Research And Realization Of Forest Volume Quantitative Estimation System By Remote Sensing

As human society is now facing great crisis of ecological environment. Strengthen themanagement of forest resources, to achieve the sustainable development of forestry hasbecome more and more national consensus. The forest stock volume is one of an importantpart of forestry resource survey, the traditional survey of the forest resources is a cycle long,the task is heavy, large labor intensity, and requires a large number of funds work. How to use3S technology, combined with a small amount of ground sample information, to establish theestimation equation of the forest volume in the monitoring regions to estimation of the foreststock volume, maximum reduction of field survey work, which has always been a concern ofby the domestic and foreign forestry remote sensing science and technology workers.This research developed a computer system via C#and ArcGIS Engine9.3, which basedon the theory of forest stock volume quantitative estimation by remote sensing. The systemwould selects the best impact of remote sensing factors and GIS factors from the forestrysurvey data and remote sensing image of the monitoring area; depicts sample plots by the bestimpact of factor, classifies those sample plots; extract the establishment of reservesEstimation of sample plots. The next step is establishing the volume estimation model by theextracted sample plots, converts remote sensing image cell size to the size of a sample plot;getting the values of main remote sensing and GIS factors with DEM and survey data of themonitoring regional; creating the forest volume distribution map for the monitoring area andthe system would compute the volume the total stock volume of the monitoring regional orthe stock volume of any compartment or sub compartment.Finally, I text “the system of forest volume quantitative estimation by remotesensing” by Yunnan province Simao7500km2areas within the81sample plots, themonitoring area of high resolution of2.5m SPOT5remote sensing image、resolution of25mDEM data and the Sub-compartment distribution map. The results show that the monitoringarea of the volume estimation model accuracy reached85.21%, to meet the precision requirement of forestry production.