Estimating and mapping Sabalan rangelands aboveground phytomass  using Landsat 8 images

Ghorbani, Ardavan; Pournemati, Ardashir; Panahande, Mohsen

doi:10.22092/ijrdr.2017.109858

Document Type : Research Paper

Authors

https://doi.org/10.22092/ijrdr.2017.109858

Abstract

The aim of this study was to estimate and map the plant group and total aboveground phytomass using Landsat 8 images in the rangelands of Sabalan Mountain. Images were selected on the 19th of July 2013 and field data were collected in April and July based on maximum matching with the phenology of the study area and in the closest date to the time of image acquisition. Twenty-four sampling sites on six vegetation types were determined. In each site, 9 sampling plots, based on previous studies, which are required for minimum sample number according to the variance of vegetation distribution, were determined in a systematic-random method, and the aboveground phytomass of vegetation groups, such as grasses, forbs, shrubs and total, were determined using the harvesting method. Initially, to calculate vegetation indices, the averages of 16 pixel values of the location of sample units from the corrected images were derived and transferred to the software environment. The correlation matrices between the derived pixel values and field collected data for the 24 selected vegetation indices were calculated and used for the estimation of grasses, forbs, shrubs and total aboveground phytomass. The results showed that indices such as RVI, TNDVI and GNDVI had the highest correlation with the aboveground phytomass of grasses, PD312, IO and PD311 with the aboveground phytomass of forbs, RDVI, DVI and RVI with the shrubs, and PD311, PD321 and PD312 with the total aboveground phytomass (P <0.01). In the second stage, three of the indices, having the highest correlation with the aboveground phytomass of each group and entire previous stage, were selected, and Landsat8 images were used to calculate the aboveground phytomass of each vegetation group and the total aboveground phytomass was calculated. The aboveground phytomass maps of each group and the total aboveground phytomass were controlled with sampling points to assess the accuracy. The results of this study showed that the best maps were obtained using the TNDVI index for grasses aboveground phytomass, PD312 for forbs, RVI for shrubs groups and PD311 for the total aboveground phytomass. Moreover, some indices, such as PD311 and RVI, could be used for all growth forms and estimation of total aboveground phytomass (P<0.01) and (P<0.01). In general, Landsat 8 data could be used to estimate and map the aboveground phytomass of vegetation groups and to determine the carrying capacity of the total aboveground phytomass in Sabalan rangelands, having advantages based on cost, time and the ability to monitor large areas with repeatability potential in comparison with the ground-based methods.

Keywords

References

Arzani, H., Noori, S., Kaboli, S. H., Moradi, H. R. and Ghelichnia, H., 2009. Determination of Suitable Indices for Vegetation Cover Assessment in Summer Rangelands in South of Mazandaran, Iranian Journal Natural Resources,
61(4): 997-1016.

-Arzani, H., Hoseini, S. Z. and Mirakhorlou, Kh., 2014. Application of Landsat ETM⁺ images for estimating vegetation production and cover in Taleghan rangelands, Iranian Journal of Range and Desert Research, 21(1): 24-31.

-Ebrahimi, A., Bossuyt, B. and Hoffmann, M., 2010. A herbivore specific grazing capacity model accounting for spatio-temporal environmental variation: A tool for a more sustainable natur conservation and rangeland management, Ecological Modeling, 221: 900-910.

-Hangs, R. D., Van Rees, J., Schoenau, K. C. J. and Guo, X., 2011. A simple technique for estimating above-ground biomass in short-rotation willow plantations, Biomass and Bioenergy, 35: 2156-2162.

-Hazarika, M. K., Yasuoka, Y., Ito, A. and Dye, D., 2004. Estimation of net primary productivity by integrating remote sensing data with an ecosystem model. Remote Sensing of Environment, 94: 289-310.

-Javanshir, A. 1988. Study the Rangelands of Sabalan. Joint project of Jihad-e-Sazandegi of East Azarbyjan and the University of Tabriz. Tabriz. 213p.

-Karathanassi, V., Andronis, V. and Rokos, D., 2000. Evaluation of Topographic Normalization Methods for a Mediterranean Forest Area, International Archives of Photogrammetry and Remote Sensing, 33(part B7): 654-661.

-Lobell, D. B., Hicke, J. A., Asner, G. P., Field, C. B., Tucker, C. J. and Los, S. O., 2002. Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–1998. Global Chenge Manegemant Biolgecal, 8: 722-735.

-Long, Y., Zhou, L., Liu, W. and Hua-Kun, Z., 2010. Using remote sensing and GIS technology to estimate grass yield and livestock carrying capacity of Alpine Grasslands in Golog Prefecture China. Pedosphere, 20(3): 342-351.

-Lu, D., 2005. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon Basin, International Journal of Remote Sensing, 27: 2509-2525.

-Mohammadifakhr, H., 2001. Determination of Suitable Vegetation Indices to Estimating of Rangeland Cover and Production in Two Stepp Regions of Markazi, M.Sc. Thesis, Tehran University, P.136.

-Mohammadi, M., Ebrahimi, A. and Haqzade, A., 2012. Capability of IRS satellite on vegetation cover estimation (Case Study: Chaharmah-va-Bakhtiari), Journal of Renewable Natural Resources, 3(1): 41-53.

-Ghorbani, A., Sharifi, J., Kavianpoor, H., Malekpoor, B. and Mirzaei Aghche Gheshlagh, F., 2013. Investigation on ecological characteristics of Festuca ovina L. in south-eastern rangelands of Sabalan, Iranian Journal of Range and Desert Research, 20(2): 379-396.

-Pairanj, J., Ebrahimi, A., Ranjbar, A. and Hassanzadeh, M., 2012. Evaluation of forage production accessibility with considering effective factors using RS and GIS, Iranian Journal of Range and Desert Research, 18(4): 593-607.

-Paruelo, J.M., Oesterheld, M., Bella, D., Carlos, M., Arzadum, M., Lafontaine, C., Rebella, M. and César M., 2000. Estimation of primary production of sub humid rangelands from remote sensing data, Applied Vegetation Science, 3: 189-195.

-Olexa, E. M. and Lawrence, R. L., 2014. Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland. International Journal of Applied Earth Observation and Geoinformation, 30: 30-31.

-Salis, S. M., Assis, M. A., Mattos, P. P. and Piaob, A. C. S., 2006. Estimating the aboveground biomass and wood volume of savanna woodlands in Brazil’s Pantanal wetlands based on allometric correlations. Forest Ecology and Management, 228: 61-68.

-Sharifi, J., Fayaz, M., Azimi, F., RostamiKia, Y. and Eshvari, P., 2013. Identification of ecological region of Iran (Vegetation of Ardabil Province). Institute Research of Forest and Rangeland Press. Report No. 42183/37.

-Soleimani, K., TamrTash, R., Alavi, F. and Lotfi, S., 2007. Application of Landsat TM data for estimation rangeland yield (A case study: sub-basin of Sefidab, Lar Dam). Journal of Agriculture and Natural Resources Science and Technology, 40: 411-422.

-Solaimani, K., Shokrian, F. TamarTash, R. and Banihashemi, M., 2011. Eveluation the capability of ETM⁺ data to detrmine the best vegetation indices (A case study: Waz watershed). Journal Iranian Remote Sensing and GIS, 2(4): 71-82.

-Theau, J., Sankey, T. T. and Weber, K. T., 2010. Multisensor analyses of vegetation indices in a semiarid environment, GIS Science and Remote Sensing, 47(2): 260-275.

-Wagel, P., Xiao, X., Torn, M. S., Cook, D. R., Matamala, R., Fischer, M. L., Jin, C., Jinwei, D. and Biradar, Ch., 2014. Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought. Remote Sensing of Environment, 152: 1-14.

-Xiaoping, W., Kai, G. N. and Jing, W., 2011. Hyper spectral Remote Sensing estimation models of aboveground biomass in Gannan rangelands Procedia. Environmental Sciences, 10: 697-702.

-Xie, Y., Sha, Z., Yu, M., Bai, Y. and Zhang, L., 2009. A comparison of two models with Landsat data for estimating aboveground grassland biomass in Inner Mongolia, China. Ecological Modelling, 220: 1810-1818.

-Xulin, G., Price, K. and Stiles, J., 2001. Modeling biophysical factors for grasslands using Landsat TM data in eastern Kansas. Kansas Applied Remote Sensing (KARS), 12: 125-130.

-Yeganeh, H., Khajeddin S. J. and Soffianian, A. R., 2008. Evaluating the Potentials of Spectral Indices of the
MODIS in Estimating the Plant Production in Semirom Pastures. Journal of Rangeland, 2(1): 63-77.

-Zareh Hesari, B., Ghorbani, A., Azimi Motam, F., Hashmi Majd, K. and Asghari, A., 2014. Study the effective ecological factors on distribution of Artemisia fragrans in southeast faced slopes of Sabalan. Rangeland Journal, 8(3): 238-250.

-Zarineh, E., Asadi Brojeni, E. and Khorasgani, M. N., 2012. Estimation range production with using satellite data IRS LISS III (Case Study of the Tang Sayyad, Chaharmahal and Bakhtiari). Journal of Iranian Remote Sensing and GIS, 3(4): 63-80.

-Zarineh, E., Naderi Khorasgani, M. and Asadi Borujeni, E., 2013. Estimating the Rangeland Vegetation Cover of Tange Sayyad Region (Chaharmahal-oBakhtiary Province) Using IRS LISS-III data. Journal of Environmental Studies, 38(1): 117-130.

-Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J. and Ryu, S., 2004. Estimating aboveground biomass using Landsat 7 ETM⁺ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93: 402-411.

Estimating and mapping Sabalan rangelands aboveground phytomass using Landsat 8 images

References

References

Volume 24, Issue 1May 2017Pages 165-180

Volume 24, Issue 1
May 2017
Pages 165-180