The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin
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This study aimed to investigate the spatial autocorrelation between precipitation and vegetation indices in the Bandar Abbas basin. For this purpose, the vegetation indices of DVI, EVI, IPVI, NDVI, NDWI, RVI, SAVI, TCI, VCI, and VHI were derived from Landsat satellite images over 20 years were studied. Precipitation data corresponding to rain gauge stations was extracted. The Pearson correlation coefficient and the GI * and I indices were used to investigate the relationship between precipitation and spatial autocorrelation. Moreover, the Pearson correlation coefficient was used to investigate the relationship between precipitation and vegetation indices, and the GI * and I indices was used to correlate spatial autocorrelation patterns. The results showed that SAVI, VHI, VCI, and NDWI were most correlated with precipitation among the Bandar Abbas basin's vegetation indices, with the SAVI index being more closely correlated than the others. However, precipitation had the least impact on the TCI index. The spatial autocorrelation of rainfall with the vegetation indices, except for the IPVI index, had a scattered pattern in the study area’s southern and eastern parts. Of the indices studied in terms of spatial pattern, the IPVI and NDWI indices formed a positive spatial correlation pattern with precipitation over a greater spatial range.
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Azizi G., Miri M., Mohamadi H., Pourhashemi M. (2015): Analysis of relationship between forest decline and precipitation changes in Ilam province. Iranian Journal of Forest and Poplar Research, 23: 502–515. (In Persian)
Ahl, D.E.; Gower, S.T.; Burrows, S.N.; Shabanov, N.V.; Myneni, R.B.; Knyazikhin, Y. (2006). Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 2006, 104, 88–95. [CrossRef]
Abdulhafedh, A. (2017) A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord * Gi Statistic. Open Journal of Civil Engineering, 7, 208-221. https://doi.org/10.4236/ojce.2017.72013
Anselin, L. (1995). Local Indicators of Spatial Association LISA, Geographical Analysis, 27, 93-115.
Bing, G.; Yi, Z.; Shi-Xin, W.; He-Ping, T. The relationship between normalized difference vegetation index (NDVI) and climate factors in the semiarid region: A case study in Yalu Tsangpo River basin of Qinghai-Tibet Plateau. J. Mt. Sci. 2014, 11, 926–940
Boles, S.H.; Xiao, X.; Liu, J.; Zhang, Q.; Munkhtuya, S.; Chen, S.; Ojima, D. (2004). Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sens. Environ. 2004, 90, 477–489. [CrossRef]
Carvalho, F. M. V.; Ferreira, L. G.; Lobo, F. C.; Diniz-filho, A. F.; Bini, L.M. (2008). Padrões de autocorrelação espacial de índices de vegetação MODIS no bioma cerrado. Revista Árvore, Viçosa-MG, v.32, n.2, p.279-290
Ceccato, P.; Flasse, S.; Grégoire, J.-M. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sens. Environ. 2002, 82, 198–207. [CrossRef]
Ding, W.R. (2016). Temporal and spatial Evolution characteristics of vegetation NDVI and its Driving Factors in Karst Area of Southeast Yunnan, China. Res. Soil Water Conserv. 2016, 23, 227–231.
Diouf, A.; Lambin, E.F. (2001). Monitoring land-cover changes in semi-arid regions: Remote sensing data and field observations in the Ferlo, Senegal. J. Arid Environ. 2001, 48, 129–148. [CrossRef]
Fischer, M.M. Wang, J. (2011) Spatial Data Analysis: Models, Methods, and Techniques. Springer, New York. https://doi.org/10.1007/978-3-642-21720-3
Fensholt, R.; Sandholt, I. (2003). Derivation of a Shortwave Infrared Water Stress Index From MODIS Near- and Shortwave Infrared Data in a Semiarid Environment. Remote Sens. Environ. 2003, 87, 111–121. [CrossRef]
Ghavidel, M., Bayat, P., Farashia, M.E. (2021). Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran. Journal of Forest Science, 67, 2021 (2): 71–79
Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [CrossRef]
Gu, Y.; Brown, J.F.; Verdin, J.P.; Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys. Res. Lett. 2007, 34, L06407. [CrossRef]
GISGeography (2019): What is NDVI (Normalized Difference Vegetation Index). Available at: https://gisgeography .com/ ndvi-normalized-difference-vegetation-index/
Houborg, R.; Soegaard, H.; Boegh, E. (2007). Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sens. Environ. 2007, 106, 39–58. [CrossRef]
Huete, A.R.; Jackson, R.D. (1988). Soil and atmosphere influences on the spectra of partial canopies. Remote Sens. Environ. 1988, 25, 89–105. [CrossRef]
Irons, J.R.; Dwyer, J.L.; Barsi, J.A. T (2012). The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 2012, 122, 11–21. [CrossRef]
Jian, P.; You, L.; Lu, T. (2015). Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008. Remote Sens. 2015, 7, 13641–13663.
Jiang, W.G.; Yuan, L.H.; Wang, W.J. (2014). Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecol. Indic. 2014, 51, 117–126. [CrossRef]
Jei, L., Peters, A.J. (2004). A Spatial Regression Procedure for Evaluating the Relationship between AVHRR-NDVI and Climate in the Northern Great Plains, Int. J. Remote Sensing, 25, pp. 297-311.
Karimi, M., Shahedi, K., Raziei, T., Miryaghoubzadeh, M. (2020). Analysis of Performance of vegetation indices on agricultural drought using remote sensing technique in Karkheh basin. REMOTE SENSING & GIS. 2020 [cited 2021June18];11(4):29-46. Available from: https://www.sid.ir/en/journal /ViewPaper .aspx ?id =761416
Kazeminia A. (2018): Application of remote sensing and GIS in the investigating vegetation coverage. Geospatial Engineering Journal, 9: 75–85
Lin, X.; Niu, J.; Berndtsson, R.; Yu, X.; Chen, X. (2020). NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China. Remote Sens. 2020, 12, 4138. [CrossRef]
Liu, Y.; Fu, B.J. (2013). Topographical variation of vegetation cover evolution and the impact of land use/cover change in the Loess Plateau. Arid Land Geogr. 2013, 36, 1097–1102
Li, J., Lewis, J., Rowland, J., Tappan, G. and L. L. Tieszen, (2004). Evaluation of Land Performance in Senegal Using Multi-temporal NDVI and Rainfall Series. Journal of Arid Environments, 59, pp. 463-480.
Maglione P., Parente C., Vallario A. (2013). Using WorldView-2 satellite imagery to support geoscience studies on Phlegraean area. American Journal of Geoscience, 3: 1–12.
Miura, T.; Huete, A.; Yoshioka, H. (2000). Evaluation of sensor calibration uncertainties on vegetation indices for MODIS. IEEE Trans. Geosci. Remote Sens. 2000, 38, 1399–1409. [CrossRef]
Mokarm, M., Mazin, M., Faraji, M., Mousavi, K. (2016). "Investigation of vegetation changes in different growing seasons using satellite imagery and its relation to temperature changes (study area: north of Darab city)." Iranian Natural Ecosystems, 8 (3), 1-20. [Persian]
Nagler, P.L.; Cleverly, J.; Glenn, E.; Lampkin, D.; Huete, A.; Wan, Z. (2005). Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data. Remote Sens. Environ. 2005, 94, 17–30. [CrossRef]
Naji T.A: (2018): Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot. Journal of Physics Conference Series, 1003: 012083
Ord, J.K., and Geties, A. (1995).Local Spatial Autocorrelation Statistics: Distributional Issues and an Application, Geographical Analysis, 27(4), 286-306.
Robinson N.P., Allred B.W., Jones M.O., Moreno A., Kumball J.S., Naugle D.E., Erickson T.A., Richardson A.D. (2017): A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote Sensing, 9: 863.
Sims, D.A.; Rahman, A.F.; Cordova, V.D.; El-Masri, B.Z.; Baldocchi, D.D.; Bolstad, P.V.; Flanagan, L.B.; Goldstein, A.H.; Hollinger, D.Y.; Misson, L. (2008). A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sens. Environ. 2008, 112, 1633–1646. [CrossRef]
Syed, S.S.; Imran, A.S. (2004). Spatial pattern analysis of seeds of an arable soil seed bank and its relationship with above-ground vegetation in an arid region. J. Arid Environ. 2004, 3, 311–327.
Safari A., Sohrabi H., Powell S. (2018). Comparison of satellite-based estimates of aboveground biomass in coppice oak forests using parametric, semiparametric, and nonparametric modeling methods. Journal of Applied Remote Sensing, 12: 046026.
Solano, R.; Didan, K.; Jacobson, A.(2012). Huete, A. MODIS Vegetation Index User’s Guide. Version 2.00, May 2010; Collection 5. Available online: http://vip.arizona.edu /documents/MODIS /MODIS_VI _UsersGuide _01_ 2012.pdf (accessed on 6 June 2018).
Thiele J.C., Nuske R.S., Ahrends B., Panferov O., Albert M., Staupendahl K., Junghans U., Jansen M., Saborowski J. (2017). Climate change impact assessment – a simulation experiment with Norway spruce for a forest district in Central Europe. Ecological Modelling, 346: 30–47.
United States Geological Survey-USGS. (2019). Available online: https://www.usgs.gov/land-resources /nli/ landsat (accessed on 1 July 2019).
Virgílio A. Bento, V.A., Trigo, I.F., Gouveia, C.M., DaCamara, C.C. (2018). Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records. Remote Sens. 2018, 10, 1324; doi:10.3390/rs10091324
Vani V., Mandla V.R. (2017). Comparative study of NDVI and SAVI vegetation indices in Anantapur district semiarid areas. International Journal of Civil Engineering & Technology, 8: 287–300.
Wardlow, B.D.; Egbert, S.L.; Kastens, J.H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ. 2007, 108, 290–310. [CrossRef]
Waring, R.H.; Coops, N.C.; Fan, W.; Nightingale, J.M. (2006). MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous USA. Remote Sens. Environ. 2006, 103, 218–226. [CrossRef]
Xiao, X.; Hagen, S.; Zhang, Q.; Keller, M.; Moore, B. (2006). Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images. Remote Sens. Environ. 2006, 103, 465–473. [CrossRef]
Yengoh, G.T.; Dent, D.; Olsson, L.; Tengberg, A.E.; Iii, C.J.T. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations. Springer Brief Environ. Sci. 2015, 100
Yang, L., Wylie, B., Tieszen, L. L., and B. C., Reed. (1998). An analysis of Relationships Among Climate Forcing and Time-integrated NDVI of Grasslands over the U.S. Northern and Central Great Plains, Remote Sens. Environ, 65, pp. 25–37.
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