Application of Different Methods of Decision Tree Algorithm for Mapping Rangeland Using Satellite Imagery (Case Study: Doviraj Catchment in Ilam Province)

Document Type : Research and Full Length Article

Authors

1 Rangeland and Watershed Management Group, Faculty of Agriculture, Ilam University, Ilam

2 Combating Desertification, Faculty of Agriculture, Ilam University, Ilam

3 Agronomy, Agriculture College, Ilam University, Ilam

4 Rangeland Management, Faculty of Natural Resources, Tarbiat Modares University

Abstract

Using satellite imagery for the study of Earth's resources is attended by many
researchers. In fact, the various phenomena have different spectral response in
electromagnetic radiation. One major application of satellite data is the classification of
land cover. In recent years, a number of classification algorithms have been developed for
classification of remote sensing data. One of the most notable is the decision tree. The aim
of this study was to compare three types of decision trees split algorithm for land cover
classification in Doviraj catchment in Ilam province, Iran. For this, propose, first, the
geometric and radiometric corrections were performed on the 2007 ETM+ data. Field data
as training sites were collected in the various classes of land use. The results of image
classification accuracy assessment showed that the Gini split classification. With kappa
value 89.98 and the entire accuracy 91.17% was significantly higher, then categorization of
branching and the branching ratio and Entropy with kappa values of 88.45 and 90.65 and
the entire accuracy of 86.21 and 86.15%, respectively.

Keywords


Alavipanah, S. K., 2005. Application of remote
sensing evaluating in earth sciences. Tehran
University Press, p. 478. (In Persian).
Alavipanah, S. K., and Valdani, M., 2010.
Remote sensing evaluating and GIS, Tehran
University Press, pp. 149-153. (In Persian).
Arekhi, S., 2012. assesment of decision tree
method for mapping land use using sattelite
imagery in the Chamgardelan in Ilam Province,
Jour. Geography and Land Use Planning, 2
(4):75-84.
Bonyad, A. I., and Haji Ghaderi, T., 2007. The
provision of Zanjan province natural forest map
using Landsat 7 ETM+
sensor data. Jour.
Agriculture Technologies and Sciences and
Natural Resources, 42(11): 627-638. (In
Persian).
Borak, J. S.,and Strahler, A. H., 1999. Feature
selection and land cover classification of a
MODIS-like dataset for a semiarid environment.
INT. Jour. Remote Sensing, 20: 919-938.
Breiman, L., Friedman, J. H., Olshen, R. A., and
Stone, C. J., 1984. Classification and regression
trees. Monterey, CA: Wadsworth, 358 p.
Chubey, M. S., Franklin, S. E., and Wulder, M.
A., 2006. Object-based Analysis of Ikonos-2
Imagery for Extraction of Forest Inventory
Parameters. Photogrammetric Engineering &
Remote Sensing, 72 (4): 383-394.
Defries, R. S., and Townshend, J. R. G., 1994.
Global land cover: comparison of
groundbaseddata sets to classi. cations with
AVHRR data. In Environmental Remote
Sensingfrom Regional to Global Scales, edited
by G. M. Foody and P. J. Curran (New York:
Wiley. 84–110.
Hansen, MC., Dub ayah, R. and Defries, R.S.,
1996. Classification trees: an alternative to
traditional land cover classifers. Int. Jour.
Remote Sensing, 17:1075-1081.
Huete, A., 2004. Remote Sensing for Natural
Resources Management and Enviromental
Monitoring: Manual of Remote Sensing 3 ed,
Vol.4. Univercity of Arizona.
Loveland, T. R., Reed, B.C., Brown, J. F., Ohlen,
D. O., Zhu, Z., Yang, L., and Merchant, J. W.,
2000. Development of a global land cover
characteristics database and IGBP discover
from 1 km AVHRR data. International Jour.
Remote Sensing, 21: 1303-1330.
Lu, D., Mausel, P., Brondi´zio, E., and Moran, E.,
2004. Change detection techniques. Int. Jour.
Remote Sensing, 25(12): 2365–2407.
Mokhtari, A., Feiznia, S., Ahmadi, H.,
Khawajaldin, S. J., and Rahnema, F. A., 2000.
Application of remote sensing evaluating in data
layers provision of lands use and land coverage
in soil erosion model. MPSIAC, Jour.
Construction and Research, 46: 82-87. (In
Persian).
Otukei, J. R., and Blaschke, T., 2010. Land cover
change assessment using decision trees, support
vector machines and maximum likelihood
classification algorithms. International Jour.
Applied Earth Observation and
Geoinformation, 12: 27S31.
Pettorelli. N., Vik, J. O., Mysterud. A., Gaillard.
J. M., Tucker, C. J., and Stenseth, N. C., 2005.
Using the satellite-derived NDVI to assess
ecological responses to environmental change.
Jour. Trends in ecology and evolution. 20(9):
503–510.
Shahriari, A., Gholami, H., Fakhireh, A., Arkhi,
S., and Nouri, S., 2010. Comparison of different
methods for monitoring vegetation coverage in
desert area of Dehloran Ein Khosh using RS and
GIS. Geomatics National Conference. Iran
Mapping Organization. (In Persian).
Swain, P. H., and Davis, S. M., 1978. Remote
Sensing: the Quantitative Approach (New York:
McGraw-Hill).
Wang, F., 1990. Fuzzy supervised classification
of remote sensing images. IEEE Transactions
on Geoscience and Remote Sensing, 28: 194-
201.
Xu, M., Watanachaturaporn, P., Varshney, P. K.
and Arora, M. K., 2005. Decision tree
regression for soft classification of remote
sensing data. Remote Sens. Environ., 97: 322-
336.
Yang, C., Prasher, S. O., Enright, P.,
Madramootoo, C., Burgess, M., Goel, P. K., and
Callum, I., 2003. Application of decision tree
technology for image classification using
remote sensing data. Agricultural Systems, 76:
1101–1117.
Zambon, M., Lawrence, R., Bunn, A., and
Powell, S., 2006. Effect of alternative splitting
rules on image processing using classification
tree analysis. Photogrammetric Engineering
and Remote Sensing, 72(1): 25–30.