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Small Unmanned Aerial System Development and Applications in Precision Agriculture and Natural Resource Management

Virtual Special Issue

European Journal of Remote Sensing: an Open Access journal

Special Issue Editors: Amr Abd-Elrahman, Bruce Quirk, Jordi Corbera, Ayman Habib

Advances in small Unmanned Aerial Systems (sUAS) are carving a new paradigm in remote sensing sensor development, integration, data processing, and exploitation. Led by the rapid development in navigation sensors and motivated by an ever-increasing application fields, the utilization of remote sensing sensors onboard sUAS is taking off, literally. The suite of sensors onboard sUAS, including navigation, Lidar, multispectral, hyperspectral, microwave, and thermal sensors sets the stage for new applications that take advantage of the combined increase in the sUAS spatial, spectral, and temporal resolutions. The integration and use of these sensors is an active research area, but without robust calibration and validation procedures, benefits of this technology will be, at best, diminished. These data require accurate calibration procedures to account for all sensor-, geometry-, and environment-induced discrepancies that can mask the targeted signal and make it harder to detect. Similarly, the application-specific algorithms applied to process and analyze the data are essential for successful use of sUAS. New natural resource management applications in fields such as invasive plant detection and control, disaster management, environmental monitoring, wetland classification, wildlife monitoring, and coastal management are emerging every day. Precision agriculture is also one of the most promising application areas for sUAS remote sensing. Still, the methods used in, for example, plant phenotyping, pesticide/fertilization management, and stress detection and diagnoses, are evolving.

This special issue presents diverse topics demonstrating the expanding role of small Unmanned Aircraft Systems (sUAS) in the remote sensing field. The majority of the manuscripts present sUAS applications in the agricultural and natural resource management fields. sUAS agricultural applications were presented in four of the eleven manuscripts in this special issue. Primicerio et al. (2017) presented a new methodology for identifying missing plants in a vineyard using RGB images. Marino and Alveno (2018) used cluster analysis of multi-temporal images to identify homogeneous wheat areas. Jorge et al. (2019) tested different vegetation indices extracted from multispectral camera to detect irrigation inhomogeneities in an olive grove. Wheat damage caused by wild game animals was detected and quantified using RGB camera in the study authored by Kuzelka and Surovy (2018).

Natural resources management applications involved the use of multi-resolution object-based image analysis for wetland landcover classification (Pande-chhetri et al., 2017), performing water budget analysis in wetland area using structure-from-motion (sfm) point cloud (Garcia-Lopez et al., 2018), evaluating potentials for forest monitoring in Ethiopia (Berie and Burud, 2018), single-tree detection in high-density LiDAR data (Balso et al., 2019). Radiometric calibration of multispectral images is a challenging and highly demanded subject. A paper presenting a simplified radiometric calibration method for sUAS multispectral camera is authored by (Iqbal et al., 2018). Qayuum et al. (2019) designed a new deep learning convolutional neural network model based on sparse networks coding technique using sUAS imagery. Finally, a framework of hierarchical cooperation of sUAS swarm was introduced by (Rotter and Chmiel, 2018).

We hope that success stories of sUAS applications as well as image calibration, classification and implementation frameworks presented in this special issue enrich the sUAS knowledge base benefitting the remote sensing community and stimulating new research in this area. The following is a list of the research studies presented in the special issue.

European Journal of Remote Sensing

The latest articles from the European Journal of Remote Sensing, ahead of print issues.

Language: en-US

Publisher: tandf

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