联合LiDAR和多光谱数据森林地上生物量反演研究

【目的】森林地上生物量的准确估测对于实时掌握全球碳储量变化及应对气候变化有着重要的意义。组合多种遥感数据特征优选,分类建模反演森林地上生物量,是提高森林地上生物量精度的有效方法。【方法】以根河市大兴安岭生态观测站寒温带天然林为研究对象,以机载激光雷达(LiDAR)、Landsat8 OLI两种遥感数据源结合55块地面调查数据,采用偏最小二乘算法优化筛选变量,再以线性多元逐步回归和快速迭代特征选择的最近邻算法(KNN-FIFS)构建模型,在两种数据源的不同组合方式下进行森林地上生物量反演。【结果】①基于线性多元逐步回归模型下的单一LiDAR数据反演精度决定系数(R²)为 0.76,均方根误差(RMSE)为 21.78 t/hm²;单一Landsat8 OLI数据的反演精度R²为 0.24,RMSE为39.27 t/hm²;LiDAR和Landsat8 OLI联合反演精度R² 为 0.84,RMSE为18.16 t/hm²;②基于KNN-FIFS模型下的单一LiDAR数据反演精度R²为 0.74,RMSE为23.83 t/hm²;单一Landsat8 OLI数据的反演精度R²为0.60,RMSE为 29.63 t/hm²;LiDAR和Landsat8 OLI联合反演精度R²为0.80,RMSE为21.15 t/hm²。【结论】①特征优选支持下的3种组合方式中,LiDAR和Landsat8 OLI两种数据的组合在两种模型中反演精度均最高,其中线性多元逐步回归模型的反演精度最高,说明LiDAR和Landsat8 OLI数据组合,激光雷达与光学数据优势特征互补,协同反演可有效提高森林地上生物量的反演精度;②单一数据源反演森林地上生物量精度中,LiDAR数据比Landsat8 OLI数据在两种模型反演精度中均较高,这与LiDAR数据空间分辨高、可获得垂直结构特征参数有关。

Inversion of forest aboveground biomass using combination of LiDAR and multispectral data

【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 R²of 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.