Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2023

English name

CSTR

CSTR:11738.11.NCDC.HHSTBC.DB6736.2025

DOI

10.12072/ncdc.hhstbc.db6736.2025

Share type

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Category

Soil and Water Conservation

contributors

LI Suoya

Datasets description

 <p>The data set of soil erosion in the national key prevention areas of the Yellow River flood plain in 2023 includes the statistical tables of soil erosion in Yanjin County, Fengqiu County, Changyuan county and fan County of Henan Province in 2023. It is obtained by processing the satellite remote sensing image with a spatial resolution of 2m, and the saved format is xlsx. The data is named in the form of "key control area + year + soil erosion statistical table", such as“ ×× Key governance areas ×× Statistical table of soil erosion . Soil erosion intensity can be divided into 6 grades: Micro erosion, light erosion, moderate erosion, strong erosion, very strong erosion and severe erosion.</p>

Base information

collect time	2022/01/01 - 2022/12/31
collect place	Yellow River flood plain national key prevention area

Data source description

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The data sources of land use are ZY-3 and Gao FEN-1 satellite images, which are mainly obtained from the information center of the Ministry of water resources. 2. Vegetation 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. 3.1:50000 DEM is mainly obtained from the information center of the Ministry of water resources.

Data processing method

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Based on the thematic maps of land use, vegetation coverage and slope, the soil erosion intensity was graded by ArcGIS software. 2. The land use processing method is based on ecognition software platform, using the method of object-oriented computer automatic classification and manual visual interpretation to extract the land use data of the study area year by year. Finally, three methods are used to verify the accuracy of the data: field sample point survey, high-resolution image recognition and Google Earth sample point recognition. 3. The processing method of vegetation coverage is based on remote sensing estimation, and the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by pixel dichotomy model. First, the NDVI of each pixel is calculated by using the near-infrared and red band data of multispectral images. Then, the model is used to calculate the vegetation coverage of the whole region, and the vegetation coverage is classified according to the classification rules. Finally, the land use type data obtained from remote sensing interpretation and the vegetation coverage data obtained from remote sensing estimation are used for superposition operation, The vegetation coverage information of each pixel was obtained. 4. Slope data processing method is based on 1:50000 DEM extraction.

Data quality description

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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.

Citations and annotations

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For the achievements published in English, please refer to the following specifications： The dataset is provided by National Cryosphere Desert Data Center. (http://www.ncdc.ac.cn).

Data reference

LI Suoya. Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2023. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2025. https://cstr.cn/CSTR:11738.11.NCDC.HHSTBC.DB6736.2025.
LI Suoya. Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2023. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2025. https://www.doi.org/10.12072/ncdc.hhstbc.db6736.2025.

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license agreement

This work is licensed under CC BY 4.0 (Creative Commons Attribution 4.0 International License).

Relevant data
File list

#	Dataset title
1	Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2021
2	Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2022
3	Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2019
4	Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2020
5	Soil erosion data set of national key prevention areas in the Yellow River floodplain in 2024
6	Data set of soil erosion statistical table in national key prevention area of soil and water loss in Yellow River flood plain in 2018
7	Land use data set of national key prevention areas in the Yellow River floodplain in 2019
8	Vegetation coverage data set of national key prevention areas in the Yellow River floodplain in 2023
9	Data set of soil and water loss plots in national key prevention area of Yellow River floodplain in 2023
10	Data set of soil and water conservation measures in national key prevention areas of Yellow River floodplain in 2023

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How to get the data

Theme:
Statistical table of soil erosion
Time:
2023
Place:
Yanjin County

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Contact information

contributors:: LI Suoya
author:: LI Suoya
manager:: LI Suoya

Platform Service information

contacts:: Li Hongxing
phone:: 0931-4967592
mailbox:: ncdc@lzb.ac.cn