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

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.