Net model in conversion. These results prove the proposed model’s capability of estimating and monitoring canopy heights with high accuracy and fine spatial resolution.