MODELING OF SATELLITE DATA TO IDENTIFY THE SEASONAL PATTERNS AND TRENDS OF VEGETATION INDEX IN KATHMANDU VALLEY, NEPAL FROM 2000 TO 2015
DOI:
https://doi.org/10.11113/jt.v80.11728Keywords:
Satellite data, normalised difference vegetation index, cubic spline function, linear model, generalized estimating equationsAbstract
Normalised difference vegetation index (NDVI) data were analysed to identify the seasonal patterns and the time series trends of vegetation in Kathmandu. The data were managed in three steps: reordering, removal of unreliable values and validating. A cubic spline function was used to examine annual seasonal patterns that revealed regular seasonal peaks (day 225 to 280) and troughs (day 50 to 81) of vegetation and start of greening from April and of browning from November. Linear regression models were fitted to seasonally adjusted NDVI, which statistically showed 40.70% of the grid cells had a significant increase and 24.71% of it had decreasing trends. To adjust for autocorrelation, generalized estimating equations (GEE) were fitted to the data for whole area that showed, the overall vegetation has been significantly declining at a rate of -0.005 ̊C and -0.006 ̊C per decade for 2000-2004 and 2010-2015 respectively. The recent period of decline is alarming for a growing city like Kathmandu.
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References
Archibold, W. O. 1995. Ecology of World Vegetation. Springer Science and Business Media. Dordrecht.
Eckert, S., F. Husler, H. Liniger, and E. Hodel. 2015 Trend Analysis of MODIS NDVI Time Series for Detecting Land Degradation and Regeneration in Mongolia. Journal of Arid Environment. 113: 16-28.
DOI: http://dx.doi.org/10.1016/j.jaridenv.2014.09.001.
. Liu, Y., Y. Li, S. Li, and S. Motesharrei. 2015. Spatial and temporal patterns of global NDVI trends: correlations with climate and human factors. Remote Sensing. 7: 13233-13250.
DOI : http://dx.doi.org/10.3390/rs71013233
Bounoua, L., G. J. Collatz, S. O. Los, P. J. Sellers, D. A. Dazlich, C. J. Tucker, and D. A. Randall. 2000. Sensitivity of Climate to Changes in NDVI. Journal of Climate.13: 2277-229.
Goward, S. N., Y. Xue, and K. P. Czajkowski. 2002. Evaluating Land Surface Moisture Conditions from the Remotely Sensed Temperature/Vegetation Index Measurements: An Exploration with the Simplified Simple Biosphere Model. Remote Sensing Environment. 79: 225-242.
Kaufmann, R. K., L. Zhou, R. B. Myneni, C. J. Tucker, D. Slayback, N. V. Shabanov, and J. Pinzon. 2003. The Effect of Vegetation on Surface Temperature: A Statistical Analysis of NDVI and Climate Data. Geophysical Research Letters. 30 (22): (3)1-(3)4.
DOI: http://dx.doi.org/10.1029/2003GL018251.
Piao, S., J. Fang, L. Zhou, Q. Guo, M. Henderson, W. Ji, Y. Li, and S. Tao. 2003. Interannual Variations of Monthly and Seasonal Normalised Difference Vegetation Index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research. 108.
DOI: http://dx.doi.org/10.1029/2002JD002848.
Evrendilek, F., and O. Gulbeyaz. 2008. Deriving Vegetation Dynamics of Natural Terrestrial Ecosystems from MODIS NDVI/EVI data over Turkey. Sensors. 8: 5270-5302.
DOI: http://dx.doi.org/10.3390/s8095270.
Karnieli, N., R. T. Agam, M. Pinker, M. L. Anderson, G. G. Imhoff, N. Gutman, N. Panov, and A. Goldberg. 2010. Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations. Journal of Climate. 23: 618-633.
DOI: http://dx.doi.org/ 10.1175/2009JCLI2900.1.
[NASA] National Aeronautics and Space Administration. 2015. Global Climate Change: Vital Signs of the Planet. NASA. URL: http://climate.nasa.gov/effects.
Kumar, L., K. Schmidt, S. Dury, and A. Skidmore. 2002. Imaging Spectrometry. Imaging Spectrometry and Vegetation Science. Chapter 5. Springer, Netherlands. 111-155.
Yin, G., Z. Hu, and X. Chen. 2016. Vegetation Dynamics and Its Response to Climate Change in Central Asia. Journal of Arid Land. 8(3): 375-388.
DOI: http://dx.doi.org/10.1007/s40333-016-0043-6.
Zhang, Y., J. Gao, L. Liu, Z.Wang, M. Ding, and Yang X. 2013. NDVI-based Vegetation Changes and Their Responses to Climate Change from 1982 to 2011: A Case Study in the Koshi River Basin in the Middle Himalayas. Global and Planetary Change. 108: 139-148.
DOI: https://doi.org/10.1016/j.gloplacha.2013.06.012.
DFRS] Department of Forest Research and Survey. 2015. State of Nepal’s Forest. Ministry of Forest and Soil Conservation (MFSC), Government of Nepal. Kathmandu.
Haack, B. N., and G. Khatiwada. 2007. Rice and Bricks: Environmental Issues and Mapping of the Unusual Crop Rotation Pattern in the Kathmandu Valley, Nepal. Environmental Management. 39: 774-782.
DOI: http://dx.doi.org/10.1007/s00267-006-0167-0.
Thapa, R. B., and Y. Murayama. 2011. Urban Growth Modeling of Kathmandu Metropolitan Region, Nepal. Computers, Environment and Urban Systems. 35: 25-34.
DOI:http://dx.doi.org/10.1016/j.compenvurbsys.2010.07.005
Gumma, M. K., D. Gauchan, A. Nelson, S. Pandey, and A. Rala. 2011 Temporal Changes in Rice-Growing Area and Their Impact on Livelihood Over a Decade: A Case Study of Nepal. Agriculture Ecosystems and Environment. 142: 382-392.
DOI: http://dx.doi.org/10.1016/j.agee.2011.06.010.
Poudel, B., Z. Yi-Li, L. Shi-Cheng, L. Lin-Shan, W. Xue, and N. R. Khanal. 2016. Review of Studies on Land Use and Land Cover Change in Nepal. Journal of Mountain Science. 13(4): 643-660.
Poudel, K. P., and P. Anderson. 2010. Assessing Rangeland Degradation Using Multi Temporal Satellite Images and Grazing Pressure Surface Model in Upper Mustang, Trans Himalaya, Nepal. Remote Sens Environ. 114: 1845-1855.
DOI: http://dx.doi.org/10.1016/j.rse.2010.03.011.
Maharjan, S. R., D. R. Bhuju, and C. Khadka. 2006. Plant Community Structure and Species Diversity in Ranibari Forest, Kathmandu. Nepal Journal of Science and Technology. 7: 35-43.
[ORNL DAAC] Oak Ridge National Laboratory Distributed Active Achieve Center. 2015. MODIS subset of NASA Earth Data.
URL: http://daacmodis.ornl.gov/cgi-bin/MODIS/GLBVIZ_1_ Glb/modis_subset_order _global_col5.pl.
Weier, J., and D. Herring. 2000. Measuring Vegetation (NDVI and EVI). NASA Earth Observatory.
URL:http://earthobservatory.nasa.gov/Features/Measuring Vegetation/.
R Core Team. 2015. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015. URL: http://www.R-project.org/.
Wongsai, N., S. Wongsai and A. R. Huete. 2017. Annual Seasonality Extraction Using the Cubic Spline Function and Decadal Trend in Temporal Daytime Modis lst Data. Remote Sensing. 9: 1254. DOI: http://dx.doi.org/10.3390/rs9121254
Liang, K., and S. L. Zeger. 1986. Longitudinal Data Analysis Using Generalized Linear Models. Biometrika. 73: 13-22.
Dormann, C. F., J. M. McPherson, M. B. Arau´jo, R. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. D. Kissling, I. Ku¨hn, R. Ohlemu¨ller, P. R. Peres-Neto, B. Reineking, B. Schro¨der, F. M. Schurr, and R. Wilson. 2007. Methods to Account for Spatial Autocorrelation in the Analysis of Species Distributional Data: A Review. Ecography. 30: 609-628.
DOI: http://dx.doi.org/10.1111/j.2007.0906-7590.05171.x.
Fitter, H., and R. K. M. Hay. 2002. Environmental Physiology of Plants. 3rd ed. New York (NY), Academic Press.
Uddin, K., S. Chaudhary, N. Chettri, R. Kotru, M. Murthy, R. P. Chaudhary, W. Ning, M. S. Sahash, and S. K. Gautam. 2015. The Changing Land Cover and Fragmenting Forest on the Roof of the World: A Case Study in Nepal's Kailash Sacred Landscape. Landscape and Urban Planing. 141: 1-10.
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