Abstract
Effective forest degradation monitoring is crucial for devising targeted interventions to curb carbon emissions and safeguard ecosystem services. In Ethiopia, where coffee farming is intricately tied to forest health, understanding and managing degradation are essential for sustaining both agricultural productivity and environmental integrity. This study rigorously assesses the impact of different management interventions on forest degradation in Ethiopian coffee plots, with a specific focus on quantifying carbon emissions. By integrating field data with freely available high-resolution Sentinel-2 imagery and employing a neural network model to predict NDVIs, we achieved a high level of accuracy, as demonstrated by a strong correlation between a predicted greenness indicator (NDVI) and field biomass data (R2 = 0.97), while also establishing a robust framework for monitoring forest degradation. Our degradation mapping from 2021 to 2023 demonstrated a notable reduction in degraded areas within managed coffee plots, although baseline plots exhibited a more significant reduction in later years. These findings underscore the transformative potential of combining machine learning with remote sensing to effectively monitor and mitigate forest degradation, enhancing the precision of carbon accounting and promoting sustainable land management practices. This approach holds significant potential for use in company-internal sustainability audits, compliance with the upcoming European Union Deforestation Regulation (EUDR), and the generation of carbon credits for both insetting and offsetting carbon emissions.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ICC2024-18175/s1.
Author Contributions
Conceptualization, J.P.; methodology, M.K. and K.W.; validation, M.K. and K.W.; formal analysis, M.K. and K.W.; writing—original draft preparation, M.K.; writing—review and editing, M.K., J.P. and K.W.; visualization, M.K. and K.W.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.
Funding
This work was financed by the German International Climate Initiative (IKI) by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors M. Kalamandeen, K. Weyhermüller and J. Pirker are employed by Unique land use GmbH. The authors declare that this study received funding from the German Federal Ministry for Economic Affairs and Climate Action, the International Climate Initiative (IKI) and the Hanns R. Neumann Stiftung (HRNS). The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).