Estimating of  above ground biomass of Haloxylon using sattelite based vegetation indices (Case study: Marak, Birjand )

Sabaghzade, Sahar; Zare, Mohammad; Mokhtari, Mohamad Hosein; Afkham Olshoara, Mohamadreza

doi:10.22092/ijrdr.2017.109517

Document Type : Research Paper

Authors

yazd university

https://doi.org/10.22092/ijrdr.2017.109517

Abstract

Arid land degradation is the biggest environmental challenges in 21st century. Saxaul (Haloxylon spp) is one of the most important species for sand dunes fixation, widely used in reclamation of arid lands. One of the applications of satellite data is to estimate the biomass of plants. In this study, Landsat 8 satellite images were used for mapping the biomass of Haloxylon plants in the South Khorasan province. Then, 11 vvegetation indices (DVI, IPVI, NDVI, PVI, RVI, SAVI, TSAVI, WDVI, Brightness, Wetness, Greenness) were calculated. To determine the correlation between biomass and satellite data, 30 plots of 30*30 m
²
were used in the region and vegetation parameters were measured. Principal component analysis and cluster analysis were used to classify the indices. Finally, the effective indices were selected and the regression models were fitted. Results showed that the regression model obtained from the TSAVI index with RMSE of 14.9 and R
²
=0.43 had the best estimation for the above ground biomass of Haloxylon.

Keywords

References

Baret, F., Jacquemoud., S. and Hanocq., J. F., 1993. About the soil line concept in remote sensing. Advances in Space Research, 13(5): 281-284.

Buyantuyev, A., Wu, J. and Gries, C., 2007. Estimating Vegetation Cover in an Urban Environment Based on Landsat ETM+ Imagery: A Case Study in Phoenix, USA. International Journal of Remote Sensing, 28(2): 269-291.

Camdevyren, H., Demyr, N., Kanik, A., and Keskyn, S. 2005. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling, 181: 581-589.

Darvishzade, R., Motakan, A. A., Hoseini asl, A. and Ebrahimi Khosfi, M., 2012. Estimation of vegetation fraction in the Central arid region of Iran using satellite images (Case study: Sheitoor basin, Bafgh). Arid Biome Scientific and Research Journal. 2 (1): 25-38.

Hoseini, S. M., Ekhtesasi, M. R. and Mohtasham Niya, S., 2013. The effect of drought on vegetation cover degradation Sistan region . Iranian Journal of Range and Desert Research. 20 (2): 227-239.

Huete, A. R., 1988. A Soil-adjusted vegetation index. Remote Sensing of Environment, 25: 295-309.

Ishiyama, T., Nakajima, Y., Kajiwara, K. and Tsuchiya, K., 1997. Extraction of vegetation cover in an arid area based on satellite data. Advances in Space Research, Calibration and Intercalibration of Satellite Sensors and Early Results of Radarsat, 19: 1375-1378.

Jackson, R. B. and Schlesinger, W. H., 2004. Curbing the US carbon deficit. Proceedings of the National Academy of Sciences USA, 101 (45): 15827-15829.

Kafman, L. and Rousseeuw, P. J., 1990. Finding groups in data Wiely pub.

Kielland, K. and Bryant, J. P., 1998. Moose herbivory in Taiga: Effects on biogeochemistry and vegetation dynamics in primary succession. Oikos, 82: 377-383.

Khosravi, M. R., Keshavarz, A., Rostami, H. and Mansouri, S., 2014. Statistical intergrates High-resolution Landsat Satellite Images to Rngysazy band. Twentieth Annual National Conference Of Computers Society Of Iran.

Launchbaugh, J. L., 1957. The effects of stocking rate on cattle gains and on native short grass vegetation in west central Kansas. Kansas Agricultural Experiment Station, Kansas State College of Agriculture and Applied Science, Bulletin: 394, 29p.

Leblon, B., 1993. Soil and vegetation optical properties. In: Applications in Remote Sensing, Volume 4, The International Center for Remote Sensing Education. Wiley Press, New York, USA.

Mohammadi, j., Shetabi, Sh., 2009. The vegetation indices used to assess the sensitivity of the sensitivity of the estimated standing volume stands. Journal of Wood and Forest Science and Technology.16(2): 101-120.

Rahimi Zade, A., Farzadmehr, J., Rastaghi, A. A. and Ramazani Ghasgh, M., 2010. The effect of planting Haloxylon and Atriplex species , the vegetation and soil properties of desert pastures Salm Abad Sarbishe. Renewable Natural Resources Research. 1(2): 1-11.

Rodgarmi, P., Khorasani, N. A., Monavari, S. M. and Nori, J., 2007. Anticipated environmental impact of using satellite imagery and remote sensing techniques. Journal of Environmental Sciences and Technology. 11(1): 161-172.

Stahl, K. and Demuth, S., 1999. Methods for regional classification of stream flow drought series: Cluster analysis,University of Freiburg,Germany.

Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127–150.

Estimating of above ground biomass of Haloxylon using sattelite based vegetation indices (Case study: Marak, Birjand )

References

References

Volume 23, Issue 4March 2017Pages 843-855

Volume 23, Issue 4
March 2017
Pages 843-855