Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data

Zhen, Jianing; Liao, Jingjuan; Shen, Guozhuang

doi:10.3390/s18114012

Cited by 31 publications

(21 citation statements)

References 44 publications

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“…To ensure that all the data products had the same pixel size for deep learning for mangrove extraction, we read the Level-C 2A image through SNAP, resampled the band needed by the image to 10 m pixel size, and converted it to a format that could be used by ENVI to facilitate subsequent data processing in ENVI. According to previous experiments and previous research results [35,38], the red-edge bands from Sentinel-2 and the SAR data from Sentinel-1 are also useful for differentiating different vegetation types. However, many redundant and even noise data [39] are observed for only mangrove extraction using deep learning after our preliminary research.…”

Section: Remote Sensing Data and Preprocessingmentioning

confidence: 85%

“…(2) Multispectral indices of the image are required for mangrove extraction. Given that the band selection is not our research focus, six multispectral indices were used in this study based on the vegetation index commonly used in remote sensing images and the existing research results in mangrove extraction research [34][35][36][37]. According to previous experiments and research results [35,38], the red-edge bands from Sentinel-2 and the SAR data from sentinel-1 are also useful for differentiating different vegetation types.…”

Section: Methodsmentioning

confidence: 99%

“…DNNR is the most resource-rich area of all mangrove types. It is a typical mangrove wetland composed of major mangrove species in southern China and is becoming a major area for mangrove classification research [35]. There are five families and eight genera of mangroves in DNNR.…”

Section: Study Areamentioning

confidence: 99%

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ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Guo

et al. 2021

Remote Sensing

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Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

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Section: Remote Sensing Data and Preprocessingmentioning

confidence: 85%

Section: Methodsmentioning

confidence: 99%

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ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Guo

et al. 2021

Remote Sensing

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“…Mangrove mapping with the combination of optical and SAR datasets has been reported by previous studies [1,21,27] and few studies have investigated the classification performance with full-pol SAR (e.g., RADARSAT-2 and ALOS-PALSAR-2) [57,58].…”

Section: Contribution From the Full-polarimetric Sarmentioning

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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“…On the other hand, climate change-induced impacts, such as sea level rise [16], heightened frequency and severity of storm surges, coastal flooding [17,18] and salinization of soils and freshwater resources, increase the risk to humans, infrastructures and economies [19]. and X-band SAR data [29][30][31] or for the monitoring of coastal wetlands with C-band [24,[32][33][34][35] or X-band [35][36][37] sensors. Advanced high-resolution X-band SAR sensors, such as TerraSAR-X/Tandem-X and COnstellation of small Satellites for the Mediterranean basin Observation (COSMO)-Skymed, enable acquisition of high-resolution imagery of up to a 1 m resolution.…”

Section: Introductionmentioning

confidence: 99%

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

Ottinger

Kuenzer

2020

Remote Sensing

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The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.

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Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data

Cited by 31 publications

References 44 publications

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

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

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

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