the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Apr 2023
Vectorized dataset of check dams on the Chinese Loess Plateau using object-based classification method from Google Earth images
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).
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Status: closed
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RC1: 'Comment on essd-2023-120', Anonymous Referee #1, 10 May 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-120/essd-2023-120-RC1-supplement.pdf
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RC2: 'Comment on essd-2023-120', Anonymous Referee #2, 21 May 2023
Review for ESSD-2023-120
Title: Vectorized dataset of check dams on the Chinese Loess Plateau using object-based classification method from Google Earth images
This study developed a vectorized dataset of dam lands in the Chinese Loess Plateau. The multiresolution segmentation algorithm in the eCognition Developer was applied to Google Earth images to classify dam lands. The dam land dataset is a unique map product and may be useful for communities working on soil and water conservation projects in the Chinese Loess Plateau. However, the manuscript is riddled with poor syntax and unnecessary words. Good editing is necessary.
Major concerns:
- The dataset only provides silted land (dam land) data without the distribution map of check dams. Is it possible to add dam location data to this product? Otherwise, the dam lands should be used in the title and the abstract.
- The development of this dataset relied on two assumptions: (1) the dam lands are used for cultivation, and (2) the dam lands are distributed along river networks. Are these two assumptions enough to classify dam lands? Are there any dam lands that were not used for cultivation? Does the dam location be considered when classifying dam lands? If yes, I think it is necessary to add dam locations to this dataset. If not, please justify how to distinguish between dam lands and natural floodplains.
- How did the dam lands change with time? Google images from 2016 to 2020 were used to develop data. The dataset was compared with the official report (CMWR, 2013) to evaluate accuracy. Please provide evidence or reference to justify it is appropriate to use data in very different periods to conduct validation. I suggest clarifying the time period of this dataset. If the dam lands didn’t change too much with time, how did they influence the variability of flow and sediment (Line 18)?
- The slope and R used in the validation are misleading. Given the large differences among different regions (and a total number) and few data points, the slope and R are largely decided by the high values. Please consider changing the linear regression plot to a table.
- Does Fig 3a present the dam land data developed in this study? I also see a similar figure in (Zeng et al, 2022a). Has this data already been used and published in a previous study?
- The authors indicated that there is a large uncertainty in the estimation of check dams according to the two reports (CMWR, 2003) and (CMWR, 2013). It seems like the two reports were released by the same institution. Is it appropriate to claim it is uncertainty just according to one very old version report and an updated version of the report? Since the result in this study is close to the new version of the report, it is weird to claim the number is still unclear.
Minor:
Abstract
- L16-17. Wordy. Rephrase this sentence.
- If the number is still unclear, why the result of this study is close to the official reports?
- Analyzed.
- R-value is misleading.
Introduction
- Do you mean the whole country is a study area?
- Variations
- L74-75. Revise this sentence. Maybe “Currently, only two studies…”.
- Delete the repeated “check dams”.
Methods
- Please use past tense when describing the methods.
- Rephrase this sentence.
- L195-200. Such a simple and empirical method was used to estimate sediment volume. Please discuss the uncertainty of this method. Can this method represent large-scale conditions? What about other impact factors, such as slope?
Results and discussion
- “Compared”?
- “We provide the check dam dataset on the CLP for the first time by combining high-resolution and easily accessible Google Earth images and object-based classification strategy”.
Citation: https://doi.org/10.5194/essd-2023-120-RC2 -
RC3: 'Comment on essd-2023-120', Anonymous Referee #3, 23 May 2023
This paper presents a significant contribution by utilizing high-resolution Google Earth images and object-based classification methods to establish a vectorized dataset of check dams on the Chinese Loess Plateau. The accuracy and reliability of the dataset are demonstrated through validation with test samples and comparison with official statistics. This dataset provides a valuable resource for assessing the ecosystem service functions of check dams, including sediment retention, carbon sequestration, and grain supply. The findings have implications for understanding the impact of check dams on sediment dynamics in the Yellow River and planning future soil and water conservation projects. It is worth noting that while this study is commendable, there is still room for improvement. The paper would benefit from improvements in the accuracy of language expression, the coherence of structural organization, and the conciseness of language. Addressing these areas would result in a more authentic and higher-quality writing. I suggest it to be accepted after a major revision.
The introduction needs improvement in terms of logical flow and structure. It should briefly introduce soil erosion as a significant environmental issue and its threat to sustainable development. Transition to the measures addressing soil erosion, including dam construction. Highlight the advantages of dam construction in arid regions, such as soil retention and erosion prevention. Mention additional benefits like carbon sequestration and food supply. Discuss the lack of accurate data on dam numbers and spatial distribution, posing challenges in assessing their impact on sediment transport. Finally, clarify the study's objectives, methods, and innovation, emphasizing the creation of a vectorized dataset using high-resolution imagery for assessing ecosystem services and informing conservation projects. A more coherent and concise introduction would improve readability and convey the research's significance.
The section on data and methodology in the paper is comprehensive. However, it is essential to provide a detailed description of the data collection process, including data sources, collection methods, and tools used. For example, relevant details like the timing of image acquisition should be included. Additionally, the overall study design and methodology should be explained, highlighting the chosen research methods and the reasons behind their selection. For instance, it is important to clarify why an object-oriented approach was adopted for image segmentation and discuss the model parameters. In conclusion, emphasizing the effectiveness and advantages of the employed data and methods is crucial.
The results and discussion section of the paper should be approached with attention to the following aspects. It is important to integrate the results with the discussion, providing interpretations of the findings and presenting evidence that supports or contradicts the research hypotheses. The main discoveries of the study should be summarized, and their significance and contribution to the relevant field should be assessed and discussed. Furthermore, I would suggest the author to include a discussion on the strengths of the present study.
Citation: https://doi.org/10.5194/essd-2023-120-RC3 - AC1: 'Comment on essd-2023-120', Yi Zeng, 13 Jul 2023
Status: closed
-
RC1: 'Comment on essd-2023-120', Anonymous Referee #1, 10 May 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-120/essd-2023-120-RC1-supplement.pdf
-
RC2: 'Comment on essd-2023-120', Anonymous Referee #2, 21 May 2023
Review for ESSD-2023-120
Title: Vectorized dataset of check dams on the Chinese Loess Plateau using object-based classification method from Google Earth images
This study developed a vectorized dataset of dam lands in the Chinese Loess Plateau. The multiresolution segmentation algorithm in the eCognition Developer was applied to Google Earth images to classify dam lands. The dam land dataset is a unique map product and may be useful for communities working on soil and water conservation projects in the Chinese Loess Plateau. However, the manuscript is riddled with poor syntax and unnecessary words. Good editing is necessary.
Major concerns:
- The dataset only provides silted land (dam land) data without the distribution map of check dams. Is it possible to add dam location data to this product? Otherwise, the dam lands should be used in the title and the abstract.
- The development of this dataset relied on two assumptions: (1) the dam lands are used for cultivation, and (2) the dam lands are distributed along river networks. Are these two assumptions enough to classify dam lands? Are there any dam lands that were not used for cultivation? Does the dam location be considered when classifying dam lands? If yes, I think it is necessary to add dam locations to this dataset. If not, please justify how to distinguish between dam lands and natural floodplains.
- How did the dam lands change with time? Google images from 2016 to 2020 were used to develop data. The dataset was compared with the official report (CMWR, 2013) to evaluate accuracy. Please provide evidence or reference to justify it is appropriate to use data in very different periods to conduct validation. I suggest clarifying the time period of this dataset. If the dam lands didn’t change too much with time, how did they influence the variability of flow and sediment (Line 18)?
- The slope and R used in the validation are misleading. Given the large differences among different regions (and a total number) and few data points, the slope and R are largely decided by the high values. Please consider changing the linear regression plot to a table.
- Does Fig 3a present the dam land data developed in this study? I also see a similar figure in (Zeng et al, 2022a). Has this data already been used and published in a previous study?
- The authors indicated that there is a large uncertainty in the estimation of check dams according to the two reports (CMWR, 2003) and (CMWR, 2013). It seems like the two reports were released by the same institution. Is it appropriate to claim it is uncertainty just according to one very old version report and an updated version of the report? Since the result in this study is close to the new version of the report, it is weird to claim the number is still unclear.
Minor:
Abstract
- L16-17. Wordy. Rephrase this sentence.
- If the number is still unclear, why the result of this study is close to the official reports?
- Analyzed.
- R-value is misleading.
Introduction
- Do you mean the whole country is a study area?
- Variations
- L74-75. Revise this sentence. Maybe “Currently, only two studies…”.
- Delete the repeated “check dams”.
Methods
- Please use past tense when describing the methods.
- Rephrase this sentence.
- L195-200. Such a simple and empirical method was used to estimate sediment volume. Please discuss the uncertainty of this method. Can this method represent large-scale conditions? What about other impact factors, such as slope?
Results and discussion
- “Compared”?
- “We provide the check dam dataset on the CLP for the first time by combining high-resolution and easily accessible Google Earth images and object-based classification strategy”.
Citation: https://doi.org/10.5194/essd-2023-120-RC2 -
RC3: 'Comment on essd-2023-120', Anonymous Referee #3, 23 May 2023
This paper presents a significant contribution by utilizing high-resolution Google Earth images and object-based classification methods to establish a vectorized dataset of check dams on the Chinese Loess Plateau. The accuracy and reliability of the dataset are demonstrated through validation with test samples and comparison with official statistics. This dataset provides a valuable resource for assessing the ecosystem service functions of check dams, including sediment retention, carbon sequestration, and grain supply. The findings have implications for understanding the impact of check dams on sediment dynamics in the Yellow River and planning future soil and water conservation projects. It is worth noting that while this study is commendable, there is still room for improvement. The paper would benefit from improvements in the accuracy of language expression, the coherence of structural organization, and the conciseness of language. Addressing these areas would result in a more authentic and higher-quality writing. I suggest it to be accepted after a major revision.
The introduction needs improvement in terms of logical flow and structure. It should briefly introduce soil erosion as a significant environmental issue and its threat to sustainable development. Transition to the measures addressing soil erosion, including dam construction. Highlight the advantages of dam construction in arid regions, such as soil retention and erosion prevention. Mention additional benefits like carbon sequestration and food supply. Discuss the lack of accurate data on dam numbers and spatial distribution, posing challenges in assessing their impact on sediment transport. Finally, clarify the study's objectives, methods, and innovation, emphasizing the creation of a vectorized dataset using high-resolution imagery for assessing ecosystem services and informing conservation projects. A more coherent and concise introduction would improve readability and convey the research's significance.
The section on data and methodology in the paper is comprehensive. However, it is essential to provide a detailed description of the data collection process, including data sources, collection methods, and tools used. For example, relevant details like the timing of image acquisition should be included. Additionally, the overall study design and methodology should be explained, highlighting the chosen research methods and the reasons behind their selection. For instance, it is important to clarify why an object-oriented approach was adopted for image segmentation and discuss the model parameters. In conclusion, emphasizing the effectiveness and advantages of the employed data and methods is crucial.
The results and discussion section of the paper should be approached with attention to the following aspects. It is important to integrate the results with the discussion, providing interpretations of the findings and presenting evidence that supports or contradicts the research hypotheses. The main discoveries of the study should be summarized, and their significance and contribution to the relevant field should be assessed and discussed. Furthermore, I would suggest the author to include a discussion on the strengths of the present study.
Citation: https://doi.org/10.5194/essd-2023-120-RC3 - AC1: 'Comment on essd-2023-120', Yi Zeng, 13 Jul 2023
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
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