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Advances in Remote Sensing of Land-Sea Ecosystems

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 26433

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Special Issue Editors

Dr. Jiwei Li

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Guest Editor

Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA
Interests: coral reefs; coastal regions; ocean color; water quality (inland waters, coastal and open sea waters); benthic habitat mapping; land subsidence; SAR; InSAR; machine learning; google earth engine

Dr. Di Yang

E-Mail Website
Guest Editor

Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY 82071, USA
Interests: remote sensing applications on human-environment interactions; geospatial analytics; digital image processing; machine learning and GeoAI; citizen science; cloud-based big data analysis and management; land change science
Special Issues, Collections and Topics in MDPI journals

Dr. Qunshan Zhao

E-Mail Website
Guest Editor

Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, UK
Interests: geographic information science; urban remote sensing; location modeling and analysis; spatial statistics; urban climate modeling and instrumentation; urban green infrastructure; human and environmental systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coral reefs, seagrass and mangroves (CMS) interact and coexist in tropical regions. CMS and associated land–sea ecosystems are among the most productive and vulnerable in the world. These land–sea ecosystems provide habitat for thousands of species, maintain coastal organisms, and deliver a variety of goods and services to millions of people living in coastal regions. Both the CMS interaction zones and boundaries between coral reefs, seagrass and mangroves represent key areas of high conservation and management efficiency. With the ongoing rapid urbanization, coastal ecosystems have been rapidly changed and disrupted because of human activities and reclaimed land. Thus, it is very important to better understand how to balance land–sea ecosystems and avoid further disruption to the unique CMS system. Advances in remote sensing technology are providing accurate and up-to-date spatially explicit information for guiding the effective conservation and management of land–sea ecosystems. CubeSats constellations are providing daily coverage with a high spatial resolution for CMS ecosystems monitoring. Airborne and spaceborne imaging spectroscopy (hyperspectral) measurements will help to discover unrevealed processes and patterns. Cloud-based remote sensing platforms enable the global-scale analysis of land–sea interactions.

This Special Issue will focus on newly developed technology, algorithms, approaches, analyses, and applications to enable the next generation of remote sensing for coral reefs, seagrass, and mangroves. Topics include CubeSats, hyperspectral remote sensing techniques and insights, unmanned aerial and underwater vehicles, bio-optical algorithm development and application, novel machine learning methods, spectral signature analysis, etc. Submissions which describe solution-based approaches to promote the conservation and management of coral reefs, seagrass and mangroves are encouraged.

Coastal environment benthic classification and monitoring
Coral reefs monitoring
Seagrass spatial and temporal analysis
Mangrove remote sensing
Hyperspectral
Machine learning
Google Earth Engine
Drone
High resolution

Dr. Jiwei Li
Dr. Di Yang
Dr. Qunshan Zhao
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

coral reefs
seagrass
mangrove
land–water ecosystem
human–environment interaction
coastal environment
coastal sustainability

Published Papers (5 papers)

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21 pages, 1692 KiB

Open AccessArticle

Assessment of Active LiDAR Data and Passive Optical Imagery for Double-Layered Mangrove Leaf Area Index Estimation: A Case Study in Mai Po, Hong Kong

by Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung, Luke A. Brown and Jadunandan Dash

Remote Sens. 2023, 15(10), 2551; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102551 - 12 May 2023

Cited by 3 | Viewed by 1542

Abstract

Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R²_adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R²_adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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15 pages, 3722 KiB

Open AccessArticle

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

by Xingguang Yan, Jing Li, Di Yang, Jiwei Li, Tianyue Ma, Yiting Su, Jiahao Shao and Rui Zhang

Remote Sens. 2022, 14(20), 5154; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205154 - 15 Oct 2022

Cited by 8 | Viewed by 3233

Abstract

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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32 pages, 12352 KiB

Open AccessEditor’s ChoiceArticle

Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0

by Pete Bunting, Ake Rosenqvist, Lammert Hilarides, Richard M. Lucas, Nathan Thomas, Takeo Tadono, Thomas A. Worthington, Mark Spalding, Nicholas J. Murray and Lisa-Maria Rebelo

Remote Sens. 2022, 14(15), 3657; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153657 - 30 Jul 2022

Cited by 80 | Viewed by 14841

Abstract

Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of deforestation caused by the expansion of agriculture and aquaculture in coastal environments. However, a limited number of studies have attempted to estimate changes in global mangrove extent, particularly into the 1990s, despite much of the loss in mangrove extent occurring pre-2000. This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4% (95th conf. int.: 86.2–88.6%), although the accuracies of the individual gain and loss change classes were lower at 58.1% (52.4–63.9%) and 60.6% (56.1–64.8%), respectively. Sources of error included misregistration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km² (133,996–176,910) of mangroves were identified for 1996, with this decreasing by −5245 km² (−13,587–1444) resulting in a total extent of 147,359 km² (127,925–168,895) in 2020, and representing an estimated loss of 3.4% over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress toward conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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26 pages, 35942 KiB

Open AccessArticle

Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

by Jundong Chen and Jun Sasaki

Remote Sens. 2021, 13(23), 4880; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234880 - 01 Dec 2021

Cited by 6 | Viewed by 3083

Abstract

Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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16 pages, 3066 KiB

Open AccessEditor’s ChoiceTechnical Note

Mapping the National Seagrass Extent in Seychelles Using PlanetScope NICFI Data

by C. Benjamin Lee, Lucy Martin, Dimosthenis Traganos, Sylvanna Antat, Stacy K. Baez, Annabelle Cupidon, Annike Faure, Jérôme Harlay, Matthew Morgan, Jeanne A. Mortimer, Peter Reinartz and Gwilym Rowlands

Remote Sens. 2023, 15(18), 4500; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184500 - 13 Sep 2023

Cited by 2 | Viewed by 1796

Abstract

Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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