scholarly journals STRUCTURING A G.I.S. FOR THE INTEGRATED APPLICATION OF GEOLOGICAL, HYDROGEOLOGICAL AND REMOTE SENSING METHODS AND TECHNIQUES IN THE STUDY OF GROUND WATER IN THE SITIA TECTONIC GRABEN

2018 ◽  
Vol 36 (2) ◽  
pp. 885
Author(s):  
Ε. Ρόκος ◽  
Β. Ανδρώνης
Keyword(s):  
Remote Sensing ◽  
Ground Water ◽  
Digital Processing ◽  
Remotely Sensed ◽  
In Situ Studies ◽  
Doctoral Research ◽  
Hydrogeological Study ◽  
Methods And Techniques ◽  
Remote Sensing Methods

This paper utilizes data from the first author's doctoral research on the geological and hydrogeological study of the broader area of the Sitia tectonic graben, using geological, hydrogeological, remote sensing and G.I.S. methods and techniques. In this work a G.I.S. which has been developed to include the information levels produced in the course of the research, is presented. The information levels can thus be appropriately correlated, with the aim of integrating the results of geological and hydrogeological methods and related in situ studies and measurements with evaluations of the potential of digital processing and analysis of remotely sensed images. Some conclusions of this study are documented in the paper.

scholarly journals Remote Sensing Approaches and Related Techniques to Map and Study Landslides

2020 ◽  
Author(s):  
Ram L. Ray ◽  
Maurizio Lazzari ◽  
Tolulope Olutimehin
Keyword(s):  
Remote Sensing ◽  
Hazard Assessment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Landslide Monitoring ◽  
In Situ Observation ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

scholarly journals Auroral ionospheric E region parameters obtained from satellite-based far ultraviolet and ground-based ionosonde observations: Effects of proton precipitation

2019 ◽  
Author(s):  
Harold K. Knight
Keyword(s):  
Remote Sensing ◽  
Optical Emission ◽  
Electron Precipitation ◽  
E Region ◽  
Emission Models ◽  
Proton Precipitation ◽  
Far Ultraviolet ◽  
Proton Aurora ◽  
Remote Sensing Methods

Abstract. Coincident auroral far ultraviolet (FUV) and ground-based ionosonde observations are compared for the purpose of determining whether auroral FUV remote sensing algorithms that assume pure electron precipitation are biased in the presence of proton precipitation. Auroral particle transport and optical emission models, such as the Boltzmann 3-Constituent (B3C) model, predict that maximum E region electron density (NmE) values derived from auroral Lyman-Birge-Hopfield (LBH) emission assuming electron precipitation will be biased high by up to ~ 20 % for pure proton aurora, while comparisons between LBH radiances and radiances derived from in situ particle flux observations (i.e., Knight et al., 2008, 2012) indicate that the bias associated with proton aurora should be much larger. Surprisingly, in the comparisons with ionosonde observations described here, no bias associated with proton aurora is found in FUV-derived auroral NmE, which means that auroral FUV remote sensing methods for NmE are more accurate in the presence of proton precipitation than was suggested in the aforementioned earlier works. Possible explanations for the discrepancy with the earlier results are discussed.

scholarly journals Remote Sensing of Aboveground Biomass in Tropical Secondary Forests: A Review

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
J. M. Barbosa ◽  
E. N. Broadbent ◽  
M. D. Bitencourt
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Structural Changes ◽  
Forest Succession ◽  
Conservation Status ◽  
Remotely Sensed ◽  
Secondary Forests ◽  
Forest Regrowth ◽  
Ground Truthing ◽  
Remote Sensing Methods

Tropical landscapes are, in general, a mosaic of pasture, agriculture, and forest undergoing various stages of succession. Forest succession is comprised of continuous structural changes over time and results in increases in aboveground biomass (AGB). New remote sensing methods, including sensors, image processing, statistical methods, and uncertainty evaluations, are constantly being developed to estimate biophysical forest changes. We review 318 peer-reviewed studies related to the use of remotely sensed AGB estimations in tropical forest succession studies and summarize their geographic distribution, sensors and methods used, and their most frequent ecological inferences. Remotely sensed AGB is broadly used in forest management studies, conservation status evaluations, carbon source and sink investigations, and for studies of the relationships between environmental conditions and forest structure. Uncertainties in AGB estimations were found to be heterogeneous with biases related to sensor type, processing methodology, ground truthing availability, and forest characteristics. Remotely sensed AGB of successional forests is more reliable for the study of spatial patterns of forest succession and over large time scales than that of individual stands. Remote sensing of temporal patterns in biomass requires further study, in particular, as it is critical for understanding forest regrowth at scales useful for regional or global analyses.

scholarly journals Remote Sensing

2017 ◽  
Vol 58 ◽  
pp. 10.1-10.21 ◽  
Author(s):  
J. Bühl ◽  
S. Alexander ◽  
S. Crewell ◽  
A. Heymsfield ◽  
H. Kalesse ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Measurements ◽  
Ice Formation ◽  
Literature Study ◽  
Passive Remote Sensing ◽  
Level Information ◽  
Formation And Evolution ◽  
Remote Sensing Techniques ◽  
Remote Sensing Methods

Abstract State-of-the-art remote sensing techniques applicable to the investigation of ice formation and evolution are described. Ground-based and spaceborne measurements with lidar, radar, and radiometric techniques are discussed together with a global view on past and ongoing remote sensing measurement campaigns concerned with the study of ice formation and evolution. This chapter has the intention of a literature study and should illustrate the major efforts that are currently taken in the field of remote sensing of atmospheric ice. Since other chapters of this monograph mainly focus on aircraft in situ measurements, special emphasis is put on active remote sensing instruments and synergies between aircraft in situ measurements and passive remote sensing methods. The chapter concentrates on homogeneous and heterogeneous ice formation in the troposphere because this is a major topic of this monograph. Furthermore, methods that deliver direct, process-level information about ice formation are elaborated with a special emphasis on active remote sensing methods. Passive remote sensing methods are also dealt with but only in the context of synergy with aircraft in situ measurements.

scholarly journals Documentation of archaeological sites in northern iraq using remote sensing methods

2015 ◽  
Vol XL-5/W7 ◽  
pp. 331-336
Author(s):  
E. Matoušková ◽  
K. Pavelka ◽  
K. Nováček ◽  
L. Starková
Keyword(s):  
Remote Sensing ◽  
Urban Landscape ◽  
Archaeological Sites ◽  
Urban Network ◽  
Northern Iraq ◽  
West Bohemia ◽  
Historical Sites ◽  
The University ◽  
Remote Sensing Methods

The MULINEM (The Medieval Urban Landscape in Northeastern Mesopotamia) project is aiming to investigate a Late Sasanian and Islamic urban network in the land of Erbil, historic province of Hidyab (Adiabene) that is located in the northern Iraq. The research of the hierarchical urban network in a defined area belongs to approaches rarely used in the study of the Islamic urbanism. The project focuses on the cluster of urban sites of the 6th–17th centuries A.D. This paper focuses on remote sensing analysis of historical sites with special interest of FORMOSAT-2 data that have been gained through a research announcement: Free FORMOSAT-2 satellite Imagery. Documentation of two archaeological sites (Makhmúr al-Qadima and Kushaf) are introduced. FORMOSAT-2 data results have been compared to historic CORONA satellite data of mentioned historical sites purchased earlier by the University of West Bohemia. Remote sensing methods were completed using in-situ measurements.

Features of river plume parameter determination by in situ and remote sensing methods

2019 ◽  
Vol 16 (2) ◽  
pp. 227-243 ◽  
Author(s):  
K.R. Nazirova ◽  
◽  
O.Yu. Lavrova ◽  
E.V. Krayushkin ◽  
D.M. Soloviev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Plume ◽  
Parameter Determination ◽  
Remote Sensing Methods

scholarly journals Remote Sensing Methods in the Identification of Oil Contaminations / Možnosti Využití Vybraných Metod Dpz Pro Identifikaci Ropných Kontaminací (Starých Ekologických Zátěží)

2012 ◽  
Vol 58 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Eva Smejkalová ◽  
Petr Bujok
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
In Situ Measurements ◽  
Oil Wells ◽  
Digital Data ◽  
Ecological Hazards ◽  
Remote Sensing Methods

Abstract The article deals with the possibilities of using remote sensing methods for analysis, observation and identification of old ecological hazards caused by petroleum contaminations from insufficiently plugged and abandoned oil wells in the area of Hodonín. It is focused on the description and determination of areas of interests, remote sensing approaches to the problems of petroleum substance detection and establishing the methodology of an acquired data analysis. Moreover, proper methods, algorithms and satellite digital data parameters for this aim are discussed. The article also describes in-situ measurements, technical instruments and further research advancements. Finally, the proposals of the results evaluation, interpretation and complex comparisons with the results of past and future researches in the area of Nesyt - Hodonín are specified.

scholarly journals Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

2021 ◽  
Vol 13 (1) ◽  
pp. 143
Author(s):  
Ksenia Nazirova ◽  
Yana Alferyeva ◽  
Olga Lavrova ◽  
Yuri Shur ◽  
Dmitry Soloviev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Suspended Particulate Matter Concentration ◽  
Water Sampling ◽  
Study Region ◽  
Sentinel 2A ◽  
Remote Sensing Methods

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.

scholarly journals A 55-Year Time Series Station for Primary Production in the Adriatic Sea: Data Correction, Extraction of Photosynthesis Parameters and Regime Shifts

2018 ◽  
Vol 10 (9) ◽  
pp. 1460 ◽  
Author(s):  
Žarko Kovač ◽  
Trevor Platt ◽  
Živana Ninčević Gladan ◽  
Mira Morović ◽  
Shubha Sathyendranath ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Water Column ◽  
Seasonal Cycle ◽  
Adriatic Sea ◽  
Remotely Sensed ◽  
Linear Extrapolation ◽  
Daily Production ◽  
Assimilation Number

In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth than the estimates based on linear extrapolation. Subsequently, water column production is calculated, and here, the model predicted on average 26% lower water column production. With the recovered parameters and the known magnitude of the overestimation, the time-series of water column production is then re-established with the non-linearly-corrected data. During this 55-year period, distinct regimes were observed, which were classified with a regime shift detection method. It is then demonstrated how the recovered parameters can be used in a remote sensing application. A seasonal cycle of the recovered assimilation number is constructed along with the seasonal cycle of remotely-sensed chlorophyll. The two are then used to model the seasonal cycle of water column production. An upper and a lower bound on the seasonal cycle of water column production based on remotely-sensed chlorophyll data are then presented. Measured water column production was found to be well within the range of remotely-sensed estimates. With this work, the utility of in situ measurements as a means of providing information on the assimilation number is presented and its application as a reference for remote sensing models highlighted.

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