Simulation and analysis of ecological early-warning of urban construction land expansion based on digital sensing feature recognition and remote sensing spatial analysis technology

Abstract

To improve the analysis effect of the simulation and prediction of the expansion effect of urban construction land, this paper combines the digital sensing feature recognition and remote sensing analysis technology for the earth's surface, and uses Artificial-neural-network-based cellular automaton (ANN-CA) Markov model and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to study and analyze the expansion effect and trend of urban construction land, and subdivides the spatial and temporal characteristics of land cover. In addition to that, multi-based sensing data is used to generate urban construction land expansion effect drivers. The multi-source sensing data and InVEST model are used to conduct spatial information analysis and ecological early-warning. The research shows that combining digital sensing feature identification and remote sensing analysis technology, using the analysis model of ANN-CA-Markov model and InVEST model can effectively improve the analysis effect of urban construction land expansion effect prediction, and carry out ecological early-warning of construction land expansion on this basis. Overall territorial spatial planning stage of Xuzhou city, the designated ecological space area accounts for 20% of the whole land area. There is a sharp contradiction between constructive land expansion and ecological security in the whole region, so it is appropriate to conduct precise intervention and efficient management of constructive land expansion according to ecological early-warning.