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Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring
PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science ( IF 2.1 ) Pub Date : 2020-10-28 , DOI: 10.1007/s41064-020-00128-7
Alexander Jenal , Ulrike Lussem , Andreas Bolten , Martin Leon Gnyp , Jürgen Schellberg , Jörg Jasper , Jens Bongartz , Georg Bareth

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 \(R^2\) of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with \(R^{2}\) 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.



中文翻译:

研究基于新开发的基于无人机的VNIR / SWIR成像系统对草料质量进行监测的潜力

基于无人机(UAV)的遥感系统非常适合在农业应用中对中小型农田进行空中监测。成像系统通常以多光谱多相机系统的形式使用,以有效地导出完善的植被指数(VI)。这项研究调查了这种多相机系统的潜力,该方法采用一种新颖的方法来将光谱灵敏度从可见光到近红外(VNIR)扩展到短波红外(SWIR)(400-1700 nm),以便从高空作业平台。该系统测试于2019年7月下旬在德国科隆附近的德国一项草地肥料试验中进行。在37分钟内,连续两次飞行均获得了NIR和SWIR范围内四个不同波段的光谱响应。使用两种不同的方法将光谱图像数据校准为反射率。对于每个波段,将得到的反射率数据集处理为正交拼合。从这两种校正方法的正交拼合计算出四波段NIR / SWIR GnyLi VI和两波段NIR / SWIR归一化比率指数(NRI)。在两次无人机飞行过程中,用分光辐射计在总共12个地块上记录了光谱地面真相数据,以验证基于摄像机的光谱数据。直接比较相机和光谱辐射仪的数据集的反射率,并通过简单的线性回归(SLR)模型进行进一步分析,以预测干物质(DM)的产量。在基于相机的SLR中,NRI在 对于每个波段,将得到的反射率数据集处理为正交拼合。从这两种校正方法的正交拼合计算出四波段NIR / SWIR GnyLi VI和两波段NIR / SWIR归一化比率指数(NRI)。在两次无人机飞行过程中,用分光辐射计在总共12个地块上记录了光谱地面真相数据,以验证基于摄像机的光谱数据。直接比较相机和光谱辐射仪的数据集的反射率,并通过简单的线性回归(SLR)模型进行进一步分析,以预测干物质(DM)的产量。在基于相机的SLR中,NRI在 对于每个波段,将得到的反射率数据集处理为正交拼合。从这两种校正方法的正交拼合计算出四波段NIR / SWIR GnyLi VI和两波段NIR / SWIR归一化比率指数(NRI)。在两次无人机飞行过程中,用分光辐射计在总共12个地块上记录了光谱地面真相数据,以验证基于摄像机的光谱数据。直接比较相机和光谱辐射仪的数据集的反射率,并通过简单的线性回归(SLR)模型进行进一步分析,以预测干物质(DM)的产量。在基于相机的SLR中,NRI在 使用分光辐射计在总共12个地块上记录了光谱地面真实数据,以验证基于相机的光谱数据。直接比较相机和光谱辐射仪的数据集的反射率,并通过简单的线性回归(SLR)模型进行进一步分析,以预测干物质(DM)的产量。在基于相机的SLR中,NRI在 使用分光辐射计在总共12个地块上记录了光谱地面真实数据,以验证基于相机的光谱数据。直接比较相机和光谱辐射仪的数据集的反射率,并通过简单的线性回归(SLR)模型进行进一步分析,以预测干物质(DM)的产量。在基于相机的SLR中,NRI在GnyLi之前的\(R ^ 2 \)为0.73和0.75(RMSE:0.18和0.17),其中\(R ^ {2} \)为0.71和0.73(RMSE:0.19和0.18)。这些结果清楚地表明了相机系统在草料质量监控中的应用潜力。

更新日期:2020-10-30
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