应用滤波方法提高高山松地上生物量遥感估测精度的研究

基于1987年、1992年、1997年、2002年、2007年、2012年分布在香格里拉市的34个高山松固定样地数据，以及Landsat时间序列数据集，利用谷歌地球引擎和Python，通过3种滤波算法对时间序列数据进行重构，应用随机森林算法对森林地上生物量进行估测，根据模型评价指标对重构前后时间序列数据的估测效果进行分析。结果表明：采用3种不同滤波方法重构的时间序列数据训练的非参数模型，其拟合精度和预测精度均高于滤波前时间序列的预测精度，整体均方根误差和相对均方根误差指标均优于滤波前数据，其中ARMIA方法最佳。应用滤波方法在一定程度消除了影像自身所携带的大量噪声和不确定性，有效地提高了数据质量，提高了高山松地上生物量遥感估测的精度。

Research on Improving the Accuracy of Estimating Aboveground Biomass for Pinus densata Based on Remote Sensing Using Filtering Algorithm

Using data from 34 Pinus densata permanent sampling plots distributed in Shangri-La in 1987, 1992, 1997, 2002, 2007 and 2012, and time series datasets created based on Landsat images, combined Google Earth Engine and Python to reconstruct time series data with 3 different filtering algorithms. The random forest algorithm is used to estimate the aboveground biomass, and the estimation results of time series data before and after reconstruction are analyzed according to the model evaluation indicators. The results show that the non-parametric model trained by time series data reconstructed by 3 different filtering methods has higher fitting accuracy and prediction accuracy than the pre-filtering time series. The overall root mean square error and relative root mean square error are both better than the pre-filter data, and the ARMIA method performs best. The application of the filtering method does eliminate a large amount of noise and uncertainty carried by the image itself to a certain of extent, which effectively improves the data quality and improves the accuracy of remote sensing estimation of Pinus densata aboveground biomass.