Volume 7, Issue 3 (2019)
ECOPERSIA 2019, 7(3): 149-154 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Naseri M, Motazedian M. Investigation of Quickbird Satellite Image Capability in the Separation of the Canopy of Zagros Forest Trees. ECOPERSIA 2019; 7 (3) :149-154
URL: http://ecopersia.modares.ac.ir/article-24-29492-en.html
URL: http://ecopersia.modares.ac.ir/article-24-29492-en.html
1- Forest Sciences Faculty, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran , Mohammadhasan_n71@yahoo.com
2- Agriculture Faculty, Yasuj University, Yasuj, Iran
2- Agriculture Faculty, Yasuj University, Yasuj, Iran
Abstract: (4764 Views)
Aims: 2005 DashteBarm forests of Fars province image is used to investigate and evaluate the capability of Quickbird satellite imagery to differentiate tree canopies regions from no-tree areas.
Materials and Methods First, the validity geometric correction of satellite image was assured. By systematic random sampling, 79 square footages of (20*20) in ARCGIS 9.3 software was designed and on the footages’ places of the combined image from Quickbird panchromatic band and multispectral band, the samples of no tree canopies and tree canopies areas was obtained. Then, 20% of the footages were considered as test samples and the rest was studied as training samples. In the next step, processes on a multivariate image were performed by ENVI 4.3 software and some indexes such as NDVI, GNDVI, RVI Partial Component Analysis (PCA) were created and integrated and were combined. Then, two classifications on the original image and processed bands with two methods of maximum likelihood and Support Vector Machine (SVM) were categorized, in which the images were classified into two classes of trees and non-trees.
Findings: Evaluation of the classified images using the test samples showed that the accuracy and Kappa coefficient in the classified images of the original bands were 94.478% and 0.789 for the maximum likelihood method and 94.848% and 0.877 for the support vector machine, respectively. Also, the results of the processed band's classifications by maximum likelihood and support vector machine methods showed that these images have 94.274 and 94.683% accuracy and Kappa coefficient of 0.875 and 0.882, respectively.
Conclusion: The results of this study show that the Quickbird satellite image is suitable for separating tree canopies and no tree canopies areas in Zagros forests and similar areas.
Materials and Methods First, the validity geometric correction of satellite image was assured. By systematic random sampling, 79 square footages of (20*20) in ARCGIS 9.3 software was designed and on the footages’ places of the combined image from Quickbird panchromatic band and multispectral band, the samples of no tree canopies and tree canopies areas was obtained. Then, 20% of the footages were considered as test samples and the rest was studied as training samples. In the next step, processes on a multivariate image were performed by ENVI 4.3 software and some indexes such as NDVI, GNDVI, RVI Partial Component Analysis (PCA) were created and integrated and were combined. Then, two classifications on the original image and processed bands with two methods of maximum likelihood and Support Vector Machine (SVM) were categorized, in which the images were classified into two classes of trees and non-trees.
Findings: Evaluation of the classified images using the test samples showed that the accuracy and Kappa coefficient in the classified images of the original bands were 94.478% and 0.789 for the maximum likelihood method and 94.848% and 0.877 for the support vector machine, respectively. Also, the results of the processed band's classifications by maximum likelihood and support vector machine methods showed that these images have 94.274 and 94.683% accuracy and Kappa coefficient of 0.875 and 0.882, respectively.
Conclusion: The results of this study show that the Quickbird satellite image is suitable for separating tree canopies and no tree canopies areas in Zagros forests and similar areas.
Article Type: Original Research |
Subject:
Forest Ecosystems
Received: 2019/01/16 | Accepted: 2019/05/15 | Published: 2019/07/21
Received: 2019/01/16 | Accepted: 2019/05/15 | Published: 2019/07/21
References
1. Mosaddegh A. Forestry. 2nd edition. Tehran: Tehran University Press; 2002. p. 481. [Persian] [Link]
2. Erfanifard S, Zobeiri M, Feghhi J, Namiranian M. Determination of the appropriate area and shape of the sample plot in the canopy estimation using forest simulation in Zagros. Iran J of Forest Poplar Res. 2006;15(3):278-88. [Persian] [Link]
3. Hekkila J, Nevalanen S, Tokol A. Estimating defoliation in boreal coniferous forests by combining Landsat TM, aerial photographs and field data. Forest Ecol Manag. 2002;158(1-3):9-23. [Link] [DOI:10.1016/S0378-1127(00)00671-X]
4. Amini MR, ShataeeJouibari Sh, Ghazanfar H, Moaieeri MH. 2008. Investigation of changes in range of Zagros forests using aerial photographs and satellite images (Case study: Armand Baneh forests). J Agricult Sci Nat Resour. 2008;16(3):431-43. [Link]
5. Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL. 2006. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. Forest Ecol Manag. 2006;221(1-3):27-41. [Link] [DOI:10.1016/j.foreco.2005.09.021]
6. Barazmand S, Shataeejouibari Sh. Investigating the possibility of recognizing the distribution of crown princely trees using images with high spatial resolution (Case study area of educational and research forest of Dr. Bahramnia) [Dissertation]. Gorgan: Gorgan University of Agricultural Sciences and Natural Resources; 2009. p. 92. [Link]
7. Naseri, F. Classification of forest types and estimation of its quantitative characteristics using satellite data in arid and semi-arid forests [Dissertation]. Tehran: University of Tehran; 2003. p. 155. [Persian] [Link]
8. Afshar S, ShataeeJouibari Sh. Estimation of Zagros forest conservation characteristics using combination of satellite images and aid data (Case study of forests around Ilam city) [Dissertation]. Gorgan: Gorgan University of agricultural sciences and natural resources; 2012. p.86. [Persian] [Link]
9. Farzadmehr J, Arzani H, Darvishseffat AA, Jafari M. Investigating the capability of Landsat7 satellite data to estimate coverage and plant production (Case study: Hana-Semirom Semi-Steppe area). Iran Nat Resour J. 2004;57(2):339-52. [Persian] [Link]
10. Noorian NA, Shataeejouibari Sh. Investigating the capability of satellite data with different spatial resolution with emphasis on spectral indices in the separation of pure forest stands. J Sci Technol Wood For. 2014;21(3):149-66. [Persian] [Link]
11. Alavipanah SK, Ehsani AH, Omidi P. Investigation of desertification and land degradation of Damghan plains using satellite, multi-time and multi-spectral data. Biaban. 2004;9(1):143-50. [Persian] [Link]
12. Alishaharatbani F, Arzani H, Hosseini SZ, Babaeekafaki S, Meerakhorloo Kh. Preparation of rangeland vegetation map using IRS-LissIII satellite images (Case study: Sorkhabad Basin of Mazandaran). Q J Res Range Desert Iran. 2013;20(3):454-62. [Persian] [Link]
13. Barzafkan AA, Pirbavaghar M, Fathi P. Investigation of LissIII Data capability for mapping Map of Zagros forests (Case study: Marivan forests). Forest J Iran. 2014;6(4):378-401. [Link]
14. Rahdari V, Safyanian A, Khajehdiin SJ, Najafabadi SA. Investigating satellite data capability in mapping the canopy cover percentage of arid and semiarid regions (Case study: Muteh wildlife refuge). Environ Sci Technol. 2010;15(4):43-54. [Persian] [Link]
15. Rahimi HA, Peerbavaghar M, Ahmadi M, Amini MR. 2014. TM imagery capability in detecting burned forests (Case study: Qourigaleh region in Kermanshah province). Forest Poplar Res Iran. 2014;22(3):485-95. [Link]
16. Roodgarmi P, Khorasani NA, Monavvari SM, Noori J. Environmental impact exposure development using satellite images and measurement techniques. Environ Sci Technol. 2007;11(1):161-72. [Persian] [Link]
17. Salmanmahini AA, Nadali A, Feghhi J, Riazi B. Classification of forest areas of Golestan province by maximum likelihood using satellite image ETM+2001. Environ Sci Technol. 2007;14(3):47-56. [Persian] [Link]
18. Hosseini FS, Darvishseffat AA, Zargham NA. Investigation of IRS-P6-LISS-IV image capability for storing pistachio wildlife forests (Case study: KhajehKalat Forest in Khorasan). Forest J Iran. 2012;4(4):311-20. [Persian] [Link]
19. Barazmand S, ShataeeJouibari SH, Abdi O. Investigating the possibility of tree crown degradation by using Quick Bird satellite imagery images (Case study: Shastkolteh forest in Gorgan). J Forest Poplar Res Iran. 2011;19(4):466-77. [Persian] [Link]
20. Hosseinzadeh J, Najafifar A. Investigation of the relationship between diameter and height of trees with dry matter distribution in Ilam forests. J Sci Technol Wood For. 2015;23(2):75-87. [Persian] [Link]
21. Cano F, NavaroCerriilo RM, Garcia Ferrer A, SanchézdelaOrden S. Detection of forest decline using IKONOS sensor for cork oak (Quercus suber L.) Woods in South Spain. Geocarto Int. 2006;21(3):13-8. [Link] [DOI:10.1080/10106040608542388]
22. Hamzehpoor M, Kayadakiri H, Bordbar SK. Preliminary study on the drying of Iranian oak trees in Bam Kazeroun Plain. Iran J Res Poplar. 2010;19(2):352-63. [Persian]. [Link]
23. Fathizadeh H, Safari A, Bazgir M, Khosravi GhR. Evaluation and comparison of the supporting machine carriers with linear, polynomial and radial kernels with artificial neural network for classification of land use. Q J of Iran Derby Desert Res. 2015;23(4):729-43. [Link]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |