Assessing Trees Canopy Cover by Using Ikonos Satellite Imagery Retrieved from Google Earth and Field Measurements (Case Study: Iran; Sari)

Document Type : Research Paper

Authors

1 tehran

2 sari

Abstract

One of the most important information in urban forestry is acquaintance of urban landscape’s area or proportion. Nowadays, in several countries variable methods are used to achieve this aim. Since there is not any complete method to study urban forest’s status in IRAN, it is impossible to exert authentic management for these resources. Hence, in this research, sampling methods and Ikonos satellite imagery in Google Earth software were used to assess the area of road trees’ canopy cover for evaluating the accuracy of satellite images in road trees’ canopy cover estimation. Besides, random sampling was used as the base of comparison, the crown of trees’ in street margins was calculated. Afterwards, canopy of these trees in satellite images scale 1/2000 was accounted. In this study, the georeference and perform the geometric correction of the image in ENVI software, environment action provide cover and area of each of the trees on the street. The results of double t-test showed (df = 118, t =1.69) that the outcomes of two calculation methods do not have any significant difference (95%). Also the result of regression analysis showed that satellite images usage in road trees’ crown is become (R2=0.95). Thereupon, with results of this research assessing urban forest canopy cover using Ikonos satellite imagery in Google Earth software can be proposed to calculate the total canopy cover of road trees and optimum management of them and increased awareness of changes in these valuable resources in short periods of time.

Keywords

References

[1]. Zangiabadi, A.,Rakhshaninasab,H. (2009). Statistical analysis - spatial urban green space development indices (case study: Isfahan). Journal of Environmental Studies, 11(6): 49-105 pp.
 [2]. Network, F. (2005). Urban Forests: New Tools for Growing More Livable Communities. Livable Communities Work,  2: 1-8.
[3]. Konijnendijk, C. C., Nilsson, K., Randrup, T. B., and Schipperijn, J. (2005). Urban Forests and Trees. Springer, Berlin.185 p.
 [4]. Miller, R. W. (1997). Planning and Management Urban Green Spaces. University of Wisconsin  Press, USA. 502 p.
 [5]. Mathieu, R., and Aryal, J., (2005). Object-oriented classification and Ikonos multispectral imagery mapping vegetation communities in urban areas. Annual colloquium of the spatial information research center University of Otago, Dunedin, Newszealand. 24-27pp.
[6]. Valentine, F. A., Westfall, R. D., and Manion, P.D., (1978). Street tree assessment by a survey sampling procedure. Journal of Arboriculture, 4(3): 49-57.
[7]. Simpson, J., Mcpherson, G., and Delany, C. (2005). State of the urban forestry Francisco bay area progress report. Center for Urban Forest Research USDA Forest Service, PSW Research Station Davis. 147-159 p.
[8]. Boggs, G. S. (2009). Assessment of SPOT5 and Quickbird remotely sensed imagery for mapping tree cover in savannas. College of Environmental and Life Sciences, University of Charles Darwin, Australia. 217-224 pp.
 [9]. Karami, J., Shataee joybari, Sh., and Hosseini, M. (2010). Assess the ability of satellite images to map urban green coverage. Iranian Journal of Wood and Forest Science and Technology, 17(2):89-103.
[10]. Golshani, P. (2011). Evaluating the ability of multiple images Geo Eye and inventory methods for estimating ground level canopy street trees (Zone 3 Tehran), In: Proceedings of Geomatics 91.May.8-10. Tehran. Iran. 10 p.
[11]. Zobeiry, M. (2002). Forest Biometry, University of Tehran Press, Tehran. 409 p.
[12]. Zobeiry, M., Majd, A.R. (2008). An Introduction to Remote Sensing Technology and its Application in Natural Resources, University of Tehran Press, Tehran. 317 p.
 

Files

Share

How to cite

Statistics