Method for dynamicaly predicting land use change

Abstract

The invention relates to a dynamic prediction method of landuse change. The basic idea of GeoCA-Landuse model simulating landuse change can introduce the life character of the land element and simulate the dynamic evolvement of the type of the landuse. Beginning with the superimposition analysis and the transfer analysis of the two phase landuse thematic map, combining with the measurement model of landuse change, analyzing the the landuse change character. According to the model controlling factors of river system and road map analysis, constructing the layer of the controlling factors, determining the reasonable neighbors and transfer rules in light of the historical land-use change process, with the combination of adjusting the model parameters of the social economic data. Setting a powerful spatial data processing of GIS, the land use trend is simulated and predicted. The invention can simulate the dynamic trend of the landuse change and provide a scientific basis for decision-making.

Description

technical field [0001] The invention relates to the technical field of geographic modeling, in particular to a method for dynamic prediction of land use change. Background technique [0002] The land use change model is to reveal the magnitude, speed and spatial distribution of land use change on the basis of the analysis of land use change. It analyzes the driving factors of land use and their relationship, explores the mechanism of land use change, and predicts The trend of future land use change and its impact on the ecological environment and socio-economic development. The current background technologies of land use trend prediction can be summarized into the following types, all of which have their own advantages and disadvantages: [0003] 1) Regression prediction method is a method of forecasting by using mathematical equations that indicate the relationship between variables to infer the value of the predictor variable from the known values ​​​​of other variables. ...

AI Technical Summary

Problems solved by technology

The disadvantage of this model is that it is not suitable for large-scale forecasting and the error of forecast value caused by the difficulty of quantifying economic factors

The model calculation only needs to consider the current information of land use, without considering the internal mechanism of land use change. The disadvantage is that the model lacks the ability to reveal the driving mechanism of land dynamic change and the lack of spatial expression ability. This model is suitable for the driving force of land use change Change Mechanisms Are Unclear and Status of Predicting Short-term Land Use Change

The advantage of this model is that it can analyze dynamic data and induce laws based on historical data. The disadvantage is that it is limited by the size of the forecast area and the length of time. It is difficult to determine the characteristics of some main factors, and the forecast results are not very accurate.

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