Processing Model of LUCC Temporal Characteristics in Urban and Rural Space Based on Remote Sensing Data Validation

Abstract

The spatial land change of villages and towns can be analyzed through external spatio-temporal characteristics, which can realize the evaluation of historical development and assist the implementation of planning and design norms and the implementation of problem rectification. To explore the specific process and the overall pattern of land use change, it is necessary to analyze and predict all kinds of data in a unified way to grasp the law of development and the direction of future development. This paper takes the spatial development and planning of villages and towns as the research object, selects the remote sensing image data of regional historical development as the driving factor, combines with Gaussian function fitting to calculate the training samples, and constructs the regional spatial environment analysis system. According to the processing model of external characteristics of time series, the spatial distribution and evolution of Risk-Screening Environmental Indicators (RSEI) and its components are analyzed. The multi-dimensional remote sensing data is innovatively fused into the time series processing model. Besides, the curve which can characterize the spatial change law and is not completely symmetrical is obtained through the least square method fitting so that the data with unequal time series intervals can be flexibly processed. Finally, through the experimental analysis, the traditional Asymmetric Gaussian model (A-G) and Savitzky-Golay (S-G) are taken to compare and analyze the effect of various factors on the processing of time series characteristics. Tests show that the proposed algorithm is superior to the comparison method in noise removal of remote sensing data. The data fitting coefficient is 0.02, and the root mean square measurement error is more than 7% higher than the comparison method. It can predict the trend of land use change distribution, play a key role in assisting decision-making for different land use transformations and utilization in villages and towns, and make a scientific evaluation for the impact of social and economic development potential.