Qianyang Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Qianyang Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Huanna Zheng
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China
1 shared publication - Hui Lin
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China
1 shared publication - Jiangping Long
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, China
1 shared publication - Peisong Yang
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
1 shared publication - Zhaohua Liu
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
1 shared publication - Zilin Ye
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
1 shared publication
Latest Publications
- Open Access
Mapping Forest Stock Volume Using Phenological Features Derived from Time-Serial Sentinel-2 Imagery in Planted Larch
As one of the important types of forest resources, mapping forest stock volume (FSV) in larch (Larix decidua) forests holds significant importance for forest resource management, carbon cycle research, and climate change monitoring. However, the accuracy of FSV mapping using common spectral and texture features is often limited due to their failure in fully capturing seasonal changes and growth cycle characteristics of vegetation. Phenological features can effectively provide essential information regarding the growth status of forests. In this study, multi-temporal Sentinel-2 satellite imagery were initially acquired in the Wangyedian Forest Farm in Chifeng City, Inner Mongolia. Subsequently, various phenological features were extracted from time series variables constructed by Gaussian Process Regression (GPR) using Savitzky–Golay filters, stepwise differentiation, and Fourier transform techniques. The alternative features were further refined through Pearson’s correlation coefficient analysis and the forward selection algorithm, resulting in six groups of optimal subsets. Finally, four models including the Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multiple Linear Regression (MLR) algorithms were developed to estimate FSV. The results demonstrated that incorporating phenological features significantly enhanced model performance, with the SVM model exhibiting the best performance—achieving an R2 value of 0.77 along with an RMSE value of 46.36 m3/hm2 and rRMSE value of 22.78%. Compared to models without phenological features, inclusion of these features led to a 0.25 increase in R2 value while reducing RMSE by 10.40 m3/hm2 and rRMSE by 5%. Overall, integration of phenological feature variables not only improves the accuracy of larch forest FSV mapping but also has potential implications for delaying saturation phenomena.
Open AccessMapping Forest Stock Volume Using Phenological Features Derived from Time-Serial Sentinel-2 Imagery in Planted Larch
As one of the important types of forest resources, mapping forest stock volume (FSV) in larch (Larix decidua) forests holds significant importance for forest resource management, carbon cycle research, and climate change monitoring. However, the accuracy of FSV mapping using common spectral and texture features is often limited due to their failure in fully capturing seasonal changes and growth cycle characteristics of vegetation. Phenological features can effectively provide essential information regarding the growth status of forests. In this study, multi-temporal Sentinel-2 satellite imagery were initially acquired in the Wangyedian Forest Farm in Chifeng City, Inner Mongolia. Subsequently, various phenological features were extracted from time series variables constructed by Gaussian Process Regression (GPR) using Savitzky–Golay filters, stepwise differentiation, and Fourier transform techniques. The alternative features were further refined through Pearson’s correlation coefficient analysis and the forward selection algorithm, resulting in six groups of optimal subsets. Finally, four models including the Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multiple Linear Regression (MLR) algorithms were developed to estimate FSV. The results demonstrated that incorporating phenological features significantly enhanced model performance, with the SVM model exhibiting the best performance—achieving an R2 value of 0.77 along with an RMSE value of 46.36 m3/hm2 and rRMSE value of 22.78%. Compared to models without phenological features, inclusion of these features led to a 0.25 increase in R2 value while reducing RMSE by 10.40 m3/hm2 and rRMSE by 5%. Overall, integration of phenological feature variables not only improves the accuracy of larch forest FSV mapping but also has potential implications for delaying saturation phenomena.