Sparkpr: An Efficient Parallel Inversion of Forest Canopy Closure

Forest canopy closure is an important parameter to study forest ecosystem and understand the status of forest resources. With the development of remote sensing big data, the amount of remote sensing data has increased sharply, which makes the existing serial processing of remote sensing data face se...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.135949-135956
Main Authors: Chen, Guangsheng, Lou, Tongtong, Jing, Weipeng, Wang, Zeyu
Format: Article
Language:English
Subjects:
Algorithms
Animal behavior
Canopies
Closures
Cloud computing
Data processing
Density
Earth
forest crown closure
Forest management
Forestry
Forests
Indexes
parallel computing
Parallel processing
Remote sensing
spark model
Sparks
Vegetation mapping
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Summary:Forest canopy closure is an important parameter to study forest ecosystem and understand the status of forest resources. With the development of remote sensing big data, the amount of remote sensing data has increased sharply, which makes the existing serial processing of remote sensing data face severe challenges.In order to satisfy the requirements of efficient remote sensing data processing, Spark open source framework is applied to the parallel processing of remote sensing images, and a parallel forest canopy density inversion algorithm based on Spark is proposed. We call this algorithm Sparkpr. Based on the GF-1 remote sensing images and 80 actual measured sample points obtained by Maoershan Laoshan Experimental Forest Farm of Northeast Forestry University in 2016. In this paper, a multi-element linear regression algorithm is used to carry out parallel inversion of the forest canopy density in the Laoshan Experimental Forest Farm of the Maoershan. The comparison experiment between single machine mode and spark standalone and spark on yarn mode is carried out. The experimental results show that the serial and parallel inversion results of forest depression density based on the model are consistent, and the parallel inversion results are accurate and credible. With the increase of computing nodes, the efficiency of parallel inversion is also improving.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2941966