Abstract
Annual normalized difference vegetation index (NDVI) and chlorophyll-a (Chl-a) concentration are the most important large-scale indicators of terrestrial and oceanic ecosystem net primary productivity. In this paper, the Sea-viewing Wide Field-of-view Sensor level 3 standard mapped image annual products from 1998 to 2009 are used to study the spatial–temporal characters of terrestrial NDVI and oceanic Chl-a concentration on two sides of the coastline of China by using the methods of mean value (M), coefficient of variation (CV), the slope of unary linear regression model (Slope), and the Hurst index (H). In detail, we researched and analyzed the spatial–temporal dynamics, the longitudinal zonality and latitudinal zonality, the direction, intensity, and persistency of historical changes. The results showed that: (1) spatial patterns of M and CV between NDVI and Chl-a concentration from 1998 to 2009 were very different. The dynamic variation of terrestrial NDVI was much mild, while the variation of oceanic Chl-a concentration was relatively much larger; (2) distinct longitudinal zonality was found for Chl-a concentration and NDVI due to their hypersensitivity to the distance to shoreline, and strong latitudinal zonality existed for Chl-a concentration while terrestrial NDVI had a very weak latitudinal zonality; (3) overall, the NDVI showed a slight decreasing trend while the Chl-a concentration showed a significant increasing trend in the past 12 years, and both of them exhibit strong self-similarity and long-range dependence which indicates opposite future trends between land and ocean.
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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.
Campbell, J. W., Bleisdell, J. M., & Darzi, M. (1995). Level-3 SeaWiFS data products: Spatial and temporal binning algorithms. NASA Tech. Memo. 104566, Vol. 32, S.B. Hooker, E.R. Firestone, & J.G. Acker, NASA Goddard Space Flight Center, Greenbelt, Maryland, 73. http://oceancolor.gsfc.nasa.gov/SeaWiFS/TECH_REPORTS/PreLPDF/PreLVol32.pdf
Chauhan, P., Mohan, M., Sarngi, R. K., Kumari, B., Nayak, S., & Matondkar, S. G. P. (2002). Surface chlorophyll a estimation in the Arabian Sea using IRS-P4 Ocean Colour Monitor (OCM) satellite data. International Journal of Remote Sensing, 23, 1663–1676.
Dogliotti, A. I., Schloss, I. R., Almandoz, G. O., & Gagliardini, D. A. (2009). Evaluation of SeaWiFS and MODIS chlorophyll-a products in the Argentinean Patagonian Continental Shelf (38° S–55° S). International Journal of Remote Sensing, 30, 251–273.
Falkowski, P. G., Barber, R. T., & Smetacek, V. (1998). Biogeochemical controls and feedbacks on ocean primary production. Science, 281, 200–206.
Gobron, N., Mélin, F., Pinty, B., Verstraete, M. M., Widlowski, J. L., & Bucini, G. (2003). A global vegetation index for SeaWiFS: Design and applications. Remote Sensing and Climate Modeling: Synergies and Limitations, 7, 5–21.
Gordon, H. R., & Morel, A. Y. (1983). Remote assessment of ocean color for interpretation of satellite visible imagery: A review. In M. Bowman (Ed.), Lecture notes on coastal and estuarine studies, vol. 4 (pp. 1–114). New York: Springer.
Han, J. T., & Tang, D. L. (2004). Some problems in estimating a Hurst exponent—A case study of applications to climatic change. Scientia Geographica Sinica, 24, 177–182 (in Chinese).
Han, X. Z., Li, S. M., Luo, J. N., & Ji, X. (2008). Study on spatiotemporal change of vegetation in China since 20 years. Arid Zone Research, 25, 753–759 (in Chinese).
Han, G. F., Zhao, K., & Xu, J. H. (2009). Spatial-temporal change of vegetation in the Yangtze River Delta based on time series remote sensing. Chinese Landscape Architecture, 25, 60–64 (in Chinese).
Hong, H. S., Liu, X., Chiang, K. P., Huang, B. Q., Zhang, C. Y., Hua, J., et al. (2011). The coupling of temporal and spatial variations of chlorophyll a concentration and the East Asian monsoons in the southernTaiwan Strait. Continental Shelf Research, 31, S37–S47.
Huete, A., Justice, C., & Leeuwen, W. V. (1999). MODIS Vegetation index (MOD 13): Algorithm theoretical basis document, version 3. 1-133. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf
Hyde, K. J. W., O'Reilly, J. E., & Candace, A. O. (2007). Validation of SeaWiFS chlorophyll a in Massachusetts Bay. Continental Shelf Research, 27, 1677–1691.
Karakaya, N., & Evrendilek, F. (2011). Monitoring and validating spatio-temporal dynamics of biogeochemical properties in Mersin Bay (Turkey) using Landsat ETM. Environmental Monitoring and Assessment, 181, 457–464.
Kim, D., Choi, S. H., Kim, K. H., Shim, J. H., Yoo, S., & Kim, C. H. (2009). Spatial and temporal variations in nutrient and chlorophyll-a concentrations in the northern East China Sea surrounding Cheju Island. Continental Shelf Research, 29, 1426–1436.
Krishna, K. M. (2008). Seasonal and interannual variability of SeaWiFS-derived chlorophyll-a concentrations in waters off the southwest coast of India, 1998-2003. Journal of Applied Remote Sensing, 2, 023543. doi:10.1117/1.3026540.
Lasaponara, R. (2006). On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series. Ecological Modelling, 194, 429–434.
Morel, A., & Berthon, J. F. (1989). Surface pigments, algal biomass profiles, and potential production of the euphotic layer: Relationships reinvestigated in view of remote sensing applications. Limnology and Oceanography, 34, 1545–1562.
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegcl, D. A., Carder, K. L., Garver, S. A., et al. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research, 103(11), 24,937–24,953.
Rao, A. R., & Bhattachary, D. (1999). Hypothesis testing for long-term memory in hydrologic series. Journal of Hydrology, 216, 183–196.
Sackmann, B., Mack, L., Logsdon, M., & Perry, M. J. (2004). Seasonal and inter-annual variability of SeaWiFS- derived chlorophyll a concentrations in waters off the Washington and Vancouver Island coasts, 1998–2002. Deep-Sea Research Part II, 51, 945–965.
Song, Y., & Ma, M. G. (2007). Study on vegetation cover change in northwest China based on SPOT Vegetation data. Journal of Desert Research, 27, 89–93 (in Chinese).
Sun, J., Wang, X., Chen, A., Ma, Y., Cui, M., & Piao, S. (2011). NDVI indicated characteristics of vegetation cover change in China’s metropolises over the last three decades. Environmental Monitoring and Assessment, 179, 1–14.
Uz, B. M., & Yoder, J. A. (2004). High frequency and mesoscale variability in SeaWiFS chlorophyll imagery and its relation to other remotely sensed oceanographic variables. Deep-Sea Research Part II, 51, 1001–1017.
Acknowledgments
The authors acknowledge a number of individuals, groups, and organizations for their excellent works on SeaWiFS sensor designing and launching and the NDVI and chlorophyll product processing and sharing. We would like to thank Prof. Ping Shi, Dr. Song Qian, Prof. Dongyan Liu, and Dr. Dejuan Jiang for their pertinent amendments on the manuscript. The research was founded by the Knowledge Innovation Program of the Chinese Academy of Sciences (kzcx2-yw-224), the CAS Strategic Priority Research Program (XDA05130703). And, the authors gratefully acknowledge the support of K.C. Wong Education Foundation, Hong Kong.
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Hou, X., Li, M., Gao, M. et al. Spatial–temporal dynamics of NDVI and Chl-a concentration from 1998 to 2009 in the East coastal zone of China: integrating terrestrial and oceanic components. Environ Monit Assess 185, 267–277 (2013). https://doi.org/10.1007/s10661-012-2551-y
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DOI: https://doi.org/10.1007/s10661-012-2551-y