Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts.

Urban blue–green spaces (UBGSs) serve as irreplaceable ecological infrastructure that underpins ecosystem service provision and human well-being improvement in densely populated urban regions. Based on 10-year (2014–2023) Landsat 8 OLI remote sensing imagery, this paper systematically investigates the temporal–spatial evolutionary characteristics and potential socioeconomic and policy-related associations with UBGS changes in the Shandong Peninsula urban agglomeration (SPUA), a pivotal coastal urban agglomeration in eastern China. The results demonstrate that the total UBGSs in the SPUA exhibited a pronounced increasing trend throughout the study period: the area of urban green spaces (UGSs) expanded from 28,311.66 km² to 30,194.39 km², while urban blue spaces (UBSs) grew from 1108.02 km² to 1699.04 km². Concurrently, the ecological quality of UGSs has markedly improved, with NDVI showing a significant upward trend in over one-third of the built-up areas, and vegetation greenness in cities such as Binzhou, Jinan, and Zibo increasing by more than 35%. Landscape pattern analysis reveals that the spatial structure of UGSs has transformed from a fragmented and scattered distribution to a centralized and contiguous layout. Specifically, the aggregation index (AI) and largest patch index (LPI) increased overall, while the landscape shape index (LSI) decreased by approximately 18.3%, indicating that the connectivity and structural integrity of urban green spaces have been substantially enhanced. National strategic policies, particularly the outline of ecological protection and high-quality development planning for the Yellow River basin, have effectively alleviated the encroachment pressure of population agglomeration and economic expansion on UBGSs, and played a decisive regulatory role in promoting the structural optimization of blue–green spaces. These findings provide empirical evidence for cross-city collaborative planning and integrated ecological governance of blue–green spaces at the urban agglomeration level, and offer valuable reference for achieving sustainable urban development in other rapidly urbanizing areas.