Preprints
https://doi.org/10.5194/essd-2023-120
https://doi.org/10.5194/essd-2023-120
25 Apr 2023
 | 25 Apr 2023
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

Vectorized dataset of check dams on the Chinese Loess Plateau using object-based classification method from Google Earth images

Yi Zeng, Tongge Jing, Baodong Xu, Xiankun Yang, Jinshi Jian, Renjie Zong, Bing Wang, Wei Dai, Lei Deng, Nufang Fang, and Zhihua Shi

Abstract. The Chinese government has invested tens of billions of dollars and about 60 years to implement a large-scale check dam project on the Chinese Loess Plateau (CLP) to control severe soil erosion. These check dams have trapped billions of tons of eroded sediment over the past few decades, significantly reducing the sediment load of the Yellow River, which was once the river with the largest sediment load in the world. However, there is still great uncertainty about how much sediment is trapped by check dams and what roles they play in the flow and sediment variability in the Yellow River, because the number and spatial distribution of check dams are still unclear. In this study, we produced the first vectorized dataset of check dam on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. We first investigated and analysed the key characteristics of check dams, and obtained the 0.3–1.0 m resolution Google Earth image of the best extraction period. Then we preliminarily obtained the rough check dam layer through multi-scale segmentation, threshold classification, and river network superposition. Finally, a self-developed human-computer interaction program combined with auxiliary data, visual interpretation, and expert knowledge is used to improve the classification accuracy of check dams. The accuracy of the dataset is verified by 1947 collected test samples, and the producer’s accuracy and user’s accuracy of the check dam are 88.9 % and 99.5 %, respectively. Furthermore, at the provincial level, the area and number of check dams in our dataset are highly consistent with the latest official statistics of check dams, with R2 > 0.99. Our study provides fundamental dataset for accurately assessing the ecosystem service functions of check dams, including sediment retention, carbon sequestration, grain supply, and will help to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects. The check dam dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.7857443 (Zeng et al., 2023).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yi Zeng, Tongge Jing, Baodong Xu, Xiankun Yang, Jinshi Jian, Renjie Zong, Bing Wang, Wei Dai, Lei Deng, Nufang Fang, and Zhihua Shi

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-120', Anonymous Referee #1, 10 May 2023
  • RC2: 'Comment on essd-2023-120', Anonymous Referee #2, 21 May 2023
  • RC3: 'Comment on essd-2023-120', Anonymous Referee #3, 23 May 2023
  • AC1: 'Comment on essd-2023-120', Yi Zeng, 13 Jul 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-120', Anonymous Referee #1, 10 May 2023
  • RC2: 'Comment on essd-2023-120', Anonymous Referee #2, 21 May 2023
  • RC3: 'Comment on essd-2023-120', Anonymous Referee #3, 23 May 2023
  • AC1: 'Comment on essd-2023-120', Yi Zeng, 13 Jul 2023
Yi Zeng, Tongge Jing, Baodong Xu, Xiankun Yang, Jinshi Jian, Renjie Zong, Bing Wang, Wei Dai, Lei Deng, Nufang Fang, and Zhihua Shi

Data sets

Vectorized dataset of check dams on the Chinese Loess Plateau using object-based classification method from Google Earth images Yi Zeng, Tongge Jing, Baodong Xu, Xiankun Yang, Jinshi Jian, Renjie Zong, Bing Wang, Wei Dai, Lei Deng, Nufang Fang, and Zhihua Shi https://doi.org/10.5281/zenodo.7857443

Yi Zeng, Tongge Jing, Baodong Xu, Xiankun Yang, Jinshi Jian, Renjie Zong, Bing Wang, Wei Dai, Lei Deng, Nufang Fang, and Zhihua Shi

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Short summary
In this study, the first vectorized dataset of check dams on the Chinese Loess Plateau containing spatial distribution, silted land area, and sediment volume, was provided through object-based classification method and Google Earth images. The accuracy of the dataset is verified by 1947 collected test samples and the latest official statistics. This dataset can be used to quantify the ecosystem service function of check dams, including sediment retention, carbon sequestration, and grain supply.
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