Affiliation:
1. College of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
2. College of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
3. Resources, Environment and Geographic Information Engineering Technology Research Center of Anhui Province, Wuhu 241002, China
4. College of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China
5. College of Remote Sensing and Surveying Engineering, Nanjing University of Information Science & Technology, Nanjing 211500, China
Abstract
Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R2) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R2 of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion.
Funder
the Major science and technology Project of High-Resolution Earth Observation System
Anhui Science and Technology Major Program
Key Research and Development Program of Anhui Province
the Science Foundation for Distinguished Young Scholars of Anhui Universities
Collaborative Innovation Project of Universities in Anhui Province
Anhui Provincial Special Support Plan
Science Research Key Project of Anhui Educational Committee