利用巴西塞拉多遥控飞机的图像估算入侵草的高度:来自Phantom 4的全球导航卫星系统的精度

D. D. Pessi, Jefferson Vieira José, C. L. Mioto, M. A. Diodato, A. M. Grigio, A. C. Paranhos Filho, Normandes Matos da Silva
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引用次数: 0

摘要

本研究的目的是利用集成到无人机中的GNSS从无人机图像中估计入侵植物的高度,并评估GNSS的准确性。DSM和DTM高程模型是根据遥控飞机(RPA)收集的图像生成的。CHIS的产生是通过DSM和DTM的相减来实现的。为了评估CHIS+GNSS模型的准确性,生成CHIS+RTK模型作为观测变量。以塞拉多典型植被和腕子草为代表的两个样区进行了模型间的比较。采用统计学检验:Spearman相关、RMSE、MAE、Wilcoxon检验。对所选影像的目视解译结果表明,CHIS+GNSS模型对入侵禾草类地表覆盖的识别存在误差,对入侵物种冠层高度的分类精度低于CHIS+RTK模型。统计检验表明,CHIS+GNSS模型对入侵物种的识别存在显著差异,样本区域高度误差较大(0.24 cm)。从这些结果可以看出,CHIS+RTK模型比CHIS+GNSS模型在检测由外来草组成的地表覆盖方面更加自信。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating invasive grasses heights with images from a remotely piloted aircraft in Brazilian Cerrado: accuracy of Global Navigation Satellite System from Phantom 4
The purpose of this study was to estimate the height of invasive plants from UAV images using the GNSS integrated into the UAV and to evaluate the accuracy of the GNSS. DSM and DTM elevation models were produced from images collected by remotely piloted aircraft (RPA). The production of CHIS occurred through the subtraction of the DSM and the DTM. In order to assess the accuracy of the CHIS+GNSS model, the CHIS+RTK model was generated as the observed variable. The comparison between the models took place in two sample areas represented by typical vegetation of Cerrado and Brachiaria grass. The statistical tests adopted were: Spearman correlation, RMSE, MAE and Wilcoxon test. The visual interpretation of the selected images showed that the CHIS+GNSS model presented errors in the identification of the ground cover represented by invasive grasses when compared to the CHIS+RTK model, being less accurate in the classification of the canopy heights of the invasive species. Statistical tests indicated that the CHIS+GNSS model showed significant differences in the identification of invasive species, with greater height error (0.24 cm) in the sample area. From these results it can be seen that the CHIS+RTK model is more assertive in detecting ground cover composed by exotic grasses than the CHIS+GNSS model.
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