Abstract
The recent advances in multimedia modeling with deep learning methods have significantly affected remote sensing applications, such as canopy height mapping. Estimating canopy height maps in large-scale is an important step towards sustainable ecosystem management. Apart from the standard height estimation method using LiDAR data, other airborne measurement techniques, such as very high-resolution passive airborne imaging, have also shown to provide accurate estimations. However, those methods suffer from high cost and cannot be used at large-scale nor frequently. In our study, we adopt a neural network architecture to estimate pixel-wise canopy height from cost-effective spaceborne imagery. A deep convolutional encoder-decoder network, based on the SegNet architecture together with skip connections, is trained to embed the multi-spectral pixels of a Sentinel-2 input image to height values via end-to-end learned texture features. Experimental results in a study area of 942 \(\mathrm{km}^2\) yield similar or better estimation accuracy resolution in comparison with a method based on costly airborne images as well as with another state-of-the-art deep learning approach based on spaceborne images.
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Notes
- 1.
For example, Samaria’s Data Cube is a tool for satellite data users to monitor and evaluate land resources and land change. http://datacube.iti.gr/.
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Acknowledgements
This study has been partially funded and supported by the European Union’s Horizon 2020 innovation program under Grant Agreement No. 820852, e-shape (https://e-shape.eu/). LIDAR data was granted by the Cross-border cooperation programme Czech Republic–Bavaria Free State ETC goal 2014–2020, the Interreg V project No. 99 “Přeshranični mapovani lesnich ekosystemů – cesta ke společnemu managementu NP Šumava a NP Bavorsky les /Grenzuberschreitende Kartierung der Waldokosysteme – Weg zum gemeinsamen Management in NP Sumava und NP Bayerischen Wald”. We acknowledge the support of the “Data Pool Initiative for the Bohemian Forest Ecosystem” data-sharing initiative of the Bavarian Forest National Park.
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Alagialoglou, L., Manakos, I., Heurich, M., Červenka, J., Delopoulos, A. (2021). Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_26
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