To Extract The Optimal Sample Method Of Research Of Forest Stock Volume Estimation Model Is Established

Along with the high resolution satellite launch, It，forest stock volume estimation， hasbecome a hot research topic by the use of high resolution remote sensing images combinedwith a small amount of sample plot survey. After determining remote sensing and GIS factorsthat have a greater influence on forest stock volume estimate, establish the estimationequation to forecast the forest stock volume on the basis of a small amount of sample plotsurvey data corresponding to remote sensing and GIS factors, thereby furthest, reduce theforest survey work.The traditional sampling, generally establish equation by extracting partly, forecastaccuracy of the remaining sample, by using a small amount of sample to setup estimateequation. After all, need how many sample, that is too little to guarantee the accuracy tomeeting the requirements, and too many to demonstrate the superiority of the estimationbecause of manpower and material resources wastage. And sample quantity is proportional tothe contained information, more number of samples make the equation more stable. Use allsample to establish equation can use furthest the information contained in samples. But alsothere are two problems. Firstly, the total sample contains the abnormal samples information,what is harmful for establishing equation. Second, if all samples used in establishing equation,it cannot test the forecast accuracy of the equation, and cannot know whether the equationssatisfy the requirements of accuracy. So need a sampling method, can use farthest thefavorable information without harmful information, and get the prediction precision of theequation.It, multiple random sampling average method, is proposed in this paper. For randomsampling, the selective probability of every sample pumped are equal, and multiple samplingcan guarantee that all samples are selected, meanwhile control each sampling accuracy, toreduce the abnormal sample information and guarantee forecast accuracy. If have a bigresidual error, there is reason to suspect that contain the abnormal samples, and if the forecast accuracy is less than85%, it cannot satisfy the actual demand. Ultimately, meet all above, wethink that this sample meets the requirements. Multiple random sample, get the averagecoefficient by average of coefficients.For verify efficiency and accuracy of the sample, this article use the A sample survey dataand TM remote sensing image, that adjusted, in the same period of forest resource fromMiyun county of Beijing, by software to extract the information of the wave band and theratio of wave band to estimate the forest stock volume. For three sampling methods, bytesting in least squares model, partial least squares model and the robust estimation model,mean square error of hierarchical multiple random method is the smallest and have a highaccuracy, can be used in the forest stock volume estimation.