Special Issue "Advancements in Remote Sensing of Land Surface Change"

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: 30 June 2020.

Special Issue Editors

Dr. George Xian
E-Mail Website
Guest Editor
USGS Center for Earth Resources Observation and Science, Sioux Falls, SD 57198, USA
Interests: remote sensing of land cover; urban–wildland interface; surface thermal properties; regional climate change
Special Issues and Collections in MDPI journals
Dr. Robert Kennedy
E-Mail Website
Guest Editor
Department of Forest Science, Oregon State University, Corvallis, OR 97331-5503, USA
Interests: geospatial analysis; remote sensing; modeling; landscape ecology; disturbance dynamics; computational methods
Dr. Yuyu Zhou
E-Mail Website
Guest Editor
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011-1051, USA
Interests: ecosystem and human system dynamics; phenology indicators urban heat island; climate change; urbanization
Special Issues and Collections in MDPI journals
Dr. Seth Munson
E-Mail Website
Guest Editor
US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ,86001, USA
Interests: plant ecology; ecosystem ecology; landscape ecology; restoration ecology

Special Issue Information

Dear Colleagues,

Remote sensing information has been used in a wide range of earth science research and applications, such as over land, water, ecosystems, and geology. Remote sensing-derived land change information has been applied to quantify and model physical properties of the Earth’s surface, as well as to monitor land cover and use changes. Satellite sensors, such as the Landsat series of satellites (1980s to the present), ASTER and MODIS sensors on Terra (1999 to the present), MODIS on Aqua (2002 to the present), and VIIRS (2012 to the present), have routinely provided remotely sensed imagery of Earth’s surface condition, allowing for change assessment. With recent advances in remote sensing technologies, multiple remotely sensed data products are readily available to the scientific community with the potential to advance our scientific understanding of various dynamic processes associated with the terrestrial ecosystem.

This Special Issue invites manuscripts that focus on advancements in methodologies relating to and new knowledge gained by using remote sensing datasets to characterize land surface changes across large geographical areas and assess how ecosystem processes respond to land use and climate change. Topics on overcoming the challenges of using these data and advancements in understanding dynamic land processes, including the types, trends, magnitudes, causes, and consequences of land surface change and ecosystem responses, will also be considered.

Dr. George Xian
Dr. Robert Kennedy
Dr. Yuyu Zhou
Dr. Seth Munson
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

  • land surface
  • change
  • remote sensing
  • monitoring
  • ecosystem
  • climate

Published Papers (1 paper)

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Research

Open AccessArticle
Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping
Remote Sens. 2019, 11(22), 2603; https://doi.org/10.3390/rs11222603 - 06 Nov 2019
Abstract
Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades [...] Read more.
Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m. Full article
(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)
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