ObjectiveBased on the ZY-3 satellite imagery and the LULUCF carbon sink monitoring plot data covering Zhejiang Province of southern China, the study attempted to construct a technical method for automatically extracting the above-ground carbon density of arbor forest in this area.

MethodTaking the carbon density of arbor forest in Zhejiang Province as the research object, relevant research tests were carried out in the aspects of vector sign constructing, extraction of spectral information, purification of interpretation sign, ZY-3 satellite image classification, optimization of independent variables, optimization of modeling methods, production of carbon density map, etc.

ResultThe results showed that the accuracy of classification of ZY-3 imagery after purification of interpretation signs was higher than that of image classification before purification. The accuracy of classification of ZY-3 images by kNN method (average total accuracy was 80.31%, average Kappa coefficient was 0.69, average user accuracy of arbor forest was 91.86%, and the average producer accuracy of arbor forest was 80.85%), which was higher than the maximum likelihood classification method (average total accuracy was 78.56%, average Kappa coefficient was 0.62, average user accuracy of arbor forest was 89.68%, and the average producer accuracy of arbor forest was 77.79%). Among the selected modeling methods, the model accuracy constructed by the kNN method (average RMSE was 15.64 t/ha, average RRMSE was 23.53%) was better than the robust estimation method (average RMSE was 17.63 t/ha, average RRMSE was 25.11%). Finally, the above-mentioned carbon density distribution map of arbor forest in Zhejiang Province was generated.

ConclusionThis study provides a new path for arbor forest or forest carbon density estimation at the provincial or larger scale, providing a reference for automated estimation of carbon density and other forest parameters.