Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest
Abstract In recent decades, the increasing frequency and severity of wildfires have been linked to climate change and human activities. Understanding the dynamics of post-fire vegetation recovery (PVR) is therefore critical for forest ecosystem restoration and management. The present study analysed...
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Springer
2025-07-01
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| Series: | Discover Geoscience |
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| Online Access: | https://doi.org/10.1007/s44288-025-00196-5 |
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| author | Loventa Anyango Otieno Terry Amolo Otieno Brian Rotich Katharina Löhr Harison Kiplagat Kipkulei |
| author_facet | Loventa Anyango Otieno Terry Amolo Otieno Brian Rotich Katharina Löhr Harison Kiplagat Kipkulei |
| author_sort | Loventa Anyango Otieno |
| collection | DOAJ |
| description | Abstract In recent decades, the increasing frequency and severity of wildfires have been linked to climate change and human activities. Understanding the dynamics of post-fire vegetation recovery (PVR) is therefore critical for forest ecosystem restoration and management. The present study analysed burn severities and investigated the impact of environmental variables on post-fire recovery (PVR) in the Mount Kenya Forest Ecosystem (MKFE). The Random Forest (RF) regression model was employed to predict PVR and identify factors that significantly contribute to PVR in the Mount Kenya Forest ecosystem. Landsat satellite imageries from 2011 to 2021 were used to classify burn severity into seven classes based on the differenced Normalized Burn Ratio (dNBR) index. Climate data, soil organic carbon, and topographic variables were integrated into the RF model to predict trends in PVR. The RF model achieved excellent accuracy with a coefficient of determination (R²) of 0.9013 and a Root Mean Square Error (RMSE) of 0.0280 on the training dataset, and R² of 0.8753 and RMSE of 0.0406 on the validation set. The model further revealed a strong positive relationship between temperature and Land Surface Temperature (LST), as well as vegetation recovery. On the other hand, topographic variables demonstrated a strong negative relationship with vegetation recovery. The combined influence of topographic and temperature condition variables highlights the heterogeneous nature of recovery processes, hence the need for spatially targeted management strategies. These findings have significant implications for adaptive management strategies in tropical montane ecosystems facing increasing wildfire risks. |
| format | Article |
| id | doaj-art-df63b68e50de471bb8706f7207fd5643 |
| institution | Kabale University |
| issn | 2948-1589 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
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| series | Discover Geoscience |
| spelling | doaj-art-df63b68e50de471bb8706f7207fd56432025-08-20T03:45:52ZengSpringerDiscover Geoscience2948-15892025-07-013111810.1007/s44288-025-00196-5Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forestLoventa Anyango Otieno0Terry Amolo Otieno1Brian Rotich2Katharina Löhr3Harison Kiplagat Kipkulei4Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology (JKUAT)Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology (JKUAT)Faculty of Environmental Studies and Resources Development, Chuka UniversityFaculty of Forest and Environment, Eberswalde University for Sustainable Development (HNEE)Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology (JKUAT)Abstract In recent decades, the increasing frequency and severity of wildfires have been linked to climate change and human activities. Understanding the dynamics of post-fire vegetation recovery (PVR) is therefore critical for forest ecosystem restoration and management. The present study analysed burn severities and investigated the impact of environmental variables on post-fire recovery (PVR) in the Mount Kenya Forest Ecosystem (MKFE). The Random Forest (RF) regression model was employed to predict PVR and identify factors that significantly contribute to PVR in the Mount Kenya Forest ecosystem. Landsat satellite imageries from 2011 to 2021 were used to classify burn severity into seven classes based on the differenced Normalized Burn Ratio (dNBR) index. Climate data, soil organic carbon, and topographic variables were integrated into the RF model to predict trends in PVR. The RF model achieved excellent accuracy with a coefficient of determination (R²) of 0.9013 and a Root Mean Square Error (RMSE) of 0.0280 on the training dataset, and R² of 0.8753 and RMSE of 0.0406 on the validation set. The model further revealed a strong positive relationship between temperature and Land Surface Temperature (LST), as well as vegetation recovery. On the other hand, topographic variables demonstrated a strong negative relationship with vegetation recovery. The combined influence of topographic and temperature condition variables highlights the heterogeneous nature of recovery processes, hence the need for spatially targeted management strategies. These findings have significant implications for adaptive management strategies in tropical montane ecosystems facing increasing wildfire risks.https://doi.org/10.1007/s44288-025-00196-5Post-fire vegetation recovery (PVR)Land surface temperatureBurn severityRandom forest modelRemote sensingMount Kenya forest ecosystem |
| spellingShingle | Loventa Anyango Otieno Terry Amolo Otieno Brian Rotich Katharina Löhr Harison Kiplagat Kipkulei Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest Discover Geoscience Post-fire vegetation recovery (PVR) Land surface temperature Burn severity Random forest model Remote sensing Mount Kenya forest ecosystem |
| title | Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest |
| title_full | Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest |
| title_fullStr | Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest |
| title_full_unstemmed | Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest |
| title_short | Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest |
| title_sort | integrating remote sensing and machine learning to evaluate environmental drivers of post fire vegetation recovery in the mount kenya forest |
| topic | Post-fire vegetation recovery (PVR) Land surface temperature Burn severity Random forest model Remote sensing Mount Kenya forest ecosystem |
| url | https://doi.org/10.1007/s44288-025-00196-5 |
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