Aboveground Biomass Estimation in Shrub-Encroached Grasslands by Using Multi-Source UAV Remote Sensing

doi:10.11733/j.issn.1007-0435.2026.02.018

Acta Agrestia Sinica ›› 2026, Vol. 34 ›› Issue (2): 559-572.DOI: 10.11733/j.issn.1007-0435.2026.02.018

Aboveground Biomass Estimation in Shrub-Encroached Grasslands by Using Multi-Source UAV Remote Sensing

MA Lei-chao^1,2, WANG Xing¹, YANG Xiu-chun¹, YANG Dong¹, WANG Zi-chao¹, XING Xiao-yu¹

1. School of Grassland Science, Beijing Forestry University, Beijing 100083, China;
2. Command Center for Natural Resources Comprehensive Survey, China Geological Survey, Beijing 100055, China

Received:2025-03-28 Revised:2025-05-16 Published:2026-01-22

基于无人机多源遥感数据的灌丛草原地上生物量估算

马磊超^1,2, 王兴¹, 杨秀春¹, 杨东¹, 王子超¹, 邢晓语¹

1. 北京林业大学草业与草原学院, 北京 100083;
2. 中国地质调查局自然资源综合调查指挥中心, 北京 100055

通讯作者: 杨秀春，E-mail:yangxiuchun@bjfu.edu.cn
作者简介:马磊超（1981-），男，汉族，河南沈丘人，硕士研究生，主要从事草原遥感研究，E-mail:leichaoma@126.com；
基金资助:
国家自然科学基金项目（41571105）资助

Abstract

Abstract: Shrub encroachment in grasslands， intensified by climate change and anthropogenic activities， represents a substantial threat to the health of grassland ecosystems. This underscores the pressing need for effective and precise biomass monitoring methodologies. The present study was carried out in the shrub-encroached grasslands of the Xilingol League， to investigate optimal above ground biomass （AGB） estimation model by utilizing unmanned aerial vehicle-based LiDAR and hyperspectral data validated against field measurements. Stepwise multiple linear regression， support vector machine， and random forest algorithms were employed for analysis. The results demonstrated a robust correlation between AGB and both LiDAR-derived mean canopy height and hyperspectral reflectance within the red-edge spectral region. Hyperspectral data demonstrated superior accuracy to LiDAR for herbaceous AGB inversion （R²=0.87 vs. 0.24）， whereas LiDAR performed better than hyperspectral data for shrub AGB inversion （R²=0.58 vs. 0.47）. Data fusion significantly improved the model performance， with the random forest model integrating features from both LiDAR and hyperspectral data achieving R² values of 0.88 and 0.64 for herbaceous and shrub AGB， respectively. The related findings offer critical scientific support for the sustainable utilization and precise management of shrub-encroached grasslands.

Key words: Grassland above-ground biomass, LiDAR, Hyperspectral, Machine learning, Shrub-encroached grassland

摘要： 气候变化与人类活动加剧了全球草原灌丛化，严重威胁草原生态系统健康，亟需高效精准的生物量监测技术。本研究以锡林郭勒盟灌丛草原为研究区，基于无人机激光雷达（LiDAR）和高光谱数据，结合地面实测数据，采用逐步多元线性回归、支持向量机和随机森林等方法探究了灌丛草原地上生物量（Above-ground biomass，AGB）的最佳反演模型。结果表明，基于LiDAR数据计算的草层平均高度、高光谱数据的红边区间反射率与AGB显著相关。高光谱在草本AGB反演中优于LiDAR精度（R2=0.87 vs. 0.24），而LiDAR较高光谱则更适用于灌木AGB反演（R2=0.58 vs. 0.47）。数据融合能显著提升模型精度，LiDAR和高光谱数据多源特征融合的随机森林模型使草本和灌木AGB的R2分别达到0.88和0.64。相关成果可为灌丛草原可持续利用和精细化管理提供重要科学支撑。

关键词: 草原地上生物量, 激光雷达, 高光谱, 机器学习, 灌丛草原

MA Lei-chao, WANG Xing, YANG Xiu-chun, YANG Dong, WANG Zi-chao, XING Xiao-yu. Aboveground Biomass Estimation in Shrub-Encroached Grasslands by Using Multi-Source UAV Remote Sensing[J]. Acta Agrestia Sinica, 2026, 34(2): 559-572.

马磊超, 王兴, 杨秀春, 杨东, 王子超, 邢晓语. 基于无人机多源遥感数据的灌丛草原地上生物量估算[J]. 草地学报, 2026, 34(2): 559-572.

References

[1] FANG J Y，YANG Y H，MA W H，et al. Ecosystem carbon stocks and their changes in China’s grasslands[J]. SCIENTIA SINICA Vitae，2010，40（7）：566-576 方精云，杨元合，马文红，等. 中国草地生态系统碳库及其变化[J]. 中国科学：生命科学，2010，40（7）：566-576
[2] GRÜN A L，STRASKRABA S，SCHULZ S，et al. Long-term effects of environmentally relevant concentrations of silver nanoparticles on microbial biomass，enzyme activity，and functional genes involved in the nitrogen cycle of loamy soil[J]. Journal of Environmental Sciences，2018，69：12-22
[3] LIU Z，CHEN R，SONG Y，et al. Aboveground biomass and water storage allocation in alpine willow shrubs in the Qilian Mountains in China[J]. Journal of Mountain Science，2015，12（1）：207-217
[4] XU B，YANG X C，HOU X Y，et al. Advance of technology of remote sensing monitoring for grassland vegetation[J]. Science & Technology Review，2007（9）：5-8 徐斌，杨秀春，侯向阳，等. 草原植被遥感监测方法研究进展[J]. 科技导报，2007（9）：5-8
[5] KNAPP N，FISCHER R，CAZCARRA-BES V，et al. Structure metrics to generalize biomass estimation from LiDAR across forest types from different continents[J]. Remote Sensing of Environment，2020，237：111597
[6] ZHAO Y J，LIU X L，WANG Y，et al. UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland[J]. International Journal of Applied Earth Observation and Geoinformation，2021，101：102358
[7] JIN Y X，XU B，YANG X C， et al. Remote sensing dynamic estimation of grass production in Xilinguole， Inner Mongolia[J]. SCIENTIA SINICA Vitae，2011，41（12）：1185-1195 金云翔，徐斌，杨秀春，等. 内蒙古锡林郭勒盟草原产草量动态遥感估算[J]. 中国科学：生命科学，2011，41（12）：1185-1195
[8] HE L，LI A N，YIN G F，et al. Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery[J]. Remote Sensing，2019，11（13）：1597
[9] GAO H Y，HOU M J， GE J，et al. Hyperspectral Estimation of Aboveground Biomass of Alpine Grassland based on Random Forest Algorithm[J]. Acta Agrestia Sinica，2021，29（8）：1757-1768 高宏元，侯蒙京，葛静，等. 基于随机森林的高寒草地地上生物量高光谱估算[J]. 草地学报，2021，29（8）：1757-1768
[10] XING X Y，YANG X C，XU B，et al. Remote sensing estimation of grassland aboveground biomass based on random forest[J]. Journal of Geo-information Science，2021，23（7）：1312-1324 邢晓语，杨秀春，徐斌，等. 基于随机森林算法的草原地上生物量遥感估算方法研究[J]. 地球信息科学学报，2021，23（7）：1312-1324
[11] SHEN X J，LIU Y W，LIU B H，et al. Effect of shrub encroachment on land surface temperature in semi-arid areas of temperate regions of the Northern Hemisphere[J]. Agricultural and Forest Meteorology，2022，320：108943
[12] BAZEZEW M N，HUSSIN Y A，KLOOSTERMAN E H. Integrating Airborne LiDAR and Terrestrial Laser Scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest， Malaysia[J]. International Journal of Applied Earth Observation and Geoinformation，2018，73：638-652
[13] GUO Q H，LIU J，TAO S L，et al. Perspectives and prospects of LiDAR in forest ecosystem monitoring and modeling[J]. Chinese Science Bulletin，2014，59（6）：459-478 郭庆华，刘瑾，陶胜利，等. 激光雷达在森林生态系统监测模拟中的应用现状与展望[J]. 科学通报，2014，59（6）：459-478
[14] YAO X C，QU Y T，CHANG W J，et al. Estimation of grassland biomass using MODIS data and plant community characteristics[J]. Chinese Journal of Eco-Agriculture，2017，25（4）：530-541 姚兴成，曲恬甜，常文静，等. 基于MODIS数据和植被特征估算草地生物量[J]. 中国生态农业学报，2017，25（4）：530-541
[15] YANG S X，ZHANG W J，FENG Q S，et al. Monitoring of grassland herbage accumulation by remote sensing using MODIS daily surface reflectance data in the Qingnan Region[J]. Acta Prataculturae Sinica，2016，25（8）：14-26 杨淑霞，张文娟，冯琦胜，等. 基于MODIS逐日地表反射率数据的青南地区草地生长状况遥感监测研究[J]. 草业学报，2016，25（8）：14-26
[16] SONG K X，JIANG F G，HU Z D，et al. Remote sensing inversion of above-ground biomass of grassland in the Tibet Autonomous Region. Acta Ecologica Sinica，2023，43（13）：5600-5613 宋柯馨，蒋馥根，胡宗达，等. 西藏自治区草地地上生物量遥感反演研究[J]. 生态学报，2023，43（13）：5600-5613
[17] HUANG J X，WU J，LI C B，et al. Remote sensing retrieval of grassland above-ground biomass in tianzhu county based on Sentinel-2 and Landsat 8 data[J]. Acta Agrestia Sinica，2021，29（9）：2023-2030 黄家兴，吴静，李纯斌，等. 基于Sentinel-2和Landsat 8数据的天祝县草地地上生物量遥感反演[J]. 草地学报，2021，29（9）：2023-2030
[18] TONG X，YANG Z L，ZHANG Y R，et al. Estimation of pasture aboveground biomass using different orders of differential hyperspectral vegetation indices[J]. Acta Agrestia Sinica，2022，30（9）：2438-2448 童新，杨震雷，张亦然，等. 基于不同阶微分高光谱植被指数的牧区草场地上生物量估算[J]. 草地学报，2022，30（9）：2438-2448
[19] ZHANG Y R，LIU Y X，TONG X，et al. Hyperspectral remote sensing inversion of meadow aboveground biomass based on an XGBoost algorithm[J]. Acta Prataculturae Sinica，2021，30（4）：1-12 张亦然，刘廷玺，童新，等. 基于XGBoost算法的草甸地上生物量的高光谱遥感反演[J]. 草业学报，2021，30（4）：1-12
[20] YIN X F，WANG C G， WANG H C，et al. Study on biomass estimation of desert steppe based on hyperspectral characteristic[J]. Journal of Agricultural Mechanization Research，2018，40（10）：154-159 殷晓飞，王春光，王海超，等. 基于高光谱特征参数荒漠草原生物量估测研究[J]. 农机化研究，2018，40（10）：154-159
[21] TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society Series B：Statistical Methodology，1996，58（1）：267-288
[22] VAPNIK V N. The nature of statistical learning theory[M]. Cham：Springer New York，2000：156-163
[23] CHEN A，WANG X，ZHANG M，et al. Fusion of LiDAR and multispectral data for aboveground biomass estimation in mountain grassland[J]. Remote Sensing，2023，15：405
[24] XU C，ZHAO D，ZHENG Z，et al. Correction of UAV LiDAR-derived grassland canopy height based on scan angle[J]. Frontiers in Plant Science，2023，14：1108109
[25] XU K H，SHI Z，MA L C，et al. Retrieval of grassland aboveground biomass based on airborne LiDAR and SuperView-1 data[J]. Acta Prataculturae Sinica，2023，32（5）：40-49 许开宏，施招，马磊超，等. 基于机载激光雷达与高景一号数据的草原地上生物量反演研究[J]. 草业学报，2023，32（5）：40-49
[26] CHEN A，XU C，ZHANG M，et al. Cross-scale mapping of above-ground biomass and shrub dominance by integrating UAV and satellite data in temperate grassland[J]. Remote Sensing of Environment，2024，304：114024
[27] WULAN T Y，BAO G，WUYUN D J，et al. Hyper-spectral remote sensing estimates of aboveground biomass of grassland[J]. Journal of Inner Mongolia Normal University （Natural Science Edition），2015，44（5）：660-666 乌兰吐雅，包刚，乌云德吉，等. 草地地上生物量高光谱遥感估算研究[J].内蒙古师范大学学报（自然科学汉文版），2015，44（5）：660-666
[28] THOMSON E R，SPIEGEL M P，ALTHUIZEN I H J，et al. Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy， UAV imagery and Sentinel-2A data[J]. Environmental Research Letters，2021，16（5）：055006
[29] REN H R，ZHOU G S，ZHANG X S. Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method[J]. Biosystems Engineering，2011，109（4）：385-395
[30] MUTANGA O， SKIDMORE A K. Red edge shift and biochemical content in grass canopies[J]. ISPRS journal of Photogrammetry and Remote Sensing，2007，62（1）：34-42

Aboveground Biomass Estimation in Shrub-Encroached Grasslands by Using Multi-Source UAV Remote Sensing

基于无人机多源遥感数据的灌丛草原地上生物量估算

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	LI Jia-xin, JIN Gui-li, LIU Wen-hao, WANG Sheng-ju, CHEN Meng-tian, LI Wen-xiong, DU Wen-lin, LI Chao, HU Xiu-wen. Analyzing and Modifying of Grassland Plant Information Loss of Aboveground Biomass based on UAV Data [J]. Acta Agrestia Sinica, 2025, 33(7): 2206-2218.
[2]	ZHANG Guo-jing, YAN Qing-song, QIN Wen-qiang, ZHANG Zi-yu, LI Xi-lai, HUANG Jian-qiang. A Comparison Study on the Prediction Methods of Vegetation Index in the Sanjiangyuan Region Based on Gradient Boosting Regression Trees [J]. Acta Agrestia Sinica, 2025, 33(5): 1655-1668.
[3]	HUANG Ke-pan, YANG Xue-mei, LIU Zhi-fei, ZHANG Zhong, WANG Jing-rui, XU Hao-jie. Estimation of the Photosynthetic Pigment Content in Assimilated Branches of Haloxylon ammodendron Based on Hyperspectral Data and Machine Learning Methods [J]. Acta Agrestia Sinica, 2025, 33(1): 250-261.
[4]	SUN Yu-yan, DONG Jian-jun, WANG Xiu-mei. Hyperspectral Inversion of Above-ground Biomass model of Typical Steppe in Xilin Gol Based on ASD the Ground Object [J]. Acta Agrestia Sinica, 2024, 32(7): 2234-2244.
[5]	QI Hui-min, CHEN Ang, YANG Xiu-chun. Inversion of Nitrogen,Phosphorus and Potassium Content in Natural Grassland in Inner Mongolia Based on UAV Hyperspectral Data [J]. Acta Agrestia Sinica, 2024, 32(5): 1500-1512.
[6]	LIU Jun-ze, HE Ying-cai, MA Xiu-qin, NI Hao-ran, WANG Xue-meng, WANG Qing, HE Xiao-fan, NIE Jia-xin, HU Hao, MAO Pei-sheng, JIA Shan-gang. Characteristics of Natural Ageing Phenotypes and Multispectral Changes in Seeds of Different Oat Varieties [J]. Acta Agrestia Sinica, 2024, 32(12): 3688-3697.
[7]	WANG Meng-tao, HE Jing, BU He, LIU Yu, DIAO Zhao-yan, ZHOU Yu-jing, SU De-rong. Hyperspectral Inversion of Phosphorus Content in Surface Soil under Different Utilization Modes in Hulunbuir Grassland [J]. Acta Agrestia Sinica, 2024, 32(10): 3141-3150.
[8]	LI Gui-xin, AN Dong, YU Ying-wen, SHEN Yu-ying. Estimation of Above-Ground Biomass of Forage Soybean Based on UAV Multispectral and Texture Features [J]. Acta Agrestia Sinica, 2024, 32(10): 3262-3271.
[9]	LI Xi-lai, GAO Jay, SHI Yan. Aboveground Biomass Simulation and Its Temporal-Spatial Variation of Yongqu River Basin in the Alpine Meadow in the Yellow River Source Zone [J]. Acta Agrestia Sinica, 2023, 31(7): 1964-1976.
[10]	TONG Xin, YANG Zhen-lei, ZHANG Yi-ran, WU Yu-chen, DUAN Li-min. Estimation of Pasture Aboveground Biomass using Different Orders of Differential Hyperspectral Vegetation Indices [J]. Acta Agrestia Sinica, 2022, 30(9): 2438-2448.
[11]	WEN Tong, LIU Xiao-ni, JI Tong, YANG Dong-yang, QIAO Huan-huan, JIANG Jia-chang, PAN Dong-rong. Studying on Plant Classification and Recognition Method for Three-River Source Alpine Grassland Plant Based on Vegetation Index [J]. Acta Agrestia Sinica, 2022, 30(7): 1811-1818.
[12]	BU Ling-xin, LAI Quan, LIU Xin-yi. Adaptation of Different Machine Learning Algorithms for Grassland Biomass Estimation [J]. Acta Agrestia Sinica, 2022, 30(11): 3156-3164.
[13]	GAO Hong-yuan, HOU Meng-jing, GE Jing, BAO Xu-ying, LI Yuan-chun, LIU Jie, FENG Qi-sheng, LIANG Tian-gang, HE Jin-sheng, QIAN Da-wen. Hyperspectral Estimation of Aboveground Biomass of Alpine Grassland based on Random Forest Algorithm [J]. Acta Agrestia Sinica, 2021, 29(8): 1757-1768.
[14]	JI Tong, WANG Bo, WANG Zhan-jun, YANG Jun-ying, LI Qiang, LIU Zhi-gang, GUAN Wen-hao, HE Guo-xing, PAN Dong-rong, LIU Xiao-ni. Hyperspectral-based Estimation on the Chlorophyll Content of Turfgrass [J]. Acta Agrestia Sinica, 2020, 28(3): 675-683.
[15]	WU Hong-qi, FAN Yan-min, JIN Gui-li, XIA Xiao-wei. Estimation of the Restoration Status of Seriphidium transiliense Desert Grassland Based on Spectral Index [J]. Acta Agrestia Sinica, 2018, 26(4): 853-858.