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СКАЧАТЬ UAS, on the other hand, are configured with multiple propellers in a symmetrical distribution around a central hub that allows positioning very precisely while the platform is in the air (Sámano et al., 2013). As Nascimento and Saska (2019) noted, there has been significant technological growth in multi‐rotor platforms over the last two decades. More specifically, the core components necessary for multi‐rotor designs to function in an efficient manner have improved in their technological capabilities, which has encouraged the large growth of the commercial UAS market. This technological growth can be seen in the surge of relatively low‐cost, yet high‐quality multi‐rotor platforms becoming more widely available for both research and commercial purposes since 2010 (Norouzi Ghazbi et al., 2016; Yao et al., 2019). Since a multi‐rotor UAS has multiple propellers operating at the same time, the flight times tend to be much shorter in duration compared to fixed‐wing platforms. The multiple propellers are in constant motion which generates vertical lift for the UAS, thus enabling the platform to hover in place. The ability to hover in place is what arguably makes multi‐rotor platforms more user‐friendly for less experienced UAS operators, as well as allow operators to conduct flights in more crowded areas. The ability to change direction with multi‐rotor UAS is enabled by varying each individual propeller’s thrust and torque, all of which are controlled by an onboard autopilot system that assists the pilot in controlling the positioning of the UAS so the pilot does not have to control each individual propeller. Due to the user‐friendliness, the ability for precision positioning and to carry heavier payloads a multi‐rotor frequently becomes the preferred platform for urban applications (González‐Jorge et al., 2017; Singh and Frazier, 2018; Watkins et al., 2020).

      There are other UAS models beyond the above two categories, which are much less commonly used in aerial remote sensing. These models are hybridized platforms that maintain certain characteristics of both fixed‐wing and multi‐rotor models. Hybrid platforms typically maintain the aerodynamic design of a fixed‐wing platform (large wingspan, lightweight) but can perform vertical take‐off and landing (VTOL) operations, much like a multi‐rotor platform. The ability for VTOL operations enables these platforms to be used in environments that generally do not allow for fixed‐wing UAS to safely take‐off and land in. Although these hybrid platforms are still less common than their fixed‐wing and multi‐rotor counterparts, we have witnessed a surge in developing more commercially viable systems that can meet unique industry demands using these hybridized designs (Floro da Silva and Branco, 2013; Thamm et al., 2015; Aktas et al., 2016; Hu and Lanzon, 2018; Joshi et al., 2019), which suggests a trend of hybrid models being more common and potentially more viable for commercial purposes as well.

      3.2.2 CAMERAS AND SENSORS

During the early integration of UAS technology into remote sensing in the early 2000s, visiblespectrum cameras (RGB) were the most popular type of payload sensor carried by commercially available UAS (Pajares, 2015). With the expansion of UAS technology into diverse industries more recently, the scaled‐down versions of many airborne and space imaging instruments are becoming available for UAS platforms, including multispectral sensors, hyperspectral sensors, thermal cameras, and LiDAR sensors. Detailed discussions about the selection of models and parameters (e.g. focal length and pixel size) for UAS sensing payloads can be found elsewhere (e.g. Colomina and Molina, 2014; Pepe et al., 2018; Yao et al., 2019). Here, we only focused on the characteristics of the most common types of sensor data and their potential applications, which are important for researchers to consider when planning their specific projects. A brief description of the different sensor data and their advantages and disadvantages are provided as follows.

      3.2.2.1 RGB Cameras

      Visible‐spectrum cameras are the most used sensor paired with UAS platforms. These cameras generally collect high spatial resolution color imagery that can be used to generate digital elevation models (DEMs) and derive orthophoto mosaics.

      3.2.2.2 Multispectral Sensors

      Multispectral sensors extend beyond the visible portion of the electromagnetic spectrum. Multispectral images can be used to derive vegetation indices like Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI). This type of sensor is primarily used in the fields of vegetation and agriculture (Adam et al., 2010). With much higher spatial resolution than traditional multispectral sensors mounted on airplanes or satellites, multispectral data collected with UAS allows for detailed examinations of phenomena like leave level farming (Calderón et al., 2014) and pads level water pollution issues (Kislik et al., 2018). However, they are significantly more expensive than RGB cameras. There is currently a lack of processing software that can handle various formats of multispectral data efficiently (Yao et al., 2019).

      3.2.2.3 Hyperspectral Sensors

      Hyperspectral sensors can capture spectral response at many narrow bands. With such a high spectral resolution, hyperspectral data are useful in many applications including vegetation analyses (Adam et al., 2010), precision agriculture (Haboudane et al., 2004), and urban mapping (Benediktsson et al., 2005). However, the high spectral resolution is often achieved at the cost of spatial resolution, and it is challenging to derive high‐accuracy products with limited meta‐information from the sensor manufacture (Yao et al., 2019).

      3.2.2.4 Thermal Cameras

      Thermal cameras are designed to detect thermal emission in the mid‐infrared range (Prakash, 2000). They are commonly used for temperature measurement in vegetation studies (Berni et al., 2009), environmental applications (Zarco‐Tejada et al., 2012), and real‐time detection of objects. Given the low flying height of UAS, the products can have a much higher spatial resolution and negligible atmospheric influence. However, UAS‐based thermal cameras usually do not have cooled detectors because of their size, which can lead to low sensitivity and capture rates (Yao et al., 2019).

      3.2.2.5 LiDAR

LiDAR is an active remote sensor that sends out light pulses and records reflections of the pulses to measure distances. LiDAR is well known for its high geometric accuracy and its ability to penetrate forest canopies (Dalponte et al., 2009). Because LiDAR accuracy is highly influenced by the positional accuracy of the platform and UAS is usually unstable in flight and their GPS is inaccurate considering sensor resolution, it is hard to obtain accurate point clouds with UAS LiDAR without differential GPS stations. Without regard to cost, integration of RGB and LiDAR data can be promising to improve measurement and interpretation accuracy (Campos‐Taberner et al., 2016).

3.3 DATA COLLECTION AND PROCESSING

      When using UAS for remote data collection, there are several different approaches one can take depending on the desired data outcomes and the specific UAS platform and sensor available. UAS are versatile in their ability to be used for various data collection techniques, but the types of data one can collect are highly dependent on the specific type of UAS platform and sensors being used. Therefore, UAS are increasingly being designed and manufactured for specific data collection applications, such as vegetation monitoring in rural areas and 3D modeling of building construction in urban areas. Due to the diversity of scenarios where one can incorporate the use of UAS, professionals should pay close attention to what methods they utilize to collect data because there is no one‐size‐fits‐all approach. This does not mean, however, that there are no best practices associated with UAS data collection. In recent years, UAS and remote sensing researchers have identified effective methodologies and best practices associated with UAS data collection (Hodgson and Koh, 2016; Pepe et al., 2018; Wu and An, 2019; Stecz and Gromada, 2020). In addition to familiarizing oneself with the latest best practices for a specific application, individuals who are interested in using a UAS for data collection should pay attention СКАЧАТЬ