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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 56400

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Special Issue Editors

Dr. Fariba Mohammadimanesh

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Guest Editor

Research Scientist, C-CORE and Memorial University of Newfoundland, St. John’s, NL, Canada
Interests: remote sensing; PolSAR data analysis; InSAR for geo-hazard monitoring; deep learning; geo big data
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Dr. Masoud Mahdianpari

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Guest Editor

Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
Interests: remote sensing; geospatial data; machine learning; geo big data; wetland; GHG monitoring
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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
Special Issues, Collections and Topics in MDPI journals

Dr. Bahram Salehi

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Guest Editor

College of Environmental Science and Forestry (SUNY-ESF), State University of New York, Syracuse, NY 13210, USA
Interests: remote sensing of environment (wetland, permafrost, forest, oil spill, land cover, harmful algal bloom, etc.); SAR (PolSAR and InSAR) remote sensing; photogrammetry and image processing of UAVs; machine learning and image processing; nanosatellite data processing
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Special Issue Information

Dear Colleagues,

Wetlands provide valuable ecosystem services to humankind, including, but not limited to, water filtration, flood prevention, and carbon storage. Despite these benefits, wetlands are under particular threat because of the impacts of anthropogenic and climate-related land cover change, rendering these habitats amongst the most endangered on Earth. Remote sensing tools and data have been frequently used to collect information on wetlands for policymaking and conservation efforts. However, the important components for operational wetland monitoring, including the public availability of satellite imagery, advanced machine learning algorithms, and cloud computing infrastructures, have recently become jointly available.

This Special Issue provides an opportunity to bring together research in the “remote sensing of wetlands” and highlight ongoing investigations and new applications of remote sensing in the field of wetlands. In particular, this issue was designed to highlight currently applied research using satellite imagery, aerial photography, drone imaging, GIS-based mapping, spatial analysis, machine learning, and big data processing applications to better understand and solve problems related to wetland management. Therefore, potential topics for original research papers and review articles on applications of remote sensing to wetlands include, but are not limited to, the following:

Wetland mapping and monitoring using multi-source remote sensing data, including optical, LiDAR, Synthetic Aperture Radar (SAR), and UAV;
Wetland conservation using remote sensing tools and data;
The application of geo big data processing for wetland monitoring;
Wetland change detection;
Wetland species mapping and biodiversity assessment;
Advanced machine learning and data processing for wetland applications ;
Climate change impacts on wetlands;
Estimating carbon fluxes and wetland productivity.

Dr. Fariba Mohammadimanesh
Dr. Masoud Mahdianpari
Dr. Brian Brisco
Dr. Bahram Salehi
Guest Editors

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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

wetland monitoring
habitat classification
remote sensing
machine-learning
deep learning
big data
change detection
climate change

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21 pages, 11055 KiB

Open AccessArticle

Factors Influencing Seasonal Changes in Inundation of the Daliyaboyi Oasis, Lower Keriya River Valley, Central Tarim Basin, China

by Jinhua Wang, Feng Zhang, Guangming Luo, Yuchuan Guo, Jianghua Zheng, Shixin Wu, Dawei Wang, Suhong Liu and Qingdong Shi

Remote Sens. 2022, 14(19), 5050; https://doi.org/10.3390/rs14195050 - 10 Oct 2022

Cited by 6 | Viewed by 2085

Abstract

The ecological water diversion project (EWDP) in the Tarim River Basin, China, aims to allocate more surface water to downstream reaches to restore the degraded ecosystems. However, seasonal changes in ecological water diversion; the factors (natural and anthropogenic) controlling the ecological water diversion, whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands; and the benefits of the ecological water diversion through overflowing surface water irrigation are unclear. To address the above issues, this study examines the intra-annual changes and its influencing factors in ecological water diversion (inundation) in the Daliyaboyi Oasis in the lower Keriya River valley within the Tarim Basin, discusses whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands, and assesses the ecological benefits of overflowing surface water irrigation. Inundation was quantified by digitizing monthly changes in the inundated area from 2000 to 2018 in the oasis using 184 Landsat images. The results demonstrate that seasonal changes in the inundated area varied significantly, with maximum peaks occurring in February and August; a period of minimal inundation occurred in May. Differences in the July/August peak (i.e., July or August) in inundation dominated the inter-annual variations in the inundated area over the 19-year study period. The two peaks in the inundation area were temporally consistent with the vegetation’s seasonal water demand. Local residents have used ecological water to irrigate vegetation in different parts of the oasis during different seasons, an approach that expanded the inundated area. The February peak in the inundated area is closely linked to elevated downstream groundwater levels and the melting of ice along the river. The August peak is related to a peak in runoff from headwater areas. The minimum May value is correlated to a relatively low value in upstream runoff and an increase in agricultural water demand. Thus, natural factors control the intra-annual and inter-annual variations in the inundated area. Humans changed the spatial distribution of the inundated area and enhanced the water’s ecological benefits, but did not alter the correlation between peak periods of inundation and vegetation water demand. The results from this study improve our understanding of the benefits of the EWDP in the Tarim River Basin. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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23 pages, 60172 KiB

Open AccessArticle

Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine

by Meisam Amani, Mohammad Kakooei, Arsalan Ghorbanian, Rebecca Warren, Sahel Mahdavi, Brian Brisco, Armin Moghimi, Laura Bourgeau-Chavez, Souleymane Toure, Ambika Paudel, Ablajan Sulaiman and Richard Post

Remote Sens. 2022, 14(15), 3778; https://doi.org/10.3390/rs14153778 - 6 Aug 2022

Cited by 23 | Viewed by 3439

Abstract

Wetlands provide many benefits, such as water storage, flood control, transformation and retention of chemicals, and habitat for many species of plants and animals. The ongoing degradation of wetlands in the Great Lakes basin has been caused by a number of factors, including climate change, urbanization, and agriculture. Mapping and monitoring wetlands across such large spatial and temporal scales have proved challenging; however, recent advancements in the accessibility and processing efficiency of remotely sensed imagery have facilitated these applications. In this study, the historical Landsat archive was first employed in Google Earth Engine (GEE) to classify wetlands (i.e., Bog, Fen, Swamp, Marsh) and non-wetlands (i.e., Open Water, Barren, Forest, Grassland/Shrubland, Cropland) throughout the entire Great Lakes basin over the past four decades. To this end, an object-based supervised Random Forest (RF) model was developed. All of the produced wetland maps had overall accuracies exceeding 84%, indicating the high capability of the developed classification model for wetland mapping. Changes in wetlands were subsequently assessed for 17 time intervals. It was observed that approximately 16% of the study area has changed since 1984, with the highest increase occurring in the Cropland class and the highest decrease occurring in the Forest and Marsh classes. Forest mostly transitioned to Fen, but was also observed to transition to Cropland, Marsh, and Swamp. A considerable amount of the Marsh class was also converted into Cropland. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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26 pages, 6214 KiB

Open AccessArticle

Evaluating the Performance of High Spatial Resolution UAV-Photogrammetry and UAV-LiDAR for Salt Marshes: The Cádiz Bay Study Case

by Andrea Celeste Curcio, Gloria Peralta, María Aranda and Luis Barbero

Remote Sens. 2022, 14(15), 3582; https://doi.org/10.3390/rs14153582 - 26 Jul 2022

Cited by 23 | Viewed by 4013

Abstract

Salt marshes are very valuable and threatened ecosystems, and are challenging to study due to their difficulty of access and the alterable nature of their soft soil. Remote sensing methods in unmanned aerial vehicles (UAVs) offer a great opportunity to improve our knowledge in this type of complex habitat. However, further analysis of UAV technology performance is still required to standardize the application of these methods in salt marshes. This work evaluates and tunes UAV-photogrammetry and UAV-LiDAR techniques for high-resolution applications in salt marsh habitats, and also analyzes the best sensor configuration to collect reliable data and generate the best results. The performance is evaluated through the accuracy assessment of the corresponding generated products. UAV-photogrammetry yields the highest spatial resolution (1.25 cm/pixel) orthomosaics and digital models, but at the cost of large files that require long processing times, making it applicable only for small areas. On the other hand, UAV-LiDAR has proven to be a promising tool for coastal research, providing high-resolution orthomosaics (2.7 cm/pixel) and high-accuracy digital elevation models from lighter datasets, with less time required to process them. One issue with UAV-LiDAR application in salt marshes is the limited effectiveness of the autoclassification of bare ground and vegetated surfaces, since the scattering of the LiDAR point clouds for both salt marsh surfaces is similar. Fortunately, when LiDAR and multispectral data are combined, the efficiency of this step improves significantly. The correlation between LiDAR measurements and field values improves from R² values of 0.79 to 0.94 when stable reference points (i.e., a few additional GCPs in rigid infrastructures) are also included as control points. According to our results, the most reliable LiDAR sensor configuration for salt marsh applications is the nadir non-repetitive combination. This configuration has the best balance between dataset size, spatial resolution, and processing time. Nevertheless, further research is still needed to develop accurate canopy height models. The present work demonstrates that UAV-LiDAR technology offers a suitable solution for coastal research applications where high spatial and temporal resolutions are required. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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17 pages, 12907 KiB

Open AccessArticle

Wetland Hydroperiod Analysis in Alberta Using InSAR Coherence Data

by Meisam Amani, Brian Brisco, Rebecca Warren, Evan R. DeLancey, Seyd Teymoor Seydi and Valentin Poncos

Remote Sens. 2022, 14(14), 3469; https://doi.org/10.3390/rs14143469 - 19 Jul 2022

Cited by 5 | Viewed by 2003

Abstract

Wetlands are dynamic environments, the water and vegetation of which can change considerably over time. Thus, it is important to investigate the hydroperiod status of wetlands using advanced techniques such as remote sensing technology. Wetland hydroperiod analysis has already been investigated using optical satellite and synthetic aperture radar (SAR) backscattering data. However, interferometric SAR (InSAR) coherence products have rarely been used for wetland hydroperiod mapping. Thus, this study utilized Sentinel-1 coherence maps produced between 2017 and 2020 (48 products) to map the wetland hydroperiod over the entire province of Alberta, Canada. It was observed that a coherence value of 0.45 was an optimum threshold value to discriminate flooded from non-flooded wetlands. Moreover, the results showed that most wetlands were inundated less than 50% of the time over these four years. Furthermore, most wetlands (~40%) were seasonally inundated, and there was a small percentage of wetlands (~5%) that were never flooded. Overall, the results of this study demonstrated the high capability of InSAR coherence products for wetland hydroperiod analysis. Several suggestions are provided to improve the results in future works. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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17 pages, 3837 KiB

Open AccessArticle

Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada

by Evan R. DeLancey, Agatha Czekajlo, Lyle Boychuk, Fiona Gregory, Meisam Amani, Brian Brisco, Jahan Kariyeva and Jennifer N. Hird

Remote Sens. 2022, 14(14), 3401; https://doi.org/10.3390/rs14143401 - 15 Jul 2022

Cited by 6 | Viewed by 4120

Abstract

Wetlands in the Prairie Pothole Region (PPR) of Canada and the United States represent a unique mapping challenge. They are dynamic both seasonally and year-to-year, are very small, and frequently altered by human activity. Many efforts have been made to estimate the loss of these important habitats but a high-quality inventory of pothole wetlands is needed for data-driven conservation and management of these resources. Typical landcover classifications using one or two image dates from optical or Synthetic Aperture Radar (SAR) Earth Observation (EO) systems often produce reasonable wetland inventories for less dynamic, forested landscapes, but will miss many of the temporary and seasonal wetlands in the PPR. Past studies have attempted to capture PPR wetland dynamics by using dense image stacks of optical or SAR data. We build upon previous work, using 2017–2020 Sentinel-2 imagery processed through the Google Earth Engine (GEE) cloud computing platform to capture seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada. Using 36 different image dates, wetland flood frequency (hydroperiod) was calculated by classifying water/flooding in each image date. This product along with the Global Ecosystem Dynamics Investigation (GEDI) Canopy Height Model (CHM) was then used to generate a seven-class wetland inventory with wetlands classified as areas with seasonal but not permanent water/flooding. Overall accuracies of the resulting inventory were between 95% and 96% based on comparisons with local photo-interpreted inventories at the Canadian Wetland Classification System class level, while wetlands themselves were classified with approximately 70% accuracy. The high overall accuracy is due, in part, to a dominance of uplands in the PPR. This relatively simple method of classifying water through time generates reliable wetland maps but is only applicable to ecosystems with open/non-complex wetland types and may be highly sensitive to the timing of cloud-free optical imagery that captures peak wetland flooding (usually post snow melt). Based on this work, we suggest that expensive field or photo-interpretation training data may not be needed to map wetlands in the PPR as self-labeling of flooded and non-flooded areas in a few Sentinel-2 images is sufficient to classify water through time. Our approach demonstrates a framework for the operational mapping of small, dynamic PPR wetlands that relies on open-access EO data and does not require costly, independent training data. It is an important step towards the effective conservation and management of PPR wetlands, providing an efficient method for baseline and ongoing mapping in these dynamic environments. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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19 pages, 5319 KiB

Open AccessArticle

Monitoring the Spring Flood in Lena Delta with Hydrodynamic Modeling Based on SAR Satellite Products

by Avi Putri Pertiwi, Achim Roth, Timo Schaffhauser, Punit Kumar Bhola, Felix Reuß, Samuel Stettner, Claudia Kuenzer and Markus Disse

Remote Sens. 2021, 13(22), 4695; https://doi.org/10.3390/rs13224695 - 20 Nov 2021

Cited by 4 | Viewed by 2901

Abstract

Due to the remote location and the extreme climate, monitoring stations in Arctic rivers such as Lena in Siberia have been decreasing through time. Every year, after a long harsh winter, the accumulated snow on the Lena watershed melts, leading to the major annual spring flood event causing heavy transport of sediments, organic carbon, and trace metals, both into as well as within the delta. This study aims to analyze the hydrodynamic processes of the spring flood taking place every year in the Lena Delta. Thus, a combination of remote sensing techniques and hydrodynamic modeling methodologies is used to overcome limitations caused by missing ground-truth data. As a test site for this feasibility study, the outlet of the Lena River to its delta was selected. Lena Delta is an extensive wetland spanning from northeast Siberia into the Arctic Ocean. Spaceborne Synthetic Aperture Radar (SAR) data of the TerraSAR-X/TanDEM-X satellite mission served as input for the hydrodynamic modeling software HEC-RAS. The model resulted in inundation areas, flood depths, and flow velocities. The model accuracy assessed by comparing the multi-temporal modeled inundation areas with the satellite-derived inundation areas ranged between 65 and 95%, with kappa coefficients ranging between 0.78 and 0.97, showing moderate to almost perfect levels of agreement between the two inundation boundaries. Modeling results of high flow discharges show a better agreement with the satellite-derived inundation areas compared to that of lower flow discharges. Overall, the remote-sensing-based hydrodynamic modeling succeeded in indicating the increase and decrease in the inundation areas, flood depths, and flow velocities during the annual flood events. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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17 pages, 6859 KiB

Open AccessArticle

Response of Multi-Incidence Angle Polarimetric RADARSAT-2 Data to Herbaceous Vegetation Features in the Lower Paraná River Floodplain, Argentina

by Natalia Soledad Morandeira, Matías Ernesto Barber, Francisco Matías Grings, Frank Ahern, Patricia Kandus and Brian Brisco

Remote Sens. 2021, 13(13), 2518; https://doi.org/10.3390/rs13132518 - 27 Jun 2021

Cited by 3 | Viewed by 3444

Abstract

Wetland ecosystems play a key role in hydrological and biogeochemical cycles. In emergent vegetation targets, the occurrence of double-bounce scatter is indicative of the presence of water and can be valuable for hydrological monitoring. Double-bounce scatter would lead to an increase of σ⁰_HH over σ⁰_VV and a non-zero co-polarized phase difference (CPD). In the Lower Paraná River floodplain, a total of 11 full polarimetric RADARSAT-2 scenes from a wide range of incidence angles were acquired during a month. Flooded targets dominated by two herbaceous species were sampled: Schoenoplectus californicus (four sites, Bulrush marshes) and Ludwigia peruviana (three sites, Broadleaf marshes). As a general trend, σ⁰_HH was higher than σ⁰_VV, especially at the steeper incidence angles. By modeling CPD with maximum likelihood estimations, we found results consistent with double-bounce scatter in two Ludwigia plots, at certain scene incidence angles. Incidence angle accounted for most of the variation on σ⁰_HH, whereas emergent green biomass was the main feature influencing σ⁰_HV. Multivariate models explaining backscattering variation included the incidence angle and at least two of these variables: emergent plant height, stem diameter, number of green stems, and emergent green biomass. This study provides an example of using CPD to decide on the contribution of double-bounce scatter and highlights the influence of vegetation biomass on radar response. Even with the presence of water below vegetation, the contribution of double-bounce scatter to C-band backscattering depends on scene incidence angles and may be negligible in dense herbaceous targets. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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20 pages, 15350 KiB

Open AccessArticle

Hyperspectral Identification of Chlorophyll Fluorescence Parameters of Suaeda salsa in Coastal Wetlands

by Wei Zheng, Xia Lu, Yu Li, Shan Li and Yuanzhi Zhang

Remote Sens. 2021, 13(11), 2066; https://doi.org/10.3390/rs13112066 - 24 May 2021

Cited by 24 | Viewed by 3105

Abstract

The stomata of Suaeda salsa are closed and the photosynthetic efficiency is decreased under conditions of water–salt imbalance, with the change to photosynthesis closely related to the chlorophyll fluorescence parameters of the photosystem PSII. Accordingly, chlorophyll fluorescence parameters were selected to monitor the growth status of Suaeda salsa in coastal wetlands under conditions of water and salt. Taking Suaeda salsa in coastal wetlands as the research object, we set up five groundwater levels (0 cm, −5 cm, −10 cm, −20 cm, and −30 cm) and six NaCl salt concentrations (0%, 0.5%, 1%, 1.5%, 2%, and 2.5%) to carry out independent tests of Suaeda salsa potted plants and measured the canopy reflectance spectrum and chlorophyll fluorescence parameters of Suaeda salsa. A polynomial regression method was used to carry out hyperspectral identification of Suaeda salsa chlorophyll fluorescence parameters under water and salt stress. The results indicated that the chlorophyll fluorescence parameters

F v / F m

F m^{'}, and Φ P S I I

of Suaeda salsa showed significant relationships with vegetation index under water and salt conditions. The sensitive canopy band ranges of Suaeda salsa under water and salt conditions were 680–750 nm, 480–560 nm, 950–1000 nm, 1800–1850 nm, and 1890–1910 nm. Based on the spectrum and the first-order differential spectrum, the spectral ratio of A/B was constructed to analyze the correlation between it and the chlorophyll fluorescence parameters of Suaeda salsa. We constructed thirteen new vegetation indices. In addition, we discovered that the hyperspectral vegetation index D₆₉₀/D₁₃₂₀ retrieved Suaeda chlorophyll fluorescence parameter

F v / F m

with the highest accuracy, with a multiple determination coefficient R² of 0.813 and an RMSE of 0.042, and that D₇₂₅/D₁₂₈₄ retrieved Suaeda chlorophyll fluorescence parameter

Φ P S I I

model with the highest accuracy, with a multiple determination coefficient R² of 0.848 and an RMSE of 0.096. The hyperspectral vegetation index can be used to retrieve the chlorophyll fluorescence parameters of Suaeda salsa in coastal wetlands under water and salt conditions, providing theoretical and technical support for future large-scale remote sensing inversion of chlorophyll fluorescence parameters. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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38 pages, 11626 KiB

Open AccessReview

Remote Sensing and Machine Learning Tools to Support Wetland Monitoring: A Meta-Analysis of Three Decades of Research

by Hamid Jafarzadeh, Masoud Mahdianpari, Eric W. Gill, Brian Brisco and Fariba Mohammadimanesh

Remote Sens. 2022, 14(23), 6104; https://doi.org/10.3390/rs14236104 - 1 Dec 2022

Cited by 17 | Viewed by 5995

Abstract

Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progress that has been made in the field, this paper constitutes an overview of studies utilizing RS methods in wetland monitoring. It investigates publications from 1990 up to the middle of 2022, providing a systematic survey on RS data type, machine learning (ML) tools, publication details (e.g., authors, affiliations, citations, and publications date), case studies, accuracy metrics, and other parameters of interest for RS-based wetland studies by covering 344 papers. The RS data and ML combination is deemed helpful for wetland monitoring and multi-proxy studies, and it may open up new perspectives for research studies. In a rapidly changing wetlands landscape, integrating multiple RS data types and ML algorithms is an opportunity to advance science support for management decisions. This paper provides insight into the selection of suitable ML and RS data types for the detailed monitoring of wetland-associated systems. The synthesized findings of this paper are essential to determining best practices for environmental management, restoration, and conservation of wetlands. This meta-analysis establishes avenues for future research and outlines a baseline framework to facilitate further scientific research using the latest state-of-art ML tools for processing RS data. Overall, the present work recommends that wetland sustainability requires a special land-use policy and relevant protocols, regulation, and/or legislation. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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33 pages, 7371 KiB

Open AccessReview

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

by MohammadAli Hemati, Mahdi Hasanlou, Masoud Mahdianpari and Fariba Mohammadimanesh

Remote Sens. 2021, 13(15), 2869; https://doi.org/10.3390/rs13152869 - 22 Jul 2021

Cited by 114 | Viewed by 22427

Abstract

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency. Full article

(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)

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