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Methodology of Lithuanian climate atlas mapping

Donatas Valiukas Audronė Galvonaitė Algimantas Česnulevičius
Geodesy and Cartography | 2015 | vol. 64 | No 1 | DOI: 10.1515/geocart-2015-0008
Keywords Climate atlas Climate normal ArcGIS
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Abstract

Climate atlases summarize large sets of quantitative and qualitative data and are results of complex analytical cartographic work. These special geographical publications summarize long term meteorological observations, provide maps and figures which characterise different climate elements. Visual information is supplemented with explanatory texts. A lot of information on short and long term changes of climate elements were provided in published Lithuanian atlases (Atlas of Lithuanian SDR, 1981; Climate Atlas of Lithuania, 2013), as well as in prepared but unpublished Lithuanian Atlas (1989) and in upcoming new national atlas publications (National Atlas of Lithuania. 1 st part, 2014). Climate atlases has to be constantly updated to be relevant and to describe current climate conditions. Comprehensive indicators of Lithuanian climate are provided in different cartographic publications. Different time periods, various data sets and diverse cartographic data analysis tools and visualisation methods were used in these different publications.
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Authors and Affiliations

Donatas Valiukas
Audronė Galvonaitė
Algimantas Česnulevičius

Monitoring the change in land surface temperature and urban areas using Satellite images. Case study Kafr El-Sheikh City – Egypt

Zaki M. Zeidan Ashraf A.A. Beshr Sanaa S. Soliman
Geodesy and Cartography | 2019 | vol. 68 | No 2 | 389-404 | DOI: 10.24425/gac.2019.128465
Keywords Urban area ERDAS Imagine software satellite image LST ArcGIS 10.3 forecasting
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Abstract

In recent years, the rate of urban growth has increased rapidly especially in Egypt, due to the increase in population growth. The Egyptian government has set up new cities and established large factories, roads and bridges in new places to solve this trouble. This paper investigates the change monitoring of land surface temperature, urban and agricultural area in Egypt especially Kafr EL-Sheikh city as case study using high resolution satellite images. Nowadays, satellite images are playing an important role in detecting the change of urban growth. In this paper, cadastral map for Kafr El-Sheikh city with scale 1:5000, images from Landsat 7 with accuracy 30 meters; images from Google Earth with accuracy 0.5 meter; and images from SAS Planet with accuracy 0.5 m are used where all images are available during the study period (for year’s 2003, 2006, 2009, 2012, 2015 and 2017). The analysis has been performed in a platform of Geographical Information System (GIS) configured with Remote Sensing system using ArcGIS 10.3 and ERDAS Imagine image processing software. From the processing and analysis of the specified images during the studied time period, it is found that the building area was increased by 28.8% from year 2003 up to 2017 from Google Earth images and increased by percentage 34.4% from year 2003 up to year 2017 from supervised Landsat 7 images but for unsupervised Landsat 7 images, the building area was increased by percentage 35.9%. In this study, land surface temperature (LST) was measured also from satellite images for different years through 2003 until 2017. It is deduced that the increase in the building area (urban growth) in the specified city led to increase the land surface temperature (LST) which will affect some agricultural crops. Depending on the results of images analysis, Forecasting models using different algorithms for the urban and agricultural area was built. Finally, it is deduced that integration of spacebased remote sensing technology with GIS tools provide better platform to perform such activities.

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Authors and Affiliations

Zaki M. Zeidan
Ashraf A.A. Beshr
Sanaa S. Soliman

Extraction of urban construction development with using Landsat satellite images and geoinformation systems

Aybek M. Arifjanov Shamshodbek B. Akmalov Luqmon N. Samiev
Journal of Water and Land Development | 2021 | No 48 | 65-69 | DOI: 10.24425/jwld.2021.136147
Keywords ArcGIS Geographic Information System (GIS) Landsat satellite image remote sensing (RS) urban
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Abstract

In recent times there have been many changes on Earth, which have appeared after anthropogenic impact. Finding solu-tions to problems in the environment requires studying the problems quickly, make proper conclusions and creating safe and useful measures. Humanity has always had an effect on the environment. There can be many changes on the Earth be-cause of direct and indirect effects of humans on nature. Determining these changes at the right time and organizing meas-urements of them requires the creation of quick analysing methods. This development has improved specialists’ interest for remote sensing (RS) imagery. Moreover, in accordance with analysis of literature sources, agriculture, irrigation and ecolo-gy have the most demand for RS imagery. This article is about using geographic information system (GIS) and RS technol-ogies in cadastre and urban construction branches. This article covers a newly created automated method for the calculation of artificial surface area based on satellite images. Accuracy of the analysis is verified according to the field experiments. Accuracy of analysis is 95%. According to the analysis from 1972 to 2019 artificial area enlargement is 13.44%. This method is very simple and easy to use. Using this data, the analysis method can decrease economical costs for field measures. Using this method and these tools in branches also allows for greater efficiency in time and resources.
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Bibliography

ARIFJANOV A., APAKHODJAEVA T., AKMALOV SH. 2019a. Calculation of losses for transpiration in water reservoirs with using new computer technologies. In: International Conference on Information Science and Communication Technologies (ICISCT). 04–06.11.2019 Tashkent. IEEE p. 1–4. DOI 10.1109/ICISCT47635.2019.9011883.
ARIFJANOV A., SAMIEV L., APAKHODJAEVA T., AKMALOV SH. 2019b Distribution of river sediment in channels. In: XII International Scientific Conference on Agricultural Machinery Industry. 10–13.09.2019 Don State Technical University, Russian Federation. IOP Conference Series: Earth and Environmental Science. Vol. 403, 012153. DOI 10.1088/1755-1315/403/1/012153.
AYRES-SAMPAIO D., TEODORO A.C., FREITAS T.A., SILLERO N. 2012. The use of remotely sensed environmental data in the study of asthma disease. Remote Sensing for Agriculture, Ecosystems, and Hydrology 14. Vol. 8531, 853124. DOI 10.1117/12. 974539.
BALAWEJDER M., NoGa K. 2016. The influence of the highway route on the development of patchwork of plots. Journal of Water and Land Development. No. 30 p. 3–11. DOI 10.1515/jwld-2016-0015.
BEKHIRA A., HABI M., MORSLI B. 2019. Management of hazard of flooding in arid region urban agglomeration using HEC-RAS and GIS software: The case of the Bechar's city. Journal of Water and Land Development. No. 42 (VII–IX) p. 21–32. DOI 10.2478/jwld-2019-0041.
BIEDA A., BYDŁOSZ J., WARCHOŁ A., BALAWEJDER M. 2020. Historical underground structures as 3D cadastral objects. Remote Sensing. Vol. 12. Iss. 10, 1547 p. 1–29. DOI 10.3390/rs12101547.
BRIGANTE R., RADICIONINI F. 2014. Use of multispectral sensors with high spatial resolution for territorial and environmental analysis. Geographia Technica. Vol. 9. No. 2 p. 9–20.
CAPOLUPO A., MONTERISI C., TARANTINO E. 2020. Landsat Images Classification Algorithm (LICA) to automatically extract land cover information in Google Earth engine environment. Remote Sensing. Vol. 12. Iss. 7, 1201. DOI 10.3390/ rs12071201.
CHEN Z., NING X., ZHANG J. 2012. Urban land cover classification based on WorldView-2 image data. In: International Symposium on Geomatics for Integrated Water Resource Management. IEEE p. 1–5.
DINKA M.O., CHAKA D.D. 2019. Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia. Journal of Water Land Development. No. 41 p. 146–153. DOI 10.2478/jwld-2019-0025.
GINIYATULLINA O.L., POTAPOV V.P., SCHACTLIVTCEV E.L. 2014 Integral methods of environmental assessment at mining regions based on remote sensing data. International Journal of Engineering and Innovative Technology (IJEIT). Vol. 4. Iss. 4 p. 220–224.
Impactmin 2010. WP4-Satelite remote sensing deliverable D4. 1 Report on the limitations and potentials of satelite EO data [online]. Contract No. 244166. Impact Monitoring of Mineral Resources Exploitation pp. 143. [Access 08.05.2020]. Available at: https://impactmin.geonardo.com/downloads/impactmin_d41.pdf
MACHAULT V., VIGNOLLES C., BORCHI F., VOUNATSOU P., BRIOLANT S., LACAUX J.P., ROGIER C. 2011. The use of remotely sensed environmental data in the study of malaria. Geospatial Health. Vol. 5. No. 2 p. 151–168. DOI 10.1117/12.974539.
NAVULUR K. 2006. Multispectral image analysis using the object-oriented paradigm. UK CRC Press. ISBN 987-1-4200-4306-8 pp. 204.
NAVULUR K., PACIFICI F., BAUGH B. 2013. Trends in optical commercial remote sensing industry [Industrial profiles]. IEEE Geoscience and Remote Sensing Magazine. Vol. 1. Iss. 4 p. 57–64. DOI 10.1109/MGRS.2013.2290098.
RAMOELO A., CHO M. 2014. Dry season biomass estimation as an indicator of rangeland quantity using multi-scale remote sensing data. In: 10th International Conference on African Association of Remote Sensing of Environment (AARSE). University of Johannesburg p. 27–31.
RONCZYK M., WOJTASZEK-LEVENTE H. 2012. Object-based classification of urban land cover extraction using high spatial resolution imagery. In: The impact of urbanization, industrial, agricultural and forest technologies on the natural environment. Eds. M. Neményi, B. Heil. Sopron. Nyugat-magya¬rországi Egyetem p. 171–181.
TOGAEV I., NURKHODJAEV A., AKMALOV SH. 2020. Structurally decryptable complexes-a new taxonomic unit in cosmo-geological research. In: E3S Web of Conferences. EDP Sciences. Vol. 164 p. 07027. DOI 10.1051/e3sconf/2020164 07027
TUKHLIEV N., KREMENSOVA А. 2007. O’zbekiston milliy ensiklopediyasi [National encyclopedy of Uzbekistan]. State Scientific Publishing. Tashkent. Uzbekistan p. 560.
Uzkommunkhizmat 2010. Water supply of Syr Darya province. World Bank Project [online]. Uzbekistan, Tashkent Agency «Uzkommunservice» pp. 152. [Access 12.02.2020]. Available at: http://documents1.worldbank.org/curated/pt/198941468127470671/pdf/E23850P11176001C10EIA71Report1Final.pdf
XU D., GUO X., LI Z., YANG X., YIN X. 2014. Measuring the dead component of mixed grassland with Landsat Imagery. Remote Sensing of Environment. Vol. 142 p. 33–43. DOI 10.1016.j.rse.2013.11.017.

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Authors and Affiliations

Aybek M. Arifjanov
1
ORCID: ORCID
Shamshodbek B. Akmalov
1
ORCID: ORCID
Luqmon N. Samiev
1
ORCID: ORCID
  1. Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, 39 Kari Niyazov Str. Tashkent 100000, Uzbekistan

Mapping of impervious surfaces with the use of remote sensing imagery: Support Vector Machines classification and GIS-based approach

Janusz Sobieraj Marcos Fernández Marín Dominik Metelski
Archives of Civil Engineering | 2023 | vol. 69 | No 3 | 129-146 | DOI: 10.24425/ace.2023.146071
Keywords classification impervious surface support vector machines (SVM) remote sensing geographicinformation system (GIS) Land Use/Land Cover (LULC) ArcGIS machine learning
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Abstract

This study focuses on the problem of mapping impervious surfaces in urban areas and aims to use remote sensing data and orthophotos to accurately classify and map these surfaces. Impervious surface indices and green space assessments are widely used in land use and urban planning to evaluate the urban environment. Local governments also rely on impervious surface mapping to calculate stormwater fees and effectively manage stormwater runoff. However, accurately determining the size of impervious surfaces is a significant challenge. This study proposes the use of the Support Vector Machines (SVM) method, a pattern recognition approach that is increasingly used in solving engineering problems, to classify impervious surfaces. The research results demonstrate the effectiveness of the SVM method in accurately estimating impervious surfaces, as evidenced by a high overall accuracy of over 90% (indicated by the Cohen’s Kappa coefficient). A case study of the “Parkowo-Lesne” housing estate in Warsaw, which covers an area of 200,000 m², shows the successful application of the method. In practice, the remote sensing imagery and SVM method allowed accurate calculation of the area of the surface classes studied. The permeable surface represented about 67.4% of the total complex and the impervious surface corresponded to the remaining 32.6%. These results have implications for stormwater management, pollutant control, flood control, emergency management, and the establishment of stormwater fees for individual properties. The use of remote sensing data and the SVM method provides a valuable approach for mapping impervious surfaces and improving urban land use management.
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Authors and Affiliations

Janusz Sobieraj
1
ORCID: ORCID
Marcos Fernández Marín
2
ORCID: ORCID
Dominik Metelski
3
ORCID: ORCID
  1. Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16,00-637 Warsaw, Poland
  2. Universitat Politccnica de Valcncia, Department of Computer Science and Artificial Intelligence,46980 Paterna (Valencia), Spain
  3. University of Granada, Faculty of Economics and Business Sciences, Campus Cartuja, 18071Granada, Spain

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