scholarly journals Fusion of Remote Sensing and Applied Geophysics for Sinkholes Identification in Tabular Middle Atlas of Morocco (the Causse of El Hajeb): Impact on the Protection of Water Resource

Resources ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 51
Author(s):  
Anselme Muzirafuti ◽  
Mustapha Boualoul ◽  
Giovanni Barreca ◽  
Abdelhamid Allaoui ◽  
Hmad Bouikbane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Geophysical Methods ◽  
Remote Sensing Data ◽  
Resistivity Tomography ◽  
Geophysical Surveys ◽  
Middle Atlas ◽  
Karst Landforms ◽  
Water Springs ◽  
Self Potential

The Causse of El Hajeb belongs to the Tabular Middle Atlas (TMA), in which thousands of karst landforms have been identified. Among them, collapse dolines and dissolution sinkholes have been highlighted as a source of environmental risks and geo-hazards. In particular, such sinkholes have been linked to the degradation of water quality in water springs located in the junction of the TMA and Saïss basin. Furthermore, the developments of collapse dolines in agricultural and inhabited areas enhance the risk of life loss, injury, and property damage. Here, the lack of research on newly formed cavities has exacerbated the situation. The limited studies using remote sensing or geophysical methods to determine the degree of karstification and vulnerability of this environment fail to provide the spatial extent and depth location of individual karst cavities. In order to contribute to the effort of sinkhole risk reduction in TMA, we employed remote sensing and geophysical surveys to integrate electrical resistivity tomography (ERT) and self-potential (SP) for subsurface characterization of four sinkholes identified in the Causse of El Hajeb. The results revealed the existence of sinkholes, both visible and non-accessible at the surface, in carbonate rocks. The sinkholes exhibited distinct morphologies, with depths reaching 35 m. Topography, geographic coordinates and land cover information extracted on remote sensing data demonstrated that these cavities were developed in depressions in which agricultural activities are regularly performed. The fusion of these methods benefits from remote sensing in geophysical surveys, particularly in acquisition, georeferencing, processing and interpretation of geophysical data. Furthermore, our proposed method allows identification of the protection perimeter required to minimize the risks posed by sinkholes.

scholarly journals Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data

2019 ◽  
Vol 116 (7) ◽  
pp. 1124 ◽  
Author(s):  
C. S. Jha ◽  
, Rakesh ◽  
J. Singhal ◽  
C. S. Reddy ◽  
G. Rajashekar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Forest Health ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Detection and characterization of boreal coniferous forests from remote sensing data

2001 ◽  
Vol 106 (D24) ◽  
pp. 33405-33419 ◽  
Author(s):  
Jean-Luc Widlowski ◽  
Bernard Pinty ◽  
Nadine Gobron ◽  
Michel M. Verstraete
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Coniferous Forests ◽  
Sensing Data

Landscape Biodiversity Characterization in Ecoregion 29 Using MODIS

2013 ◽  
pp. 815-831
Author(s):  
Nitin Kumar Tripathi ◽  
Aung Phey Khant
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Global Warming ◽  
Forest Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Modis Data ◽  
Moderate Resolution ◽  
Landscape Biodiversity

Biodiversity conservation is a challenging task due to ever growing impact of global warming and climate change. The chapter discusses various aspects of biodiversity parameters that can be estimated using remote sensing data. Moderate resolution satellite (MODIS) data was used to demonstrate the biodiversity characterization of Ecoregion 29. Forest type map linked to density of the study area was also developed by MODIS data. The outcome states that remote sensing and geographic information systems can be used in combination to derive various parameters related to biodiversity surveillance at a regional scale.

scholarly journals Deep Electrical Resistivity Tomography for the Hydrogeological Setting of Muro Lucano Mounts Aquifer (Basilicata, Southern Italy)

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
E. Rizzo ◽  
V. Giampaolo ◽  
L. Capozzoli ◽  
S. Grimaldi
Keyword(s):  
Karst Aquifer ◽  
Geophysical Methods ◽  
Necessary Condition ◽  
Resistivity Tomography ◽  
Well Drilling ◽  
Groundwater Exploitation ◽  
Debris Slope ◽  
Fractured Limestone ◽  
Resistive Zone

The proposed work concerns the application of a deep geoelectrical survey to a carbonate aquifer in order to define the best location for exploitation well drilling for increasing water supply. However, an optimal characterization of a groundwater resource is the necessary condition to reach the indicated aim. Therefore, the geoelectrical investigation was guided from the previous geological and hydrogeological characterization. Moreover, geophysical methods are good tools to improve the groundwater model when detailed information is necessary, such as the localization of a pumping well. The work summarizes the hydrogeological knowledge at the West of the Basilicata Region (Muro Lucano, Italy). The investigated area is characterized by the presence of a karst aquifer which is made up of a carbonate ridge (Castelgrande, Muro Lucano) that tectonically dips southward and is widely covered by Pliocene deposits (sands and conglomerates), by the Irpinian unit and Sicilide unit formations, and by debris slope and landslide deposits. The assessment of the complex hydrogeological framework of the area was detailed by the use of a new multichannel deep geoelectrical technique (DERT). In details, the proposed technique was able to successfully locate a less resistive zone connected to a more fractured limestone and then it was suitable for the localization of a groundwater exploitation well.

scholarly journals Characterization of the northern Red Sea's oceanic features with remote sensing data and outputs from a global circulation model

Oceanologia ◽  
2017 ◽  
Vol 59 (3) ◽  
pp. 213-237 ◽  
Author(s):  
Ahmed Eladawy ◽  
Kazuo Nadaoka ◽  
Abdelazim Negm ◽  
Sommer Abdel-Fattah ◽  
Mahmoud Hanafy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Circulation Model ◽  
Remote Sensing Data ◽  
Global Circulation Model ◽  
Sensing Data ◽  
Global Circulation

Monitoring of levee breaching through remote sensing and artificial intelligence

2020 ◽  
Author(s):  
Rosa Di Maio ◽  
Eleonora Vitagliano ◽  
Rosanna Salone
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Time Series ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Ground Level ◽  
Model Parameters ◽  
Levee Failure ◽  
Geophysical Surveys ◽  
Derived Data

<p>The study of flooding events resulting from bank over-flooding and levee breaching is of large interest for both society and environment, because flood waves, resulting from levee failure, might cause loss of lives and destruction of properties and ecosystems. Understanding the subsoil mechanics and the fluid-solid interplay allows the stability condition estimate of dams, embankments and slopes and the development of early warning alarm systems. Changes in soil and hydraulic parameters are usually monitored by geotechnical and geophysical investigations that also provide the basic assumptions for developing hydraulic models. Nowadays, remote sensing approaches, including satellite techniques, are mainly used for flooding simulation studies. Indeed, remote sensing observations, such as discharge, flood area extent and water stage, have been used for retrieving flood hydrology information and modeling, calibrating and validating hydrodynamic models, improving model structures and developing data assimilation models. Although all these studies have contributed significantly to the recent advances, uncertainty in observations, as well as in model parameters and prediction, represents a critical aspect for using remote sensing data in the flooding defence. Compared to past and current methods for monitoring the fluvial levee failure, we propose a new procedure that provides a wide and fast alert system. The proposed methodological path is based on presumed relationships between ground level deformation and hydrological and surface soil properties, due to physical mechanisms and exhibited by geodetic and hydrological time series. The procedure is accomplished first through multi-methodological comparative analyses applied to geodetic, hydrological and soil-properties patterns, then through the mapping of the river zones prone to failure. Since the input consists of time series satellite-derived data, the geospatial Artificial Intelligence is applied for extracting knowledge from spatial big data and for increasing the performance of data computing. In particular, machine learning is initially developed for selecting the relevant geographical areas (i.e. rivers, levees and riverbanks) from large geo-referential datasets. Then, since the spatial-distributed points are also time-dependent, the trends of different datasets are compared point by point by selected analytical techniques. Finally, in accordance with the acquired knowledge from previous steps, the system extracts information on the correlation indexes in order to make sense of patterns in space and time and to identify hierarchic orders for the realization of hazard maps. The proposed method is “wide” because, unlike other direct surveys, it is able to monitor large spatial areas since it is based on satellite-derived data. It is also “fast” because it is based on the Earth’s surface observation and is not connected with Earth’s inland investigations (such as the geotechnical and geophysical ones) or with forecasting models (e.g. hydraulic and flooding simulations). Due to these peculiarities, the method can support flood protection studies and can be used for driving the localization of river portions prone to failure, where focusing detailed geotechnical and geophysical surveys.</p>

scholarly journals Geomorphological Analysis and Hydrological Potential Zone of Baira River Watershed, Churah in Chamba District of Himachal Pradesh, India

2017 ◽  
Vol 2 (1) ◽  
pp. 26 ◽  
Author(s):  
Kuldeep Pareta ◽  
Upasana Pareta
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Himachal Pradesh ◽  
Morphometric Parameters ◽  
Landsat 8 ◽  
Potential Zone ◽  
Gis Technology ◽  
River Watershed ◽  
Geomorphological Analysis

In the present study, an attempt has been made to study the quantitative geomorphological analysis and hydrological characterization of 95 micro-watersheds (MWS) of Baira river watershed in Himachal Pradesh, India with an area of 425.25 Km2. First time in the world, total 173 morphometric parameters have been generated in a single watershed using satellite remote sensing data (i.e. IRS-P6 ResourceSAT-1 LISS-III, LandSAT-7 ETM+, and LandSAT-8 PAN & OLI merge data), digital elevation models (i.e. IRS-P5 CartoSAT-1 DEM, ASTER DEM data), and soI topographical maps of 1: 50,000 scale. The ninety-five micro-watersheds (MWS) of Baira river watershed have been prioritized through the morphometric analysis of different morphometric parameters (i.e. drainage network, basin geometry, drainage texture analysis, and relief characterizes ). The study has concurrently established the importance of geomorphometry as well as the utility of remote sensing and GIS technology for hydrological characterization of the watershed and there for better resource and environmental managements.

scholarly journals Integrated Archaeological Research: Archival Resources, Surveys, Geophysical Prospection and Excavation Approach at an Execution and Burial Site: The German Nazi Labour Camp in Treblinka

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 336
Author(s):  
Sebastian Różycki ◽  
Rafał Zapłata ◽  
Jerzy Karczewski ◽  
Andrzej Ossowski ◽  
Jacek Tomczyk
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Archaeological Research ◽  
Burial Site ◽  
Sensing Data ◽  
Non Invasive ◽  
Geophysical Surveys ◽  
Forced Labour ◽  
Labour Camp

This article presents the results of multidisciplinary research undertaken in 2016–2019 at the German Nazi Treblinka I Forced Labour Camp. Housing 20,000 prisoners, Treblinka I was established in 1941 as a part of a network of objects such as forced labour camps, resettlement camps and prison camps that were established in the territory of occupied Poland from September 1939. This paper describes archaeological research conducted in particular on the execution site and burial site—the area where the “death pits” have been found—in the so-called Las Maliszewski (Maliszewa Forest). In this area (poorly documented) exhumation work was conducted only until 1947, so the location of these graves is only approximately known. The research was resumed at the beginning of the 21st century using, e.g., non-invasive methods and remote-sensing data. The leading aim of this article is to describe the comprehensive research strategy, with a particular stress on non-invasive geophysical surveys. The integrated archaeological research presented in this paper includes an analysis of archive materials (aerial photos, witness accounts, maps, plans, and sketches), contemporary data resources (orthophotomaps, airborne laser scanning-ALS data), field work (verification of potential objects, ground penetrating radar-GPR surveys, excavations), and the integration, analysis and interpretation of all these datasets using a GIS platform. The results of the presented study included the identification of the burial zone within the Maliszewa Forest area, including six previously unknown graves, creation of a new database, and expansion of the Historical-GIS-Treblinka. Obtained results indicate that the integration and analyses within the GIS environment of various types of remote-sensing data and geophysical measurements significantly contribute to archaeological research and increase the chances to discover previously unknown “graves” from the time when the labour camp Treblinka I functioned.

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