Research on vegetation coverage optimisation of urban landscape based on vegetation index
Hua Yang
Abstract
Hua Yang
Abstract
In order to overcome the problem of fuzziness and randomness of remote sensing data, a vegetation coverage optimisation of urban landscape based on vegetation index is designed. The remote sensing image of vegetation cover is preprocessed by the principal component analysis. According to the fuzzy principal component image, GIS software is used as the platform of input data, and Fragstat software package is used to calculate the vegetation index of urban landscape. The NDVI value of shadow pixels is changed into background value, and Band Math is used on ENVI platform to get the NDVI data after removing the outliers. According to the principle of pixel dichotomy, the mixed pixel decomposition model is established to estimate the vegetation coverage and realise the vegetation coverage optimisation of urban landscape. The experimental results show that the vegetation coverage rate is 53%, the performance breakthrough is achieved and the pixel accuracy is more than 90%.
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In order to overcome the problem of fuzziness and randomness of remote sensing data, a vegetation coverage optimisation of urban landscape based on vegetation index is designed. The remote sensing image of vegetation cover is preprocessed by the principal component analysis. According to the fuzzy principal component image, GIS software is used as the platform of input data, and Fragstat software package is used to calculate the vegetation index of urban landscape. The NDVI value of shadow pixels is changed into background value, and Band Math is used on ENVI platform to get the NDVI data after removing the outliers. According to the principle of pixel dichotomy, the mixed pixel decomposition model is established to estimate the vegetation coverage and realise the vegetation coverage optimisation of urban landscape. The experimental results show that the vegetation coverage rate is 53%, the performance breakthrough is achieved and the pixel accuracy is more than 90%.
Key concepts: Normalized Difference Vegetation Index, Vegetation (pathology), Pixel, Remote sensing, Principal component analysis, Shadow (psychology), Outlier, Vegetation Index