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145 results on '"Li, Linlong"'

4. UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level

Author

Yao, Yao, Li, Linlong, Liang, Zhaotang, Cheng, Tao, Sun, Zhenhui, Luo, Peng, Guan, Qingfeng, Zhai, Yaqian, Kou, Shihao, Cai, Yuyang, Li, Lefei, and Ye, Xinyue

Subjects
Computer Science - Computers and Society, Computer Science - Computational Complexity
Abstract

Vector-based cellular automata (CA) based on real land-parcel has become an important trend in current urban development simulation studies. Compared with raster-based and parcel-based CA models, vector CA models are difficult to be widely used because of their complex data structures and technical difficulties. The UrbanVCA, a brand-new vector CA-based urban development simulation framework was proposed in this study, which supports multiple machine-learning models. To measure the simulation accuracy better, this study also first proposes a vector-based landscape index (VecLI) model based on the real land-parcels. Using Shunde, Guangdong as the study area, the UrbanVCA simulates multiple types of urban land-use changes at the land-parcel level have achieved a high accuracy (FoM=0.243) and the landscape index similarity reaches 87.3%. The simulation results in 2030 show that the eco-protection scenario can promote urban agglomeration and reduce ecological aggression and loss of arable land by at least 60%. Besides, we have developed and released UrbanVCA software for urban planners and researchers., Comment: 27 pages, 7 figures, 6 tables

Published
2021

14. Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China.

Author

Bai, Wenqian, He, Zhengwei, Tan, Yan, Robinson, Guy M., Zhang, Tingyu, Wang, Xueman, He, Li, Li, Linlong, and Wu, Shuang

Subjects
VEGETATION classification, CONVOLUTIONAL neural networks, FEATURE selection, RANDOM forest algorithms, MACHINE learning, CLASSIFICATION
Abstract

Developing an effective vegetation classification method for mountain–plain transition zones is critical for understanding ecological patterns, evaluating ecosystem services, and guiding conservation efforts. Existing methods perform well in mountainous and plain areas but lack verification in mountain–plain transition zones. This study utilized terrain data and Sentinel-1 and Sentinel-2 imagery to extract topographic, spectral, texture, and SAR features as well as the vegetation index. By combining feature sets and applying feature elimination algorithms, the classification performance of one-dimensional convolutional neural networks (1D-CNNs), Random Forest (RF), and Multilayer Perceptron (MLP) was evaluated to determine the optimal feature combinations and methods. The results show the following: (1) multi-feature combinations, especially spectral and topographic features, significantly improved classification accuracy; (2) Recursive Feature Elimination based on Random Forest (RF-RFE) outperformed ReliefF in feature selection, identifying more representative features; (3) all three algorithms performed well, with consistent spatial results. The MLP algorithm achieved the best overall accuracy (OA: 81.65%, Kappa: 77.75%), demonstrating robustness and lower dependence on feature quantity. This study presents an efficient and robust vegetation classification workflow, verifies its applicability in mountain–plain transition zones, and provides valuable insights for small-region vegetation classification under similar topographic conditions globally. [ABSTRACT FROM AUTHOR]

Published
2025
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28. Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoigê Plateau, China.

Author

Wang, Zhifei, He, Li, He, Zhengwei, Wang, Xueman, Li, Linlong, Kang, Guichuan, Bai, Wenqian, Chen, Xin, Zhao, Yang, and Xiao, Yixian

Subjects
FOREST biomass, BIOMASS, GRASSLANDS, SUPPORT vector machines, CARBON cycle, CONSTRUCTION planning, INVERSE problems
Abstract

Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R2 = 0.64, RMSE = 43.52 g/m2, RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R2 = 0.69, RMSE = 44.07 g/m2, RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Bioactive Scaffold Fabricated by 3D Printing for Enhancing Osteoporotic Bone Regeneration

Author

Zhang, Xiaoting, primary, Wang, Xinluan, additional, Lee, Yuk-wai, additional, Feng, Lu, additional, Wang, Bin, additional, Pan, Qi, additional, Meng, Xiangbo, additional, Cao, Huijuan, additional, Li, Linlong, additional, Wang, Haixing, additional, Bai, Shanshan, additional, Kong, Lingchi, additional, Chow, Dick Ho Kiu, additional, Qin, Ling, additional, Cui, Liao, additional, Lin, Sien, additional, and Li, Gang, additional

Published
2022
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47. Improved hemostatic effects by Fe3+modified biomimetic PLLA cotton-like mat via sodium alginate grafted with dopamine

Author

Lv, Caili, Li, Linlong, Jiao, Zixue, Yan, Huanhuan, Wang, Zongliang, Wu, Zhenxu, Guo, Min, Wang, Yu, and Zhang, Peibiao

Abstract

The development of an excellent, bioabsorbable hemostatic material for deep wound remains a challenge. In this work, a biodegradable cotton-like biomimetic fibrous mat of poly (l-lactic acid) (PLLA) was made by melt spinning. Subsequently, SD composite was prepared by cross-linking sodium alginate (SA) with dopamine (DA). It was immobilized on the fibre surface, which inspired by mussel byssus. Finally, Fe3+was loaded onto the 0.5SD/PLLA composite by chelation with the carboxyl of alginate and phenolic hydroxy of dopamine. The haemostasis experiment found that the hemostatic time 47 s in vitro. However, the bleeding volume was 0.097 g and hemostatic time was 23 s when 20Fe3+-0.5SD/PLLA was applied in the haemostasis of the rat liver. As a result of its robust hydrophilicity and bouffant cotton-like structure, it could absorb a large water from blood, which could concentrate the component of blood and reduce the clotting time. Furthermore, the addition of Fe3+in the 0.5SD/PLLA had a significant effect on improve hemostatic property. It also displayed excellent antibacterial property for Escherichia coliand Staphylococcus aureus. Notably, it possesses superior hemocompatibility, cytocompatibility and histocompatibility. Hence, 20Fe3+-0.5SD/PLLA has high potential application in haemostasis for clinical settings due to its outstanding properties.

Published
2021
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