利用无人机影像构建作物表面模型估测甘蔗LAI

发布时间：2018-05-17 16:40

  本文选题：遥感 + 无人机　； 参考：《农业工程学报》2017年08期

【摘要】：为探讨从作物表面模型(crop surface models,CSMs)中提取株高来估算糖料蔗叶面积指数(leaf area index,LAI)的可行性,该文采用无人机-RGB高清数码相机构成的低空遥感平台,以广西糖料蔗为研究对象,采集了糖料蔗全生育期的高清数码影像,分别在有无地面控制点条件下建立各生育期CSMs并提取株高。此外,该文利用高清数码影像计算了6种可见光植被指数并建立LAI估算模型,用以对比从CSMs提取的株高对LAI的估算效果。结果表明:全生育期CSMs提取的株高与实测株高显著相关(P0.01),株高预测值与实测值高度拟合(R2=0.961 2,RMSE=0.215 2)。选取的6种可见光植被指数中,绿红植被指数对糖料蔗伸长末期以前的LAI的估测效果最好(R2=0.779 0,RMSE=0.556 1,MRE=0.168 0)。相同条件下,株高对LAI有更高的估测精度,其中CSMs提取的株高估测效果优于地面实测株高,预测模型R2=0.904 4,RMSE=0.366 2,MRE=0.124 3。研究表明,使用无人机拍摄RGB影像来提取株高并运用于糖料蔗重要生育期LAI的估算是可行的,CSMs提取的株高拥有较高的精度。该研究可为大区域进行精准快速的农情监测提供参考。
[Abstract]:In order to study the feasibility of estimating leaf area index (Lai) from crop surface model surface models CSMs, a low altitude remote sensing platform based on UAV RGB high-definition digital camera was used to study sugarcane in Guangxi. The high-definition digital images of sugarcane growth period were collected, and the CSMs of each growth period was established under the condition of ground control point or not and the plant height was extracted. In addition, six kinds of visible light vegetation indices were calculated by using high-definition digital images and LAI estimation models were established to compare the effect of plant height extracted from CSMs on LAI estimation. The results showed that the plant height extracted by CSMs at the whole growth stage was significantly correlated with the measured plant height (P0.01). Of the 6 visible light vegetation indices selected, the green red vegetation index had the best effect on estimating LAI of sugarcane before the end of sugarcane elongation. Under the same conditions, the plant height had higher estimation accuracy to LAI, and the plant height estimated by CSMs extraction was better than that of ground measured plant height, and the prediction model R2O0.9044 RMSEN 0.366 2 MREE 0.124 3 124. The results show that it is feasible to extract plant height from RGB images and estimate LAI in sugar cane at important growth stage. This study can provide a reference for accurate and rapid monitoring of agricultural conditions in large areas.
【作者单位】： 武汉大学水资源与水电工程科学国家重点实验室;广西壮族自治区水利科学研究院;
【基金】：高等学校全国优秀博士学位论文作者专项资金(201248) 广西水利厅科技项目(201615)
【分类号】：S127;S566.1
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本文编号：1902094