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Study on Growth Monitoring Technique Based on Pixel Un-Mixing Method and HJ Remote Sensing Images in Paddy Rice

Author: MaMengLi
Tutor: CaoWeiXing
School: Nanjing Agricultural College
Course: Cartography and Geographic Information Systems
Keywords: Remote sensing Rice Image sub- block Hierarchical classification Spectral unmixing Information extraction Growth monitoring Production forecast
CLC: S511
Type: Master's thesis
Year: 2011
Downloads: 26
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Real-time and non-destructive monitoring of crop growth and accurate grain yield and qualities predicting could improve traditional crop cultivation and real-time forecasting techniques, and it’s also very important for developments of food security and sustainable agriculture. The ability of monitoring and predicting crop growth has been improved significantly with the spatial and real-time remote sensing images. In this study, the main research are methods of extracting area information based on pixel un-mixing and of quantitative remote sensing reversed based on HJ remote sensing images in Rugao county, Jiangsu. Following are the detail results:To resolve the serious pixel mixing problem of coarse spatial resolution sensors, and improve the cultivation area extraction accuracy of paddy rice, two pixel un-mixing methods were proposed, which were fit for multispectral remote sensed images:Pixel un-mixing based on Image blocking (IBPU) and the stratified multiple endmember spectral mixture analysis (SMESMA). For the first one both spatial feature and spectral information was combined, and the endmember was selected from the "blocked images" containing more simple landscape, which could avoid mistakes of endmember selection caused by deficiency of spectral bands and spectral information of multispectral images, and improve the accuracy of pixel un-mixing. For the later one, the complexity of landscape will be mitigated using stratified classification method, and MESMA represent an alternative approach, in which the number and types of endmembers vary in a per-pixel basis, and overcome the spectral variations within classes. The accuracy of classification was improved significantly by combining these two methods. The result showed that SMESMA had the best classification accuracy of 85.78% and kappa coefficient of 0.85, than the IBPU of 83.65% and 0.82, and both of the two method based on pixel outperformed maximum likelihood classification (MLC). Over all, SMESMA is a mechanism method with higher accuracy, which can solve the "same object with different spectra" phenomenon, IBPU can not solve the it, but has advantages such as simple and high operation efficiency. The results of our study indicated that the two proposed methods were useful and fit for paddy cultivation area extracting with coarse spatial resolution images.Based on the extracting information of paddy rice, the relationships between measured leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA) and "pure" spectral indices of rice were analyzed. For LNC models, DVI(4,2)(differential vegetation index) was the best index with determination coefficient (R2) of 0.74. And for the LNA, RVI(4,2)(ratio vegetation index) was the best index with R2 of 0.80. In addition, the Savitzky-Golay filter was used to remove noise or unusual values such as cloud vapor et al in the HJ images. and then the relationships between time-series HJ-NDVI(normalized difference vegetation index), HJ-EVI(enhanced vegetation index) data and leaf area index (LAI) were analyzed, meanwhile, the relationship between rice yield and LAI was analyzed too, finally, the optimum phase was selected and the rice yield prediction model was constructed base on "EVI-LAI-rice yield".The inspection result with different years showed that this construction of leaf nitrogen nutrient monitoring model and rice yield prediction model is feasible and reliable, which are fit for widespread application.

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CLC: > Agricultural Sciences > Crop > Cereal crops > Rice
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