Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mosaic reflectance image. Hundreds of samples were collected for each burn severity class, and they were used as training and validation samples for classification. Maximum likelihood (MLH), spectral angle mapper (SAM), and thresholding of a normalized difference vegetation index (NDVI) were used as classifiers. In the results, all classifiers showed high overall accuracy. The classifiers also showed high accuracy for classification of the burned surface, even though there was some confusion among spectrally similar classes, unburned pine, and unburned deciduous. Therefore, multispectral UAV images can be used to analyze burn severity after a forest fire. Additionally, NDVI thresholding can also be an easy and accurate method, although thresholds should be generalized in the future.

Airborne light detection and ranging (LiDAR) systems provide precise geometric information over the surface of the Earth, as well as radiometric information. studies on LiDAR have primarily focused on topographic mapping and urban 3-D modeling. The radiometric characteristics of backscattered laser intensity are meanwhile not yet clearly understood. Recently, the interest in the radiometric properties of LiDAR has been rapidly expanding, and several works have performed the normalization or correction of laser intensity data. However, the effect of radiometric correction is still unclear, and a suitable radiometric correction method has yet to be proposed. This letter investigated the radiometric properties of LiDAR intensity data as a prerequisite procedure for the practical use of intensity. On the basis of a comparison with the surface reflectance measured by a portable spectroradiometer, it was found that the LiDAR intensity of vegetation was unusual. For land cover types other than vegetation, it was observed that range distance was the dominant factor of the intensity of LiDAR. Therefore, this letter suggests that the correction of LiDAR intensity in terms of range is effective to the land cover types except vegetation.

A frozen or wet road surface is a cause of skidding and accidents, so road surface condition is important information for driving safety. Some instruments and methods have been developed to investigate road surface conditions based on optical imagery, although an active sensor is needed, regardless of the time of day. Recently, the laser scanner, which acquires backscattering intensity data related to reflectivity, has become popular in various fields. There is a need to investigate road surface conditions (frozen, wet, or dry) using laser backscattering intensity. This study tries to analyze signal characteristics of laser backscattering intensity to detect frozen and wet surfaces on roads. An ice target with a 7 cm thickness was placed on a road surface, and a wet surface was made due to the melting ice. The ice target, wet surface, dry surface, and roadside vegetation were scanned using a laser scanner. As a result, backscattering signals from the top surface of the ice target were missing due to its smoothness. Dry and wet asphalt surfaces showed distinguishable intensity ranges in their signals. The thick sidewall of the ice target and vegetation at the roadside showed overlapping intensity ranges. An ice sheet is only a few millimeters thick on a real road surface, and the roadside vegetation might be easily distinguished by using texture or auxiliary data. Therefore, laser backscattering intensity can be used to detect frozen, wet, and dry road surfaces, regardless of the time of day. The laser scanner can be installed to acquire information about road surface conditions from observation stations and vehicles in an application for transportation.

As the use of unmanned aerial vehicle (UAV) images rapidly increases so does the need for precise radiometric calibration. For UAV images, relative radiometric calibration is required in addition to the traditional vicarious radiometric calibration due to the small field of view. For relative radiometric calibration, some UAVs install irradiance sensors, but most do not. For UAVs without them, an intelligent scheme for relative radiometric calibration must be applied. In this study, a relative radiometric calibration method is proposed to improve the quality of a reflectance map without irradiance measurements. The proposed method, termed relative calibration by the optimal path (RCOP), uses tie points acquired during geometric calibration to define the optimal paths. A calibrated image from RCOP was compared to validation data calibrated with irradiance measurements. As a result, the RCOP method produces seamless mosaicked images with uniform brightness and reflectance patterns. Therefore, the proposed method can be used as a precise relative radiometric calibration method for UAV images.