Vegetation classification in typical areas of the demonstration zone of integrated regional development of Yangtze River Delta based on hyperspectral satellite images
Monitoring the spatial pattern of vegetation is a crucial aspect of ecological environment monitoring.However,traditional methods still have significant limitations in large-scale,long-term and dynamic monitoring tasks.Hyperspectral remote sensing technology emerges as a promising solution,addressing these shortcomings while meeting the requirements for meticulous management.The demonstration zone of integrated regional development of Yangtze River Delta prioritizes sustainable ecological green development and places great emphasis on the construction of ecological civilization.Nonetheless,the diversity of vegetation types and fragmented distribution patterns within this demonstration zone pose challenges to investigating vegetation spatial dynamics.Therefore,there is an urgent need for a comprehensive understanding of the ecological environment in this area,particularly in accurately depicting the spatial distribution of vegetation using hyperspectral remote sensing technology.This article introduces a method for vegetation classification and spatial pattern extraction,using ZiYuan1 02D(ZY1E)hyperspectral satellite images across various seasons.The methodology encompasses hyperspectral satellite image data processing,the establishment of a vegetation spectral library,and the optimization of a deep learning network.Through field investigations of vegetation types at sample points,a comprehensive vegetation spectral library for the demonstration zone is established,capturing the distinct spectral characteristics of vegetation in hyperspectral remote sensing images across different seasons.Utilizing the lightweight deep learning network of ShuffleNet and incorporating the spatial-spectral attention mechanism,a tailored deep learning network for vegetation classification is constructed to realize vegetation classification using hyperspectral satellite images for the studied area.Aiming at the characteristics of diverse vegetation types,spatial distribution and different spectral characteristics,the network incorporates dilated convolution and spatial pyramid pooling to capture information at different scales.The experimental results show that the deep learning network constructed in this article significantly outperforms conventional CNN networks and SVM methods in terms of precision,recall,Fl score and overall accuracy.The overall accuracy of vegetation classification across different seasons exceeds 0.85.The network effectively delineates the contours of ground objects while preserving the integrity of patches.Furthermore,statistical analysis of vegetation spatial patterns reveals distinctive characteristics of different vegetation types,which is of great significance for monitoring the ecosystem within the demonstration zone.The article concludes that hyperspectral satellite remote sensing images can be used to complete the classification of vegetation quickly and timely across extensive areas.Although the vegetation types included in the spectral library may not be exhaustive,the establishment method lays a solid foundation for the subsequent application of hyperspectral remote sensing within the demonstration zone.
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