Monitoring vegetation condition is essential for ecological sustainability, restoration planning, and climate change adaptation, particularly in urban-adjacent conservation areas such as Entoto Natural Park in Addis Ababa, Ethiopia. However, vegetation condition assessments in the park have been limited and lack quantitative evidence based on geospatial approaches. This study evaluates natural vegetation conditions using multispectral remote sensing, spectral indices, and a Random Forest machine learning model. Landsat imagery from 1995, 2005, 2015, and 2025 was processed to generate NDVI, GNDVI, and NDWI indices, which were used to classify vegetation health and analyze temporal trends. The Random Forest classifier was trained using field-based reference samples and validated using out-of-bag accuracy metrics. Results indicate a general improvement in vegetation condition between 1995 and 2025, with higher chlorophyll content and water availability in recently rehabilitated areas, while eucalyptus-dominated zones exhibited comparatively lower moisture and greenness values. The prediction model also forecasted future vegetation conditions, suggesting continued improvement under ongoing restoration programs. This study demonstrates the effectiveness of spectral indices combined with machine learning for vegetation condition monitoring and provides a geospatial foundation to support sustainable management and restoration efforts under Ethiopia’s Green Legacy Initiative.
| Published in | Engineering Science (Volume 11, Issue 1) |
| DOI | 10.11648/j.es.20261101.11 |
| Page(s) | 1-17 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Entoto Natural Park, GNDVI, NDVI, NDWI, Random Forest, Remote Sensing, Spectral Indices, Vegetation Condition
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APA Style
Selato, A. W., Taddesse, A. D. (2026). Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, Ethiopia. Engineering Science, 11(1), 1-17. https://doi.org/10.11648/j.es.20261101.11
ACS Style
Selato, A. W.; Taddesse, A. D. Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, Ethiopia. Eng. Sci. 2026, 11(1), 1-17. doi: 10.11648/j.es.20261101.11
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Download@article{10.11648/j.es.20261101.11,
author = {Amanuel Wolde Selato and Adamu Dessalegn Taddesse},
title = {Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, Ethiopia},
journal = {Engineering Science},
volume = {11},
number = {1},
pages = {1-17},
doi = {10.11648/j.es.20261101.11},
url = {https://doi.org/10.11648/j.es.20261101.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.es.20261101.11},
abstract = {Monitoring vegetation condition is essential for ecological sustainability, restoration planning, and climate change adaptation, particularly in urban-adjacent conservation areas such as Entoto Natural Park in Addis Ababa, Ethiopia. However, vegetation condition assessments in the park have been limited and lack quantitative evidence based on geospatial approaches. This study evaluates natural vegetation conditions using multispectral remote sensing, spectral indices, and a Random Forest machine learning model. Landsat imagery from 1995, 2005, 2015, and 2025 was processed to generate NDVI, GNDVI, and NDWI indices, which were used to classify vegetation health and analyze temporal trends. The Random Forest classifier was trained using field-based reference samples and validated using out-of-bag accuracy metrics. Results indicate a general improvement in vegetation condition between 1995 and 2025, with higher chlorophyll content and water availability in recently rehabilitated areas, while eucalyptus-dominated zones exhibited comparatively lower moisture and greenness values. The prediction model also forecasted future vegetation conditions, suggesting continued improvement under ongoing restoration programs. This study demonstrates the effectiveness of spectral indices combined with machine learning for vegetation condition monitoring and provides a geospatial foundation to support sustainable management and restoration efforts under Ethiopia’s Green Legacy Initiative.},
year = {2026}
}
TY - JOUR T1 - Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, Ethiopia AU - Amanuel Wolde Selato AU - Adamu Dessalegn Taddesse Y1 - 2026/02/02 PY - 2026 N1 - https://doi.org/10.11648/j.es.20261101.11 DO - 10.11648/j.es.20261101.11 T2 - Engineering Science JF - Engineering Science JO - Engineering Science SP - 1 EP - 17 PB - Science Publishing Group SN - 2578-9279 UR - https://doi.org/10.11648/j.es.20261101.11 AB - Monitoring vegetation condition is essential for ecological sustainability, restoration planning, and climate change adaptation, particularly in urban-adjacent conservation areas such as Entoto Natural Park in Addis Ababa, Ethiopia. However, vegetation condition assessments in the park have been limited and lack quantitative evidence based on geospatial approaches. This study evaluates natural vegetation conditions using multispectral remote sensing, spectral indices, and a Random Forest machine learning model. Landsat imagery from 1995, 2005, 2015, and 2025 was processed to generate NDVI, GNDVI, and NDWI indices, which were used to classify vegetation health and analyze temporal trends. The Random Forest classifier was trained using field-based reference samples and validated using out-of-bag accuracy metrics. Results indicate a general improvement in vegetation condition between 1995 and 2025, with higher chlorophyll content and water availability in recently rehabilitated areas, while eucalyptus-dominated zones exhibited comparatively lower moisture and greenness values. The prediction model also forecasted future vegetation conditions, suggesting continued improvement under ongoing restoration programs. This study demonstrates the effectiveness of spectral indices combined with machine learning for vegetation condition monitoring and provides a geospatial foundation to support sustainable management and restoration efforts under Ethiopia’s Green Legacy Initiative. VL - 11 IS - 1 ER -
Department of Surveying Engineering, Wachemo University, Hosanna, Ethiopia
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