| 引用本文: | 宋奕欣,裴青宝,吴立峰,等.利用无人机多光谱估算平卧菊三七叶面积指数和叶绿素量[J].灌溉排水学报,2026,45(8):25-33. |
| Song Yixin,Pei Qingbao,Wu Lifeng,et al.利用无人机多光谱估算平卧菊三七叶面积指数和叶绿素量[J].灌溉排水学报,2026,45(8):25-33. |
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| 摘要: |
| 【目的】探究无人机多光谱反演平卧菊三七叶面积指数（Leaf area index，LAI）和叶绿素量的估算潜力。【方法】在3个飞行高度（20、40、60 m）采集多光谱影像，利用12种经验植被指数与地面实测数据进行相关性分析，获得不同高度下的植被指数与LAI和叶绿素量的决定系数，以决定系数为依据，优选3种相关度最高的植被指数分别构建随机森林（RF）、梯度提升决策树（GBDT）、岭回归（RR）模型，分析无人机不同飞行高度多光谱反演平卧菊三七冠层LAI和叶绿素量的精度。【结果】①20 m飞行高度下，植被指数与LAI均不相关；40 m飞行高度下，12种经验植被指数中叶绿素吸收比值指数（CARI）与平卧菊三七叶绿素量、LAI的相关性最高，相关系数分别为0.797、0.570；60 m飞行高度下，TVI与LAI的相关性最高，相关系数为0.707；②利用随机森林的遥感反演模型得到平卧菊三七叶绿素量、LAI的精度最高，决定系数为0.837、0.659，均方根误差为0.041、4.046。【结论】综上可知，飞行高度为40 m时，基于RF算法构建的平卧菊三七叶绿素与LAI反演模型综合效果最好。 |
| 关键词: 无人机；多光谱；平卧菊三七；LAI；叶绿素量 |
| DOI：10.13522/j.cnki.ggps.2025397 |
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| 基金项目: |
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| Estimating leaf area index and chlorophyll content of Gynura procumbens (Lour.) Merr. using UAV multispectral remote sensing |
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Song Yixin, Pei Qingbao, Wu Lifeng, Li Liang, Han Yu, Luo Taiyou, Wu Runnan
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1. Jiangxi University of Water Resources and Electric Power, Nanchang 330099, China;
2. East China University of Technology, Nanchang 330013, China
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| Abstract: |
| 【Objective】Leaf area index (LAI) and chlorophyll content are key physiological traits, and their traditional measurement methods are destructive and labor-intensive. The advances in UAV multispectral remote sensing provide a non-destructive alternative for monitoring them in the field. This paper studies the potential of UAV multispectral data for estimating LAI and chlorophyll content of Gynura procumbens (Lour.) Merr.【Method】Multispectral imagery was acquired using a UAV flying at three heights: 20, 40 and 60 m. We selected 12 empirical vegetation indices derived from imagery acquired at different flying heights and analyzed their correlations with field-measured LAI and chlorophyll content. Based on the correlation results, the three most sensitive vegetation indices were used to construct a Random Forest, a Gradient Boosting Decision Tree, and a Ridge Regression model to improve the estimation of LAI and chlorophyll content of Gynura procumbens (Lour.) Merr.【Result】①There were no significant correlations between LAI and vegetation indices derived from imageries acquired at flying height of 20 m. At flying height of 40 m, the chlorophyll absorption ratio index (CARI) derived from the imageries correlated strongly with both chlorophyll content and LAI, with correlation coefficients of 0.797 and 0.570, respectively. At flying height of 60 m, the triangular vegetation index derived from the imageries had a strong correlation with LAI, with a correlation coefficient of 0.707. ②Among all three models we compared, the Random Forest inversion model was most accurate for estimating chlorophyll content and LAI, with their associated coefficient of determination being 0.837 and 0.659, and root mean square error (RMSE) being 0.041 and 4.046, respectively.【Conclusion】The Random Forest model using UAV imagery acquired at a flying height of 40 m is most accurate for estimating canopy chlorophyll content and LAI of Gynura procumbens (Lour.) Merr. |
| Key words: UAV; multispectral; Gynura procumbens (Lour.) Merr; leaf area index; chlorophyll content |
