Enhancing carbon storage in terrestrial ecosystems has become a key strategy for mitigating climate change. The Tibetan Plateau holds a pivotal position in achieving carbon neutrality, with the structural pattern of its land use types directly impacting the region’s ecosystem carbon storage capacity. However, there is still a lack of understanding of the spatial distribution of carbon storage in their ecosystems. This study targeted the Tibetan Plateau, utilizing land use data from 2000 to 2020, and employed the Patch-generating Land Use Simulation (PLUS) model to project land use patterns for 2030. By integrating future climate change projections, this study forecasted land use under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was employed to quantify carbon storage from 2000 to 2030, while the GeoDetector model was used to explore the driving influences of factors such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Leaf Area Index (LAI), Net Primary Productivity (NPP), population density, and road network density on carbon storage. The results revealed that: (1) Grassland predominated the land use types on the Tibetan Plateau, with most types having a stability of over 70%, whereas significant changes were observed in the western Tibet Autonomous Region and southern Xinjiang Uygur Autonomous Region. (2) Carbon storage on the Tibetan Plateau generally followed a tendency towards an initial decrease followed by an increase, with an average annual reduction of 50,107,371.79 Mg. The SSP1-2.6 scenario demonstrated the most substantial increase in carbon storage, being 18 times the natural trend, while the SSP5-8.5 scenario indicated the largest decrease. (3) Over the two decades, NDVI emerged as the most influential driver of carbon storage on the Tibetan Plateau, which was maintained at around 0.4, with the interaction between NDVI and NDWI exerting the strongest driving force, which was maintained at around 0.45. The conversion to forestland and grassland was the primary factor accounting for the change in carbon storage. Based on these results, despite the absence of empirical carbon density data, the SSP1-2.6 scenario could be regarded as a reference pathway for carbon storage changes on the Tibetan Plateau. Solely focusing on enhancing carbon storage by converting low-carbon land uses to high-carbon land uses is misguided; sustainable development represents the optimal approach for augmenting carbon storage on the Tibetan Plateau.

Background: To evaluate the accuracy of a computer numerical control (CNC) milled surgical guide for implant placement in edentulous jaws. Methods: Edentulous patients seeking implants treatment were recruited in this prospective cohort study. Radiographic guides with diagnostic templates were fabricated from wax-up dentures. Patients took cone-beam computed tomography (CBCT) wearing the radiopaque radiographic guides. Implant positions were virtually designed in the planning software based on the CBCT data, and the radiographic templates were converted into surgical guides using CNC milling technique. Forty-four implants were placed into 12 edentulous jaws following guided implant surgery protocol. Post-surgery CBCT scans were made for each jaw, and the deviations between the planned and actual implant positions were measured. Deviation of implant position was compared between maxilla and mandible, and between cases with and without anchor pins using independent t-test. Results: Nine patients (3 males and 6 females) with 12 edentulous jaws were recruited. The mean age of patients was 59.2 ± 13.9 years old. All 44 implants was placed without complication and survived, the mean three dimensional linear deviation of implant position between virtual planning and actual placement was 1.53 ± 0.48 mm at the implant neck and 1.58 ± 0.49 mm at the apex. The angular deviation was 3.96 ± 3.05 degrees. No significant difference was found in the deviation of implant position between maxilla and mandible (P = 0.28 at neck, 0.08 at apex), nor between cases with and without anchor pins (P = 0.87 at neck, 0.06 at apex). Conclusions: The guides fabricated using the CNC milling technique provided comparable accuracy as those fabricated by Stereolithography. The displacement of the guides on edentulous arch might be the main contributing factor of deviation. Trial registration: Chinese Clinical Trial Registry, ChiCTR-ONC-17014159 (July 26, 2017).

Timely and rapidly mapping impervious surface area (ISA) and monitoring its spatial-temporal change pattern can deepen our understanding of the urban process. However, the complex spectral variability and spatial heterogeneity of ISA caused by the increased spatial resolution poses a great challenge to accurate ISA dynamics monitoring. This research selected Jinan City as a case study to boost ISA mapping performance through integrating the dual-attention CBAM module, SE module and focal loss function into the Deeplabv3+ model using Sentinel-2 data, and subsequently examining ISA spatial-temporal evolution using the generated annual time-series ISA data from 2017 to 2021. The experimental results demonstrated that (a) the improved Deeplabv3+ model achieved satisfactory accuracy in ISA mapping, with Precision, Recall, IoU and F1 values reaching 82.24%, 92.38%, 77.01% and 0.87, respectively. (b) In a comparison with traditional classification methods and other state-of-the-art deep learning semantic segmentation models, the proposed method performed well, qualitatively and quantitatively. (c) The time-series analysis on ISA distribution revealed that the ISA expansion in Jinan City had significant directionality from northeast to southwest from 2017 to 2021, with the number of patches as well as the degree of connectivity and aggregation increasing while the degree of fragmentation and the complexity of shape decreased. Overall, the proposed method shows great potential in generating reliable times-series ISA data and can be better served for fine urban research.

The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10 m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km² in 2018 to 5147.02 km² in 2022 with an average rate of −16.09 km²/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas.

Rapid and timely evaluation of ecological environment change is of great significance to the sustainable development of the region. Owing to climate change and more anthropological intervention, ecological environment is deteriorating in the Yellow River Delta High-Efficiency Ecological Economic Zone (YRDHEEZ). To evaluate ecological environment change of the YRDHEEZ, a comprehensive evaluation index (CEI) was built in this investigation, which integrated vegetation cover (VC), land surface temperature (LST) and fine particulate matter (PM2.5) concentration. The experimental results demonstrated that significant regional differences existed in the ecological environment of the YRDHEEZ from 2000 to 2018. The ecological environmental changes in most districts and counties are complex and diverse. More specifically, regions with improvement, moderate improvement and the unchanged ecological environment accounted for more than 70 % of the study area, which suggested that the ecological environment has improved. In order to further verify the validity of the CEI, a field survey was performed, and it found that the result of field survey corresponded with the CEI evaluation, which demonstrated the potential of multi-source remote sensing data to quickly monitor the change of ecological environment quality in the YRDHEEZ.