Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran)
Biomass holds great importance in the environment, as it not only allows us to measure the carbon stored in forests but also facilitates the assessment of biodiversity and the evaluation of ecological integrity within these crucial ecosystems. In this study, we employed a Genetic Algorithm (GA) to e...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Kuwait Journal of Science |
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| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410823002006 |
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| author | Armin Moghimi Ava Tavakoli Darestani Nikrouz Mostofi Mahdiyeh Fathi Meisam Amani |
| author_facet | Armin Moghimi Ava Tavakoli Darestani Nikrouz Mostofi Mahdiyeh Fathi Meisam Amani |
| author_sort | Armin Moghimi |
| collection | DOAJ |
| description | Biomass holds great importance in the environment, as it not only allows us to measure the carbon stored in forests but also facilitates the assessment of biodiversity and the evaluation of ecological integrity within these crucial ecosystems. In this study, we employed a Genetic Algorithm (GA) to estimate forest Above-Ground Biomass (AGB) by selecting the most applicable features from both Sentinel-2 optical and Sentinel-1 Synthetic Aperture Radar (SAR) images in the Noor forest. The study area was divided into four distinct regions (north, near north, middle, and south), and each region was documented with 100 sample plots through fieldwork to enable comprehensive analysis. In our workflow, Sentinel-2-derived features (i.e., spectral bands, vegetation indices (VIs), soil indices (SIs), and water indices (WIs), along with Sentinel-1 SAR features were initially extracted. Subsequently, GA was employed to select the most optimal features among them within both Random Forest (RF) and Multiple Linear Regression (MLR) models, leading to enhanced accuracy in the forest AGB estimation process. The experimental results demonstrated that the RF model outperformed the MLR model in estimating forest AGB. Furthermore, incorporating GA-based feature selection substantially improved the accuracy of both models, resulting in more dependable AGB estimations. The selected features from the combined Sentinel-1 and Sentinel-2 data also provided the best AGB estimation, surpassing the individual use of each dataset. The selected features from Sentinel-2 particularly played a more substantial role in achieving this overall enhanced performance in AGB estimation. The AGB estimates based on GA-RF were more accurate in all cases, with an average coefficient of determination (R2) of 0.5 and average RMSE of 13.17 Mg ha−1, while the MLR-based estimates were less accurate, with an average R2 value lower than 0.3 and average RMSE higher than 16 Mg ha−1. Furthermore, the GA-RF model selected a wider variety of features including spectral bands, indices, and SAR features compared to GA-MLR, resulting in accurate AGB estimation in the Noor forest. |
| format | Article |
| id | doaj-art-4584ebd8a876448d8959ed1e46d0d7d5 |
| institution | Kabale University |
| issn | 2307-4116 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-4584ebd8a876448d8959ed1e46d0d7d52025-08-20T03:46:07ZengElsevierKuwait Journal of Science2307-41162024-04-01512100159https://doi.org/10.1016/j.kjs.2023.11.008Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran)Armin Moghimi0https://orcid.org/0000-0002-0455-4882Ava Tavakoli Darestani1Nikrouz Mostofi2Mahdiyeh Fathi3Meisam Amani4Department of Geomatics, Faculty of Engineering, Islamic Azad University South Branch of Tehran, Tehran, IranDepartment of Geomatics, Faculty of Engineering, Islamic Azad University South Branch of Tehran, Tehran, IranDepartment of Geomatics, Faculty of Engineering, Islamic Azad University South Branch of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranWSP Environment and Infrastructure Canada Limited, Ottawa, ON, K2E 7L5, CanadaBiomass holds great importance in the environment, as it not only allows us to measure the carbon stored in forests but also facilitates the assessment of biodiversity and the evaluation of ecological integrity within these crucial ecosystems. In this study, we employed a Genetic Algorithm (GA) to estimate forest Above-Ground Biomass (AGB) by selecting the most applicable features from both Sentinel-2 optical and Sentinel-1 Synthetic Aperture Radar (SAR) images in the Noor forest. The study area was divided into four distinct regions (north, near north, middle, and south), and each region was documented with 100 sample plots through fieldwork to enable comprehensive analysis. In our workflow, Sentinel-2-derived features (i.e., spectral bands, vegetation indices (VIs), soil indices (SIs), and water indices (WIs), along with Sentinel-1 SAR features were initially extracted. Subsequently, GA was employed to select the most optimal features among them within both Random Forest (RF) and Multiple Linear Regression (MLR) models, leading to enhanced accuracy in the forest AGB estimation process. The experimental results demonstrated that the RF model outperformed the MLR model in estimating forest AGB. Furthermore, incorporating GA-based feature selection substantially improved the accuracy of both models, resulting in more dependable AGB estimations. The selected features from the combined Sentinel-1 and Sentinel-2 data also provided the best AGB estimation, surpassing the individual use of each dataset. The selected features from Sentinel-2 particularly played a more substantial role in achieving this overall enhanced performance in AGB estimation. The AGB estimates based on GA-RF were more accurate in all cases, with an average coefficient of determination (R2) of 0.5 and average RMSE of 13.17 Mg ha−1, while the MLR-based estimates were less accurate, with an average R2 value lower than 0.3 and average RMSE higher than 16 Mg ha−1. Furthermore, the GA-RF model selected a wider variety of features including spectral bands, indices, and SAR features compared to GA-MLR, resulting in accurate AGB estimation in the Noor forest.https://www.sciencedirect.com/science/article/pii/S2307410823002006above-ground biomassfeature selectiongenetic algorithmmultiple linear regressionrandom forest regressionsatellite imagery |
| spellingShingle | Armin Moghimi Ava Tavakoli Darestani Nikrouz Mostofi Mahdiyeh Fathi Meisam Amani Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) Kuwait Journal of Science above-ground biomass feature selection genetic algorithm multiple linear regression random forest regression satellite imagery |
| title | Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) |
| title_full | Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) |
| title_fullStr | Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) |
| title_full_unstemmed | Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) |
| title_short | Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) |
| title_sort | improving forest above ground biomass estimation using genetic based feature selection from sentinel 1 and sentinel 2 data case study of the noor forest area in iran |
| topic | above-ground biomass feature selection genetic algorithm multiple linear regression random forest regression satellite imagery |
| url | https://www.sciencedirect.com/science/article/pii/S2307410823002006 |
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