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Big Earth Data and Remote Sensing in Coastal Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 36159

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Special Issue Editors

Prof. Dr. Cuizhen (Susan) Wang

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Guest Editor

Department of Geography, University of South Carolina, Columbia, SC 29208, USA
Interests: bio-environmental remote sensing; environmental modeling; coastal wetlands; sUAS
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Prof. Dr. Li Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
Interests: remote sensing; coast; drylands carbon
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Deepak R. Mishra

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Guest Editor

Department of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USA
Interests: water quality (inland waters, estuaries, coastal, and open ocean waters); wetlands health, productivity, and carbon sequestration; benthic habitat mapping; cyber-innovated environmental sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,
Remote Sensing has launched a Special Issue (SI) entitled “Big Earth Data and Remote Sensing in Coastal Environments”.

Coastal environments are steadily subjected to natural and anthropogenic stresses such as hurricanes, floods, sea level rise and coastal development. Big Earth Data is a new frontier in earth and information sciences to study our living planet from excessive earth observations. This SI solicits papers highlighting its recent advancements with a focus on addressing various environmental problems by means of innovative data collection, processing, and analytical solutions in the coastal zone. Topics of interest may include but are not limited to the following:

Remote sensing of coastal wetlands and dynamics
Coastal adaptation to sea level rise
Response of coastal ecosystems to hurricanes and floods
New advances of systems and geospatial technologies in coastal monitoring: Cube satellites, sUAS (drone technology), sensor networks, citizen science, and cloud computing, etc.

The Special Issue is now extended to 31 March 2022. We invite you to contribute a research or review paper. The accepted manuscript will receive a 30% discount on the Article Processing Charge (APC).

Prof. Cuizhen (Susan) Wang
Dr. Li Zhang
Prof. Dr. Deepak R. Mishra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing
Big earth data
Coast
Natural and developed lands
Sea level rise
Environmental stress

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Published Papers (7 papers)

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Research

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17 pages, 5860 KiB

Open AccessArticle

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

by Léa Enguehard, Nicola Falco, Myriam Schmutz, Michelle E. Newcomer, Joshua Ladau, James B. Brown, Laura Bourgeau-Chavez and Haruko M. Wainwright

Remote Sens. 2022, 14(14), 3285; https://doi.org/10.3390/rs14143285 - 8 Jul 2022

Cited by 9 | Viewed by 2893

Abstract

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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18 pages, 2723 KiB

Open AccessArticle

Coastal Wetland Responses to Sea Level Rise: The Losers and Winners Based on Hydro-Geomorphological Settings

by Li Wen and Michael G. Hughes

Remote Sens. 2022, 14(8), 1888; https://doi.org/10.3390/rs14081888 - 14 Apr 2022

Cited by 9 | Viewed by 3435

Abstract

Many coastal wetlands are under pressure due to climate change and the associated sea level rise (SLR). Many previous studies suggest that upslope lateral migration is the key adaptive mechanism for saline wetlands, such as mangroves and saltmarshes. However, few studies have explored the long-term fate of other wetland types, such as brackish swamps and freshwater forests. Using the current wetland map of a micro-tidal estuary, the Manning River in New South Wales, Australia, this study built a machine learning model based on the hydro-geomorphological settings of four broad wetland types. The model was then used to predict the future wetland distribution under three sea level rise scenarios. The predictions were compared to compute the persistence, net, swap, and total changes in the wetlands to investigate the loss and gain potential of different wetland classes. Our results for the study area show extensive gains by mangroves under low (0.5 m), moderate (1.0 m), and high (1.5 m) sea level rise scenarios, whereas the other wetland classes could suffer substantial losses. Our findings suggest that the accommodation spaces might only be beneficial to mangroves, and their availability to saltmarshes might be limited by coastal squeeze at saline–freshwater ecotones. Furthermore, the accommodation spaces for freshwater wetlands were also restrained by coastal squeeze at the wetland-upland ecotones. As sea level rises, coastal wetlands other than mangroves could be lost due to barriers at the transitional ecotones. In our study, these are largely manifested by slope impacts on hydrology at a higher sea level. Our approach provides a framework to systematically assess the vulnerability of all coastal wetland types. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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19 pages, 7926 KiB

Open AccessArticle

Dynamic Expansion of Urban Land in China’s Coastal Zone since 2000

by Peipei Du, Xiyong Hou and He Xu

Remote Sens. 2022, 14(4), 916; https://doi.org/10.3390/rs14040916 - 14 Feb 2022

Cited by 31 | Viewed by 3952

Abstract

Although a major region with strong urbanization, there is not yet a systematic and comprehensive understanding of urban expansion during the last 20 years for China’s coastal zone. In this paper, based on remote sensing techniques, and using indicators such as new urban land proportion, annual urban increase, and annual growth rate, as well as a landscape expansion index reflecting the urban expansion type (e.g., edge-expansion, infilling, and outlying), we measured the dynamic expansion of urban land in China’s coastal zone since 2000. The results indicated that: (1) China’s coastal zone experienced rapid urbanization from 2000 to 2020, with the new urban land and annual urban growth rate at 17,979.72 km² and 4.83%, respectively. The new urban land was mainly concentrated in economically advanced regions, such as Bohai Rim, Shandong Peninsula, the Yangtze River delta, and the Pearl River delta. (2) The urban growth rates of coastal cities in Liaoning, Hebei, Shandong, southeast Fujian, and Taiwan became slower over time, with a sharp decline during 2015–2020. In the mid and south of China’s coastal zone, such as coastal cities in Jiangsu, Guangxi, and Hainan, there was slow urbanization before 2015, and urban land expanded dramatically during 2015–2020. (3) The urban expansion of China’s coastal zone was dominated by edge-expansion after 2000, but it went through a low-speed and intensive development stage during 2010–2015, with an increase in urban land less than 50% of that in the other three five-year periods, and the most significant filling of urban space compared with the other three five-year periods, which was probably caused by the global financial crisis. (4) The spatial-temporal differences in the urbanization process in China’s coastal zone were largely consequent on national economic development strategies and regional development plans implemented in China’s coastal zone. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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16 pages, 61207 KiB

Open AccessArticle

Modelling Aboveground Biomass Carbon Stock of the Bohai Rim Coastal Wetlands by Integrating Remote Sensing, Terrain, and Climate Data

by Shaobo Sun, Yafei Wang, Zhaoliang Song, Chu Chen, Yonggen Zhang, Xi Chen, Wei Chen, Wenping Yuan, Xiuchen Wu, Xiangbin Ran, Yidong Wang, Qiang Li and Lele Wu

Remote Sens. 2021, 13(21), 4321; https://doi.org/10.3390/rs13214321 - 27 Oct 2021

Cited by 25 | Viewed by 4265

Abstract

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m⁻² and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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18 pages, 87768 KiB

Open AccessArticle

RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System

by Grayson R. Morgan, Cuizhen Wang and James T. Morris

Remote Sens. 2021, 13(17), 3406; https://doi.org/10.3390/rs13173406 - 27 Aug 2021

Cited by 19 | Viewed by 4158

Abstract

Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R² = 0.39) and excess green index (ExG; R² = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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20 pages, 7683 KiB

Open AccessArticle

Mapping Large-Scale Mangroves along the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model and Landsat Data

by Yujuan Guo, Jingjuan Liao and Guozhuang Shen

Remote Sens. 2021, 13(2), 245; https://doi.org/10.3390/rs13020245 - 13 Jan 2021

Cited by 55 | Viewed by 5574

Abstract

Mangroves are important ecosystems and their distribution and dynamics can provide an understanding of the processes of ecological change. Meanwhile, mangroves protection is also an important element of the Maritime Silk Road (MSR) Cooperation Project. Large amounts of accessible satellite remote sensing data can provide timely and accurate information on the dynamics of mangroves, offering significant advantages in space, time, and characterization. In view of the capability of deep learning in processing massive data in recent years, we developed a new deep learning model—Capsules-Unet, which introduces the capsule concept into U-net to extract mangroves with high accuracy by learning the spatial relationship between objects in images. This model can significantly reduce the number of network parameters to improve the efficiency of data processing. This study uses Landsat data combined with Capsules-Unet to map the dynamics of mangrove changes over the 25 years (1990–2015) along the MSR. The results show that there was a loss in the mangrove area of 1,356,686 ha (about 21.5%) between 1990 and 2015, with anthropic activities such as agriculture, aquaculture, tourism, urban development, and over-development appearing to be the likely drivers of this decline. This information contributes to the understanding of ecological conditions, variability characteristics, and influencing factors along the MSR. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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Review

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24 pages, 3785 KiB

Open AccessReview

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

by Ghada Y.H. El Serafy, Blake A. Schaeffer, Merrie-Beth Neely, Anna Spinosa, Daniel Odermatt, Kathleen C. Weathers, Theo Baracchini, Damien Bouffard, Laurence Carvalho, Robyn N. Conmy, Liesbeth De Keukelaere, Peter D. Hunter, Cédric Jamet, Klaus D. Joehnk, John M. Johnston, Anders Knudby, Camille Minaudo, Nima Pahlevan, Ils Reusen, Kevin C. Rose, John Schalles and Maria Tzortziou Show full author list Hide full author list

Remote Sens. 2021, 13(15), 2899; https://doi.org/10.3390/rs13152899 - 23 Jul 2021

Cited by 31 | Viewed by 9228

Abstract

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making. Full article

(This article belongs to the Special Issue Big Earth Data and Remote Sensing in Coastal Environments)

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