Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R² and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R² and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest.

Light Detection and Ranging (LiDAR) has proved to be an effective technology for accurately extracting forest structural parameters. Unmanned Aerial Vehicles (UAVs) are characterized by its flexibility and low cost. Combining the advantages of both technologies, UAV-LiDAR exhibits great potential in the accurate surveying of large forests. However, for forests dominated by deciduous tree species, the accuracy of individual tree detection and height extraction is inevitably impacted by the leaf-on and leaf-off seasons when UAV-LiDAR scans point clouds. In this study, a planted forest of dawn redwood (Metasequoia glyptostroboides Hu & W. C. Cheng) in Ma’anxi Wetland Park of Chongqing, China, was chosen as the study object. The UAV-LiDAR was first leveraged to capture the point clouds of summer and winter seasons. Then, the canopy height models (CHMs) with different spatial resolutions were generated, based on which the tree quantity and individual heights were extracted. The achieved outcomes included the following: (1) The CHMs of the two seasons could be used to obtain the tree quantity, and the accuracy of individual tree detection from the point cloud scanned in the winter was relatively higher than that in the summer. (2) The spatial resolution of CHM impacted the accuracy of individual tree segmentation and height extraction, and the optimum spatial resolution was 0.3 m (approximately 1/10 of the average canopy diameter of the dawn redwoods). Therefore, to obtain more accurate individual tree heights of the deciduous forest, it is better to scan the point cloud using UAV-LiDAR in the leaf-off season and choose the appropriate spatial resolution of the CHM.

Capturing the spatial heterogeneity and characteristic scale is the key to determining the spatial patterns of land surfaces. In this research, the core observation area of the middle reaches of the Heihe River Basin was selected as the study area, and the scale identification of several typical objects was carried out by implementing experiments on moderate- and high-resolution remotely sensed ASTER and CASI NDVI images. The aim was to evaluate the potential of the local variance and semivariance analysis to characterize the spatial heterogeneity of objects, track their changes with scale, and obtain their scales. Our results show that natural objects have multiscale structures. For a single object with a recognizable size, the results of the two methods are relatively consistent. For continuously distributed samples of indistinctive size, the scale obtained by the local variance is smaller than that obtained by the semivariance. As the image resolution becomes coarser and the research scopes expand, the scales of objects are also increasing. This article also indicates that with a small research area of uniform objects, the local variance and semivariance are easy to facilitate researchers to quickly select the appropriate spatial resolution of remote sensing data according to the research area.