Deep learning in the woods

Introduction

This page documents the progress of my research on the global canopy height model by Nico Lang (2024). For the full data and scripts please visit my GitHub and for other projects and more information about me visit my Main Site.

Research question

Can canopy height be derived from spectral data?

The main objective of this research is to uncover the prediction mechanisms of the CNN. Central question of the work is What can be learned about canopy height from optical remote sensing.

Hypothesis

1. The model has not learned a connection of spectral values to canopy height.
   
2. Other variables like location and biome play a more important role.

Results

The preliminary results already show no clear trend after spectral manipultation (Fig. 1). Differences between tiles have shown to be more significant than spectral manipulation, suggesting that the actual influence of spectral properties in the creation of the prediction is very low or none. Relationships between the location of the image tile and the predicted canopy height could already be observed. They will be properly analysed and shown at a later stage of this project.

Fig. 1: Average difference to the original prediction for all manipulated bands and degrees of manipulation.

Colour blind friendly result plots

Soon to come:)

Data

These are the 11 tiles that were used for the analysis of the model. They were choosen to be distributed globally, cover multiple different biomes and based on the presented tiles by the original paper.

Slides

Conference slides (PDF)

Sources

Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023).
A high-resolution canopy height model of the Earth.
Nature Ecology & Evolution, 1-12.

@article{lang2023high,
  title={A high-resolution canopy height model of the Earth},
  author={Lang, Nico and Jetz, Walter and Schindler, Konrad and Wegner, Jan Dirk},
  journal={Nature Ecology \& Evolution},
  pages={1--12},
  year={2023},
  publisher={Nature Publishing Group UK London}
}