Journal of Ecohydrology

Journal of Ecohydrology

Land degradation trend in the climatic types of Fars province using remote sensing and climatic variables

Document Type : Research Article

Authors
Behnaz Asefjah 1
Yahya Esmaeilpour 2
Ommolbanin Bazrafshan 3
Amirreza Keshtkar 3
Hossein Zamani 4
1 PhD Student in Desert Management and Control, Faculty of Agricultural Engineering and Natural Resources, Hormozgan University, Bandar Abbas, Iran
2 Associate Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran
3 Associate Professor, International Desert Research Center University of Tehran, Tehran, Iran
4 Assistant Professor, Department of Statistics, Faculty of Sciences, University of Hormozgan, Bandar Abbas, Iran
10.22059/ije.2023.353644.1707
Abstract
Land degradation rapidly increased in developing countries. Changes in climate and land use in Fars province in the past few decades have intensified the process of destruction and desertification. In the present research, satellite data was used to investigate the temporal and spatial changes of vegetation and its relationship with climate changes in the climate samples of Fars province in the years 2000 to 2020. The trend of changes of these variables in time with the Mann-Kendall method and in determining the time of change and spatial correlation, Pettit's test and Pearson's correlation test were used, respectively. The trend of NDVI in arid and semi-arid climates is increasing and the point of change is from 2010 onwards. Based on this, it can be expected that in most regions of Fars, we see a decrease in the ratio of precipitation to potential evaporation (increase in evaporation) and the decadence trend is increasing. Precipitation has no trend and the surface temperature is decreasing. The spatial pattern of NDVI and precipitation trend is increasing in more than 70% of the region (south), and AI and LST are decreasing in more than 65% of the region (central). NDVI changes spatial correlation with LST, precipitation and AI variables showed that the type of relationship and the strength of correlation were different in climatic regions. The strongest correlations were seen in the cold ultra-arid climates in the northeast and the temperate Mediterranean located in the northwest of the province.
Keywords
Land degradation trend
Climatic types
Remote Sensing
Climate factor
Subjects
  • Sanjuán, M. E., del Barrio, G., Ruiz Moreno, A., Rojo, L., Puigdefábregas, J., Martínez, A. Evaluación y seguimiento de la desertificación en España: Mapa de la Condición de la Tierra 2000-2010. 2014; 08.
  • Alves TLB, de Azevedo PV, Costa dos Santos CA. Influence of climate variability on land degradation (desertification) in the watershed of the upper Paraíba River. Theoretical and Applied Climatology. 2017; 127(3), 741-751.
  • Geist HJ, Lambin EF. Dynamic causal patterns of desertification. Bioscience. 2004; 54(9), 817-829.‏
  • Wu S, Yin Y, Zheng D, Yang Q. Aridity/humidity status of land surface in China during the last three decades. Science in China Series D: Earth Sciences. 2005; 48(9), 1510-1518.‏
  • Zhang Q, Xu YC, Zhang Z. Observed changes of drought/wetness episodes in the Pearl River basin, China, using the standardized precipitation index and aridity index. Theoretical and Applied Climatology. 2009; 98(1), 89-99.‏

 

  • Liu X, Zhang D, Luo Y, Liu C. Spatial and temporal changes in aridity index in northwest China: 1960 to 2010.Theoretical and Applied Climatology. 2013; 112(1), 307-316.‏
  • Wu S, Yi Y, Zheng D, Yang Q. Moisture conditions and climate trends in China during the period 1971–2000. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2006; 26(2), 193-206.
  • Wu S, Gao X, Lei J, Zhou N, Wang Y. Spatial and temporal changes in the normalized difference vegetation index and their driving factors in the desert/grassland biome transition zone of the Sahel region of Africa. Remote Sensing. 2020; 12(24), 4119.‏
  • Liu H, Zheng L, Yin S. Multi-perspective analysis of vegetation cover changes and driving factors of long time series based on climate and terrain data in Hanjiang River Basin, China. Arabian Journal of Geosciences. 2018; 11(17), 1-16.
  • Chakraborty A, Seshasai M.V.R, Reddy C.S, Dadhwal V.K. Persistent negative changes in seasonal greenness over different forest types of India using MODIS time series NDVI data (2001–2014). Ecological Indicators.2018; 85, 887-903.
  • Guo B, Zang W, Zhang R. Soil salizanation information in the Yellow River Delta based on feature surface models using Landsat 8 OLI data. IEEE Access. 2020; 8, 94394-94403.
  • Sun G, Guo B, Zang W, Huang X, Han, B., Yang, et al. Spatial–temporal change patterns of vegetation coverage in China and its driving mechanisms over the past 20 years based on the concept of geographic division. Geomatics, Natural Hazards and Risk. 2020; 11(1), 2263-2281.
  • Fensholt R, Proud S R. Evaluation of earth observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series. Remote sensing of Environment. 2012; 119, 131-147.
  • Guo B, Wen Y. An optimal monitoring model of desertification in naiman banner based on feature space utilizing landsat8 OLI image. IEEE Access. 2020; 8:4761–4768.
  • Kern A , Marjanović H, Barcza Z. Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data. Agricultural and Forest Meteorology. 2020; 287, 107969.
  • Liu Y, Wan, X, Guo, M., Tani H, Matsuoka, N, Matsumura S. Spatial and temporal relationships among NDVI, climate factors, and land cover changes in Northeast Asia from 1982 to 2009. GIScience & Remote Sensing. 2011; 48(3), 371-393.
  • Ding M, Zhang Y, Liu L, Zhang W, Wang Z, Bai W. The relationship between NDVI and precipitation on the Tibetan Plateau. Journal of Geographical Sciences.2007; 17(3), pp.259-268.
  • Prince SD. “Satellite Remote Sensing of Primary Production: Comparison of Results for Sahelian Grassland 1981–1988,” International Journal of Remote Sensing.1991; 12:1301–1312.
  • Guo B, Zhou Y, Wang SX, Tao HP. 2014. Relationship between Normalized Difference Vegetation Index (NDVI) and Climate Factors in the semi-arid region: a case study Yalu Tsangpo River Basin of Qinghai-Tibet Plateau. Journal of Mountain Science.2014; 11(4), 926-940.‏
  • Barbosa HA, Huete AR, Baethgen WE. A 20-year study of NDVI variability over the Northeast Region of Brazil. J Arid Environ. 67(2):288–307.
  • Verbesselt J, Hyndman R, Newnham G, Culvenor D. “Detecting Trend and Seasonal Changes in Satellite Image Time Series,” Remote Sensing of Environment. 2010; 114:106–115.
  • Chuai XW, Huang XJ, Wang WJ, Bao G. NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China. 2013; International journal of climatology, 33(7), 1696-1706.‏
  • Yang L, Wei W, Wang T, Li L. Temporal-spatial variations of vegetation cover and surface soil moisture in the growing season across the mountain-oasis-desert system in Xinjiang, China. 2021; Geocarto International, 1-29.
  • Nasserzadeh MH, Hejazizadeh Z, Gholampour Z, Alijani B. Spatiotemporal Response of MODIS Derived Vegetation index to climatic condition Case study: Kohgiloyeh O Boirahmad Province of Iran. Scientific Journals Management System. 2020; 20 (57), 355-370.[Persian]
  • Ghanbari Motlagh M, Amraei B. Detecting the Spatiotemporal Relationship of Vegetation Changes with Climatic Elements in Mazandaran Province. Geography and Environmental Sustainability. 2020; 10(2), 37-55.[Persian]
  • Lu D, Mausel P, Brondızio E, Moran E. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest ecology and management. 2004; 198(1): 149-167.
  • Wessels KJ, Van Den Bergh F, Scholes RJ. Limits to detectability of land degradation by trend analysis of vegetation index data. Remote sensing of Environment.2012; 125: 10-22.
  • Eckert S, Hüsler F, Liniger H, Hodel, E. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments. 2015; 113, 16-28.
  • Nikpour N, negaresh H, Fotoohi S, Hosseini S Z, Bahrami S. Monitoring the trend of vegetation changes one of the most important indicators of land degradation (in Ilam province). jsaeh 2019; 5 (4) :21-48.[Persian]
  • Azare A, Rafien Sardoie E, Barkhori S. Investigating the trend of vegetation changes using the NDVI index and MODIS sensor images. Contemporary Agricultural and Sustainable Natural Resources.2019.[Persian]
  • Rahimi, J., Ebrahimpour, M., & Khalili, A. Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theoretical and Applied Climatology, 2013 ;112(3–4).[Persian]
  • Hashemi Dareh Badami S, Nouraeisefat I, Karimi S, Nazari S. Development trend analysis of urban heat island regarding land use/cover changes using time series of landSat images. Journal of RS and GIS for Natural Resources.2015; 6(3), 15-28.[Persian]
  • Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 1979; 8(2), 127-150.‏
  • Cihlar J, Laurent LS, Dyer JA. Relation between the normalized difference vegetation index and ecological variables. Remote sensing of Environment, 35(2-3). 1991; 279-298.
  • Fuller DO. Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993. International Journal of Remote Sensing. 1998; 19(10), 2013-2018.
  • Zarch MAA, Sivakumar B, Malekinezhad H, Sharma A. Future aridity under conditions of global climate change, J. Hydrol. 2017; 554, 451–469.
  • UNEP: World atlas of desertification, Edward Arnold, London, 1992.
  • Wu D, Wu H, Zhao X, Zhou T, Tang B, Zhao W, Jia, K. Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011. Remote Sensing. 2014; 6: 5. 4217-4239
  • Pettitt AN. A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society. Series C (Applied Statistics). 1979; 28(2):126-135.
  • Gandomkar, A., Dehghani, R. Study of Temperature Changes in Fars Province. World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering. 2012; 6 (3), 127-129.
  • Keshavarz, M., Karami, E., Zibaei, M. Adaptation of Iranian farmers to climate variability and change. Regional environmental change. 2014; 14 (3), 1163-1174. [Persian]
  • Eskandari Damaneh H, Gholami H, Mahdavi R, Khoorani A, Li J. Evaluation of land degradation trend using satellite imagery and climatic data (Case study: Fars province). DEEJ 2019; 8 (24):49-64. [Persian]
  • Andales, A.A., Derner, J.D., Ahuja, L.R. & Hart, R.H. Strategic and tactical prediction of forage production in northern mixed-grass prairie. Rangeland Ecology Management. 2006; 59:576–584.
  • Nemani, R. R., C. D. Keeling., H. Hashimoto., W. M. Jolly., S. C. Piper., C. J. Tucker., R. B. Myneni., and S. W. Running.. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. 2003; Science 300: 1560–1563.
  • Nicholson, S. E., M. L. Davenport., and A. R. Malo. A comparison of the vegetation response to rainfall in the Sahel and East Africa, using Normalized Difference Vegetation Index from NOAA AVHRR. Climate Change. 1990; 17: 209–241.
  • Le Hourou, H.N., Bingham, R.L. & Skerbek, W. Relationship between the variability of primary production and the variability of annual precipitation in world arid lands. Journal of Arid Environments. 1988; 15: 1―18.
  • Los, S.O., G. J. Collatz., L. Bounoua., P. J. Sellers., and C. J. Tucker. Global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation. Journal of Climate.2001; 14: 1535–1549
  • Mallick, K., Bhattacharya, B.K., Patel, N.K. Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology.2009; 149, 1327–1342.
  • Zhu, W.Q., Pan, Y.Z., Liu, X. & Wang, A.L. Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic change. Journal of Forestry Research.2006; 17(2): 93-98
  • Odorico P, Bhattachan A, Davis KF, Ravi S, Runyan CW.Global desertification: drivers and feedbacks. Adv. Wat. Res.2013; 51:326–344.

 

  • Schucknecht A, Matschullat J, Erasmi S. Spatial and temporal variability of vegetation status in Paraíba, Northeastern Brazil. Geoscience and Remote Sensing Symposium (IGARSS).2012; 32–35.
  • Xu D, Li C, Song X, Ren H. The dynamics of desertification in the farming-pastoral region of North China over the past 10 years and their relationship to climate change and human activity. Catena. 2014; 123:11–22.
  • Marland G, Pielke RA, Apps M, Avissar R, Betts RA, Davis KJ, et al. The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy. Clim Pol.2003; 3:149–157.
Journal of Ecohydrology
Volume 9, Issue 4
Winter 2023
Pages 833-851

  • Receive Date 11 October 2022
  • Revise Date 30 October 2022
  • Accept Date 02 December 2022
  • First Publish Date 22 December 2022
  • Publish Date 22 December 2022