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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)

Abstract:

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.

 

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