Estimation of forest canopy closure in northwest Yunnan based on multi-source remote sensing data colla-boration

Forest canopy closure (FCC) is an important parameter to evaluate forest resources and biodiversity. Using multi-source remote sensing collaborative means to achieve regional forest canopy closure inversion with low cost and high-precision is a research hotspot. Taking ICESat-2/ATLAS data as the main information source and combined with data of 54 measured plots, we estimated FCC value by the Bayesian optimization (BO) algorithm improved random forest (RF), K-nearest neighbor (KNN), and gradient boosting regression tree (GBRT) model at footprint-scale. Combined with multi-source remote sensing image Sentinel-1/2 and terrain factors, we estimated the regional-scale FCC value of Shangri-La in the northwest Yunnan based on deep neural network (DNN) optimized by BO algorithm. The results showed that six characteristic parameters (percentage of tree canopy, standard deviation of relative height of photons at the top of the canopy, minimum canopy height, difference between 98% canopy height and median canopy height in the segment, number of top canopy photons, apparent surface reflectance) out of the 50 parameters that were extracted from ATLAS lidar footprint had higher contribution rate after RF characteristic variable optimization, which could be used as model variable for footprint-scale remote sensing estimation. Among BO-RF, BO-KNN, and BO-GBRT models, the FCC results estimated by the BO-GBRT model were the best at footprint-scale. The coefficient of determination (R) was 0.65, the root mean square error (RMSE) was 0.10, the mean absolute residual (RS) was 0.079, and the prediction accuracy (P) was 0.792 for leave-one-out cross validation. It could be used as the FCC estimation model of 74808 ATLAS footprints for forest in the study area. We used the ATLAS footprint-scale FCC value of forest as the large sample data of the regional-scale BO-DNN model and combined with multi-source remote sensing factors to estimate FCC in the study area, the accuracy of the 10-fold cross-validation BO-DNN model was R²=0.47, RMSE=0.22, P=0.558. The mean values of FCC in the study area estimated by BO-DNN model and ordinary Kriging (OK) interpolation were 0.46 and 0.52, respectively, and the values mainly distributed in 0.3-0.6, accounting for 77.8% and 81.4%, respectively. The FCC efficiency obtained directly by the OK interpolation method was higher (R²=0.26), but the prediction accuracy was significantly lower than the BO-DNN model (R²=0.49). The FCC high value was distributed from northwest to southeast in the study area, and the northern and southeastern regions were the main distribution areas of high and low FCC values, respectively. It had certain advantages to estimate mountain area FCC based on ICESat-2/ATLAS high-density footprint, and the estimation results of small sample data at footprint-scale could be used as large sample data of deep learning model at region-scale, which would provide a reference for the low-cost and high-precision to FCC estimation on the footprint-scale up to the extrapolated regional-scale.