In order to establish the forest canopy density and stock volume estimation equation based on remote sensing and GIS in the monitoring area

it’s needed to sample certain amounts of representative sample plots.How to rationally select certain amounts of representative sample plots belongs to the problem of multi-objective optimization.It’s hard to do in practical work because of the heavy calculation workload from the selecting by certain optimizing standards with all the combination method according to the known amounts of sample plots in monitoring area.Therefore

to classify the sample plots firstly and then select certain representative ones is needed.Because of the manifold of statistic measuring distance between sample plots and concrete clustering method

different categorized results appeared in the same monitoring area result in different statistic and clustering methods.Designing different factors of remote sensing and GIS that influencing the estimation of canopy density and stock volume

there will be a large difference in classifying results.To a specific monitoring area

the influencing law of different factors to classifying is studied systematically by means of computer simulation.Meanwhile how to choose the statistic measuring distance between sample plots and clustering methods in practical work is also studied in this paper.The results can be useful to real work.