基于無人機(jī)遙感的荒漠草地地上生物量反演研究

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中圖分類號：TP79；S812 文獻(xiàn)標(biāo)識碼：A 文章編號：1007-0435（2025）04-1258-09

Abstract： Aboveground biomass（AGB） is an important index to evaluate vegetation status and desertification process in desert grassand. In order to evaluate the aboveground biomass （AGB）of desert grassland rapidly， accurately and efficiently，the desert grasslandof Seriphidium transiliense in Xinjiangwas takenas the research area in this study. The AGB data of grassland were collected in the vegetation growth season，and the unmanned aerial vehicle（UAV） data were obtained simultaneously. Ten vegetation indices were selected as thecharacteristic variables，and three machine leaming algorithms were used to construct the AGB inversion model. The genetic algorithm （GA） was introduced to optimize the model parameters，and then the best AGB inversion model was selected.The results showed that the three algorithms all had high prediction perfor mance，among which the XGBoost model had significant advantages.Especially after integrating four typical vegetation indices and using genetic algorithm（GA）optimization，the prediction accuracy reached the highest （ ， ，of which RVI contributed the most，accounting for 3 5 % .Therefore，the XGBoost model based on four typical vegetation indices combined with GA optimization was identified as the most suitable model for grassland AGB remote sensing inversion in the study area.The results of this study could provide a reference for the selection ofremote sensing inversion methods for monitoring grassland biomass and the improvement of accuracy.

Key words： Desert grassland； Aboveground biomass； Unmanned aerial vehicle； eXtreme gradient boosting： Random forest；Light gradient boosting machine

荒漠草地約占全國草原總面積的 $8 . 1 ＼% ^ { [ 1 ] }$ ，在維持區(qū)域生態(tài)和生產(chǎn)平衡方面發(fā)揮著關(guān)鍵作用，但由于其生態(tài)特性較為脆弱，對環(huán)境變化具有較高的敏感性，從而極易遭受損害。（剩余11688字）