Remote Sensing

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Special Issue Editor

Dr. Ute Herzfeld

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Guest Editor

Geomathematics, Remote Sensing and Cryospheric Sciences, Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Interests: spatial statistics; geomathematics; remote sensing; geoinformatics; statistical signal processing and information retrieval

Special Issue Information

Dear Colleagues,

The focus of this Special Issue lies on mathematical models and algorithms developed for and applied to the analysis of remotely sensed data and remotely sensed imagery.

Especially invited are contributions on methods and algorithms for new sensor types, including new imaging sensors and new types of altimeters, operated from satellites, manned or unmanned airborne platforms, and ground-based observing systems.

Examples include but are not limited to the following missions, sensor, and data types:

Altimeter data from NASA’s ICESat-2 Mission (launched Sept 15, 2018), including airborne simulator instrumentation (MABEL, SIMPL, SIGMA-space sensors);
Image and SAR data from ESA’s Copernicus Sentinel Missions;
CryoSat-2 SIRAL data (European Space Agency);
RADARSAT-2 Data (Canadian Space Agency);
TSX (TerraSAR-X) Mission Data (German Aerospace Center (DLR) and Airbus Defence and Space);
Modern image data, such as DigitalGlobe WorldView 1-4, GeoEye and others;
GPS and GNSS data;
Ground-penetrating radar data;
Airborne campaign or airborne mission data, including Operation ICEBridge Data (NASA);
Data from new sensors collected during principal-investigator-led campaigns, experiments conducted by individual scientists or small business ventures.

Contributions to all application areas are welcome, ranging from cryospheric sciences over oceanography, ecology, hydrology, marine geophysics, and solid earth geophysics to atmospheric sciences and space sciences, especially those applications that demonstrate the advancement facilitated by a new mathematical approach.

Dr. Ute Herzfeld
Guest Editor

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

mathematical algorithms
satellite imagery
altimetry
GPS
ICESat-2
Copernicus Sentinel Data
radar
RADARSAT
cryospheric sciences
oceanography
ecology
hydrology
geophysics
atmospheric sciences
space sciences

Published Papers (2 papers)

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Research

22 pages, 2325 KiB

Open AccessArticle

Near Real-Time Characterization of Spatio-Temporal Precursory Evolution of a Rockslide from Radar Data: Integrating Statistical and Machine Learning with Dynamics of Granular Failure

by Sourav Das and Antoinette Tordesillas

Remote Sens. 2019, 11(23), 2777; https://doi.org/10.3390/rs11232777 - 25 Nov 2019

Cited by 8 | Viewed by 2714

Abstract

This study builds on fundamental knowledge of granular failure dynamics to develop a statistical and machine learning approach for characterization of a landslide. We demonstrate our approach for a rockslide using surface displacement data from a ground based radar monitoring system. The algorithm [...] Read more.

t_{0}

marking the departure from statistical invariance of the global velocity field, (ii) characterization of the clustering pattern formed by the velocity time series at

t_{0}

, and (iii) classification of velocity patterns for

t > t_{0}

to deliver a measure of risk of failure from

t_{0}

and estimates of the time of emergent and imminent risk of failure. Unlike the prevailing approach of analysing time series data from one or a few chosen locations, we make full use of data from all monitored points on the slope (here 1803). We do not make a priori assumptions on the monitored domain and base our characterization of the complex spatial patterns and associated dynamics only from the data. Our approach is informed by recent developments in the physics and micromechanics of failure in granular media and is configured to accommodate additional data on landslide triggers and other determinants of landslide risk readily. Full article

(This article belongs to the Special Issue Mathematical Models for Remote Sensing Image and Data Processing)

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Graphical abstract

19 pages, 4567 KiB

Open AccessArticle

waveformlidar: An R Package for Waveform LiDAR Processing and Analysis

by Tan Zhou and Sorin Popescu

Remote Sens. 2019, 11(21), 2552; https://doi.org/10.3390/rs11212552 - 30 Oct 2019

Cited by 12 | Viewed by 7010

Abstract

A wealth of Full Waveform (FW) LiDAR (Light Detection and Ranging) data are available to the public from different sources, which is poised to boost extensive applications of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, Adaptive Gaussian and Weibull decompositions and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed by converting waveforms into points to form the Hyper Point Cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales. Full article

(This article belongs to the Special Issue Mathematical Models for Remote Sensing Image and Data Processing)

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