Special Issue "Remote Sensing of Wetlands"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Mingming Jia
E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, 130102, Changchun, China
Interests: coastal wetlands remote sensing; fine-scale wetland monitoring; mangrove mapping
Dr. Dehua Mao
E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, 130102, Changchun, China
Interests: wetland mapping; wetland ecological parameter inversion; remote sensing assessment of wetland ecosystems
Dr. Zongming Wang
E-Mail
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, 130102, Changchun, China
Interests: remote sensing of wetlands; remote sensing of ecosystem services
Dr. Monica Rivas Casado
E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, Senior Lecturer in Integrated Environmental Monitoring, College Road, Cranfield, Bedfordshire, MK430AL, UK
Interests: unmanned aerial vehicles; structure from motion; monitoring; ecological modeling; freshwater ecosystems; statistics; environmental engineering; autonomous systems
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Special Issue Information

Dear Colleagues,

Wetlands cover approximately 6% of the terrestrial surface and provide important and diverse benefits to people around the world. However, an increasing number of wetlands are being converted to agricultural or urban uses or affected by natural factors like drought. Despite efforts to restore natural wetlands for human well-being, more than half of the global wetlands have disappeared during the last century. These changes directly affect the world biotic diversity and contribute to local and regional climate changes as well as to global warming. Thus, in recent years, changes impacting on the size and quality of the world’s wetland ecosystems have raised increasing concerns.

Remote Sensing provides unique capabilities and advantages to characterize and measure the state, conditions, and functioning of inaccessible wetlands. Since the launch of the Landsat series in 1972, there has been an exponential increase in the number of satellites and airborne sensors conveying information about wetlands. Today, more than 300 earth observation satellites from more than 15 countries are operational. Meanwhile, with the development of computer science, numerous methods have been utilized for remote sensing of wetlands, ranging from pixel- to object-oriented approaches and from manual to machine learning methods. More recently, the operation environment has evolved from personal computers to cloud computing severs. Therefore, the numerous imagery and high-performance computing facilities around the world are offering great oppurtunities to remote sensing scientists. However, due to the complex and varied environment of wetland ecosystems, it is still very challenging to achieve accurate remote sensing of wetlands.

The aim of this Special Issue is to collect original manuscripts on innovative research using state-of-the-art remote sensing technologies. Articles on biodiversity, functioning, services, and sustainability of wetlands are also welcome. The potential topics of this Special Issue include, but are not limited to:

  • Large-scale long-term wetland identification, delineation and habitat classification.
  • Remote sensing technologies for capturing accurate wetland vegetation parameters, such as species composition, leaf area index, productivity, etc.
  • Applications of remote sensing in conservation and management of wetlands.
  • Human activities and climate change impacts and resilience of wetlands.

Dr. Mingming Jia
Dr. Dehua Mao
Dr. Zongming Wang
Dr. Monica Rivas Casado
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 papers will be 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 2000 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.

Published Papers (7 papers)

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Research

Open AccessArticle
Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands
Remote Sens. 2020, 12(3), 407; https://doi.org/10.3390/rs12030407 - 28 Jan 2020
Abstract
The utilization of advanced remote sensing methods to monitor the coastal wetlands is essential for conservation and sustainable development. With multiple polarimetric channels, the polarimetric synthetic aperture radar (PolSAR) is increasingly employed in land cover classification and information extraction, as it has more [...] Read more.
The utilization of advanced remote sensing methods to monitor the coastal wetlands is essential for conservation and sustainable development. With multiple polarimetric channels, the polarimetric synthetic aperture radar (PolSAR) is increasingly employed in land cover classification and information extraction, as it has more scattering information than regular SAR images. Polarimetric decomposition is often used to extract scattering information from polarimetric SAR. However, distinguishing all land cover types using only one polarimetric decomposition in complex ecological environments such as coastal wetlands is not easy, and thus integration of multiple decomposition algorithms is an effective means of land cover classification. More than 20 decompositions were used in this research to extract polarimetric scattering features. Furthermore, a new algorithm combining random forest (RF) with sequential forward selection (SFS) was applied, in which the importance values of all polarimetric features can be evaluated quantitatively, and the polarimetric feature set can be optimized. The experiments were conducted in the Jiangsu coastal wetlands, which are located in eastern China. This research demonstrated that the classification accuracies were improved relative to regular decision tree methods, and the process of polarimetric scattering feature set optimization was intuitive. Furthermore, the scattering matrix elements and scattering features derived from H / α , Yamaguchi3, VanZyl3, and Krogager decompositions were determined to be very supportive of land cover identification in the Jiangsu coastal wetlands. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017
Remote Sens. 2020, 12(2), 220; https://doi.org/10.3390/rs12020220 - 08 Jan 2020
Abstract
Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing [...] Read more.
Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Changes in Lake Area in Response to Climatic Forcing in the Endorheic Hongjian Lake Basin, China
Remote Sens. 2019, 11(24), 3046; https://doi.org/10.3390/rs11243046 - 17 Dec 2019
Abstract
Endorheic lakes are key components of the water cycle and the ecological system in endorheic basins. The endorheic Hongjian Lake wetland is China’s national nature reserve for protecting the vulnerable species of Relict Gull. The Hongjian Lake, once China’s largest desert freshwater lake, [...] Read more.
Endorheic lakes are key components of the water cycle and the ecological system in endorheic basins. The endorheic Hongjian Lake wetland is China’s national nature reserve for protecting the vulnerable species of Relict Gull. The Hongjian Lake, once China’s largest desert freshwater lake, has been suffering from severe shrinkage in the last two decades, yet the variations in the lake area and its responses to climate change are poorly understood due to a lack of in situ observations. In this study, using Landsat remote sensing images, the Modified Normalized Difference Water Index, and nonparametric tests, we obtained the Hongjian Lake area changes on the annual, seasonal, and quasi-monthly scales during 1988–2014, analyzed the corresponding variations of the six climatic factors in the Hongjian Lake Basin (HJLB) using satellite-based products, and investigated the multi-scale response characteristics of lake area to climatic forcing using correlation analysis. The results showed that the lake area decreased during 1988–2014, and this process can be divided into two sub-stages, namely the first slight increasing sub-phase in 1988–1999 and the second significant declining sub-phase in 2000–2014. The shifts in patterns of the seasonal cycle had three types: as the natural rhythm of the lake changes has been broken by intensive human activities since the late 1990s, the natural bimodal type I has obviously changed into non-natural bimodal type II and unimodal type III, featured by a declining peak in July–September. The climatic wet/dry regime on multi-scales during 1988–2014 in the HJLB was generally warming and drying, mainly reflected by the increase in temperature (T), arid index (AI) and evaporation (ET0, ETa), and the decrease in the precipitation (Pre) and actual water difference (AWD). There were large differences in the climatic factors at different time scales, especially in the wet and dry seasons. When the lagged effect, the cumulative effect, and the lagged and cumulative combined effect were gradually considered, the correlation coefficient significantly increased, and the direction of the correlation coefficient became coincident with common sense. The correlation analysis identified a lag period of approximately 1–3 years on an annual scale, and a lag period of approximately 1–3 months on a monthly scale. This study could provide a certain scientific reference for climate change detection, water resource management, and species habitat protection in the HJLB and similar endorheic basins or inland arid regions. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
The Impact of Artificial Wetland Expansion on Local Temperature in the Growing Season—the Case Study of the Sanjiang Plain, China
Remote Sens. 2019, 11(24), 2915; https://doi.org/10.3390/rs11242915 - 05 Dec 2019
Abstract
Land use and land cover change (LUCC) has been increasingly recognized as having important effects on climate systems. Paddy fields, one kind of artificial wetland, have seen a significant increase in the Sanjiang Plain, China since 2000 and have become the most typical [...] Read more.
Land use and land cover change (LUCC) has been increasingly recognized as having important effects on climate systems. Paddy fields, one kind of artificial wetland, have seen a significant increase in the Sanjiang Plain, China since 2000 and have become the most typical LUCC at the regional scale. Against this background, in this paper, we discuss the effects of this artificial wetland increase on surface temperature, in addition to its driving mechanisms. Firstly, the spatiotemporal variations of land surface temperature (LST) and its two driving variables (albedo and latent heat flux (LE)) in the Sanjiang Plain are analyzed and assessed based on remote sensing observation information from 2001 to 2015. Our results from both spatial distribution difference and time series analysis show that paddy field expansion led to day-time cooling and night-time warming over the study area. However, the LST changes show different characteristics and magnitudes in the spring (May to June) compared to the other months of the growing season (July to September). The daytime cooling trend is found to be −0.3842 K/year and the warming trend at night 0.1988 K/year during the period 2001 to 2015, resulting in an overall cooling effect in May and June. In July–September, the LST changes have the same sign but a smaller magnitude, with a −0.0686 K/year temperature trend seen for the day-time and a 0.0569 K/year increase for the night-time. As a consequence, a pronounced decrease in the diurnal temperature range is detected in the growing season, especially in spring. Furthermore, albedo and LE are demonstrated to be very sensitive to land use changes, especially in the earlier periods of the growing season. Correlation analysis between LST and albedo and LE also indicates the dominant role played by evapotranspiration in paddy fields in regulating local temperature. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Wetland Loss Identification and Evaluation Based on Landscape and Remote Sensing Indices in Xiong’an New Area
Remote Sens. 2019, 11(23), 2834; https://doi.org/10.3390/rs11232834 - 29 Nov 2019
Abstract
Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. [...] Read more.
Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. In this study, the Xiong’an New Area was selected as the study area. For this site, we built a new method to identify the patterns of wetland loss integrated the landscape variation and wetland elements loss based on seven land use maps and Landsat series images from the 1980s to 2015. The calculated results revealed the following: (1) From the 1980s to 2015, wetland area decreased by 40.94 km2, with a reduction of 13.84%. The wetland loss was divided into three sub stages: the wet stage from 1980s to 2000, the reduction stage from 2000 to 2019 and the recovering stage from 2009 to 2015. The wetland area was mainly replaced by cropland and built-up land, accounting for 98.22% in the overall loss. The maximum wetland area was 369.43 km2 in the Xiong’an New Area. (2) From 1989 to 2015, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil moisture monitoring index (SMMI) showed a degradation, a slight improvement and degradation trend, respectively. The significantly degraded areas were 80.40 km2, 20.71 km2 and 80.05 km2 by the detection of the remote sensing indices, respectively. The wetland loss was mainly dominated by different elements in different periods. The water area (NDWI), soil moisture (SMMI) and vegetation (NDVI) caused the wetland loss in the three sub-periods (1980s–2000, 2000–2009 and 2009–2015). (3) According to the analysis in the landscape and elements, the wetland loss was summarized with three patterns. In the pattern 1, as water became scarce, the plants changed from aquatic to terrestrial species in sub-region G, which caused the wetland vegetation loss. In the pattern 2, due to the water area decrease in sub-regions B, C, D and E, the soil moisture decreased and then the aquatic plants grew up, which caused the wetland loss. In the pattern 3, in sub-region A, due to the reduction in water, terrestrial plants covered the region. The three patterns indicated the wetland loss process in the sub region scale. (4) The research integrated the landscape variation and element loss appears potential in the identification of the loss of wetland areas. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data
Remote Sens. 2019, 11(21), 2549; https://doi.org/10.3390/rs11212549 - 30 Oct 2019
Abstract
Wetland ecosystems are important resources, providing great economic benefits for surrounding communities. In this study, we developed a new stress indicator called “Rapidly Assessed Wetlands Stress Index” (RAWSI) by combining several natural and anthropogenic stressors of wetlands in Delaware, in the United States. [...] Read more.
Wetland ecosystems are important resources, providing great economic benefits for surrounding communities. In this study, we developed a new stress indicator called “Rapidly Assessed Wetlands Stress Index” (RAWSI) by combining several natural and anthropogenic stressors of wetlands in Delaware, in the United States. We compared two machine-learning algorithms, support vector machine (SVM) and random forest (RF), to quantify wetland stress by classifying satellite images from Landsat 8 and Sentinel-1 Synthetic Aperture Radar (SAR). An accuracy assessment showed that the combination of Landsat 8 and Sentinel SAR data had the highest overall accuracy (93.7%) when used with an RF classifier. In addition to the land-cover classification, a trend analysis of the normalized difference vegetation index (NDVI) calculated from Landsat images during 2004–2018 was used to assess changes in healthy vegetation. We also calculated the stream sinuosity to assess human alterations to hydrology. We then used these three metrics to develop RAWSI, and to quantify and map wetland stress due to human alteration of the landscape. Hot-spot analysis using Global Moran’s I and Getis-Ord Gi* identified several statistically significant hot spots (high stress) in forested wetlands and cold spots (low values) in non-forested wetlands. This information can be utilized to identify wetland areas in need of further regulation, with implications in environmental planning and policy decisions. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform
Remote Sens. 2019, 11(21), 2479; https://doi.org/10.3390/rs11212479 - 24 Oct 2019
Abstract
Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still [...] Read more.
Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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