Jung-Il Shin

Land Cover Characteristics of Airborne LiDAR Intensity Data: A Case Study

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.

Spectral characteristics of forest vegetation in moderate drought condition observed by laboratory measurements and spaceborne hyperspectral data

Although there have been several studies on the spectral characteristics related to leaf water content, it remains unclear whether the spectral property of leaves can be extended to the canopy-level. In this study, we attempt to compare the spectral characteristics of forest vegetation in moderate drought condition observed by laboratory measurement and satellite hyperspectral image data. Spectral reflectance data were measured from detached pine needles and oak leaves in the laboratory with a spectroradiometer. Canopy reflectance spectra of the same species were collected from temperate forest stands with dense canopy conditions using EO-1 Hyperion imaging spectrometer data obtained during the moderate drought season in 2001, and then compared with those obtained in the normal precipitation season of 2002. The relationship between leaf-level spectral reflectance and leaf water content was the clearest at the shortwave infrared (SWIR) regions. However, the canopy-level spectral characteristics of forest stands did not quite correspond with the leaf-level reflectance spectra. Further, four water-related spectral indices (WI, NDWI, MSI, and NDII) developed mainly with leaf-level reflectance were not very effective to be used with the canopy-level reflectance in dense forest condition. Forest canopy spectra under moderate drought status may be more influenced by canopy foliage mass, rather than by canopy moisture level.

Development of a Target Detection Algorithm using Spectral Pattern Observed from Hyperspectral Imagery

In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.