ZCZongxin Cui

Zongxin Cui

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


Zongxin Cui has a total of 7 co-authors. Here are the top co-authors by number of shared publications:

  • Linhao Sun

    Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China

    2 shared publications
  • Luming Fang

    Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China

    2 shared publications
  • Ao Xu

    Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China

    1 shared publication
  • Chaowen Li

    Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China

    1 shared publication
  • Hongwen Yao

    Forest Resources Monitoring Center for Zhejiang Province, 71 Fengqi East Road, Hangzhou 310020, China

    1 shared publication
  • Jie Wang

    Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China

    1 shared publication

Latest Publications

  • Open Access

    Development and Performance Validation of a UWB–IMU Fusion Tree Positioning Device with Dynamic Weighting for Forest Resource Surveys

    by Zongxin Cui, Linhao Sun, Ao Xu, Hongwen Yao, Luming Fang

    In forest resource plot surveys, tree relative positioning is a crucial task with profound silvicultural and ecological significance. However, traditional methods such as compasses and total stations suffer from low efficiency, high costs, or poor environmental adaptability, while single-sensor technologies (e.g., UWB or IMU) struggle to balance accuracy and stability in complex forest environments. To address these challenges, this study designed a multi-sensor fusion-based tree positioning device. By integrating the high-precision ranging capability of Ultra-Wideband (UWB) with the dynamic motion perception advantages of an Inertial Measurement Unit (IMU), a dynamic weight fusion algorithm was proposed, effectively mitigating UWB static errors and IMU cumulative errors. Experimental results demonstrate that the device achieves system biases of −1.54 cm (X-axis) and 1.27 cm (Y-axis), with root mean square errors (RMSE) of 21.34 cm and 23.93 cm, respectively, across eight test plots. The average linear distance error was 26.23 cm. Furthermore, in single-operator mode, the average measurement time per tree was only 20.89 s, approximately three times faster than traditional tape measurements. This study confirms that the proposed device offers high positioning accuracy and practical utility in complex forest environments, providing efficient and reliable technical support for forest resource surveys.

  • Open Access

    Development and Testing of a Tree Height Measurement Device

    by Chaowen Li, Jie Wang, Shan Zhu, Zongxin Cui, Luming Fang, Linhao Sun

    Tree height is a key indicator in forest resource inventories, playing a vital role in evaluating forest resources, carbon stocks, and biomass. However, conventional tree height measurement methods often suffer from limitations such as inadequate accuracy and low efficiency. This paper proposes a portable tree height measurement device based on the integration of ultra-wideband (UWB) technology and an accelerometer, enabling high-precision, low-cost, and rapid tree height measurements. The device adopts a modular design, integrating a UWB ranging sensor, a triaxial accelerometer, a main control unit, and wireless communication modules. It acquires precise distance information via the double-sided two-way ranging (DS-TWR) algorithm and computes tree height by incorporating the pitch angle measured by the accelerometer. Through measurements on 80 trees of various species, compared to results from Total Station, the root mean square error (RMSE) was 0.621 m, with an overall bias of 0.104 m (0.79%) and an overall device accuracy of 95.75%. Additionally, the device features real-time data transmission and cloud storage capabilities, offering an efficient and convenient technical solution for the digital management of forest resources. It holds promising application prospects in areas such as forest resource inventories, ecological monitoring, and forestry production management.