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
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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.
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页码:493 / 507
页数:14
相关论文
共 228 条
[21]  
Bareth G(2005)An unmanned aerial vehicle for rangeland photography Rangeland Ecol Manag 58 439-2375
[22]  
Berni JAJ(2011)Assessing structural effects on PRI for stress detection in conifer forests Remote Sens Environ 115 2360-74
[23]  
Zarco-Tejada PJ(2019)Improving productivity and increasing the efficiency of soil nutrient management on grassland farms in the UK and Ireland using precision agriculture technology Eur J Agron 106 67-5454
[24]  
Suarez L(2017)Distinguishing intensity levels of Grassland fertilization using vegetation indices Remote Sens 9 81-305
[25]  
Fereres E(2016)Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small unmanned airborne vehicle (UAV) IEEE Trans Geosci Remote Sens 54 5440-313
[26]  
Boval M(2010)Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring Remote Sens 2 290-49
[27]  
Dixon RM(2018)Simplified radiometric calibration for UAS-mounted multispectral sensor Eur J Remote Sens 51 301-178
[28]  
Camino C(2014)Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands Remote Sens Environ 153 40-298
[29]  
González-Dugo V(2019)Development of a VNIR/SWIR multispectral imaging system for vegetation monitoring with unmanned aerial vehicles Sensors 19 5507-115
[30]  
Hernández P(2020)Geopandas/geopandas: V0.7.0 Zenodo 3 167-1108