Estimation Method Of Forest Resources Land Type Variations And Its Application Based On TM Images

?This research is mainly about the variation information of the forest land type. Find the variation information by the use of TM remote sensing image data, and then do the variation detection and estimation combined with subcompartment data updating.This research mainly abstracts the variation information of the type of the landscape situation between year 2003 and 2005 using two-temporal TM images data and the filed survey data, with the help of remote sensing image conduct and data analysis applications such as ENVI4.6, ERDAS9.2, ArcGIS9.3, MATLAB7.0, PHOTOSHOP, EXCEL, etc. There are two ways to abstract the variation information, by classify the images and compare the differences or transform and analysis image data based on pixels. The pretreatment of the remote sensing images includes geometrical correction, image clipping, radial correction, eliminating cloud and shadow. And then estimate the precision of two ways of variation information collecting by filed survey data.The main contents concluded as follows:(1)For the remote sensing images from the same sensor, there are two ways to abstract the variation information, by classify the images and compare the differences or transform and analysis image data based on pixels. The first method is just as describe, the other one is to calculate and convert the value of the specifically pixels from two images to obtain the change information.(2)Use maximum likelihood method and support vector machine method to classify the two images. The result shows that for the two images from 2003 and 2005, the classification using the first method has the overall accuracy of 89.94% and 85.23%, and the kappa index is 0.7895 and 0.7290; the classification using the second method has the overall accuracy of 92.23% and 89.82%, and the kappa index is 0.8312 and 0.8045. For the experiment area we choose, the support vector machine method has the better result than maximum likelihood method. The classifications by computer can recognize the forestry land well, as the accuracies could reach 98.11% and 97.69%; they can also recognize the non-productive forest land and other land by the accuracies of 88.77% and 90.13%; the accuracies of the immature forest land are 69.67% and 64.76%.(3)The pixel analysis method includes two ways: D-value and PCA (principal component analysis). Estimate the variation information extractives qualitatively and quantitatively, the result shows that in those images which could show the differences of the type of the land during the period, the RVI D-value image has highest entropy of 4.8027 which means it has highest information content; and the first component of the principal component analysis of D-value image has the highest average gradient which means it has highest average sharpness. Combine the two, and collect the variation information by classify those images.(4)From the TM images and the field survey data of forest department, we can summary that during year 2003 and 2005, there are 4 kinds of situation in the majority: forest land, non-productive forest land and other land which didn't change during the period, immature forest land grow up to forest land or the immature forest land replace forest land.(5)Using two ways to abstract the variation information (by classify the images and compare the differences or transform and analysis image data based on pixels), the precision evaluation of the result shows that both of them has a precision of more than 90% with the no-changing area. For the areas that land type changed between immature forest land and forest land, the latter has a higher detection precision which reaches to 74.7% and 85.1%, and the former only has 55.54% and 54.55%.(6)This research indicates that estimate the variation of the forest resource subcompartment landscape by using two-temporal TM images data to control the overall accuracy of the forest resource investigation is applicable. Although the estimation has a certain error with the landscape variation in specific subcompartments, as an investigate, the detection and estimation of the forest resource's variation information especially the land type change information with the help of remote sensing images has got a preferable effect. As more researches done with this issue now and in the future, using remote sensing images and the sampling investigation method could make the forest resource survey system based on second type of forest resource investigation more applicable and high-efficiency.