Monitoring temporal and spatial changes of vegetation and its relationship with climatic variables (case study: Fars province)

Asefjah, Behnaz; Esmaeilpour, Yahya; Bazrafshan, Ommolbanin; Zamani, Hossein

doi:10.22059/jrwm.2022.337022.1637

Document Type : Research Paper

Authors

¹ PhD Student in Desert Management and Control, Faculty of Agricultural Engineering and Natural Resources, Hormozgan University, Bandar Abbas, Iran

² Assistant Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas

³ Associate Professor, Department of Natural Resources Engineering, Faculty of Agricultural Engineering and Natural Resources, Hormozgan University, Bandar Abbas, Iran

⁴ Assistant Professor, Department of Statistics, Faculty of Basic Sciences, Hormozgan University, Bandar Abbas, Iran

10.22059/jrwm.2022.337022.1637

Abstract

The past decade has seen a major revolution in vegetation monitoring using satellite imagery, resulting in quantitative indicators of vegetation with a professional processor in a web-based interactive development environment. In this study, using MOD13A1 and MOD13Q1 products of Modis sensor, the trend of temporal and spatial changes of NDVI and EVI indices in Fars province in a period of 16 days from 2000 to 2020 was coded and processed monthly in Google Earth engine system. The results of this study showed that the average index of NDVI index is from minimum 0.11 to maximum 0.495 and the average index of EVI index is 0.1. According to the results obtained in this survey, in all the years from 2000 to 2020 in January, NDVI and EVI values had the highest values compared to other months, so that in January 2019 and January 2020, the highest EVI values averaged 0.22 and the NDVI values Was estimated to be 0.18. The lowest monthly average values of both indices occurred between 2000 and 2005, which indicates that the vegetation has been severely degraded during these years. The results of spatial changes using EVI index showed that the level of vegetation in Fars province in different months varied from 10,000 square kilometers to 22,000 square kilometers and from the perspective of NDVI index from 15,000 square kilometers to 30,000 square kilometers.

Keywords

20.1001.1.50442008.1401.75.3.6.4

References

[1] Abolverdi, J., Ferdosifar, G., Khalili, D., and Kamgar-Haghighi, A. A. (2016). Spatial and temporal changes of precipitation concentration in Fars province, southwestern Iran. Meteorology and Atmospheric Physics, 128(2), 181-196.

[2] Ahl, D. E., Gower, S. T., Burrows, S. N., Shabanov, N. V., Myneni, R. B., and Knyazikhin, Y. (2006). Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sensing of Environment, 104(1), 88-95.

[3] Alonso, A., Muñoz-Carpena, R., Kennedy, R. E., and Murcia, C. (2016). Wetland landscape spatio-temporal degradation dynamics using the new Google Earth Engine cloud-based platform: Opportunities for non-specialists in remote sensing. Transactions of the ASABE, 59(5), 1331-1342.

[4] Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S.,… and Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7), 842.‏

[5] Barbosa, H. A., Huete, A. R., Baethgen, W. E. (2006). A 20-year study of NDVI variability over the Northeast Region of Brazil, Journal of Arid Environments, 67, 288–307.

[6] Behrang Manesh, M., Khosravi, H., Azarnivand, H., and Senatore, A. (2020). Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province). PEC, 7 (15) ,295-318.

[7] Chen, X. Q., Xu, C. X., and Tan, Z. J. (2001). An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China. International Journal of Biometeorology, 45, 170–177.

[8] Chuai, X., Qi, X., Zhang, X., Li, J., Yuan, Y., Guo, X., ... and Feng, J. (2018). Land degradation monitoring using terrestrial ecosystem carbon sinks/sources and their response to climate change in C hina. Land Degradation & Development, 29(10), 3489-3502.

[9] Entezari, A., Zandi, R., and Khosravian, M. (2019). Evaluation of spatial variations of vegetation and surface temperature using Landsat and midsize images, case study: Fars Province, 1967-2017. Watershed Engineering and Management, 11(4), 929-940. doi: 10.22092/ijwmse.2018.122914.1528

[10] Eskandari Damaneh, H., Gholami, H., Mahdavi, R., Khorani, A. and Li, J. (2021). Monitoring Land Degradation and Desertification in the Arid and Semi-arid Regions with an Emphasis in Response to Gross Primary Production Relative to the Climatic Variables during the 2001-2017 in the Province of Fars. Watershed Management Research Journal, 34(1), 41-58.

[11] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.

[12] Huete, A., and Ustin, S. (2004). Remote Sensing for Natural Resources Management and Environmental Monitoring. Manual of remote sensing University of Arizona.

[13] Jacquin, A., Sheeren, D., and Lacombe, J. P. (2010). Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series. International Journal of Applied Earth Observation and Geoinformation, 12, S3-S10.

[14] Jiang, F., Deng, M., Long, Y., & Sun, H. (2022). Spatial Pattern and Dynamic Change of Vegetation Greenness from 2001 to 2020 in Tibet, China. Frontiers in Plant Science, 1292.

[15] Jiang, W., Yuan, L., Wang, W., Cao, R., Zhang, Y. and Shen, W. (2015). Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51, 117-126.

[16] Karkauskaite, P., Tagesson, T., and Fensholt, R. (2017). Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone. Remote Sensing, 9(5), 485.‏ 64

[17] Kumari, N., Srivastava, A. and Dumka, U. C. (2021). A long-term spatiotemporal analysis of vegetation greenness over the Himalayan Region using Google Earth Engine. Climate, 9(7), 109.

[18] Lamchin, M., Lee, W. K., Jeon, S. W., Wang, S. W., Lim, C. H., Song, C. and Sung, M. (2018). Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data. Science of the Total Environment, 618, 1089-1095.

[19] Li, H., Wei, X. and Zhou, H. (2015). Rain-use efficiency and NDVI-based assessment of karst ecosystem degradation or recovery: a case study in Guangxi, China. Environmental Earth Sciences, 74(2), 977-984.

[20] Li, Z., Li, X., Wei, D., Xu, X. and Wang, H. (2010). An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China. Procedia Environmental Sciences, 2, 964-969.

‏[21] Liu, E., Xiao, X., Shao, H., Yang, X., Zhang, Y., and Yang, Y. (2021). Climate change and livestock management drove extensive vegetation recovery in the Qinghai-Tibet plateau. Remote Sens. 13:4808.

[22] Ma, M., and Veroustraete, F. (2006). Interannual variability of vegetation cover in the Chinese Heihe River Basin and its relation to meterological parameters. International Journal of Remote Sensing, 27(22), 5127-5127.‏

[23] Matsushita, B., Yang, W., Chen, J., Onda, Y. and Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11), 2636-2651.‏

[24] Nasserzadeh, M. H., Hejazizadeh, Z., Gholampour, Z., and Alijani, B. (2020). Spatiotemporal Response of MODIS Derived Vegetation index to climatic condition Case study: Kohgiloyeh O Boirahmad Province of Iran. Scientific Journals Management System, 20 (57), 355-370.

[25] Nzabarinda, V., Bao, A., Xu, W., Uwamahoro, S., Jiang, L., Duan, Y., ... and Long, G. (2021). Assessment and evaluation of the response of vegetation dynamics to climate variability in Africa. Sustainability, 13(3), 1234.‏

[26] Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J. and Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution, 20(9), 503-510.‏

[27] Rahimi, M., Damavandi, A. and Jafarian, V. (2014). Investigating remote sensing applications in evaluating and monitoring land degradation and desertification. Scientific- Research Quarterly of Geographical Data (SEPEHR), 22(88), 115-128.

[28] Sulla-Menashe, D. and Friedl, M. A. (2018). User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. USGS: Reston, VA, USA, 1-18.

[29] Wang, J., Rich, P. M. and Price, K. P. (2003). Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International journal of remote sensing, 24(11), 2345-2364.

[30] Wardlow, B. D., and Egbert, S. L. (2010). A comparison of MODIS 260-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. International Journal of Remote Sensing, 31(3), 805-830.‏ 63

[31] Xia, P., Yong, G., and Ji, W. (2018). Response differences of MODIS-NDVI and MODIS-EVI to climate factors. Journal of Resources and Ecology, 9(6), 673-680.‏

[32] Yan, E., Wang, G., Lin, H., Xia, C., and Sun, H. (2015). Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series. International Journal of Remote Sensing, 36(2), 489-512.‏

[33] Yang, L., Wei, W., Wang, T., and Li, L. (2021). Temporal-spatial variations of vegetation cover and surface soil moisture in the growing season across the mountain-oasis-desert system in Xinjiang, China. Geocarto International, 1-29.‏

[34] Yuchuan, H., Junnan, X., Ahemaitihali, A., Weiming, C., Chongchong, Y., Wen, H., ... and Jie, T. (2021). Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine. Journal of Resources and Ecology, 12(6), 729-742.‏

[35] Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C., Gao, F., ... and Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote sensing of environment, 84(3), 471-475.

[36] Zhe, M. and Zhang, X. (2021). Time-lag effects of NDVI responses to climate change in the Yamzhog Yumco Basin, South Tibet. Ecological Indicators, 124, 107-134.

Journal of Range and Watershed Managment

Monitoring temporal and spatial changes of vegetation and its relationship with climatic variables (case study: Fars province)

References

References

Volume 75, Issue 3
December 2022
Pages 409-427

Monitoring temporal and spatial changes of vegetation and its relationship with climatic variables (case study: Fars province)

References

References

Volume 75, Issue 3December 2022Pages 409-427

Volume 75, Issue 3
December 2022
Pages 409-427