scholarly journals Historical memory of the geology of Antioquia: Emil Grosse and The Carboniferous Tertiary of Antioquia

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Felipe Velásquez Ruiz ◽  
Marion Weber Scharff ◽  
Verónica Botero Fernández
Keyword(s):  
Historical Memory ◽  
Temporal Analysis ◽  
Google Earth ◽  
Field Trips ◽  
Geospatial Information ◽  
Thin Sections ◽  
Gold Silver ◽  
Multi Temporal ◽  
Western Side ◽  
Coal Reserves

The Carboniferous Tertiary of Antioquia (TCA), published by Dr. Jakob Emil Grosse in 1926, is one of the most influential scien­tific results of the Ordinance 16 of 1918 of the Honorable Departmental Assembly of Antioquia. The work began with the main objective of quantifying the coal reserves of Antioquia, and showing their surface extension on a scale of 1:50 000, in a region that includes the Arma river to the Puente de Occidente and from the western side of the Cauca River to the Romeral lineament and the plains of Ovejas. As a result, extensive work comprising petrological, structural, and economic geology studies was published in a manuscript published in Spanish and German, plus four attached maps, including coal, gold, silver, kaolin, and carbonate mines, among others. In the present work, the four TCA maps were digitized at a scale of 1:50 000 with Bessel 1841 datum and created a unified file in .kml format, which can be used directly in field trips, via Google Earth on cell phones, tablets, or computers. The metadata associates the information in the TCA with the Servicio Geológico Colombiano for the year 2015. In addition, 480 thin sections were scanned, which were donated by Dr. Grosse to the Escuela Nacional de Minas and today are in the Museum of Geosciences of the Faculty of Mines of the Universidad Nacional de Colombia. The geospatial information contained in each thin section was interpreted and georeferenced, obtaining, as a result, a list with north and west geographic coordinates, in degrees, minutes, and seconds. This unpublished information is available in the supplementary material of this article. Finally, nine field trips were made to the places referenced in 23 photographs of the TCA between 1920 and 1923 to take their current equivalent and thus carry out a multi-temporal analysis of the TCA.

scholarly journals Sand Dune Dynamics Exploiting a Fully Automatic Method Using Satellite SAR Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3993
Author(s):  
José Manuel Delgado Blasco ◽  
Marco Chini ◽  
Gert Verstraeten ◽  
Ramon F. Hanssen
Keyword(s):  
Temporal Analysis ◽  
Google Earth ◽  
The South ◽  
Western Sahara ◽  
Migration Rates ◽  
Multi Temporal ◽  
Barchan Dunes ◽  
Radar Satellite ◽  
Thresholding Algorithm ◽  
Dune Dynamics

This work presents an automatic procedure to quantify dune dynamics on isolated barchan dunes exploiting Synthetic Aperture RADAR satellite data. We use C-band datasets, allowing the multi-temporal analysis of dune dynamics in two study areas, one located between the Western Sahara and Mauritania and the second one located in the South Rayan dune field in Egypt. Our method uses an adaptive parametric thresholding algorithm and common geospatial operations. A quantitative dune dynamics analysis is also performed. We have measured dune migration rates of 2–6 m/year in the NNW-SSE direction and 11–20 m/year NNE-SSW for the South Rayan and West-Sahara dune fields, respectively. To validate our results, we have manually tracked several dunes per study area using Google Earth imagery. Results from both automatic and manual approaches are consistent. Finally, we discuss the advantages and limitations of the approach presented.

scholarly journals Morphological Changes Detection of a Large Earthflow Using Archived Images, LiDAR-Derived DTM, and UAV-Based Remote Sensing

2020 ◽  
Vol 13 (1) ◽  
pp. 120
Author(s):  
Massimo Conforti ◽  
Michele Mercuri ◽  
Luigi Borrelli
Keyword(s):  
Morphological Changes ◽  
Source Area ◽  
Temporal Analysis ◽  
Earth Satellite ◽  
Google Earth ◽  
Aerial Photographs ◽  
Temporal Data ◽  
Transport Zone ◽  
Multi Temporal ◽  
System Data

In mountainous landscapes, where strongly deformed pelitic sediments outcrop, earthflows can dominate denudation processes and landscape evolution. This paper investigated geological and geomorphological features and space-time evolution over a 65-year time span (1954–2019) of a large earthflow, representative of wide sectors of the Apennine chain of southern Italy. The landslide, with a maximum length of 1.85 × 103 m, affects an area of 4.21 × 105 m2 and exhibits two source zones: a narrow and elongated transport zone and a lobate accumulation zone. Spatial and temporal morphological changes of the earthflow were assessed, comparing multi-source and multi-temporal data (aerial photographs, Google Earth satellite images, Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAV) system data). Geomorphic changes, quantified using Digital Terrain Models (DTMs) of differences, highlighted an extensive lowering of the topographic surface in the source area and a significant uplift at the landslide toe. Moreover, the multi-temporal analysis showed a high increase of landslide surface (more than 66%) during the last 65 years. The volumetric analyses showed that different sectors of the earthflow were active at different times, with different rates of topographic change. Overall, the used approach highlighted the great potentiality of the integration of multi-source and multi-temporal data for the diachronic reconstruction of morphological landslide evolution.

scholarly journals MULTI-TEMPORAL ANALYSIS OF DENSE AND SPARSE FORESTS’ RADAR BACKSCATTER USING SENTINEL-1A COLLECTION IN GOOGLE EARTH ENGINE

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 23-30
Author(s):  
C. M. Arellano ◽  
A. A. Maralit ◽  
E. C. Paringit ◽  
C. J. Sarmiento ◽  
R. A. Faelga ◽  
...  
Keyword(s):  
Data Extraction ◽  
Temporal Analysis ◽  
Radar Data ◽  
The Philippines ◽  
Google Earth ◽  
Radar Backscatter ◽  
Multi Temporal ◽  
Climatic Drivers ◽  
Google Earth Engine ◽  
Computing Platforms

Abstract. Radar data has been historically expensive and complex to process. However, in this milieu of cloud-computing platforms and open-source datasets, radar data analysis has become convenient and can now be performed for more exploratory researches. This study aims to perform multi-temporal analysis of radar backscatter to characterize dense and sparse forest from Sentinel-1 images. The area of study are reforested sites under the National Greening Program (NGP) of the Philippines. Ground data were collected: (1) in 2019, from a 1.35 ha -site in Brgy. Calula, Ipil, Zamboanga Sibugay, (2) in 2019, from a 1.10 ha- site in Brgy. Cabatuanan, Basay, Negros Oriental, and (3) from PhilLiDAR 2 – Project 3: FRExLS’ 2.4 ha -validated site in Ubay, Bohol. SAR intensity values were derived from Sentinel-1 from Google Earth Engine, which is a cloud-based platform with a repository of satellite images and functionalities for data extraction and processing. The temporal variation in C-band radar backscatter from 2014 to 2018 were analyzed. The results show, for the whole period of analysis, that: in VH polarization, dense forest samples backscatter range from −11 to −18 dB in VH and −2 to -13 dB in VV; sparse forest samples range from −12 to -21 dB in VH and −7 to −14 dB in VV; ground samples range from −12 to −24 dB in VH and −6 to −15 dB in VV; and water samples range from −21 to −30 dB in VH and −11 to −26 dB in VV. Forest backscatter are expected to saturate over time, especially in dense forests. These variations are due to differences in forest species, landscape, environmental and climatic drivers, and phenomenon or interventions on the site.

scholarly journals Cloud-Native Coastal Turbid Zone Detection Using Multi-Temporal Sentinel-2 Data on Google Earth Engine

2021 ◽  
Vol 8 ◽  
Author(s):  
Avi Putri Pertiwi ◽  
Chengfa Benjamin Lee ◽  
Dimosthenis Traganos
Keyword(s):  
Coastal Waters ◽  
Light Attenuation ◽  
Aquatic Ecology ◽  
Temporal Analysis ◽  
Google Earth ◽  
Spectral Unmixing ◽  
Benthic Habitat ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Sentinel 2

The lack of clarity in turbid coastal waters interferes with light attenuation and hinders remotely sensed studies in aquatic ecology such as benthic habitat mapping and bathymetry estimation. Although turbid water column corrections can be applied on regions with seasonal turbidity by performing multi-temporal analysis, different approaches are needed in regions where the water is constantly turbid or only exhibits subtle turbidity variations through time. This study aims to detect these turbid zones (TZs) in optically shallow coastal waters using multi-temporal Sentinel-2 surface reflectance datasets to improve the aforementioned studies. The herein framework can be paired with other aquatic ecology remote sensing studies to establish the clear water focus area and can also be used by decision makers to identify rehabilitation areas. We selected the coastlines of Guinea-Bissau, Tunisia, and west Madagascar as our case studies which feature wide-ranging turbidity intensities across tropical, subtropical, and Mediterranean waters and applied three different methods for the TZ detection: Otsu’s method for bimodal thresholding, linear spectral unmixing, and Random Forest (RF) machine learning method on Google Earth Engine as an end-to-end process. Based on our experiments, the RF method yields good results in all study regions with overall accuracies ranging between 88 and 96% and F1-scores between 0.87 and 0.96. TZ detection is highly site-specific due to the inter-class variability that is mainly affected by the nature of the suspended materials and the environmental characteristics of the site.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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