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Remote Sensing in Geology

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

Deadline for manuscript submissions: closed (30 November 2015) | Viewed by 128593

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Special Issue Editors

Dr. James Jin-King Liu

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Guest Editor

LIDAR Technology Co., Ltd. and Secretary General, Taiwan Group on Earth Observations, Zhubei City 30274, Taiwan
Interests: aerial photo-interpretation; airborne LiDAR; DTM; remote sensing in geology; landform analysis; landslides; soil erosion; geohazards; remote sensing for mineral resources and hot springs; mobile mapping

Dr. Yu-Chang Chan

E-Mail Website
Guest Editor

Structural Geology and Remote Sensing Laboratory, Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan
Interests: remote sensing in geology; geologic structures; active faults; landslides; geologic erosion; geohazards; orogeny

Special Issue Information

Dear Colleagues,

Geology is the science comprising the study of the solid Earth, the rocks of which it is composed, and the processes by which they change. Geologists use remote sensing and a number of field, laboratory, and numerical modeling methods to decipher the Earth and understand the processes that occur on and inside it. Remote sensing technology can be used for geological investigations, explorations of minerals and geothermal energy, and evaluation for environmental geology and geotechnical engineering. Remote sensing is also an important tool for understanding the important natural hazards pertinent to geology such as avalanches, earthquakes, floods, landslides and debris flows, river channel migration and avulsion, liquefaction, sinkholes, subsidence, tsunamis, and volcanoes.

The state-of-the-art tools of remote sensing include LiDAR and digital elevation models, very high resolution optical remote sensing, thermal remote sensing, hyperspectral remote sensing, microwave and SAR, and remote sensing with historical aerial photographs or archival images which span from a century ago to present. Thus, we can perceive the Earth beyond our visual capability and transact the temporal and spatial limitations of earth observations.

The special issue of Remote Sensing in Geology is designed to explore knowledge on the scientific applications of these state-of-the-art tools. Prospective authors are invited to contribute to this Special Issue by submitting an original manuscript of their latest research results in the field of advances of remote sensing in geology, including:

New technological developments of platforms and/or sensors
Analytical methods and algorithms for datasets of

-LiDAR and Digital Elevation Models

-Thermal Infrared Sensors

-Hyperspectral and Multispectral Sensors

-Synthetic Aperture Radar (SAR)

-Optical and High Resolution Sensors

-Data-fusion

-Historical aerial photographs or archival images

Applied use of Remote Sensing in:

-Geological applications (geological mapping, lithological classification, geological structures, neotectonics, seismology, etc.)

-Geomorphology

-Geohazards, engineering/geotechnical, and environmental and/or contamination

-Terrain, bathymetry and DEM analytical techniques

-Mineral and Geothermal exploration

-Oil & Gas

-Classification, multi-temporal analysis and modelling

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. James Jin-King Liu
Dr. Yu-Chang Chan
Guest Editor

Published Papers (13 papers)

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Research

4958 KiB

Open AccessArticle

Monitoring Riverbank Erosion in Mountain Catchments Using Terrestrial Laser Scanning

by Laura Longoni, Monica Papini, Davide Brambilla, Luigi Barazzetti, Fabio Roncoroni, Marco Scaioni and Vladislav Ivov Ivanov

Remote Sens. 2016, 8(3), 241; https://doi.org/10.3390/rs8030241 - 14 Mar 2016

Cited by 54 | Viewed by 10171

Abstract

Sediment yield is a key factor in river basins management due to the various and adverse consequences that erosion and sediment transport in rivers may have on the environment. Although various contributions can be found in the literature about sediment yield modeling and bank erosion monitoring, the link between weather conditions, river flow rate and bank erosion remains scarcely known. Thus, a basin scale assessment of sediment yield due to riverbank erosion is an objective hard to be reached. In order to enhance the current knowledge in this field, a monitoring method based on high resolution 3D model reconstruction of riverbanks, surveyed by multi-temporal terrestrial laser scanning, was applied to four banks in Val Tartano, Northern Italy. Six data acquisitions over one year were taken, with the aim to better understand the erosion processes and their triggering factors by means of more frequent observations compared to usual annual campaigns. The objective of the research is to address three key questions concerning bank erosion: “how” erosion happens, “when” during the year and “how much” sediment is eroded. The method proved to be effective and able to measure both eroded and deposited volume in the surveyed area. Finally an attempt to extrapolate basin scale volume for bank erosion is presented. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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12214 KiB

Open AccessArticle

Wide-Area Landslide Deformation Mapping with Multi-Path ALOS PALSAR Data Stacks: A Case Study of Three Gorges Area, China

by Xuguo Shi, Mingsheng Liao, Menghua Li, Lu Zhang and Cory Cunningham

Remote Sens. 2016, 8(2), 136; https://doi.org/10.3390/rs8020136 - 06 Feb 2016

Cited by 75 | Viewed by 6652

Abstract

In recent years, satellite synthetic aperture radar interferometry (InSAR) has been adopted as a spaceborne geodetic tool to successfully measure surface deformation of a few well-known landslides in the Three Gorges area. In consideration of the fact that most events of slope failure happened at places other than those famous landslides since the reservoir impoundment in 2003, focusing on a limited number of slopes is insufficient to meet the requirements of regional-scale landslide disaster prevention and early warning. As a result, it has become a vital task to evaluate the overall stability of slopes across the vast area of Three Gorges using wide-coverage InSAR datasets. In this study, we explored the approach of carrying out joint analysis of multi-path InSAR data stacks for wide-area landslide deformation mapping. As an example, three ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data stacks of neighboring ascending paths covering the area along the Yangtze River from Fengjie to Zigui were analyzed. A key problem to be solved is the separation of the tropospheric signal from the interferometric phase, for which we employed a hybrid description model of the atmospheric phase screen (APS) to improve APS estimation from time series interferograms. The estimated atmospheric phase was largely correlated with the seasonal rainfall in the temporal dimension. The experimental results show that about 30 slopes covering total areas of 48 km² were identified to be landslides in active deformation and should be kept under routine surveillance. Analyses of time series displacement measurements revealed that most landslides in the mountainous area far away from Yangtze River suffered from linear deformation, whereas landslides located on the river bank were destabilized predominantly by the influences of reservoir water level fluctuation and rainfall. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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12372 KiB

Open AccessArticle

An Object-Oriented Approach to Extracting Productive Fossil Localities from Remotely Sensed Imagery

by Charles Emerson, Bryan Bommersbach, Brett Nachman and Robert Anemone

Remote Sens. 2015, 7(12), 16555-16570; https://doi.org/10.3390/rs71215848 - 08 Dec 2015

Cited by 12 | Viewed by 7401

Abstract

Most vertebrate fossils are rare and difficult to find and although paleontologists and paleoanthropologists use geological maps to identify potential fossil-bearing deposits, the process of locating fossiliferous localities often involves a great deal of luck. One way to reduce the role of serendipity is to develop predictive models that increase the likelihood of locating fossils by identifying combinations of geological, geospatial, and spectral features that are common to productive localities. We applied GEographic Object-Based Image Analysis (GEOBIA) of high resolution QuickBird and medium resolution images from the Landsat 8 Operational Land Imager (OLI) along with GIS data such as slope and surface geology layers to identify potentially productive Eocene vertebrate fossil localities in the Great Divide Basin, Wyoming. The spectral and spatial characteristics of the image objects that represent a highly productive locality (WMU-VP-222) were used to extract similar image objects in the area covered by the high resolution imagery and throughout the basin using the Landsat imagery. During the 2013 summer field season, twenty-six locations that would not have been spotted from the road in a traditional ground survey were visited. Fourteen of the eighteen localities that were fossiliferous were identified by the predictive model. In 2014, the GEOBIA techniques were applied to Landsat 8 imagery of the entire basin, correctly identifying six new productive localities in a previously unsurveyed part of the basin. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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3010 KiB

Open AccessArticle

Remotely Sensed Trajectory Analysis of Channel Migration in Lower Jingjiang Reach during the Period of 1983–2013

by Chao Yang, Xiaobin Cai, Xuelei Wang, Ranran Yan, Ting Zhang, Qing Zhang and Xiaorong Lu

Remote Sens. 2015, 7(12), 16241-16256; https://doi.org/10.3390/rs71215828 - 03 Dec 2015

Cited by 55 | Viewed by 6112

Abstract

In China, the Lower Jingjiang Reach (LJR) of the Yangtze River could be one of the most complicated areas in terms of channel migration. The river had undergone many channel changes in the reach since the 18th century. Intensive human activities in recent decades, such as the construction of upstream dams and revetments, had directly affected the channel migration characteristics. The revetment would significantly diminish migration, whereas the reduced sediment caused by dams would increase bank erosion and bank failure risks. Satellite imageries of Landsat Multi Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) were employed to quantify the planform migration of the reach from 1983 to 2013 and to investigate the possible effect of human activities on the LJR channel evolution. Apart from the commonly used planform morphometric parameters, the migration direction was introduced to predict the future migration trends. Results showed that the LJR was gradually changing to a straighter channel, with sinuosity reducing from 2.09 to 1.9 and river length decreasing from 125.32 km to 113.31 km in the past 30 years. Planform morphometric parameters, such as migration rate of the channel centerline and erosion and deposition areas and rates, also decreased drastically in the past 30 years. The migration rate of the channel centerline decreased from 31.05 m·year⁻¹ in 1983–1988 to 11.62 m·year⁻¹ in 2009–2013. The lateral erosion and deposition areas decreased from 21.32 and 25.73 km² in 1983–1988 to 4.83 and 5.83 km² in 2009–2013. All of these findings indicate that the LJR tended to be in a steady state from 1983 to 2013 and was totally controlled by the bank revetments. However, the undercutting was strengthened because of the restrictive effect of revetments on lateral migration in the LJR. Moreover, the channel migrated to the left bank with a visible tendency as the total migration area to the left bank was approximately two times that of the right bank during the period. Consequently, the left bank of the LJR should be the focus of more attention in future migrations, and bank revetments of the left bank should be kept reinforced and adjusted with the change of water and sediment conditions. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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3195 KiB

Open AccessArticle

Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM

by Rou-Fei Chen, Ching-Weei Lin, Yi-Hui Chen, Tai-Chien He and Li-Yuan Fei

Remote Sens. 2015, 7(11), 15443-15466; https://doi.org/10.3390/rs71115443 - 18 Nov 2015

Cited by 43 | Viewed by 10486

Abstract

Steep topographic reliefs and heavy vegetation severely limit visibility when examining geological structures and surface deformations in the field or when detecting these features with traditional approaches, such as aerial photography and satellite imagery. However, a light detection and ranging (LiDAR)-derived digital elevation model (DEM), which is directly related to the bare ground surface, is successfully employed to map topographic signatures with an appropriate scale and accuracy and facilitates measurements of fine topographic features. This study demonstrates the efficient use of 1-m-resolution LiDAR for tectonic geomorphology in forested areas and to identify a fault, a deep-seated landslide, and the regional cleavage attitude in southern Taiwan. Integrated approaches that use grayscale slope images, openness with a tint color slope visualization, the three-dimensional (3D) perspective of a red relief image map, and a field investigation are employed to identify the aforementioned features. In this study, the previously inferred Meilongshan Fault is confirmed as a NE–SW-trending, eastern dipping thrust with at least a 750 m-wide deformation zone. The site where future paleoseismological studies should be performed has been identified, and someone needs to work further on this site. Signatures of deep-seated landslides, such as double ridges, trenches, main escarpments, and extension cracks, are successfully differentiated in LiDAR DEM images through the use of different visualization techniques. Systematic parallel and continuous lineaments in the images are interpreted as the regional cleavage attitude of cleavage, and a field investigation confirms this interpretation. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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1619 KiB

Open AccessArticle

Polarimetric Scattering Properties of Landslides in Forested Areas and the Dependence on the Local Incidence Angle

by Takashi Shibayama, Yoshio Yamaguchi and Hiroyoshi Yamada

Remote Sens. 2015, 7(11), 15424-15442; https://doi.org/10.3390/rs71115424 - 18 Nov 2015

Cited by 27 | Viewed by 7217

Abstract

This paper addresses the local incidence angle dependence of several polarimetric indices corresponding to landslides in forested areas. Landslide is deeply related to the loss of human lives and their property. Various kinds of remote sensing techniques, including aerial photography, high-resolution optical satellite imagery, LiDAR and SAR interferometry (InSAR), have been available for landslide investigations. SAR polarimetry is potentially an effective measure to investigate landslides because fully-polarimetric SAR (PolSAR) data contain more information compared to conventional single- or dual-polarization SAR data. However, research on landslide recognition utilizing polarimetric SAR (PolSAR) is quite limited. Polarimetric properties of landslides have not been examined quantitatively so far. Accordingly, we examined the polarimetric scattering properties of landslides by an assessment of how the decomposed scattering power components and the polarimetric correlation coefficient change with the local incidence angle. In the assessment, PolSAR data acquired from different directions with both spaceborne and airborne SARs were utilized. It was found that the surface scattering power and the polarimetric correlation coefficient of landslides significantly decrease with the local incidence angle, while these indices of surrounding forest do not. This fact leads to establishing a method of effective detection of landslide area by polarimetric information. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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2252 KiB

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Integration of Concentration-Area Fractal Modeling and Spectral Angle Mapper for Ferric Iron Alteration Mapping and Uranium Exploration in the Xiemisitan Area, NW China

by Jun-Ting Qiu, Chuan Zhang and Xiao Hu

Remote Sens. 2015, 7(10), 13878-13894; https://doi.org/10.3390/rs71013878 - 22 Oct 2015

Cited by 19 | Viewed by 6783

Abstract

The high-grade uranium deposits in the Xiemisitan area, northwestern China, are genetically associated with the faulting of felsic volcanic or sub-volcanic rocks. Ferric iron alteration indicates that oxidizing hydrothermal fluids percolated through the rocks. In this study, we measured the gamma-ray intensities of rocks in the Xiemisitan area and we propose a hybrid method for the mapping of ferric iron alteration using concentration-area fractal modeling and spectral angle mapper. The method enables ferric iron alteration to be distinguished from potash-feldspar granitic rocks. The mapping results were integrated with structural data to assist with exploration for uranium in the study area. Using this approach, six prospective areas of mineralization were proposed. Of these areas, two anomalies with high gamma-ray intensities of 104 and 650 Uγ were identified and verified by field inspection. These observations suggest that Enhanced Thematic Mapper Plus images are a valuable tool that can improve the efficiency of uranium exploration. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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2613 KiB

Open AccessArticle

Sentinel-2 for Mapping Iron Absorption Feature Parameters

by Harald Van der Werff and Freek Van der Meer

Remote Sens. 2015, 7(10), 12635-12653; https://doi.org/10.3390/rs71012635 - 25 Sep 2015

Cited by 72 | Viewed by 13859

Abstract

Iron is an indicator for soil fertility and the usability of an area for cultivating crops. Remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval, yet a relative high spectral resolution is needed for mapping iron contents with reflectance data. Sentinel-2 has several bands that cover the 0.9 μm iron absorption feature, while space-borne sensors traditionally used for geologic remote sensing, like ASTER and Landsat, had only one band in this feature. In this paper, we introduce a curve-fitting technique for Sentinel-2 that approximates the iron absorption feature at a hyperspectral resolution. We test our technique on library spectra of different iron bearing minerals and we apply it to a Sentinel-2 image synthesized from an airborne hyperspectral dataset. Our method finds the wavelength position of maximum absorption and absolute absorption depth for minerals Beryl, Bronzite, Goethite, Jarosite and Hematite. Sentinel-2 offers information on the 0.9 μm absorption feature that until now was reserved for hyperspectral instruments. Being a satellite mission, this information comes at a lower spatial resolution than airborne hyperspectral data, but with a large spatial coverage and frequent revisit time. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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1197 KiB

Open AccessArticle

Mineral Classification of Makhtesh Ramon in Israel Using Hyperspectral Longwave Infrared (LWIR) Remote-Sensing Data

by Gila Notesco, Yaron Ogen and Eyal Ben-Dor

Remote Sens. 2015, 7(9), 12282-12296; https://doi.org/10.3390/rs70912282 - 21 Sep 2015

Cited by 27 | Viewed by 7513

Abstract

Hyperspectral remote-sensing techniques offer an efficient procedure for mineral mapping, with a unique hyperspectral remote-sensing fingerprint in the longwave infrared spectral region enabling identification of the most abundant minerals in the continental crust—quartz and feldspars. This ability was examined by acquiring airborne data with the AisaOWL sensor over the Makhtesh Ramon area in Israel. The at-sensor radiance measured from each pixel in a longwave infrared image represents the emissivity, expressing chemical and physical properties such as surface mineralogy, and the atmospheric contribution which is expressed differently during the day and at night. Therefore, identifying similar features in day and night radiance enabled identifying the major minerals in the surface—quartz, silicates (feldspars and clay minerals), gypsum and carbonates—and mapping their spatial distribution. Mineral identification was improved by applying the radiance of an in situ surface that is featureless for minerals but distinctive for the atmospheric contribution as a gain spectrum to each pixel in the image, reducing the atmospheric contribution and emphasizing the mineralogical features. The results were in agreement with the mineralogy of selected rock samples collected from the study area as derived from laboratory X-ray diffraction analysis. The resulting mineral map of the major minerals in the surface was in agreement with the geological map of the area. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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6323 KiB

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Monitoring Mining Subsidence Using A Combination of Phase-Stacking and Offset-Tracking Methods

by Hongdong Fan, Xiaoxiong Gao, Junkai Yang, Kazhong Deng and Yang Yu

Remote Sens. 2015, 7(7), 9166-9183; https://doi.org/10.3390/rs70709166 - 17 Jul 2015

Cited by 106 | Viewed by 8745

Abstract

An approach to study the mechanism of mining-induced subsidence, using a combination of phase-stacking and sub-pixel offset-tracking methods, is reported. In this method, land subsidence with a small deformation gradient was calculated using time-series differential interferometric synthetic aperture radar (D-InSAR) data, whereas areas with greater subsidence were calculated by a sub-pixel offset-tracking method. With this approach, time-series data for mining subsidence were derived in Yulin area using 11 TerraSAR-X (TSX) scenes from 13 December 2012 to 2 April 2013. The maximum mining subsidence and velocity values were 4.478 m and 40 mm/day, respectively, which were beyond the monitoring capabilities of D-InSAR and advanced InSAR. The results were compared with the GPS field survey data, and the root mean square errors (RMSE) of the results in the strike and dip directions were 0.16 m and 0.11 m, respectively. Four important results were obtained from the time-series subsidence in this mining area: (1) the mining-induced subsidence entered the residual deformation stage within about 44 days; (2) the advance angle of influence changed from 75.6° to 80.7°; (3) the prediction parameters of mining subsidence; (4) three-dimensional deformation. This method could be used to predict the occurrence of mining accidents and to help in the restoration of the ecological environment after mining activities have ended. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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6465 KiB

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Land subsidence, Ground Fissures and Buried Faults: InSAR Monitoring of Ciudad Guzmán (Jalisco, Mexico)

by Carlo Alberto Brunori, Christian Bignami, Matteo Albano, Francesco Zucca, Sergey Samsonov, Gianluca Groppelli, Gianluca Norini, Michele Saroli and Salvatore Stramondo

Remote Sens. 2015, 7(7), 8610-8630; https://doi.org/10.3390/rs70708610 - 07 Jul 2015

Cited by 44 | Viewed by 11013

Abstract

We study land subsidence processes and the associated ground fissuring, affecting an active graben filled by thick unconsolidated deposits by means of InSAR techniques and fieldwork. On 21 September 2012, Ciudad Guzmán (Jalisco, Mexico) was struck by ground fissures of about 1.5 km of length, causing the deformation of the roads and the propagation of fissures in adjacent buildings. The field survey showed that fissures alignment is coincident with the escarpments produced on 19 September 1985, when a strong earthquake with magnitude 8.1 struck central Mexico. In order to detect and map the spatio-temporal features of the processes that led to the 2012 ground fissures, we applied InSAR multi-temporal techniques to process ENVISAT-ASAR and RADARSAT-2 satellite SAR images acquired between 2003 and 2012. We detect up to 20 mm/year of subsidence of the northwestern part of Ciudad Guzmán. These incremental movements are consistent with the ground fissures observed in 2012. Based on interferometric results, field data and 2D numerical model, we suggest that ground deformations and fissuring are due to the presence of areal subsidence correlated with variable sediment thickness and differential compaction, partly driven by the exploitation of the aquifers and controlled by the distribution and position of buried faults. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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38983 KiB

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Hyperspectral REE (Rare Earth Element) Mapping of Outcrops—Applications for Neodymium Detection

by Nina Kristine Boesche, Christian Rogass, Christin Lubitz, Maximilian Brell, Sabrina Herrmann, Christian Mielke, Sabine Tonn, Oona Appelt, Uwe Altenberger and Hermann Kaufmann

Remote Sens. 2015, 7(5), 5160-5186; https://doi.org/10.3390/rs70505160 - 24 Apr 2015

Cited by 59 | Viewed by 13381

Abstract

In this study, an in situ application for identifying neodymium (Nd) enriched surface materials that uses multitemporal hyperspectral images is presented (HySpex sensor). Because of the narrow shape and shallow absorption depth of the neodymium absorption feature, a method was developed for enhancing and extracting the necessary information for neodymium from image spectra, even under illumination conditions that are not optimal. For this purpose, the two following approaches were developed: (1) reducing noise and analyzing changing illumination conditions by averaging multitemporal image scenes and (2) enhancing the depth of the desired absorption band by deconvolving every image spectrum with a Gaussian curve while the rest of the spectrum remains unchanged (Richardson-Lucy deconvolution). To evaluate these findings, nine field samples from the Fen complex in Norway were analyzed using handheld X-ray fluorescence devices and by conducting detailed laboratory-based geochemical rare earth element determinations. The result is a qualitative outcrop map that highlights zones that are enriched in neodymium. To reduce the influences of non-optimal illumination, particularly at the studied site, a minimum of seven single acquisitions is required. Sharpening the neodymium absorption band allows for robust mapping, even at the outer zones of enrichment. From the geochemical investigations, we found that iron oxides decrease the applicability of the method. However, iron-related absorption bands can be used as secondary indicators for sulfidic ore zones that are mainly enriched with rare earth elements. In summary, we found that hyperspectral spectroscopy is a noninvasive, fast and cost-saving method for determining neodymium at outcrop surfaces. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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3013 KiB

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Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

by Jie Dou, Kuan-Tsung Chang, Shuisen Chen, Ali P. Yunus, Jin-King Liu, Huan Xia and Zhongfan Zhu

Remote Sens. 2015, 7(4), 4318-4342; https://doi.org/10.3390/rs70404318 - 13 Apr 2015

Cited by 123 | Viewed by 11633

Abstract

This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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