广域空间尺度上植被净初级生产力的精确推算
收稿日期: 2003-09-07
修回日期: 2003-11-22
网络出版日期: 2004-01-25
基金资助
亚洲太平洋地区环境创新战略项目(APEIS) 环境综合监侧子课题(IEM)
Accurate Estimation of Net Primary Productivity of Terrestrial Ecosystem at a Regional Scale
Received date: 2003-09-07
Revised date: 2003-11-22
Online published: 2004-01-25
Supported by
Integrated Environmental Monitoring (IEM) Subproject, the Asia-Pacific Environmental Innovation Strategy Project (APEIS)
作者介绍了使用遥感、GIS数据和BEPS生态过程模型推算植被净初级生产力 (NPP)的方法。为了准确推算北海道地区NPP，我们改进了BEPS模型，而且使用了高质量GIS数据作为模型的输入数据。通过计算得出1998年北海道NPP的平均值为644 g C/m2，总量为0.078 Gt C。我们还进行了模型输入数据质量对应用生态过程模型推算NPP的精度影响测试。结果表明，高质量的GIS输入数据可以提高NPP推算精度16.6%~39.7%。
关键词： 生态过程模型；遥感和GIS数据；NPP
松下文经,杨翠芬,陈晋,王勤学,龟山哲,田村正行 . 广域空间尺度上植被净初级生产力的精确推算[J]. 地理学报, 2004 , 59(1) : 80 -87 . DOI: 10.11821/xb200401010
This paper describes a method for the estimation of the net primary productivity (NPP) by integrating remotely sensed and GIS data with a process-based ecosystem model. Hokkaido Island of Japan was selected as a case to validate our method. In order to improve the estimation accuracy, we improved the BEPS model (Boreal Ecosystem Productivity Simulator) for NPP estimation by incorporating new land cover classification logic, a robust Normalized Difference Vegetation Index-Leaf Area Index (NDVI-LAI) algorithm and employing GIS data with high quality as the input of the model. As the results of the model calculation, the mean and total NPP for the study area in 1998 was 644 g C/m2/yr and 0.078 Gt C/yr, respectively. In addition, the effect of the quality of the model input requirements on accurate NPP estimation using a process-based model was also assessed. The results show that the higher quality input data obtained from GIS datasets for a process-based model improved the NPP estimation accuracy for Hokkaido Island by about 16.6%-39.7%.
[1] Melillo J M, Prentice I C, Farquhar G D et al. Terrestrial Biotic Responses to Environmental Change and Feedbacks to Climate. In: J T Houghton, L G Meira Filho, B A Callander (eds.), Climate Change 1995: The Science of Climate Change. Cambridge University Press, 1996. 444-481.
[2] Cramer W, Field C B. Comparing global models of terrestrial net primary productivity (NPP): introduction. Global Change Biology 5(supp 1): iii-iv (1924). Angstrom A. Solar and terrestrial radiation. Q. J. R. Meteorol. Soc., 1999, 50: 121-126.
[3] Tian H, Hall C A S, Qi Y. Modeling primary productivity of the terrestrial biosphere in changing environments: toward a dynamic biosphere model. Critical Reviews in Plant Science, 1998, 15(5): 541-557.
[4] Liu J, Chen J M, Cihlar J et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 1997, 62: 158-175.
[5] Running S W, Coughlan J C. A general model of forest ecosystem processes for regional applications (I): hydrological balance, canopy gas exchange and primary production processes. Ecological Modelling, 1988, 42: 125-154.
[6] Chen J M, Liu J, Cihlar J et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling, 1999, 124: 99-119.
[7] Myneni R B, Nemani R R, Running S W. Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(6): 1380-1393.
[8] Holben B N. Characteristics of maximum value composite images from temporal AVHRR data. International Journal of Remote Sensing, 1986, 7: 1417-1434.
[9] Vermonte E F, Tanre D, Deuze J L et al. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675-686.
[10] Viovy N, Arino O. The best index slope extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing, 1992, 13(8): 1585-1590.
[11] Angstrom A. Solar and terrestrial radiation. Q. J. R. Meteorol. Soc., 1924, 50: 121-126.
[12] Kondo J. Meteorology in Water Environment. Tokyo: Asakura Press, 1994. (in Japanese)
[13] Landon J R. Booker Tropical Soil Manual: A Handbook for Soil Survey and Agricultural Land Evaluation in the Tropics and Subtropics. New York: Longman Scientific & Technical, 1991.
[14] Matsushita B, Tamura M. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment, 2002, 81/1: 58-66.
[15] Lotsch A. Biome level classification of land cover at continental scales using decision trees. Masters Thesis, Boston University, 1999.
/
| 〈 |
|
〉 |