A detailed mangrove map of China for 2019 derived from Sentinel‐1 and ‐2 images and Google Earth images

Zhao, Chenyang; Qin, Cheng‐Zhi

doi:10.1002/gdj3.119

Cited by 25 publications

(12 citation statements)

References 54 publications

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“…Due to its strong ability to tolerate harsh coastal conditions (e.g. hypersalinity and overflooding), high reproductive capability by both seeds and clonal rhizomes, and great competitive ability (Li et al, 2009; Qiu et al, 2020; Xu, Zhang, et al, 2022), S. alterniflora has rapidly invaded coastal China (ranging from 20 to 40 °N) since it was introduced in 1979 and now covers more than 40% of the coastal wetlands in China (Hu et al, 2021; Zhao & Qin, 2021). S. alterniflora has a suite of traits that impact the cycling and accumulation of soil organic C (SOC) content, including an extensive underground system (Darby & Turner, 2008; H. Liu et al, 2021), high photosynthetic rates (Jiang et al, 2009; Liao et al, 2007) and primary productivity (Liu et al, 2020), making the native ranges of S. alterniflora one of the most important blue C sinks across the world (Ouyang & Lee, 2014).…”

Section: Introductionmentioning

confidence: 99%

Effects of Spartina invasion on the soil organic carbon content in salt marsh and mangrove ecosystems in China

Wei

Chen

et al. 2022

Journal of Applied Ecology

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1. Coastal wetlands are large reservoirs of soil carbon (C) and have been invaded globally by the exotic species Spartina alterniflora. However, the effects of these invasions on soil C content remain unclear.2. We performed a meta-analysis of 2479 soil organic C (SOC) content observations collected from 91 field studies conducted in coastal China, the world's largest introduced range for S. alterniflora.3. Spartina alterniflora invasions had no significant effect on the SOC content in vegetated native wetlands in coastal China. S. alterniflora increased the SOC content in the top 30 cm of salt marshes after invading only one of the nine marsh types, which was dominated by a dwarf succulent species, Suaeda salsa in the northern subtropics and decreased the SOC content in the top 60 cm of mangroves dominated by Kandelia obovata and mixed communities after invasion in the southern subtropics. S. alterniflora invasions did not significantly affect the SOC content in the other salt marsh or mangrove ecosystems. Moreover, the SOC content in S. alterniflora-invaded ecosystems increased only on a decadal scale and then decreased, rather than increasing monotonically. 4. Synthesis and applications. Our findings reveal that the SOC content of S. alterniflora-invaded ecosystems is similar to or lower than that of most native ecosystems. As soil bulk density is often reduced by S. alterniflora invasions, the C storage capabilities of vegetated native ecosystems might be substantially reduced after S. alterniflora invasions. Moreover, the SOC content in these invaded ecosystems gradually decreased after long-term S. alterniflora invasions (>20 years). Therefore, the displacement of native salt marsh or mangrove species by S. alterniflora may not be considered a natural climate solution to increase the C sink in coastal China. Future efforts are required to control S. alterniflora preferentially in coastal wetlands where S. alterniflora invasions do not increase

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Section: Introductionmentioning

confidence: 99%

Effects of Spartina invasion on the soil organic carbon content in salt marsh and mangrove ecosystems in China

Wei

Chen

et al. 2022

Journal of Applied Ecology

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“…1). 3) A ne-resolution mangrove map of China for 2019 was derived from 10-m-resolution satellite observations and Google Earth images (Zhao and Qin 2020b). The data have a resolution of 10 m and are mainly based on the Google Earth Engine cloud computing platform, using Sentinel SAR and optical time-series images, together with a global digital surface model dataset (Zhao and Qin 2020b).…”

Section: Mangrove Datamentioning

confidence: 99%

Can the Dongzhaigang Mangrove in China Adapt to Sea Level Rise in the Future?

Ding

Cai

Yan

et al. 2021

Preprint

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Considering climate change, coastal mangroves are facing serious threats from rising sea levels. However, whether the largest contiguous Dongzhaigang mangrove in China can adapt to future sea level rise, which is very critical for mangrove restoration and management, has been little known. Using the data of historical monitor since the 1950s, supplementary field research of mangrove wetland sediment rates measured, satellite remote sensing, digital elevation model, global climate models, and ArcGIS, we investigated the Dongzhaigang mangrove area changes, related causes, and the impacts of future sea level rise under greenhouse gas emission scenarios, as representative concentration pathways (RCPs) 2.6, 4.5, and 8.5. The study revealed that: (1) during 1956–1987, total mangrove area had decreased by ~ 50%, from ~ 3417 hm2 to ~ 1710 hm2. This was mainly because of the impacts of human activities, such as fish pond reclamation and the use of former mangrove land for economic tree planting. After the 1990s, the total mangrove area was maintained at ~ 1711 hm2, mainly because of the establishment of the nature reserve in 1986, along with protective and restorative measures; (2) under the intermediate and high RCP 4.5 and 8.5, sea level increases are likely to cause > 25% of the mangroves to disappear by 2100, whereas for the low RCP 2.6, only 17% of the mangroves are likely to be affected; and (3) taking measures such as reestablishing ponds as mangrove forests, plant restoration, and biological shore protection could improve the adaptation of mangroves to the impacts of rising sea levels.

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“…Hu et al [28] mapped global mangrove AGB using multisource satellite imagery, spaceborne LiDAR, and ground inventory data. Zhao et al [29] mapped mangroves in China using Google Earth images, Sentinel-2 images, and Sentinel-1 data. Sharifi et al [30] mapped mangrove forests in Qeshm Island, Iran using Sentinel-1 and Sentinel-2 satellite images.…”

Section: Introductionmentioning

confidence: 99%

“…Sentinel-2 is an accessible passive remote-sensing system with global coverage that has 13 bands with varying spatial resolutions (10, 20, and 60 m). Several studies have effectively utilized machine-learning and deep-learning methods with Sentinel-2 data to understand the condition of mangroves [22,24,[27][28][29][30][39][40][41][42]. The FCN algorithm can extract more refined deep features and contribute to determining the spatial-spectral pattern of the images.…”

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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A detailed mangrove map of China for 2019 derived from Sentinel‐1 and ‐2 images and Google Earth images

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 25 publications

References 54 publications

Effects of Spartina invasion on the soil organic carbon content in salt marsh and mangrove ecosystems in China

Effects of Spartina invasion on the soil organic carbon content in salt marsh and mangrove ecosystems in China

Can the Dongzhaigang Mangrove in China Adapt to Sea Level Rise in the Future?

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

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