With rapid advancement and low costs of unmanned aerial vehicles, or drones, there have also been calls for new or improved methodology to take advantage of the various data acquisition possibilities in geography. Whether it is reconstructing landscapes across time or even measuring specific features and cultural artefacts, multiple disciplines and applied work may need to develop or take advantage of methodologies developed to best maximize the potential in geography.
Drones, along with improvements in areas such as photogrammetry and computer vision, enable rapid and easy to develop measuring tools, such as volumetric measurements of complex shapes that are best viewed from a distance. This is the case, for instance, if one were to look at a large statue such as Christ the Redeemer in Rio de Janeiro. In essence, many overlapping photographs using structure for motion (SfM) photogrammetric methods can create 3D images that provide accurate measurements of a given feature or the landscape.
Repeat and Rapid Geographic Data Acquisition
The ease of sampling also means that areas visited by drones can be repeatedly visited to acquire new data to measure change. Digital surface models (DSMs) can be generated by high resolution cameras using SfM methods. These DSMs are then compared in different periods to determine such change as erosion in valleys. The accuracy in measuring, when compared to fieldwork techniques, can be as low as 1 cm, indicating that a cheap, UAV (or done)-based method is often far more preferable due to the low costs than fieldwork measuring landscape change such as erosion.
Measuring Social and Cultural Data with Drones
Much of the focus of developing methods has looked at physical measurements or data acquisition of relatively fixed objects on the landscape. Another possibility, however, is measuring social or cultural data. In social geography, there is a need for methods that can enable drones to monitor and access data. For instance, measuring crowds, enabling an understanding of interactions, or even evaluating crime and then extrapolating that data to meaningful understanding are areas where drones could be useful. This includes sampling conducted over long-term periods, as drones can capture areas by flying over longer periods or sampling at various intervals to develop estimates. The use of drones in social science and social geography, in general, is still relatively limited in contrast to growing areas such as the use of GPS data from phones for measuring social data. Drones, however, offer the possibility to measure without having to depend on GPS data. Image recognition software, for instance, can also be used in digital images to help rapidly count numbers of individuals or objects in scenes, enabling possibilities in sampling to be more automated.
In fact, many current developments in the use of automated, image recognition techniques have been mostly confined to military-related applications, particularly for smart platforms that can recognize objects they intend to focus on without operator input. Application to transport, such as unmanned transport of goods, are one area where automated image recognition can be used to better assess how items can be moved using unmanned, aerial techniques.
Multi- and hyperspectral Drones for Measuring Plant Growth and Vigor
In agricultural and biological sciences, the use of multi- and hyperspectral sensors has opened the possibility of enabling methods to capture much more subtle features in plant phenotypes. This includes measuring vigor in plant growth. Traditional technologies, such as multispectral satellites using multispectral coverage, enabled large-scale, relatively gross measurements of the health of plant growth. However, with multi- or hyperspectral drones, measurements can enable individual plants to be measured for crop stress or vigor. Different parts of the electromagnetic spectrum covered by drone sensors enable more subtle indication of plant growth, where methods such as principal component analysis and other multivariate statistics can differentiate vigor more clearly between different plants of the same species.
With the widespread use of drones, methods are likely to be further refined to improve not only the use of UAVs in physical measurements in geography but even expanded to areas of the social sciences. This includes measuring and surveying social data such as population and interaction of population. Methods have particularly focused on computer vision and photogrammetry, but information science techniques, including automated image recognition, show potential to expand the utility of drones for different disciplines.
 For more on some methodologies used such as SfM, see: Garrett, B. & Anderson, K. (2018). Drone methodologies: Taking flight in human and physical geography. Transactions of the Institute of British Geographers. DOI: 10.1111/tran.12232.
 For more on using SfM for measuring erosion, see: Eltner, A., Baumgart, P., Maas, H.-G., & Faust, D. (2015). Multi-temporal UAV data for automatic measurement of rill and interrill erosion on loess soil: UAV DATA FOR AUTOMATIC MEASUREMENT OF RILL AND INTERRILL EROSION. Earth Surface Processes and Landforms, 40(6), 741–755. https://doi.org/10.1002/esp.3673
 For more on drones in social geography areas, see: Birtchnell, T., & Gibson, C. (2015). Less talk more drone: social research with UAVs. Journal of Geography in Higher Education, 39(1), 182–189. https://doi.org/10.1080/03098265.2014.1003799.
 For more on automated techniques such as image recognition for unmanned transport in the use of drones, see: Kim, J., Lee, Y. S., Han, S. S., Kim, S. H., Lee, G. H., Ji, H. J., … Choi, K. N. (2015). Autonomous flight system using marker recognition on drone (pp. 1–4). IEEE. https://doi.org/10.1109/FCV.2015.7103712.
 For more on multi- and hyperspectral methods for measuring plant phenotypes and vigor, see: Di Gennaro, S. F., Rizza, F., Badeck, F. W., Berton, A., Delbono, S., Gioli, B., … Matese, A. (2017). UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices. International Journal of Remote Sensing, 1–15. https://doi.org/10.1080/01431161.2017.1395974.
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