Username   Password       Forgot your password?  Forgot your username? 


Net Primary Productivity Evaluation for Mao’er Mountain Forest Vegetation based on Cloud Computing and GIS

Volume 14, Number 4, April 2018, pp. 699-708
DOI: 10.23940/ijpe.18.04.p13.699708

Huiling Liu, Guangsheng Chen, Yanjuan Li, and Weipeng Jing

School of Information and Computer Engineering, Northeast forestry University, Harbin, 150040, China

(Submitted on January 11, 2018; Revised on February 18, 2018; Accepted on March 21, 2018)


For the problems in net primary productivity estimation of forest vegetation such as complex model, great difficulty in parameter acquisition, only appropriate for specific area and slow remote-sensing data processing platform computation speed, etc., the improved net vegetation primary productivity estimation model (Cloud-ICASA) is proposed by using the domestic GF-1 high resolution image based on the specific ecological environment of research region Mao’er Mountain forest farm. The Spark-based remote-sensing data processing platform is constructed to process the remote-sensing image in parallel environment. The research results show that the improved Cloud-ICASA model simplifies the parameters, improves the estimation accuracy and is appropriate for estimation of net primary productivity for the vegetation in research region. The Spark based remote-sensing data processing improves the node utilization rate, increase the computation speed and can satisfy the real-time dynamic evaluation requirements.


References: 17

    1. J. H. Bian, A. N. Li, and W. Deng, “Estimation and Analysis of Net Primary Productivity of Ruoergai Wetland in China for the Recent 10 Years based on Remote Sensing,” Environmental Sciences, vol. 2, no.35, pp.288-301, February,2010
    2. D. Cenk, B. Suha, and J. C. Paul, “Modelling the Current and Future Spatial Distribution of NPP in a Mediterranean Watershed,” International Journal of Applied Earth Observations and Geo-information, vol.13, no.3, pp.336-345,June, 2011
    3. L. Christopher, C. Jeffrey, L. Reid and G. Thomas, “Role of Forest Products in the Global Carbon Cycle: From the Forest to Final Disposal,” Springer Netherlands, pp. 257–282, June 2012
    4. G. F. Dong, X. L. Fu, H. H. Li, and P. F. Xie, “Cooperative Ant Colony-genetic Algorithm based on Spark,” Computers and Electrical Engineering, vol. 12, no.5, pp.1-10, October,2016
    5. C. B. Field, J. T. Randerson, and C. M. Malmstrom, “Global Net Primary Reduction: Recombining Ecology and Remote Sensing,” Remote Sensing of Environment Remote, vol. 51, no.31, pp.74-88, November 1995
    6. J. J. He, X. Y. Pen, Z. J. Chen, M. X. Cui, X. L. Zhang, and C.H. Zhou, “Responses of Pinus Tabulaeformis Forest Ecosystem in North China to Climate Change and Elevated CO2: a Simulation based on BIOME-BGC Model and Tree-ring Data,” Journal of applied ecology , vol. 23, no.7, pp. 1733-1742, July, 2012
    7. L. H. He, H. Y. Wang, and X. D. Lei, “Parameter Sensitivity of Simulating Net Primary Productivity of Larix Olgensis Forest based on BIOME-BGC Model,” Journal of applied ecology, vol. 27, no.2, pp. 412-420, May 2016
    8. 8. C. L. Hung, G. J. Hua, and C. Y. Lin. “Local Alignment Tool Based on Hadoop Framework and GPU Architecture,” Biomed Research International, vol. 10, no.7, pp. 1155-1162, May 2014
    9. P. Jozef , K. Bohdan, and M. Robert, “Above-Ground Net Primary Productivity in Young Stands of Beech and Spruce,” Lesnick Casopis-Forestry Journal, vol. 59, no.3, November 2013
    10. K. Michel, and S. Ivo, “A Modular Software Architecture for Processing of Big Geospatial Data in the Cloud,” Computers and Graphics, vol.49, no.2, pp. 69-81, June,2015
    11. C. S. Potter ,J. Randerson, and C. B. Field, “Terrestrial Ecosystem Production: a Process Model based on Global Satellite and Surface,” Global Biogeochemical Global Cycle, vol. 7, no.2, pp.811-841, May 1993
    12. Y. Wang, N. Zhang, and G. R. Yu, “Simulation of Carbon Cycle in Qianyanzhou Artificial Masson Pine Forest Ecosystem and Sensitivity Analysis of Model Parameters,” Journal of applied ecology,vol.21, no.7, pp.1656-1666, July, 2010
    13. Z. H. Wang, P. Lv and C. W. Zhen, “CUDA on Hadoop : A Mixed Computing Framework for Massive Data Processing,” Springer Berlin Heidelberg, vol. 215,pp.253-260, September 2014
    14. Z. P. Yang, J. X. Gao, and M. R. Tian, “Spatial and Temporal Patterns of Net Primary Productivity in the Source Regions of Yangtze and Yellow Rivers,” Advanced Materials Research, vol.1793, no.518, pp.5130-5137, May,2012
    15. D. Y. Yu, P. J. Shi, H. B. Shao, W. Q. Zhu, and Y. Z. Pan, “Modelling Net Primary Productivity of Terrestrial Ecosystems in East Asia based on an Improved CASA Ecosystem Model,” International Journal of Remote Sensing, vol. 30, no.18, pp.4851-4866,2009
    16. T. L. Zhang, R. Sun, R. H. Zhang, and L. Zhang, “Simulation of Water and Carbon Fluxes in Harvard Forest Area based on Data Assimilation Method,” Journal of applied ecology , vol. 24, no.10, pp.2746-2754, November 1995
    17. W. Q. Zhu, Y. Z. Pan, H. He, D. Y. Yu, and H. B. Hu, “Simulation of Maximum Light Use Efficiency for Some Typical Vegetation Types in China,” Chinese Science Bulletin , vol. 51, no.4, pp.457-463, April, 2016


      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      This site uses encryption for transmitting your passwords.