Camellia oleifera is an important economic tree species in China. Accurate estimation of canopy structural parameters of C. oleifera is essential for yield prediction and plantation management. However, this remains challenging in mountainous plantations due to canopy occlusion and background interference. This study developed a multi-level object-oriented segmentation method integrating UAV-based LiDAR and visible-light data to address this issue. The proposed approach progressively eliminates background objects (bare soil, weeds, and forest gaps) through hierarchical segmentation and classification in eCognition, ultimately enabling precise canopy delineation. The method was validated in a high-canopy-closure plantation characterized by a mountainous area. The results demonstrated exceptional performance; canopy area extraction and individual plant extraction achieved average F-scores of 97.54% and 91.69%, respectively. The estimated tree height and mean crown diameter were strongly correlated with field measurements (both R² = 0.75). This study provides a method for extracting the parameters of C. oleifera canopies that is suitable for mountainous regions with high canopy closure, demonstrating significant potential for supporting digital management and precision forestry optimization in such wooded areas.

In global navigation satellite systems (GNSSs)-based positioning, user receiver clock jump is a common phenomenon on the low-cost receiver clocks and can break the continuity of observation time tag, carrier phase and pseudo range. The discontinuity may affect precise point positioning-related parameter estimation, including receiver clock error, position, troposphere and ionosphere parameters. It is important to note that these parameters can be used for timing, positioning, atmospheric inversion and so on. In response to this problem, the receiver clock jumps are divided into two types. The first one can be expressed by the carrier phase and pseudo range having the same scale jump, and the second one is that they are having different scale jumps. For the first type, if a small priori variance of receiver clock error is provided, it can affect the accuracy of ionospheric delay estimation both in static and kinematic mode, while in the latter mode, it also affects position estimation. However, if large process noise is provided, numerical problems may arise since other parameters’ process noises are usually small, it is proposed to use the single point positioning with pseudo ranges to provide a priori value of receiver clock error, and an empiric value is assigned to its prior variance, this handle can avoid the above problems. For the second type, instead of compensating so many raw observations in the traditional methods, it is proposed to compensate the ambiguities at the clock jump epochs only in a new method. The new method corrects the Melbourne–Wubbena (MW) combination firstly in order to avoid the misjudging of cycle slips for current epoch, and the second step is to compensate the corresponding ambiguities, then, after Kalman filtering, the MW and its mean should be corrected back in order to avoid the misjudging of cycle slips at the next epoch. This approach has the advantage of handling the clock jump epoch-wise and can avoid correcting the rest of the observations as the traditional methods used to. With the numerical validation examples both in static and kinematic modes, it shows the new method is simple but efficient for real time precise point positioning (PPP).