# Country-wide high-resolution vegetation height mapping with Sentinel-2

## TL;DR

This study develops a deep learning approach using Sentinel-2 images to produce high-resolution vegetation height maps across entire countries, validated with LiDAR and photogrammetric data, achieving accurate results.

## Contribution

It introduces a CNN-based method for large-scale vegetation height mapping using Sentinel-2 data, with validation in Gabon and Switzerland, demonstrating feasibility at country scale.

## Key findings

- Mean absolute error of 1.7 m in Switzerland
- Vegetation heights up to >50 m accurately estimated
- High-resolution maps generated at 10 m GSD for entire countries

## Abstract

Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to >50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km$^2$ in Gabon and $\approx$5800 km$^2$ in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.