基于无人机与Sentinel–2数据的滇西北高山松LAI估测研究

LAI Estimation of Pinus densata in Northwest Yunnan Based on UAV and Sentinel–2 Data

    摘要
  • 摘要: 以香格里拉市典型高山松天然林为研究对象，应用无人机获取低空航拍的多光谱影像，使用冠层间隙率模型计算LAI，联合Sentinel–2影像提取的植被指数，基于随机森林和贝叶斯优化算法改进后的随机森林模型（BO–RF）研究建立高山松叶面积指数估测模型，运用留一交叉验证方法的决定系数（R2）、均方根误差（RMSE）、预测精度（P）和平均绝对误差（MRE）评价估测模型拟合精度，使用BO–RF模型进行区域尺度LAI遥感反演。结果表明：基于无人机多光谱遥感影像，使用冠层间隙率模型计算的LAI均值为4.24，标准差为0.96。贝叶斯优化算法能有效提高机器学习模型估测精度，BO–RF模型的R2=0.82、RMSE=0.41、P=90.03%、MRE=8.78%，较未优化前，R2提高了20.59%、RMSE减小了24.07%、P提升了2.87%、MRE降低了1.78%。使用BO–RF模型估测研究区LAI和空间制图，均值为4.25，主要分布在4, 6区间，占比63.15%，预测值与实测值具有较高的一致性，相关系数达0.75，R2=0.58。LAI总体分布趋势为中间高、四周低，中部和北部区域是LAI高值主要分布区，低值主要分布于东南部。研究结果可为使用机载超高分辨率光学数据耦合星载中分辨率卫星数据快速精准遥感估测大空间尺度的森林叶面积指数提供参考。

     

    Abstract: The research focused on the typical Pinus densata natural forest in Shangri-La and utilized multi-spectral images obtained by low-altitude aerial photography using a UAV. The canopy gap rate model was applied to calculate leaf area index, and the extracted vegetation index from the Sentinel–2 image was integrated. Subsequently, an estimation model for the leaf area index of P. densata was developed using the random forest (RF), Bayesian optimization algorithm improved RF(BO–RF). At the same time, the leave one out cross-validation method was used to evaluate the fitting accuracy of the estimation model by the determination coefficient (R2), root mean square error(RMSE), prediction accuracy(P) and mean absolute error(MRE). The BO–RF model with the highest accuracy was used for LAI remote sensing inversion at the regional scale. The results showed that the mean LAI calculated by the canopy gap rate model was 4.24, and the standard deviation was 0.96 based on the multi-spectral remote sensing image of UAV. Bayesian optimization algorithm can effectively improve the estimation accuracy of the machine learning model and the R2 = 0.82, RMSE=0.41, P=90.03% and MRE=8.78% of the BO–RF model. The R2 increased by 20.59%, RMSE decreased by 24.07%, P increased by 2.87%, and MRE decreased by 1.78%, compared to the values before optimization. The BO–RF model was used to estimate LAI and spatial mapping in the study area. The average value was 4.25, which was mainly distributed in the range of 4,6, accounting for 63.15 %. This distribution aligns with the measured and predicted values, displaying a strong correlation coefficient of 0.75 and R2 = 0.58. The overall distribution of LAI tends to be high in the center and low in the periphery, with the central and northern regions being the main distribution areas for high LAI values, and low values mainly in the southeast. The research results can provide a reference for rapid and precise remote sensing estimation of forest LAI at a large spatial scale using ultra-high resolution optical data in conjunction with spaceborne medium-resolution satellite data.

     

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