Alage Bloom Remote Sensing Monitoring Based On Cellular Automaton

With the development of space science and remote sensing techniques, remotesensing images has become an important data source for earth observation, dataprocessing and the application of current research focusEutrophication of Taihu Lake water body increasingly frequent algal blooms,water quality problems seriously affect the production and living of the masses ofaround Taihu Lake. Use of remote sensing images, the effective extraction ofcyanobacteria dynamic distribution of information on the analysis of algal blooms andpollution warning and governance are important. Conventional water qualitymonitoring and time-consuming and labor-intensive and difficult to reflect the lakewater quality, remote sensing image analysis, with its large-scale, fast phase, a largeamount of information, such as the characteristics of the analysis of cyanobacteriabloom extract results achieved good results in the lake water quality monitoring hasgreat potentialDue to the limitations of remote sensing shooting instrument spatial resolutionremote sensing images in a corresponding pixel in surface area and usually cover avariety of surface features pixel spectrum is actually several pure spectroradiometerlinear or nonlinearmixed pixel. Mixed pixel decomposition for a typical surfacefeatures (ie, endmembers) and their mixing ratio (ie, abundance), you can get theinformation of the sub-pixel level, improve the accuracy of remote sensingclassification. For mixed pixel, the use of cellular automata model can not onlyquickly but also accurately analyze mixed pixel, this article of MA TLAB as platform for the preparation of cellular automation program, to achieve the water andcyanobacteria gathering area of the sub-pixel level classification and statistics.The thesis made the following findings:(1) Through the control of algal blooms and algae-containing water spectralcharacteristics curve obtained algal blooms in NDVI values greater than the NDVIvalue of algae-bearing water bodies, so NDVI threshold to preliminary identify thealgal blooms and algae-bearing water bodies. Band op to further optimize the selectedalgal blooms region of interest, preliminary outlines the distribution of the Taihu Lake,China, and Chinese pollution of Statistics water area in the HJ-CCD image.(2) Cellular automata composed of cellular, cellular space, rules, time, based oncellular automata, composition and characteristics of cellular automata theory, thetheory of cellular automata is the introduction of remote sensing of water Chineseextract, first through gray value of the linear decomposition of C and MATLABprogramming designed to achieve the identification of algal blooms and algae ofwater bodies.(3) Of cellular automata theory applied to the model of the monitoring of algalblooms, improved water Chinese extraction system to distinguish between water andalgae gathering area sub pixel level and area statistics. Tested the accuracy of the newtheoretical model, the results confirmed that the new method to improve themonitoring accuracy of Taihu Lake, China.