Abstract:

【Objective】 The accurate estimation of forest aboveground biomass is important to determine changes in global carbon reserves and the corresponding climate change in real time. Combining a variety of remote sensing data, feature optimization, and classification modeling is an effective means to improve the accuracy of estimating forest aboveground biomass.【Method】 In this study, the research object was defined as the temperate natural forest at the Daxinganling ecological observation station in Genhe City, China. Additionally, fifty-five ground survey data containing airborne light detection and ranging (LiDAR) and Landsat8 operational land imager (OLI) remote sensing imagery were utilized. The partial least squares algorithm was used to optimize the selected variables, and the model was constructed by linear multiple stepwise regression and constructed by the k-nearest neighbor algorithm (KNN-FIFS) for fast iterative feature selection; the forest aboveground biomass was retrieved under different combinations of the two data sources.【Result】 The inversion accuracy of the single LiDAR data based on linear multiple stepwise regression model had a R² of 0.76, and a root mean squared error (RMSE) of 21.78 t/hm². The inversion accuracy of the single Landsat8 OLI data had a R² of 0.24, and a RMSE of 39.27 t/hm². The accuracy of LiDAR and Landsat8 OLI combined inversion had a R² of 0.84, and a RMSE of 18.16 t/hm². The inversion accuracy of single LiDAR data based on the KNN-FIFS model had a R² of 0.74, and a RMSE of 23.83 t/hm². The inversion accuracy of the single Landsat8 OLI data had a R² of 0.60, and a RMSE of 29.63 t/hm². The accuracy of the LiDAR and Landsat8 OLI combined inversion had a R² of 0.80, and a RMSE of 21.15 t/hm².【Conclusion】 Among the three combination methods supported by feature optimization, the combination of LiDAR and Landsat8 OLI data demonstrated the highest inversion accuracy in both models. Among the models, the inversion accuracy of the linear multiple stepwise regression model was the highest, with a R² of 0.84, and a RMSE of 18.16 t/hm². This result indicates that the LiDAR and Landsat8 OLI data complement each other, and collaborative inversion can effectively improve the inversion accuracy of forest aboveground biomass. The inversion accuracy of forest aboveground biomass from a single data source using LiDAR data was higher than Landsat8 OLI data of the two models; this was related to the high spatial resolution of LiDAR data and the availability of vertical structure parameters.

Key words: airborne light detection and ranging (LiDAR), Landsat8 OLI, forest aboveground biomass, partial least squares, linear multiple stepwise regression, k-nearest neighbor with fast iterative features selection (KNN-FIFS)