A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia

Gezahiegn Tessema; Mahlet Agegnehu Asfaw

doi:10.61969/jai.1922074

A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia

Abstract

Nech Sar National Park (NSNP), a vital biodiversity sanctuary in Ethiopia, is experiencing increasing pressure from human activities. This research employs Google Earth Engine (GEE) and machine learning (ML) techniques to examine vegetation patterns and land cover transformations from 2015 to 2024. The study utilized high-resolution Planet NICFI (4.77 m) imagery and evaluated four ML algorithms for land cover classification: Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), and Gradient Tree Boost (GTB). RF emerged as the most accurate model, with an overall accuracy of 97.81% and a Kappa coefficient of 0.9715, surpassing SVM (79.56%), CART (96.35%), and GTB (94.16%). The findings revealed substantial ecological changes: dense vegetation decreased from 34.2% (2015) to 22.9% (2024), while savanna coverage expanded from 27.9% to 61.1%, primarily due to agricultural expansion and deforestation. Bushland saw a dramatic reduction (19.4% to 0.8%), and grassland fluctuated, reaching a peak of 21.5% in 2019. Bare land remained relatively constant (2–8%). Hotspot analysis identified the eastern part of NSNP as susceptible to land-use alterations. The study introduced an innovative integrated model, combining GEE's cloud computing capabilities with RF, enabling real-time monitoring of land cover dynamics. This framework allows for scalable, high-resolution analysis, providing policymakers with valuable insights for developing adaptive conservation strategies. The research highlights the transformative potential of ML and cloud-based platforms in addressing ecological degradation, offering a replicable approach for protected areas worldwide facing similar challenges.

Keywords

Supporting Institution

Dilla University, Ethiopia

Project Number

DU-25-2345

Ethical Statement

The study does not involve human participants or animals. In the study, the author/s declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval.

Thanks

I would like to express my heartfelt gratitude to Almighty God for guiding me throughout this study. I extend my sincere thanks to Gebriel Asfaw, the Office Head and Advisor of the General Director of the Ethiopian Wildlife Conservation Authority, for his invaluable support and guidance. I am also grateful to Miss Firehiiwot Samuel Kelibe, Head of General Services at Nech Sar National Park, for her collaboration and insights that enriched my research. The contributions of my colleagues and the dedicated staff of the Department of Geography and Environmental Studies have been instrumental in shaping this work. I would like to acknowledge the exceptional mentorship of Principal Advisor Dr. Abiot and Co-Advisor Dr. Amiro, whose expertise and encouragement have greatly influenced my academic journey. Special thanks to Dr. Arega for shaping the scope of this study with his insightful perspectives. Finally, I would like to express my appreciation to my classmates for their camaraderie and support throughout this endeavor.

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Details

Primary Language

English

Subjects

Machine Learning Algorithms

Journal Section

Research Article

Authors

Gezahiegn Tessema ^*
0000-0001-9575-3395
Ethiopia

Mahlet Agegnehu Asfaw
0009-0003-4859-5816
Ethiopia

Publication Date

June 22, 2026

Submission Date

April 2, 2026

Acceptance Date

June 5, 2026

Published in Issue

Year 2026 Number: 10

DOI

https://doi.org/10.61969/jai.1922074

IZ

https://izlik.org/JA79CM32ZW

Cite

RIS / Bibtex

APA

Tessema, G., & Agegnehu Asfaw, M. (2026). A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia. Journal of AI, 10, 151-166. https://doi.org/10.61969/jai.1922074

AMA

1.Tessema G, Agegnehu Asfaw M. A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia. Journal of AI. 2026;(10):151-166. doi:10.61969/jai.1922074

Chicago

Tessema, Gezahiegn, and Mahlet Agegnehu Asfaw. 2026. “A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia”. Journal of AI, nos. 10: 151-66. https://doi.org/10.61969/jai.1922074.

EndNote

Tessema G, Agegnehu Asfaw M (June 1, 2026) A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia. Journal of AI 10 151–166.

IEEE

[1]G. Tessema and M. Agegnehu Asfaw, “A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia”, Journal of AI, no. 10, pp. 151–166, June 2026, doi: 10.61969/jai.1922074.

ISNAD

Tessema, Gezahiegn - Agegnehu Asfaw, Mahlet. “A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia”. Journal of AI. 10 (June 1, 2026): 151-166. https://doi.org/10.61969/jai.1922074.

JAMA

1.Tessema G, Agegnehu Asfaw M. A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia. Journal of AI. 2026;:151–166.

MLA

Tessema, Gezahiegn, and Mahlet Agegnehu Asfaw. “A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia”. Journal of AI, no. 10, June 2026, pp. 151-66, doi:10.61969/jai.1922074.

Vancouver

1.Gezahiegn Tessema, Mahlet Agegnehu Asfaw. A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia. Journal of AI. 2026 Jun. 1;(10):151-66. doi:10.61969/jai.1922074

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Cyberphysical Systems and Internet of Things, Information Security and Cryptology, Cryptography, Wireless Communication Systems and Technologies (Incl. Microwave and Millimetrewave)

Vijaya Kittu MANDA

Metaverse, Natural Language Processing, Planning and Decision Making, Green Economy, Banking Management, Business Administration, Sustainability Accounting and Reporting

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A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia

A Google Earth Engine and Machine Learning Model for Near-Real Time Spatiotemporal Change Detection: Enhancing Vegetation Cover Assessment in Nech Sar National Park, Ethiopia

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