Optimization of forest canopy closure estimation model based on spaceborne photon counting LiDAR data
[Objective]This paper aims to evaluate the potential of spaceborne photon counting LiDAR data to estimate forest canopy closure(FCC),also in order to propose a new technical method for optimizing forest management planning with high efficiency and low cost to estimate regional-scale FCC.[Method]The study took the spaceborne lidar ICESat-2/ATLAS photon point cloud data as the information source,and took the ecologically fragile area of Shangri-La in northwest Yunnan as the study area,combined with the data of 54 ground measured sample plots.In the early,based on the pre-processing of point cloud data such as denoising and classification,74 808 footprints canopy parameters of forested land in the study area were extracted(59 in total).The recursive feature elimination algorithm of support vector machine(SVM-RFE)was used to optimize the feature variables,and the spatial distribution of regional-scale feature variables was obtained by ordinary Kriging(OK)interpolation.Modeling of improved random forest(RF),k-nearest neighbor(KNN),and gradient regression tree(GBRT)models by Bayesian Optimization(BO)algorithms,the determination coefficient(R2),root mean square error(RMSE),overall prediction accuracy(P),residual sum of squares(RSS)and relative root mean square error(RRMSE)were used as model evaluation indexes,which construct the FCC estimation model in the study area.[Result]1)The average cross-validation accuracy of the six(asr,n_toc_photons,n_ca_photons,h_min_canopy,toc_roughness,photon_rate_can)footprint canopy parameters extracted by ICESat-2/ATLAS after SVM-RFE optimization was 0.60,which could be used as OK interpolation variables.2)The optimal canopy parameter was used as the OK interpolation variable to fit the best variance function,the nugget effect(SR＜25%)of all variables was weaker and had strong spatial autocorrelation.Excepting that the best fitting model of asr variable was spherical model(R2=0.689,RSS=2.05×10-6,RRMSE=0.160 2),and the best fitting models of the other 5 variables were exponential models(R2,0.71-0.93;RSS,2.34×10-9-1.54×10-4;RRMSE,0.023 9-0.188 6).3)Among BO-RF,BO-GBRT and BO-KNN of forest canopy closure estimation models,the BO-RF model had the highest comprehensive modeling accuracy(R2=0.73,RMSE=0.09,P=80.13%),which could be used as FCC remote sensing estimation model in the study area.4)The spatial mapping of the study area FCC estimated by the BO-RF model had a mean value of 0.53,mainly distributed between 0.3 and 0.6,accounting for 77.44%.The high value area of FCC generally showed a trend of extending the distribution from southeast to north,which was basically consistent with the distribution of forest resources in the study area.[Conclusion]This method can provide a technical and methodological reference for optimizing forest resource management.
ICESat-2/ATLASBayesian optimization algorithmmachine learning methodSVM-RFEvariance function
NETL
NSTL
万方数据
