Spectral processing of the spatial data at geoelectric monitoring

2011 ◽  
Author(s):  
O. Kuzichkin ◽  
N. Chaykovskay
Keyword(s):  
Spatial Data ◽  
Spectral Processing ◽  
Geoelectric Monitoring

scholarly journals A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

2019 ◽  
Vol 11 (9) ◽  
pp. 1042 ◽  
Author(s):  
Sofia Hakdaoui ◽  
Anas Emran ◽  
Biswajeet Pradhan ◽  
Chang-Wook Lee ◽  
Salomon Cesar Nguemhe Fils
Keyword(s):  
Spatial Data ◽  
Flash Flood ◽  
Optical Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Spectral Processing ◽  
Linear Classifiers ◽  
Gaussian Kernels ◽  
Before And After ◽  
Efficient Information

This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

An empirical analysis of town-forming enterprises' corporate social responsibility: Evidence from the Volga Federal District

2020 ◽  
Vol 18 (10) ◽  
pp. 1894-1909
Author(s):  
I.R. Badykova
Keyword(s):  
Regression Analysis ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Empirical Analysis ◽  
Spatial Data ◽  
Univariate Analysis ◽  
Federal District ◽  
Current Situation ◽  
Economic Situation ◽  
Corporate Social

Subject. This article explores the determinants of social responsibility of backbone enterprises. Objectives. The article aims to investigate the relationships between the socio-economic situation of the monotown where the backbone company operates, and corporate social responsibility (CSR). Methods. For the study, I used a regression analysis and univariate analysis of spatial data. The rating estimates calculated using an original methodology are used as a CSR proxy (dependent variable). Results. Presenting information about the current situation of backbone enterprises and monotowns in Russia, the article reveals the existence of relationships between the backbone enterprise's affiliation to a monotown with a certain socio-economic situation and the level of corporate social responsibility. Conclusions. The situation of the backbone companies is likely to deteriorate. Increasing the level of social responsibility during a crisis seems unlikely.

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