Remote sensing monitoring data of land use status in China in 2023

Mondo Three rural Updated on 2024-02-02

The remote sensing monitoring data of land use status in China in 2023 is based on the Landsat 8 remote sensing images of the United States in 2023 as the main data source, and is generated by manual visual interpretation on the basis of the land use data of 2020 in 2020 and through the comparative analysis of remote sensing images in 2020 and 2023.

Since the reform and opening up, China's rapid economic development has had a profound impact on land use patterns. At the same time, China has a complex natural environment background and a vast land area, and its land use change has an important impact not only on national development, but also on global environmental change. In order to restore and reconstruct the modern process of land use change in China, and better predict the trend of land use change, the Chinese Academy of Sciences has built a national 1:10 scale multi-period land use thematic database on the basis of the national resource and environment database, using the Landsat remote sensing image data of the United States Land Satellite as the main information source, and through manual visual interpretation.

The classification system of the multi-period land use remote sensing monitoring dataset (CNLUCC) in China adopts a two-level classification system (Table 1): the first level is divided into six categories, which are mainly divided into cultivated land, forest land, grassland, water area, construction land and unused land according to land resources and their use attributes; The second level is mainly divided into 23 types according to the natural attributes of land resources.

The remote sensing monitoring data of land use status in China in 2023 follows the classification system of previous periods, and the land use types include 6 primary types and 25 secondary types of cultivated land, forest land, grassland, water area, residential land and unused land. The specific classification system is as follows:

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