scholarly journals Institutional and legal model for public procurement of products of Earth observation in Ukraine

2021 ◽  
Vol 27 (3) ◽  
pp. 93-107
Author(s):  
A.M. Hurova ◽  
◽  
V.K. Malolitneva ◽  
Keyword(s):  
Remote Sensing ◽  
Public Procurement ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Economic Feasibility ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Technical Requirements ◽  
Procurement Contract ◽  
Depth Analysis

The article explores the mechanism of public procurement in the sphere of providing the remote sensing services to public entities. Authors emphasize on the benefits of the centralized procurement of remotely sensed data that will lead to cost savings of state funds through the avoidance of duplicate purchases. Authors give special consideration to the difference in products obtained within the Earth observation (EO) process, the purchase of which can be carried out according to different procedures, as well as the duality of the status of the National Center for the Management and Testing of Space Facilities (NCMTSF) as a supplier and intermediary in public procurement legal relations. It is determined that NCMTSF as a centralized procurement organization will collect requests from contracting authorities (public consumers) for remote sensing services, analyze them for the possibility of satisfying it with products from the existing own fund or the need to purchase remote sensing data. It is argued that in case of impossibility to provide relevant services from the available resources of the remote sensing fund, but economic feasibility of purchasing raw remote sensing data, considering the consolidated application of public consumers, NCMTSF will act as a centralized procurement organization. It is emphasized that, unlike other centralized procurement organizations, the procurement contract with the winner of the procurement will be concluded by NCMTSF. NCMTSF will provide free of charge public authorities, enterprises, institutions, and organizations with raw data information products which were purchased at the expense of state budget according to their requests. It is suggested that in case of impossibility and economic inefficiency of providing data processing services, NCMTSF as a centralized procurement organization organizes procurement in the interests of public consumers. The procurement contract is concluded between the public consumer and the winner of the procurement. That is, in this case, the NCMTSF performs an intermediary function of procurement organization, which can professionally qualify the participants and determine the most economically advantageous tender. In addition, several procedures have been discovered that will unify suppliers’ offers according to contracting authorities’ technical requirements and optimize budget spending, that was based on an analysis of domestic legislation on public procurement and models for the procurement of remote sensing products in space states such as the USA, Australia and India. The article provides an in-depth analysis of the implementation of multi-use supplier list as prequalification system for potential economic operators of remote sensing services.

Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Biswajeet Pradhan
Keyword(s):  
Remote Sensing ◽  
Process Model ◽  
Remote Sensing Data ◽  
Groundwater Potential ◽  
Remotely Sensed Data ◽  
Drainage Pattern ◽  
Ancillary Data ◽  
Sensing Data ◽  
Gis Techniques ◽  
Gis Tools

AbstractThis paper summarizes the findings of groundwater potential zonation mapping at the Bharangi River basin, Thane district, Maharastra, India, using Satty’s Analytical Hierarchal Process model with the aid of GIS tools and remote sensing data. To meet the objectives, remotely sensed data were used in extracting lineaments, faults and drainage pattern which influence the groundwater sources to the aquifer. The digitally processed satellite images were subsequently combined in a GIS with ancillary data such as topographical (slope, drainage), geological (litho types and lineaments), hydrogeomorphology and constructed into a spatial database using GIS and image processing tools. In this study, six thematic layers were used for groundwater potential analysis. Each thematic layer’s weight was determined, and groundwater potential indices were calculated using groundwater conditions. The present study has demonstrated the capabilities of remote sensing and GIS techniques in the demarcation of different groundwater potential zones for hard rock basaltic basin.

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 Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night

2020 ◽  
Vol 12 (24) ◽  
pp. 4139
Author(s):  
Ruirui Wang ◽  
Wei Shi ◽  
Pinliang Dong
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Important Indicator ◽  
Artificial Lighting ◽  
Sensing Data ◽  
Dragon Fruit ◽  
Positive Correlations

The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.

scholarly journals Modeling regional evaporation through ANFIS incorporated solely with remote sensing data

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.

scholarly journals Integrated geological interpretation of remote sensing data (Boysun structural-tectonic zone)

2021 ◽  
Vol 929 (1) ◽  
pp. 012002
Author(s):  
L R Bikeeva ◽  
Z Kh Safarov ◽  
M G Yuldasheva ◽  
N M Akramova ◽  
Sh A Umarov
Keyword(s):  
Remote Sensing ◽  
Oil And Gas ◽  
Sedimentary Cover ◽  
Remote Sensing Data ◽  
Remotely Sensed Data ◽  
Tectonic Zone ◽  
Geological Interpretation ◽  
Sensing Data ◽  
Local Structures ◽  
Ring Structures

Abstract In recent years, remote sensing data are increasingly used in the practice of oil and gas prospecting. This article discusses the main methodological aspects of identifying oil and gas promising structures by using materials for interpreting remote sensing data and a complex of geological and geophysical data. Remotely sensed data exhibit a regional review of the various geological formations and tectonic fracture zone and faults that are otherwise not possible detection by human eyes on the ground. The method of structural interpretation space image allows you to: detail the internal structure of oil and gas regions; to reveal the position and features of the tectonic blocks, structures of the second and third (anticlines, synclines, monoclines, etc.) orders; identify major disruptive violations; identify chains of local structures; fix the transverse structural elements that determine tectonic fragmentation. By deciphering the remote sensing data, the distribution and nature of the lineament network marking disjunctive dislocations and zones of increased fracturing are revealed and analyzed, as well as ring structures are detected, which in most cases indicate local structures of the sedimentary cover at different depth sections. The lithology and lineament interpreted from these multi-level data were integrated with data collected from the ground.

scholarly journals Monitoring military landscapes and detection of underground man-made critical infrastructures in Cyprus using Earth Observation

2018 ◽  
Vol 45 ◽  
pp. 335-342 ◽  
Author(s):  
George Melillos ◽  
Athos Agapiou ◽  
Silas Michaelides ◽  
Diofantos G. Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Underground Structures ◽  
Spectral Signatures ◽  
Sensing Data ◽  
Phenological Cycle ◽  
In Situ Observations

Abstract. This paper aims to explore the importance of monitoring military landscapes in Cyprus using Earth Observation. The rising availability of remote sensing data provides adequate opportunities for monitoring military landscapes and detecting underground military man-made structures. In order to study possible differences in the spectral signatures of vegetation so as to be used for the systematic monitoring of military landscapes that comprise underground military structures, field spectroscopy has been used. The detection of underground and ground military structures based on remote sensing data could make a significant contribution to defence and security science. In this paper, underground military structures over vegetated areas were monitored, using both ground and satellite remote sensing data. Several ground measurements have been carried out in military areas, throughout the phenological cycle of plant growth, during 2016–2017. The research was carried out using SVC-HR1024 ground spectroradiometers. Field spectroradiometric measurements were collected and analysed in an effort to identify underground military structures using the spectral profile of the vegetated surface overlying the underground target and the surrounding area, comprising the in situ observations. Multispectral vegetation indices were calculated in order to study their variations over the corresponding vegetation areas, in presence or absence of military underground structures. The results show that Vegetation Indices such as NDVI, SR, OSAVI, DVI and MSR are useful for determining areas where military underground structures are present.

scholarly journals Analysis of Effects of Selected Aerosol Particles to the Global Climate Change and Health using Remote Sensing data: The Focus on Africa

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
G Rushingabigwi ◽  
W Kalisa ◽  
P Nsengiyumva ◽  
F Zimulinda ◽  
D Mukanyiligira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Global Climate ◽  
Fine Particulate Matter ◽  
Remote Sensing Data ◽  
Aerosol Extinction ◽  
Sensible Heat ◽  
Remotely Sensed Data ◽  
Tropical Regions ◽  
Sensing Data

The desert's dust and anthropogenic biomass burning's black carbon (BC) in the tropical regions are associated with many effects on climate and air quality. The dust and BC are the selected aerosols, which affect health by polluting the breathable air. This research discusses the effects of both the aerosols, especially while they interact with the clouds. The respective aerosol extinction optical thickness (AOT) extinction was analysed with the sensible heat from Turbulence. The research purposes to quantitatively study the remote sensing data for fine particulate matter, PM2.5, heterogeneously mixing both the dust and the pulverized black carbon's soot or ash, to analyse at which levels PM2.5 can endanger human health in the sub-Saharan region. The mainly analysed data had been assimilated from different remote sensing tools; the Goddard interactive online visualization and analysis infrastructure (GIOVANNI) was in the centre of data collection; GIS, the research data analysis software. In results, the rise and fall of the averaged sensible heat were associated with the rise and fall of averaged aerosol extinction AOT; the direct effects of the selected aerosols on the clouds are also presented. Regarding the health effects, PM2.5 quantities are throughout beyond the tolerably recommended quantity of 25μg/m3; thus, having referred to erstwhile research, inhabitants would consume food and drug supplements which contain vanillic acid during dusty seasons. Keywords: Geographic Information System (GIS), remotely sensed data, spatio-temporal (data) analysis

scholarly journals GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 201-215
Author(s):  
M. R. Mohd Salleh ◽  
N. I. Ishak ◽  
K. A. Razak ◽  
M. Z. Abd Rahman ◽  
M. A. Asmadi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Active Region ◽  
Probability Model ◽  
Remote Sensing Data ◽  
Landslide Inventory ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Landslide Activity ◽  
Tectonically Active ◽  
Vegetation Characteristics

<p><strong>Abstract.</strong> Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia.</p>

scholarly journals Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

2020 ◽  
Vol 12 (6) ◽  
pp. 972 ◽  
Author(s):  
Yinyi Cheng ◽  
Kefa Zhou ◽  
Jinlin Wang ◽  
Jining Yan
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Data Integration ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Integration Framework ◽  
Sensing Data ◽  
Storage Method

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.

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