Pixel-level pm2.5 prediction method based on land change prediction and spatial lag model

CN116402199BActive Publication Date: 2026-06-26TONGJI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TONGJI UNIV
Filing Date
2023-03-21
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies do not adequately consider the impact of land use and land cover in pixel-level PM2.5 prediction, leading to biased prediction accuracy and a lack of consideration for the spatial dependence of PM2.5.

Method used

Land use patterns are predicted using the Futureland model. Land cover index is obtained by combining Landsat remote sensing imagery and spectral calculations. A spatial lag model is used to construct a relationship model between PM2.5 and land cover index, and the spatial dependence of PM2.5 is considered for prediction.

Benefits of technology

It achieves large-scale, long-term pixel-level PM2.5 prediction, improving the accuracy and precision of prediction, and overcoming the problems of short time span and neglect of spatial dependence in traditional methods.

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Abstract

The application relates to a pixel-level PM2.5 prediction method based on land change prediction and a spatial lag model, which comprises the following steps: acquiring a Landsat remote sensing image, performing land use type classification based on a random forest method to obtain a land use classification map; predicting a future land use mode by using a Futureland model based on the land use classification map and driving factors; acquiring a land cover index by using a spectrum calculation method based on the Landsat remote sensing image; predicting a future land cover index by using a global and local change method based on the land cover index and the land use mode; acquiring PM2.5 concentration data of a ground monitoring station, considering the spatial dependence of PM2.5 by using a spatial lag model, and constructing a relationship model of PM2.5 and the land cover index; and predicting the pixel-level PM2.5 based on the relationship model of PM2.5 and the land cover index and in combination with the predicted future land cover index. Compared with the prior art, the application has the advantages that long-time accurate prediction of pixel-level PM2.5 in a large range is realized.
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