Russian Federation
Russian Federation
GRNTI 37.15 Геомагнетизм и высокие слои атмосферы
GRNTI 37.25 Океанология
GRNTI 37.31 Физика Земли
GRNTI 38.01 Общие вопросы геологии
Vegetation, precipitation, and surface temperature are three important elements of the environment. By increasing the concerns about climate change and global warming, monitoring vegetation dynamics are considered to be crucial. In this study, the cross-relationship between vegetation, surface temperature, and precipitation, and their fluctuations over the past 21 years are evaluated. Day time LST from Terra sensor of MODIS, nir and red bands of Landsat 7 ETM+ and Landsat 8 OLI, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are used in this research. Data were evaluated and processed using the google earth engine cloud processing platform. According to the results, it was concluded that the correlations between the annual average of normalized difference vegetation index and precipitation are not significant. Evaluation of the cross-seasonal correlations exhibited the availability of the strong and significant correlation with a value of r2 = 0.82 between vegetation thickness and precipitation, during the spring and summer, especially from April to August. Moreover, surface temperature exposed an inverse correlation with precipitation and NDVI with the values of r2= 0.776 and r2= 0.68 respectively, these relationships are highly significant. According to the results of this study, vegetation declined sharply in particular years, and this decrease occurred due to insufficient rainfalls.
Land surface temperature, Landsat, NDVI, Balkh Province
1. Alan, S., Groninger, J. W., & Myers, O. (2012). Rebuilding Afghanistan’s Agricultural Economy: Vegetable Production in Balkh Province. 41(1), 7-13. https://doi.org/10.5367/OA.2012.0073
2. Campos-Taberner, M., Moreno-Martínez, Á., García-Haro, F. J., Camps-Valls, G., Robinson, N. P., Kattge, J., & Running, S. W. (2018a). Global estimation of biophysical variables from Google Earth Engine platform. Remote Sensing, 10(8). https://doi.org/10.3390/rs10081167
3. Campos-Taberner, M., Moreno-Martínez, Á., García-Haro, F. J., Camps-Valls, G., Robinson, N. P., Kattge, J., & Running, S. W. (2018b). Global estimation of biophysical variables from Google Earth Engine platform. Remote Sensing, 10(8). https://doi.org/10.3390/rs10081167
4. Ding, M., Zhang, Y., Liu, L., Zhang, W., Wang, Z., & Bai, W. (2007). The relationship between NDVI and precipitation on the Tibetan Plateau. Journal of Geographical Sciences, 17(3), 259-268. https://doi.org/10.1007/s11442-007-0259-7
5. Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., & Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Quarterly Journal of the Royal Meteorological Society, 144, 292-312. https://doi.org/10.1002/qj.3244
6. Fuller, D. O. (1998). Trends in ndvi time series and their relation to rangeland and crop production in senegal, 1987-1993. International Journal of Remote Sensing, 19(10), 2013-2018. https://doi.org/10.1080/014311698215135
7. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
8. Hamel, S., Garel, M., Festa-Bianchet, M., Gaillard, J. M., & Côté, S. D. (2009). Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates. Journal of Applied Ecology, 46(3), 582-589. https://doi.org/10.1111/j.1365- 2664.2009.01643.x
9. Hilker, T., Lyapustin, A. I., Tucker, C. J., Hall, F. G., Myneni, R. B., Wang, Y., Bi, J., de Moura, Y. M., & Sellers, P. J. (2014). Vegetation dynamics and rainfall sensitivity of the Amazon. Proceedings of the National Academy of Sciences of the United States of America, 111(45), 16041-16046. https://doi.org/10.1073/pnas.1404870111
10. Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., & Zhu, Z. (2017). Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment, 202, 166-176. https://doi.org/10.1016/j.rse.2017.02.021
11. Jamali, A. A., Ghorbani Kalkhajeh, R., Randhir, T. O., & He, S. (2022). Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni. Journal of Environmental Management, 302. https://doi.org/10.1016/j.jenvman.2021.113970
12. Johansen, K., Phinn, S., & Taylor, M. (2015). Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine. Remote Sensing Applications: Society and Environment, 1, 36-49. https://doi.org/10.1016/j.rsase.2015.06.002
13. Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Panov, N., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3), 618-633. https://doi.org/10.1175/2009JCLI2900.1
14. Kuhn, C., de Matos Valerio, A., Ward, N., Loken, L., Sawakuchi, H. O., Kampel, M., Richey, J., Stadler, P., Crawford, J., Striegl, R., Vermote, E., Pahlevan, N., & Butman, D. (2019). Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity. Remote Sensing of Environment, 224, 104-118. https://doi.org/10.1016/j.rse.2019.01.023
15. Kumari, N., Srivastava, A., & Dumka, U. C. (2021). A long-term spatiotemporal analysis of vegetation greenness over the himalayan region using google earth engine. Climate, 9(7). https://doi.org/10.3390/cli9070109
16. Luyssaert, S., Jammet, M., Stoy, P. C., Estel, S., Pongratz, J., Ceschia, E., Churkina, G., Don, A., Erb, K., Ferlicoq, M., Gielen, B., Grünwald, T., Houghton, R. A., Klumpp, K., Knohl, A., Kolb, T., Kuemmerle, T., Laurila, T., Lohila, A., … Dolman, A. J. (2014). Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate Change, 4(5), 389-393. https://doi.org/10.1038/nclimate2196
17. Maimaitiyiming, M., Ghulam, A., Tiyip, T., Pla, F., Latorre-Carmona, P., Halik, Ü., Sawut, M., & Caetano, M. (2014). Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 89, 59-66. https://doi.org/10.1016/j.isprsjprs.2013.12.010
18. Martín-Ortega, P., García-Montero, L. G., & Sibelet, N. (2020). Temporal patterns in illumination conditions and its effect on vegetation indices using Landsat on Google Earth Engine. Remote Sensing, 12(2). https://doi.org/10.3390/rs12020211
19. Masoudi, M., & Tan, P. Y. (2019). Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature. Landscape and Urban Planning, 184, 44-58. https://doi.org/10.1016/j.landurbplan.2018.10.023
20. Mohd Jaafar, W. S. W., Maulud, K. N. A., Muhmad Kamarulzaman, A. M., Raihan, A., Sah, S. M., Ahmad, A., Maizah Saad, S. N., Mohd Azmi, A. T., Syukri, N. K. A. J., & Khan, W. R. (2020). The influence of deforestation on land surface temperature-A case study of Perak and Kedah, Malaysia. Forests, 11(6). https://doi.org/10.3390/F11060670
21. Mutanga, O., & Kumar, L. (2019). Google earth engine applications. In Remote Sensing (Vol. 11, Issue 5). MDPI AG. https://doi.org/10.3390/rs11050591
22. Pettorelli, N., Ryan, S., Mueller, T., Bunnefeld, N., Jedrzejewska, B., Lima, M., & Kausrud, K. (2011). The Normalized Difference Vegetation Index (NDVI): Unforeseen successes in animal ecology. In Climate Research (Vol. 46, Issue 1, pp. 15-27). https://doi.org/10.3354/cr00936
23. Piao, S., Mohammat, A., Fang, J., Cai, Q., & Feng, J. (2006). NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change, 16(4), 340-348. https://doi.org/10.1016/j.gloenvcha.2006.02.002
24. Potter, C. S., & Brooks, V. (1998). Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing, 19(15), 2921-2948. https://doi.org/10.1080/014311698214352
25. Richard, Y., & Poccard, I. (1998). A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensing, 19(15), 2907-2920. https://doi.org/10.1080/014311698214343
26. Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. https://doi.org/10.1080/014311600209814
27. Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., & Egorov, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185, 57-70. https://doi.org/10.1016/J.RSE.2015.12.024
28. Sharma, M., Bangotra, P., Gautam, A. S., & Gautam, S. (2021). Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-021-02066-1
29. Shen, M., Piao, S., Cong, N., Zhang, G., & Jassens, I. A. (2015). Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Global Change Biology, 21(10), 3647-3656. https://doi.org/10.1111/gcb.12961
30. Song, J., Chen, W., Zhang, J., Huang, K., Hou, B., & Prishchepov, A. v. (2020). Effects of building density on land surface temperature in China: Spatial patterns and determinants. Landscape and Urban Planning, 198. https://doi.org/10.1016/j.landurbplan.2020.103794
31. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. In ISPRS Journal of Photogrammetry and Remote Sensing (Vol. 164, pp. 152-170). Elsevier B.V. https://doi.org/10.1016/j.isprsjprs.2020.04.001
32. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, 459-469. https://doi.org/10.1016/j.landusepol.2017.11.023
33. Wang, J., Rich, P. M., & 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. https://doi.org/10.1080/01431160210154812
34. Wang, Q., & Tenhunen, J. D. (2004). Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT). International Journal of Applied Earth Observation and Geoinformation, 6(1), 17-31. https://doi.org/10.1016/j.jag.2004.07.002
35. Weiss, J. L., Gutzler, D. S., Coonrod, J. E. A., & Dahm, C. N. (2004). Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA. Journal of Arid Environments, 58(2), 249-272. https://doi.org/10.1016/j.jaridenv.2003.07.001
36. Wu, Z., Dijkstra, P., Koch, G. W., Peñuelas, J., & Hungate, B. A. (2011). Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation. In Global Change Biology (Vol. 17, Issue 2, pp. 927-942). https://doi.org/10.1111/j.1365- 2486.2010.02302.x
37. Yue, W., Xu, J., Tan, W., & Xu, L. (2007). The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15), 3205-3226. https://doi.org/10.1080/01431160500306906
38. Zhang, X., Yamaguchi, Y., Li, F., He, B., & Chen, Y. (2017). Assessing the Impacts of the 2009/2010 Drought on Vegetation Indices, Normalized Difference Water Index, and Land Surface Temperature in Southwestern China. Advances in Meteorology, 2017. https://doi.org/10.1155/2017/6837493
