مدل‌سازی تخریب مراتع نیمه‌استپی استان اصفهان با استفاده از تصاویر مودیس

نوع مقاله : علمی - پژوهشی

نویسندگان

1 ‌دانشجوی دکتری علوم مرتع، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان-

2 دانشیار گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان

3 ‌ استادیار گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان

چکیده

گیاهان، یکی از مهم‌ترین اجزای اکوسیستم هستند که تحت‌تاثیر عوامل طبیعی و انسانی قرار می‌گیرند. به‌طوری‌که مطالعه تولید خالص اولیه، یکی از مهم‌ترین موضوعات در بوم‌شناسی به‌حساب می‌آید. مهمترین هدف این تحقیق مدل‌سازی توزیع مکانی و زمانی تولید خالص اولیه (مدل CASA) و تاثیر تابش موثر نور خورشید (LUE)  و همچنین اندازه‌گیری تخریب اراضی با شاخص تاثیر بارندگی ( RUE ) در مراتع نیمه‌استپی استان اصفهان است. برای انجام این مطالعه، تصاویر - NDVI ۱۶ روزه مودیس،‌ داده‌های هواشناسی، نقشه پوشش اراضی و داده‌های زمینی به کار گرفته شد و نتایج نشان داد که نرخ NPP از ماه مارس (C/m2/mo 44/11) تا ماه می‌ (C/m2/mo/07/41) افزایش داشته است، در حالیکه سیر نزولی را از اوایل ماه ژوئن  (C/m2/mo 2/2) به دلیل خشکی خاک نشان می‌دهد. اقلیم،‌ تیپ گیاهی و وضعیت مرتع نقش مهمی‌درNPP سالیانه داشتند، لذا بیشترین و کمترین NPP به ترتیب  در  Astragalus- Daphnae 85/38  gC/m2 y-1) 85/38 )  و Artemisia sieberi – Scariola gC/m2 y-1) 4 ) همراه با حداکثر ( g C (MJ)-1 13/0) و حداقل تاثیر تابش موثر نور خورشید  g C (MJ)-1)   LUE 005/0 ) مشاهده شد. مقدار (RUE)، در مراتع تخریب یافته کاهش داشت. علاوه بر این از همبستگی بین داده‌های زمینی و مدل CASA در اقلیم نیمه‌خشک گرم و مراتع تخریب‌یافته، کاسته شد. بنابراین توجه به وضعیت مرتع، تیپ گیاهی و اقلیم در پایش NPP و مدیریت مراتع ضروری است. 

کلیدواژه‌ها


عنوان مقاله [English]

Modeling of semi-steppe rangelands degradation in Isfahan Province using MODIS images

نویسندگان [English]

  • Fatemeh Hadian 1
  • Reza Jafari 2
  • Hossein Bashari 3
  • Mostafa Tarkesh 3
1 Graduate Ph D in Range Management, Isfahan University of Technology
2 Associate Professor, Faculty of Natural Resources, Isfahan University of Technology
3 Associate Professor, Faculty of Natural Resources, Isfahan University of Technology
چکیده [English]

Plants are one of the most important components of the ecosystem which are affected by natural and human factors. Therefore, the study of net primary production (NPP) is one of the main subjects in ecology. The main purpose of this research was to model spatial and temporal distributions of NPP  and also to determine the degradation of vegetation types using Carnegie Ames Stanford Approach (CASA), Rain Use Efficiency  (RUE)  and  Light Use Efficiency (LUE) models in semi-steppe rangelands of Isfahan Province. For this purpose, the 16- day MODIS NDVI  images, metrological data, land cover maps and field study  were applied in the study area. The  results showed that the NPP rate increased from March (11.44gC/m2/mo) to May (41.07gC/m2/mo) while demonstrating a decreasing trend from the onset of June  (2.2 g C/m2/mo) due to soil dryness. Climate , vegetation type and rangeland conditions had important roles  in  annual plant NPP and therefore the highest and lowest NPP were observed in Astragalus- Daphnae (38.85 gC/m2 y-1) and Artemisia sieberi - Scariola  (4 g C/m2 y-1) vegetation types  with maximum (0.13 g C (MJ)-1) and minimum (0.005 g C (MJ)-1) LUE, respectively. The amount of RUE decreased in degraded rangelands. Moreover, the correlation between field measurements and the CASA model decreased in semiarid warm climate and degraded rangelands. Therefore, rangeland conditions, vegetation type and climate condition must be taken into consideration in NPP monitoring and rangelands management.

کلیدواژه‌ها [English]

  • NPP
  • CASA
  • RUE
  • LUE
  • range condition
  1. Adler, P., Raff, D. & Lauenroth, W., 2001, The effect of grazing on the spatial heterogeneity of vegetation, Oecologia, 128(4): 465-479.
  2. Alamdari, P., Nematollahi, O. & Alemrajabi, A.A., 2013, Solar energy potentials in Iran: A review, Renewable and Sustainable Energy Reviews, 21: 778-788.
  3. Almorox, J., Benito, M. & Hontoria, C., 2005, Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain, Renewable Energy, 30(6): 931-936.
  4. Attorre, F., Alfo, M., De Sanctis, M., Francesconi, F. & Bruno, F., 2007, Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale, International Journal of Climatology, 27(13): 1825-1843.
  5. Bai, Z., Dent, D., Olsson, L., Schaepman, M., 2008, Global assessment of land degradation and improvement: 1, identification by remote sensing, Citeseer.
  6. Binbol, N. & Zemba, A., 2007, Analysis of Rainfall Data for Effective Agricultural Producation in Adamawa State, Nigeria.
  7. Bogan, M.T., Boersma, K.S. & Lytle, D.A., 2015, Resistance and resilience of invertebrate communities to seasonal and supraseasonal drought in arid‐land headwater streams, Freshwater Biology, 60(12): 2547-2558.
  8. Dale, V.H., Joyce, L.A., McNulty, S., Neilson, R.P., Ayres, M.P., Flannigan, M.D., Hanson, P.J., Irland, L.C., Lugo, A.E. & Peterson, C.J., 2001, Climate change and forest disturbances: climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides, BioScience, 51(9): 723-734.
  9. de Jong, R., de Bruin, S., Schaepman, M. & Dent, D., 2011, Quantitative mapping of global land degradation using Earth observations, International Journal of Remote Sensing, 32(21): 6823-6853.
  10. Eisfelder, C., Kuenzer, C., Dech, S. & Buchroithner, M.F., 2013., Comparison of two remote sensing based models for regional net primary productivity estimation—a case study in semi-arid Central Kazakhstan, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4): 1843-1856.
  11. Faramarzi, M., Heidarizadi, Z., Mohamadi, A. & Heydari, M., 2018, Detection of Vegetation Changes in Relation to Normalized Difference Vegetation Index (NDVI) in Semi-Arid Rangeland in Western Iran, Journal of Agricultural Science and Technology, 20(1): 51-60.
  12. Fensholt, R., Sandholt, I., Rasmussen, M.S., Stisen, S. & Diouf, A., 2006, Evaluation of satellite based primary production modelling in the semi-arid Sahel, Remote Sensing of Environment, 105(3): 173-188.
  13. Friedlingstein, P., Joel, G., Field, C.B. & Fung, I.Y., 1999, Toward an allocation scheme for global terrestrial carbon models, Global Change Biology, 5(7): 755-770.
  14. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Penuelas, J. & Valentini, R., 1995, Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types, Ecological Applications, 5(1): 28-41.
  15. Ghazanfar, S.A., 1997, The phenology of desert plants: a 3-year study in a gravel desert wadi in northern Oman, Journal of Arid Environments, 35(3): 407-417.
  16. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., Leavitt, B., Ciganda, V., Burba, G.G. & Suyker, A.E., 2006, Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity, Journal of Geophysical Research: Atmospheres, 111(D8).
  17. Gyssels, G., Poesen, J., Bochet, E. & Li, Y., 2005, Impact of plant roots on the resistance of soils to erosion by water: a review, Progress in physical geography, 29(2): 189-217.
  18. Hadian, F., Jafari, R., Bashari, H., Tarkesh, M. & Clarke, K.D., 2019, Effects of drought on plant parameters of different rangeland types in Khansar region, Iran, Arabian Journal of Geosciences, 12(3): 93.
  19. Haxeltine, A. & Prentice, I., 1996., A general model for the light-use efficiency of primary production, Functional Ecology, 551-561.
  20. Hibbard, K., Law, B., Reichstein, M. & Sulzman, J., 2005, An analysis of soil respiration across northern hemisphere temperate ecosystems, Biogeochemistry, 73(1): 29-70.
  21. Holechek, J.L., Pieper, R.D. & Herbel, C.H., 1989, Range management, Principles and practices, Prentice-Hall.
  22. Hua, L., Liu, H., Zhang, X., Zheng, Y., Man, W. & Yin, K., 2014, Estimation Terrestrial Net Primary Productivity Based on CASA Model: a Case Study in Minnan Urban Agglomeration, China, IOP Conference Series: Earth and Environmental Science. IOP Publishing, p. 012153.
  23. Jafari, R., Bashari, H. & Tarkesh, M., 2016, Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands, Arid Land Research and Management, 1-17.
  24. Jafari, R., Bashari, H. & Tarkesh, M., 2017, Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands, Arid land research and management, 31(1): 94-110.
  25. Kern, A., Marjanović, H., Dobor, L., Anić, M., Hlasny, T., Barcza, Z., 2017, Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data, South-east European forestry, 8(1): 1-20.
  26. Khajeddin, S.J., 1995, A survey of the plant communities of the Jazmorian, Iran, using Landsat MSS data, University of Reading, Pages.
  27. Krishnan, V., Murugaiya, R., Shanmugham, R. & Mariappan, M., 2015, Assessing the Impact of Natural Factors on Desertification in Tamilnadu, India using Integrated Remote Sensing, 1st International Electronic Conference on Remote Sensing. Multidisciplinary Digital Publishing Institute, pp. 1-16.
  28. Lazaro, R., Rodrigo, F., Gutierrez, L., Domingo, F. & Puigdefabregas, J., 2001, Analysis of a 30-year rainfall record (1967–1997) in semi–arid SE Spain for implications on vegetation, Journal of arid environments, 48(3):373-395.
  29. Li, J., Wang, Z., Lai, C., Wu, X., Zeng, Z., Chen, X. & Lian, Y., 2018a, Response of net primary production to land use and land cover change in mainland China since the late 1980s, Science of The Total Environment, 639: 237-247.
  30. Li, W., Li, C., Liu, X., He, D., Bao, A., Yi, Q., Wang, B. & Liu, T., 2018b, Analysis of spatial-temporal variation in NPP based on hydrothermal conditions in the Lancang-Mekong River Basin from 2000 to 2014, Environmental monitoring and assessment, 190(6): 321.
  31. Li, X., Li, G., Wang, H., Wang, H. & Yu, J., 2015, Influence of meadow changes on net primary productivity: a case study in a typical steppe area of XilinGol of Inner Mongolia in China, Geosciences Journal, 19(3): 561.
  32. Maestre, F.T., Cortina, J., 2002, Spatial patterns of surface soil properties and vegetation in a Mediterranean semi-arid steppe, Plant and soil, 241(2): 279-291.
  33. Manning, A.D., Cunningham, R.B. & Lindenmayer, D.B., 2013, Bringing forward the benefits of coarse woody debris in ecosystem recovery under different levels of grazing and vegetation density, Biological Conservation, 157: 204-214.
  34. McCoy, R.M., 2005, Field Methods in Remote Sensing, Guilford New York.
  35. Melillo, J.M., McGuire, A.D., Kicklighter, D.W., Moore, B., Vorosmarty, C.J. & Schloss, A.L., 1993, Global climate change and terrestrial net primary production, Nature, 363(6426): 234.
  36. Meyfroidt, P., Lambin, E.F., Erb, K.-H. & Hertel, T.W., 2013, Globalization of land use: distant drivers of land change and geographic displacement of land use, Current Opinion in Environmental Sustainability, 5(5): 438-444.
  37. Middleton, E.M., Huemmrich, K.F., Cheng, Y.-B. & Margolis, H.A., 2016, 12 Spectral Bioindicators of Photosynthetic Efficiency and Vegetation Stress, Hyperspectral remote sensing of vegetation, 265.
  38. Miranda, J.d.D., Armas, C., Padilla, F. & Pugnaire, F., 2011, Climatic change and rainfall patterns: effects on semi-arid plant communities of the Iberian Southeast, Journal of Arid Environments, 75(12): 1302-1309.
  39. Monteith, J., 1972, Solar radiation and productivity in tropical ecosystems, Journal of applied ecology, 9(3): 747-766.
  40. Mortimer, S.R., 1992, Root length/leaf area ratios of chalk grassland perennials and their importance for competitive interactions, Journal of vegetation science, 3(5): 665-673.
  41. Ols, C., Girardin, M.P., Hofgaard, A., Bergeron, Y. & Drobyshev, I., 2017, Monitoring climate sensitivity shifts in tree-rings of eastern boreal North America using model-data comparison, Ecosystems, 1-16.
  42. Pack, S.M., 2009, A MODIS Imagery Toolkit for ArcGIS Explorer,
  43. Pan, G., Sun, G.-J. & Li, F.-M, 2009, Using QuickBird imagery and a production efficiency model to improve crop yield estimation in the semi-arid hilly Loess Plateau, China, Environmental Modelling & Software, 24(4): 510-516.
  44. Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.-M., Tucker, C.J., Stenseth, N.C., 2005, Using the satellite-derived NDVI to assess ecological responses to environmental change, Trends in ecology & evolution, 20(9): 503-510.
  45. Piao, S., Fang, J. & He, J., 2006, Variations in vegetation net primary production in the Qinghai-Xizang Plateau, China, from 1982 to 1999, Climatic Change, 74(1-3): 253-267.
  46. Raich, J.W. & Schlesinger, W.H., 1992, The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate, Tellus B, 44(2): 81-99.
  47. Rey, A., Pegoraro, E., Oyonarte, C., Were, A., Escribano, P. & Raimundo, J., 2011, Impact of land degradation on soil respiration in a steppe (Stipa tenacissima L.) semi-arid ecosystem in the SE of Spain, Soil Biology and Biochemistry, 43(2): 393-403.
  48. Richerson, P.J. & Lum, K.-l., 1980, Patterns of plant species diversity in California: relation to weather and topography, The American Naturalist, 116(4): 504-536.
  49. Rohli, R.V. & Vega, A.J., 2013, Climatology, Jones & Bartlett Publishers.
  50. Sanaienejad, S.H., Shah Tahmasbi, A.R., Sadr Abadi Haghighi, R. & Kelarestani, K., 2008, A Study of Spectral Reflection on Wheat Fields in Mashhad Using MODIS Data, Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science 12(45): 11-19.
  51. Schwinning, S., Davis, K., Richardson, L. & Ehleringer, J.R., 2002, Deuterium enriched irrigation indicates different forms of rain use in shrub/grass species of the Colorado Plateau, Oecologia, 130(3): 345-355.
  52. Shabanov, N., Wang, Y., Buermann, W., Dong, J., Hoffman, S., Smith, G., Tian, Y., Knyazikhin, Y. & Myneni, R., 2003, Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests, Remote Sensing of Environment, 85(4): 410-423.
  53. Snyman, H., 2004, Short-term response in productivity following an unplanned fire in a semi-arid rangeland of South Africa, Journal of Arid Environments, 56(3): 465-485.
  54. Tesfaye, S., Birhane, E., Leijnse, T. & van der Zee, S., 2017, Climatic controls of ecohydrological responses in the highlands of northern Ethiopia, Science of the Total Environment, 609: 77-91.
  55. Turner, D.P., Gower, S.T., Cohen, W.B., Gregory, M. & Maiersperger, T.K., 2002, Effects of spatial variability in light use efficiency on satellite-based NPP monitoring, Remote Sensing of Environment, 80(3): 397-405.
  56. van Minnen, J.G., Onigkeit, J. & Alcamo, J., 2002., Critical climate change as an approach to assess climate change impacts in Europe: development and application, Environmental Science & Policy, 5(4): 335-347.
  57. Woodward, F.I. & Williams, B., 1987, Climate and plant distribution at global and local scales, Vegetatio, 69(1-3): 189-197.
  58. Xu, B., Park, T., Yan, K., Chen, C., Zeng, Y., Song, W., Yin, G., Li, J., Liu, Q. & Knyazikhin, Y., 2018a, Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016, Forests, 9(2): 73.
  59. Xu, L., Tu, Z., Zhou, Y. & Yu, G., 2018b, Profiling Human-Induced Vegetation Change in the Horqin Sandy Land of China Using Time Series Datasets, Sustainability, 10(4): 1068.
  60. Yaghmaei, L., Soltani, S. & Khodagholi, M., 2009, Bioclimatic classification of Isfahan province using multivariate statistical methods, International Journal of Climatology, 29(12): 1850-1861.