Using Geospatial Technologies to Detect and Monitor Changes Land Cover and Land Management
Keywords:
RS, GIS, land fund, land use and land cover, landscape, indices, change analysis
Abstract
Today, as a result of rapid population growth, there are many cases of unauthorized and illegal occupation of lands used for other purposes, including agricultural irrigated lands, as well as irregular settlements. This is leading to a sharp decline in irrigated agriculture, which is important for the economy and threatens the country's food security in the future. Implementing important issues such as finding and preventing the right solution for such situations through GIS and remote sensing technologies could give high efficiency. That is why this research paper focuses on the use of remote sensing data and GIS technologies in the land-use change analysis in order to study the current state of the residential areas of the Zangiota district of the Tashkent region, Uzbekistan. The analyses NDBI, BUI, BUAEI, NBI, VIBI, BSI, UI carried out by using indices on the basis of Landsat 5-TM and 8-OLI satellite data in order to create a land-use change map during the period of 2000-2020 for identifying changes of build-up areas. As a result, it was found that over 20 years, the population's living space and common land use have increased by 3,402 hectares. It was found that 90% of the total land area of the identified residential is agricultural irrigated land.
References
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2. N. E. M. Asselman and H. Middelkoop, “Floodplain sedimentation: quantities, patterns and processes,” Earth Surface Processes & Landforms, vol. 20, no. 6, pp. 481–499, 1995
3. M. K. Jat, P. K. Garg, and D. Khare, “Monitoring and modellingof urban sprawl using remote sensing and GIS techniques,”International Journal of Applied Earth Observation and Geoinformation,vol. 10, no. 1, pp. 26–43, 2008.
4. T.L.Sohl, B.M.Sleeter Role of remote sensing for land –use and land cover change modeling Remote Sensing and Land Cover: Principles and Applications pp.225-239 may 2012
5. J. G. M. Tziztiki, F. M. Jean, and A. H. Everett, “Land cover mapping applications with MODIS: a literature review,” International Journal of Digital Earth, vol. 5, no. 1, pp. 63–87, 2012.
6. Zhe Zhu and others Benefits of the free and open Landsat data policy, April 2019, Pages 382-385
7. Maktav D, Erbek FS, Jürgens C (2005) Remote sensing of urban areas.International Journal of Remote Sensing 26: 655-659.
8. Treitz P, Rogan J (2004) Remote sensing for mapping and monitoringland cover and land-use change. Progress in Planning 61: 269-279.
9. Goodchild, M.F.; Hill, L.L. Introduction to digital gazetteer research. Int. J. Geogr. Inf. Sci. 2008, 22, 1039–1044.
10. J. Morgan, “A User-centered Design for the Addition of Interactive Masking Capability within an existing Web GIS: Adding Interactive Masking Capability into a Web GIS,” Trans. GIS, vol. 20, Apr. 2016, doi: 10.1111/tgis.12197.
11. T. Sasidhar, K. Sreelakshmi, V. Mt, S. Vishvanathan, and S. Kp, Land Cover Satellite Image Classification Using NDVI and SimpleCNN. 2019.
12. L. Zhao, P. Zhang, X. Ma, and Z. Pan, “Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination,” Math. Probl. Eng., vol. 2017, p. 6824051, 2017, doi: 10.1155/2017/6824051
13. Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areasfrom TM imagery. Int. J. Remote Sens. 2003, 24, 583–594.
14. He, C.; Shi, P.; Xie, D.; Zhao, Y. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens. Lett. 2010, 1, 213–221.
15. Bouzekri, S.; Lasbet, A.A.; Lachehab, A. A New Spectral Index for Extraction of Built-Up Area Using Landsat-8 Data. J. Indian Soc. Remote Sens. 2015, 43, 867–873.
16. Jieli, C.; Manchun, L.; Yongxue, L.; Chenglei, S.; Wei, H. Extract residential areas automatically by New Built-up Index. In Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010; pp. 1–5.
17. Stathakis, D.; Perakis, K.; Savin, I. Efficient segmentation of urban areas by the VIBI. Int. J. Remote Sens. 2012, 33, 6361–6377.
18. Xu, H. A new index for delineating built-up land features in satellite imagery. Int. J. Remote Sens. 2008, 29, 4269–4276.
19. Kawamura,M.; Jayamana, S.; Tsujiko, Y. Relation between Social and Environmental Conditions in Colombo Sri Lanka and the Urban Index Estimated by Satellite Remote Sensing Data. Int. Arch. Photogramm. Remot Sens. 1996, 31, 321–326.
20. Modeling of land resource valuation processes. Abdullaev Z. Publishing House of the National Library of Uzbekistan, 2013, pages 31-51
21. IOP Conference Series: Earth and Environmental Science Modelling LULC for the period 2010-2030 using GIS and Remote sensing: a case study of Tikrit, Iraq Sinan JasimHadiet al 2014 IOP Conf. Ser.: Earth Environ. Sci. 20
22. Analysis of Land Use/Land Cover Changes Using Remote SensingData and GIS at an Urban Area, Tirupati, IndiaDepartment of Chemistry, Sri Venkateswara University, Tirupati, Andhra Pradesh 517 502, India
23. Future land use changes in a peri-urban context: Local stakeholder viewsScience of the Total Environment Eduardo Gomes, Arnaud Banos , Patrícia Abrantes , Jorge Rocha , Markus Schläpfer, 718 (2020) 137381
24. Praveen Kumar Mallupattu and Jayarama Reddy Sreenivasula Reddy The scientific world journal. Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India
25. N. E. M. Asselman and H. Middelkoop, “Floodplain sedimentation: quantities, patterns and processes,” Earth Surface Processes & Landforms, vol. 20, no. 6, pp. 481–499, 1995
26. J. Morgan, “A User-centered Design for the Addition of Interactive Masking Capability within an existing Web GIS: Adding Interactive Masking Capability into a Web GIS,” Trans. GIS, vol. 20, Apr. 2016, doi: 10.1111/tgis.12197.
27. T. Sasidhar, K. Sreelakshmi, V. Mt, S. Vishvanathan, and S. Kp, Land Cover Satellite Image Classification Using NDVI and SimpleCNN. 2019.
28. L. Zhao, P. Zhang, X. Ma, and Z. Pan, “Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination,” Math. Probl. Eng., vol. 2017, p. 6824051, 2017, doi: 10.1155/2017/6824051.
29. Khamidov M.,Isabaev K., Urazbaev I., Islamov U., Inamov A., and MamatkulovZ. 2020. “Application of Geoinformation Technologies for Sustainable Use of Water Resources.” European Journal of Molecular and Clinical Medicine 7 (2): 1639–48.
30. Mamatkulov Z., SafarovE., OymatovR., AbdurahmanovI., and RajapbaevM. 2021. “Application of GIS and RS in Real Time Crop Monitoring and Yield Forecasting: A Case Study of Cotton Fields in Low and High Productive Farmlands.” E3S Web of Conferences 227. https://doi.org/10.1051/e3sconf/202122703001.
31. Matyakubov B Sh., MamatkulovZ. J., OymatovR.K., KomilovU. N., and. EshchanovaG. E. 2020. “Assessment of the Reclamation Conditions of Irrigated Areas by Geospatial Analysis and Recommendations for Their Improvement.” InterCarto, InterGIS 26: 229–39. https://doi.org/10.35595/2414-9179-2020-3-26-229-239.
32. Malgorzata W. V, RonczykL., MamatkulovZ., and ReimovM. 2021. “Object-Based Approach for Urban Land Cover Mapping Using High Spatial Resolution Data.” E3S Web of Conferences 227. https://doi.org/10.1051/e3sconf/202122701001.
Published
2021-11-27
How to Cite
Saipova, B. (2021). Using Geospatial Technologies to Detect and Monitor Changes Land Cover and Land Management. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 2(11), 103-111. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/133
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Articles
