Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing in Coastal Ecosystem Monitoring
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing in Coastal Ecosystem Monitoring

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

Deadline for manuscript submissions: closed (1 September 2023) | Viewed by 18799

Special Issue Editors

Dr. Valeria Tomaselli

E-Mail Website
Guest Editor
Department of Biology, Università Degli Studi di Bari, 70126 Bari, Italy
Interests: vegetation monitoring and mapping; habitat monitoring and mapping; land cover classification; land cover change; analysis of plant diversity at the community level in Mediterranean environments
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Adamo

E-Mail Website
Guest Editor
Institute of Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), c/o Interateneo Physics Department, University of Bari, Via Amendola 173, 70126 Bari, Italy
Interests: optical remote sensing; land cover/land use mapping; habitat mapping; time series analysis; oil spill monitoring; wind fields retrieval from SAR
Special Issues, Collections and Topics in MDPI journals
Cristina Tarantino

E-Mail Website
Guest Editor
Institute of Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), c/o Interateneo Physics Department, University of Bari, Via Amendola 173, 70126 Bari, Italy
Interests: remote sensing; classification; land cover/land use mapping; habitat mapping; change detection; invasive species monitoring; time series analysis; GIS environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal locations and their adjacent areas are characterized by a wide biodiversity, a variety of ecosystems, and remarkable biological productivity. Their high accessibility, along with the large amount of ecosystem services provided, have made them areas of major economic and social importance for millennia.

In recent decades, the development and utilization of coastal zones has greatly increased, with even higher rates of population growth. Coasts are undergoing continuous pressure and degradation with huge socioeconomic and environmental changes. Climate change, urbanization, and agricultural intensification are the main drivers of these trends.

Quantitative and qualitative assessment of coastal areas is essential to guarantee the preservation of their ecological richness and economic importance. Proper conservation and management actions imply continuous monitoring and mapping of their spatial distribution, landscape pattern, land-cover/land-use changes, etc.

Native vegetation in coastal areas plays an important role in determining morphology, stabilizing surface and providing habitat for wildlife. Monitoring and conservation of coastal vegetation is important for the long-term protection of coastal ecosystems. Evaluating changes at land use/land cover (LU/LC), vegetation, and habitat level implies different thematic and spatial scales of observation and is essential to develop efficient management strategies.

Remote sensing techniques have proven to be powerful and cost-effective tools for the long-term monitoring of the Earth’s surface on a global, regional, and even local scale, by providing important coverage, mapping, and classification of land cover features such as vegetation, soil, and water.

We are inviting submissions including but not limited to:

  • Multiscale and long-term monitoring in coastal areas;
  • Use of expert knowledge for vegetation/habitat identification and classification in coastal areas;
  • Monitoring LC/LU and habitat changes in coastal environments;
  • Phenology trends and changes in coastal vegetation types;
  • Sand dune systems;
  • Coastal wetlands;
  • Coastal urbanization trends;
  • Coastal erosion.

Dr. Valeria Tomaselli
Dr. Maria Adamo
Dr. Cristina Tarantino
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

  • coastal vegetation
  • habitat
  • land cover and land use
  • classification
  • mapping
  • change detection
  • phenological characterization of vegetation communities
  • EO data classification
  • time-series data
  • spatial statistics

Related Special Issue

Published Papers (9 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 25366 KiB  
Article
Mapping Suspended Sediment Changes in the Western Pacific Coasts
by Tao Zhou, Bowen Cao, Junliang Qiu, Shirong Cai, Haidong Ou, Wei Fan, Xiankun Yang, Xuetong Xie, Yu Bo and Gaige Zhang
Remote Sens. 2023, 15(21), 5192; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15215192 - 31 Oct 2023
Viewed by 928
Abstract
The Western Pacific Coasts (WPC) are the outlets of many large Asian rivers. In recent years, the interplay of climate changes and human activities has persistently altered the suspended sediment concentrations (SSC) in the WPC, triggering substantial shifts in coastal ecosystems. However, the [...] Read more.
The Western Pacific Coasts (WPC) are the outlets of many large Asian rivers. In recent years, the interplay of climate changes and human activities has persistently altered the suspended sediment concentrations (SSC) in the WPC, triggering substantial shifts in coastal ecosystems. However, the scarcity of coastal observation stations hampered comprehensive investigations at large scales. This study employed three representative SSC retrieval models and utilized Landsat images acquired from 1990 to 2020 to estimate the SSC in the WPC with a focused endeavor to dissect the intricate spatial and temporal variability of SSC in the region. The findings revealed the following insights: (1) The outcomes derived from the three distinct SSC models consistently manifested a discernible decreasing pattern in SSC changes over the past three decades across all six major estuaries (Liao River Estuary, Yellow River Estuary, Yangtze River Estuary, Hangzhou Bay, Pearl River Estuary, and Mekong River Estuary). (2) The seasonal attributes of the six major estuaries differed, primarily due to distinct dominant influencing factors like precipitation, upstream sediment load, wind, and tides. (3) Collectively, SSC tends to be relatively higher in the Yangtze River Estuary, Hangzhou Bay, and Yellow River Estuary, while the Pearl River and Mekong River Estuaries exhibit relatively lower levels. Notably, the SSC exhibited distinct spatial traits along the coastlines of different estuaries. (4) SSC in the non-estuarine regions along the WPC, a similar significant declining trend in SSC is observed as in the estuaries, albeit the rate of decline generally appeared to be less pronounced. Furthermore, regions with faster rates of SSC reduction are typically concentrated near major estuaries in the northern part of the Coasts. The decline in estuarine SSC plays an important role in the overall decrease in SSC across the WPC. These study outcomes held substantial significance for advancing the stability and sustainable evolution of the WPC. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

37 pages, 7107 KiB  
Article
Assessing Spectral Band, Elevation, and Collection Date Combinations for Classifying Salt Marsh Vegetation with Unoccupied Aerial Vehicle (UAV)-Acquired Imagery
by Michael Routhier, Gregg Moore and Barrett Rock
Remote Sens. 2023, 15(20), 5076; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15205076 - 23 Oct 2023
Viewed by 1382
Abstract
New England salt marshes provide many services to humans and the environment, but these landscapes are threatened by drivers such as sea level rise. Mapping the distribution of salt marsh plant species can help resource managers better monitor these ecosystems. Because salt marsh [...] Read more.
New England salt marshes provide many services to humans and the environment, but these landscapes are threatened by drivers such as sea level rise. Mapping the distribution of salt marsh plant species can help resource managers better monitor these ecosystems. Because salt marsh species often have spatial distributions that change over horizontal distances of less than a meter, accurately mapping this type of vegetation requires the use of high-spatial-resolution data. Previous work has proven that unoccupied aerial vehicle (UAV)-acquired imagery can provide this level of spatial resolution. However, despite many advances in remote sensing mapping methods over the last few decades, limited research focuses on which spectral band, elevation layer, and acquisition date combinations produce the most accurate species classification mappings from UAV imagery within salt marsh landscapes. Thus, our work classified and assessed various combinations of these characteristics of UAV imagery for mapping the distribution of plant species within these ecosystems. The results revealed that red, green, and near-infrared camera image band composites produced more accurate image classifications than true-color camera-band composites. The addition of an elevation layer within image composites further improved classification accuracies, particularly between species with similar spectral characteristics, such as two forms of dominant salt marsh cord grasses (Spartina alterniflora) that live at different elevations from each other. Finer assessments of misclassifications between other plant species pairs provided us with additional insights into the dynamics of why classification total accuracies differed between assessed image composites. The results also suggest that seasonality can significantly affect classification accuracies. The methods and findings utilized in this study may provide resource managers with increased precision in detecting otherwise subtle changes in vegetation patterns over time that can inform future management strategies. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

30 pages, 8577 KiB  
Article
Mapping of Ecological Environment Based on Google Earth Engine Cloud Computing Platform and Landsat Long-Term Data: A Case Study of the Zhoushan Archipelago
by Chao Chen, Liyan Wang, Gang Yang, Weiwei Sun and Yongze Song
Remote Sens. 2023, 15(16), 4072; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15164072 - 17 Aug 2023
Cited by 10 | Viewed by 1702
Abstract
In recent years, with the rapid advancement of China’s urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor [...] Read more.
In recent years, with the rapid advancement of China’s urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

20 pages, 6630 KiB  
Article
Long-Term Change of Coastline Length along Selected Coastal Countries of Eurasia and African Continents
by Fan Yang, Li Zhang, Bowei Chen, Kaixin Li, Jingjuan Liao, Riffat Mahmood, Mohammad Emran Hasan, M. M. Abdullah Al Mamun, Syed Ahmed Raza and Dewayany Sutrisno
Remote Sens. 2023, 15(9), 2344; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092344 - 28 Apr 2023
Cited by 3 | Viewed by 1910
Abstract
The acquisition of dynamic coastline change at fine spatial and temporal resolution is essential for enhancing sustainable coastal economic development and coastal environmental conservation. Port construction, land reclamation, urban development, and sediment deposition have resulted in extensive coastline change. In this study, the [...] Read more.
The acquisition of dynamic coastline change at fine spatial and temporal resolution is essential for enhancing sustainable coastal economic development and coastal environmental conservation. Port construction, land reclamation, urban development, and sediment deposition have resulted in extensive coastline change. In this study, the coastlines along the 56 coastal countries in 1990, 2000, 2010, 2015, and 2020 were delineated and classified into six categories using Landsat time–series images. Five relevant indices, i.e., the length, length ratio, length change rate, index of coastline utilization degree (ICUD), and fractal dimension (FD), were calculated to analyze and explore the spatiotemporal pattern of the coastlines. The results indicate that: (1) The overall length of the coastlines has increased from 3.45 × 105 km to 3.48 × 105 km in the past 30 years, with a net increase of nearly 3904 km. Between 1990 and 2020, the length of the artificial coastline increased by about 13,835 km (4.9~8.8%), while the length of the natural coastline decreased by 9932 km (95.1~91.2%). The increase in artificial coastline is concentrated in Southeast Asia and South Asia. (2) The coastline fractal dimensions (FDs) of countries and continents show that the average FD values of countries in South Asia (1.3~1.4) and Southeast Asia (1.2~1.3) were higher than other countries in the study regions, meaning that the coastlines in South Asia and Southeast Asia are more complex and curved. (3) The value of the ICUD index increased consistently between 1990 and 2015 (177.7~186.6) but decreased sharply between 2015 and 2020 (186.6~162.4), implying that the impact of human activities on the coastline continued to increase until 2015 and began to decrease after 2015. Our study examined the changes in various types of coastlines, which could be significant for sustainable development and environmental protection in coastal areas. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

18 pages, 19023 KiB  
Article
A Global Remote-Sensing Assessment of the Intersite Variability in the Greening of Coastal Dunes
by Petya G. Petrova, Steven M. de Jong and Gerben Ruessink
Remote Sens. 2023, 15(6), 1491; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061491 - 08 Mar 2023
Cited by 2 | Viewed by 1772
Abstract
In recent decades, the vegetation on many coastal dunes has expanded spatially, which is attributed, among other things, to global-scale climate change. The intersite variability in this dune greening has not yet been substantially investigated, nor is it known whether it is consistent [...] Read more.
In recent decades, the vegetation on many coastal dunes has expanded spatially, which is attributed, among other things, to global-scale climate change. The intersite variability in this dune greening has not yet been substantially investigated, nor is it known whether it is consistent with intersite variability in climate change. Therefore, the objectives of this work were firstly to quantify and analyse the change in vegetation cover from multitemporal NDVI time series at a large number (186) of dune fields worldwide, calculated from Landsat satellite imagery available between 1984 and 2021 and secondly, to correlate the identified trends with trends in the main climate variables influencing vegetation growth (temperature, precipitation and wind speed). We show that greening is strongest in cool temperate climates (35° to 66.5° north/south latitudes) and that the rate of greening is accelerating at many sites. We find no dependence between the rate of greening and the local temporal change in temperature, precipitation and/or wind speed. Based on existing literature, sand supply and anthropogenic activities are discussed as possible reasons for the absence of a clear global relationship between variability in dune greening and climate change. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

23 pages, 13033 KiB  
Article
Remote Sensing of Coastal Wetland Degradation Using the Landscape Directional Succession Model
by Linlin Cui, Guosheng Li, Huajun Liao, Ninglei Ouyang, Xingyue Li and Dan Liu
Remote Sens. 2022, 14(20), 5273; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205273 - 21 Oct 2022
Cited by 8 | Viewed by 1638
Abstract
In recent decades, human activities have impaired the structure, function, and diversity of coastal wetland ecosystems, and there is a need for the rational planning of ecological restoration to curb wetland degradation. However, the challenge remains to quickly and accurately identify degraded wetland [...] Read more.
In recent decades, human activities have impaired the structure, function, and diversity of coastal wetland ecosystems, and there is a need for the rational planning of ecological restoration to curb wetland degradation. However, the challenge remains to quickly and accurately identify degraded wetland areas and their degradation levels. In this study, we used remote sensing interpretation data from 1980 to 2020 and the wetland degradation evaluation method based on a landscape directional succession model to quantify the spatial and temporal characteristics of wetland degradation in Jiangsu Province, China. The key findings showed that 3020.67 km2 of wetlands became degraded over the 40 years of this study, accounting for 42.74% of the total area of coastal wetlands, and that the overall degradation was mild. This degradation presented significant spatial differences, with the wetland degradation in Yancheng City observed to be more serious than that in Nantong City. Degradation mainly occurred in Sheyang County, Dafeng District, Dongtai City, and Rudong County, and the spatial distribution pattern of severe and moderate degradation, mild degradation, and non-degradation was observed from land to sea in that order. The degradation of wetlands was observed to have obvious stages, and the degradation of coastal wetlands in the study area from 1980 to 2020 showed a significant increasing trend. The comprehensive score of wetland degradation in 2020 (1.67) was 3.70 times that in 1985 (0.45), and the turning point occurred in 2000. The types of wetland degradation were dominated by the transformation of natural wetlands into construction land (coastal industry), fish farming, and arable land, as well as the invasion of exotic species. Although great efforts have been made in recent years to protect and restore coastal wetlands, the development and utilization of coastal wetland resources should be strictly controlled to achieve the goal of sustainable development in coastal areas. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Figure 1

18 pages, 26292 KiB  
Article
Watching the Saltmarsh Grow: A High-Resolution Remote Sensing Approach to Quantify the Effects of Wetland Restoration
by Ashley J. Rummell, Javier X. Leon, Hayden P. Borland, Brittany B. Elliott, Ben L. Gilby, Christopher J. Henderson and Andrew D. Olds
Remote Sens. 2022, 14(18), 4559; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184559 - 12 Sep 2022
Cited by 9 | Viewed by 2630
Abstract
Coastal wetlands are restored to regenerate lost ecosystem services. Accurate and frequent representations of the distribution and area of coastal wetland communities are critical for evaluating restoration success. Typically, such data are acquired through laborious, intensive and expensive field surveys or traditional remote [...] Read more.
Coastal wetlands are restored to regenerate lost ecosystem services. Accurate and frequent representations of the distribution and area of coastal wetland communities are critical for evaluating restoration success. Typically, such data are acquired through laborious, intensive and expensive field surveys or traditional remote sensing methods that can be erroneous. Recent advances in remote sensing techniques such as high-resolution sensors (<2 m resolution), object-based image analysis and shallow learning classifiers provide promising alternatives but have rarely been applied in a restoration context. We measured the changes to wetland communities at a 200 ha restoring coastal wetland in eastern Australia, using remotely sensed Worldview-2 imagery, object-based image analysis and random forest classification. Our approach used structural rasters (digital elevation and canopy height models) and a multi-temporal technique to distinguish between spectrally similar land cover. The accuracy of our land cover maps was high, with overall accuracies ranging between 91 and 95%, and this supported early detection of increases in the area of key ecosystems, including mixed she-oak and paperbark (10 ha), mangroves (0.91 ha) and saltmarsh (4.31 ha), over a 5-year monitoring period. Our approach provides coastal managers with an accurate and frequent method for quantifying early responses of coastal wetlands to restoration, which is essential for informing adaptive management in the regeneration of ecosystem services. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Figure 1

17 pages, 2859 KiB  
Article
Habitat Classification Predictions on an Undeveloped Barrier Island Using a GIS-Based Landscape Modeling Approach
by Emily R. Russ, Bianca R. Charbonneau, Safra Altman, Molly K. Reif and Todd M. Swannack
Remote Sens. 2022, 14(6), 1377; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061377 - 12 Mar 2022
Cited by 1 | Viewed by 2274
Abstract
Landscape models are essential tools that link landscape patterns to ecological processes. Barrier island vegetation communities are strongly correlated with geomorphology, which makes elevation-based metrics suitable for developing a predictive habitat classification model in these systems. In this study, multinomial logistic regression is [...] Read more.
Landscape models are essential tools that link landscape patterns to ecological processes. Barrier island vegetation communities are strongly correlated with geomorphology, which makes elevation-based metrics suitable for developing a predictive habitat classification model in these systems. In this study, multinomial logistic regression is used to predict herbaceous, sparse, and woody habitat distributions on the North End of Assateague Island from slope, distance to shore, and elevation change, all of which are derived from digital elevation models (DEMs). Sparse habitats, which were generally found closest to shore in areas that are exposed to harsh conditions, had the highest predictive accuracy. Herbaceous and woody habitats occupied more protected inland settings and had lower predictive accuracies. A majority of woody cells were misclassified as herbaceous likely because of the similarity in the predictive parameter distributions. This relatively simple model is transparent and does not rely on subjective interpretations. This makes it an effective tool that can directly aid practitioners making coastal management decisions surrounding storm response planning and conservation management. The model results were used in a nutrient sequestration application to quantify carbon and nitrogen stored in barrier island vegetation. This represents an example of how the model results can be used to assign economic value of ecosystem services in a coastal system to justify different management and conservation initiatives. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

23 pages, 8927 KiB  
Article
Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing
by Canran Tu, Peng Li, Zhenhong Li, Houjie Wang, Shuowen Yin, Dahui Li, Quantao Zhu, Maoxiang Chang, Jie Liu and Guoyang Wang
Remote Sens. 2021, 13(21), 4444; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214444 - 04 Nov 2021
Cited by 20 | Viewed by 3150
Abstract
The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita [...] Read more.
The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-9
Back to TopTop