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Remote Sensing for Wetland Inventory, Mapping and Change Analysis

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 21077

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Special Issue Editors

Dr. Meisam Amani

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Guest Editor

Wood Environment & Infrastructure Solutions, 210 Colonnade Road, Ottawa, ON K2E 7L5, Canada
Interests: remote sensing; wetlands; met-ocean; classification; machine learning; big data
Special Issues, Collections and Topics in MDPI journals

Dr. Brian Brisco

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Guest Editor

Sr. Scientist, Canada Center for Mapping and Earth Observation, Ottawa, ON, Canada
Interests: wetlands; SAR; surface water mapping
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Dr. Hooman Latifi

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Guest Editor

1. Department of Remote Sensing, University of Würzburg, Würzburg, Germany
2. Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
Interests: ecosystem monitoring; vegetation health; time series remote sensing; LiDAR
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Dr. Qiusheng Wu

E-Mail Website1 Website2
Guest Editor

Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: remote sensing; GIS; wetland hydrology; LiDAR; google earth engine
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Dr. Sahel Mahdavi

E-Mail Website
Guest Editor

Wood Environment & Infrastructure Solutions, 210 Colonnade Road, Ottawa, ON K2E 7L5, Canada
Interests: remote sensing of environment; classification; synthetic aperture radar; feature selection; change detection

Dr. Arsalan Ghorbanian

E-Mail Website
Guest Editor

1. Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi of University of Technology, Tehran 19967-15433, Iran
2. Department of Technology and Society, Lund University, 221 00 Lund, Sweden
Interests: remote sensing; land cover mapping; Google Earth Engine (GEE); Big Geo Data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetlands provide numerous services to humans and the environment. They facilitate water purification, reduce the risk of natural hazards, play an important role in soil and water conservation, act as natural filters of sediments, remove a considerable amount of pollution, and provide recreational and aesthetic values. Wetlands are negatively impacted by anthropogenic and natural activities, such as land cover changes, urbanization, industrial wastes, groundwater depletion, and climate change. Therefore, they should be monitored and preserved for managing their biodiversity and wildlife and sustainable development. Several international organizations (e.g., the Wetland International, Ramsar Convention, International Union for Conservation of Nature) have developed strategies for wetland ecosystem monitoring, conservation, and restoration. In this regard, generating wetland inventories is an important step that should be followed by consistent and timely monitoring and assessment of wetland ecological conditions for better management, protection, preservation, restoration, and conservation.

Remote sensing has proved to be an efficient tool for wetland mapping and monitoring. The possibility of acquiring images with various spectral and spatial resolutions enables researchers to efficiently study wetlands at local, regional, continental, and global scales in a cost-effective manner. Moreover, the availability of multitemporal open-source remote sensing datasets makes this technology invaluable for long-term monitoring of wetlands to measure wetland state, condition, and change. Finally, the availability of advanced machine learning algorithms and big data processing platforms facilitates effective wetland mapping and monitoring.

This Special Issue of Remote Sensing aims to collect the most recent research works related to different aspects of wetland mapping and change analysis using remote sensing methods. Potential topics for this Special Issue include but are not limited to:

Wetland mapping using different types of remote sensing datasets (e.g., multispectral, hyperspectral, SAR, LiDAR, and UAV data);
Decadal/annual/seasonal change analysis of wetlands;
Condition assessment of wetlands using multispectral and hyperspectral remote sensing images;
Polarimetric and interferometric techniques for wetland classification and change detection;
Advanced machine learning algorithms (e.g., deep learning) for wetland classification and monitoring;
Applications of geospatial big data processing platforms (e.g., Google Earth Engine) for wetland mapping and change analysis;
Hydrological dynamics of wetland ecosystems

Dr. Meisam Amani
Dr. Brian Brisco
Dr. Hooman Latifi
Dr. Qiusheng Wu
Dr. Sahel Mahdavi
Dr. Arsalan Ghorbanian
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

Remote sensing
Wetland mapping
Wetland change analysis
Wetland conservation
Wetland condition assessment
Machine learning
Geospatial big data

Published Papers (6 papers)

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Research

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21 pages, 6195 KiB

Open AccessArticle

Using Time Series Optical and SAR Data to Assess the Impact of Historical Wetland Change on Current Wetland in Zhenlai County, Jilin Province, China

by Sixue Shi, Yu Chang, Yuehui Li, Yuanman Hu, Miao Liu, Jun Ma, Zaiping Xiong, Ding Wen, Binglun Li and Tingshuang Zhang

Remote Sens. 2021, 13(22), 4514; https://doi.org/10.3390/rs13224514 - 10 Nov 2021

Cited by 7 | Viewed by 2331

Abstract

Wetlands, as the most essential ecosystem, are degraded throughout the world. Wetlands in Zhenlai county, with the Momoge National Nature Reserve, which was included on the Ramsar list, have degraded by nearly 30%. Wetland degradation is a long-term continuous process with annual or interannual changes in water area, water level, or vegetation presence and growth. Therefore, it requires sufficiently frequent and high-spatial-resolution data to represent its dynamics. This study mapped yearly land-use maps with 30-m resolution from 1985 to 2018 using Landsat data in Google Earth Engine (GEE) to explore the wetland degradation process and mapped 12-day interval land-use maps with 15-m resolution using the Sentinel-1B and Sentinel-2 data in GEE and other assistant platforms to study the characteristics of wetland dynamics in 2018. Four sets of maps were generated using Sentinel-1B (S1), Sentinel-2 (S2), the combination of Sentinel-1B and Sentinel-2 (S12), and S12 with multitemporal remote sensing (S12’). All of the classifications were performed in the Random Forest Classification (RFC) method using remote sensing indicators. The results indicate that S12’ was the most accurate. Then, the impact of the historic land-use degradation process on current wetland change dynamics was discussed. Stable, degradation, and restoration periods were identified according to the annual changes in wetlands. The degraded, stable, restored, and vulnerable zones were assessed based on the transformation characteristics among wetlands and other land-use types. The impact of historical land-use trajectories on wetland change characteristics nowadays is diverse in land-use types and distributions, and the ecological environment quality is the comprehensive result of the effect of historical land-use trajectories and the amount of rainfall and receding water from paddy fields. This study offers a new method to map high-spatiotemporal-resolution land-use (S12’) and addresses the relationship between historic wetland change characteristics and its status quo. The findings are also applicable to wetland research in other regions. This study could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Inventory, Mapping and Change Analysis)

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33 pages, 18851 KiB

Open AccessArticle

A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine

by Erfan Fekri, Hooman Latifi, Meisam Amani and Abdolkarim Zobeidinezhad

Remote Sens. 2021, 13(20), 4169; https://doi.org/10.3390/rs13204169 - 18 Oct 2021

Cited by 27 | Viewed by 3888

Abstract

Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Inventory, Mapping and Change Analysis)

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19 pages, 9186 KiB

Open AccessArticle

Analysis of Salt Lake Volume Dynamics Using Sentinel-1 Based SBAS Measurements: A Case Study of Lake Tuz, Turkey

by Burhan Baha Bilgilioğlu, Esra Erten and Nebiye Musaoğlu

Remote Sens. 2021, 13(14), 2701; https://doi.org/10.3390/rs13142701 - 09 Jul 2021

Cited by 2 | Viewed by 2987

Abstract

As one of the largest hypersaline lakes, Lake Tuz, located in the middle of Turkey, is a key waterbird habitat and is classified as a Special Environmental Protection Area in the country. It is a dynamic lake, highly affected by evaporation due to its wide expanse and shallowness (water depth <40 cm), in addition to being externally exploited by salt companies. Monitoring the dynamics of its changes in volume, which cause ecological problems, is required to protect its saline lake functions. In this context, a spatially homogeneous distributed gauge could be critical for monitoring and rapid response; however, the number of gauge stations and their vicinity is insufficient for the entire lake. The present study focuses on assessing the feasibility of a time-series interferometric technique, namely the small baseline subset (SBAS), for monitoring volume dynamics, based on freely available Sentinel-1 data. A levelling observation was also performed to quantify the accuracy of the SBAS results. Regression analysis between water levels, which is one of the most important volume dynamics, derived by SBAS and levelling in February, April, July and October was 67%, 80%, 84%, and 95% respectively, for correlation in the range of 10–40 cm in water level, and was in line with levelling. Salt lake components such as water, vegetation, moist soil, dry soil, and salt, were also classified with Sentinel-2 multispectral images over time to understand the reliability of the SBAS measurements based on interferometric coherence over different surface types. The findings indicate that the SBAS method with Sentinel-1 is a good alternative for measuring lake volume dynamics, including the monitoring of water level and salt movement, especially for the dry season. Even though the number of coherent, measurable, samples (excluding water) decrease during the wet season, there are always sufficient coherent samples (>0.45) over the lake. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Inventory, Mapping and Change Analysis)

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23 pages, 322 KiB

Open AccessArticle

State of Science Assessment of Remote Sensing of Great Lakes Coastal Wetlands: Responding to an Operational Requirement

by Lori White, Robert A. Ryerson, Jon Pasher and Jason Duffe

Remote Sens. 2020, 12(18), 3024; https://doi.org/10.3390/rs12183024 - 16 Sep 2020

Cited by 9 | Viewed by 2790

Abstract

The purpose of this research was to develop a state of science synthesis of remote sensing technologies that could be used to track changes in Great Lakes coastal vegetation for the Great Lakes-St. Lawrence River Adaptive Management (GLAM) Committee. The mapping requirements included a minimum mapping unit (MMU) of either 2 × 2 m or 4 × 4 m, a digital elevation model (DEM) accuracy in x and y of 2 m, a “z” value or vertical accuracy of 1–5 cm, and an accuracy of 90% for the classes of interest. To determine the appropriate remote sensing sensors, we conducted an extensive literature review. The required high degree of accuracy resulted in the elimination of many of the remote sensing sensors used in other wetland mapping applications including synthetic aperture radar (SAR) and optical imagery with a resolution >1 m. Our research showed that remote sensing sensors that could at least partially detect the different types of wetland vegetation in this study were the following types: (1) advanced airborne “coastal” Airborne Light Detection and Ranging (LiDAR) with either a multispectral or a hyperspectral sensor, (2) colour-infrared aerial photography (airplane) with (optimum) 8 cm resolution, (3) colour-infrared unmanned aerial vehicle (UAV) photography with vertical accuracy determination rated at 10 cm, (4) colour-infrared UAV photography with high vertical accuracy determination rated at 3–5 cm, (5) airborne hyperspectral imagery, and (6) very high-resolution optical satellite data with better than 1 m resolution. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Inventory, Mapping and Change Analysis)

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43 pages, 44808 KiB

Open AccessReview

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

by S. Mohammad Mirmazloumi, Armin Moghimi, Babak Ranjgar, Farzane Mohseni, Arsalan Ghorbanian, Seyed Ali Ahmadi, Meisam Amani and Brian Brisco

Remote Sens. 2021, 13(20), 4025; https://doi.org/10.3390/rs13204025 - 09 Oct 2021

Cited by 18 | Viewed by 4977

Abstract

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Inventory, Mapping and Change Analysis)

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Other

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14 pages, 5252 KiB

Open AccessTechnical Note

Assessment of Peat Extraction Range and Vegetation Succession on the Baligówka Degraded Peat Bog (Central Europe) Using the ALS Data and Orthophotomap

by Witold Jucha, Paulina Mareczka and Daniel Okupny

Remote Sens. 2022, 14(12), 2817; https://doi.org/10.3390/rs14122817 - 12 Jun 2022

Cited by 2 | Viewed by 1616

Abstract

The Baligówka peat bog is one of the peat bogs of the Orawa-Nowy Targ Basin—the largest complex of wetlands in the Polish Carpathians. Its area has declined in the past as a result of drainage and peat exploitation, which caused a bad hydrological condition and it is gradually overgrown by non-peat bog medium and high vegetation. The research uses models derived from airborne laser scanning (ALS) and an orthophotomap to delimit the bog and divide it into parts and assess the range of drainage ditches and vegetation. The area of the peat dome along with 3 sites of peat exploitation is currently 159.6 ha, while the ecotone zone is 105.9 ha. Both sections are separated by a steep post-mining slope. The medium and high vegetation areas cover 44% of the peat bog; its location is related to the dense drainage system in the southern part of the dome. The parameters of the Baligówka peat bog: area, size and extent of drainage system, and the degree of overgrowth by high vegetation, are the subject of research towards the protection under the Natura 2000 network (PLH120016) and the establishment of a plan for restoration activities. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Inventory, Mapping and Change Analysis)

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