Urban Diversity Index Analysis and Visual Modeling Based on Remote Sensing Data Inversion

Abstract

The invention proposes an urban diversity index analysis and visualization modeling system and a diversity analysis method based on remote sensing data inversion. The system includes urban data acquisition subsystem, data scale selection subsystem, data separation processing subsystem, diversity index determination subsystem and data inversion subsystem. The data scale selection subsystem automatically recommends the urban data type and urban data scale required for the target diversity index; the data separation processing subsystem is used to separate the target urban data corresponding to the urban data type and urban data scale from the urban data acquisition subsystem ; The data inversion subsystem inverts the target city data based on a predetermined data inversion model to obtain the target diversity index, and displays the different first diversity indices over time and space on the human-computer interaction interface Change trend graph. The present invention can realize the visual presentation of different types of diversity indices.

Description

technical field [0001] The invention belongs to the technical field of urban diversity analysis, and in particular relates to an urban diversity index analysis and visualization modeling system and a diversity analysis method based on remote sensing data inversion. Background technique [0002] Diversity is one of the basic factors for the survival of human society, an important measure for human society to protect the natural ecological environment, and a fundamental guarantee for realizing the common and sustainable development goals of man and nature. Urban diversity is reflected in many aspects, including biodiversity, functional diversity, landscape diversity and land use diversity. [0003] With the breakthrough of emerging scientific theories and research technologies at home and abroad, the development from traditional field evaluation to remote sensing evaluation methods and model evaluation methods in recent years has been realized. The diversity of evaluation and ...

AI Technical Summary

Problems solved by technology

[0006] However, in the actual decision-making and application process, the solutions proposed by the above-mentioned prior art are only for the analysis of a specific type of single index

When urban decision makers need to predict urban diversity from multiple perspectives, they need to collect and evolve data one by one, resulting in low efficiency and poor applicability

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