Automated Remote Sensing Forest Inventory Using Satellite Imagery

TL;DR

This paper explores using autoencoders and classical machine learning algorithms on satellite imagery to classify tree crowns, offering a scalable alternative to UAV-based forest inventories for large areas.

Contribution

It introduces an autoencoder-based method for tree crown classification from satellite images, comparing its performance to traditional CNN classifiers.

Findings

Autoencoder embeddings effectively classify tree crowns.

Autoencoder approach performs comparably to CNN classifiers.

Method suitable for large-scale forest inventory applications.

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.