Volume 8, Issue 2 (2020)                   ECOPERSIA 2020, 8(2): 97-107 | Back to browse issues page

XML Print


1- Watershed Management Engineering Department, Natural Resources Faculty, Tarbiat Modares University, Nur, Iran
2- The Centre for Advanced Modelling & Geospatial Information Systems (CAMGIS), Engineering & Information Technology Faculty, University of Technology Sydney, Ultimo, Australia , vafakhah@modares.ac.ir
3- Energy & Mineral Resources Engineering Department, Sejong University, Seoul, South Korea
4- Forestry Department, Natural Resources Faculty, Tarbiat Modares University, Nur, Iran
Abstract:   (2639 Views)
Aims: Generally, optical satellite images are used to produce a land use map. Due to spectral mixing, these data can affect the accuracy of land use classifications, especially in areas with diverse vegetation.
Materials & Methods: In the present study, in order to achieve the correct land use classification in a mountainous-forested basin, four Landsat 8 thermal images were used with a few additional information (Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope angle and slope aspect) along with optical data and data of multi-temporal images.
Findings: Results showed that thermal data, slope angle and DEM have a significant role in increasing the accuracy of land use classification, so that they increase the overall accuracy by about 3-10% from late spring to the beginning of autumn. Among the data used, slope angle and elevation data have a significant role in increasing the accuracy of agricultural classes. The total accuracy and Kappa coefficient in land use maps obtained from monotemporal images in the wet season (late spring; 83.93 and 0.82) and early summer (83.79 and 0.81)) are more than the dry season (late summer; 81.25 and 0.79) and early autumn).
Conclusion: Generally, the highest total accuracy among monotemporal images generated from optical data is about 83.95%, while the application of thermal and additional data along with optical data and the combination of monotemporal images of the wet season, the accuracy of the information multitemporal increased to 91.60% of the land use map.
 
Full-Text [PDF 1391 kb]   (767 Downloads)    
Article Type: Original Research | Subject: Ecosystem Management, Monitoring, Policy and Law
Received: 2019/10/8 | Accepted: 2019/12/7 | Published: 2020/05/19
* Corresponding Author Address: Natural Resources Faculty, Tarbiat Modares University, Imam Khomeini Street, Imam Reza Boulevard, Nur, Mazandaran Province, Iran. Postal Code: 64414356.

References
1. Afrasinei GM, Melis MT, Buttau C, Bradd JM, Arras C, Ghiglieri G. Assessment of remote sensing-based classification methods for change detection of salt-affected areas (Biskra area, Algeria). J Appl Remote Sens. 2017;11(1):016025. [Link] [DOI:10.1117/1.JRS.11.016025]
2. Naseri MH, MotazedianM. Investigation of quickbird satellite image capability in the separation of the canopy of Zagros forest trees. Ecopersia. 2019;7(3):149-54. [Link]
3. Thakkar AK, Desai VR, Patel A, Potdar MB. Post-classification corrections in improving the classification of land use/land Cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India. Egypt J Remote Sens. 2017;20(1):79-89. [Link] [DOI:10.1016/j.ejrs.2016.11.006]
4. Hazarika N, Das AK, Borah SB. Assessing land-use changes driven by river dynamics in chronically flood affected Upper Brahmaputra plains, India, using RS-GIS techniques. Egypt J Remote Sens Sp Sci. 2015;18(1):107-18. [Link] [DOI:10.1016/j.ejrs.2015.02.001]
5. Phukan P, Thakuriah G, Saikia R. Land use land cover change detection using remote sensing and GIS techniques: A case study of Golaghat district of Assam, India. Int Res J Earth Sci. 2013;1(1):11-5. [Link]
6. Karan SK, Samadder SR. Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas. Environ Monit Assess. 2016;188(8):486. [Link] [DOI:10.1007/s10661-016-5494-x]
7. López-Granados E, Mendoza ME, González DI. Linking geomorphologic knowledge, RS and GIS techniques for analyzing land cover and land use change: A multitemporal study in the Cointzio watershed, Mexico. Rev Ambient Água. 2013;8(1):18-37. [Link] [DOI:10.4136/ambi-agua.956]
8. Manandhar R, Odeh IO, Ancev T. Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. Remote Sens. 2009;1(3):330-44. [Link] [DOI:10.3390/rs1030330]
9. Ustuner M, Sanli FB, Dixon B. Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis. Eur J Remote Sens. 2015;48(1):403-22. [Link] [DOI:10.5721/EuJRS20154823]
10. Gomariz-Castillo F, Alonso-Sarría F, Cánovas-García F. Improving classification accuracy of multi-temporal Landsat Images by Assessing the Use of different algorithms, textural and ancillary information for a mediterranean semiarid area from 2000 to 2015. Remote Sens. 2017;9(10):1058. [Link] [DOI:10.3390/rs9101058]
11. Gheitury M, Heshmati M, Ahmadi M. Longterm land use change detection in Mahidasht watershed, Iran. Ecopersia. 2019;7(3):141-8. [Link]
12. Luyssaert S, Jammet M, Stoy PC, Estel S, Pongratz J, Ceschia E, et al. Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat Clim Change. 2014;4(5):389-93. [Link] [DOI:10.1038/nclimate2196]
13. Prasad SV, Savithri TS, Krishna IV. Comparison of accuracy measures for RS image classification using SVM and ANN classifiers. Int J Electr Comput Eng. 2017;7(3): 1180-7. [Link] [DOI:10.11591/ijece.v7i3.pp1180-1187]
14. Senf C, Leitão PJ, Pflugmacher D, Van Der Linden S, Hostert P. Mapping land cover in complex Mediterranean landscapes using landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens Environ. 2015;156:527-536. [Link] [DOI:10.1016/j.rse.2014.10.018]
15. Kantakumar LN, Neelamsetti P. Multi-temporal land use classification using hybrid approach. Egypt J Remote Sens Sp Sci. 2015;18(2):289-95. [Link] [DOI:10.1016/j.ejrs.2015.09.003]
16. Beyer F, Jarmer T, Siegmann B, Fischer P. Improved crop classification using multitemporal RapidEye data. 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015 July 22-24, Annecy, France. Piscataway: IEEE; 2015. [Link] [DOI:10.1109/Multi-Temp.2015.7245780]
17. Alganci U, Sertel E, Ozdogan M, Ormeci C. Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in Southeastern Turkey. Photogramm Eng Remote Sens. 2013;79(11):1053-65. [Link] [DOI:10.14358/PERS.79.11.1053]
18. Eisavi V, Homayouni S, Yazdi AM, Alimohammadi A. Land cover mapping based on random forest classification of multitemporal spectral and thermal images. Environ Monit Assess. 2015;187(5):291. [Link] [DOI:10.1007/s10661-015-4489-3]
19. Basukala AK, Oldenburg C, Schellberg J, Sultanov M, Dubovyk O. Towards improved land use mapping of irrigated croplands: Performance assessment of different image classification algorithms and approaches. Eur J Remote Sens. 2017;50(1):187-201. [Link] [DOI:10.1080/22797254.2017.1308235]
20. Nguyen TT, Pham TT. Incorporating ancillary data into landsat 8 image classification process: A case study in Hoa Binh, Vietnam. Environ Earth Sci. 2016;75(5):430. [Link] [DOI:10.1007/s12665-016-5278-1]
21. Ildoromi A, Safari Shad M. Land use change prediction using a hybrid (CA-Markov) model. Ecopersia. 2017;5(1):1631-40. [Link] [DOI:10.18869/modares.ecopersia.5.1.1631]
22. Mushore TD, Mutanga O, Odindi J, DubeT. Assessing the potential of integrated landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes. Geocarto Int. 2017;32(8):886-99. [Link] [DOI:10.1080/10106049.2016.1188168]
23. Sinha S, Sharma LK, Nathawat MS. Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. Egypt J Remote Sens Sp Sci. 2015;18(2):217-33. [Link] [DOI:10.1016/j.ejrs.2015.09.005]
24. Sun L, Schulz K . The improvement of land cover classification by thermal remote sensing. Remote Sens. 2015;7(7):8368-90. [Link] [DOI:10.3390/rs70708368]
25. Barrett B, Nitze I, Green S, Cawkwell F. Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote Sens Environ. 2014;152:109-24. [Link] [DOI:10.1016/j.rse.2014.05.018]
26. Pencue-Fierro EL, Solano-Correa YT, Corrales-Muñoz JC, Figueroa-Casas A. A semi-supervised hybrid approach for multitemporal multi-region multisensor landsat data classification. IEEE J Sel Top Appl Earth Obs Remote Sens. 2016;9(12):5424-35. [Link] [DOI:10.1109/JSTARS.2016.2623567]
27. Mohammady M, Amiri M, Dastorani J. Modeling land use changes of Ramin city in the Golestan province. J Spat Plan. 2016;19(4):141-58. [Link]
28. Wulder MA, White JC, Loveland TR, Woodcock CE, Belward AS, Cohen WB, et al. The global landsat archive: Status, consolidation, and direction. Remote Sens Environ. 2016;185:271-83. [Link] [DOI:10.1016/j.rse.2015.11.032]
29. Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens. 2007;28(5):823-70. [Link] [DOI:10.1080/01431160600746456]
30. Madhura M, Venkatachala S. Comparison of supervised classification methods on remote sensed satellite data: An application in Chennai, South India. Int J Sci Res. 2015;4(2):1407-11. [Link]
31. Castillo M, Muñoz-Salinas E. Controls on peak discharge at the lower course of Ameca River (Puerto Vallarta graben, west-central Mexico) and its relation to flooding. CATENA. 2017;151:191-201. [Link] [DOI:10.1016/j.catena.2016.12.019]
32. Jia K, Wei X, Gu X, Yao Y, Xie X, Li B. Land cover classification using landsat 8 operational land imager data in Beijing, China. Geocarto Int. 2014;29(8):941-51. [Link] [DOI:10.1080/10106049.2014.894586]
33. Namdar M, Adamowski J, Saadat H, Sharifi F, Khiri A. land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification. Int J Remote Sens. 2014;35(24):8057-73. [Link] [DOI:10.1080/01431161.2014.978035]
34. Chuvieco E. Fundamentals of satellite remote sensing. Boca Raton: CRC Press; 2009. [Link] [DOI:10.1201/b18954]
35. Zoungrana BJ, Conrad C, Amekudzi LK, Thiel M, Da ED, Forkuor G, et al. Multi-temporal landsat images and ancillary data for land use/cover change (LULCC) detection in the Southwest of Burkina Faso, West Africa. Remote Sens. 2015;7(9):12076-102. [Link] [DOI:10.3390/rs70912076]
36. Sesnie SE, Hagell SE, Otterstrom SM, Chambers CL, Dickson BG. SRTM-DEM and landsat ETM+ data for mapping tropical dry forest cover and biodiversity assessment in Nicaragua. Rev Geogr Acad. 2008;2(2):53-65. [Link]

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.