Utilizing remote sensing data in forest inventory sampling via Bayesian optimization

TL;DR

This paper introduces a Bayesian optimization-based sampling method that leverages remote sensing data to improve forest inventory estimates, especially when sample sizes are limited, by effectively utilizing the relationship between RS and inventory variables.

Contribution

The paper presents a novel sampling approach that incorporates remote sensing data into Bayesian optimization for forest inventory, enhancing estimation accuracy over traditional methods.

Findings

Proposed method outperforms baseline sampling techniques in MSE reduction.

Effective when the RS-inventory relationship is accurately modeled.

Validated with synthetic and real data from Finland.

Abstract

In large-area forest inventories a trade-off between the amount of data to be sampled and the costs of collecting the data is necessary. It is not always possible to have a very large data sample when dealing with sampling-based inventories. It is therefore necessary to optimize the sampling design in order to achieve optimal population parameter estimation. On the contrary, the availability of remote sensing (RS) data correlated with the forest inventory variables is usually much higher. The combination of RS and the sampled field measurement data is often used for improving the forest inventory parameter estimation. In addition, it is also reasonable to study the utilization of RS data in inventory sampling, which can further improve the estimation of forest variables. In this study, we propose a data sampling method based on Bayesian optimization which uses RS data in forest inventory sample selection. The presented method applies the learned functional relationship between the RS and inventory data in new sampling decisions. We evaluate our method by conducting simulated sampling experiments with both synthetic data and measured data from the Aland region in Finland. The proposed method is benchmarked against two baseline methods: simple random sampling and the local pivotal method. The results of the simulated experiments show the best results in terms of MSE values for the proposed method when the functional relationship between RS and inventory data is correctly learned from the available training data.