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This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop and cruising modes) and two ground control point (GCP) settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds) for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve OPEN ACCESS Remote Sens. 2015, 7 12794 every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely.

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE).

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Sorghum halepense (johnsongrass) is a perennial weed with a vegetative reproductive system and one of the most competitive weeds in maize showing a spatial distribution in compact patches. When maize is irrigated, successive weed emergences occur in the early phenological phases of the crop, which require several herbicide applications. Our aim was to provide an accurate tool for an early detection and mapping of johnsongrass patches and delineate the actual surface area requiring a site-specific herbicide treatment based on the weed coverage. This early detection represents a major challenge in actual field scenarios because both species are in the Poaceae family, and show analogous spectral patterns, an extraordinarily similar appearance and a parallel phenological evolution. To solve this, an automatic OBIA (object-basedimage-analysis) procedure was developed to be applied on orthomosaicked images using visible (red-green-blue bands) and multispectral (red-green-blue and near infrared bands) cameras collected by an unmanned aerial vehicle (UAV) that flew at altitudes of 30, 60 and 100 m on two maize fields. One of our first phases was the generation of accurate orthomosaicked images of an herbaceous crop such as maize, which presented a repetitive pattern and nearly no invariant parameters to conduct the aerotriangulation. Here, we show that high-quality orthomosaicks were produced from both cameras and that they were able to be the first step for mapping the johnsongrass patches. The most accurate weed maps were obtained using the multispectral camera at an altitude of 30 m in both fields. These maps were then used to design a site-specific weed management program, and we demonstrated that potential herbicide savings ranged from 85 to 96 %. Our results showed that accurate and timely maps of johnsongrass patches in maize can be a key element in achieving sitespecific and sustainable herbicide applications for reducing spraying herbicides and costs.

The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 • C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1 • C compared to those where no drift correction was applied.

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