Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (4): 699-708.doi: 10.23940/ijpe.18.04.p13.699708

• Original articles • Previous Articles     Next Articles

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

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

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

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.


Submitted on January 11, 2018; Revised on February 18, 2018; Accepted on March 21, 2018
References: 17