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Study On Information Extraction Technology Of Dendrolimus Punctatus Damage Based On SPOT-5 Remote Sensing Images

Posted on:2012-10-17Degree:DoctorType:Dissertation
Country:ChinaCandidate:X L QiFull Text:PDF
GTID:1483303350974029Subject:Forest management
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Dendrolimus Punctatus Walker (the main insect pests of pinus massoniana) is seriously restrain the growth of pinus massoniana, it occurs frequently, often rampant hazard, known as "the forest fire of smokeless". In southern China region, dendrolimus punctatus perennial happen, cause disaster generally three to five years, as once flood-gates pestcide area 2.0106?3.3106hm2, reduce wood quantity about 3.0106 m3, economic loss of one billion yuan, is the most pest in the case of area of forest pest and serious harm. Realize the effective monitoring of Dendrolimus punctatus Damage, is the key to reduce harm. Traditional ground sampling survey method is time-consuming,and cannot satisfied with forestry production actual need.Remote sensing technology has the advantages of covering and real-time detection of large area, has the technical feasibility and economic save. Therefore, ShaXian was chosen as study area in this paper, based on multi-spectral and panchromatic images of SPOT-5, combined with the field survey data, research area forest resources database, research area DEM auxiliary data, analyze spectral changes and textures change of pests images, the information of Dendrolimus punctatus Damage was extracted with the spectrum index selection and the analysis of the texture characteristics etc. Thus the remote sensing monitoring forest pest technology system was established, and the remote sensing monitoring Dendrolimus punctatus Damage index system was constructed, and a new method based on pests in pinus massoniana slice layer of image classification -- facing classes was established, provide technical support and theoretical basis for production monitoring, and as reference for high resolution images used for forest pest monitoring. The main research results are as follows:(1) Referenced to land use classification system of ministry of land resources, combined with actual situation of study area, according to research needs, the land use classification system of Shaxian county was determined:woodland and unwoodland, woodland were divided into Pinus assoniana, fir,and hardwood Based on the spectral features of images and sub-region and hierarchical theory, combining the threshold set and decision tree algorithm of QUEST classification, the woodland image was extracted, on this basis, the pinus massoniana project information was extracted, the extraction accuracy reached 92.89%.(2) The victims of pinus massoniana of images changed greatly in 2,3,4 band with SPOT-5 spectral analysis. Based on this, the remote sensing monitoring index system of Dendrolimus punctatus Damage was firstly constructed. (3) Based on the spectral index, the insect scouting series estimation model that Characterizing pest degree was established, its model estimation accuracy reached 81.69%. So as to realize the inversion of insect scouting series and extract the project information of pinus massoniana damage, the information extraction total precision was 70.75%, Kappa=0.6759, the classification accuracy was not high, Salt-pepper phenomenon was serious, and Health stand with mild victims stand confused seriously.(4) The image texture feature were extracted and analysised, and the information of pinus massoniana wool pests were extracted with the maximum likelihood method and object-oriented classification method. The former classification total precision was 72.75%, Kappa=0.6913,The latter classification total precision was 74.75%, Kappa=0.7283, the latter is higher than that of the former. compared to the classification that based on spectral information, both classification accuracy have improved, as followed:the classification method that based on pixel statistics raised 2%, object-oriented method increased 4%.It showed that the texture characteristics for image classification played a key role.(5) Study on fusion algorithms of spectral information and texture information, introducing texture information to the image classification of Dendrolimus punctatus damage, extracting pests information with SVM classification method based on multi-scale texture and spectrum fusion, compared to the single yardstick texture method, the former classification total precision was 82.50%, Kappa=0.8059, the latter classification total precision was 80.75%, Kappa= 0.7824, the latter classification accuracy was higher than the former. Meanwhile compared to only based on spectral information and texture information, both classification precision had increased, as followed:monoscale texture respectively increased by 10%?6%, multi-scale texture improved 11.75%,7.75%. Spectrum information and texture information fusion significantly enhanced the pests spectral response, and the introduction of multi-source information were helpful to improve the image classification accuracy.(6) Fusing spectrum information and texture information of image, considering the ecological and forest itself factor that impacting occurrence and development and change of insects, the remote sensing monitoring index system of Dendrolimus punctatus damage were firstly constructed. Based on the monitoring index system, the project information of Dendrolimus punctatus damage were extracted by ridge regression model its classification total precision was 85.75%, Kappa=0.8328. Compared to the method without terrain and forest factor, its accuracy increased 15%,11%,3%.(7) Based on the characteristics of image of pests, the new method-based on the ecological factors patches for class was put forward.Based on the altitude, slope and aspect, the pinus massoniana were partited in space, then according to the characteristics of each piece, the decision-making tree, object-oriented classification method were used comprehensively to extract information of pests, the classification total precision was 87.50%, Kappa=0.8559, its precision was higher than ridge regression modeling method, and the classification total accuracy improved 1.75%. This method considered comprehensively many factors, eliminated the influence of terrain, effectively avoid salt-pepper happening.(8) Analysising various methods to extracting information of Pinus massoniana wool pest, the infonnation extraction accuracy of health stand and severe suffer stand were highest, the information extraction accuracy of mild suffer stand and moderate suffer stand were slightly lower. Among various method of extracting pest information, the classification precision of new method -based on the ecological factors patches for class was highest, This new method was the best information extraction method.(9) The remote sensing monitoring of forest diseases and pests is not just a question of image recognition, the plant diseases and pests occurrence and development have a close relationship with its survival environment, and the spectrum responses of pests were influenced by the forest itself. So remote sensing monitoring the forest diseases and pests, the ecological conditions and its own stand condition factor of host should be considered, thus the pest areas are correctly identified and the monitoring accuracy are improved.
Keywords/Search Tags:Dendrolimus punctatus damage, Spectrum information, Texture information, Information extraction, SPOT-5 remote sensing images
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