This data is based on TM data of about 1-2 years in 2005. It is vector data produced through re projection, geometric correction, manual interpretation and other work. The data production time is 2009, and the scale is 1:250000. The codes of counties and cities are subject to the national administrative division code implemented in 1998. The land classification system is subject to the "national resources and environment macro remote sensing survey project" of Chinese Academy of Sciences The land classification system of Chinese Academy of Sciences established by group "is divided into 6 first class and 24 second class.
The data comes from Landsat TM and ETM image data downloaded from the website of University of Maryland before and after 2005, and 1:100000 topographic map published in the 1970s in Xinjiang branch of Chinese Academy of Sciences.
Firstly, the TM data is screened, and the cloud coverage ratio is less than 4%, or the main ground objects are less covered. Combined with 1:100000 topographic map, the selected image is geometrically corrected, then spliced, and leveled. Finally, it becomes a vectorized product, and each TM data is visually interpreted and manually vectorized by professionals according to the interpretation mark and classification system to generate a coverage grid Then, ArcGIS software is used to convert coverage data into SHP data, and cost data is generated through projection conversion.
This data is processed by the geographic information and cartography professionals of Xinjiang Institute of ecology and geography, Chinese Academy of Sciences. From the scanning of paper data, screening of image data, precise geometric correction, splicing, color equalization to digital vectorization and manual interpretation, the whole operation process complies with the relevant national standards, and the digital error is controlled within 2 pixels, ensuring the vectorization boundary The boundary between the line and the topographic map is exactly the same. The vector data truly reflects the shape and attribute information of the features, and the accuracy of the data is as high as 99%.
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CSTR:11738.11.ncdc.nieer.2020.1454
