基于无人机多光谱影像的水稻生物量估测
S252; 水稻是中国主要粮食作物之一,对水稻生物量进行及时、准确、快速、高效地监测具有重要作用.该研究以无人机多光谱影像作为数据源,提取10种多光谱植被指数与水稻生物量进行相关性分析,结果表明全生育期的生物量与植被指数的相关性比单期更高,在植被覆盖度接近100％并趋于稳定后,将全生育期数据划分为营养生长期和生殖生长期,也能提高生物量与植被指数的相关性.全生育期10种植被指数全为显著相关,其中差值植被指数(difference vegetation index,DVI)相关性最高,为0.689;营养生长期10种植被指数均为显著相关,其中红边波段比值植被指数(red-edge ratio veg...
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| Published in | 农业工程学报 Vol. 40; no. 17; pp. 161 - 170 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
西南石油大学土木工程与测绘学院,成都 610500
01.09.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202401024 |
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| Abstract | S252; 水稻是中国主要粮食作物之一,对水稻生物量进行及时、准确、快速、高效地监测具有重要作用.该研究以无人机多光谱影像作为数据源,提取10种多光谱植被指数与水稻生物量进行相关性分析,结果表明全生育期的生物量与植被指数的相关性比单期更高,在植被覆盖度接近100％并趋于稳定后,将全生育期数据划分为营养生长期和生殖生长期,也能提高生物量与植被指数的相关性.全生育期10种植被指数全为显著相关,其中差值植被指数(difference vegetation index,DVI)相关性最高,为0.689;营养生长期10种植被指数均为显著相关,其中红边波段比值植被指数(red-edge ratio vegetation index,RE-RVI)相关性最高,为0.894;生殖生长期植被指数除DVI外均为显著相关,其中稻穗部分红边波段归一化植被指数(red-edge normalized difference spectral reflectance index,RE-NDVI)相关性最高,为-0.794,茎秆叶部分RE-RVI相关性最高,为0.629.分别利用营养生长期与生殖生长期的植被指数构建生物量估测模型,营养生长期主要模型为二次回归模型和指数模型,较优的多光谱指数为RE-RVI,验证精度决定系数R2为0.90,均方根误差RMSE为119.36g/m2;生殖生长期稻穗主要模型为二次回归模型,较优的多光谱指数为比值植被指数(ratio vegetation index,RVI),验证精度决定系数R2为0.78,均方根误差RMSE为124.98 g/m2.总体上看,利用植被覆盖度接近100％时来划分全生育期数据构建生物量估测模型能够提升模型精度,而生殖生长期将稻穗与茎秆叶分别构建模型也能提高生物量的估测精度.研究结果可为无人机多光谱影像技术对全生育期水稻生物量监测提供理论依据和技术支持. |
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| AbstractList | S252; 水稻是中国主要粮食作物之一,对水稻生物量进行及时、准确、快速、高效地监测具有重要作用.该研究以无人机多光谱影像作为数据源,提取10种多光谱植被指数与水稻生物量进行相关性分析,结果表明全生育期的生物量与植被指数的相关性比单期更高,在植被覆盖度接近100％并趋于稳定后,将全生育期数据划分为营养生长期和生殖生长期,也能提高生物量与植被指数的相关性.全生育期10种植被指数全为显著相关,其中差值植被指数(difference vegetation index,DVI)相关性最高,为0.689;营养生长期10种植被指数均为显著相关,其中红边波段比值植被指数(red-edge ratio vegetation index,RE-RVI)相关性最高,为0.894;生殖生长期植被指数除DVI外均为显著相关,其中稻穗部分红边波段归一化植被指数(red-edge normalized difference spectral reflectance index,RE-NDVI)相关性最高,为-0.794,茎秆叶部分RE-RVI相关性最高,为0.629.分别利用营养生长期与生殖生长期的植被指数构建生物量估测模型,营养生长期主要模型为二次回归模型和指数模型,较优的多光谱指数为RE-RVI,验证精度决定系数R2为0.90,均方根误差RMSE为119.36g/m2;生殖生长期稻穗主要模型为二次回归模型,较优的多光谱指数为比值植被指数(ratio vegetation index,RVI),验证精度决定系数R2为0.78,均方根误差RMSE为124.98 g/m2.总体上看,利用植被覆盖度接近100％时来划分全生育期数据构建生物量估测模型能够提升模型精度,而生殖生长期将稻穗与茎秆叶分别构建模型也能提高生物量的估测精度.研究结果可为无人机多光谱影像技术对全生育期水稻生物量监测提供理论依据和技术支持. |
| Abstract_FL | Rice is one of the major food crops in China.It is important to timely,accurately,efficiently,and rapidly monitor the rice biomass.In this study,ten multispectral vegetation indices were extracted from Unmanned Aerial Vehicles(UAV)multispectral images.The data source was obtained for the correlation analysis with rice biomass.The results showed that the correlation between biomass and vegetation index was higher for the full reproductive period than for a single period.The correlation between biomass and vegetation index was also improved to divide the data for the full reproductive period into a nutritive and a reproductive period,according to the vegetation cover up to 100％.All ten vegetation indices were significantly correlated throughout the reproductive and nutrient growth period,with the highest DVI and RE-RVI correlation of 0.689 and 0.894,respectively;Except the DVI,the vegetation indices were significantly correlated in the productive growth period,with the highest RE-NDVI correlation of-0.794 for the rice spike portion and 0.629 for the stem and leaf portion.The biomass estimation models were then constructed using vegetation indices for the nutritive and reproductive growth periods.Quadratic regression and exponential models were also obtained in the nutritive growth period.The optimal multispectral index was achieved in the RERVI,with the validation accuracies of R2=0.90,and RMSE=119.36 g/m2;In the reproductive growth period,the rice spike was a quadratic regression model with an optimal multispectral index of RVI and validation accuracies of R2=0.78 and RMSE=124.98 g/m2,respectively.Overall,the accuracy of the model was improved in the biomass estimation with the full-birth period data using the vegetation cover close to 100％.The accuracy of biomass estimation was also improved to separate the rice spike from the stalk and leaf during the reproductive growth period.Relatively less saturation was suffered in the early growth period of rice,whereas,the accuracy was slightly higher than that in the late growth period;Moreover,the experimental data significantly improved the estimation precision and accuracy of the model.The rice biomass was segmented for modeling and accuracy validation during the whole life cycle,in terms of plant nutritional and reproductive growth.The finding can also provide effective information and references for field crop growth monitoring and decision-making on farmland production. |
| Author | 苏勇 王帝 杨博 孙榕 |
| AuthorAffiliation | 西南石油大学土木工程与测绘学院,成都 610500 |
| AuthorAffiliation_xml | – name: 西南石油大学土木工程与测绘学院,成都 610500 |
| Author_FL | SU Yong SUN Rong YANG Bo WANG Di |
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| Author_xml | – sequence: 1 fullname: 王帝 – sequence: 2 fullname: 孙榕 – sequence: 3 fullname: 苏勇 – sequence: 4 fullname: 杨博 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Rice biomass estimation based on multispectral imagery from unmanned aerial vehicles |
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| Keywords | vegetation index biomass 生物量 无人机 植被指数 水稻 多光谱 UAV rice multispectral |
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| Title | 基于无人机多光谱影像的水稻生物量估测 |
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