Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification
This work provides a method for environmental management and policy-making to better assess ecosystem services, though it appears incremental as it builds on existing remote sensing and machine learning techniques.
The paper tackles the challenge of quantifying heterogeneous ecosystem services by using land use proxy labels with a soft, multi-label classifier to predict these services, addressing the limitations of expensive ground truth measurements and simplistic proxies.
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.