Application of Landsat ETM+ satellite images in forest cover mapping; case study: Arasbaran protected area

Paper Details

Research Paper 01/02/2014
Views (190) Download (3)

Application of Landsat ETM+ satellite images in forest cover mapping; case study: Arasbaran protected area

M. Farahnak Ghazani, M. Najibzadeh
J. Bio. Env. Sci.4( 2), 272-277, February 2014.
Certificate: JBES 2014 [Generate Certificate]


Current research was carried out in Arasbaran protected area in order to investigate the capability of landsat ETM+ satellite images in forest cover mapping. For this perpose the ETM+ image of the study area acquired on june 16th, 2001 was used. Radiometric and geometric correction were done for the images. The best spectral bands were selected using Optimum Index Factor (OIF) method. False color composite map was created using bands 4, 3, 2 as the best combination of spectral bands. Sufficient number of training samples of each land cover class were collected for image classification. Supervised classification was accomplished using maximum likelihood classifier and forest cover map of the study area was created. According to the results the area of forest and non-forest classes are 20777.7 ha and 59813.9 ha respectively. Ground truth data was collected using Systematic-cluster sampling method In order to assess the accuracy of classification. The results showed that overal accuracy and kappa statistic are %95 and %88 respectively.


Bagheri R, Shataee Jouybari SH. 2010. Modeling Forest Areas Decreases, Using Logistic Regression (Case Study: Chehl-Chay Catchment, Golestan Province). Iranian Journal of Forest 2(3), 243-252.

Beaubien J. 1994. Landsat TM satellite images of forests: from enhancement to classification. Canadian Journal of Remote Sensing 20, 17–26.

Bruce CM, Hilbert DW. 2004. Pre-processing Methodology for Application to Landsat TM/ETM+ Imagery of the Wet Tropics. Cooperative Research Centre for Tropical Rainforest Ecology and Management. Rainforest CRC, Cairns, 44.

Congalton RG, Green K. 1999. Assessing the accuracy of remotely sensed data, Principles and practices, Boca Rotan, Florida: Lewis Publishers, 43– 64.

Cooke WH. 1999. Forest/non-forest stratification in Georgia with Landsat TM data. In: Proceedings of the First Annual Forest Inventory and Analysis Symposium. General Technical Report No. NC-213. USDA Forest Service, North Central Research Station, St. Paul, MN, 28–30.

Czaplewski RL. 1999. Multistage remote sensing: toward an annual national inventory. Journal of forestry 97(12), 44–48.

Danaher TJ, Bisshop G, Kastanis L, Carter J. 1998. The Statewide Landcover and Trees Study (SLATS) – monitoring landcover change and greenhouse gas emissions in Queensland. Proceedings of the 9th Australasian Remote Sensing and Photogrammetry Conference, Sydney, Australia, July 1998.

Dymond CC, Mlandenoff DJ, Radeloff VJ. 2002. Phenological differences in tasseled cap indices improve deciduous forest classification. Remote Sensing of Environment 80, 460–472.

Eurisy (a European Non-profit Association Bridging Space and Society). 2011. Forest and biomass management using satellite information and services, Paris, France. Availble: management-using-satellite-information-and-services.html/, accessed 7 December, 2013.

Fleiss JL. 1981. Statistical methods for rates and proportions. 2nd ed. New York: John Wiley, 38–46.

Freeman A, Chapman B, Siqueira P. 2002. The JERS-1 Amazon Multi-season Mapping Study (JAMMS): Science Objectives and Implications for Future Missions. International Journal of Remote Sensing 23(7), 1447-1460.

Gould W. 2000. Remote Sensing of Vegetation, Plant Species Richness and Regional Biodiversity Hotspots. Ecological Applications 10(6), 1861-1870.

Hadjimitsis DG, Clayton CRI, Hope VS. 2004. An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs, International Journal of Remote Sensing 25(18), 3651–3674.

Lannom KB, Evans DL, Zhu Z. 1995. Comparison of AVHRR Classification and Aerial Photography Interpretation for Estimation of Forest Area. Research Paper No. SO-292. USDA Forest Service, Southern Forest Experiment Station, New Orleans, LA.

Lillesand TM, Kiefer RW. 2000. Remote Sensing and Image Interpretation. New York: John Wiley and Sons.

Mayaux P, De Grandi G, Malingreau, JP. 2000. Central African Forest Cover Revisited: A Multisatellite Analysis. Remote Sensing of Environment 71(2), 183-196.

Mohammadi J, Shataee Sh, Yaghmaee F, Mahiny AS. 2010. Modeling Forest Stand Volume and Tree Density Using Landsat ETM+ Data. International Journal of Remote Sensing 31(11, 10), 2959–2975.

Rafieian A, Darvishsefat AA, Namiranian M. 2006. The Area Change Detection in the Northern Forests of Iran Using ETM+ Data. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science 10(3), 277-286.

Sagheb-Talebi kh, Sajedi T, Yazdian F. 2004. Forests of Iran. Research Institude of forests and Rangelands. Tehran, Iran, 10-11.

Shataee Sh, Abdi O. 2007. Land cover mapping in mountainous lands of Zagros using ETM+ data (Case study: Shorkhab watershed, Lorestan province). Journal of Agricultural Sciences And Natural Resources 14(1), 129-138.

Torahi AA, Rai SC. 2011. Land Cover Classification and Forest Change Analysis, Using Satellite Imagery – A Case Study in Dehdez Area of Zagros Mountain in Iran. Journal of Geographic Information System 3, 1-11.

Varjo J. 1995. Forest change detection by satellite remote sensing in Eastern Finland. EARSel advances in remote sensing 4(3-XII), 102.

Watt P, watt M. 2011. Applying satellite imagery for forest planning. NZ journal of forestry 56(1), 23.

Wayman JP, Wynne RH, Scrivani JA, Reams GR. 2001. Landsat TM-based forest area estimation using iterative guided spectral class rejection. Photogrammetric Engineering and Remote Sensing 67(10), 1155–1165.

Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment 98, 317-328.