Impact of Regional Rangeland Cover Degradation on Increasing Dusty Days in West of Iran
الموضوعات :Hamid Nouri 1 , mohamad faramarzi 2 , seyed hadi sadeghi 3
1 - Department of Range and Watershed Management, Faculty of Natural Resources engineering, and Research Institute of Grapes and Raisins ,Malayer University, Malayer, Iran.
2 - Department of Range and Watershed Management, Faculty of Natural Resources engineering, Malayer University, Malayer, Iran.
3 - Research institute of grape and raisin, Malayer University, Iran
الکلمات المفتاحية: Markov chain, Dusty days, Degradation trend, Rangeland cover,
ملخص المقالة :
Dust events of Iran mainly originate from Iraq, Syria, Saudi Arabia, Kuwait and inland territories which are influenced by droughts and Land Use/Land Cover (LU/LC) degradation in regional scale. The aim of this research was to investigate the impact of Regional Vegetation Cover Degradation (RVCD), particularly Regional Rangeland Cover Degradation (RRCD) on frequency of dusty days in western provinces of Iran (Khorramabad, Ahvaz, Hamadan and Kermanshah) since 2000. Therefore, the LU/LC and RRCD were evaluated with respect to time-series MODIS satellite images and NDVI Index. The trend of RRCD was predicted by Markov chain analysis for 2030, 2060 and 2100. The accuracy analysis of comparing the observed and predicted LU/LC classes to 2000 and 2016 indicated the absolute value of error around 5.49%. The findings showed that probability of changing from water body, high-cover and low-cover classes to non-cover class would probably be 55% and 62% during 2016-2030 and 2030-2060, respectively. Durability of non-cover class was 89% during 2000-2016. Thus, the area of non-cover class increased to 410 km2 in the study region. In general, it could be noted that increasing of RRCD and drought are the main causes of dusty days increasing from 2000 to 2060.
Abd El-Kawy, O. R., Rod, J.K., Ismail, H.A., Suliman A.S. 2011. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2): 483-494.
Asrar, G., Fuchs, M., Kanemasu, E.T., Hatiield, J.L. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat, Journal of Agron. 76: 300-306.
Bakr, N., Bahnassy, M., Weindorf, D., El-Badawi, M. 2010. Monitoring land cover changes in a newly reclaimed area of Egypt using multi temporal Landsat data. Applied Geography, 30 (4): 592-605.
Baugh W M., Groeneveld D P. 2006. Broadband vegetation index performance evaluated for a low-cover environment. Int. Jour. Remote Sensing, 27: 4715-4730.
Brink, A., Bodart, C., Brodsky, L., Defourney, P., Ernst, C., Donney, F., Lupi, A., Tuckova, K. 2014. Anthropogenic pressure in East Africa monitoring 20 years of land cover changes by means of medium resolution satellite data. Int. J. Appl. Earth Obs. Geo inf. 28: 60–69.
Choupani, M. 2009. Environmental pollutants and environment protection. 1st ed., Manpower Training, National Iranian Gas Company Publishers. (In Persian).
Congalton, R.G. 1991. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sensing of Environment, 37: 35-46.
Coppedge, B., Engle, D., Fuhlendorf, S. 2007. Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecology 22 (9): 1383-1393.
Coppin, P., Bauer, M. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews13 (3): 207-234.
Dengler, J., Janisova, M., Torok, P., Wellstein, C. 2014. Biodiversity of Palaearctic grasslands: a synthesis. Agric. Ecosys. Environ 182:1–14
Eastman, J.R. 2006. IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA.
Elie, A., Padonoua, B., Anne, M., Lykkec, Y., Bachmannd, R., Idohoue, B., Sinsinb. B.2017. Mapping changes in land use/land cover and prediction of future extension of bowé in Benin, West Africa. Land Use Policy. 69: 85–92.
Fan, F., Wang, Y., Wang, Z. 2008. Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental Monitoring Assessment 137(1-3): 127-147.
Fensholt, R., Rasmussen, K., Nilson, T.T., Mbow, C. 2009. Evaluation of Earth Observation Based Long Term Vegetation Trends-Inter comparing NDVI Time series Trend Analysis Consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT Data. Remote Sensing of Environment, 12: 1-13.
Foody, G.M. 2002. Status of land cover classification accuracy assessment, Remote Sensing of Environment, 80: 185-201.
Fraser, R.H., Abuelgasim, A., Latifovic, R. 2005. A method for detecting large-scale forest cover change using coarse spatial resolution imagery, Remote Sensing of Environment, 95: 414-427.
Gao, J., Liu, Y. 2010. Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. Int. J. Appl. Earth Obs. Geo inf. 12: 9–16.
Ghaffari, D. and Nouri, H. 2016. Relative Humidity and Moisture Flux Convergence during the Dusty Days in Alvand Mountain. Ecopersia 4(4): 527-540. (In Persian).
Gholamalifard, M., Joorabian Shooshtari, S., Hosseini Kahnuj, SH, Mirzaei, M. 2013. Land Cover Change Modeling of Coastal Areas of Mazandaran Province Using LCM in a GIS Environment. Jour Environment Ecology.38 (4): 109-124. (In Persian).
Ghorbani, A., Mirzaei Mossivand A., Esmali Ouri A. 2012. Utility of the Normalized Difference Vegetation Index (NDVI) for land/canopy cover mapping in Khalkhal County (Iran). Annals of Biological Research, 3(12): 5494-5503.
Gu, Y., Brown, J., Verdin, J., Wardlow, A. 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34: 1-6.
Haibo, Y., Longjiang, D., Hengliang, G., Jie, Z. 2011. Tai'an land use Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences 10: 2625-2630.
Hall, C., Tian, H., Qi, Y., Pontius, G., Cornell, J. 1995. Modelling spatial and temporal patterns of tropical land use change. Jour. Biogeogr. 22: 753–757.
Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI), Remote sensing of environment, 25: 295-309.
Kanianska, R., Kizeková, M., Nováček, J., Zeman, M., 2014. Land-use and land-cover changes in rural areas during different political systems: a case study of Slovakia from 1782 to 2006. Land Use Policy 36: 554–566.
Khoi, D.D., Murayama, Y. 2010. Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing 2(5): 1249-1272
Linkie, M., Smith, R.J., Leader-Williams N. 2004. Mapping and predicting deforestation patterns in the lowlands of Sumatra. Biodiversity and Conservation 13 (10): 1809-1818.
Lo, C.P., Quattrochi, D.A. 2003. Land-Use and Land-Cover Change, Urban Heat Island Phenomenon, and Health Implications: A Remote Sensing Approach, Photogrammetric Engineering and Remote Sensing, 69: 1053- 1063.
McTainsh, G., Chan, Y.C., McGowan, H., Leys J., Tews K. 2005. The 23rd October 2002 dust storm in eastern Australia: characteristics and meteorological conditions. Atmos Environ 39: 1227–1236.
Mendoza, M.E., Geneletti, D., Granados, E.L. 2011. Analyzing land cover and land use change processes at watershed level: A multi temporal study in the Lake Cuitzeo Watershed, Mexico (1975-2003). Applied Geography, 31(1): 237-250.
Merten, B., Lambin, E. 1997. Spatial modeling of tropical deforestation in southern Cameroon: spatial disaggregation of diverse deforestation processes. Applied Geography 17(2): 143-162.
Mousavi-Bayegi, M., Batoul, A. 2012. Weather instability condition and the synoptic pattern influencing dust events in Mashhad. Journal of Geography and Regional Development, 18:46-59 (In Persian).
Miri, M. 2011. Statistical analysis of synoptically dust phenomenon in the western half of Iran. Master thesis, Faculty of Geography, University of Tehran. (In Persian).
Powers, J.S. 2004. Changes in soil carbon and nitrogen after contrasting land-use transitions in northeastern Costa Rica. Ecosystems 7: 134–146.
Rouse, J.W., Haas, R.S., Schell J.A., Deering D.W. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings, 3rd ERTS Symposium, 1: 48–62.
Russell-Smith, J., Yates, C., Edwards, A., Allen, G.E., Cook, G.D., Cooke, P., Craig, R., Heath, B., Smith, R. 2003. Contemporary fire regimes of northern Australia, 1997–1380: change since Aboriginal occupancy, challenges for sustainable management, International Journal of Wildland Fire, 12: 283-297.
Sabins, F.F. 1987. Remote sensing: principles and interpretation [M]. New York: W.H. Freeman.
Schippers, P., Vermaat, J.E., de Klein, J., Mooij, W.M. 2004. The effect of atmospheric carbon dioxide elevation on plant growth in freshwater ecosystems. Ecosystems 7: 63–74.
Schulz, J.J., Cayuela L., Echeverria, C., Salas, J., Rey Benayas J.M. 2010. Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008). Applied Geography 30 (3): 436–447.
Shooshtari, S.J., Esmaili-Sari, A., Hosseini, S.M., Gholamalifard M. 2014. Application logistic regression and Markov Chain in land cover change prediction in east of Mazandaran province. Iranian Journal of Natural Environment. 66 (4): 351-363 (In Persian).
Sujatha, G., Dwivedi, R. S., Sreenivas K.S, Venkaratathan L. 2000. Mapping and monitoring of degraded lands in part of Jaunpur district of Uttar Pradesh using temporal space borne multispectral data. Int. Jour. Rem. Sen., 21(3): 519-531.
Sun, H., Forsythe, W., Waters, N. 2007. Modeling Urban Land Use Change and Urban Sprawl: Calgary, Alberta, Canada. Networks and Spatial Economics 7 (4): 353-376.
Shao, Y., Wang, J. 2003. A climatology of Northeast Asian dust events. Meteorol Z 12(4): 187–196.
Szema, A.M., Reeder, R.J, Harrington, A.D. Schmidt, M., Liu J., Golightly, M., Rueb, T., Hamidi, S.A. 2015. Iraq Dust is Respirable, Sharp, Metal-Laden, and Induces Lung Inflammation with Fibrosis in Mice via IL-2 Upregulation and Depletion of Regulatory T Cells. Jour. Occup Environ Med. 56(3): 243–251.
Takada, T., Miyamoto, A., Hasegawa, S.F. 2010. Derivation of a yearly transition probability matrix for land-use dynamics and its applications, Landscape Ecology, 25: 561–572.
Verburg, P.H., Neumann, K., Nol, L., 2011. Challenges in using land use and land cover data for global change studies. Global Change Biol. 17: 974–989.
Verburg, P.H., van de Steeg, J., Veldkamp, A., Willemen, L., 2009. From land cover change to land function dynamics: a major challenge to improve land characterization. Jour. Environ. Manage. 90: 1327–1335.
Wu, Q., Li, H., Wang, R., Paulussen, J., He, Y., Wang, M., Wang, B., Wang, Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning 78 (4): 322-333.
Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y. and Huang, Z. 2006. Development of topsoil grain size index for monitoring desertification in arid land. Int. Jour. Remote Sens., 27(12): 2411-2422
Yatagai, A., Yasunari, T. 1995. Inter annual variations of summer precipitation in the arid/semi-arid regions in China and Mongolia: their regionality and relation to the Asian summer monsoon. Jour. Meteorol Soc Jpn Ser 2(73): 909–923.
Yuan, F., Sawaya, K., Loeffelholz, B., Bauer, M. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multi temporal Landsat remote sensing. Remote Sensing of Environment 98: 317–328.
Zhao, S. 2012. Asian Dust Detection from the Satellite Observations of Moderate Resolution Imaging Spectro-radiometer (MODIS). Aerosol and Air Quality Research, 12: 1073–1080.
Zazuli, M.F., Vafaeinezhad, A., Kheirkhah Zarkesh, M.M., Ahmadi Dehka, F. 2014. Monitoring of dust haze phenomenon using remote sensing and GIS and its synoptic analysis. Journal of geography information.23: 69-80. (In Persian).
