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

Deadline for manuscript submissions: closed (1 June 2013) | Viewed by 55768

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Special Issue Editor

Prof. Dr. Nancy F. Glenn

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Department of Geosciences, Boise State University, 1910 University Drive, Boise, Idaho 83725-1535, USA
Interests: remote sensing of semiarid environments and development of LiDAR processing techniques

Special Issue Information

Dear Colleagues,
Investigations in earth science have played a strong role in the development of remote sensing sensors and image processing techniques that are now used across multiple disciplines. For example, hyperspectral sensors and spectral unmixing techniques were developed for surface composition and mineral exploration, while novel InSAR processing techniques were cultivated for tectonics and hydrology. Data integration has always been a major focus of geologic remote sensing and most recently, the use of visualization has facilitated both quantitative and qualitative data integration.

This special issue of Remote Sensing focuses on examining the current and future trends in remote sensing of the earth sciences. We are interested in research using a range of spectral-, spatial-, and temporal scales and sensors for earth science investigations. Papers on new spectral unmixing techniques, the use of multitemporal data, and integration of multiple sensor types are especially encouraged. Papers offering insight on quantitative validation techniques, ground sampling (e.g. spectrometer, terrestrial laser scanning) and at multiple scales are invited. Innovative papers on data processing or new computational methods for retrieving surface change, surface composition, and filtering/removing vegetation are also sought. New applications for earth science remote sensing data visualization, with an emphasis on data fusion may also be considered.

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

spectral unmixing
validation
multitemporal
spatial scaling
data fusion
visualization
spectral discrimination

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Published Papers (5 papers)

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960 KiB

Open AccessArticle

Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts

by Jessica J. Mitchell, Rupesh Shrestha, Carol A. Moore-Ellison and Nancy F. Glenn

Remote Sens. 2013, 5(10), 4857-4876; https://doi.org/10.3390/rs5104857 - 8 Oct 2013

Cited by 11 | Viewed by 7819

Abstract

Basalt outcrops are significant features in the Western United States and consistently present challenges to Natural Resources Conservation Service (NRCS) soil mapping efforts. Current soil survey methods to estimate basalt outcrops involve field transects and are impractical for mapping regionally extensive areas. The purpose of this research was to investigate remote sensing methods to effectively determine the presence of basalt rock outcrops. Five Landsat 5 TM scenes (path 39, row 29) over the year 2007 growing season were processed and analyzed to detect and quantify basalt outcrops across the Clark Area Soil Survey, ID, USA (4,570 km²). The Robust Classification Method (RCM) using the Spectral Angle Mapper (SAM) method and Random Forest (RF) classifications was applied to individual scenes and to a multitemporal stack of the five images. The highest performing RCM basalt classification was obtained using the 18 July scene, which yielded an overall accuracy of 60.45%. The RF classifications applied to the same datasets yielded slightly better overall classification rates when using the multitemporal stack (72.35%) than when using the 18 July scene (71.13%) and the same rate of successfully predicting basalt (61.76%) using out-of-bag sampling. For optimal RCM and RF classifications, uncertainty tended to be lowest in irrigated areas; however, the RCM uncertainty map included more extensive areas of low uncertainty that also encompassed forested hillslopes and riparian areas. RCM uncertainty was sensitive to the influence of bright soil reflectance, while RF uncertainty was sensitive to the influence of shadows. Quantification of basalt requires continued investigation to reduce the influence of vegetation, lichen and loess on basalt detection. With further development, remote sensing tools have the potential to support soil survey mapping of lava fields covering expansive areas in the Western United States and other regions of the world with similar soilscapes. Full article

(This article belongs to the Special Issue Geological Remote Sensing)

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1532 KiB

Open AccessArticle

Targeting Mineral Resources with Remote Sensing and Field Data in the Xiemisitai Area, West Junggar, Xinjiang, China

by Lei Liu, Jun Zhou, Dong Jiang, Dafang Zhuang, Lamin R. Mansaray and Bing Zhang

Remote Sens. 2013, 5(7), 3156-3171; https://doi.org/10.3390/rs5073156 - 25 Jun 2013

Cited by 36 | Viewed by 10797

Abstract

The Xiemisitai area, West Junggar, Xinjiang, China, is situated at a potential copper mineralization zone in association with small granitic intrusions. In order to identify the alteration zones and mineralization characteristics of the intrusions, Landsat Enhanced Thematic Mapper (ETM+) and Quickbird data of the study area were evaluated in mapping lithological units, small intrusions, and alteration zones. False color composites of the first principal component analyses (PCA1), PCA2, and PCA4 in red (R), green (G), and blue (B) of the ETM+ image, and relevant hue-saturation-intensity (HSI) color model transformations, were performed. This led to the identification of lithologic units and discrimination of granitic intrusions from wall-rocks. A new geological map was generated by integrating the remote sensing results with two internally published local geologic maps and field inspection data. For the selected region, false color composites from PCA and relevant HSI-transformed images of the Quickbird data delineated the details of small intrusions and identified other unknown similar intrusions nearby. Fifteen separate potash-feldspar granites and three separate hornblende biotite granites were identified using ETM+ and Quickbird data. The principal component analysis-based Crosta technique was employed to discriminate alteration minerals. Some of the mapped alteration zones using the Crosta technique agreed very well with the known copper deposits. Field verification led to the discovery of three copper mineralizations and two gold mineralizations for the first time. The results show that the PCA and HSI transformation techniques proved to be robust in processing remote sensing data with moderate to high spatial resolutions. It is concluded that the utilized methods are useful for mapping lithology and the targeting of small intrusion-type mineral resources within the sparsely vegetated regions of Northwest China. Full article

(This article belongs to the Special Issue Geological Remote Sensing)

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1598 KiB

Open AccessArticle

Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region

by Arnaldo De Q. Da Silva, Waldir R. Paradella, Corina C. Freitas and Cleber G. Oliveira

Remote Sens. 2013, 5(6), 3101-3122; https://doi.org/10.3390/rs5063101 - 20 Jun 2013

Cited by 17 | Viewed by 7996

Abstract

This study evaluates the potential of C- and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajás Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world’s largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band. Full article

(This article belongs to the Special Issue Geological Remote Sensing)

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3819 KiB

Open AccessArticle

Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska

by Theodore B. Barnhart and Benjamin T. Crosby

Remote Sens. 2013, 5(6), 2813-2837; https://doi.org/10.3390/rs5062813 - 31 May 2013

Cited by 124 | Viewed by 13651

Abstract

Terrestrial laser scanners (TLS) allow large and complex landforms to be rapidly surveyed at previously unattainable point densities. Many change detection methods have been employed to make use of these rich data sets, including cloud to mesh (C2M) comparisons and Multiscale Model to Model Cloud Comparison (M3C2). Rather than use simulated point cloud data, we utilized a 58 scan TLS survey data set of the Selawik retrogressive thaw slump (RTS) to compare C2M and M3C2. The Selawik RTS is a rapidly evolving permafrost degradation feature in northwest Alaska that presents challenging survey conditions and a unique opportunity to compare change detection methods in a difficult surveying environment. Additionally, this study considers several error analysis techniques, investigates the spatial variability of topographic change across the feature and explores visualization techniques that enable the analysis of this spatiotemporal data set. C2M reports a higher magnitude of topographic change over short periods of time (~12 h) and reports a lower magnitude of topographic change over long periods of time (~four weeks) when compared to M3C2. We found that M3C2 provides a better accounting of the sources of uncertainty in TLS change detection than C2M, because it considers the uncertainty due to surface roughness and scan registration. We also found that localized areas of the RTS do not always approximate the overall retreat of the feature and show considerable spatial variability during inclement weather; however, when averaged together, the spatial subsets approximate the retreat of the entire feature. New data visualization techniques are explored to leverage temporally and spatially continuous data sets. Spatially binning the data into vertical strips along the headwall reduced the spatial complexity of the data and revealed spatiotemporal patterns of change. Full article

(This article belongs to the Special Issue Geological Remote Sensing)

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3308 KiB

Open AccessArticle

Mineral Mapping Using Simulated Worldview-3 Short-Wave-Infrared Imagery

by Fred A. Kruse and Sandra L. Perry

Remote Sens. 2013, 5(6), 2688-2703; https://doi.org/10.3390/rs5062688 - 27 May 2013

Cited by 94 | Viewed by 14161

Abstract

WorldView commercial imaging satellites comprise a constellation developed by DigitalGlobe Inc. (Longmont, CO, USA). Worldview-3 (WV-3), currently planned for launch in 2014, will have 8 spectral bands in the Visible and Near-Infrared (VNIR), and an additional 8 bands in the Short-Wave-Infrared (SWIR); the approximately 1.0–2.5 μm spectral range. WV-3 will be the first commercial system with both high spatial resolution and multispectral SWIR capability. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected at 3 m spatial resolution with 86 SWIR bands having 10 nm spectral resolution were used to simulate the new WV-3 SWIR data. AVIRIS data were converted to reflectance, geographically registered, and resized to the proposed 3.7 and 7.5 m spatial resolutions. WV-3 SWIR band pass functions were used to spectrally resample the data to the proposed 8 SWIR bands. Characteristic reflectance signatures extracted from the data for known mineral locations (endmembers) were used to map spatial locations of specific minerals. The WV-3 results, when compared to spectral mapping using the full AVIRIS SWIR dataset, illustrate that the WV-3 spectral bands should permit identification and mapping of some key minerals, however, minerals with similar spectral features may be confused and will not be mapped with the same detail as using hyperspectral systems. The high spatial resolution should provide detailed mapping of complex alteration mineral patterns not achievable by current multispectral systems. The WV-3 simulation results are promising and indicate that this sensor will be a significant tool for geologic remote sensing. Full article

(This article belongs to the Special Issue Geological Remote Sensing)

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