Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

McCarthy, Matthew J.; Jessen, Brita; Barry, Michael J.; Figueroa, Marissa; McIntosh, Jessica; Murray, Tylar; Schmid, Jill; Karger, F.

doi:10.3390/rs12111740

Cited by 20 publications

(22 citation statements)

References 28 publications

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“…Hurricanes are also among natural events that have substantially contributed to mangrove degradation [13,19]. In September 2017, southwest Florida was hit by one such storm, classed as a Category 3 hurricane, named Hurricane Irma [20]. Studies have reported on the Hurricane Irma-induced mangrove degradation in southwest Florida [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Mangroves are grown in intertidal zones along tropical and subtropical climate areas, which have many benefits for humans and ecosystems. The knowledge of mangrove conditions is essential to know the statuses of mangroves. Recently, satellite imagery has been widely used to generate mangrove and degradation mapping. Sentinel-2 is a volume of free satellite image data that has a temporal resolution of 5 days. When Hurricane Irma hit the southwest Florida coastal zone in 2017, it caused mangrove degradation. The relationship of satellite images between pre and post-hurricane events can provide a deeper understanding of the degraded mangrove areas that were affected by Hurricane Irma. This study proposed an MDPrePost-Net that considers images before and after hurricanes to classify non-mangrove, intact/healthy mangroves, and degraded mangroves classes affected by Hurricane Irma in southwest Florida using Sentinel-2 data. MDPrePost-Net is an end-to-end fully convolutional network (FCN) that consists of two main sub-models. The first sub-model is a pre-post deep feature extractor used to extract the spatial–spectral–temporal relationship between the pre, post, and mangrove conditions after the hurricane from the satellite images and the second sub-model is an FCN classifier as the classification part from extracted spatial–spectral–temporal deep features. Experimental results show that the accuracy and Intersection over Union (IoU) score by the proposed MDPrePost-Net for degraded mangrove are 98.25% and 96.82%, respectively. Based on the experimental results, MDPrePost-Net outperforms the state-of-the-art FCN models (e.g., U-Net, LinkNet, FPN, and FC-DenseNet) in terms of accuracy metrics. In addition, this study found that 26.64% (41,008.66 Ha) of the mangrove area was degraded due to Hurricane Irma along the southwest Florida coastal zone and the other 73.36% (112,924.70 Ha) mangrove area remained intact.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Exploring classification models is an effective way since it can make up the limitations of previous proposed classifiers. Various related studies mainly used ensemble learning [14]- [16], support vector machine (SVM) [17]- [19], decision tree (DT) [20], [21], and deep learning [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [20] developed DT based on multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM) to map mangrove forests. Furthermore, by using DT, McCarthy et al [21] not only mapped the extent of mangroves, but also distinguished between healthy and degraded mangroves. • Deep learning is gaining widespread popularity in the remote sensing community recently since it can extract high-level features directly from the raw input data.…”

Section: Introductionmentioning

confidence: 99%

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

Xue

Qian

2022

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

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Monitoring mangroves is critical to protect the coastal ecosystems. Some studies resorted to remote sensing for constructing mangrove indices (MIs). However, there are still some drawbacks in existing MIs. On the one hand, difficulty still persists in distinguishing mangroves from non-mangrove vegetation and non-vegetated areas at the same time. On the other hand, the existing MIs have not fully utilized the phenological trajectories, which can greatly help to distinguish mangroves from other land covers. To overcome these issues, we built a novel mangrove index, namely generalized composite mangrove index (GCMI) by compositing vegetation indices (VIs) and water indices (WIs) based on Sentinel-2 time series data. Firstly, to determine the optimal indices, a similarity trend distance (ST distance) measure was proposed based on Pearson correlation coefficient and dynamic time warping (DTW). Secondly, in order to optimize the weights of selected indices, a population reconstruction genetic algorithm (PRGA) was designed. Finally, mangroves were mapped by feeding the time series of GCMI into random forest (RF) classifier. Experiments conducted over three areas along the southern coast of China demonstrate that: 1) GCMI enhances the separability between mangroves and other land covers compared to the existing VIs, WIs, and MIs, with an averaged OA of 91.45%; 2) ST distance outperforms Euclidean distance, Cosine distance, Pearson correlation coefficient, and DTW in optimizing the weights of GCMI; and 3) PRGA greatly improves the probability of attaining global optimal result. The innovation lies in the presented GCMI considering both the vegetation trajectory information and water inundation using time series.

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“…Wan et al [13] have investigated four mangrove species in Hong Kong using the new hyperspectral Gaofen-5 dataset (330 spectral bands). Besides the conventional satellite sensors, such as Worldview [14] and Pleiades [15], unmanned aerial vehicles (UAVs) [16] have been employed for mangrove mapping, especially for individual mangrove analysis, in which the fuzzy-based and objected-based approaches are often adopted [8,12]. Since SAR images can penetrate the canopy and sensitive to the surface and vertical structure, thus, they are useful for mapping and monitoring mangrove structure and biomass [17,18].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam

2020

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Mangrove forests play an important role in maintaining water quality, mitigating climate change impacts, and providing a wide range of ecosystem services. Effective identification of mangrove species using remote-sensing images remains a challenge. The combinations of multi-source remote-sensing datasets (with different spectral/spatial resolution) are beneficial to the improvement of mangrove tree species discrimination. In this paper, various combinations of remote-sensing datasets including Sentinel-1 dual-polarimetric synthetic aperture radar (SAR), Sentinel-2 multispectral, and Gaofen-3 full-polarimetric SAR data were used to classify the mangrove communities in Xuan Thuy National Park, Vietnam. The mixture of mangrove communities consisting of small and shrub mangrove patches is generally difficult to separate using low/medium spatial resolution. To alleviate this problem, we propose to use label distribution learning (LDL) to provide the probabilistic mapping of tree species, including Sonneratia caseolaris (SC), Kandelia obovata (KO), Aegiceras corniculatum (AC), Rhizophora stylosa (RS), and Avicennia marina (AM). The experimental results show that the best classification performance was achieved by an integration of Sentinel-2 and Gaofen-3 datasets, demonstrating that full-polarimetric Gaofen-3 data is superior to the dual-polarimetric Sentinel-1 data for mapping mangrove tree species in the tropics.

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Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

Cited by 20 publications

References 28 publications

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

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

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam

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