Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

Mohseni, Farzane; Amani, Meisam; Mohammadpour, Pegah; Kakooei, Mohammad; Jin, Shuanggen; Moghimi, Armin

doi:10.3390/rs15143495

Cited by 8 publications

(7 citation statements)

References 68 publications

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“…The accuracy of the map produced is 89% which is comparable to previous wetland mapping studies [7,[47][48][49][50] especially large scale wetland studies in Minnesota [51,52]. This study leverages Sentinel-2 and Sentinel-1 data for wetland mapping.…”

Section: Discussionsupporting

confidence: 59%

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Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

Igwe,

Salehi,

Mahdianpari

2023

Remote Sensing

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Rapid impacts from both natural and anthropogenic sources on wetland ecosystems underscore the need for updating wetland inventories. Extensive up-to-date field samples are required for calibrating methods (e.g., machine learning) and validating results (e.g., maps). The purpose of this study is to design a dataset generation approach that extracts training data from already existing wetland maps in an unsupervised manner. The proposed method utilizes the LandTrendr algorithm to identify areas least likely to have changed over a seven-year period from 2016 to 2022 in Minnesota, USA. Sentinel-2 and Sentinel-1 data were used through Google Earth Engine (GEE), and sub-pixel water fraction (SWF) and normalized difference vegetation index (NDVI) were considered as wetland indicators. A simple thresholding approach was applied to the magnitude of change maps to identify pixels with the most negligible change. These samples were then employed to train a random forest (RF) classifier in an object-based image analysis framework. The proposed method achieved an overall accuracy of 89% with F1 scores of 91%, 81%, 88%, and 72% for water, emergent, forested, and scrub-shrub wetland classes, respectively. The proposed method offers an accurate and cost-efficient method for updating wetland inventories as well as studying areas impacted by floods on state or even national scales. This will assist practitioners and stakeholders in maintaining an updated wetland map with fewer requirements for extensive field campaigns.

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Section: Discussionsupporting

confidence: 59%

“…To achieve successful training for wetland mapping using remote sensing, it is crucial to have a substantial training dataset that encompasses a diverse array of class variations [50]. The size of the study area also plays a critical role in determining the number of required samples [50].…”

Section: Dataset Generation Using Change Detectionmentioning

confidence: 99%

Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

Igwe,

Salehi,

Mahdianpari

2023

Remote Sensing

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“…Our analysis considered the backscattering coefficient products derived from dual-band cross-polarization VV + VH, with a pixel size of 10 m. A composite summer image was generated by calculating the mean values of backscattering data spanning from June to September 2021 (four months). This approach is employed to mitigate the impact of speckle noise inherent in SAR images [ 31 ].…”

Section: Study Sites and Datasetsmentioning

confidence: 99%

“…A summer composite of the Sentinel-2 surface reflectance products was generated by employing the four-month extraction of Sentinel-2 observations and determining the median values across all pixels. This approach effectively helped to reduce the effects of cloudy and other unwanted pixels [ 31 ].…”

Section: Study Sites and Datasetsmentioning

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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“…The coastal wetlands in Jiangsu are crucial for migratory birds, providing key locations for resting, breeding, and wintering. Changes in wetland vegetation significantly affect the stability of migratory bird populations, and it is essential to closely monitor these changes and accurately assess the ecological functions and values of coastal wetlands [1][2][3][4]. Remote sensing technology is a vital tool for monitoring coastal wetlands, with high-resolution imagery offering a comprehensive understanding of vegetation's spatial distribution [5,6].…”

Section: Introductionmentioning

confidence: 99%

Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images

Wang,

Jin,

Dardanelli

2024

Remote Sensing

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The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands’ health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification.

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Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

Cited by 8 publications

References 68 publications

Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

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

Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images

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