A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land
Accurate estimation of above-ground biomass (AGB) and shrub dominance in sandy lands is crucial for monitoring desertification risk and guiding effective management policies. This study proposed a novel strategy for large-scale AGB estimation in sandy landscapes, focusing on Horqin Sandy Land, by in...
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| Format: | Article |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002912 |
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| author | Yiran Zhang Tingxi Liu Asaad Y. Shamseldin Xin Tong Limin Duan Tianyu Jia Shuo Lun Simin Zhang |
| author_facet | Yiran Zhang Tingxi Liu Asaad Y. Shamseldin Xin Tong Limin Duan Tianyu Jia Shuo Lun Simin Zhang |
| author_sort | Yiran Zhang |
| collection | DOAJ |
| description | Accurate estimation of above-ground biomass (AGB) and shrub dominance in sandy lands is crucial for monitoring desertification risk and guiding effective management policies. This study proposed a novel strategy for large-scale AGB estimation in sandy landscapes, focusing on Horqin Sandy Land, by integrating ground reference data with unmanned aerial vehicle (UAV) observations and Landsat 8 OLI imagery. This integration enhanced both the accuracy and efficiency of mapping AGB and shrub dominance. Initially, UAV data were employed to classify shrub and herbaceous vegetation using an object-oriented method, followed by estimating shrub and herbaceous AGB using an allometric growth model (AGM) and partial least squares regression (PLSR). UAV-derived biomass estimates were then aggregated into landscape-scale samples and combined with Landsat imagery to develop Shapley Additive explanation-extreme gradient boosting (SHAP-XGBoost) models for shrub and total AGB. Finally, shrub dominance was mapped as the shrub AGB /total AGB across the region. At the plot scale, AGM coupled with shrub volume provided the highest accuracy for shrub AGB estimation (R2 = 0.97, MAE = 176.24 g). Visible-light features from UAV data significantly contributed to herbaceous AGB estimation, achieving a PLSR model accuracy of R2 of 0.91 and an MAE of 14.76 g/m2. At the landscape scale, the SHAP-XGBoost models demonstrated excellent accuracy, yielding R2 values of 0.78 (MAE = 14.96 g/m2) for shrub AGB and 0.83 (MAE = 30.47 g/m2) for total AGB. These high-precision estimation results facilitated the mapping of the shrub dominance. |
| format | Article |
| id | doaj-art-90a4bf4c19c0496c9f49da996080a647 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-90a4bf4c19c0496c9f49da996080a6472025-08-20T05:05:32ZengElsevierEcological Informatics1574-95412025-12-019010328210.1016/j.ecoinf.2025.103282A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy landYiran Zhang0Tingxi Liu1Asaad Y. Shamseldin2Xin Tong3Limin Duan4Tianyu Jia5Shuo Lun6Simin Zhang7State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, ChinaState Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China; Corresponding authors at: State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China.Department of Civil and Environmental Engineering, The University of Auckland, Auckland 1010, New ZealandState Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China; Corresponding authors at: State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China.State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaState Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, ChinaState Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, ChinaState Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, ChinaAccurate estimation of above-ground biomass (AGB) and shrub dominance in sandy lands is crucial for monitoring desertification risk and guiding effective management policies. This study proposed a novel strategy for large-scale AGB estimation in sandy landscapes, focusing on Horqin Sandy Land, by integrating ground reference data with unmanned aerial vehicle (UAV) observations and Landsat 8 OLI imagery. This integration enhanced both the accuracy and efficiency of mapping AGB and shrub dominance. Initially, UAV data were employed to classify shrub and herbaceous vegetation using an object-oriented method, followed by estimating shrub and herbaceous AGB using an allometric growth model (AGM) and partial least squares regression (PLSR). UAV-derived biomass estimates were then aggregated into landscape-scale samples and combined with Landsat imagery to develop Shapley Additive explanation-extreme gradient boosting (SHAP-XGBoost) models for shrub and total AGB. Finally, shrub dominance was mapped as the shrub AGB /total AGB across the region. At the plot scale, AGM coupled with shrub volume provided the highest accuracy for shrub AGB estimation (R2 = 0.97, MAE = 176.24 g). Visible-light features from UAV data significantly contributed to herbaceous AGB estimation, achieving a PLSR model accuracy of R2 of 0.91 and an MAE of 14.76 g/m2. At the landscape scale, the SHAP-XGBoost models demonstrated excellent accuracy, yielding R2 values of 0.78 (MAE = 14.96 g/m2) for shrub AGB and 0.83 (MAE = 30.47 g/m2) for total AGB. These high-precision estimation results facilitated the mapping of the shrub dominance.http://www.sciencedirect.com/science/article/pii/S1574954125002912Sandy landAbove-ground biomassShrub dominanceUAVSHAP-XGBoostRemote sensing |
| spellingShingle | Yiran Zhang Tingxi Liu Asaad Y. Shamseldin Xin Tong Limin Duan Tianyu Jia Shuo Lun Simin Zhang A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land Ecological Informatics Sandy land Above-ground biomass Shrub dominance UAV SHAP-XGBoost Remote sensing |
| title | A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land |
| title_full | A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land |
| title_fullStr | A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land |
| title_full_unstemmed | A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land |
| title_short | A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land |
| title_sort | synergistic uav landsat novel strategy for enhanced estimation of above ground biomass and shrub dominance in sandy land |
| topic | Sandy land Above-ground biomass Shrub dominance UAV SHAP-XGBoost Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002912 |
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