Development of a VNIR/SWIR Multispectral Imaging System for Vegetation Monitoring with Unmanned Aerial Vehicles

Abstract

Short-wave infrared (SWIR) imaging systems with unmanned aerial vehicles (UAVs) are rarely used for remote sensing applications, like for vegetation monitoring. The reasons are that in the past, sensor systems covering the SWIR range were too expensive, too heavy, or not performing well enough, as, in contrast, it is the case in the visible and near-infrared range (VNIR). Therefore, our main objective is the development of a novel modular two-channel multispectral imaging system with a broad spectral sensitivity from the visible to the short-wave infrared spectrum (approx. 400 nm to 1700 nm) that is compact, lightweight and energy-efficient enough for UAV-based remote sensing applications. Various established vegetation indices (VIs) for mapping vegetation traits can then be set up by selecting any suitable filter combination. The study describes the selection of the individual components, starting with suitable camera modules, the optical as well as the control and storage parts. Special bandpass filters are used to select the desired wavelengths to be captured. A unique flange system has been developed, which also allows the filters to be interchanged quickly in order to adapt the system to a new application in a short time. The characterization of the system was performed in the laboratory with an integrating sphere and a climatic chamber. Finally, the integration of the novel modular VNIR/SWIR imaging system into a UAV and a subsequent first outdoor test flight, in which the functionality was tested, are described.

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

“…Thus, taking multiple variables (e.g., in a Random Forest model) might lead to better predictive ability in terms of grassland differentiation. Future studies should be directed short-wave infrared wavelengths (Honkavaara et al 2016;Camino et al 2018;Jenal et al 2019).…”

First Investigation of Mediterranean Oak Tree Vitality With High-Resolution Worldview-3 Satellite Data: Comparing Ten Vegetation Indices and Three Machine Learning Classifiers

“…Alternatively, spectral ground truth could be captured with low-flying airborne systems such as unmanned aerial vehicles (UAVs). Jenal et al (2019) have recently introduced a novel UAV-borne VNIR/SWIR sensor, which might produce appropriate data.…”

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

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“…Section 2.5 describes the spectral workflow that transforms the raw DN image data sets into plot-wise VI reflectance data sets. For more details on the performance of the two VIs, see also [9,15,[23][24][25]27,38]. (IV) Regression models (RM) were created for the destructive field sampling plots (18 plots per date: n = 108) from (I) the UAV-derived crop height data and from (II) the manual and destructive sampling data, resulting in four regression models for biomass (fresh and dry), crop moisture, and N C .…”