scholarly journals Detailed geological mapping of the Olon-Ovoot gold-ore cluster (South Mongolia) based on the interpretation of satellite imagery of medium and high spatial resolution.

2019 ◽  
pp. 59-71
Author(s):  
M. V. Zadorozhnyy ◽  
I. D. Zolnikov ◽  
N. V. Glushkova
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Geological Structure ◽  
Google Earth ◽  
Geological Mapping ◽  
Geological Interpretation ◽  
Gold Ore ◽  
Sentinel 2

Detailed geological mapping of Olon-Ovoot gold-ore cluster (South Mongolia) on the basis of interpretation of satellite imagery of medium and high spatial resolution The article presents the results of geological interpretation the territory of the Olon-Ovoot ore cluster by space imagery of medium and high spatial resolution. A Sentinel-2 imagery, chosen for interpretation, was orthorectified and reduced to a common spatial resolution (10m) The iron-hydroxid and ferrous-silicates indices in Sentinel-2 imagery were used to detect the perspective gold-bearing objects. The sub-pixel structure of the imagery Sentinel-2 were analyzed by means of satellite imagery of high spatial resolution by Google Earth for detecting areas concentration of the quartz-carbonate veins. The study of the spectral domain in high-resolution imagery not necessary for detecting lineaments by structural and morphological interpretation. The interpretation of the remote sensing data provide a unique opportunity to substantial specify the geological structure of the territory and change the level of mapping from the scale of 1 : 200 000 to the scale of 1 : 20 000 for the perspective areas. The integration of satellite images of different functional scale provided an tenfold increase for some geological objects (for example dikes). Detailed mapping of the territory allowed to come for geoinformation modeling of geological structural elements and predictive indicators.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

Enhancement and Application of WorldView-2 to Geological Interpretation

2014 ◽  
Vol 1010-1012 ◽  
pp. 1237-1242 ◽  
Author(s):  
Jia Jing Zhou ◽  
Shu Fang Tian ◽  
Na Wang ◽  
Xiao Hu
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Good Effect ◽  
Geological Interpretation ◽  
Spectral Bands ◽  
Texture Information ◽  
Kunlun Mountain ◽  
West Kunlun

By comparing WorlView-2 with other remote sensing data in the characteristics of spectral bands and spatial resolution, we found that all the eight bands of WorldView-2 are sensitive to lithology and helpful to distinguish them; besides, WorldView-2 provides a richer texture information with a high spatial resolution of 0.46m, which is also very important in geological interpretation of remote sensing. Therefore, WorldView-2 data has a strong advantage in geological applications. In the geological interpretation of the Kezile area in West Kunlun Mountain, different enhancement methods based on the spectrum, texture and geomorphology/vegetation were applyed to enhance the lithology information of WorldView-2 image, and it achieved a good effect. With the enhanced images of Kezile area, we subdivided the Jurassic, Cretaceous, Paleogene and Neogene into lithologies in detail, and completed the remote sensing geological interpretation map.

scholarly journals EXPLORING THE CAPABILITIES OF COMBINING THE SENTINEL-2 MSI DATA AND HIGH RESOLUTION GOOGLE EARTH IMAGE FOR MAPPING MANGROVE SPECIES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1001-1007
Author(s):  
H. Z. Li ◽  
Y. Han ◽  
J. S. Chen
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Svm Classifier ◽  
Textural Features ◽  
Species Classification ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Sentinel 2

Abstract. Knowledge gained about the mangrove species mapping is essential to understand mangrove species development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. In this study, mangrove species classification in Shenzhen Bay, China was performed by using Sentinel 2 (S2) Multi Spectral Instrument (MSI) data and Google Earth (GE) high resolution imagery as data sources and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The SVM classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the Kappa coefficient was 0.74, which indicated great potential of using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

scholarly journals Catching Geomorphological Response to Volcanic Activity on Steep Slope Volcanoes Using Multi-Platform Remote Sensing

2020 ◽  
Vol 12 (3) ◽  
pp. 438 ◽  
Author(s):  
Federico Di Traglia ◽  
Alessandro Fornaciai ◽  
Massimiliano Favalli ◽  
Teresa Nolesini ◽  
Nicola Casagli
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Morphological Changes ◽  
Remote Sensing Data ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Geomorphological Evolution ◽  
Sentinel 2 ◽  
Sciara Del Fuoco

The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed for topographic changes estimation. Data were comprised of high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images that allowed for the mapping of areas that were affected by major lithological and morphological changes. PLÉIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared and, although there are artefacts in the smaller structures (e.g., ridges and valleys), there is still a clear consistency between the two DEMs for the larger structures (as the main valleys and ridges). The period between July 2010 and May 2012 showed only minor changes consisting of volcanoclastic sedimentation and some overflows outside the crater. Otherwise, between May 2012 and May 2017, large topographic changes occurred that were related to the emplacement of the 2014 lava flow in the NE part of the Sciara del Fuoco and to the accumulation of a volcaniclastic wedge in the central part of the Sciara del Fuoco. Between 2017 and 2019, minor changes were again detected due to small accumulation next to the crater terrace and the erosion in lower Sciara del Fuoco.

Preliminary spectral and geological analyses of the Degas crater on Mercury - supporting the SIMBIO-SYS instrument onboard BepiColombo

2021 ◽  
Author(s):  
Nicolas Bott ◽  
Océane Barraud ◽  
Laura Guzzetta
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Mineralogical Composition ◽  
Geological Mapping ◽  
Absorption Bands ◽  
Spectral Parameters ◽  
Very High Spatial Resolution ◽  
Multispectral Analysis

<p>The BepiColombo spacecraft was launched in October 19th, 2018 (local time) towards Mercury, carrying 16 instruments in two orbiters (MPO and MMO). Among this impressive set of devices, the SIMBIO-SYS (Spectrometer and Imagers for MPO BepiColombo Integrated Observatory SYStem) instrument [Cremonese et al., 2020] will map at an unprecedented high resolution the surface of the innermost planet of the Solar system, thanks to 3 cameras: STC (Stereo Channel), a stereo camera; HRIC (High spatial Resolution Imaging Channel), a multispectral camera with a very high spatial resolution; VIHI (Visible Infrared Hyperspectral Imager channel), a hyperspectral imager to with a good spectral resolution and a good S/N ratio. The last one aims to map the global mineralogical composition of Mercury, which has not yet been precisely determined due to the absence of diagnostic absorption bands in the remote sensing data of the previous MESSENGER mission [Izenberg et al., 2014]. The choice and the list of targets SIMBIO-SYS will have to analyse are still in progress and are continuously updated. Therefore, preliminary studies of potential targets of interest can be very useful to support their selection.</p><p>For that purpose, we started investigating a particular crater, Degas, which occurs in the Shakespeare quadrangle (H-03) [Guzzetta et al., 2017; Bott et al., 2019], located at mid-latitudes of the northern hemisphere of Mercury (37.08 ◦ N - 232.66 ◦ E). Its well-preserved ray system of ejecta are a strong hint in favor of its chronostratigraphic classification as a Kuiperian (-1 Gyr – today) crater [Banks et al., 2017]. By using MESSENGER data, we analysed the Degas crater with a three-fold approch: a multispectral analysis based on MDIS-WAC data have been combined with a spectroscopic analysis of MASCS data and a geological analysis based on MDIS-NAC images. Here, we would like to present the first outputs of our works, including a set of color and monochrome mosaics, spectral parameters maps and spectra of each kind of terrain identified with the mosaics, and the first results of the high-resolution geological mapping of the Degas crater performed on a NAC images mosaic of 23 m/pixel. Other findings and initial discussions will be presented during the virtual talk.</p><p>Acknowledgements: This work is partly supported by the Centre National d' Études Spatiales. We gratefully acknowledge funding from the Italian Space Agency (ASI) under ASI-INAF agreement2017-47-H.0. The authors acknowledge the use of MESSENGER data.</p>

scholarly journals A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1290
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Local Scale ◽  
Google Earth ◽  
Forest Composition ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Remotely Sensed Imagery ◽  
Composition Classes

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

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