*2.3. Data Collection*

## 2.3.1. Sequoia and P4M Data

The UAV flight was conducted during sunny and clear sky (without clouds) conditions from 11:00 to 13:00 on 22 August 2019. During data collection, the Sequoia sensor was mounted on the EM6-800 hexarotor UAV, while the P4M was mounted on its own aircraft. For the Sequoia, a calibration target provided by the manufacturer was recorded to perform radiometric calibration in postprocessing.

In this experiment, the two sensors acquired a total of three sets of image data. The Sequoia images were collected while flying at 56 m height with 5 cm resolution and 100 m height with 10 cm resolution. The P4M images were collected while flying at 100 m height with 5 cm resolution. All of those flights were started within a one-hour period (11:27 to 12:22) to maintain similar illumination among each set of images. These images were acquired with 80% overlap. Sequoia acquired a total of 1715 images and P4M acquired 960 images. The specific parameter settings for the sensors are shown in Table 2.

**Table 2.** Parameters of sensors used for comparison. During the data collection process, the P4M camera failed to successfully acquire image data with a resolution of 10 cm. The 10 cm image data used in the comparative experiment was obtained by resampling the 5 cm image.


#### 2.3.2. ASD Data

We used the FieldSpec HandHeld 2 field spectroradiometer produced by Analytical Spectral Devices to measure the ground object spectral data. The coordinates and photos of the measured points were collected for ground object identification and visual interpretation of images. The spectroradiometer can perform continuous spectrum measurement in the wavelength range of 325–1075 nm, with the spectral resolution <3.0 nm at 700 nm, wavelength accuracy of ±1 nm, and field angle of 25◦. It can measure the reflection, transmission, radiance, or irradiance in real time and obtain the continuous spectral curve of the measured object. The spectrum measurement was carried out in sunny and cloudless weather and the time was between 11:00 to 13:00. During the UAV flight, synchronous ground observation was carried out to ensure that the solar elevation angle, zenith angle, and weather conditions measured by the ground object spectrum were consistent with the UAV data. During data collection, the surveyors wore black clothing to absorb sunlight and reduce spectral interference. The spectrum measurement was carried out under natural light conditions, the spectroradiometer was held vertically downward at 1 m above the ground, and the sensor covered about 0.68 m<sup>2</sup> ground area. To improve the accuracy of measurement, each ground point was repeatedly measured (ten times), and then the average value was taken. The spectroradiometer was calibrated every 10 min to reduce the interference of weather change on the spectrum measurement. Considering the small area, similar vegetation species, and relatively uniform ground surface in the study area, a total of eight ground points were selected (Figure 1). These points were selected randomly. Finally, according to Equation (1), the radiance of the ground object was converted into reflectance using the calibration coefficient provided by the reference plate.

$$R\_{\bar{i}} = \frac{\int\_{\lambda\_{\rm{win}}}^{\lambda\_{\rm{imw}}} R\_{\lambda} \mathbb{C}\_{\lambda} \mathbf{d} \lambda}{\int\_{\lambda\_{\rm{imw}}}^{\lambda\_{\rm{imw}}} \mathbb{C}\_{\lambda} \mathbf{d} \lambda} \tag{1}$$

where *Ri* is the reflectance of band *i* (*i* = 1, 2, 3, 4), λ*imax* and λ*imin* are the maximum and minimum values of wavelength *i*, *C*<sup>λ</sup> is the transmittance of wavelength, and *R*<sup>λ</sup> is the reflectance of wavelength λ.

The reflectance data can be used to calculate the true NDVI of the ground point (ASD-NDVI). The area of each ground point is about 0.68 m2. Therefore, when comparing the NDVI obtained by the two sensors with the true NDVI (ASD-NDVI), 272 pixels were selected and the average NDVI was taken from the 5 cm resolution image. Similarly, when comparing the NDVI obtained by the two sensors with the true NDVI (ASD-NDVI), 68 pixels were selected and the average NDVI was taken from the 10 cm resolution image.

#### 2.3.3. GCP

Five ground control points (GCPs) were evenly established on the field using printed white crosses to ensure the overlap between the Sequoia and P4M imagery at different times (Figure 1). The GCP and ASD coordinates were measured with 0.025 m horizontal accuracy and 0.035 m vertical accuracy. A geodetic dual-frequency global navigation satellite system (GNSS) receiver was used in a rapid-static manner (approximately 4 min for each measurement) using the relative positioning approach from a master station located at a point with known coordinates.

#### *2.4. Methodology*

This research methodology involved data acquisition (Section 2.3), preprocessing (Section 2.4), VI selection (Section 2.4), ROI selection (Section 2.4), and analysis (Section 3). The details are described after methodology chart (Figure 4).

**Figure 4.** Methodological flowchart of the research.

#### 2.4.1. Image Resampling

To standardize the spatial resolution of images acquired by different sensors, it is necessary to resample images that are very suitable for experimental comparison [46]. In order to avoid the contingency of the experimental results and to ensure the maneuverability of the UAV flight process, we compared the images of Sequoia and P4M with different spatial resolutions (5 and 10 cm). Therefore, we used ENVI software to resample the P4M images with the spatial resolution of 5 cm to obtain images with the spatial resolution of 10 cm. The pixel aggregate method was adopted in the resampling process [47].

#### 2.4.2. Image Preprocessing

For preprocessing, Sequoia and P4M images were imported into Pix4D mapper [48] and DJI Terra software, respectively. Different steps of initial processing were followed, including point cloud processing, 3D model construction, feature extraction, feature construction, and orthophoto generation. As the Sequoia images can be used to directly obtain the reflectance data of the study area after processing, the VIs were calculated using reflectance data from VI equations. As the P4M images can be used to directly obtain the VIs, there was no need to get reflectance data for these images. Then, the processed images were imported into ENVI software to clip, match, and select different ROIs in a single band for comparison.

Figure 5 shows the processed 5 cm spatial resolution image. From Figure 5, it can be seen that there is a slight difference between a and b. For example, the building in the bottom left corner appears white in the P4M image but red in Sequoia, the stones on the right are white in P4M but red in Sequoia, and some roads which are white in P4M are yellow in Sequoia. These differences may be caused by the saturation of the red band in the Sequoia sensor. There are also some small differences between c and d. The Sequoia-derived NDVI (Sequoia-NDVI) is greater than the P4M-derived NDVI (P4M-NDVI); the range of Sequoia-NDVI is −0.19 to 0.93, and the range of P4M-NDVI is −0.43 to 0.85. There are some errors of Sequoia-NDVI in the visual range: Some buildings in the bottom left corner have the

NDVI of about 0.7 (yellow part), but the building does not correspond to such a large NDVI value in reality.

**Figure 5.** Image pairs of Sequoia and P4M (5 cm) after data processing: (**a**,**b**) two false color composites formed by the combination of near-infrared, red, and green bands; (**c**,**d**) normalized difference vegetation index (NDVI) products of the two sensors. The left side corresponds to the Sequoia camera, and the right side corresponds to the P4M camera. The yellow squares indicate the difference between Sequoia-derived RGB and P4M-derived RGB; the black squares indicate the difference between Sequoia-NDVI and P4M-NDVI.

Figure 6 shows the processed 10 cm spatial resolution images of Sequoia. Compared with the 5 cm spatial resolution results, both the false color RGB images (Figure 5a) and NDVI products (Figure 5c) are different. In the 10 cm resolution RGB image, the red part of the building in the bottom left corner still exists, but it is significantly smaller than in the 5 cm resolution image; the stones on the right are shown in white instead of red, and the road is shown in white instead of yellow. The problem of red band saturation does not seem to be obvious in 10 cm resolution images. Similarly, the NDVI value of the building in the bottom left corner seems normal.

**Figure 6.** Image pairs of Sequoia (10 cm) after data processing: (**a**) the false color composite formed by the combination of near-infrared, red, and green bands; (**b**) normalized difference vegetation index (NDVI) product of Sequoia.
