Development of a geographical information system for risk mapping of reinforced concrete buildings subjected to atmospheric corrosion in Cyprus using optical remote sensing data

2014 ◽  
Author(s):  
Kyriacos Neocleous ◽  
Athos Agapiou ◽  
Andreas Christofe ◽  
Kyriacos Themistocleous ◽  
Zenon Achillides ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Reinforced Concrete ◽  
Geographical Information System ◽  
Atmospheric Corrosion ◽  
Remote Sensing Data ◽  
Risk Mapping ◽  
Geographical Information ◽  
Optical Remote Sensing ◽  
Sensing Data

scholarly journals Assessment of Irrigation Potentiality over an Area through the Combined Application of Remote Sensing (RS) and Geographic Information System (GIS)

2020 ◽  
Vol 11 (2) ◽  
pp. 141-148
Author(s):  
Md Abdus Salam ◽  
Farhana Tazneen ◽  
Md Shafiqul Islam ◽  
SM Noman Chy
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Ground Water ◽  
Water Level ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Ground Water Level ◽  
Secondary Source ◽  
Landsat 8 ◽  
Sensing Data

There is a great influence of irrigated land of an area to the acreage and productions of agricultural crops and thus maintain the food security. Recent awareness about climate change and its impacts on global environmental challenges has drawn the great attention on rational and sustainable handling of irrigation resources and its networks. As one of the cutting-edge technologies remote sensing data and geographical information system (GIS) are very much useful for efficient management of irrigation networks and optimum utilization of irrigation schemes for the sustainable agricultural development. Irrigation potentiality is the total area which can be irrigated from a project on its full utilization. This implies that an area where water is available for irrigation in each season during a complete irrigation year. In the present study an attempt has been made to investigate the irrigation potentiality of an area using remote sensing data as primary source and field data and as well as ground water level data from secondary source. Landsat 8 OLI (Operational Land Imager) data of 2016 and 2017 have been used for this purpose. Existing irrigation system has also been identified through the investigation of natural and artificial sources of irrigation water of the study area. Seasonal irrigated area was also monitored during the crop growing season. Ground water level fluctuation was also studied using ancillary data. Journal of Engineering Science 11(2), 2020, 141-148

ANALISIS KESESUAIAN LAHAN DAN EVALUASI JENIS TANAH DALAM BUDIDAYA TANAMAN TEBU UNTUK PENGEMBANGAN DAERAH KABUPATEN TEGAL

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Plant Productivity ◽  
Saccharum Officinarum ◽  
Geographical Information ◽  
Sugarcane Plant ◽  
Plant Cultivation ◽  
Suitability Map

The objectives of the research were to make land suitability map for sugarcane plant (Saccharum officinarum), to give recommendation of location including area for sugarcane plant cultivation and to increase sugarcane plant productivity. The research used maps overlay and Geographical Information System (GIS) which used Arch-View Spatial Analysis version 2,0 A in Remote Sensing Laboratory, Agency for the Assessment and Application of Technology (BPPT), Jakarta. The research was carried out in Tegal Regency starting from June to October 2004.The results of the research showed that the suitable, conditionally suitable, and not suitable land for sugarcane cultivation in Tegal Regency reached to a high of 20,227 ha, 144 ha, and 81,599 ha respectively. There were six most dominant kind of soil: alluvial (32,735 ha), grumosol 5,760 ha), mediteran (17,067 ha), latosol   (18,595 ha), glei humus (596 ha), and regosol (22,721 ha).

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

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