Home Journals Articles Updates About
CN
image
收藏切换
Vegetation nitrogen inversion of typical grassland in Inner Mongolia combined with ASD and UAV hyperspectral
收藏切换
PDF
Li-shan JIN1, Xiu-mei WANG1, *, Jian-jun DONG2, **, Ruo-chen WANG1, He-fei WEN2, Yu-yan SUN1, Wen-bo WU2, Zhi-hang ZHANG1, Can KANG2
China Environmental Science | 2025, 45(5) : 2713 - 2723
Less
收藏切换
China Environmental Science | 2025, 45(5): 2713-2723
• Environmental Ecology •
Vegetation nitrogen inversion of typical grassland in Inner Mongolia combined with ASD and UAV hyperspectral
Full
Li-shan JIN1, Xiu-mei WANG1, *, Jian-jun DONG2, **, Ruo-chen WANG1, He-fei WEN2, Yu-yan SUN1, Wen-bo WU2, Zhi-hang ZHANG1, Can KANG2
Affiliations
  • 1.School of Resources and Environmental Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
  • 2.School of Ecological Environment, Inner Mongolia University, Hohhot 010021, China
Published: 2025-05-20
Outline
收藏切换

The combination of ground remote sensing and UAV remote sensing was used to estimate the nitrogen content of typical grassland vegetation in Inner Mongolia. This experiment was carried out in the grassland ecology research base of Inner Mongolia University from August to September 2023, and the ground ASD spectral data and UAV Resonon data were collected. Based on ASD data, four spectral parameters of vegetation index, hyperspectral characteristic variable, continuum removal variable and wavelet coefficient were constructed, and LASSO was used to screen sensitive parameters. Five models of multiple linear, XGBoost, SVM, ANN and KNN were constructed to estimate the nitrogen content of vegetation. The results showed that the SVM method based on wavelet coefficients was the optimal model（validation set R2=0.72, RMSE and MAE were 0.26 and 0.18, respectively）. Finally, the model was used to estimate and map the UAV Resonon data（validation set R2=0.41, RMSE and MAE were 0.42 and 0.32, respectively）. The research showed that the combination of ASD and UAV images with machine learning algorithms could be used to realize the estimation of grassland vegetation nitrogen content, and was provided basic data and technical support for optimizing fertilization and improving forage quality.

hyperspectral  /  nitrogen  /  typical grassland in Inner Mongolia  /  inversion model
Li-shan JIN, Xiu-mei WANG, Jian-jun DONG, Ruo-chen WANG, He-fei WEN, Yu-yan SUN, Wen-bo WU, Zhi-hang ZHANG, Can KANG. Vegetation nitrogen inversion of typical grassland in Inner Mongolia combined with ASD and UAV hyperspectral[J]. China Environmental Science, 2025 , 45 (5) : 2713 -2723 .
Appendix
Less
Year 2025 volume 45 Issue 5
PDF
35
14
Cite this Article
BibTeX
Article Info
  • Receive Date：2024-08-31
  • Online Date：2026-03-18
  • Published：2025-05-20
Article Data
Affiliations
History
  • Received：2024-08-31
Funding
Affiliations
    1.School of Resources and Environmental Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    2.School of Ecological Environment, Inner Mongolia University, Hohhot 010021, China
References
Share
https://castjournals.cast.org.cn/joweb/zghjkx/EN/
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House