Determining the Relationship between NDVI/Leaf Area Index and Plant Production in Vegetation Cover Studies using Remote Sensing

Document Type : Original Article

Authors

1 Department of Environment, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

2 Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran

3 Department of Environment, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

10.22052/jdee.2023.248303.1082

Abstract

Plant production is one of the most important living elements in ecosystems for example in the food cycle. In arid and desert areas, due to the fragility of these ecosystems, vegetation cover is of particular importance in reducing wind and water erosion. Therefore, the main purpose of this study is to study vegetation cover and plant production in desert and semi-desert climates of Khuzestan as a coastal province. In this study, while separating climatic classes, vegetation type and determining the status of rangeland, the relationship between vegetation cover, plant production, and NDVI/ Leaf Area Index (MODIS image products with the resolution of 250*250 m2) in the separated layers of vegetation was calculated. The results showed that among the studied climates, the relationship between vegetation cover and satellite images decreases in semi-arid, arid, and ultra-arid climates, respectively, and in a climatic classification with vegetation type degradation, the relationship between vegetation and NDVI index weakens. The amount of leaf area index in this research was between 0.13 to 0.002, and Quercus brantii and Scirpus spp. showed the highest and lowest values, respectively. A comparison of the relationship between plant production and leaf area index shows that this relationship is stronger in the leaf area index than plant production as the main factor of plant production. Therefore, considering the importance of plant reflectivity, remote sensing studies and reduction of leaf area index, dry conditions and destruction of plant types can be the main reasons for reducing the relationship between vegetation and NDVI index.

Keywords

  1. Abatzoglou, J. T., McEvoy, D. J. and Redmond, K. T., 2017. The West Wide Drought Tracker: Drought Monitoring at Fine Spatial Scales. Bulletin of the American Meteorological Society, Vol. 98- 9: 1815–1820.
  2. Abdi, N., Maddah Arefi, H., Zahedi Amiri, G. and Arzani, H., 2009. Investigation of carbon sequestration content in astragalus rangelands in Gholestankoh of Khansar. Watershed Management Researches., Vol.1:58-68.
  3. Adl, H. 2007. Estimation of leaf biomass and leaf area index of two major species in Yasuj forests.  Iranian Journal Of Forest and Poplar Research., Vol. 15: 417–426.
  4. Almorox, J., Benito, M. and Hontoria, C., 2005, Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain. Journal Of Renewable Energy, Vol. 30- 6: 931-936.
  5. Andalibi, A., Ghorbani, A., Moameri, M., Hazbavi, Z., Nothdurft, A., Jafari, R. and Dadjo, F. 2021. Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran. Journal of Remote Sens. Vol. 13: 2879.
  6. Aronson, J., Floret, C., Floc'h, E., Ovalle, C. and Pontanier, R. 1993. Restoration and rehabilitation of degraded ecosystems in arid and semi‐arid lands. I. A View from the South. Restoration ecology., Vol.1: 8-17.
  7. Asadi, S. Bannayan, M. Jahan, M. and Farid, A. 2018. Comparison of different spectral vegetation indices for the remote assessment of winter wheat leaf area index in Mashhad. Journal Of Agroecology (Quarterly)., Vol. 10:913–934.
  8. Attorre, F., Alfo, M., De Sanctis, M., Francesconi, F. and Bruno, F., 2007, Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. International Journal of Climatology, Vol. 27- 13: 1825-1843.
  9. Behbahani, N., Falah Shamsi. S.R., Farzadmehr, J., Erfanifard, S.R. and Ramezani Gasak, M. 2010. Using vegetation indices of Aster-LB imagery of estimate single trees crown cover in arid rangelands, case study: Tag-Ahmadshhi, Southern of Khorasan. Iran. Journal of Rangel., Vol. 4: 93–103.
  10. Blanco, F.F. and Folegatti, M.V. 2003. A new method for estimating the leaf area index of cucumber and tomato plants. Journal of Horticulture Brasileria, Vol. 21: 666–669.
  11. Bucci, S.J., Scholz, F.G., Goldstein, G., Meinzer, F.C. and Arce, M.E. 2009. Soil water availability and rooting depth as determinants of hydraulic architecture of Patagonian woody species. Oecologia., Vol.160: 631-641.
  12. Campanella, M.V. and Bertiller, M.B. 2008. Plant phenology, leaf traits and leaf litterfall of contrasting life forms in the arid Patagonian Monte, Argentina. Journal of Vegetation Science., Vol.19: 75-85.
  13. Carlson, T. N. and Ripley, D. A. 1997, On the relation between NDVI, fractional vegetation cover, and leaf area index. Journal of Remote sensing of Environment, Vol. 62- 3: 241-252.
  14. Cohen, W.B., Maiersperger, T.K., Gower, S. T. and Turner, D.P., 2003, An Improved Strategy for Regression of Biophysical Variables and Landsat ETM+ Data. Journal of Remote Sensing of Environment, Vol. 84: 561–571.
  15. DeFries, R. and Townshend, J. 1994. NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, Vol. 15- 17: 3567-3586
  16. Fang, H., Yinghui, Z., Wei, S., Li, W., Ye, Y., Sun, T. and Liu, W. 2019. The field measurements and high resolution reference LAI data in Hailun and Honghe, China. PANGAEA, Data Publisher for Earth & Environmental Science
  17. Freitas, S. R., Mello, M. C. S. and Carla B.M.C. 2005. Relationships Between Forest structure and Vegetation Indices in Atlantic Rainforest. Journal of Forest Ecology and Management, Vol. 218: 353–362.
  18. Friedel, M., 1991, Range condition assessment and the concept of thresholds: a viewpoint. Journal of range management, Vol. 44- 5:422-426.
  19. Gerber, L., 2000, Development of a ground truthing method for determination of rangeland biomass using canopy reflectance properties, African Journal of Range and Forage Science, Vol. 17- 1-3: 93-100.
  20. Ghorbani, A., Dadjou, F., Moameri, M. and Biswas, A. 2020. Estimating aboveground net primary production (ANPP) using Landsat 8-based indices: A case study from Hir-Neur rangelands, Iran. Journal of Rangeland Ecology & Management., Vol 73: 649–657.
  21. Goswami, S., Gamon, J.A., Vargas, S. and Tweedie, S.E. 2015. Relationships of NDVI, biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska. Journal of PeerJ Prepr., Vol. 3: e913v1.
  22. Hadian, F., Jafari, R., Bashari, H. and Tarkesh, M. 2019. Modeling of semi-steppe rangelands degradation in Isfahan Province using MODIS images. Iran. Journal of Remote Sens. GIS, Vol 11: 1–20.
  23. Hadian, F., Jafari, R., Bashari, H. and Soltani, S., 2014, Monitoring the Effects of Precipitation on Vegetation Cover Changes Using Remote Sensing Techniques in 12 Years Period (Case study: Semirom Isfahan). Journal of Range & Watershed Management, Vol. 66- 4: 621-632.
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  33. Ma, H., Song, J., Wang, J., Xiao, Z. and Fu, Z. 2014. Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi angle optical data. Agric. Journal of Agricultural and Forest Meteorology, Vol. 189-190: 60–70.
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36. Meng, S., Xie, X., Zhu, B. and Wang, Y. 2020. The relative contribution of vegetation greening to the hydrological cycle in the three-north region of China: A modelling analysis. Journal of Hydrology., Vol. 591: 125689.

  1. McCoy, R. M., 2005, Field Methods in Remote Sensing, Guilford.
  2. Moeser, D., Roubinek, J., Schleppi, P., Morsdorf, F. and Jonas, T. Canopy closure. 2014. LAI and radiation transfer from airborne LiDAR synthetic images. Journal of Agricultural and Forest Meteorology., Vol, 197: 158–168.
  3. Moreno-de las Heras, M., Díaz-Sierra, R., Turnbull, L. and Wainwright, J. 2015. Assessing vegetation structure and ANPP dynamics in a grassland-shrubland Chihuahuan ecotone using NDVI-rainfall relationships. Biogeosciences discussions., Vol.12:51-92.
  4. Nowruzi, A., Jalali, N., Miri, M. and Abbasi, M. 2013. Estimation of rice leaf area index in northern Iran. Journal of Soil Water Resour. Prot., Vol. 3:1–10.
  5. Peterson, D. L., Price, K. P. and Martinko, E. A. 2002. Investigating grazing intensity and range condition of grasslands in northeastern Kansas using Landsat thematic mapper data. Journal of Geocarto International, Vol. 17, No. 4, 15-26.
  6. Penuelas, J., Gamon, J. A., Griffin, K. L. and Field, C. B. 1993. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Journal of Remote Sensing of Environment, Vol. 46- 2: 110-118.
  7. Pourhashemi, M., Eskandari, S., Dehghani, M., Najafi, T., Asadi, A. and Panahi, P., 2011. Biomass and leaf area index of Caucasian Hackberry (Celtis caucasica Willd.) in Taileh urban forest, Sanandaj, Iran. Journal of Iran. Jungle Poplar Res. Q. Vol. 19: 609–620.
  8. Qiao, K., Zhu, W., Xie, Z. and Li, P. 2019. Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases. Journal of Remote Sensing., Vol 11: 689.
  9. Reynolds, J. F., Virginia, R. A., Kemp, P. R., De Soyza, A. G. and Tremmel, D. C., 1999, Impact of drought on desert shrubs: effects of seasonality and degree of resource island development, Journal of Ecological Monographs, Vol. 69- 1: 69-106.
  10. Silveira, E.M., Carvalho, L.M., Acerbi-Júnior, F.W. and Mello, J.M. 2008. The assessment of vegetation seasonal dynamics using multitemporal NDVI and EVI images derived from MODIS. Cerne Lavras, Vol. 14: 177–184.
  11. Sims, D. A. and Gamon, J. A. 2002, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Journal of Remote sensing of environment, Vol. 81-2:337-354.
  12. Singh, V. and Singh, N. 2019. Assessment and comparison of phytoremediation potential of selected plant species against Endosulfan. International Journal of Environmental Science and Technology, Vol 16: 3231–3248.
  13. Siuki, S.K. Saghafian, B. and Moazami., S. 2017. Comprehensive evaluation of 3-hourly TRMM and half-hourly GPM-IMERG satellite precipitation products. Journal of Remote Sensing., Vol. 38: 558–571.
  14. Tan, C.W., Zhang, P.P., Zhou, X.X., Wang, Z.X., Xu, Z.Q. and Mao, W., 2020. Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law. Journal of Scientific Reports., Vol. 10: 929.
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55. Yan, G., Hu, R., Luo, J., Weiss, M., Jiang, H., Mu, X., Xie, D. and Zhang, W. 2019. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Journal of Agricultural and Forest Meteorology, Vol.265: 390-411.

56. Yaghmaei, L., Jafari., R.Soltani, S.2021. Investigating net primary production in climate regions of central Zagros, Iran, using MODIS and meteorological data, climate research, 83:173-186.

  1. Yeganeh, H., jamale Khajedein, S., Amiri, F. and Shariff, A.R.B.M. 2014, Monitoring rangeland ground cover vegetation using multitemporal MODIS data. Arabian Journal of Geosciences, Vol. 7- 1:287-298.
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References

  1. Abatzoglou, J. T., McEvoy, D. J. and Redmond, K. T., 2017. The West Wide Drought Tracker: Drought Monitoring at Fine Spatial Scales. Bulletin of the American Meteorological Society, Vol. 98- 9: 1815–1820.
  2. Abdi, N., Maddah Arefi, H., Zahedi Amiri, G. and Arzani, H., 2009. Investigation of carbon sequestration content in astragalus rangelands in Gholestankoh of Khansar. Watershed Management Researches., Vol.1:58-68.
  3. Adl, H. 2007. Estimation of leaf biomass and leaf area index of two major species in Yasuj forests.  Iranian Journal Of Forest and Poplar Research., Vol. 15: 417–426.
  4. Almorox, J., Benito, M. and Hontoria, C., 2005, Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain. Journal Of Renewable Energy, Vol. 30- 6: 931-936.
  5. Andalibi, A., Ghorbani, A., Moameri, M., Hazbavi, Z., Nothdurft, A., Jafari, R. and Dadjo, F. 2021. Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran. Journal of Remote Sens. Vol. 13: 2879.
  6. Aronson, J., Floret, C., Floc'h, E., Ovalle, C. and Pontanier, R. 1993. Restoration and rehabilitation of degraded ecosystems in arid and semi‐arid lands. I. A View from the South. Restoration ecology., Vol.1: 8-17.
  7. Asadi, S. Bannayan, M. Jahan, M. and Farid, A. 2018. Comparison of different spectral vegetation indices for the remote assessment of winter wheat leaf area index in Mashhad. Journal Of Agroecology (Quarterly)., Vol. 10:913–934.
  8. Attorre, F., Alfo, M., De Sanctis, M., Francesconi, F. and Bruno, F., 2007, Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. International Journal of Climatology, Vol. 27- 13: 1825-1843.
  9. Behbahani, N., Falah Shamsi. S.R., Farzadmehr, J., Erfanifard, S.R. and Ramezani Gasak, M. 2010. Using vegetation indices of Aster-LB imagery of estimate single trees crown cover in arid rangelands, case study: Tag-Ahmadshhi, Southern of Khorasan. Iran. Journal of Rangel., Vol. 4: 93–103.
  10. Blanco, F.F. and Folegatti, M.V. 2003. A new method for estimating the leaf area index of cucumber and tomato plants. Journal of Horticulture Brasileria, Vol. 21: 666–669.
  11. Bucci, S.J., Scholz, F.G., Goldstein, G., Meinzer, F.C. and Arce, M.E. 2009. Soil water availability and rooting depth as determinants of hydraulic architecture of Patagonian woody species. Oecologia., Vol.160: 631-641.
  12. Campanella, M.V. and Bertiller, M.B. 2008. Plant phenology, leaf traits and leaf litterfall of contrasting life forms in the arid Patagonian Monte, Argentina. Journal of Vegetation Science., Vol.19: 75-85.
  13. Carlson, T. N. and Ripley, D. A. 1997, On the relation between NDVI, fractional vegetation cover, and leaf area index. Journal of Remote sensing of Environment, Vol. 62- 3: 241-252.
  14. Cohen, W.B., Maiersperger, T.K., Gower, S. T. and Turner, D.P., 2003, An Improved Strategy for Regression of Biophysical Variables and Landsat ETM+ Data. Journal of Remote Sensing of Environment, Vol. 84: 561–571.
  15. DeFries, R. and Townshend, J. 1994. NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, Vol. 15- 17: 3567-3586
  16. Fang, H., Yinghui, Z., Wei, S., Li, W., Ye, Y., Sun, T. and Liu, W. 2019. The field measurements and high resolution reference LAI data in Hailun and Honghe, China. PANGAEA, Data Publisher for Earth & Environmental Science
  17. Freitas, S. R., Mello, M. C. S. and Carla B.M.C. 2005. Relationships Between Forest structure and Vegetation Indices in Atlantic Rainforest. Journal of Forest Ecology and Management, Vol. 218: 353–362.
  18. Friedel, M., 1991, Range condition assessment and the concept of thresholds: a viewpoint. Journal of range management, Vol. 44- 5:422-426.
  19. Gerber, L., 2000, Development of a ground truthing method for determination of rangeland biomass using canopy reflectance properties, African Journal of Range and Forage Science, Vol. 17- 1-3: 93-100.
  20. Ghorbani, A., Dadjou, F., Moameri, M. and Biswas, A. 2020. Estimating aboveground net primary production (ANPP) using Landsat 8-based indices: A case study from Hir-Neur rangelands, Iran. Journal of Rangeland Ecology & Management., Vol 73: 649–657.
  21. Goswami, S., Gamon, J.A., Vargas, S. and Tweedie, S.E. 2015. Relationships of NDVI, biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska. Journal of PeerJ Prepr., Vol. 3: e913v1.
  22. Hadian, F., Jafari, R., Bashari, H. and Tarkesh, M. 2019. Modeling of semi-steppe rangelands degradation in Isfahan Province using MODIS images. Iran. Journal of Remote Sens. GIS, Vol 11: 1–20.
  23. Hadian, F., Jafari, R., Bashari, H. and Soltani, S., 2014, Monitoring the Effects of Precipitation on Vegetation Cover Changes Using Remote Sensing Techniques in 12 Years Period (Case study: Semirom Isfahan). Journal of Range & Watershed Management, Vol. 66- 4: 621-632.
  24. Hadian, F., Jafari, R., Bashari, H. Tarkesh, M., Clarke, K. 2019. Effects of drought on plant parameters of different rangeland types in Khansar region, Iran. Arabian Journal of Geoscience, Vol 12: 93.
  25. Hosseini, A.F., Astaraei, A., Sanaeinejad, S.H. and Mousavi, P.M.H. 2013. Estimation of leaf area index using IRS satellite data in Neishabour region. Iran Journal of Crop Research., Vol. 3: 577–582.
  26. Hoveyzeh, H., Nemati, H. and Ashouri, P. Investigating the ecological regions of the country, plant types of Khuzestan province, Publications of the Research Institute of Forests and Ranges, 2015, 186 pages.
  27. Jarocinska, A., Kacprzyk, M., Marcinkowska-Ochtyra, A., Ochtyra, A., Zagajewski, B. and Meuleman, K. 2016. The application of APEX images in the assessment of the state of non-forest vegetation in the Karkonosze Mountains. Journal of Miscellanea Geographica., Vol. 20: 21–27.
  28. Jenkins, J., Richardson, A.D., Braswell, B., Ollinger, S.V., Hollinger, D.Y. and Smith, M.L., 2007. Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Journal of Agricultural and Forest Meteorology, Vol. 143- 1: 64-79.
  29. Khaleghi, M. R. and 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), Vol. 6- 2: 33-40.
  30. Li, X., Li, G., Wang, H., Wang, H. and 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, Vol. 19- 3: 561.
  31. Liu, Y., Ju, W., Chen, J., Zhu, G., Xing, B., Zhu, J. and He, M. 2012. Spatial and temporal variations of forest LAI in China during 2000–2010. Journal of Chinese Science Bulletin., Vol. 57: 2846–2856.
  32. Lovynska, V., Lakyda, P., Sytnyk, S., Kharytonov, N, and Piestova, I. 2018, LAI estimation by direct and indirect methods in Scots pine stands in Northern Steppe of Ukraine. Journal of Remote Sensing Environment., Vol. 64-514–522.
  33. Ma, H., Song, J., Wang, J., Xiao, Z. and Fu, Z. 2014. Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi angle optical data. Agric. Journal of Agricultural and Forest Meteorology, Vol. 189-190: 60–70.
  34. Majasalmi, T., Rautiainen, M., Stenberg, P. and Lukeš, P. 2013. An assessment of ground reference methods for estimating LAI of boreal forests. Journal of Forest Ecology and Management,Vol. 292: 10–18.
  35. Mani, J.K. Varghese, A.O. and Rao, K. Estimation of leaf area index of teak forests of Central India using satellite remote sensing. In Proceedings of the 38th Asian Conference on Remote Sensing, New Delhi, India, 23–27 October 2017.

36. Meng, S., Xie, X., Zhu, B. and Wang, Y. 2020. The relative contribution of vegetation greening to the hydrological cycle in the three-north region of China: A modelling analysis. Journal of Hydrology., Vol. 591: 125689.

  1. McCoy, R. M., 2005, Field Methods in Remote Sensing, Guilford.
  2. Moeser, D., Roubinek, J., Schleppi, P., Morsdorf, F. and Jonas, T. Canopy closure. 2014. LAI and radiation transfer from airborne LiDAR synthetic images. Journal of Agricultural and Forest Meteorology., Vol, 197: 158–168.
  3. Moreno-de las Heras, M., Díaz-Sierra, R., Turnbull, L. and Wainwright, J. 2015. Assessing vegetation structure and ANPP dynamics in a grassland-shrubland Chihuahuan ecotone using NDVI-rainfall relationships. Biogeosciences discussions., Vol.12:51-92.
  4. Nowruzi, A., Jalali, N., Miri, M. and Abbasi, M. 2013. Estimation of rice leaf area index in northern Iran. Journal of Soil Water Resour. Prot., Vol. 3:1–10.
  5. Peterson, D. L., Price, K. P. and Martinko, E. A. 2002. Investigating grazing intensity and range condition of grasslands in northeastern Kansas using Landsat thematic mapper data. Journal of Geocarto International, Vol. 17, No. 4, 15-26.
  6. Penuelas, J., Gamon, J. A., Griffin, K. L. and Field, C. B. 1993. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Journal of Remote Sensing of Environment, Vol. 46- 2: 110-118.
  7. Pourhashemi, M., Eskandari, S., Dehghani, M., Najafi, T., Asadi, A. and Panahi, P., 2011. Biomass and leaf area index of Caucasian Hackberry (Celtis caucasica Willd.) in Taileh urban forest, Sanandaj, Iran. Journal of Iran. Jungle Poplar Res. Q. Vol. 19: 609–620.
  8. Qiao, K., Zhu, W., Xie, Z. and Li, P. 2019. Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases. Journal of Remote Sensing., Vol 11: 689.
  9. Reynolds, J. F., Virginia, R. A., Kemp, P. R., De Soyza, A. G. and Tremmel, D. C., 1999, Impact of drought on desert shrubs: effects of seasonality and degree of resource island development, Journal of Ecological Monographs, Vol. 69- 1: 69-106.
  10. Silveira, E.M., Carvalho, L.M., Acerbi-Júnior, F.W. and Mello, J.M. 2008. The assessment of vegetation seasonal dynamics using multitemporal NDVI and EVI images derived from MODIS. Cerne Lavras, Vol. 14: 177–184.
  11. Sims, D. A. and Gamon, J. A. 2002, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Journal of Remote sensing of environment, Vol. 81-2:337-354.
  12. Singh, V. and Singh, N. 2019. Assessment and comparison of phytoremediation potential of selected plant species against Endosulfan. International Journal of Environmental Science and Technology, Vol 16: 3231–3248.
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  14. Tan, C.W., Zhang, P.P., Zhou, X.X., Wang, Z.X., Xu, Z.Q. and Mao, W., 2020. Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law. Journal of Scientific Reports., Vol. 10: 929.
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