Recent Surge Behavior of Walsh Glacier Revealed by Remote Sensing Data

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.

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

Remote Sensing ◽

10.3390/rs11121408 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1408 ◽

Cited By ~ 16

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.

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Evaluation of Geospatial Tools for Generating Accurate Glacier Velocity Maps from Optical Remote Sensing Data

Proceedings ◽

10.3390/ecrs-2-05154 ◽

2018 ◽

Vol 2 (7) ◽

pp. 341 ◽

Cited By ~ 5

Author(s):

Shridhar D. Jawak ◽

Shubhang Kumar ◽

Alvarinho J. Luis ◽

Mustansir Bartanwala ◽

Shravan Tummala ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Optical Remote Sensing ◽

Sensing Data ◽

Geospatial Tools ◽

Glacier Velocity

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CREATION OF GIS-RESOURCES FOR PHYSIOGRAPHIC ZONING OF THE TRANSBOUNDARY RUSSIAN-MONGOLIAN TERRITORY

Vestnik SSUGT (Siberian State University of Geosystems and Technologies) ◽

10.33764/2411-1759-2021-26-2-91-102 ◽

2021 ◽

Vol 26 (2) ◽

pp. 91-102

Author(s):

Andrew N. Beshentsev ◽

◽

Alexander A. Ayurzhanaev ◽

Bator V. Sodnomov ◽

◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Landsat Images ◽

Statistical Research ◽

Active Biomass ◽

Sensing Data ◽

Digital Elevation ◽

Geographical Regions ◽

Elevation Model ◽

Geographical Characteristics

The article is aimed at the development of methodological foundations for the creation of geoin-formation resources of transboundary territories based on cartographic materials and remote sensing data, as well as physical and geographical zoning of the transboundary Russian-Mongolian territory. The methodological basis of the study is cartographic and statistical research methods, geoinformation technology, as well as processing and analysis of remote sensing data. As a result, the study deter-mines the features of geoinformation resources, presents their characteristics, develops a classification and substantiates their integrating value in making interstate territorial decisions. The article gives the physical and geographical characteristics of the territory, determines the scale of mapping, establishes the basic units of geoinformation mapping and modeling, creates the coverage of the basin division, and proposes a scheme for creating basic geoinformation resources for the physical and geographical zoning of the territory. Based on the analysis of the digital elevation model, the territory was zoned according to the morphometric parameters of the relief. As a result of processing and analysis of Landsat images at different times, the territory was zoned in terms of the amount of photosynthetically active biomass (NDVI). As a result of zoning, 6 physical-geographical regions and 33 physical-geographical areas were identified.

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Modeling regional evaporation through ANFIS incorporated solely with remote sensing data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6153-2013 ◽

2013 ◽

Vol 10 (5) ◽

pp. 6153-6192

Author(s):

F.-J. Chang ◽

W. Sun

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Vegetation Index ◽

Remote Sensing Data ◽

Data Allocation ◽

Remotely Sensed Data ◽

Estimation Model ◽

Landsat Images ◽

Inference System ◽

Sensing Data

Abstract. The study aims to model regional evaporation that possesses the ability to present the spatial distribution of evaporation across the whole Taiwan by the adaptive network-based fuzzy inference system (ANFIS) based solely on remote sensing data. The remote sensing data used in this study consist of Landsat image products including Enhanced Vegetation Index (EVI) and land surface temperature (LST). The model construction is designed through two types of data allocation (temporal and spatial) driven with the same ten-year data of EVI and LST derived from Landsat images. Evidences indicate the estimation model based solely on remotely sensed data can effectively detect the spatial variation of evaporation and appropriately capture the evaporation trend with acceptable errors of about 1 mm day−1. The results also demonstrate the composite of EVI and LST input to the proposed estimation model improves the accuracy of estimated evaporation values as compared with the model using LST as the only input, which reveals EVI indeed benefits the estimation process. The results suggest Model-T (temporal input allocation) is suitable for making island-wide evaporation estimation while Model-S (spatial input allocation) is suitable for making evaporation estimation at ungauged sites. An island-wide evaporation map for the whole study area (Taiwan Island) is then derived. It concludes the proposed ANFIS model incorporated solely with remote sensing data can reasonably well generate evaporation estimation and is reliable as well as easily applicable for operational estimation of evaporation over large areas where the network of ground-based meteorological gauging stations is not dense enough or readily available.

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

Journal of Hyperspectral Remote Sensing ◽

10.29150/jhrs.v8.2.p47-59 ◽

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.

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The Impact of Coal Exploitation on Tidal Flat Changes, an Investigation Using Remote Sensing Data in Vietnam

Inżynieria Mineralna ◽

10.29227/im-2021-02-50 ◽

2021 ◽

Vol 1 (2) ◽

Author(s):

Thi Lan PHAM ◽

Si Son TONG ◽

Thi Thu Ha LE ◽

Thi Le LE ◽

Huu Duc HOANG

Keyword(s):

Remote Sensing ◽

Coal Mining ◽

Coal Mine ◽

Tidal Flat ◽

Remote Sensing Data ◽

Tidal Flats ◽

Mining Activities ◽

Landsat Images ◽

Sensing Data ◽

The Impact

Tidal flat plays a crucial role in socio-economic development and ecological environment.Tidal flats in Ha Long-Cam Pha in Vietnam are impacted by human activities, especially coal miningactivities. Using remote sensing data is able to detect, extract, and monitor the changes of tidal flats andexploited coal mine area with multi-temporal, in various scales, and for a large coverage. This studyaims to investigate the impact of coal mining activities on the changes of tidal flats using remote sensingin Cam Pha, Ha Long, one of the biggest coal basins in Vietnam. Digital Elevation Models (DEMs) oftidal flats constructed by Landsat satellite images acquired in years 1989, 2001, and 2014 are comparedto determine the volume changes. Besides, coal mining activities including coal production, waste rockdump area, and the expansion of open coal mine during the period 1989-2014 are investigated usingcorrespondent Landsat images and the reports from the coal mine companies in the study area. Sedimentsamples in tidal flats are analyzed to determine the origin of the sediments. As the results, organic matterin the tidal flats is dominant with the concentration of 459 g/kg to 607 g/kg, which is evidence for theimpact of coal exploitation on the coastal environment. In addition, the relationship between coal mineactivities and tidal flat variation is well observed in this study.

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

Remote Sensing ◽

10.3390/rs12121991 ◽

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.

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Exploring the Relationship between LST, LULC and NDVI in Saïss Plain using Geospatial Techniques

E3S Web of Conferences ◽

10.1051/e3sconf/202131404001 ◽

2021 ◽

Vol 314 ◽

pp. 04001

Author(s):

Manal El Garouani ◽

Mhamed Amyay ◽

Abderrahim Lahrach ◽

Hassane Jarar Oulidi

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Arable Land ◽

Remote Sensing Data ◽

Landsat Images ◽

Gis And Remote Sensing ◽

Sensing Data

Land use/land cover (LULC) change has been confirmed that have a significant impact on climate through various pathways that modulate land surface temperature (LST) and precipitation. However, there are no studies illustrated this link in the Saïss plain using remote sensing data. Thus, the aim of this study is to monitor the LST relationship between LULC and vegetation index change in the Saïss plain using GIS and Remote Sensing Data. We used 18 Landsat images to study the annual and interannual variation of LST with LULC (1988, 1999, 2009 and 2019). To highlight the effect of biomass on LST distribution, the Normalized Difference Vegetation Index (NDVI) was calculated, which is a very good indicator of biomass. The mapping results showed an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. Based on statistical analyzes, the LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of LULC type. The variation of land surface temperature with NDVI shows a negative correlation. This explains the increase in the surface temperature in rangelands and arable land while it decreases in irrigated crops and arboriculture.

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Assessment and Quantification of the Accuracy of Low- and High-Resolution Remote Sensing Data for Shoreline Monitoring

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060391 ◽

2020 ◽

Vol 9 (6) ◽

pp. 391

Author(s):

Dionysios N. Apostolopoulos ◽

Konstantinos G. Nikolakopoulos

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Spatial Resolution ◽

Remote Sensing Data ◽

Sandy Beaches ◽

Landsat Images ◽

High Resolution Data ◽

Sensing Data ◽

Shoreline Evolution ◽

Landsat Satellite

Τhe accuracy of low-resolution remote sensing data for monitoring shoreline evolution is the main issue that researchers have been trying to overcome in recent decades. The drawback of the Landsat satellite archive is its spatial resolution, which is appropriate only for low-scale mapping. The present study investigates the potentialities and limitations of remote sensing data and GIS techniques in shoreline evolution modeling, with a focus on two major aspects: (a) assessing and quantifying the accuracy of low- and high-resolution remote sensing data for shoreline mapping; and (b) calculating the divergence in the forecasting of coastline evolution based on low- and high-resolution datasets. Shorelines derived from diachronic Landsat images are compared with the corresponding shorelines derived from high-spatial-resolution airphotos or Worldview-2 images. The accuracy of each dataset is assessed, and the possibility of forecasting shoreline evolution is investigated. Two sandy beaches, named Kalamaki and Karnari, which are located in Northwestern Peloponnese, Greece, are used as test sites. It is proved that the shorelines derived from the Landsat data present a displacement error of between 6 and 11 m. The specific data are not suitable for the shoreline forecasting procedure and should not be used in related studies, as they yield less accurate results for the two study areas in comparison with the high-resolution data.

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Spatiotemporal Fusion of Satellite Images via Very Deep Convolutional Networks

Remote Sensing ◽

10.3390/rs11222701 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2701

Author(s):

Yuhui Zheng ◽

Huihui Song ◽

Le Sun ◽

Zebin Wu ◽

Byeungwoo Jeon

Keyword(s):

Remote Sensing ◽

Sparse Representation ◽

Remote Sensing Data ◽

Linear Mapping ◽

Image Features ◽

Landsat Data ◽

Fusion Model ◽

Landsat Images ◽

Sensing Data ◽

Non Linear

Spatiotemporal fusion provides an effective way to fuse two types of remote sensing data featured by complementary spatial and temporal properties (typical representatives are Landsat and MODIS images) to generate fused data with both high spatial and temporal resolutions. This paper presents a very deep convolutional neural network (VDCN) based spatiotemporal fusion approach to effectively handle massive remote sensing data in practical applications. Compared with existing shallow learning methods, especially for the sparse representation based ones, the proposed VDCN-based model has the following merits: (1) explicitly correlating the MODIS and Landsat images by learning a non-linear mapping relationship; (2) automatically extracting effective image features; and (3) unifying the feature extraction, non-linear mapping, and image reconstruction into one optimization framework. In the training stage, we train a non-linear mapping between downsampled Landsat and MODIS data using VDCN, and then we train a multi-scale super-resolution (MSSR) VDCN between the original Landsat and downsampled Landsat data. The prediction procedure contains three layers, where each layer consists of a VDCN-based prediction and a fusion model. These layers achieve non-linear mapping from MODIS to downsampled Landsat data, the two-times SR of downsampled Landsat data, and the five-times SR of downsampled Landsat data, successively. Extensive evaluations are executed on two groups of commonly used Landsat–MODIS benchmark datasets. For the fusion results, the quantitative evaluations on all prediction dates and the visual effect on one key date demonstrate that the proposed approach achieves more accurate fusion results than sparse representation based methods.

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