Mapping Coastal Wetlands Using Satellite Imagery and Machine Learning in a Highly Urbanized Landscape

Munizaga, Juan; Garcı́a, Mariano; Ureta, Fernando; Novoa, Vanessa; Rojas, Octavio; Rojas, Carolina

doi:10.3390/su14095700

Cited by 13 publications

(5 citation statements)

References 101 publications

(118 reference statements)

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“…In our study, the selection of parameters for the four classifiers (ANN, BRT, Maxent, and RF) allowed for a fairer comparative analysis rather than relying on specific classifier evaluations. This technique has been successfully implemented in other LULC investigations 78 – 80 . Across all evaluation metrics, RF consistently outperformed ANN, BRT, and Maxent, as demonstrated by the AUC, Kappa, correlation, and TSS values.…”

Section: Discussionmentioning

confidence: 99%

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Géant,

Gustave,

Schmitz

2023

Sci Rep

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There are several techniques for mapping wetlands. In this study, we examined four statistical models to assess the potential distribution of wetlands in the South-Kivu province by combining optical and SAR images. The approach involved integrating topographic, hydrological, and vegetation indices into the four most used classifiers, namely Artificial Neural Network (ANN), Random Forest (RF), Boosted Regression Tree (BRT), and Maximum Entropy (MaxEnt). A wetland distribution map was generated and classified into 'wetland' and 'non-wetland.' The results showed variations in predictions among the different models. RF exhibited the most accurate predictions, achieving an overall classification accuracy of 95.67% and AUC and TSS values of 82.4%. Integrating SAR data improved accuracy and precision, particularly for mapping small inland wetlands. Our estimations indicate that wetlands cover approximately 13.5% (898,690 ha) of the entire province. BRT estimated wetland areas to be ~ 16% (1,106,080 ha), while ANN estimated ~ 14% (967,820 ha), MaxEnt ~ 15% (1,036,950 ha), and RF approximately ~ 10% (691,300 ha). The distribution of these areas varied across different territories, with higher values observed in Mwenga, Shabunda, and Fizi. Many of these areas are permanently flooded, while others experience seasonal inundation. Through digitization, the delineation process revealed variations in wetland areas, ranging from tens to thousands of hectares. The geographical distribution of wetlands generated in this study will serve as an essential reference for future investigations and pave the way for further research on characterizing and categorizing these areas.

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Section: Discussionmentioning

confidence: 99%

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Géant,

Gustave,

Schmitz

2023

Sci Rep

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“…The specific dimension selection is designed to strengthen the correlation, enhancing the accuracy of predictions from the training model. Owing to the restricted availability of samples for artificial wetlands, this study implements downsampling techniques to attain sample equilibrium [22]. Figure 1 illustrates the technical approach employed in this research.…”

Section: Datasetmentioning

confidence: 99%

A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images

Li,

Cui,

Tian

2023

Applied Sciences

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Wetlands, situated at the vital intersection of terrestrial and aquatic ecosystems, are pivotal in preserving global biodiversity and maintaining environmental equilibrium. The escalating trend of global urbanization necessitates the utilization of high-resolution satellite imagery for accurate wetland delineation, which is essential for establishing efficacious conservation strategies. This study focuses on the wetlands of Macau, characterized by distinctive coastal and urban features. A noteworthy enhancement in this study is the integration of the Coordinate Attention mechanism with the RegNet model, forming the CA-RegNet. This combined model demonstrates superior performance, outdoing previous Macau wetlands segmentation studies that used ResNet, evidenced by an approximate rise of 2.7% in overall accuracy (OA), 4.0% in the Kappa coefficient, 1.9% in the mAcc, and 0.5% in the mIoU. Visual evaluations of the segmentation results reinforce the competence of the CA-RegNet model in precisely demarcating coastal wetlands and Saiwan Lake, thereby overcoming the former constraints of ResNet and underscoring the robustness and innovation of this study.

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“…B. Fu et al [10] mapped wetland vegetation with object-and pixel-based random forest algorithms. J. Munizaga, Ali Gonzalez-Perez and Ricardo Martínez Prentice [11][12][13] classified wetlands using machine learning and high-resolution imagery. Zou and Li [14,15] used landsat8 satellite imagery to achieve wetland classification.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of China’s Coastal Wetlands Based on Sentinel-2 and Segformer

et al. 2023

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Concerning the ever-changing wetland environment, the efficient extraction of wetland information holds great significance for the research and management of wetland ecosystems. China’s vast coastal wetlands possess rich and diverse geographical features. This study employs the SegFormer model and Sentinel-2 data to conduct a wetland classification study for coastal wetlands in Yancheng, Jiangsu, China. After preprocessing the Sentinel data, nine classification objects (construction land, Spartina alterniflora (S. alterniflora), Suaeda salsa (S. salsa), Phragmites australis (P. australis), farmland, river system, aquaculture and tidal falt) were identified based on the previous literature and remote sensing images. Moreover, mAcc, mIoU, aAcc, Precision, Recall and F-1 score were chosen as evaluation indicators. This study explores the potential and effectiveness of multiple methods, including data image processing, machine learning and deep learning. The results indicate that SegFormer is the best model for wetland classification, efficiently and accurately extracting small-scale features. With mIoU (0.81), mAcc (0.87), aAcc (0.94), mPrecision (0.901), mRecall (0.876) and mFscore (0.887) higher than other models. In the face of unbalanced wetland categories, combining CrossEntropyLoss and FocalLoss in the loss function can improve several indicators of difficult cases to be segmented, enhancing the classification accuracy and generalization ability of the model. Finally, the category scale pie chart of Yancheng Binhai wetlands was plotted. In conclusion, this study achieves an effective segmentation of Yancheng coastal wetlands based on the semantic segmentation method of deep learning, providing technical support and reference value for subsequent research on wetland values.

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Mapping Coastal Wetlands Using Satellite Imagery and Machine Learning in a Highly Urbanized Landscape

Cited by 13 publications

References 101 publications

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images

Semantic Segmentation of China’s Coastal Wetlands Based on Sentinel-2 and Segformer

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