城市森林生物量遥感估测中DN值分层抽样的应用

Application of Stratified Sampling of DN Values in Remote Sensing Estimation of Urban Forest Biomass

为了提高城市森林生物量遥感估测的精度,以杭州市西湖区为研究区,利用标准误差和平均绝对误差作为评价指标,与简单随机抽样、一般分层抽样等方法进行模拟抽样比较,根据简单随机抽样、一般分层抽样和DN值分层抽样3种方法分别进行样地实测和构建回归模型,通过均方根误差和相对均方根误差对生物量估算模型进行精度评价。结果表明:DN值分层抽样相对于简单随机抽样和一般分层抽样在稳定性上分别提升了43.3%和42.3%,在精确性上分别提升了60.1%和51.2%。基于DN值分层抽样方法建立回归模型的估算精度相对于其他2种方法有了明显的提高。

Taking Xihu District,Hangzhou as an example,a stratified sampling method based on Digital Number of remotely sensed imagery was proposed to improve the precision of urban forest biomass estimation.Standard error and average absolute error areused to compare the accuracy and stability of the Digital Number based stratified sampling with that of the simple random sampling and general stratified sampling.Based on the methods of random sampling,general stratified sampling and Digital Number based stratified sampling,the plots were collected,the regression models ware constructed,and the precisions of model fitness were evaluated with root mean square error and relative root mean square error.The results show that compared with random sampling and general stratified sampling methods,the stability of the stratified sampling are increased by 43.3%and 42.3%,respectively,and the accuracy increased by 60.1%and 51.2%,respectively.The estimation accuracy of the regression model based on the stratified sampling method of Digital Number is obviously improved compared with the other 2 methods.