A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Datasets
2.2. Modelling Framework
2.2.1. Data Preprocessing and Crude Water Extraction
2.2.2. Threshold Segmentation
2.2.3. Fine Extraction
2.2.4. Accuracy Evaluation
2.2.5. Spatiotemporal Analysis
3. Results
3.1. Framework Application
3.2. Spatial Characteristics of Aquaculture Ponds
4. Discussion
4.1. Advantages and Disadvantages of the Framework
4.2. Portability of the Framework
4.3. Implications for Sustainable Management of Aquaculture and Ecosystem Conservation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Category | Name | Time Period | Spatial Resolution | Source |
---|---|---|---|---|
Remote sensing image | Sentinel-1 SAR and Sentinel-2 | 2016, 2020, 2023 | 10 m | European Space Agency |
DEM | SRTM V3 | 2000 | 30 m | National Aeronautics and Space Administration (NASA) |
Other auxiliary data | Global Surface Water Mapping Layers | 2020 | 30 m | EC JRC (European Commission’s Joint Research Centre) |
Global urban boundary | 2020 | 30 m | Star Cloud Data Service Platform | |
Statistical data | Anhui Provincial Statistical Yearbook | 2017–2022 | / | Anhui Provincial Bureau of Statistics |
China Fishery Statistical Yearbook | 2017–2022 | / | Ministry of Agriculture and Rural Affairs of the People’s Republic of China |
Feature/Metric | Description | References |
---|---|---|
Area (A) | The coverage range of a specific feature or target in a two-dimensional geographic space | [44] |
Perimeter (P) | The total length of closed boundaries of a specific feature or target in two-dimensional geographic space | |
Shape index (SI) | Combining the perimeter and area of the ground features, the regularity of the target boundary is reflected | [45] |
Compactness (Com) | Compare the perimeter and area of the target object to evaluate its similarity to a circle | [32] |
Perimeter2/Area (P2/A) | Perimeter to area ratio | / |
MORA | Minimum outlying rectangular area ratio | / |
Year | Class | Aquaculture | Non-Aquaculture | UA/% | PA/% | OA/% | Kappa/% |
---|---|---|---|---|---|---|---|
2016 | Aquaculture | 495 | 17 | 96.68 | 88.55 | 92.11 | 84.23 |
Non-Aquaculture | 64 | 451 | 87.57 | 96.37 | |||
2020 | Aquaculture | 493 | 14 | 97.24 | 89.80 | 93.18 | 86.38 |
Non-Aquaculture | 56 | 464 | 89.23 | 97.07 | |||
2023 | Aquaculture | 503 | 15 | 97.10 | 89.50 | 92.79 | 85.58 |
Non-Aquaculture | 59 | 450 | 88.41 | 96.77 |
Techniques | Sensor | Accuracy | Study Area | Process | Reference |
---|---|---|---|---|---|
Object-oriented method | Sentinel-1 SAR | Overall accuracy 93% | Jiangsu Province, China | Object-based methods and multi-threshold segmentation for aquaculture pond extraction | [29] |
Object-oriented method | Sentinel-2 | Relative error 1.13% | Northwest Province, Sri Lanka | An iterative water body segmentation algorithm integrating grayscale morphology and edge detection | [33] |
Object-oriented method | Sentinel-1 SAR | Overall accuracy 90.16% | Vietnam | Time series Sentinel-1 SAR images, threshold segmentation, combined with object-oriented methods | [32] |
SVM classification method | Landsat OLI, Landsat TM, GaoFen-1 WFV | Overall accuracy 94% | Hubei Province, China | SVM classification method to extract natural and aquaculture ponds | [17] |
Edge Detection | Sentinel-2 | Overall accuracy 83.91 % | Global scale | Sentinel-2 time series, edge detection, morphology | [36] |
Decision tree classifier | Landsat 5 T1_SR, Landsat 8 T1_SR | Overall accuracy higher than 91% | Jiangsu Province, China | Extraction of aquaculture ponds by decision trees combined with shape index | [11] |
Decision tree classifier | Landsat 8 T1_SR | Overall accuracy 96% | Jiangsu Province, China | Extraction of aquaculture ponds by decision trees combined with water index | [12] |
Threshold segmentation | Sentinel-1 SAR | Overall accuracy 83% | The Mekong Delta, Red River Delta, Pearl River Delta, Yellow River Delta | Time series Sentinel-1 data, water threshold segmentation | [13] |
Object-oriented method | Sentinel-1 SAR, Sentinel-2 | Overall accuracy 91.90% | Coastal Asia | Object-oriented methods based on multiple sensors | [31] |
Biophysical parameters | Sentinel-2 | Overall accuracy 91% | Coastal China | A method combining spatial characteristics and biophysical parameters | [16] |
Object-oriented method | Sentinel-1 SAR | Overall accuracy 89% | Coastal India | Open-source connected component segmentation algorithm | [14] |
Object-oriented method | Landsat TM | Overall accuracy 92.90% | Coastal China | Integrate updated approach with object-oriented methods | [10] |
Object-oriented method | Sentinel-1 SAR | Overall accuracy higher than 90% | Coast of China and Vietnam | Combining neighborhood discrimination and morphological features | [2] |
Threshold segmentation | Sentinel-1 SAR | Overall accuracy 93% | Coastal China | Water index combined with object-oriented extraction methods | [26] |
Hierarchical decision trees | Sentinel-2 | Overall accuracy higher than 90% | Coastal China | A hybrid approach combining noniterative clustering with hierarchical decision trees | [38] |
Deep learning | GF-3 | F1 greater than 94% | East coast of Jiangsu Province, China | Mark-controlled watershed method combined with UN++ method | [24] |
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Qi, L.; Wang, Z.; Dai, L.; Wu, F.; Yin, H.; Zhang, K.; Guo, M.; Ye, L.; Zhang, S. A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China. Water 2025, 17, 1406. https://doi.org/10.3390/w17091406
Qi L, Wang Z, Dai L, Wu F, Yin H, Zhang K, Guo M, Ye L, Zhang S. A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China. Water. 2025; 17(9):1406. https://doi.org/10.3390/w17091406
Chicago/Turabian StyleQi, Lingyan, Zhengxin Wang, Liuyi Dai, Fengwen Wu, Han Yin, Kejia Zhang, Mingzhu Guo, Liangtao Ye, and Shanshan Zhang. 2025. "A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China" Water 17, no. 9: 1406. https://doi.org/10.3390/w17091406
APA StyleQi, L., Wang, Z., Dai, L., Wu, F., Yin, H., Zhang, K., Guo, M., Ye, L., & Zhang, S. (2025). A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China. Water, 17(9), 1406. https://doi.org/10.3390/w17091406