Investigating the Impact of Land-use and Climatic Factors on Land Degradation in North-East of Iran

Document Type : Original Article

Author

Assistant Professor, Department of Geography, University of Jiroft, Kerman, Iran.

Abstract

Vegetation is one of the most important factors in assessing land degradation. On the other hand,  remote sensing of vegetation changes can provide useful information for ecosystem management. Therefore, this study sought to investigate the trend of changes in vegetation and its correlation with land-use and climate change in northeastern Iran. To this end, the data regarding the NDVI and EVI which were extracted from the MODIS satellite and MOD13A2 product from 2000 to 2017 were used to study vegetation changes, and data obtained from the MODIS MCD12Q1 product from 2001 to 2017 were used to investigate the land-use changes. Moreover, the meteorological stations' data were examined to evaluate the trend of climate factors in the region.
The study's results showed that the trend of changes in both NDVI and EVI was significantly negative. Furthermore, the land-use analysis showed that the agricultural and rangeland area decreased and the urban and barren land area increased significantly. The temperature also increased significantly during the period while the precipitation decreased slightly. Moreover, it was found that there was a significant correlation between land-use classes, NDVI, and EVI and that the correlation between precipitation and NDVI was significant at 95% (R=0.53). on the other hand, the investigation of the relationship between climatic factors, land use, and vegetation indices based on the Pearson correlation coefficient indicated that the land-use had a higher correlation with vegetation indices compared with that of the climatic factors. Therefore, it could be argued that degradation can be affected by human activities which in turn leads to land-use changes and the overuse of water and soil resources. The degradation can also be influenced by climate change, leading to a decrease in the available water supply to be used by natural vegetation. However, land-use and human activities were found to have more influence on NDVI, EVI, and land degradation.

Keywords

References

  1. AbdelRahman M.A., Natarajan A., Hegde R. and Prakash S.S. 2019. Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder. The Egyptian Journal of Remote Sensing and Space Science Vol. 22: 323-334.
  2. Aralova D., Jahan K., Timur Kh. and Kristina T. 2018. Drought Variability and Land Degradation in Central Asia: Assessment Using Remote Sensing Data and Drought Indices." In Vegetation of Central Asia and Environs, pp. 15-47. Springer, Cham.
  3. Ardöa J.,  Tagessona T.,  Jamalib S. and Khatirc A. 2018. MODIS EVI-based net primary production in the Sahel 2000–2014. International Journal of Applied Earth Observation and Geoinformation Vol. 65: 35–45.
  1. Bannari A., Morin D., Bonn F. and Huete A. 1995. A review of vegetation indices. Remote sensing reviews Vol 13: 95-120.
  1. Batjargal Z. 1997. Desertification in Mongolia. National Agency for meteorology, hydrology and environment monitoring, Mongolia. RALA Report No. 200: 107-113.
  1. Benewinde J.B., Zoungranaa C.C. and Michael Thiela L.K.A. 2018. Evariste Dapola Dac. MODIS NDVI Trends and Fractional Land Cover Change for Improved Assessments of Vegetation Degradation in Burkina Faso, West Africa. Journal of Arid Environments Vol 153: 66-75.
  1. Binh T.N.K.D., Vromant N., Hung N.T., Hens L. and Boon E.K. 2015. Land covers changes between 1968 and 2003 in CaiNuoc, Ca Mau Peninsula, Vietnam. Environment, Development and Sustainability Vol. 7: 519- 536.
  1. Boyd D.S. and Danson F.M. 2005. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography Vol 29: 1-26.
  1. Brown M.E., Pinzón J.E., Didan K., Morisette J.T., Tucker C.J., 2006. Valuation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors. IEEE Transactions on Geoscience and Remote Sensing Vol. 44: 1787 – 1793.
  1. Clement F. and Amezaga J.M. 2008. Linking reforestation policies with land use change in northern Vietnam: Why local factors matter. Geoforum Vol 39: 265-277.
  2. da Silva V.S., Salami G., da Silva M.I.O., Silva E.A., Monteiro Junior J.J. and Alba E. 2020. Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology, and Landscapes Vol 4: 159-169.
  1. Dai A. 2011a. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. Journal of Geophysical Research: Atmospheres Vol. 116:1-26.
  2. Dai A. 2011b. Drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change Vol. 2: 45-65.
  3. Dai A. 2013. Increasing drought under global warming in observations and models. Nature climate change Vol. 3: 52-58.
  4. Dai A. 2013. Increasing drought under global warming in observations and models. Nature climate change Vol. 3: 52–71
  5. Dai A. and Zhao T. 2017. Uncertainties in historical changes and future projections of drought. Part I: estimates of historical drought changes. Climatic Change Vol. 144: 519-533.
  6. Dai A. and Zhao T. 2017. Uncertainties in historical changes and future projections of drought. Part I: estimates of historical drought changes. Climate Change Vol. 144: 519–533.
  7. Darwish T. and Faour G. 2008. Rangeland degradation in two watersheds of Lebnon. Lebanese Science Journal Vol. 9: 71-80
  8. Davenport M.L., Nicholson S.E. 1993. On the relation between rainfall and the normalized difference vegetation index for diverse vegetation types in East Africa. Int. Jornal of Remote Sensing. Vol. 14: 2369–2389.
  9. Demattê J.A.M., Sayão V.M., Rizzo R. and Fongaro C.T. 2017. Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing. Geoderma Vol. 302: 39-51.
  10. Dong L., Zhao J., Liu X. J., ZQ D., Wu Z. T. and Zhang H. 2019. Responses of vegetation growth to temperature during 1982-2015 in Xinjiang, China. Ying Yong Sheng tai xue bao= The Journal of Applied Ecology Vol. 30: 2165-2170.
  11. Dubovyk O. 2017. The role of remote sensing in land degradation assessments: opportunities and challenges. European Journal of Remote Sensing Vol. 50: 601-613.
  12. Eckert S., Hüsler F., Liniger H. and Hodel E. 2015. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments Vol. 113: 16-28.
  1. Faramarzi M., Heidarizadi Z., Mohamadi A. and 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 Vol 20: 51-60.
  1. Fensholt R., Rasmussen K., Nielsen T.T. and Mbow C. 2009. Evaluation of earth observation based long term vegetation trends—Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote sensing of environment Vol. 113: 1886-1898.
  2. Foggin J.M. and Smith A.T. 1996. Rangeland utilization and biodiversity on the alpine grasslands of Qinghai Province, People's Republic of China. Conserving China’s biodiversity II. China Environmental Science Press, Beijing, China, 247-258.
  3. Forkel M., Carvalhais N., Verbesselt J., Mahecha M.D., Neigh C.S., Reichstein M. 2013. Trend change detection in NDVI time series: effects of inter-annual variability and methodology. Remote Sensing. Vol. 5: 2113–2144.
  4. Gandhi G.M., Parthiban B., Thummalu N. and Christy A. 2015. Ndvi: Vegetation change detection using remote sensing and gis–A case study of Vellore District. Procedia computer science Vol. 57: 1199-1210.
  5. Hao Y., Zengchao H., Sifang F., Xuan Zh. and Fanghua H. 2020. Response of vegetation to El Niño-Southern Oscillation (ENSO) via compound dry and hot events in southern Africa. Global and Planetary Change Vol. 195: 1-41.
  6. Harris A., Carrb A.S., Dashc J. 2014. Remote sensing of vegetation cover dynamics andresilience across southern Africa. International Journal of Applied Earth Observation and Geoinformation Vol. 28: 131–139.
  7. Hawinkel P., Thiery W., Lhermitte S., Swinnen E., Verbist B., Van Orshoven J. and Muys B. 2016. Vegetation response to precipitation variability in East Africa controlled by biogeographical factors. Journal of Geophysical Research: Biogeosciences Vol. 121: 2422-2444.
  8. Hermans-Neumann K., Priess J. and Herold M. 2017. Human migration, climate variability, and land degradation: hotspots of socio-ecological pressure in Ethiopia. Regional Environmental Change Vol. 17: 1479-1492.
  9. Herrmann S.M., Anyamba A. and Tucker C.J. 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change Vol. 15: 394-404.
  1. Heydari Alamdarloo E., Khosravi H., Dehghan Rahimabadi P. and Ghodsi M. 2021. The Effect of Climate Fluctuations on Vegetation Dynamics in West and Northwest of Iran. Desert Ecosystem Engineering Journal Vol. 3: 19-28.
  2. Hosseini S.A.R., Gholami H., Esmaeilpoor Y. and Cerda A. 2021. Effect of the Climatic Parameters on the Trend of Vegetative Land Cover Changes with Land Degradation Approach in the Persian Gulf and Oman Sea Watershed. Watershed Management Research Journal Vol 34: 74-94.
  1. Houessou L.G., Teka O., Imorou I.T., Lykke A.M. and Sinsin B. 2013. Land use and land-cover change at" W" Biosphere Reserve and its surroundings areas in Benin Republic (West Africa). Environment and Natural Resources Research Vol. 3: 87-101.
  2. Huang C. and Asner G.P. 2013. Applications of remote sensing to alien invasive plant Studies. Sensors Vol. 9: 4869- 4889
  3. Jarchow C.J., Nagler P.L., Glenn E.P., 2017. Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: an analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data. Ecological Engineering Vol. 106: 776-783.
  4. Ji L. and Peters A.J. 2004. A Spatial Regression Procedure for Evaluating the Relationship between AVHRR-NDVI and Climate in the Northern Great Plains. International Journal of Remote Sensing Vol. 25: 297-311.
  5. Johansen B. and Tømmervik H. 2014. The relationship between phytomass, NDVI and vegetation communities on Svalbard. International Journal of Applied Earth Observation and Geoinformation Vol. 27:20-30.
  6. Kalisa W., Tertsea I., Malak H., Shahzad A., Sha Zha., Yun B. and Jiahua Zh. 2019. Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015. Scientific reports Vol. 9: 1-20.
  7. Kaptué A.T., Prihodko L. and Hanan N.P. 2015. On regreening and degradation in Sahelian watersheds. Proceedings of the National Academy of Sciences Vol. 112: 12133-12138.
  8. Khosravi H., Azareh A., Eskandari Dameneh H., Rafiei Sardoii E. and Eskandari Dameneh H. 2017. Assessing the effects of the climate change on land cover changes in different time periods. Arabian Journal of Geosciences Vol. 10:1-10.
  1. Kiani-Harchegani, M., & Sadeghi, S. H. (2020). Practicing land degradation neutrality (LDN) approach in the Shazand Watershed, Iran. Science of the Total Environment, 698, 134319.
  1. Koh C.N., Lee P.F and Lin R.S. 2016. Bird species richness patterns of northern Taiwan: primary productivity, human population density, and habitat heterogeneity. Diversity & Distributions Vol. 12: 546– 554.
  2. Krakauer N.Y., Lakhankar T. and Anadón J.D. 2017. Mapping and attributing normalized difference vegetation index trends for Nepal. Remote Sensing Vol. 9: 1-15.
  3. Lamsal P., Atreya K., Ghosh M.K. and Pant K.P. 2019. Effects of population, land cover change, and climatic variability on wetland resource degradation in a Ramsar listed Ghodaghodi Lake Complex, Nepal. Environmental monitoring and assessment Vol. 191: 1-16.
  4. Lanfredi M., Simoniello T. and Macchiato M. 2004. Temporal persistence in vegetation cover changes observed from satellite: Development of an estimation procedure in the test site of the Mediterranean Italy. Remote sensing of Environment Vol. 93: 565-576.
  5. Lawal S., Lennard C. and Hewitson B. 2019. Response of southern African vegetation to climate change at 1.5 and 2.0 global warming above the pre-industrial level. Climate Services Vol. 16: 1-16.
  6. Li J., Lewis J., Rowland J., Tappan G. and Tieszen L.L. 2004. Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series. Journal of Arid Environments Vol. 59: 463-480.
  7. Li P., Zhu D., Wang Y. and Liu, D. 2020. Elevation dependence of drought legacy effects on vegetation greenness over the Tibetan Plateau. Agricultural and Forest Meteorology Vol. 295, 1-12.
  8. Li W., Du J., Li S., Zhou X., Duan Z., Li R. ... and Li M. 2019. The variation of vegetation productivity and its relationship to temperature and precipitation based on the GLASS-LAI of different African ecosystems from 1982 to 2013. International journal of biometeorology Vol. 63: 847-860.
  9. Liu D., Zhang, C., Ogaya, R., Estiarte M. and Peñuelas J. 2020. Effects of decadal experimental drought and climate extremes on vegetation growth in Mediterranean forests and shrublands. Journal of Vegetation Science Vol. 31: 768-779.
  10. Liu Y., Li Y., Li S. and Motesharrei S. 2015. Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors. Remote Sensing Vol. 7: 13233-13250
  1. Ma W., Chen Y.H., Zhou J. and Gong A. 2008. Quantitative analysis of land surface temperature-vegetation indexes relationship based on remote sensing. In Proc. 21st ISPRS Congress, Youth Forum, pp. 261-264.
  1. Ma X., Huete A., Yu Q., Restrepo-Coupe N., Beringer J., Hutley L.B., Kanniah K.D., Cleverly J., Eamus D. 2014. Parameterization of an ecosystem light-use-efficiency model for predicting savanna GPP using MODIS EVI. Remote Sens. Environ. Vol. 154: 253–271
  1. Manesh M.B., Khosravi H., Alamdarloo E.H., Alekasir M.S., Gholami A. and Singh V.P. 2019. Linkage of agricultural drought with meteorological drought in different climates of Iran. Theoretical and Applied Climatology Vol. 138: 1025-1033.
  1. Mariano D.A., dos Santos C.A., Wardlow B.D., Anderson M.C., Schiltmeyer A.V., Tadesse T. and Svoboda M. D. 2018. Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment Vol. 213: 129-143.
  1. Masoudi M., Jokar P. and Pradhan B. 2018. A new approach for land degradation and desertification assessment using geospatial techniques. Natural Hazards and Earth System Sciences Vol 18: 1133-1140.
  1. Montfort F., Begue A., Leroux L., Blanc L., Gond V., Cambule A.H. ... and Grinand C. 2021. From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. Land Degradation & Development Vol. 32: 49-65.
  2. Nezlin N.P., Kostianoy A. G. and Li B.L. 2005. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. Journal of Arid Environments Vol. 62: 677-700.
  3. Ouedraogo I., Tigabu M., Savadogo P., Compaoré H., Odén P. C. and Ouadba J.M. 2010. Land cover change and its relation with population dynamics in Burkina Faso, West Africa. Land Degradation & Development Vol. 21: 453-462.
  1. Peilin L.I., Xiaoping L.I.U., Yinghuai H.U.A.N.G. and Honghui Z.H.A.N.G. 2020. Mapping Impervious Surface Dynamics of Guangzhou Downtown based on Google Earth Engine. Journal of Geo-information Science Vol 22: 1-11.
  1. Peng J., Li Y., Tian L., Liu Y., Wang Y. 2015. Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008. Remote Sensing Vol. 7: 13641–13663.
  2. Peng J., Liu Y., Shen H., Han Y. and Pan Y. 2012. Vegetation coverage change and associated driving forces in mountain areas of Northwestern Yunnan, China using RS and GIS. Environmental monitoring and assessment Vol. 184: 4787-4798.
  1. Peng J., Loew A., Zhang S., Wang J. and Niesel J. 2015. Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index. IEEE Transactions on Geoscience and Remote Sensing Vol 54: 558-566.
  2. Poornazari N., Khalilimoghadam B., Hazbavi Z. and Bagheri Bodaghabadi M. 2021. Land degradation assessment in the dust hotspot of southeastern Ahvaz, Iran. Land Degradation and Development Vol 32: 896-913.
  1. Raynolds M.K., Comiso J.C., Walker D.A. and Verbyla D. 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sensing of Environment Vol. 112:1884-1894.
  2. Richard Y. and Poccard, I.J. I. J.O.R.S. 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensing Vol. 19: 2907-2920.
  1. Rokni K. and Musa T.A. 2019. Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery. Catena Vol 178: 59-63.
  1. Rondeaux G., Steven M. and Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote sensing of environment Vol. 55: 95-107.
  1. Sadeghi S.H., Hazbavi Z. and Gholamalifard M. 2019. Interactive impacts of climatic, hydrologic and anthropogenic activities on watershed health. Science of the total environment Vol 648: 880-893.
  1. Shi H., Li L., Eamus D., Huete A., Cleverly J., Tian X., ... and Carrara A. 2017. Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types. Ecological Indicators Vol. 72: 153-164.
  2. Sjöström M., Ardö J., Arneth A., Boulain N., Cappelaere B., Eklundh L.,... and Veenendaal E.M. 2011. Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems. Remote sensing of environment Vol. 115: 1081-1089.
  3. Sun D. and Kafatos M. 2007. Note on the NDVI-LST relationship and the use of temperature related drought indices over North America. Geophysical Research Letters Vol. 34:1-4.
  4. Thapa S., Chhetri P.K. and Klein A.G. 2019. Cross-Comparison between MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year. Climate Vol. 7: 1-17.
  5. Trenberth K.E., Dai A., Van Der Schrier G., Jones P.D., Barichivich J., Briffa K.R. and Sheffield J. 2014. Global warming and changes in drought. Nature Climate Change Vol. 4: 17-22.
  6. Vermote E., Justice C., Claverie M. and Franch B. 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment Vol. 185: 46-56.
  7. Vogelmann J.E., Gallant A.L., Shi H. and Zhu Z. (016. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sensing of Environment Vol. 185: 258-270.
  8. Wang J., Price K.P. and Rich P.M. 2001. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International journal of remote sensing Vol. 22: 3827-3844.
  1. Wilson M.A., Burn C.R. and Humphreys E.R. 2019. Vegetation development and variation in near-surface ground temperatures at Illisarvik, Western Arctic Coast. In Cold Regions Engineering 2019, pp. 687-695. Reston, VA: American Society of Civil Engineers.
  2. Xiao J. and Moody A. 2005. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote sensing of environment Vol 98: 237-250.
  1. Xu L., Li B., Yuan Y., Gao X., Zhang T., Sun Q. 2016. Detecting different types of directional land cover changes using MODIS NDVI time series dataset. Remote Sensing Vol. 8: 1-23.
  2. Yang L., Wylie B.K., Tieszen, L.L. and Reed B.C. 1998. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains. Remote Sensing of Environment Vol. 65: 25-37.
  3. Yengoh G.T., Dent D., Olsson L., Tengberg A.E. and Tucker C.J. 2015. Use of the Normalized Difference Vegetation Index (NDVI) to assess Land degradation at multiple scales: current status, future trends, and practical considerations. Springer Briefs in Environmental Science. pp 1-109.
  1. Ying H., Zhang H., Zhao J., Shan Y., Zhang Z., Guo X., ... and Deng G. 2020. Effects of spring and summer extreme climate events on the autumn phenology of different vegetation types of Inner Mongolia, China, from 1982 to 2015. Ecological Indicators Vol 111: 105974.
  1. Zeng B. and Yang T.B., 2009. Natural vegetation responses to warming climates in Qaidam Basin 1982–2003. International Journal of Remote Sensing Vol. 30: 5685–5701.
  2. Zeng N., Neelin J.D., Lau K. M. and Tucker C. J. 1999. Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science Vol. 286: 1537-1540.
  3. Zhang J., Yao F., Zheng L., Yang L. 2007. Evaluation of grassland dynamics in the Northern-Tibet. Sensors Vol. 7: 3312–3328.
  4. Zhao Y., Tzedakis P. C., Li Q., Qin F., Cui Q., Liang C., ... and Guo Z. 2020. Evolution of vegetation and climate variability on the Tibetan Plateau over the past 1.74 million years. Science advances Vol. 6:1-13.
  5. Zoungrana B.J., Conrad C., Thiel M., Amekudzi L.K. and Da E.D. 2018. MODIS NDVI trends and fractional land cover change for improved assessments of vegetation degradation in Burkina Faso, West Africa. Journal of Arid Environments Vol. 153: 66-75.

 

 

 

 

 

 

 

 

 

 

 

 

Desert Ecosystem Engineering
Volume 10, Issue 7 in English
February 2022
Pages 68-47

Files

Share

How to cite

Statistics