Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring

被引:1
作者
Alexander Jenal
Ulrike Lussem
Andreas Bolten
Martin Leon Gnyp
Jürgen Schellberg
Jörg Jasper
Jens Bongartz
Georg Bareth
机构
[1] University of Applied Science Koblenz,Application Center for Machine Learning and Sensor Technology AMLS
[2] University of Cologne,Institute of Geography, GIS & RS Group
[3] Yara International ASA,Institute of Plant Nutrition and Environmental Science, Research Centre Hanninghof
[4] University of Bonn,INRES
来源
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science | 2020年 / 88卷
关键词
Short-wave infrared (SWIR); Unmanned aerial vehicle (UAV); Precision agriculture; Forage mass; Grassland; Biomass;
D O I
暂无
中图分类号
学科分类号
摘要
Remote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document} of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document} of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.
引用
收藏
页码:493 / 507
页数:14
相关论文
共 228 条
[1]  
Aasen H(2015)Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance ISPRS J Photogramm Remote Sens 108 245-259
[2]  
Burkart A(2015)Soukkamäki J (2015) Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: spectral comparison with portable spectroradiometer measurements Photogrammetrie - Fernerkundung - Geoinformation 1 69-79
[3]  
Bolten A(2008)Empirical proof of the empirical line Int J Remote Sens 29 665-672
[4]  
Bareth G(2015)Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley Int J Appl Earth Obs Geoinf 39 79-87
[5]  
Bareth G(2009)Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle IEEE Trans Geosci Remote Sens 47 722-738
[6]  
Aasen H(2012)The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics Animal 6 748-762
[7]  
Bendig J(2018)Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture Int J Appl Earth Obs Geoinf 70 105-117
[8]  
Gnyp ML(2015)Estimating plant traits of grasslands from UAV-acquired hyperspectral images: a comparison of statistical approaches ISPRS Int J Geo-Inf 4 2792-2820
[9]  
Bolten A(1976)A simple disc instrument for estimating herbage yield Grass Forage Sci 31 37-40
[10]  
Jung A(2004)The MERIS terrestrial chlorophyll index Int J Remote Sens 25 5403-5413