Advertisement

Journal of Arid Land

, Volume 11, Issue 4, pp 477–494 | Cite as

Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom County, Iran

  • Fatemeh Hadian
  • Reza JafariEmail author
  • Hossein Bashari
  • Mostafa Tartesh
  • Kenneth D. Clarke
Article
  • 18 Downloads

Abstract

Net primary production (NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach (CASA) model for estimating NPP and its spatial and temporal changes in semi-arid rangelands of Semirom County, Iran. Using CASA model, we estimated the NPP values based on monthly climate data and the normalized difference vegetation index (NDVI) obtained from the MODIS sensor. Regression analysis was then applied to compare the estimated production data with observed production data. The spatial and temporal changes in NPP and light utilization efficiency (LUE) were investigated in different rangeland vegetation types. The standardized precipitation index (SPI) was also calculated at different time scales and the correlation of SPI with NPP changes was determined. The results indicated that the estimated NPP values varied from 0.00 to 74.48 g C/(m2·a). The observed and estimated NPP values had different correlations, depending on rangeland conditions and vegetation types. The highest and lowest correlations were respectively observed in Astragalus spp.-Agropyron spp. rangeland (R2=0.75) with good condition and Gundelia spp.-Cousinia spp. rangeland (R2=0.36) with poor and very poor conditions. The maximum and minimum LUE values were found in Astragalus spp.-Agropyron spp. rangeland (0.117 g C/MJ) with good condition and annual grasses-annual forbs rangeland (0.010 g C/MJ), respectively. According to the correlations between SPI and NPP changes, the effects of drought periods on NPP depended on vegetation types and rangeland conditions. Annual plants had the highest drought sensitivity while shrubs exhibited the lowest drought sensitivity. The positive effects of wet periods on NPP were less evident in degraded areas where the destructive effects of drought were more prominent. Therefore, determining vegetation types and rangeland conditions is essential in NPP estimation. The findings of this study confirmed the potential of the CASA for estimating rangeland production. Therefore, the model output maps can be used to evaluate, monitor and optimize rangeland management in semi-arid rangelands of Iran where MODIS NPP products are not available.

Keywords

CASA NPP estimation light utilization efficiency vegetation type drought rangeland condition semi-arid rangelands 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alamdari P, Nematollahi O, Alemrajabi A A. 2013. Solar energy potentials in Iran: A review. Renewable and Sustainable Energy Reviews, 21: 778–788.CrossRefGoogle Scholar
  2. Ardo J, Tagesson T, Jamali S, et al. 2018. MODIS EVI-based net primary production in the Sahel 2000–2014. International Journal of Applied Earth Observation and Geoinformation, 65: 35–45.CrossRefGoogle Scholar
  3. Bao G, Bao Y H, Qin Z H, et al. 2016. Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model. International Journal of Applied Earth Observation and Geoinformation, 46: 84–93.CrossRefGoogle Scholar
  4. Biondini M E, Patton B D, Nyren P E. 1998. Grazing intensity and ecosystem processes in a northern mixed-grass prairie, USA. Ecological Applications, 8(2): 469–479.CrossRefGoogle Scholar
  5. Bonham C D. Measurement for Terrestrial Vegetation. Hoboken: John Wiley & Sons, 338.Google Scholar
  6. Caylor K K, Shugart H H. 2004. Simulated productivity of heterogeneous patches in Southern African savanna landscapes using a canopy productivity model. Landscape Ecology, 19(4): 401–415.CrossRefGoogle Scholar
  7. Chen G, Tian H, Zhang C, et al. 2012. Drought in the Southern United States over the 20th century: variability and its impacts on terrestrial ecosystem productivity and carbon storage. Climatic Change, 114(2): 379–397.CrossRefGoogle Scholar
  8. Chen T, Van der Werf G R, De Jeu R A M, et al. 2013. A global analysis of the impact of drought on net primary productivity. Hydrology and Earth System Sciences, 17: 3885–3894.CrossRefGoogle Scholar
  9. Chen T, Huang Q H, Liu M, et al. 2017. Decreasing net primary productivity in response to urbanization in Liaoning Province, China. Sustainability, 9(2): 162, doi: 10.3390/su9020162.CrossRefGoogle Scholar
  10. Clark D A, Piper S C, Keeling C D, et al. 2003. Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000. Proceedings of the National Academy of Sciences of the United States of America, 100(10): 5852–5857.CrossRefGoogle Scholar
  11. Cuadra S V, Costa M H, Kucharik C J, et al. 2012. Abiophysical model of sugarcane growth. Global Change Biolagy Bioenergy, 4(1): 36–48.CrossRefGoogle Scholar
  12. Dannenberg M P, Song C, Hwang T, et al. 2015. Empirical evidence of El Nino-Southern Oscillation influence on land surface phenology and productivity in the western United States. Remote Sensing of Environment, 159: 167–180.CrossRefGoogle Scholar
  13. Dieguez H, Paruelo J M. 2017. Disentangling the signal of climatic fluctuations from land use: changes in ecosystem functioning in South American protected areas (1982–2012). Remote Sensing in Ecology and Conservation, 3(4): 177–189.CrossRefGoogle Scholar
  14. Dintwe K, Okin G S. 2018. Soil organic carbon in savannas decreases with anthropogenic climate change. Geoderma, 309: 7–16.CrossRefGoogle Scholar
  15. Dong G T, Bai J, Yang S T, et al. 2015. The impact of land use and land cover change on net primary productivity on China's Sanjiang Plain. Environmental Earth Sciences, 74(4): 2907–2917.CrossRefGoogle Scholar
  16. Donmez C, Berberoglu S, Curran P J. 2011. Modelling the current and future spatial distribution of NPP in a Mediterranean watershed. International Journal of Applied Earth Observation and Geoinformation, 13(3): 336–345.CrossRefGoogle Scholar
  17. Feizi M T. 2018. Ecological Regions of Iran Vegetation Types of Isfahan Province. Tehran: Research Institute of Forests and Rengelands, 290.Google Scholar
  18. Fischer D G, Chapman S K, Classen A T, et al. 2014. Plant genetic effects on soils under climate change. Plant and Soil, 379(1–2): 1–19.CrossRefGoogle Scholar
  19. Fridley JD, Lynn J S, Grime J P, et al. 2016. Longer growing seasons shift grassland vegetation towards more-productive species. Nature Climate Change, 6: 865–868.CrossRefGoogle Scholar
  20. Friedel M H, Shaw K. 1987. Evaluation of methods for monitoring sparse patterned vegetation in arid rangelands. I. Herbage. Journal of Environmental Management, 25(4): 309–318.Google Scholar
  21. Friedel M H. 1991. Range condition assessment and the concept of thresholds: A viewpoint. Journal of Range Management, 44(5): 422–426.CrossRefGoogle Scholar
  22. Gao Q Z, Wan Y F, Li Y, et al. 2013. Effects of topography and human activity on the net primary productivity (NPP) of alpine grassland in northern Tibet from 1981 to 2004. International Journal of Remote Sensing, 34(6): 2057–2069.CrossRefGoogle Scholar
  23. Goetz S J, Prince S D, Goward S N, et al. 1999. Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecological Modelling, 122(3): 239–255.CrossRefGoogle Scholar
  24. Goldsmith F B. 1991. Monitoring for Conservation and Ecology. Virginia Beach: Chapman & Hall Press, 275.CrossRefGoogle Scholar
  25. Hadian F, Jafari R, Bashari H. 2013. Assessing the accuracy of spectral indices in vegetation cover mapping at vegetation type and across vegetation type scales, using TM sensor data in southern Zagros regions. Iranian Remote Sensing and GIS, 4(4): 83–100.Google Scholar
  26. Holechek J L, Pieper R D, Herbel C H. 1989. Range Management. Principles and Practices. New Jersey: Prentice Hall Press, 526.Google Scholar
  27. Hua L Z, Liu H, Zhang X L, et al. 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, 012153, doi: 10.1088/1755-1315/17/1/012153.Google Scholar
  28. IRIMO. 2016. Islamic republic of Iran meteorological organization (IRIMO). [2016-06-29]. https://doi.org/www.irimo.ir/eng/index.php. Google Scholar
  29. Jafari R, Lewis M M, Ostendorf B. 2007. Evaluation of vegetation indices for assessing vegetation cover in southern arid lands in South Australia. The Rangeland Journal, 29(1): 39–49.CrossRefGoogle Scholar
  30. Jafari R, Bakhshandehmehr L. 2013. Quantitative mapping and assessment of environmentally sensitive areas to desertification in central Iran. Land Degradation & Development, 27(2): 108–119.CrossRefGoogle Scholar
  31. Jay S, Potter C, Crabtree R, et al. 2016. Evaluation of modelled net primary production using MODIS and landsat satellite data fusion. Carbon Balance and Management, 11: 8, doi: 10.1186/sl3021-016-0049-6.CrossRefGoogle Scholar
  32. Ji L, Peters A J. 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87(1): 85–98.CrossRefGoogle Scholar
  33. Jiang C, Wu Z F, Cheng J, et al. 2015. Impacts of urbanization on net primary productivity in the Pearl River Delta, China. International Journal of Plant Production, 9(4): 581–598.Google Scholar
  34. Kaminski T, Knorr W, Rayner P J, et al. 2002. Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle. Global Biogeochemical Cycles, 16(4): 14–1–14–16.CrossRefGoogle Scholar
  35. Kehl M. 2009. Quaternary climate change in Iran-the state of knowledge. Erdkunde, 63(1): 1–17.CrossRefGoogle Scholar
  36. Khajeddin S J. 1995. A survey of the plant communities of the Jazmorian, Iran, using Landsat MSS data. Ph.D Thesis. UK: University of Reading.Google Scholar
  37. Khaleghi M R, Aeinebeygi S. 2016. An assessment of biennial enclosure effects on range production, condition and trend (case study: Taftazan rangeland, Shirvan). International Journal of Forest, Soil and Erosion (IJFSE), 6(2): 33–40.Google Scholar
  38. Khatibi R, Soltani S, Khodagholi M. 2017. Effects of climatic factors and soil salinity on the distribution of vegetation types containing Anabasis aphylla in Iran: a multivariate factor analysis. Arabian Journal of Geosciences, 10(2): 36, doi: 10.1007/sl2517-016-2812-0.CrossRefGoogle Scholar
  39. Kimball J S, White M A, Running S W. 1997. BIOME-BGC simulations of stand hydrologic processes for BOREAS. Journal of Geophysical Research, 102(D24): 29043–29051.CrossRefGoogle Scholar
  40. Lammers J M, Schubert C J, Middelburg J J, et al. 2017. Microbial carbon processing in oligotrophic Lake Lucerne (Switzerland): results of in situ 13C-labelling studies. Biogeochemistry, 136(2): 131–149.CrossRefGoogle Scholar
  41. Lei T J, Wu J J, Li X H, et al. 2015. A new framework for evaluating the impacts of drought on net primary productivity of grassland. Science of The Total Environment, 536: 161–172.CrossRefGoogle Scholar
  42. Li W, Huang H Z, Zhang Z N, et al. 2011. Effects of grazing on the soil properties and C and N storage in relation to biomass allocation in an alpine meadow. Journal of Soil Science and Plant Nutrition, 11(4): 27–39.CrossRefGoogle Scholar
  43. Liang W, Yang Y T, Fan D M, et al. 2015. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agricultural and Forest Meteorology, 204: 22–36.CrossRefGoogle Scholar
  44. Lloyd-Hughes B, Saunders M A. 2002. A drought climatology for Europe. International Journal of Climatology, 22(13): 1571–1592.CrossRefGoogle Scholar
  45. Lokupitiya E, Denning S, Paustian K, et al. 2009. Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences, 6(6): 969–986.CrossRefGoogle Scholar
  46. Lu Q S, Gao Z Q, Ning J C, et al. 2015. Impact of progressive urbanization and changing cropping systems on soil erosion and net primary production. Ecological Engineering, 75: 187–194.CrossRefGoogle Scholar
  47. McCoy R M. 2005. Field Methods in Remote Sensing. New York: Guilford Press, 575.Google Scholar
  48. Nemry B, FrançOis L, Gérard J C, et al. 1999. Comparing global models of terrestrial net primary productivity (NPP): analysis of the seasonal atmospheric CO2 signal. Global Change Biology, 5(S1): 65–76.CrossRefGoogle Scholar
  49. O'connor T G, Roux P W. 1995. Vegetation changes (1949-71) in a semi-arid, grassy dwarf shrubland in the Karoo, South Africa: influence of rainfall variability and grazing by sheep. Journal of Applied Ecology, 32(3): 612–626.CrossRefGoogle Scholar
  50. 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.CrossRefGoogle Scholar
  51. Peng J, Loew A, Zhang S Q, et al. 2015. Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index. Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2015.2462074.Google Scholar
  52. Potter C S, Klooster S, Brooks V. 1999. Interannual variability in terrestrial net primary production: exploration of trends and controls on regional to global scales. Ecosystems, 2(1): 36–48.CrossRefGoogle Scholar
  53. Potter C. 2012a. Net primary production and carbon cycling in coast redwood forests of central California. Open Journal of Ecology, 2(3): 147–153.CrossRefGoogle Scholar
  54. Potter C, Klooster S, Genovese V. 2012b. Net primary production of terrestrial ecosystems from 2000 to 2009. Climatic Change, 115(2): 365–378.CrossRefGoogle Scholar
  55. Rafique R, Xia J Y, Hararuk O, et al. 2017. Comparing the performance of three land models in global C cycle simulations: A detailed structural analysis. Land Degradation & Development, 28(2): 524–533.CrossRefGoogle Scholar
  56. Rohli R V, Vega A J. 2013. Climatology. Burlinton: Jones & Bartlett Publishers, 451.Google Scholar
  57. Roupsard O, Le Maire G L, Nouvellon Y, et al. 2009. Scaling-up productivity (NPP) using light or water use efficiencies (LUE, WUE) from a two-layer tropical plantation. Agroforestry Systems, 76(2): 409–422.CrossRefGoogle Scholar
  58. Roxburgh S H, Berry S L, Buckley T N, et al. 2005. What is NPP? Inconsistent accounting of respiratory fluxes in the definition of net primary production. Functional Ecology, 19(3): 378–382.CrossRefGoogle Scholar
  59. Ruimy A, Saugier B, Dedieu G. 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. Journal of Geophysical Research: Atmospheres, 99(D3): 5263–5283.CrossRefGoogle Scholar
  60. Sainte-Marie J, Henrot A, Barrandon M, et al. 2012. Modeling the environmental and seasonal influence on canopy dynamic and litterfall of even-aged forest ecosystems by a model coupling growth & yield and process-based approaches. In: 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. Shanghai, China: IEEE, 324–331.CrossRefGoogle Scholar
  61. Schimel D S, Braswell B H, Holland E A, et al. 1994. Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils. Global Biogeochemical Cycles, 8(3): 279–293.CrossRefGoogle Scholar
  62. Schlesinger W H, Andrews J A. 2000. Soil respiration and the global carbon cycle. Biogeochemistry, 48(1): 7–20.CrossRefGoogle Scholar
  63. Stephenson N L. 1990. Climatic control of vegetation distribution: the role of the water balance. The American Naturalist, 135(5): 649–670.CrossRefGoogle Scholar
  64. Stoddart L, Smith A, Box T. 1975. Range Management (3rd ed.). New York: McGraw-Hill Book Company, 433.Google Scholar
  65. Sun Z G, Long X H, Sun C M, et al. 2013. Evaluation of net primary productivity and its spatial and temporal patterns in southern China's grasslands. The Rangeland Journal, 35(3): 331–338.CrossRefGoogle Scholar
  66. Throop H L, Reichmann L G, Sala O E, et al. 2012. Response of dominant grass and shrub species to water manipulation: an ecophysiological basis for shrub invasion in a Chihuahuan Desert grassland. Oecologia, 169(2): 373–383.CrossRefGoogle Scholar
  67. Wang S Y, Zhang B, Yang Q C, et al. 2017. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agricultural and Forest Meteorology, 232: 235–246.CrossRefGoogle Scholar
  68. Wessels K J, Prince S D, Malherbe J, et al. 2007. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68(2): 271–297.CrossRefGoogle Scholar
  69. Whitehead D, Gower S T. 2001. Photosynthesis and light-use efficiency by plants in a Canadian boreal forest ecosystem. Tree Physiology, 21(12–13): 925–929.CrossRefGoogle Scholar
  70. Wilson A D, Abraham N A, Barratt R, et al. 1987. Evaluation of methods of assessing vegetation change in the semi-arid rangelands of southern Australia. The Rangeland Journal, 9(1): 5–13.CrossRefGoogle Scholar
  71. Xia J Y, Luo Y Q, Wang Y P, et al. 2013. Traceable components of terrestrial carbon storage capacity in biogeochemical models. Global Change Biology, 19(7): 2104–2116.CrossRefGoogle Scholar
  72. Xu X, Sherry R A, Niu S L, et al. 2013. Net primary productivity and rain-use efficiency as affected by warming, altered precipitation, and clipping in a mixed-grass prairie. Global Change Biology, 19(9): 2753–2764.CrossRefGoogle Scholar
  73. Yeganeh H, Khajedein S J, Amiri F, et al. 2014. Monitoring rangeland ground cover vegetation using multitemporal MODIS data. Arabian Journal of Geosciences, 7(1): 287–298.CrossRefGoogle Scholar
  74. Yu D Y, Yang M C, Pan Y Z, et al. 2005. Study on temporal and spatial changes of light utilization efficiency (LUE) for vegetations in Eastern Asia. In: 2005 IEEE Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. Seoul, South Korea: IEEE, 1896–1899.Google Scholar
  75. Yu D Y, Shi P J, Shao H B, et al. 2009. Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model. International Journal of Remote Sensing, 30(18): 4851–4866.CrossRefGoogle Scholar
  76. Yu R, Evans A J, Malleson N. 2018. Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index. Remote Sensing of Environment, 209: 181–194.CrossRefGoogle Scholar
  77. Yuan J G, NIU Z, WANG C L. 2006. Vegetation NPP distribution based on MODIS data and CASA Model—a case study of northern Hebei Province. Chinese Geographical Science, 16(4): 334–341.CrossRefGoogle Scholar
  78. Zhang L, Zhou G S, Ji Y H, et al. 2017. Grassland carbon budget and its driving factors of the subtropical and tropical monsoon region in China during 1961 to 2013. Scientific Reports, 7: 14717, doi: 10.1038/s41598-017-15296-7.CrossRefGoogle Scholar
  79. Zhang L X, Zhou D C, Fan J W, et al. 2015. Comparison of four light use efficiency models for estimating terrestrial gross primary production. Ecological Modelling, 300: 30–39.CrossRefGoogle Scholar
  80. Zhang N, Yu Z L, Yu G R, et al. 2007. Scaling up ecosystem productivity from patch to landscape: a case study of Changbai Mountain Nature Reserve, China. Landscape Ecology, 22(2): 303–315.CrossRefGoogle Scholar
  81. Zhou W, Gang C C, Zhou F C, et al. 2015. Quantitative assessment of the individual contribution of climate and human factors to desertification in northwest China using net primary productivity as an indicator. Ecological Indicators, 48: 560–569.CrossRefGoogle Scholar
  82. Zika M, Erb K H. 2009. The global loss of net primary production resulting from human-induced soil degradation in drylands. Ecological Economics, 69(2): 310–318.CrossRefGoogle Scholar

Copyright information

© Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Fatemeh Hadian
    • 1
  • Reza Jafari
    • 1
    Email author
  • Hossein Bashari
    • 1
  • Mostafa Tartesh
    • 1
  • Kenneth D. Clarke
    • 2
  1. 1.Department of Natural ResourcesIsfahan University of TechnologyIsfahanIran
  2. 2.School of Biological SciencesUniversity of AdelaideSouth AustraliaAustralia

Personalised recommendations