Urban Remote Sensing. Группа авторов

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СКАЧАТЬ operations, and data processing.

      3.3.1 MISSION PLANNING (PREFLIGHT)

      When using a UAS for remote sensing purposes, operators need to firstly think about their specific project and what their goals are. Because there is no one‐size‐fits‐all approach to using UAS for remote sensing, operators need to situate the technology within the intended project or application. This can often be accomplished by addressing several questions: What am I trying to address in my project? What type of data can a UAS provide to my project? Do I need a particular platform or sensor to acquire that type of data? Where will I be collecting my data (i.e. environmental context)? What potential obstacles could prevent me from acquiring that data? Are these potential obstacles physical (tall buildings, trees, powerlines), regulatory (illegal to fly in that location, limitations on altitude), or a combination of the two? By answering these questions, operators will be able to put together a cohesive and well‐structured mission plan for their project. While there are various ways one can conceptualize a mission plan, Pepe et al. (2018) proposed a useful mission planning framework consisting of several integral components, such as determining the suitable UAS platform and sensor for the application, selecting a suitable flight plan design, and analyzing the user‐determined factors that can impact the flight process. The first component, a discussion of the various types of UAS platforms and sensors, was already discussed in Section 3.2, and here we will focus on the last two components: flight design and flight factors.

The selection of a suitable flight plan design is critical to not only the final data output quality but also the time‐efficiency and cost‐effectiveness of remote sensing projects. Flight plans for data collection purposes are typically conducted in either a manual, assisted, or autonomous fashion depending on the mission’s specifications (Nex and Remondino, 2014). Manual flight plans refer to when an individual is in direct control of the UAS during the data collection process without the assistance of an autopilot system. Manual data collection allows the operator to have more direct control over the imaging process but is prone to pilot‐induced errors, such as not taking pictures with even overlap or skewed image orientations. Assisted flight plans refer to when an individual is in partial control of the system but still has the assistance of an onboard autopilot, such as GPS assisted hovering. These types of flight operations are useful when an operator needs to collect imagery in a precarious location where a completely autonomous flight might not be safe or efficient. Autonomous flight plans are the most robust and involve little‐to‐no direct user input during flight operations while the data are being collected. The automated nature of these flight plans allows the UAS to achieve higher precision in the data collection process (e.g. equal imaging intervals and/or consistent flight speed) and to feasibly collect larger datasets than possible with manual operations. Autonomous flight plans utilize a user‐created flight plan design, often generated with computer software or smartphone/tablet applications, which makes use of the UAS’s hardware and autopilot functionality to perform flight operations without direct input from the pilot. These plans utilize waypoint functionality and user‐defined flight parameters to make the UAS fly along a predetermined path at a set altitude and speed. After the pilot uploads the flight plan from the controller to the UAS itself, the UAS will then fly along the defined routes, collecting overlapping images of the target AOI. Autonomous flight plan designs vary depending on the desired data outputs after processing. However, there are several common factors of relevance impacting all autonomous flight plan designs, such as image overlap, acquisition altitude, and sensor orientation. There are other factors that can influence the outcomes of data collection flights, but the three discussed here arguably have the most significant direct impact on the data quality (Mesas‐Carrascosa et al., 2016).

Image overlap refers to the amount each image taken by a UAS along its flight path overlaps with adjacent images. Most aerial remote sensing flight designs utilize a grid‐pattern design that enables operators to designate a percentage frontal image overlap and a side overlap. Image overlap directly affects the quality of data outputs regardless of which specific data outputs are created (see Section 3.3.3). Larger image overlap is usually associated with higherquality data output but also requires longer computational time to generate the output. It is generally recommended to utilize longitudinal and lateral image overlap of 60–90% depending on the mapping purposes and the computational resources available (Torres‐Sánchez et al., 2018). Figure 3.1 presents an example flight plan design and depiction of image overlap.

FIGURE 3.1 As flying along the gridded flight path, UAS collects overlapping images that can be used to generate a point cloud of common feature points. A larger image overlap yields more matches of common feature points but can significantly increase the flight duration.

      Source: Based on Torres et al. (2018).

      Another important factor for autonomous flight plan design is image acquisition altitude. The altitude that a UAS flies at during a data collection mission can dramatically affect the amount of time it takes to complete a mission, the amount of area that can be covered, and the resolution of its sensors. Lower flight altitudes allow the UAS to collect data with higher resolution but require longer flight times as the UAS must fly much further to maintain the minimum image overlap required for reconstruction. Inversely, if a UAS is flown at a higher altitude, the spatial resolution of the output imagery becomes coarser, but the flights take less time to cover the same amount of area. According to Agüera‐Vega et al. (2017), the accuracy of DSMs and orthophotos derived from UAS images tends to improve with lower acquisition altitudes. But it takes much longer to complete the image acquisition and the output data are more computationally intensive to process. In addition, there can be physical limitations affecting the decision of appropriate flight altitudes for a project. If there are tall buildings (such as in an urban setting), the flights must be flown high enough to not collide during the mapping mission. Changes in terrain can also affect the flight altitude because the terrain elevation can change in relation to the UAS position when initially taking off.

      The third important factor influencing the quality of UAS data outputs is sensor orientation. During the early mission planning process when an operator is thinking about the intended application and goals for a project, a simple decision to be made is whether 3D data outputs are needed. This is important because image orientation can affect the quality of a 3D reconstruction significantly. It is common for operators to utilize nadir image orientations for most mapping purposes but image datasets acquired at oblique angles generally work better for constructing 3D datasets, such as point clouds, textured models, and DEMs (Küng et al., 2011; Chiabrando et al., 2017; Boonpook et al., 2018). The inclusion of oblique image orientation techniques allows the facades of tall features to be better captured in the image dataset, thus resulting in a more thoroughly captured dataset. However, capturing a complete oblique orientation dataset takes much longer than acquiring a nadir orientation dataset and is also more computationally intensive. Due to the angled perspective of oblique image datasets, double‐grid flight patterns are commonly used to capture all four sides of any features present. Since image overlap is crucial for reconstructing the image datasets into 2D and 3D data outputs, extra attention must be paid by the UAS operator when designing a flight plan with image orientations other than nadir.

      3.3.2 FLIGHT OPERATIONS (IN‐FLIGHT)

The process of in‐flight operations is much simpler than preflight mission planning. Since much of remote sensor data collection with a UAS is performed using an autonomous or assisted flight mode, there tends СКАЧАТЬ