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Fused Active and Passive UAV and Miniaturised Remote Sensing Capabilities...
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Fused Active and Passive UAV and Miniaturised Remote Sensing Capabilities and Applications in Wetlands and Drylands

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

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 5453

Special Issue Editors

Dr. Narcisa G. Pricope

E-Mail Website
Guest Editor
Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S College Rd., Wilmington, NC 28403, USA
Interests: land systems science; human–environment interactions; remote sensing and UAS applications; land degradation
Dr. Joanne N. Halls

E-Mail Website
Guest Editor
Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S College Rd., Wilmington, NC 28403, USA
Interests: geographic information science; spatial statistics; coastal ecosystems; applications of remote sensing and UAS; population dynamics; ecosystem health
Special Issues, Collections and Topics in MDPI journals
Dr. Devon O. Eulie

E-Mail Website
Guest Editor
Department of Environmental Sciences, University of North Carolina Wilmington, 601 S College Rd., Wilmington, NC 28403, USA
Interests: applications of remote sensing and UAS to coastal ecosystems; coastal resilience and restoration; living shorelines; applications of GIS for coastal management; sea-level rise
Dr. Justin T. Ridge

E-Mail Website
Guest Editor
Nicholas School of the Environment, Duke University, 135 Duke Marine Lab Rd, Beaufort, NC 28516, USA
Interests: multiscale remote sensing, including UAS and satellites, in coastal ecosystems; coastal resilience, restoration, and management

Special Issue Information

Dear Colleagues,

Wetlands and drylands, though occurring in spatially-distinct regions that may not always overlap, provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures, continued development, and global environmental changes. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands as well as drylands changes (such as woody encroachment) across a variety of stakeholder groups and programs, at scales ranging from globally to regionally and locally. Traditional wetland and dryland surveying and sampling approaches are costly, time-intensive, and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and unobtrusive. This Special Issue will be a collection of papers that demonstrate integrate integration of active (focusing on LiDAR) and passive remote sensing data collected from UAV and miniature remote sensing platforms to assess the efficacy and feasibility of using such collection methods for mapping and modeling change in wetland or dryland systems worldwide. The Special Issue will include a variety of systems such as high-resolution topography in complex forested wetlands, vegetation structure based on fused spectral (multispectral and/or hyperspectral) and LiDAR and three-dimensional data with relevance for mapping subtle changes and conversions in these previously difficult to measure ecosystems. Especially for wetland and dryland conversions from grassy to more woody-dominated habitat types, the ability to fuse active and passive remote sensing data derived from UAV platforms that can carry multiple sensors simultaneously can provide opportunities to move the science forward in ways previously impossible. Additionally, papers that discuss the fusion of UAV-collected LiDAR and spectral datasets with satellite imagery are also invited as these methods are of vital importance in the extension of local to regional scales and these resulting datasets are inter-related both spatially and temporally.

Dr. Narcisa G. Pricope
Dr. Joanne N. Halls
Dr. Devon O. Eulie
Dr. Justin T. Ridge
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

  • Active and passive UAS
  • Miniaturized remote sensing
  • Wetlands modeling
  • Drylands modeling
  • Vertical vegetation metrics
  • Active-passive remote sensing fusion

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Published Papers (2 papers)

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Research

24 pages, 14795 KiB  
Article
Detecting Woody Plants in Southern Arizona Using Data from the National Ecological Observatory Network (NEON)
by Thomas Hutsler, Narcisa G. Pricope, Peng Gao and Monica T. Rother
Remote Sens. 2023, 15(1), 98; https://doi.org/10.3390/rs15010098 - 24 Dec 2022
Cited by 2 | Viewed by 2145
Abstract
Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent [...] Read more.
Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent in drylands, where subtle changes in precipitation and disturbance regimes can have dramatic effects on vegetation structure and degrade ecosystem functions and services. Accurately determining the distribution of woody plants in drylands is critical for protecting human and natural resources through woody plant management strategies. Using an object-based approach, we have used novel open-source remote sensing and in situ data from Santa Rita Experimental Range (SRER), National Ecological Observatory Network (NEON), Arizona, USA with machine learning algorithms and tested each model’s efficacy for estimating fractional woody cover (FWC) to quantify woody plant extent. Model performance was compared using standard model assessment metrics such as accuracy, sensitivity, specificity, and runtime to assess model variables and hyperparameters. We found that decision tree-based models with a binary classification scheme performed best, with sequential models (Boosting) slightly outperforming independent models (Random Forest) for both object classification and FWC estimates. Mean canopy height and mean, median, and maximum statistics for all vegetation indices were found to have highest variable importance. Optimal model hyperparameters and potential limitations of the NEON dataset for classifying woody plants in dryland regions were also identified. Overall, this study lays the groundwork for developing machine learning models for dryland woody plant management using solely NEON data. Full article
(This article belongs to the Special Issue Fused Active and Passive UAV and Miniaturised Remote Sensing Capabilities and Applications in Wetlands and Drylands)
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19 pages, 35065 KiB  
Article
Using Hyperspatial LiDAR and Multispectral Imaging to Identify Coastal Wetlands Using Gradient Boosting Methods
by Shitij Govil, Aidan Joshua Lee, Aiden Connor MacQueen, Narcisa Gabriela Pricope, Asami Minei and Cuixian Chen
Remote Sens. 2022, 14(23), 6002; https://doi.org/10.3390/rs14236002 - 26 Nov 2022
Cited by 2 | Viewed by 2413
Abstract
Wetlands play a vital role in our ecosystems, preserving water quality, controlling flooding, and supplying aquifers. Wetlands are rapidly degrading due to threats by human encroachment and rising sea levels. Effective and timely mapping of wetland ecosystems is vital to their preservation. Unoccupied [...] Read more.
Wetlands play a vital role in our ecosystems, preserving water quality, controlling flooding, and supplying aquifers. Wetlands are rapidly degrading due to threats by human encroachment and rising sea levels. Effective and timely mapping of wetland ecosystems is vital to their preservation. Unoccupied Aircraft Systems (UAS) have demonstrated the capability to access and record data from difficult-to-reach wetlands at a rapid pace, increasing the viability of wetland identification and classification through machine learning (ML) methods. This study proposes a UAS-based gradient boosting approach to wetland classification in coastal regions using hyperspatial LiDAR and multispectral (MS) data, implemented on a series of wetland sites in the Atlantic Coastal Plain region of North Carolina, USA. Our results demonstrated that Xtreme Gradient Boosting performed the best on a cross-site dataset with an accuracy of 83.20% and an Area Under Curve (AUC) score of 0.8994. The study also found that Digital Terrain Model-based variables had the greatest feature importance on a cross-site dataset. This study’s novelty lies in utilizing cross-site validation using Gradient Boosting methods with limited amounts of UAS data while explicitly considering topographical features and vegetation characteristics derived from multi-source UAS collections for both wetland and non-wetland classes. Future work is encouraged with a larger dataset or with semi-supervised learning techniques to improve the accuracy of the model. Full article
(This article belongs to the Special Issue Fused Active and Passive UAV and Miniaturised Remote Sensing Capabilities and Applications in Wetlands and Drylands)
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