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Synergistic estimation of shrub aboveground biomass in arid and semi−arid areas based on multi−source UAV remote sensing
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Yaxin WANG1, 2, Jiamin WU1, 2, 3, Zhenping HE4, Zejiang LI5, Yue GUO5, Weina SUN5, *, Zhijie MA5, Haixia YOU6, Wen LÜ7
Science & Technology Review | 2026, 44(6) : 57 - 67
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Science & Technology Review | 2026, 44(6): 57-67
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Synergistic estimation of shrub aboveground biomass in arid and semi−arid areas based on multi−source UAV remote sensing
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Yaxin WANG1, 2, Jiamin WU1, 2, 3, Zhenping HE4, Zejiang LI5, Yue GUO5, Weina SUN5, *, Zhijie MA5, Haixia YOU6, Wen LÜ7
Affiliations
  • 1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
  • 2Laboratory of Remote Sensing for Forestry and Grassland Monitoring and Evaluation, National Forestry and Grassland Administration, Beijing 100091, China
  • 3Faculty of Geography, Yunnan Normal University, Kunming 650500, China
  • 4Ordos Forestry and Grassland Bureau, Ordos 017010, China
  • 5Ordos International Desertification Control Technology Innovation Center, Ordos 017010, China
  • 6Ordos Agricultural and Animal Husbandry Technology Extension Center, Ordos 017010, China
  • 7Ordos Vocational College of Ecological Environment, Ordos 017010, China
Published: 2026-03-28 doi: 10.3981/j.issn.1000-7857.2025.12.00119
Outline
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To address the needs for ecological monitoring in arid and semi−arid areas, this study proposes a synergistic inversion and digital representation framework for shrub aboveground biomass (AGB) driven by the fusion of UAV multispectral and LiDAR features. Leveraging the 3D structural sensing advantages of UAV−LiDAR and the spectral−texture features of UAV−MS, the study establishes a technical workflow of "object segmentation−feature selection−synergistic inversion." This framework enables the automatic identification and precise biomass accounting for typical shrubs such as Artemisia ordosica and Salix psammophila. Taking seven typical shrub communities in the Ordos region as the study area, technical validation was conducted based on ground−truth data. Experimental results demonstrate that the proposed method effectively overcomes the limitations of "same spectrum, different objects" in single optical remote sensing and the lack of spectral information in single LiDAR data. The XGBoost model achieves the best comprehensive performance under multi−source feature synergy (R2 ranging from 0.7615 to 0.8814). It exhibits good generalization capabilities across different plant types and complex backgrounds, realizing the digital representation of shrub ecological assets and significantly improving the data production efficiency and technical reliability of biomass monitoring in arid and semi−arid areas.

shrub aboveground biomass  /  UAV multispectral  /  UAV LiDAR  /  multi−source data synergy  /  feature selection
Yaxin WANG, Jiamin WU, Zhenping HE, Zejiang LI, Yue GUO, Weina SUN, Zhijie MA, Haixia YOU, Wen LÜ. Synergistic estimation of shrub aboveground biomass in arid and semi−arid areas based on multi−source UAV remote sensing[J]. Science & Technology Review, 2026 , 44 (6) : 57 -67 . DOI: 10.3981/j.issn.1000-7857.2025.12.00119
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Year 2026 volume 44 Issue 6
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Article Info
doi: 10.3981/j.issn.1000-7857.2025.12.00119
  • Receive Date：2025-12-10
  • Online Date：2026-04-16
  • Published：2026-03-28
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History
  • Received：2025-12-10
  • Revised：2026-02-28
Funding
Affiliations
    1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
    2Laboratory of Remote Sensing for Forestry and Grassland Monitoring and Evaluation, National Forestry and Grassland Administration, Beijing 100091, China
    3Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    4Ordos Forestry and Grassland Bureau, Ordos 017010, China
    5Ordos International Desertification Control Technology Innovation Center, Ordos 017010, China
    6Ordos Agricultural and Animal Husbandry Technology Extension Center, Ordos 017010, China
    7Ordos Vocational College of Ecological Environment, Ordos 017010, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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