Sparkpr: An Efficient Parallel Inversion of Forest Canopy Closure

Guangsheng Chen,Weipeng Jing,Tongtong Lou,Zeyu Wang

doi:10.1109/access.2019.2941966

Guangsheng Chen, Weipeng Jing + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2941966

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 5	License type: CC BY 4.0

Affiliation: Northeast Forestry University, Sanya University

Abstract

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.

Highlights

Forest is the largest ecosystem on land
A parallel inversion method of forest canopy density based on spark on yarn and Python Numpy was proposed by using multivariate linear regression method of remote sensing inversion
PARALLEL INVERSION OF FOREST CROWN CLOSURE BASED ON SPARK In this paper, the parallel inversion of forest depression density in Laoshan Experimental Forest Farm of Maoershan Mountain based on multiple linear regression algorithm is studied to test the processing speed of remote sensing big data based on Spark

Summary

INTRODUCTION

Forest is the largest ecosystem on land It covers a large area, distributes widely, is rich in materials and has complex structure. Forest Crown Closure is the radio of the canopy projection area to the woodland area It is one of the important characteristic factors of forest ecosystem state [2] and environmental evaluation index and it is widely used in evaluation of forestry, judgment of urban climate and heat island effect [3]. As an important index in forest survey, is an important index to reflect the spatial structure of ecosystem and tree stand density [4] It plays an important role in forest resources inventory. A parallel inversion method of forest canopy density based on spark on yarn and Python Numpy was proposed by using multivariate linear regression method of remote sensing inversion

RELATED WORK

INVERSION MODEL BASED ON MULTIVARIATE LINEAR REGRESSION

SELECTION OF CORRELATION FACTORS

ESTABLISHMENT OF MODEL BASED ON MULTIVARIATE LINEAR REGRESSION

PARALLEL INVERSION OF FOREST CROWN CLOSURE BASED ON SPARK

Findings

CONCLUSION