Cancan Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Cancan Yang has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Dexuan Zhao
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
1 shared publication - Jing Ma
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
1 shared publication - Longwei Li
School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
1 shared publication - Mengyi Hu
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
1 shared publication - Nan Li
School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
1 shared publication - Shijun Zhang
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
1 shared publication
Latest Publications
- Open Access
- 12 March 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (6) , 872
UAV-Based Stand Density Estimation for Aboveground Biomass Mapping in Moso Bamboo Forests
The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R2 = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R2 = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R2 = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15.
- 12 March 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (6) , 872
Open AccessUAV-Based Stand Density Estimation for Aboveground Biomass Mapping in Moso Bamboo Forests
The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R2 = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R2 = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R2 = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15.