Assessment on Vegetation Dynamics under Climate Change for Energy Saving with Satellite Data and Geographically Weighted Regression

Abstract:

The sensitivity of net primary productivity (NPP) to future climate change is critical for carbon dynamics and energy saving. Geographically weighted regression, which allows the use of remotely sensed NPP to establish spatial correlations with leaf area index (LAI) and topographically-based climate factors (temperature, precipitation and solar radiation), is introduced in this paper. For most area of North China, the effect of precipitation and LAI on NPP is positive, while that of temperature is negative. Grassland is most sensitive to climate change. LAI will decrease by -21.96%. Similarly, climate change may reduce NPP by -6.29%.

• Periodical: Advanced Materials Research (Volume 648)
• Pages: 265-269
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.648.265
• Online since: January 2013
• Authors: Na Zhao, Xiao Fan Zeng, Jian Zhong Zhou
• Keywords: Climate Change, Energy Saving, Geographically Weighted Regression, Leaf Area Index, Net Primary Productivity

References

[1] Gao, Q., Li, Y., Wan, Y., Qin, X., Jiangcun, W., Liu, Y., 2009. Dynamics of alpine grassland NPP and its response to climate change in Northern Tibet. Climatic Change 97, 515-528.

DOI: 10.1007/s10584-009-9617-z

[2] Lieth, H., 1975. Modeling the primary productivity of the world Primary, productivity of the biosphere, 14, Springer-Verlag Berlin, pp.237-263.

DOI: 10.1007/978-3-642-80913-2_12

[3] Cramer, W., Kicklighter, D., Bondeau, A., Moore, B., Churkina, G., Nemry, B., Ruimy, A., Schloss, A., 1999. Comparing global models of terrestrial net primary productivity (NPP) overview and key results. Global Change Biol. 5, 1-15.

DOI: 10.1046/j.1365-2486.1999.00009.x

[4] Wang, J., Meng, J., Cai, Y., 2008. Assessing vegetation dynamics impacted by climate change in the southwestern karst region of China with AVHRR NDVI and AVHRR NPP time-series. Environ. Geol. 54, 1185-1195.

DOI: 10.1007/s00254-007-0901-9

[5] Fotheringham, A., Brunsdon, C., Charlton, M., 2002. Geographically weighted regression the analysis of spatially varying relationships. John Wiley & Sons Inc.

DOI: 10.1111/j.1538-4632.2003.tb01114.x

[6] Zhao, N., Yang, Y., Zhou, X., 2010. Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment, China. Plant Ecol. 209, 349-359.

DOI: 10.1007/s11258-010-9769-y

[7] Foody, G., 2003. Geographical weighting as a further refinement to regression modelling an example focused on the NDVI-rainfall relationship. Remote Sens Environ. 88, 283-293.

DOI: 10.1016/j.rse.2003.08.004

[8] Wang, Q., Ni, J., Tenhunen, J., 2005. Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecol. Biogeogr. 14, 379-393.

DOI: 10.1111/j.1466-822x.2005.00153.x

[9] Cao, M., Woodward, F., 1998. Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change. Global Change Biol. 4, 185-198.

DOI: 10.1046/j.1365-2486.1998.00125.x

[10] Ju, W., Chen, J., Harvey, D., Wang, S., 2007. Future carbon balance of China's forests under climate change and increasing CO2. J. Environ. Manag. 85, 538-562.

DOI: 10.1016/j.jenvman.2006.04.028

[11] Yu, L., Cao, M., Li, K., 2006. Climate-induced changes in the vegetation pattern of China in the 21st century. Ecol. Res. 21, 912-919.

DOI: 10.1007/s11284-006-0042-8