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2016 | OriginalPaper | Chapter

A Comparison of Different Methods for Studying Vegetation Phenology in Central Asia

Authors : Yonggang Ma, Xinmin Niu, Jie Liu

Published in: Geo-Informatics in Resource Management and Sustainable Ecosystem

Publisher: Springer Berlin Heidelberg

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Abstract

The global inventory modeling and mapping studies (GIMMS) data has been extensively used to extract vegetation phenological data globally or regionally for phenological trend analysis. However, most of preview researches focus on the phenological change based on individual phenological metrics extracted method and the potential difference is less discussed. To compare the difference and identify the character vegetation phenology change, we use two phenological extract methods (threshold method and inflection point method) to calculate two series of phenological data for detecting the phenological change based on 25 years of satellite-derived Normalized Difference Vegetation Index (NDVI) in Central Asia. The Mann-Kendall trend analysis was used to examine the change trend of start of season (SOS), end of season (EOS) and length of season (LOS). Different phenological spatial pattern are conducted. There is an unexpected consistent distribution in detecting the significant change zone between methods. The most significant change was found in agriculture zones. The result also indicated that Vegetation phenology in central Asia does not change overall.

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Literature
1.
go back to reference Yu, F., Price, K.P., Ellis, J., Shi, P.: Response of seasonal vegetation development to climatic variations in eastern Central Asia. Remote Sens. Environ. 87, 42–54 (2003)CrossRef Yu, F., Price, K.P., Ellis, J., Shi, P.: Response of seasonal vegetation development to climatic variations in eastern Central Asia. Remote Sens. Environ. 87, 42–54 (2003)CrossRef
2.
go back to reference Yu, F., Price, K.P., Ellis, J., Kastens, D.: Satellite observations of the seasonal vegetation growth in Central Asia: 1982–1990. Photogramm. Eng. Remote Sens. 70, 461–469 (2004)CrossRef Yu, F., Price, K.P., Ellis, J., Kastens, D.: Satellite observations of the seasonal vegetation growth in Central Asia: 1982–1990. Photogramm. Eng. Remote Sens. 70, 461–469 (2004)CrossRef
3.
go back to reference Kariyeva, J., van Leeuwen, W.J.D.: Environmental drivers of NDVI-based vegetation phenology in Central Asia. Remote Sens. 3, 203–246 (2011)CrossRef Kariyeva, J., van Leeuwen, W.J.D.: Environmental drivers of NDVI-based vegetation phenology in Central Asia. Remote Sens. 3, 203–246 (2011)CrossRef
4.
go back to reference Kariyeva, J., van Leeuwen, W.J.D., Woodhouse, C.A.: Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981–2008). Front. Earth Sci. 6, 206–225 (2012)CrossRef Kariyeva, J., van Leeuwen, W.J.D., Woodhouse, C.A.: Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981–2008). Front. Earth Sci. 6, 206–225 (2012)CrossRef
5.
go back to reference White, M.A., de Beurs, K.M., Didan, K.: A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Change Biol. 15, 2335–2359 (2009)CrossRef White, M.A., de Beurs, K.M., Didan, K.: A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Change Biol. 15, 2335–2359 (2009)CrossRef
6.
go back to reference Schwartz, M.D., Hanes, J.M.: Continental-scale phenology: warming and chilling. Int. J. Climatol. 30, 614–1626 (2010) Schwartz, M.D., Hanes, J.M.: Continental-scale phenology: warming and chilling. Int. J. Climatol. 30, 614–1626 (2010)
7.
go back to reference Tucker, C., Pinzon, J., Brown, M.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2002)CrossRef Tucker, C., Pinzon, J., Brown, M.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2002)CrossRef
8.
go back to reference Jönsson, P., Eklundh, L.: Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40, 1824–1832 (2002)CrossRef Jönsson, P., Eklundh, L.: Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40, 1824–1832 (2002)CrossRef
9.
go back to reference Jönsson, P., Eklundh, L.: Timesat—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30, 833–845 (2004)CrossRef Jönsson, P., Eklundh, L.: Timesat—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30, 833–845 (2004)CrossRef
10.
go back to reference Rodrigues, A., Marcal, A.R.S., Cunha, M.: PhenoSat– a tool for vegetation temporal analysis from satellite image data. In: Proceedings of the 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-temp), pp. 45–48 (2011) Rodrigues, A., Marcal, A.R.S., Cunha, M.: PhenoSat– a tool for vegetation temporal analysis from satellite image data. In: Proceedings of the 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-temp), pp. 45–48 (2011)
11.
go back to reference Rodrigues, A., Marcal, A.R.S., Cunha, M.: Phenology parameter extraction from time-series of satellite vegetation index data using PhenoSat. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 4926–4929 (2012) Rodrigues, A., Marcal, A.R.S., Cunha, M.: Phenology parameter extraction from time-series of satellite vegetation index data using PhenoSat. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 4926–4929 (2012)
12.
go back to reference Rodrigues, A., Marcal, A.R.S., Cunha, M.: Monitoring vegetation dynamics inferred by satellite data using the PhenoSat tool. IEEE Trans. Geosci. Remote Sens. 51, 2096–2104 (2013)CrossRef Rodrigues, A., Marcal, A.R.S., Cunha, M.: Monitoring vegetation dynamics inferred by satellite data using the PhenoSat tool. IEEE Trans. Geosci. Remote Sens. 51, 2096–2104 (2013)CrossRef
13.
go back to reference Hirsch, R.M., Slack, J.R.: A nonparametric trend test for seasonal data with serial dependence. Water Resour. Res. 20, 727–732 (1984)CrossRef Hirsch, R.M., Slack, J.R.: A nonparametric trend test for seasonal data with serial dependence. Water Resour. Res. 20, 727–732 (1984)CrossRef
Metadata
Title
A Comparison of Different Methods for Studying Vegetation Phenology in Central Asia
Authors
Yonggang Ma
Xinmin Niu
Jie Liu
Copyright Year
2016
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-49155-3_30