Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Sun, Shaobo; Zhang, Yonggen; Song, Zhaoliang; Chen, Baozhang; Zhang, Yangjian; Yuan, Wenping; Chu, Chen; Chen, Wei; Ran, Xiangbin; Wang, Yidong

doi:10.3390/rs12244114

Cited by 24 publications

(13 citation statements)

References 34 publications

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“…Using the "explain" function provided by the GEE cloud platform, the importance of each of the feature variables of the random forest (RF) classifier when participating in the classification was determined. The contribution of the variables to the classification results was greater if they had a higher importance score [55]. Figure 4 shows the importance score distribution of the 31 input feature variables involved in the classification.…”

Section: Classification Results and Accuracy Of Classification Resultsmentioning

confidence: 99%

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

et al. 2021

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The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

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Section: Classification Results and Accuracy Of Classification Resultsmentioning

confidence: 99%

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

et al. 2021

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“…We used the method of Bootstrap with high efficiency to evaluate the importance of parameters [43]- [45]. The parameter evaluation method is: 30% of the original data not selected in the sampling process is used as out-of-bag (OOB) data, and the importance evaluation is carried out by calculating the OOB error of each decision tree in the RF, adding noise interference randomly to the features of the OOB data samples [46]. If the accuracy of the OOB data is significantly reduced after adding noise randomly, it means that the features have a great influence on the classification results.…”

Section: Random Forest Machine Learning Algorithmmentioning

confidence: 99%

Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine

Li¹,

Liu²,

Zhang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…RS has been demonstrated to be the most effective and economical method in wetland classification [15]. In addition, large-scale coastal wetland mapping is becoming a reality thanks to cloud computing platforms such as Google Earth Engine (GEE) [16,17].…”

Section: Introductionmentioning

confidence: 99%

Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

et al. 2021

Remote Sensing

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The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

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Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Cited by 24 publications

References 34 publications

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine

Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

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