Wildlife monitoring is carried out for diverse reasons, and monitoring methods have gradually advanced through technological development. Direct field investigations have been replaced by remote monitoring methods, and unmanned aerial vehicles (UAVs) have recently become the most important tool for wildlife monitoring. Many previous studies on detecting wild animals have used RGB images acquired from UAVs, with most of the analyses depending on machine learning–deep learning (ML–DL) methods. These methods provide relatively accurate results, and when thermal sensors are used as a supplement, even more accurate detection results can be obtained through complementation with RGB images. However, because most previous analyses were based on ML–DL methods, a lot of time was required to generate training data and train detection models. This drawback makes ML–DL methods unsuitable for real-time detection in the field. To compensate for the disadvantages of the previous methods, this paper proposes a real-time animal detection method that generates a total of six applicable input images depending on the context and uses them for detection. The proposed method is based on the Sobel edge algorithm, which is simple but can detect edges quickly based on change values. The method can detect animals in a single image without training data. The fastest detection time per image was 0.033 s, and all frames of a thermal video could be analyzed. Furthermore, because of the synchronization of the properties of the thermal and RGB images, the performance of the method was above average in comparison with previous studies. With target images acquired at heights below 100 m, the maximum detection precision and detection recall of the most accurate input image were 0.804 and 0.699, respectively. However, the low resolution of the thermal sensor and its shooting height limitation were hindrances to wildlife detection. The aim of future research will be to develop a detection method that can improve these shortcomings.

Measuring tree structure using three-dimensional (3D) mapping tools such as light detection and ranging (LiDAR) remote sensing is needed to provide well-managed and designed green spaces. The metrics used to estimate tree structure could be different depending on which LiDAR systems are used. This may lead to confusion and reduce confidence when evaluating tree structures and their derived products, such as plant area index (PAI). Therefore, studies that can determine similarities among measurements derived from different LiDAR systems are needed. In this study, structural canopy metrics in airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning (MLS) were compared to seek consistencies among the three LiDAR systems. The specific objectives were to test whether the estimates made by the metrics differed depending on single or clustered trees and to test whether LiDAR-derived errors in the metrics are related to tree structures. Tree point clouds were manually classified into single and clustered trees. Heights-related metrics, Rumple Index, area, and PAI were calculated for comparison analysis. Root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (r) were calculated to evaluate the consistencies in each metric among the LiDAR systems. The results showed that the maximum height of the point clouds (ZMAX) and max and mean heights derived from the canopy height models (minCHM and maxCHM) demonstrated good consistency (RMSE% < 10%, Bias% < 10%, and r > 0.900). Moreover, the biases from the ZMAX- and CHM-derived metrics comparisons among the LiDAR systems did not show strong linear relationships with the tree heights and canopy complexities (i.e. Pearson’s correlation coefficient r < |0.29|). On the contrary, the 95th percentile (Zq95) height and mean z height (ZMEAN) differed depending on the tree classes and showed significant linear relations with canopy heights and complexity. The configurations of LiDAR systems, such as point density and sensing locations, seem to affect the Zq95, ZMEAN metrics, and PAI. Our results suggest that assessing for consistencies among the different LiDAR systems is required before using multiple LiDAR systems interchangeably to estimate the structure of urban park areas.