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Remote Sensing in Coastal Vegetation Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 6435

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Special Issue Editors

Dr. Glenn M. Suir

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Guest Editor

Research Agronomist, U.S. Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, USA
Interests: land

Molly Reif

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Guest Editor Assistant

Research Geographer and Geospatial Data Analysis Team Lead, U.S. Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, USA
Interests: remote sensing; coastal water

Special Issue Information

Dear Colleagues,

Short- and long-term monitoring of coastal vegetation continues to be a challenge, yet these highly diverse ecosystems are critically important for protecting increasingly vulnerable coastlines, especially in the face of growing pressures from climate and other human-induced changes. Traditional field-based vegetation sampling provides essential information at fine spatio-temporal scales; however, the discrete nature of the data often precludes observing landscape-level characteristics and trends. Furthermore, coastal ecosystems typically have site access constraints that make them difficult to monitor using traditional methods. Thus, important information may be missed, resulting in knowledge gaps surrounding monitoring. Recent improvements in remote sensing technology offer ways to help overcome challenges with monitoring coastal vegetation while better understanding emerging topics, such as ecological restoration, resilience, climate change, and storm-related impacts. These technological improvements span considerable gains in spatial, temporal, and spectral resolutions across active and passive sensors (e.g., multispectral and hyperspectral imagers and LiDAR sensors) as well as platforms (e.g., satellite as well as manned and unmanned aircraft).

The aim of the Special Issue is to highlight studies covering a range of active and passive sensors and platforms and analytical methods for use in monitoring coastal vegetation through estimation of characteristics, such as habitat type or species composition, changes or trends, stress or condition, physical structure, and dynamics. Topics may address a variety of issues or approaches, such as comparative analyses (e.g., comparison of different sensors, data types, or analytical methods), new data or techniques (e.g., artificial intelligence), data integration or fusion (e.g., combining different data types), and technical improvements, challenges, or limitations for using remotely sensed data or methods. Articles may address, but are not limited to, the following topics:

Coastal vegetation or habitat characterization;
Coastal habitat trend or change analysis;
Physical structure assessment of coastal vegetation;
Coastal wetland vegetation pattern or dynamic analysis;
Coastal restoration monitoring;
Coastal storm impacts;
Coastal resilience or vulnerability;
Natural and nature-based solutions;
Prediction or modeling of coastal systems;
Coastal vegetation stress or condition assessment.

Dr. Glenn M. Suir
Guest Editor

Molly Reif
Guest Editor Assistant

Manuscript Submission Information

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Keywords

coastal wetlands
coastal dune vegetation
coastal vegetation change
coastal vegetation monitoring
coastal restoration monitoring
spectral and structure analysis of coastal vegetation

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Published Papers (7 papers)

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26 pages, 55686 KiB

Open AccessArticle

Geographic Object-Oriented Analysis of UAV Multispectral Images for Tree Distribution Mapping in Mangroves

by Luis Américo Conti, Roberto Lima Barcellos, Priscila Oliveira, Francisco Cordeiro Nascimento Neto and Marília Cunha-Lignon

Remote Sens. 2025, 17(9), 1500; https://doi.org/10.3390/rs17091500 - 24 Apr 2025

Abstract

Mangroves are critical ecosystems that provide essential environmental services, such as climate regulation, carbon storage, biodiversity conservation, and shoreline protection, making their conservation vital. High-resolution remote sensing using unmanned aerial vehicles (UAVs) offers a powerful tool for detailed mapping of mangrove species and structure. This study applies geographic object-based image analysis (GEOBIA) combined with machine learning (ML) to classify mangrove species at two ecologically distinct sites in Brazil: Cardoso Island (São Paulo State) and Suape (Pernambuco State). UAV flights at 50 m and 120 m altitudes captured multispectral data, enabling species-level classification of Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. By integrating field measurements and advanced metrics such as texture and spectral indices, the workflow achieved precise delineation of tree crowns and spatial distribution mapping. The results demonstrate the superiority of this approach over traditional methods, offering scalable and adaptable tools for ecological monitoring and conservation. The findings highlight the potential of UAV-based multispectral imaging to improve mangrove conservation efforts by delivering actionable, fine-scale data for policymakers and stakeholders. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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24 pages, 19467 KiB

Open AccessArticle

Spatiotemporal Heterogeneity of Vegetation Cover Dynamics and Its Drivers in Coastal Regions: Evidence from a Typical Coastal Province in China

by Yiping Yu, Dong Liu, Shiyu Hu, Xingyu Shi and Jiakui Tang

Remote Sens. 2025, 17(5), 921; https://doi.org/10.3390/rs17050921 - 5 Mar 2025

Viewed by 529

Abstract

Studying the spatiotemporal trends and influencing factors of vegetation coverage is essential for assessing ecological quality and monitoring regional ecosystem dynamics. The existing research on vegetation coverage variations and their driving factors predominantly focused on inland ecologically vulnerable regions, while coastal areas received relatively little attention. However, coastal regions, with their unique geographical, ecological, and anthropogenic activity characteristics, may exhibit distinct vegetation distribution patterns and driving mechanisms. To address this research gap, we selected Shandong Province (SDP), a representative coastal province in China with significant natural and socioeconomic heterogeneity, as our study area. To investigate the coastal–inland differentiation of vegetation dynamics and its underlying mechanisms, SDP was stratified into four geographic sub-regions: coastal, eastern, central, and western. Fractional vegetation cover (FVC) derived from MOD13A3 v061 NDVI data served as the key indicator, integrated with multi-source datasets (2000–2023) encompassing climatic, topographic, and socioeconomic variables. We analyzed the spatiotemporal characteristics of vegetation coverage and their dominant driving factors across these geographic sub-regions. The results indicated that (1) the FVC in SDP displayed a complex spatiotemporal heterogeneity, with a notable coastal–inland gradient where FVC decreased from the inland towards the coast. (2) The influence of various factors on FVC significantly varied across the sub-regions, with socioeconomic factors dominating vegetation dynamics. However, socioeconomic factors displayed an east–west polarity, i.e., their explanatory power intensified westward while resurging in coastal zones. (3) The intricate interaction of multiple factors significantly influenced the spatial differentiation of FVC, particularly dual-factor synergies where interactions between socioeconomic and other factors were crucial in determining vegetation coverage. Notably, the coastal zone exhibited a high sensitivity to socioeconomic drivers, highlighting the exceptional sensitivity of coastal ecosystems to human activities. This study provides insights into the variations in vegetation coverage across different geographical zones in coastal regions, as well as the interactions between socioeconomic and natural factors. These findings can help understand the challenges faced in protecting coastal vegetation, facilitating deeper insight into ecosystems responses and enabling the formulation of effective and tailored ecological strategies to promote sustainable development in coastal areas. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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20 pages, 4068 KiB

Open AccessArticle

Land Reclamation in the Mississippi River Delta

by Glenn M. Suir, Christina Saltus and Jeffrey M. Corbino

Remote Sens. 2025, 17(5), 878; https://doi.org/10.3390/rs17050878 - 1 Mar 2025

Cited by 1 | Viewed by 664

Abstract

Driven by the need to expand urban/industrial complexes, and/or mitigate anticipated environmental impacts (e.g., tropical storms), many coastal countries have long implemented large-scale land reclamation initiatives. Some areas, like coastal Louisiana, USA, have relied heavily on restoration activities (i.e., beneficial use of dredged material) to counter extensive long-term wetland loss. Despite these prolonged engagements, the quantifiable benefits of these activities have lacked comprehensive documentation. Therefore, this study leveraged remote sensing data and advanced machine learning techniques to enhance the classification and evaluation of restoration efficacy within the wetlands adjacent to the Mississippi River’s Southwest Pass (SWP). By utilizing air- and space-borne imagery, land and water data were extracted and used to compare land cover changes during two distinct restoration periods (1978 to 2008 and 2008 to 2020) to historical trends. The classification methods employed achieved an overall accuracy of 85% with a Cohen’s kappa value of 0.82, demonstrating substantial agreement beyond random chance. To further assess the success of the SWP reclamation efforts in a global context, broad-based land cover data were generated using biennial air- and space-borne imagery. Results show that restoration activities along SWP have resulted in a significant recovery of degraded wetlands, accounting for approximately a 30 km² increase in land area, ranking among the most successful land reclamation projects in the world. The findings from this study highlight beneficial use of dredged material as a critical component in large-scale, recurring restoration activities aimed at mitigating degradation in coastal landscapes. The integration of remote sensing and machine learning methodologies provides a robust framework for monitoring and evaluating restoration projects, offering valuable insights into the optimization of ecosystem services. Overall, the research advocates for a holistic approach to coastal restoration, emphasizing the need for continuous innovation and adaptation in restoration practices to address the dynamic challenges faced by coastal ecosystems globally. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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28 pages, 9564 KiB

Open AccessArticle

Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects

by Aaron N. Schad, Molly K. Reif, Joseph H. Harwood, Christopher L. Macon, Lynde L. Dodd, Katie L. Vasquez, Kevin D. Philley, Glenn E. Dobson and Katie M. Steinmetz

Remote Sens. 2025, 17(2), 223; https://doi.org/10.3390/rs17020223 - 9 Jan 2025

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Abstract

Traditional field vegetation plot surveys are critical for monitoring ecosystem restoration performance and include visual observations to quantitatively measure plants (e.g., species composition and abundance). However, surveys can be costly, time-consuming, and only provide data at discrete locations, leaving potential data gaps across a site. Uncrewed aircraft system (UAS) technology can help fill data gaps between high-to-moderate spatial resolution (e.g., 1–30 m) satellite imagery, manned airborne data, and traditional field surveys, yet it has not been thoroughly evaluated in a virtual capacity as an alternative to traditional field vegetation plot surveys. This study assessed the utility of UAS red-green-blue (RGB) and low-altitude imagery for virtually surveying vegetation plots in a web application and compared to traditional field surveys at two coastal marsh restoration sites in southeast Louisiana, USA. Separate expert botanists independently observed vegetation plots in the field vs. using UAS imagery in a web application to identify growth form, species, and coverages. Taxa richness and assemblages were compared between field and virtual vegetation plot survey results using taxa resolution (growth-form and species-level) and data collection type (RGB imagery, Anafi [low-altitude] imagery, or field data) to assess accuracy. Virtual survey results obtained using Anafi low-altitude imagery compared better to field data than those from RGB imagery, but they were dependent on growth-form or species-level resolution. There were no significant differences in taxa richness between all survey types for a growth-form level analysis. However, there were significant differences between each survey type for species-level identification. The number of species identified increased by approximately two-fold going from RGB to Anafi low-altitude imagery and another two-fold from Anafi low-altitude imagery to field data. Vegetation community assemblages were distinct between the two marsh sites, and similarity percentages were higher between Anafi low-altitude imagery and field data compared to RGB imagery. Graminoid identification mismatches explained a high amount of variance between virtual and field similarity percentages due to the challenge of discriminating between them in a virtual setting. The higher level of detail in Anafi low-altitude imagery proved advantageous for properly identifying lower abundance species. These identifications included important taxa, such as invasive species, that were overlooked when using RGB imagery. This study demonstrates the potential utility of high-resolution UAS imagery for increasing marsh vegetation monitoring efficiencies to improve ecosystem management actions and outcomes. Restoration practitioners can use these results to better understand the level of accuracy for identifying vegetation growth form, species, and coverages from UAS imagery compared to field data to effectively monitor restored marsh ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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23 pages, 5630 KiB

Open AccessArticle

MLF-PointNet++: A Multifeature-Assisted and Multilayer Fused Neural Network for LiDAR-UAS Point Cloud Classification in Estuarine Areas

by Yingjie Ren, Wenxue Xu, Yadong Guo, Yanxiong Liu, Ziwen Tian, Jing Lv, Zhen Guo and Kai Guo

Remote Sens. 2024, 16(17), 3131; https://doi.org/10.3390/rs16173131 - 24 Aug 2024

Viewed by 1273

Abstract

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F₁-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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12 pages, 4957 KiB

Open AccessTechnical Note

National Exposed Sediment Search and Inventory (NESSI): Utilizing Satellite Imagery and Machine Learning to Identify Dredged Sediment Placement Site Recovery

by Thomas P. Huff, Emily R. Russ and Todd M. Swannack

Remote Sens. 2025, 17(2), 186; https://doi.org/10.3390/rs17020186 - 7 Jan 2025

Viewed by 596

Abstract

Anthropogenic activity leads to changes in sediment dynamics, creating imbalances in sediment distributions across the landscape. These imbalances can be variable within a littoral system, with adjacent areas experiencing sediment starvation and excess sediment. Historically, sediments were viewed as an inconvenient biproduct destined for disposal; however, beneficial use of dredge material (BUDM) is a practice that has grown as a preferred methodology for utilizing sediment as a resource to help alleviate the sediment imbalances within a system. BUDM enables organizations to adopt a more innovative and sustainable sediment management approach that also provides ecological, economic, and social co-benefits. Although location data are available on BUDM sites, especially in the US, there is limited understanding on how these sites evolve within the larger landscape, which is necessary for quantifying the co-benefits. To move towards BUDM more broadly, new tools need to be developed to allow researchers and managers to understand the effects and benefits of this practice. The National Exposed Sediment Search and Inventory (NESSI) was built to show the capability of using machine learning techniques to identify dredged sediments. A combination of satellite imagery data obtained and processed using Google Earth Engine and machine learning algorithms were applied at known dredged material placement sites to develop a time series of dredged material placement events and subsequent site recovery. These disturbance-to-recovery time series are then used in a landscape analysis application to better understand site evolution within the context of the surrounding areas. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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15 pages, 15798 KiB

Open AccessTechnical Note

A Lidar Biomass Index of Tidal Marshes from Drone Lidar Point Cloud

by Cuizhen Wang, James T. Morris and Erik M. Smith

Remote Sens. 2024, 16(11), 1823; https://doi.org/10.3390/rs16111823 - 21 May 2024

Viewed by 1354

Abstract

Accompanying climate change and sea level rise, tidal marsh mortality in coastal wetlands has been globally observed that urges the documentation of high-resolution, 3D marsh inventory to assist resilience planning. Drone Lidar has proven useful in extracting the fine-scale bare earth terrain and canopy height. Beyond that, this study performed marsh biomass mapping from drone Lidar point cloud in a S. alterniflora-dominated estuary on the Southeast U.S. coast. Three point classes (ground, low-veg, and high-veg) were classified via point cloud deep learning. Considering only vegetation points in the vertical profile, a profile area-weighted height (H_PA) was extracted at a grid size of 50 cm × 50 cm. Vegetation point densities were also extracted at each grid. Adopting the plant-level allometric equations of stem biomass from long-term S. alterniflora surveys, a Lidar biomass index (Lidar_BI) was built to represent the relative quantity of marsh biomass in a range of [0, 1] across the estuary. Compared with the clipped dry biomass samples, it achieved a comparable and slightly better performance (R² = 0.5) than the commonly applied spectral index approaches (R² = 0.4) in the same marsh field. This study indicates the feasibility of the drone Lidar point cloud for marsh biomass mapping. More advantageously, the drone Lidar approach yields information on plant community architecture, such as canopy height and plant density distributions, which are key factors in evaluating marsh habitat and its ecological services. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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