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arXiv · 2110.08590

Automated Remote Sensing Forest Inventory Using Satellite Imagery

Abduragim Shtanchaev·Artur Bille·Olga Sutyrina·Sara Elelimy
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Abstract

For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large-scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.

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Abduragim Shtanchaev, Artur Bille, Olga Sutyrina, Sara Elelimy. 2021-10-16. Automated Remote Sensing Forest Inventory Using Satellite Imagery. https://arxiv.org/abs/2110.08590

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