滇西北森林郁闭度估测模型——基于全球生态系统动力学调查多波束激光雷达数据

探究全球生态系统动力学调查(GEDI)多波束激光雷达数据估测区域森林郁闭度(FCC)的潜力,对于评估森林生态系统状态和林分环境具有重要作用。选取滇西北典型生态脆弱区香格里拉为研究区,以GEDI波形数据为信息源,提取46245个有林地光斑参数,使用经验贝叶斯克里金法(EBK)获取光斑参数在研究区未知空间的连续分布,结合54块实测样地数据,采用支持向量机的递归特征消除法(SVM-RFE)、随机森林(RF)和Pearson分析分别优选特征变量,基于贝叶斯优化(BO)随机森林回归模型(BO-RFR)、贝叶斯优化梯度回归模型(BO-GBRT)和偏最小二乘法(PLSR)研建森林郁闭度最佳估测模型。结果表明:(1)EBK法预测精度高,估测结果可靠,R²:0.20-0.92,RMSE:0.004-2812.912,MAE:0.003-1996.258,MRE:0.007-4.423;(2)基于不同特征优选方法筛选的特征变量和数量略有差异,SVM-RFE 法优选出6个参数(cover、pai、sensitivity、rv_a1、rv_a4、rg_a4)的平均交叉验证精度达0.84,RF法以贡献度5%为阈值筛选出5个参数(cover、pai、pgap_theta_error、modis_treecover、modis_nonvegetated),Pearson法以相关性大于0.3且在0.01水平显著优选出5个参数(cover、pai、rv_a5、rg_a5、pgap_theta_error);(3)不同特征变量优选方法筛选的建模参数研建估测模型精度差异性较大,以SVM-RFE和RF方法优选参数构建估测模型的精度更佳,SVM-RFE方法优选参数研建估测模型精度变化相对稳定,以 RF方法中的BO-GBRT模型为最佳FCC估测模型(R²=0.85、RMSE=0.069,P=86.5%);(4)采用BO-GBRT模型估测研究区森林郁闭度和空间制图,与GEDI pai参数预测的FCC具有较高空间相关性达0.53,FCC均值分别为0.58、0.61,主要分布在0.4-0.7,分别占比65.45%、51.79%。研究区森林郁闭度主要处于中度郁闭,北部区域主要为高度郁闭区,与研究区植被覆盖度的空间分布具有一致性,说明使用GEDI数据估测森林郁闭度的方法具有可行性、结果具有可靠性。研究为使用GEDI数据高效、及时、低成本估测大空间尺度的森林水平结构参数的相关研究奠定了基础。

Construction of forest canopy closure estimation model in the northwestern Yunnan based on global ecosystem dynamics investigation multi-beam LiDAR data

Exploring the potential of global ecosystem dynamics investigation (GEDI) multi-beam LiDAR data to estimate regional forest canopy closure (FCC) plays an important role in assessing forest ecosystem status and stand environment. The typically ecologically fragile area, Shangri-la, was selected as the study area in the northwestern Yunnan. GEDI waveform data was used as the information source to extract 46245 forest footprints parameters. The empirical Bayesian kriging (EBK) method was used to obtain the continuous distribution of footprints parameters in the unknown space of the study area. Then, combined with 54 measured samples data, the recursive feature elimination method of support vector machine (SVM-RFE), random forest (RF) and Pearson analysis were chosen to optimize the characteristic variables, respectively. The best estimation model of forest canopy closure was studied and constructed by Bayesian optimal random forest regression model (BO-RFR), Bayesian optimal gradient regression model (BO-GBRT), and partial least square method (PLSR). The results showed that: (1) The EBK method had high prediction accuracy and reliable estimation results. Its R² was 0.20-0.92, RMSE was 0.004-2812.912, MAE was 0.003-1996.258, and MRE was 0.007-4.423. (2) There were slight differences in the method selection of characteristic variables and number based on different characteristic optimization methods. Among the three methods, the SVM-RFE method selected six parameters (cover, pai, sensitivity, rv_a1, rv_a4, rg_a4) with an average cross-validation accuracy of 0.84. The RF method selected five parameters (cover, pai, pgap_theta_error, modis_treecover, modis_nonvegetated) with a contribution of 5% as the threshold. The Pearson method significantly selected five parameters (cover, pai, rv_a5, rg_a5, pgap_theta_error) with a correlation greater than 0.3 and at the 0.01 level. (3) The modeling parameters selected by different characteristic variable optimization methods had great differences in the prediction accuracy of the estimation model. Among them, the accuracy of the estimation models constructed from the parameters selected by the SVM-RFE and RF methods was better, while the accuracy of the estimation models based on the optimization parameters of SVM-RFE method was relatively stable. The BO-GBRT model in the RF method was the best FCC estimation model (R²=0.85, RMSE=0.069, P=86.5%). (4) The BO-GBRT model was used to estimate the forest canopy closure and spatial mapping in the study area, which had high spatial correlation with the FCC predicted by the GEDI pai parameter of 0.53, and the mean values of the FCC were 0.58 and 0.61, which were mainly distributed in the range of 0.4-0.7, accounting for 65.45% and 51.79%, respectively. The forest canopy closure in the study area was mainly in moderate canopy closure, and the northern area was mainly in high canopy closure area, which was consistent with the spatial distribution of vegetation coverage in the study area. It indicated that the method of estimating forest canopy closure using GEDI data in this study was feasible and the results were very reliable. Our research laid a foundation for the efficient, timely and low-cost estimation of forest horizontal structural parameters at large spatial scales based on GEDI data.