Understanding and quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. The advancement of remote sensing technology and machine learning techniques has greatly facilitated this process. Yet, ground truth labels, such as biodiversity, are very difficult and expensive to measure. In addition, more easily obtainable proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem. In this paper, we demonstrate how land use proxy labels can be implemented with a soft, multi-label classifier to predict ecosystem services with complex heterogeneity.
@article{arxiv.2406.17147,
title = {Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification},
author = {Zhihui Tian and John Upchurch and G. Austin Simon and José Dubeux and Alina Zare and Chang Zhao and Joel B. Harley},
journal= {arXiv preprint arXiv:2406.17147},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication