For estimation of tree parameters at the singletree level using light detection and ranging (LiDAR), detection and delineation of individual trees is an important starting point. This paper presents an approach for delineating individual trees and estimating tree heights using LiDAR in coniferous (Pinus koraiensis, Larix leptolepis) and deciduous (Quercus spp.) forests in South Korea. To detect tree tops, the extended maxima transformation of morphological image-analysis methods was applied to the digital canopy model (DCM). In order to monitor spurious local maxima in the DCM, which cause false tree tops, different h values in the extended maxima transformation were explored. For delineation of individual trees, watershed segmentation was applied to the distance-transformed image from the detected tree tops. The tree heights were extracted using the maximum value within the segmented crown boundary. Thereafter, individual tree data estimated by LiDAR were compared to the field measurement data under five categories (correct delineation, satisfied delineation, merged tree, split tree, and not found). In our study, P. koraiensis, L. leptolepis, and Quercus spp. had the best detection accuracies of 68.1% at h = 0.18, 86.7% at h = 0.12, and 67.4% at h = 0.02, respectively. The coefficients of determination for tree height estimation were 0.77, 0.80, and 0.74 for P. koraiensis, L. leptolepis, and Quercus spp., respectively.

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This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated. In this study, we verified whether the SSVEP-based brain switch system can be practically used as an emergency call system for patients in LIS. The brain switch used for our system adopted a chromatic visual stimulus, which proved to be visually less stimulating than conventional checkerboard-type stimuli but could generate SSVEP responses strong enough to be used for brain-computer interface (BCI) applications. To verify the feasibility of our emergency call system, 14 healthy participants and 3 patients with severe ALS took part in online experiments. All three ALS patients successfully called their guardians to their bedsides in about 6.56 seconds. Furthermore, additional experiments with one of these patients demonstrated that our emergency call system maintains fairly good performance even up to 4 weeks after the first experiment without renewing initial calibration data. Our results suggest that our SSVEP-based emergency call system might be successfully used in practical scenarios.