Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale
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Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.
KeywordsBoreal ecosystem productivity simulator Heihe River Basin Landscape assessment Process model Remote sensing
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- CCICCD (Chinese Committee for Implementing UN Convention to Combat Desertification). (1997). China country paper to combat desertification. Beijing: China Forestry.Google Scholar
- Feng, Z., Wang, X., & Wu, G. (1999). Biomass and net primary productivity of forest ecosystem in China. Beijing: Science.Google Scholar
- Jobbágy, E. G., Sala, O. E., & Paruelo, J. M. (2002). Patterns and controls of primary production in the Patagonian steppe: A remote sensing approach. Ecology, 83(2), 307–319.Google Scholar
- Lv, Y., Zhang, B., Liu, F., & Zhang, Y. (2008). Game analysis on water supply and water resources utilization in the middle reaches of the Heihe River Basin. Arid Zone Reserch, 25(6), 818–823.Google Scholar
- Mao, X., & Wang, X. (1995). A method for calculation of effective soil water content using meteorological data. Eco-agriculture Research, 10(2), 83–86.Google Scholar
- Reeves, M. C., Winslow, J. C., & Running, S. W. (2001). Mapping weekly rangeland vegetation productivity using MODIS algorithms. Journal of Rangeland Management, 54, A90-A105.Google Scholar
- Rodriguez, E., Morris, C. S., & Belz, J. E. (2006). A global assessment of the SRTM performance. Photogrammetric Engineering and Remote Sensing, 72(3), 249–260.Google Scholar
- UN. (1994). United Nations Convention to Combat Desertification in countries experiencing serious drought and/or desertification, particularly in Africa. Geneva: United Nations.Google Scholar
- UNCCD. (2000). Assessment of the status of land degradationin arid, semi-arid and dry sub-humid areas. Bonn: United Nations Convention to Combat Desertification.Google Scholar
- Zhou, W., Liu, G., & Pan, J. (2003). Soil available water capacity and its empirical and statistical models. Journal of Arid Land Resources and Environment, 17, 88–95.Google Scholar