Continental-scale wetland mapping: A novel algorithm for detailed wetland types classification based on time series Sentinel-1/2 images

Peng, Kaifeng; Jiang, Weiguo; Wu, Zhifeng; Ling, Ziyan; Wang, Xiaoya; Niu, Zhenguo; Mao, Dehua

doi:10.1016/j.ecolind.2023.110113

Cited by 28 publications

(12 citation statements)

References 33 publications

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“…This indicated a 15% increase from 2010 to 2016, followed by an 11% decrease thereafter. In the three years, their areas were 12.86 km 2 (1.19% of Shenqiu), 11.99 km 2 (1.11%), and 9.37 km 2 (0.87%), revealing a continuous decline of about 7% (2010-2016) and 22% (2016-2022).…”

Section: Accuracy and Pond Extraction Resultsmentioning

confidence: 98%

“…However, their corresponding area proportion ranged from 21% to 31%. Notably, there was an increase in 1132 ponds (total of 0.67 km 2 ) from 2010 to 2016 and a decrease in 94 ponds (0.45 km 2 ) from 2016 to 2022. For level-II ponds, their number proportion ranged from 15% to 27% across the three periods, with the corresponding area proportion from 21% to 28%.…”

Section: Accuracy and Pond Extraction Resultsmentioning

confidence: 99%

“…For level-II ponds, their number proportion ranged from 15% to 27% across the three periods, with the corresponding area proportion from 21% to 28%. There was loss of 254 ponds (0.76 km 2 ) from 2010 to 2016 and 317 ponds (0.90 km 2 ) from 2016 to 2022. The level-III ponds have a different situation from level-I ponds.…”

Section: Accuracy and Pond Extraction Resultsmentioning

confidence: 99%

“…The ecological challenges of the water environment represent a substantial predicament confronting humanity in the 21st century. Contemporary attention is primarily focused on substantial water bodies, including reservoirs, lakes, and rivers, with a tendency to overlook the pivotal role played by smaller water bodies such as ponds within the broader ecological system [1,2]. Moreover, existing policies concerning the water environment appear to be inadequate in their commitment to safeguarding ponds [3].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Spatio-Temporal Variations of Ponds in Typical Rural Area in the Huai River Basin of China

Ji,

Ren,

Zha

et al. 2023

Remote Sensing

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Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km2, respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area.

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Section: Accuracy and Pond Extraction Resultsmentioning

confidence: 98%

Section: Accuracy and Pond Extraction Resultsmentioning

confidence: 99%

Section: Accuracy and Pond Extraction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring Spatio-Temporal Variations of Ponds in Typical Rural Area in the Huai River Basin of China

Ji,

Ren,

Zha

et al. 2023

Remote Sensing

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“…However, wetlands face degradation in terms of coverage and biodiversity due to natural and human-induced disturbances, such as global warming, rising sea levels, and agricultural activities [ 5 , 6 ]. As such, accurate mapping and consistent monitoring are crucial for managing and conserving wetland resources, protecting them from degradation, evaluating restoration efforts and ensuring future sustainability [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine

Jafarzadeh,

Mahdianpari,

Gill

et al. 2024

Sensors

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Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (R2) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies.

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

Sun,

Yang,

Huang

et al. 2024

Sci. China Earth Sci.

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Continental-scale wetland mapping: A novel algorithm for detailed wetland types classification based on time series Sentinel-1/2 images

Cited by 28 publications

References 33 publications

Monitoring Spatio-Temporal Variations of Ponds in Typical Rural Area in the Huai River Basin of China

Monitoring Spatio-Temporal Variations of Ponds in Typical Rural Area in the Huai River Basin of China

Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine

First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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