摘要:
叶面积指数(LAI)是评价作物长势的重要农学参数之一, 利用遥感技术准确估测作物叶面积指数(LAI)对精准农业意义重大。目前, 数码相机与无人机系统组成的高性价比遥感监测系统在农业研究中已取得一些成果, 但利用无人机数码影像开展作物LAI估测研究还少有尝试。为论证利用无人机数码影像估测冬小麦LAI的可行性, 本文以获取到的3个关键生育期(孕穗期、开花期和灌浆期)冬小麦无人机数码影像为数据源, 利用数字图像转换原理构建出10种数字图像特征参数, 并系统地分析了3个生育期内两个冬小麦品种在4种氮水平下的LAI与数字图像特征参数之间的关联性。结果表明, 在LAI随生育期发生变化的同时, 10种数字图像特征参数中R/(R+G+B)和本文提出的基于无人机数码影像红、绿、蓝通道DN值以及可见光大气阻抗植被指数(VARI)计算原理构建的数字图像特征参数UAV-based VARIRGB也有规律性变化, 说明冬小麦的施氮差异不仅对LAI有影响, 也对某些数字图像特征参数有一定影响; 在不同条件(品种、氮营养水平以及生育期)下的数字图像特征参数与LAI的相关性分析中, R/(R+G+B)和UAV-based VARIRGB与LAI显著相关。进而, 研究评价了R/(R+G+B)和UAV-based VARIRGB构建的LAI估测模型, 最终确定UAV-based VARIRGB为估测冬小麦LAI的最佳参数指标。结果表明UAV-based VARIRGB指数模型估测的LAI与实测LAI拟合性较好(R2=0.71, RMSE=0.8, P<0.01)。本研究证明将无人机数码影像应用于冬小麦LAI探测是可行的, 这也为高性价比无人机遥感系统的精准农业应用增添了新成果和经验。
Abstract:
Leaf area index (LAI) is an important agronomic parameter used in evaluating crop growth characteristics. The accurate estimation of LAI based on remote sensing technology is critical for precision agriculture. The current cost-effective unmanned aerial vehicle (UAV) of agricultural remote sensing monitoring system, which was established based on a multi-rotor UAV with a digital camera mounted on its platform, has led to significant achievements in agricultural research. However, there has been little research on retrieving crop LAI based on UAV digital imagery. To demonstrate the feasibility of using UAV digital imagery to estimate winter wheat LAI, we used this cost-effective UAV system to monitor agricultural operation in the study area. Then many UAV digital images (also known as RGB images) used as the study data source recorded at three critical growth stages — booting, anthesis and filling stages of winter wheat. We calculated ten characteristic parameters from the RGB images based on digital imaging conversion principle. Furthermore, we systematically analyzed the relationship between LAI at the three growth stages of the two winter wheat varieties with the four nitrogen levels and characteristic parameters of RGB images. It was indicated that among the ten characteristic parameters, R/(R+G+B) and UAV-based VARIRGB (visible atmospherically resistant index based on UAV RGB image, which was calculated in this paper based on DN in the red, green and blue channels of UAV digital images and the calculation principle of VARI) regularly changed with LAI of winter wheat. The change occurred regularly and simultaneously for the three growth stages. It showed that different nitrogen levels in winter wheat not only influenced LAI, but also influenced some characteristic parameters of digital images. Meanwhile, the study also indicated that R/(R+G+B) and UAV-based VARIRGB were more significantly correlated with LAI under different conditions, including variety, nitrogen level and growth stage among the ten characteristic parameters. Then two comprehensive evaluation of LAI inversion models between LAI and R/(R+G+B) and UAV-based VARIRGB were established. The evaluation demonstrated that UAV-based VARIRGB was the best parameter which optimally retrieved LAI of winter wheat. LAI estimated by the exponential model of UAV-based VARIRGB strongly matched with measured LAI, with R2 = 0.71, RMSE = 0.8 and at 0.01 significance level. Therefore, the results showed that the application of UAV digital imagery in retrieving winter wheat LAI was feasible. The study also enriched the achievements and experience of using cost-effective UAV remote sensing monitoring system in precision agriculture.
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