scholarly journals High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1991
Author(s):  
Chenhui Huang ◽  
Akinobu Shibuya
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Data Augmentation ◽  
Prediction Models ◽  
Spatial Information ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Regression Problem ◽  
Sensing Data ◽  
Multivariate Prediction

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.

scholarly journals Mapping Listvenite Occurrences in the Damage Zones of Northern Victoria Land, Antarctica Using ASTER Satellite Remote Sensing Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1408 ◽  
Author(s):  
Amin Beiranvand Pour ◽  
Yongcheol Park ◽  
Laura Crispini ◽  
Andreas Läufer ◽  
Jong Kuk Hong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Component Analysis ◽  
Spectral Unmixing ◽  
Metasomatic Alteration ◽  
Sensing Data ◽  
Victoria Land ◽  
Mineral Assemblages

Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.

scholarly journals Application of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral imagery to mineral and lithologic mapping in southern West Greenland

2018 ◽  
Vol 8 (2) ◽  
pp. 47
Author(s):  
Enton Bedini
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Multispectral Imagery ◽  
Alluvial Deposits ◽  
West Greenland ◽  
Sensing Data ◽  
Arctic Regions ◽  
Lithologic Mapping

Remote sensing data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used for mineral and lithologic mapping at the Sarfartoq carbonatite complex area in southern West Greenland. The geology of the study area consists of carbonatites, fenites, hydrothermal alteration zones, gneisses, alluvial deposits etc. The Adaptive Coherence Estimator algorithm was used to analyze the remote sensing data. The reference spectra were selected from the imagery. The mapping results show the distribution of carbonatite, hydrothermally altered zones, fenite, and sericite. In addition, lichen and tundra green vegetation were also mapped.  Due to the moderate spatial resolution of ASTER SWIR bands, it was not possible to detect and map the rock units in some parts of the study area. The study shows the possibilities and limitations of the use of the ASTER multispectral imagery for geological studies in the Arctic regions of West Greenland. The paper is the first reported study on the use of ASTER data for mineral and lithologic mapping in the Arctic regions of West Greenland. 

scholarly journals Mapping the Tso Kar basin in Ladakh

2012 ◽  
Vol 8 (2) ◽  
Author(s):  
Shashank Srinivasan
Keyword(s):  
Remote Sensing ◽  
High Altitude ◽  
Human Activities ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Climatic Changes ◽  
Pastoral Community ◽  
Sensing Data ◽  
Management Plans ◽  
Basin Wetlands

High-altitude wetlands are critical ecosystems at risk from global climatic changes and local human activities. Management plans for the conservation of these wetlands require spatial information, from remote sensing data and from local human communities. I describe my research aims and methodology working with the Changpa, a nomadic pastoral community who inhabit the high-altitude regions around the Tso Kar basin wetlands in Ladakh, India.

scholarly journals Land use/cover classification in the Brazilian Amazon using satellite images

2012 ◽  
Vol 47 (9) ◽  
pp. 1185-1208 ◽  
Author(s):  
Dengsheng Lu ◽  
Mateus Batistella ◽  
Guiying Li ◽  
Emilio Moran ◽  
Scott Hetrick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Spatial Information ◽  
Classification Tree ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Classification Performance ◽  
Sensor Data ◽  
Sensing Data

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

scholarly journals Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China

2020 ◽  
Vol 51 (5) ◽  
pp. 942-958 ◽  
Author(s):  
Jianzhu Li ◽  
Siyao Zhang ◽  
Lingmei Huang ◽  
Ting Zhang ◽  
Ping Feng
Keyword(s):  
Remote Sensing ◽  
Prediction Models ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Sensing Data ◽  
Model Driven ◽  
Drought Prediction ◽  
Svm Model

Abstract Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

scholarly journals UAV STRATEGIES VALIDATION AND REMOTE SENSING DATA FOR DAMAGE ASSESSMENT IN POST-DISASTER SCENARIOS

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 121-128 ◽  
Author(s):  
A. Calantropio ◽  
F. Chiabrando ◽  
G. Sammartano ◽  
A. Spanò ◽  
L. Teppati Losè
Keyword(s):  
Remote Sensing ◽  
Emergency Response ◽  
Spatial Information ◽  
Central Italy ◽  
Remote Sensing Data ◽  
3D Models ◽  
Point Of View ◽  
Sensing Data ◽  
Rapid Mapping ◽  
Fundamental Information

<p><strong>Abstract.</strong> The recent seismic swarms, occurred in Italy since August 2016, outlined the importance of deepen Geomatics researches for the validation of new strategies aimed at rapid-mapping and documenting differently accessible and complex environments, as in urban contexts and damaged built heritage. In the emergency response, the crucial exploitation of technological advances should obtain and efficiently organize high-scale reliable geospatial data for the early warning, impact, and recovery phases. Fulfilling these issues, among others, the Copernicus EMS, has played by now an important role in immediate and extensive damage reconnaissance, as in the case of Centre Italy. Nevertheless, the use of remote sensing data is still affected by a problem of point-of-view, scale and detectable detail. Nadir images, airborne or satellite, in fact, strongly limited the confidence level of these products. The subjectivity of the operator involvement is still an open issue, both in the first fieldwork assessment, and in the following operational approach of interpretative damage detection and rapid mapping production. To overcome these limits, the introduction of UAV platforms for photogrammetric purposes, has proven to be a sustainable approach in terms of time savings, operators’ safety, reliability and accuracy of results: the nadir and oblique integration can provide large multiscale models, with the fundamental information related to the façades conditions. The presented research, conducted within the Central Italy earthquakes events, will focus on potentialities and limits of UAV photogrammetry in the two documented sites: Pescara del Tronto and Accumoli. Here, the aim is not limited to describe a series of strategies for georeferencing, blocks orientation and multitemporal co-registration solutions, but also to validate the implemented pipelines as a workflow that could be integrated in the operative intervention for emergency response in early impact activities. Thus, it would be possible to use this 3D metric products as a reference-data for significative improvements of reliability in typical visual inspection and mapping, flanking the traditional nadir airborne- or satellite-based products. The UAV acquisitions performed in two damaged villages are displayed, in order to underline the implication of the spatial information embedded in DSM reconstruction and 3D models, supporting more reliable damage assessments.</p>

scholarly journals Recent Surge Behavior of Walsh Glacier Revealed by Remote Sensing Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 716
Author(s):  
Xiyou Fu ◽  
Jianmin Zhou
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Active Phase ◽  
Phase Changes ◽  
Landsat Images ◽  
Sensing Data ◽  
Ice Velocity ◽  
Glacier Velocity ◽  
The 1960S

Many surge-type glaciers are present on the St. Elias Mountains, but a detailed study on the surge behavior of the glaciers is still missing. In this study, we used remote sensing data to reveal detailed glacier surge behavior, focusing on the recent surge at Walsh Glacier, which was reported to have surged once in the 1960s. Glacial velocities were derived using a cross-correlation algorithm, and changes in the medial moraines were interpreted based on Landsat images. The digital elevation model (DEM) difference method was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs to evaluate the surface elevation of the glacier. The results showed that the surge initiated near the conjunction of the eastern and northern branches, and then quickly spread downward. The surge period was almost three years, with an active phase of less than two years. The advancing speed of the surge front was much large than the maximum ice velocity of ≈14 m/d observed during the active phase. Summer speed-ups and a winter speed-up in ice velocity were observed from velocity data, with the speed-ups being more obvious during the active phase. Changes in the glacier velocity and the medial moraines suggested that the eastern branch was more affected by the surge. The DEM differencing results showed that the receiving zone thickened up to about 140 m, and the upstream reservoir zone became thinner. These surge behaviors, as characterized by remote sensing data, gave us more detailed insights into the surge dynamics of Walsh Glacier.

An estimation of regional geologic structures from the geomorphology to characterize the Wadi systems, southeast Sinai, Egypt

2016 ◽  
Vol 4 (3) ◽  
pp. T323-T336
Author(s):  
Okechukwu Livinus Obiegbu ◽  
Andreas Laake ◽  
Peter Brabham
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
High Spectral Resolution ◽  
Fault System ◽  
Magnetic Data ◽  
Near Surface ◽  
Sensing Data ◽  
Rgb Images ◽  
Multispectral Remote Sensing

Complex regional geologic structural controls have generated a lot of interest in the engineering, oil, and gas industries within the past few years. Digital elevation models (DEMs), multispectral remote sensing images using ArcGIS software, in combination with data cube and geomorphologic characterization, provide important markers that aid in spatial information analysis for the study area. We have validated the characterization and classification of DEMs using spatial statistics by mineral spectroscopy of multispectral remote sensing data. Our characterization was initiated by a joint interpretation of DEMs and multispectral remote sensing data in association with stratigraphic and geologic information. We have combined Landsat ETM+ images from visible (VIS), near-infrared (NIR), and mid-infrared (MID IR) to create red-green-blue (RGB) images, superimposed with high-spectral-resolution 15 m panchromatic band 8. Principal component analysis (PCA) further enhanced the image results. To characterize the geomorphology and near surface, specific bands used included RGB Landsat 742 and 321 data sets, whereas false-color Landsat RGB images (742 and 432) provided spatial data in delineating areas of lineations and fault systems. The tectonic lineaments extracted from the escarpments of the DEM and magnetic data provided structures related to tectonic forces to better understand the major faults, lineations, and geomorphology. Results of this study showed a strikingly reliable interpretative result of these faults that controlled the low-lying areas. These faults and lineations are high-permeability zones that can be saturated by water during active rainfall and flash-flood periods thereby disrupting the equilibrium of various fault zones in the area and raising tectonic activities within the active fault system. Such saturation presents a major environmental hazard for the study area. Generally, the use of Landsat data combined with PCA indicates promising evidence of possible plays within the huge sedimentary deposits and raised concerns about safety and hazard issues.

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