Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification

TL;DR

This paper presents a soft multi-label classification approach to quantify complex ecosystem services using proxy land use labels, addressing the challenge of measuring biodiversity and heterogeneity in environmental management.

Contribution

It introduces a novel soft multi-label classification method that effectively leverages proxy land use labels to predict diverse ecosystem services.

Findings

Effective prediction of ecosystem services using proxy land use labels

Demonstrates the utility of soft multi-label classifiers in ecological modeling

Addresses the challenge of measuring biodiversity and heterogeneity

Abstract

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.