[Home ] [Archive]   [ فارسی ]  
:: Current Issue :: Search :: Submit an Article ::
Main Menu
Home
Journal Information
About the Journal
Aims & Scopes
Editorial Board
Indexing & Abstracting
Archive
All Issues
Current Issue
For Authors
Author's Guide
Reference Guide
Authorship Criteria
Submit an Article
Principle of Transparency
Publication Ethics Statement
Open Access Statement
Copyright
Contact us
::
Search in website

Advanced Search
..
Registered in


AWT IMAGE

..
:: Volume 38, Issue 1 (2023) ::
GeoRes 2023, 38(1): 35-43 Back to browse issues page
Land Use Mapping by Classification of Landsat Images Using Synthetic Method
S. Varameshc *1, S. Mohtaram Anbaran1
1- Department of Forest Science and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
Abstract:   (650 Views)
Aims: Land use mapping is very important in optimal land management. There are different methods of land use mapping that each of them has its own advantages and disadvantages. This research aimed to land use mapping by combining supervised, unsupervised, object-oriented, spectral methods and using vegetation and soil indices and ancillary maps (DEM and slope) in the Fandoghlo region.
Methodology: This research was carried out by a descriptive-analytical method in the Fandoghlo region. At the first, the Landsat8 image was pre-processed and then the normalized difference vegetation index and brightness index were extracted and stacked with image bands. Agriculture, fallow, pasture, and water body classes were extracted using the maximum likelihood method. Then matched filtering analysis and density slice methods were used to extract barren lands and forest classes respectively. Residential and garden classes’ were extracted using object-oriented method. Finally, using expert knowledge and recognition of the study area and based on ancillary maps (slope and DEM), the final land use map was extracted.
Findings: The overall accuracy and Kappa coefficient of the synthetic method were 97.28% and 0.96, respectively, that is more suitable and practical for land use planning and management than the conventional methods of satellite image classification and land use mapping.
Conclusion: Based on the results of this research, it can be said that in order to land use mapping, according to the characteristics of each region in terms of homogeneity and the number of classes, as well as the type of satellite image, it is better to use different combinations of methods, indices, and ancillary maps are used for land use mapping.
Keywords: Maximum Likelihood, Density Slice, Matched Filtering Analysis, Objected Oriented, Ancillary Maps
Full-Text [PDF 1974 kb]   (435 Downloads)    
Article Type: Original Research | Subject: Regional Planning
Received: 2022/12/16 | Accepted: 2023/03/12 | Published: 2023/03/19
* Corresponding Author Address: Department of Forest Science and Engineering, Faculty of Agriculture and Natural Resources, Mohaghegh Ardabili University, University Street, Ardabil, Iran. Postal Code: 56199-13131. (varameshs@uma.ac.ir)
References
1. Abd El-Kawy OR, Rød JK, Ismail HA, Suliman AS (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography. 31(2):483-494. [Link] [DOI:10.1016/j.apgeog.2010.10.012]
2. Alemayehu F, Nurhussen T, Jan N, Atkilt G, Amanuel Z, Mintesinot B, et al (2009). The impacts of watershed management on land use and land cover dynamics in Eastern Tigray (Ethiopia). Resources, Conservation and Recycling. 53(4):192-198. [Link] [DOI:10.1016/j.resconrec.2008.11.007]
3. Anderson JR, Hardy EE, Roach JT, Witmer RE (1976). A land use and land cover classification system for use with remote sensor data. Washington DC: US Government Printing Office. [Link] [DOI:10.3133/pp964]
4. Baatz M, Benz U, Dehghani S, Heynen M, Höltje A, Hofmann P, Lingenfelder I, et al (2004). eCognition professional user guide 4. Munich: Definiens Imaging. [Link]
5. Bakr N, Weindorf DC, Bahnassy MH, Marei SM, El-Badawi MM (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography. 30(4):592-605. [Link] [DOI:10.1016/j.apgeog.2009.10.008]
6. Brandt JS, Haynes MA, Kuemmerle T, Waller DM, Radeloff VC (2013). Regime shift on the roof of the world: Alpine meadows converting to shrublands in the southern Himalayas. Biological Conservation. 158:116-127. [Link] [DOI:10.1016/j.biocon.2012.07.026]
7. Fatahi MM, Nowrozi, AA, Abkar AA, Khalkhali A (2007). Comparison of methods of classification and preparation of land use map (Landuse) of arid regions using satellite images. Pajouhesh va Sazandgi. 20(30):129-135. [Persian] [Link]
8. Guo L (2006). Analysis of spatio-temporal changes in the landscape pattern of the Taishan mountain. Journal of Mountain Ecology. 8:1-6. [Link]
9. Guo Y, Wu Y, Ju Z, Wang J, Zhao L (2010). Remote sensing image classification by the Chaos genetic algorithm in monitoring land use changes. Mathematical and Computer Modelling. 51(11-12):1408-1416. [Link] [DOI:10.1016/j.mcm.2009.10.023]
10. Hantson S, Chuvieco E (2011). Evaluation of different topographic correction methods for landsat imagery. International Journal of Applied Earth Observation and Geoinformation. 13(5):691-700. [Link] [DOI:10.1016/j.jag.2011.05.001]
11. Hasan SS, Lin Z, Giashuddin M, Tofayel A, Abdus S (2020). Impact of land use change on ecosystem services: A review. Environmental Development. 34:100527. [Link] [DOI:10.1016/j.envdev.2020.100527]
12. Hashim BM, AL Maliki A, Sultan M, Shahid S (2022). Effect of land use land cover changes on land surface temperature during 1984-2020: A case study of Baghdad city using landsat image. Natural Hazards. 112(2):1223-1246. [Link] [DOI:10.1007/s11069-022-05224-y]
13. Kafy AA, Al Rakib A, Akter KS, Jahir DA, Sikdar S, Al Faisal A, et al (2021). Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using landsat imagery for Dhaka metropolitan area. Environmental Challenges. 4:100192. [Link] [DOI:10.1016/j.envc.2021.100192]
14. Kolios S, Stylios CD (2013). Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data. Applied Geography. 40:150-160. [Link] [DOI:10.1016/j.apgeog.2013.02.005]
15. Lillesand T, Kiefer RW, Chipman J (2015) Remote sensing and image interpretation. Toronto: John Wiley and Sons. [Link]
16. Manandhar R, Odeh IOA, Ancev T (2009). Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. Remote Sensing. 1(3):330-344. [Link] [DOI:10.3390/rs1030330]
17. Mohammady M, Zeinivand H, Moradi HR, Pourghasemi HR, Farazjoo H (2015) Investigating the effects of land use on runoff generation using WetSpa model. Iranian journal of Ecohydrology. 2(4):357-369. [Persian] [Link]
18. Mohammady M, Moradi HR, Zeinivand H, Temme AJAM (2014). A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. International Journal of Environmental Science and Technology. 12(5):1515-1526. [Link] [DOI:10.1007/s13762-014-0728-3]
19. Pakhale GK, Gupta PK (2010). Comparison of advanced pixel based (ANN and SVM) and object-oriented classification approaches using landsat-7 Etm+ data. International Journal of Engineering and Technology. 2(4):245-251. [Link]
20. Rezaei Moghadam MH, Rezaei Banafsheh M, Faizizadeh B, Nazmfar H (2010). Classification of land cover/land use based on object-oriented technique and satellite images, case study: West Azerbaijan province. Pajouhesh va Sazandgi. 23(2):19-32. [Persian] [Link]
21. Rojas C, Pino J, Basnou C, Vivanco M (2013). Assessing land-use and-cover changes in relation to geographic factors and urban planning in the metropolitan area of Concepción (Chile),implications for biodiversity conservation. Applied Geography. 39:93-103. [Link] [DOI:10.1016/j.apgeog.2012.12.007]
22. Rosenfield GH, Fitzpatrick-Lins K (1986). A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric engineering and remote sensing. 52(2):223-227. [Link]
23. Rozenstein O, Karnieli A (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography. 31(2):533-544. [Link] [DOI:10.1016/j.apgeog.2010.11.006]
24. Schulz JJ, Cayuela L, Echeverria C, Salas J, Benayas JMR (2010). Monitoring land cover change of the dryland forest landscape of central Chile (1975-2008). Applied Geography. 30(3):436-447. [Link] [DOI:10.1016/j.apgeog.2009.12.003]
25. Sexton JO, Urban DL, Donohue MJ, Song C (2013). Long-term land cover dynamics by multi-temporal classification across the landsat-5 record. Remote Sensing of Environment. 128:246-258. [Link] [DOI:10.1016/j.rse.2012.10.010]
26. Shih H, Stow DA, Chang KC, Roberts DA, Goulias KG (2022). From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping. Geocarto International.37(19):5523-5546. [Link] [DOI:10.1080/10106049.2021.1923827]
27. Stefanov WL, Ramsey MS, Christensen PR (2001). Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote sensing of Environment. 77(2):173-185. [Link] [DOI:10.1016/S0034-4257(01)00204-8]
28. Sun J, Yang J, Zhang C, Yun W, Qu J (2013). Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method. Mathematical and Computer Modelling. 58(3-4):573-581. [Link] [DOI:10.1016/j.mcm.2011.10.063]
29. Ekalagan JR (2007) Land use: The interaction of economy, ecology and hydrology. In: Tabibian M translator. Tehran: Tehran University Publications. [Persian] [Link]
30. Tokola T, Sarkeala J, Van der Linden M (2001). Use of topographic correction in landsat TM-based forest interpretation in Nepal. International Journal of Remote Sensing. 22(4):551-563. [Link] [DOI:10.1080/01431160050505856]
31. Tolessa T, Senbeta F, Kidane M (2017). The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosystem services. 23:47-54. [Link] [DOI:10.1016/j.ecoser.2016.11.010]
32. USGS [Internet] Landsat 8 Using Product (2015). Virginia: United States Geological Survey [Cited 2022, November 20]. Available from: https://www.usgs.gov/search?keywords=Landsat8+ [Link]
33. Varamesh S, Mohtaram Anbaran S, Rouhnavaz Z (2022). Evaluation and monitoring of the thirty-year physical expansion process of Ardabil city using satellite images. Research Quarterly of Geographical Data (SEPEHR). 31(123):139-153. [Persian] [Link]
34. Varamesh S, Hosseini.SM, Rahimzadegan M (2017). Comparison of conventional and advanced classification approaches by landsat-8 imagery. Applied Ecology and Environmental Research. 15(3):1407-1416. [Link] [DOI:10.15666/aeer/1503_14071416]
35. Varamesh S, Hosseini SM, Rahimzadegan M (2017). Detection of land use changes in northeastern Iran by landsat satellite data. Applied Ecology and Environmental Research. 15(3):1443-1454. [Link] [DOI:10.15666/aeer/1503_14431454]
36. Zhou W, Troy A, Grove M (2008). Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors. 8(3):1613-1636. [Link] [DOI:10.3390/s8031613]
Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Varameshc S, Mohtaram Anbaran S. Land Use Mapping by Classification of Landsat Images Using Synthetic Method. GeoRes 2023; 38 (1) :35-43
URL: http://georesearch.ir/article-1-1411-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 38, Issue 1 (2023) Back to browse issues page
تحقیقات جغرافیایی Geographical Researches
Persian site map - English site map - Created in 0.05 seconds with 40 queries by YEKTAWEB 4645