Machine Learning Methods for Environmental Monitoring

Dear Colleagues,

Today, environmental monitoring is becoming an increasingly important issue when considering climate and land cover change and its consequences for the environment. Current earth observation satellites provide information with advanced spatial and temporal details that increases the potential of remote sensing to reveal spatial and temporal patterns and trends. In this context, machine learning algorithms have shown to be a powerful method to link remote sensing information to relevant environmental variables by accounting for the complexity and nonlinearity found in nature. The combination of remote sensing data and machine learning methods hence offers great but not yet fully exploited possibilities to monitor environmental variables across disciplines (e.g., biodiversity research, agriculture, forestry, and climatology) and on different temporal and spatial scales. However, recent studies also indicate ongoing challenges when machine learning methods are applied to remote sensing data. Spatial and temporal dependencies in the data, for example, challenge the application of machine learning algorithms and call for new modelling strategies that take the characteristics of remote sensing data into account.

This Special Issue aims to advance the application of machine learning algorithms for remote sensing-based environmental monitoring. We welcome methodological contributions in terms of novel machine learning strategies and innovative developments towards the reliability and robustness of the results. We further welcome applied contributions that demonstrate the potential and the challenges of machine learning applied to remote sensing in the context of environmental monitoring.

We are looking forward to an interesting collection of contributions!

Prof. Dr. Hanna Meyer
Guest Editor

Manuscript Submission Information

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