The vegetation cover data set of national key prevention areas on the northern slope of Tianshan Mountain in 2023 includes Tianshan District, Dabancheng District, Urumqi county, Dushanzi District, Hami City, Yiwu County, Balikun Kazak Autonomous County, Changji City, Fukang City, Hutubi County, Manas County, Qitai County, jimusar County, Mulei Kazak Autonomous County, Bole City, Jinghe County, etc The vegetation coverage statistical table of Wenquan County, Tacheng City, Wusu city, Emin County, Shawan County, Tuoli County, Yumin County, Shihezi city and Wujiaqu City in 2023 is obtained by processing the satellite remote sensing image with spatial resolution of 2m, and the saved format is xlsx. The data is named in the form of "key management area + year + vegetation coverage statistical table", such as“ ×× Key governance areas ×× Statistical table of annual vegetation coverage. The vegetation coverage is divided into five levels: high coverage, medium high coverage, medium coverage, medium low coverage and low coverage
The data sources are ZY-3 and Gao FEN-1 satellite images, which are mainly obtained from the information center of the Ministry of water resources
Based on the method of remote sensing estimation, the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by using the pixel dichotomy model. The method is to first calculate the NDVI of each pixel using the near infrared band and red band data of multispectral images, and then use the model to calculate the vegetation coverage of the whole region. Then the land use type data from remote sensing interpretation and the vegetation coverage data from remote sensing estimation are superimposed to obtain the vegetation coverage information of each pixel. Finally, according to the classification rules, the vegetation coverage was classified, and the statistical table of forest and grass vegetation coverage was obtained
1. Remote sensing images are preprocessed by radiation correction, orthorectification, fusion and mosaic. 2. The actual surface area corresponding to the minimum spot area is not less than 0.1 h ^, the polygons have no overlap, no gap, and the spot attributes have no vacancy or redundancy. 3. Before the remote sensing image interpretation, the remote sensing image, typical investigation and field comparison methods are used to establish the remote sensing interpretation marks of forest and grass sample plots. 4. Based on remote sensing images, combined with interpretation marks, extract land use types. 5. Review of interpretation results: no less than 5% of the total map spots shall be selected for verification. 6. The number and results of field verification samples meet the requirements of technical specification for remote sensing monitoring of soil and water conservation (sl592-2012). For verification spots, 10% are selected as verification samples for field verification
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.HHSTBC.DB6636.2024
10.12072/ncdc.hhstbc.db6636.2024
