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, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: unmanned aerial vehicles; monitoring; ecological modelling; freshwater ecosystems; statistics; environmental engineering; robotics and 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

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Published Papers (15 papers)

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Research

Open AccessArticle
Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula
Remote Sens. 2021, 13(4), 652; https://doi.org/10.3390/rs13040652 - 11 Feb 2021
Viewed by 539
Abstract
This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 [...] Read more.
This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Ecological and Environmental Effects of Estuarine Wetland Loss Using Keyhole and Landsat Data in Liao River Delta, China
Remote Sens. 2021, 13(2), 311; https://doi.org/10.3390/rs13020311 - 18 Jan 2021
Viewed by 556
Abstract
An estuarine wetland is an area of high ecological productivity and biodiversity, and it is also an anthropic activity hotspot area, which is of concern. The wetlands in estuarine areas have suffered declines, which have had remarkable ecological impacts. The land use changes, [...] Read more.
An estuarine wetland is an area of high ecological productivity and biodiversity, and it is also an anthropic activity hotspot area, which is of concern. The wetlands in estuarine areas have suffered declines, which have had remarkable ecological impacts. The land use changes, especially wetland loss, were studied based on Keyhole and Landsat images in the Liao River delta from 1962 to 2016. The dynamics of the ecosystem service values (ESVs), suitable habitat for birds, and soil heavy metal potential ecological risk were chosen to estimate the ecological effects with the benefit transfer method, synthetic overlaying method, and potential ecological risk index (RI) method, respectively. The driving factors of land use change and ecological effects were analyzed with redundancy analysis (RDA). The results showed that the built-up area increased from 95.98 km2 in 1962 to 591.49 km2 in 2016, and this large change was followed by changes in paddy fields (1351.30 to 1522.39 km2) and dry farmland (189.5 to 294.14 km2). The area of wetlands declined from 1823.16 km2 in 1962 to 1153.52 km2 in 2016, and this change was followed by a decrease in the water area (546.2 to 428.96 km2). The land use change was characterized by increasing built-up (516.25%), paddy fields (12.66%) and dry farmland (55.22%) areas and a decline in the wetland (36.73%) and water areas (21.47%) from 1962–2016. Wetlands decreased by 669.64 km2. The ESV values declined from 6.24 billion US$ to 4.46 billion US$ from 1962 to 2016, which means the ESVs were reduced by 19.26% due to wetlands being cultivated and the urbanization process. The area of suitable habitat for birds decreased by 1449.49 km2, or 61.42% of the total area available in 1962. Cd was the primary soil heavy metal pollutant based on its concentration, accumulation, and potential ecological risk contribution. The RDA showed that the driving factors of comprehensive ecological effects include wetland area, Cd and Cr concentration, river and oil well distributions. This study provides a comprehensive approach for estuarine wetland cultivation and scientific support for wetland conservation. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Does Ecological Water Replenishment Help Prevent a Large Wetland from Further Deterioration? Results from the Zhalong Nature Reserve, China
Remote Sens. 2020, 12(20), 3449; https://doi.org/10.3390/rs12203449 - 20 Oct 2020
Cited by 1 | Viewed by 666
Abstract
Ecological water replenishment (EWR) has been increasingly applied to the restoration and maintenance of wetland hydrological conditions across China since the beginning of the 21st century. However, little is known about whether EWR projects help protect and/or restore wetland ecohydrology. As one of [...] Read more.
Ecological water replenishment (EWR) has been increasingly applied to the restoration and maintenance of wetland hydrological conditions across China since the beginning of the 21st century. However, little is known about whether EWR projects help protect and/or restore wetland ecohydrology. As one of the earliest and longest-running EWR projects in China, water has been released from the Nenjiang River into the Zhalong wetland since 2001. It is important to examine the ecohydrological effects of this EWR project. In this study, long time series remote sensing data were used to extract the water area, inundation frequency, and normalized difference vegetation index (NDVI) to explore how eco-hydrological conditions changed during the pre- (1984–2000) and post-EWR (2001–2018) periods in the Zhalong wetland. Results show that the inundation area decreased due to the reduced surface water inflow during the pre-EWR period. Similarly, monthly vegetation NDVI in the growing season generally exhibited a decreasing and an increasing trend during the pre- and post-EWR periods, respectively. In the post-EWR period, NDVI increased by 19%, 73%, 45%, 28%, 13% for the months of May through September, respectively. Due to EWR, vegetation growth in areas with low inundation frequency was better than in areas with high inundation frequency. We found that the EWR project, runoff, and precipitation contributed 25%, 11%, and 64% to changes in the NDVI, respectively, and 46%, 37%, and 17% to changes in inundation area, respectively. These results indicate that the EWR project has improved hydrological conditions in the Zhalong wetland. For further maximum benefits of EWR in the Zhalong wetlands, we suggest that implementing similar eco-hydrological projects in the future should focus on flood pulse management to increase the inundation area, improve hydrological connectivity, and create new habitats. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Vegetation Carbon Sequestration Mapping in Herbaceous Wetlands by Using a MODIS EVI Time-Series Data Set: A Case in Poyang Lake Wetland, China
Remote Sens. 2020, 12(18), 3000; https://doi.org/10.3390/rs12183000 - 15 Sep 2020
Viewed by 817
Abstract
The carbon sequestration capacity of wetland vegetation determines carbon stocks and changes in wetlands. However, modeling vegetation carbon sequestration of herbaceous wetlands is still problematic due to complex hydroecological processes and rapidly changing biomass carbon stocks. Theoretically, a vegetation index (VI) time series [...] Read more.
The carbon sequestration capacity of wetland vegetation determines carbon stocks and changes in wetlands. However, modeling vegetation carbon sequestration of herbaceous wetlands is still problematic due to complex hydroecological processes and rapidly changing biomass carbon stocks. Theoretically, a vegetation index (VI) time series can retrieve the dynamic of biomass carbon stocks and could be used to calculate the cumulative composite of biomass carbon stocks during a given interval, i.e., vegetation carbon sequestration. Hence, we explored the potential for mapping vegetation carbon sequestration in herbaceous wetlands in this study by using a combination of remotely sensed VI time series and field observation data. This method was exemplarily applied for Poyang Lake wetland in 2016 by using a 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) time series. Results show that the vegetation carbon sequestration in this area was in the range of 193–1221 g C m−2 year−1 with a mean of 401 g C m−2 year−1 and a standard deviation of 172 g C m−2 year−1 in 2016. The approach has wider spatial applicability in wetlands than the currently used global map of vegetation production (MOD17A3) because our carbon estimation in areas depicted by ‘no data’ in the MOD17A3 product is considerable, which accounts for 91.2–91.5% of the total vegetation carbon sequestration of the wetland. Thus, we determined that VI time series data shows great potential for estimating vegetation carbon sequestration in herbaceous wetlands, especially with the continuously improving quality and frequency of satellite VI images. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE
Remote Sens. 2020, 12(15), 2413; https://doi.org/10.3390/rs12152413 - 28 Jul 2020
Cited by 2 | Viewed by 936
Abstract
Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations [...] Read more.
Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Identifying Hydro-Geomorphological Conditions for State Shifts from Bare Tidal Flats to Vegetated Tidal Marshes
Remote Sens. 2020, 12(14), 2316; https://doi.org/10.3390/rs12142316 - 18 Jul 2020
Viewed by 1202
Abstract
High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the [...] Read more.
High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the restoration of tidal marsh ecosystems in particular. However, the number of studies investigating the conditions for state shifts from bare mudflats to vegetated marshes remains relatively low. We developed a GIS approach to identify the locations of expected shifts from bare intertidal flats to vegetated marshes along a large estuary (Western Scheldt estuary, SW Netherlands), by analyzing the interactions between spatial patterns of vegetation biomass, elevation, tidal currents, and wind waves. We analyzed false-color aerial images for locating marshes, LIDAR-based digital elevation models, and spatial model simulations of tidal currents and wind waves at the whole estuary scale (~326 km²). Our results demonstrate that: (1) Bimodality in vegetation biomass and intertidal elevation co-occur; (2) the tidal currents and wind waves change abruptly at the transitions between the low-elevation bare state and high-elevation vegetated state. These findings suggest that biogeomorphic feedback between vegetation growth, currents, waves, and sediment dynamics causes the state shifts from bare mudflats to vegetated marshes. Our findings are translated into a GIS approach (logistic regression) to identify the locations of shifts from bare to vegetated states during the studied period based on spatial patterns of elevation, current, and wave orbital velocities. This GIS approach can provide a scientific basis for the management and restoration of tidal marshes. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
A Comparison of Three Trapezoid Models Using Optical and Thermal Satellite Imagery for Water Table Depth Monitoring in Estonian Bogs
Remote Sens. 2020, 12(12), 1980; https://doi.org/10.3390/rs12121980 - 19 Jun 2020
Cited by 2 | Viewed by 1155
Abstract
This study explored the potential of optical and thermal satellite imagery to monitor temporal and spatial changes in the position of the water table depth (WTD) in the peat layer of northern bogs. We evaluated three different trapezoid models that are proposed in [...] Read more.
This study explored the potential of optical and thermal satellite imagery to monitor temporal and spatial changes in the position of the water table depth (WTD) in the peat layer of northern bogs. We evaluated three different trapezoid models that are proposed in the literature for soil moisture monitoring in regions with mineral soils. Due to the tight capillary connection between water table and surface soil moisture, we hypothesized that the soil moisture indices retrieved from these models would be correlated with WTD measured in situ. Two trapezoid models were based on optical and thermal imagery, also known as Thermal-Optical TRApezoid Models (TOTRAM), and one was based on optical imagery alone, also known as the OPtical TRApezoid Model (OPTRAM). The models were applied to Landsat imagery from 2008 to 2019 and the derived soil moisture indices were compared with in-situ WTD from eight locations in two Estonian bogs. Our results show that only the OPTRAM index was significantly (p-value < 0.05) correlated in time with WTD (average Pearson correlation coefficient of 0.41 and 0.37, for original and anomaly time series, respectively), while the two tested TOTRAM indices were not. The highest temporal correlation coefficients (up to 0.8) were observed for OPTRAM over treeless parts of the bogs. An assessment of the spatial correlation between soil moisture indices and WTD indicated that all three models did not capture the spatial variation in water table depth. Instead, the spatial patterns of the indices were primarily attributable to vegetation patterns. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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Open AccessArticle
Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification
Remote Sens. 2020, 12(9), 1383; https://doi.org/10.3390/rs12091383 - 27 Apr 2020
Cited by 5 | Viewed by 959
Abstract
In the late 1990s, the exotic plant Spartina alterniflora (S. alterniflora), was introduced to the Zhangjiang Estuary of China for tidal zone reclamation and protection. However, it invaded rapidly and has caused serious ecological problems. Accurate information on the seasonal invasion [...] Read more.
In the late 1990s, the exotic plant Spartina alterniflora (S. alterniflora), was introduced to the Zhangjiang Estuary of China for tidal zone reclamation and protection. However, it invaded rapidly and has caused serious ecological problems. Accurate information on the seasonal invasion of S. alterniflora is vital to understand invasion pattern and mechanism, especially at a high temporal resolution. This study aimed to explore the S. alterniflora invasion process at a seasonal scale from 2016 to 2018. However, due to the uncertainties caused by periodic inundation of local tides, accurately monitoring the spatial extent of S. alterniflora is challenging. Thus, to achieve the goal and address the challenge, we firstly built a high-quality seasonal Sentinel-2 image collection by developing a new submerged S. alterniflora index (SAI) to reduce the errors caused by high tide fluctuations. Then, an object-based random forest (RF) classification method was applied to the image collection. Finally, seasonal extents of S. alterniflora were captured. Results showed that (1) the red edge bands (bands 5, 6, and 7) of Sentinel-2 imagery played critical roles in delineating submerged S. alterniflora; (2) during March 2016 to November 2018, the extent of S. alterniflora increased from 151.7 to 270.3 ha, with an annual invasion rate of 39.5 ha; (3) S. alterniflora invaded with a rate of 31.5 ha/season during growing season and 12.1 ha/season during dormant season. To our knowledge, this is the first study monitoring S. alterniflora invasion process at a seasonal scale during continuous years, discovering that S. alterniflora also expands during dormant seasons. This discovery is of great significance for understanding the invasion pattern and mechanism of S. alterniflora and will facilitate coastal biodiversity conservation efforts. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands)
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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
Cited by 3 | Viewed by 804
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
Cited by 1 | Viewed by 1021
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
Cited by 1 | Viewed by 854
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
Cited by 1 | Viewed by 862
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
Cited by 5 | Viewed by 1142
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
Cited by 4 | Viewed by 1516
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
Cited by 10 | Viewed by 1435
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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