E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "State-of-the-Art Remote Sensing in North America 2019"

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

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Prof. Dar Roberts

Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA
Website | E-Mail
Interests: imaging spectrometry; remote sensing of vegetation; spectroscopy (urban and natural cover); land-use/land-cover change mapping with satellite time series; height mapping with lidar; fire danger assessment; remote sensing of methane
Guest Editor
Prof. Susan Ustin

Distinguished Professor of Environmental and Resource Science, University of California Davis, Davis, CA 95616, USA
Website | E-Mail
Fax: +1- 530-752-1552
Interests: remote sensing of environmental properties and landscape analysis; spectroscopy (wetlands, rangeland and forests); radiation interactions in plant canopies; detection of ecophysiological properties; vegetation stress; application to hydrological and ecological problems
Guest Editor
Dr. Cathleen Jones

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
Website | E-Mail
Interests: synthetic aperture radar; GNSS; coastal and delta subsidence; oil spill

Special Issue Information

Dear Colleagues,

Recent developments in airborne sensors and access to spaceborne data have drastically improved our ability to map the properties of land, water and air and to quantify change. Examples include new passive sensors, such as the Airborne Visible/Infrared Imaging Spectrometer Next Generation (NG), the Hyperspectral Thermal Emission Spectrometer (HYTES), the Portable Remote Imaging Spectrometer (PRISM) and active sensors, such as the Sentinel-1 C-band Synthetic Aperture Radar (SAR), the Land, Vegetation and Ice Sensor (LVIS)-Global Hawk laser altimeter, the UAVSAR L-band SAR, and the AirMOSS P-band SAR. New opportunities for sensor fusion are now possible by combining multiple sensors on a single platform, such as NEON AOP (Imaging spectrometer and LiDAR), the Goddard LiDAR, Hyperspectral and Thermal (G-LiHT) airborne sensor and the HyspIRI Airborne Campaign (AVIRIS/MASTER). Small airborne platforms, such as UAVs, offer the potential for improved near–surface imaging for applications such as precision agriculture and forestry. Finally, improved access to long-term medium-resolution-scale spaceborne data sets, such as those from the Sentinel-1A/B constellation, Landsat suite (TM, ETM+, OLI) and continuity with newer assets such as Sentinel-2, offer new opportunities for monitoring disturbance and seasonal changes in land-cover for forestry, agriculture and urban analysis, and for surface deformation studies that differentiate long term changes from seasonal and episodic events.

For this Special Issue, we encourage the submission of articles that utilize novel remote sensing datasets to address important environmental research questions pertinent to North America. Articles that focus on data fusion from multiple sensors (e.g., HyspIRI AC, NEON-AOP), from multiple platforms (airborne data combined with satellite imagery), newly available airborne datasets (e.g. HYTES, PHyTIR, AVIRIS-NG, Lidar) or the potential for novel time series analyses are particularly encouraged. Studies utilizing time series from SAR instruments like Sentinel-1 and UAVSAR to evaluate the dynamics of surface and ecosystem change are also encouraged.

Prof. Dar Roberts
Prof. Susan Ustin
Dr. Cathleen Jones
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 papers will be 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 1800 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

  • Regional or Continental analysis (North America)
  • Imaging spectroscopy
  • Hyperspectral or multiband thermal
  • Synthetic aperture radar
  • Waveform or multiband Lidar
  • Time series analysis
  • Change detection
  • SAR interferometry (InSAR)
  • Sensor Fusion
  • Lidar and Imaging spectrometry fusion

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle
Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California
Remote Sens. 2019, 11(14), 1664; https://doi.org/10.3390/rs11141664
Received: 29 April 2019 / Revised: 24 June 2019 / Accepted: 29 June 2019 / Published: 12 July 2019
PDF Full-text (6009 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth [...] Read more.
Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
Figures

Graphical abstract

Open AccessArticle
Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach
Remote Sens. 2019, 11(13), 1629; https://doi.org/10.3390/rs11131629
Received: 11 May 2019 / Revised: 19 June 2019 / Accepted: 27 June 2019 / Published: 9 July 2019
Cited by 1 | PDF Full-text (6024 KB) | HTML Full-text | XML Full-text
Abstract
The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability [...] Read more.
The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
Figures

Graphical abstract

Open AccessArticle
Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California
Remote Sens. 2019, 11(9), 1100; https://doi.org/10.3390/rs11091100
Received: 18 March 2019 / Revised: 21 April 2019 / Accepted: 6 May 2019 / Published: 8 May 2019
PDF Full-text (7097 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements [...] Read more.
Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
Figures

Figure 1

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top