MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data

Jamaluddin, Ilham; Thaipisutikul, Tipajin; Dang, Yingnong; Chuang, Chi-Hung; Hu, Chih-Lin

doi:10.3390/rs13245042

Cited by 14 publications

(17 citation statements)

References 57 publications

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“…The total mangrove loss rate per year between 2016 and 2022 in the study area is -1.506% which mean the mangrove area in the study location has declined. The mangrove forest loss is likely due to hurricane event that happened in the study area as reported in the previous research 18,21 . Based on this result, the U-Net model successfully used here foe mangrove mapping and monitoring and achieved good results.…”

Section: Evaluation Assessmentssupporting

confidence: 57%

“…This study not use all the Sentinel-2 bands, we selected some Sentinel-2 bands and produced four spectral indices that useful for mangrove mapping as the input data based on previous research 18 . The total input bands for this study are 10 bands included blue, green, red, NIR, SWIR-1, SWIR-2, normalized difference vegetation index (NDVI), combined mangrove index (CMRI), normalized difference mangrove index (NDMI), and modified mangrove recognition index (MMRI) (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…Many studies have successfully developed semantic segmentation algorithms for mangrove mapping and produced favorable results [18][19][20] . However, previous studies developed their own semantic segmentation algorithms, while the robust and reproducible semantic segmentation algorithms such as U-Net, LinkNet, FPN, and PSPNet is not yet evaluated for mangrove mapping.…”

Section: Introductionmentioning

confidence: 99%

“…This study used Sentinel-2 data to produce mangrove mapping and monitoring in the whole study area. Sentinel-2 has widely used for mangrove mapping using machine learning and deep learning 18,19 . The main goal of this study is to evaluate some deep learning semantic segmentation for mangrove mapping and use the best one to produce mangrove monitoring map.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating deep learning semantic segmentation architectures for mangrove mapping and monitoring using sentinel-2 data (case study: coastal zone of Rookery Bay, Florida, USA)

Jamaluddin,

Chen

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Mangroves play important roles in the blue carbon ecosystem. Mangrove map is important data, robust and reproducible methods for mangrove mapping and monitoring are needed. Along with the freely available optical remote sensing satellite data such as Sentinel-2 and the development of deep learning fields, mangrove mapping and monitoring are more reachable. Therefore, the main goal of this study is to evaluate and utilize some state-of-the-art deep learning semantic segmentation architectures (U-Net, LinkNet, PSPNet, and FPN) for mangrove mapping and monitoring. This study will provide evidence of the ability of state-of-the-art deep learning semantic segmentation that can provide a robust and reproducible method for mangrove mapping and monitoring. The study area is the coastal zone of Rookery Bay, Florida, USA. The Sentinel-2 bottom-of-atmosphere corrected reflectance data (2016) with the target data (water body, nonmangrove, and mangrove) used for training and evaluating the capability of U-Net, LinkNet, PSPNet, and FPN for mangrove mapping. While, for the mangrove monitoring evaluation, the best trained deep learning model based on the 2016 dataset was used here to produce a new mangrove map in 2022. The reference data were collected from google earth imagery in 2022 by visual interpretation (250 points for each class) and conducted mangroves monitoring evaluation by calculating class accuracy and overall accuracy for the produced mangrove map in 2022. Based on the experiment results, all of the state-of-the-art deep learning semantic segmentation architectures have promising results and U-Net achieved the highest performance with an average intersection over union (IoU) score of 0.926. Based on the evaluation result, the trained deep learning model based on the 2016 dataset successfully produced a mangrove map using 2022 Sentinel-2 data with an overall accuracy of around 0.98. This finding indicates the ability of U-Net, LinkNet, PSPNet, and FPN for mangrove mapping and monitoring.

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Section: Evaluation Assessmentssupporting

confidence: 57%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating deep learning semantic segmentation architectures for mangrove mapping and monitoring using sentinel-2 data (case study: coastal zone of Rookery Bay, Florida, USA)

Jamaluddin,

Chen

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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“…Various deep-learning techniques have been developed to understand the condition of mangrove ecosystems. For example, Jamaluddin et al [41] used MDPrePost-Net to determine the extent of mangrove degradation caused by the impact of Hurricane Irma as well as training data of around 40 million pixels with Sentinel-2 images and achieved an overall accuracy of 99.44%. Lin et al [42] used Convex Deep Mangrove Mapping (CODE-MM) to map mangroves in several countries, along with Sentinel-2 images and training data of around 4-50 million pixels, to achieve an overall accuracy of 86.16-97.65%.…”

Section: Introductionmentioning

confidence: 99%

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Panuntun,

Jamaluddin,

Chen

et al. 2024

Remote Sensing

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Mangroves grow in intertidal zones in tropical and subtropical regions, offering numerous advantages to humans and ecosystems. Mangrove monitoring is one of the important tasks to understand the current status of mangrove forests regarding their loss issues, including deforestation and degradation. Currently, satellite imagery is widely employed to monitor mangrove ecosystems. Sentinel-2 is an optical satellite imagery whose data are available for free, and which provides satellite imagery at a 5-day temporal resolution. Analyzing satellite images before and after loss can enhance our ability to detect mangrove loss. This paper introduces a LSST-Former model that considers the situation before and after mangrove loss to categorize non-mangrove areas, intact mangroves, and mangrove loss categories using Sentinel-2 images for a limited number of labels. The LSST-Former model was developed by integrating a fully convolutional network (FCN) and a transformer base with few-shot learning algorithms to extract information from spectral-spatial-temporal Sentinel-2 images. The attention mechanism in the transformer algorithm may effectively mitigate the issue of limited labeled samples and enhance the accuracy of learning correlations between samples, resulting in more successful classification. The experimental findings demonstrate that the LSST-Former model achieves an overall accuracy of 99.59% and an Intersection-over-Union (IoU) score of 98.84% for detecting mangrove loss, and the validation of universal applicability achieves an overall accuracy of more than 92% and a kappa accuracy of more than 89%. LSST-Former demonstrates superior performance compared to state-of-the-art deep-learning models such as random forest, Support Vector Machine, U-Net, LinkNet, Vision Transformer, SpectralFormer, MDPrePost-Net, and SST-Former, as evidenced by the experimental results and accuracy metrics.

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Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries

Dimyati,

Umarhadi,

Jamaluddin

et al. 2023

Remote Sensing Applications: Society and Environment

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MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data

Cited by 14 publications

References 57 publications

Evaluating deep learning semantic segmentation architectures for mangrove mapping and monitoring using sentinel-2 data (case study: coastal zone of Rookery Bay, Florida, USA)

Evaluating deep learning semantic segmentation architectures for mangrove mapping and monitoring using sentinel-2 data (case study: coastal zone of Rookery Bay, Florida, USA)

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries

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