Accurate measurement of the tree height and canopy cover density is important for forest biomass and management. Recently, Light Detection and Ranging (LIDAR) and Unmanned Aerial Vehicle (UAV) images have been used to estimate the tree height and canopy cover density for a forest stands. More so, UAV systems with autopilot functions, affordable Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) have created new possibilities, aided by available photogrammetric programs. In this study, we investigated the possibility of data collection methods using an Aerial LIDAR Scanner (ALS) and an UAV together with a fieldworks to evaluate accurate the tree standard metrics in Singyeri, Gyeongjusi, and Gyeongsangbukdo province. The derived metrics via statistical analyses of the ALS and UAV data and validated by field measurements were compared to a published forest type map (scale 1:5000) by the Korea Forest Service; geared towards improving the forest attributes. We collected data and analyzed and compared them with existent the forest type map produced from an aerial photographs and a digital stereo plotter. The ALS data of around 19.5 points·m^–2 were collected by an airplane, then processed and classified using the LAStools; while about 362 images of the UAV were processed via Structure from Motion algorithm in the Agisoft Metashape Pro. Thus, we calculated the metrics using the point clouds of both an ALS and an UAV, and then verified their similarity. The fieldwork was manually done on 110 sampled trees. Calculated heights of the UAV were 3.8~5.8 m greater than those for the ALS; and when correlated with the fieldwork, the UAV data overestimated, while the maximum height of the ALS data was more accurate. For the canopy cover, the ALS computed canopy cover was 10%~30% less than that of the UAV. However, the canopy cover above 2 m by an UAV was the best measurement for a forest canopy. Therefore, these results assert that the examined techniques are robust and can significantly complement methods of the conventional data acquisition for the forest type map.

The bidirectional reflectance distribution function (BRDF) is important for estimating the physical properties of a surface in remote sensing. In the laboratory, the BRDF can be estimated quickly and accurately using a goniometer, but it is very difficult to operate in the field. The purpose of this study was to evaluate whether estimating the BRDF with reasonable accuracy using an unmanned aerial vehicle (UAV) with a multispectral camera is possible in the field. Hemispherical reflectance was created from images taken using an UAV multispectral camera. The ground targets were four calibrated reference tarps (CRTs) of different reflectance, and the UAV was operated five times. Down-welling irradiance for reflectance calculation was measured in two ways: a sunlight sensor was mounted on a UAV, and a spectroradiometer with a remote cosine receptor (RCR) was installed on the ground. The BRDF was assessed through the anisotropy factor (ANIF) of the CRT reflectance derived from the collected data. As a result, the irradiance data for the reflectance calculation were more effective from the spectroradiometer with RCR on the ground than from the sunlight sensor mounted on an UAV. Furthermore, the high reflectance CRTs, ANIF, and BRDF had similar results. Therefore, when analyzing the BRDF, the effectiveness can be guaranteed when the reflectance of the target is over 21~46%, because a low reflectance tendency differs due to the adjacency effect. In addition, weather affects irradiance, so it is more effective to conduct fieldwork in clear weather.

In recent years, Korea has sustained consistent access to remote sensed data by launching Korea Multi-Purpose Satellite-3A (KOMPSAT-3A, K3A)—an updated version of the high-resolution KOMPSAT series. This KOMPSAT-3A required calibration and validation (Cal/Val) before and after its launch to enable proper functional characterization and to maintain the veracity of data collected. The Korea Aerospace Research Institute (KARI) executed the initial prelaunch calibration in the laboratory and we performed the Cal/Val of KOMPSAT-3A during the Launch and Early Operation Phase (LEOP) in the field. Two suitable sites in Korea and Mongolia with stable weather, almost uniform terrain, and near Lambertian diffusion, provided the necessary tarp reflectance to calculate the absolute radiometric calibration coefficients. The surface reflectance was determined using 12 and four well-calibrated reference reflectance tarps employing the FieldSpec^® 3(Analytical Spectral Devices Inc., Boulder, CO, USA) Spectroradiometer. Subsequently, the top of atmosphere (TOA) radiance was estimated using radiative transfer code (RTC) software based on the Atmospheric and Topographic Correction (ATCOR). In addition, cross calibration was simultaneously performed at the Libya-4 pseudo invariant calibration site (PICS) for KOMPSAT-3A TOA radiance, using the spectral band adjustment factor (SBAF) compensated Landsat 8 reflectance and the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to compute cross calibration coefficients. The results of the KOMPSAT-3A absolute calibration coefficient show that the R² values were over 0.99, implying a significant correlation for almost all bands between the TOA radiance and the KOMPSAT-3A spectral band response at both campaign sites. However, this study reveals a difference of less than 5% calibration gains for all bands compared to the prelaunch values, while the cross calibration gain is below 5% in visible bands and above 5% in the near infrared band. An effort to optimize the reliability of the absolute calibration coefficients resorted to the rigorous quantification of uncertainties amongst atmospheric conditions, the digital number (DN), the reflectance tarp, the bidirectional reflectance distribution function (BRDF), and ozone levels. Therefore, we presumed that the total uncertainty was 4.27%, which conforms to some published results.

This paper presents absolute radiometric calibration coefficients (gains) that explain the relationship between the digital number (DN) and at-sensor radiance for the multispectral camera (MSC) on Korea’s first high-resolution satellite (KOMPSAT-2). Absolute radiometric calibration was performed using a reflectance-based method. In addition, the suitability of vicarious results from radiance- and reflectance-based validations was analyzed with reference to IKONOS and QuickBird images. The latter are spectrally similar to KOMPSAT-2 images and have been validated in a large number of studies. For all bands, the $R2$ values of fitted lines for the gain ranged from 0.82 to 0.94, representing an improvement compared to previous findings for the KOMPSAT-2 MSC. To analyze the suitability of the vicarious results, same-pixel at-sensor radiances across different spectral bands were compared. In all bands, except the red band of QuickBird, the at-sensor radiances of KOMPSAT-2 MSC were highly correlated with those of IKONOS and QuickBird. In addition, same-pixel comparisons of reflectance across different spectral bands showed that the slopes of the least-squares lines for each band were similar to the results of the radiance comparison. The standard deviation among top of atmosphere (TOA) reflectances was within 0.019 for all bands. To calculate the tasseled cap transformation (TCT) coefficients for the KOMPSAT-2 MSC, an empirical method was applied using radiometric normalization. The results were similar to those obtained using the TCT coefficients for IKONOS and QuickBird in the brightness, greenness, and wetness components. The TCT images showed similar patterns. The absolute radiometric calibration coefficients presented here appear to be a good standard for maintaining the optical quality of the KOMPSAT-2 MSC, for which prelaunch, on-board, and vicarious calibration data are lacking.

Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and aerial photography. However, multiple UAV images need to be mosaicked to obtain images of large areas, and the resulting UAV multispectral image mosaics typically contain seam lines. To address this problem, we applied irradiance, vignette, and bidirectional reflectance distribution function (BRDF) filters and performed field work using a DJI Mavic 3 Multispectral (M3M) camera to collect data. We installed a calibrated reference tarp (CRT) in the center of the collection area and conducted three types of flights (BRDF, vignette, and validation) to measure the irradiance, radiance, and reflectance—which are essential for irradiance correction—using a custom reflectance box (ROX). A vignette filter was generated from the vignette parameter, and the anisotropy factor (ANIF) was calculated by measuring the radiance at the nadir, following which the BRDF model parameters were calculated. The calibration approaches were divided into the following categories: a vignette-only process, which solely applied vignette and irradiance corrections, and the full process, which included irradiance, vignette, and BRDF. The accuracy was verified through a validation flight. The radiance uncertainty at the seam line ranged from 3.00 to 5.26% in the 80% lap mode when using nine images around the CRT, and from 4.06 to 6.93% in the 50% lap mode when using all images with the CRT. The term ‘lap’ in ‘lap mode’ refers to both overlap and sidelap. The images that were subjected to the vignette-only process had a radiance difference of 4.48–6.98%, while that of the full process images was 1.44–2.40%, indicating that the seam lines were difficult to find with the naked eye and that the process was successful.

In precision agriculture, vegetation and soil are monitored by multispectral sensors that can observe outside the visible bands. In contrast to satellites and manned aircraft, unmanned aerial vehicles (UAVs) allow anyone to easily acquire near-real time data at a reasonable price. However, UAV images do not account for the anisotropic reflectance and solar irradiance from the ground surface, so extracting the reflectance of vegetation is difficult. To solve this problem, this study developed a bidirectional reflectance distribution function (BRDF) that expresses the anisotropic reflectance of the Earth’s surface as a function of the geometric relationship with the UAV sensor and the Sun. To compensate for the effect of changes in solar incident energy due to clouds and solar irradiance, the solar irradiance was measured and corrected on the ground rather than in the air to avoid errors due to the flight attitude. Before processing by the BRDF and correcting for the solar irradiance, the UAV obtained striated orthomosaic images for which the vegetation indices were affected by the position and attitude of the Sun and the UAV sensor. After the correction, consistent values were calculated for the vegetation indices throughout the images. The accuracy of the UAV data was analyzed by comparison with Sentinel 2A. Reflectance differences are 0.02% to 6.37% from the image without correction. After applying the correction, it reduced to 0.27%, 0.61%, 0.16%, and 0.65% from the blue, green, red, and near-infrared bands, respectively. This study is valuable for obtaining accurate values for vegetation indices under a wide range of weather and geometric conditions at different sites because UAVs to collect images are a rare case under optimal conditions.