基于无人机遥感的荒漠草地地上生物量反演研究

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中图分类号：TP79；S812 文献标识码：A 文章编号：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

荒漠草地约占全国草原总面积的 $8 . 1 ＼% ^ { [ 1 ] }$ ，在维持区域生态和生产平衡方面发挥着关键作用，但由于其生态特性较为脆弱，对环境变化具有较高的敏感性，从而极易遭受损害。（剩余11688字）