Multi-source data synergetic refined forest vegetation type information remote sensing updating method

Abstract

The invention relates to a multi-source data synergetic refined forest vegetation type information remote sensing updating technical method, and relates to the technical field of technologies of remote sensing and geographic information systems. The method realizes the key technology of multi-source data synergetic updating of formation level forest vegetation type information. By the aid of the multi-source data synergetic refined forest vegetation type information remote sensing updating technical method, on the basis of later-stage forest data, multi-time-phase MODIS (moderate resolution imaging spectroradiometer) data and earlier-stage forest vegetation type data, later-stage refined forest vegetation type information is updated and obtained. The method has advantages of automation, rapidness, low cost and high precision. After the method is applied in Szechwan, updating precisions of formation and vegetation types respectively can achieve 88% and 90%. The multi-source data synergetic refined forest vegetation type information remote sensing updating technical method is applicable to updating forest vegetation type information in other areas or at other time intervals.

Description

1. Technical field [0001] The technology belongs to the field of remote sensing and geographic information technology 2. Background technology [0002] Fine forest vegetation information refers to the type information of forest vegetation that can be refined down to the biome. It takes a lot of manpower, material resources and financial resources to obtain or update it using conventional ground survey techniques. Remote sensing technology provides advanced technical means for obtaining and updating information on land cover types and vegetation types. For example, my country uses the full-digital human-computer interaction remote sensing rapid extraction method of land resource information to extract land use type information from LANDSAT TM remote sensing images, and forms a multi-period national land use database. In this database, there are forest land, sparse forest land and shrub land The resulting forest has high precision [1-3] . The 1:1 million Chinese vegetation m...

AI Technical Summary

Problems solved by technology

In terms of supervised classification, a set of samples is used for all pixels to be divided. Due to the phenomenon of the same object with different spectra and different objects with the same spectrum, the extracted land cover and vegetation types are relatively rough in a large range, and its accuracy is difficult to improve.

In addition, the existing multi-source data and knowledge cannot be fully applied synergistically, making it difficult to overcome the defects of a single data source, and failing to tap and play the advantages of multiple data sources

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