Automatic estimation of soil biochar changes via hyperspecral unmixing

L Tong; J Zhou; Shahla Hosseini Bai; Cheng Yuan Xu; Y Qian; Y Gao; Z Xu

doi:10.4018/978-1-4666-9435-4.ch011

Back

Automatic estimation of soil biochar changes via hyperspecral unmixing

Book chapter

Peer reviewed

Automatic estimation of soil biochar changes via hyperspecral unmixing

L Tong, J Zhou, Shahla Hosseini Bai, Cheng Yuan Xu, Y Qian, Y Gao and Z Xu

Computer vision and pattern recognition in environmental informatics, pp.220-247

Information Science Reference

2016

DOI: https://doi.org/10.4018/978-1-4666-9435-4.ch011

Files and links (1)

url

https://doi.org/10.4018/978-1-4666-9435-4.ch011View

Published Version

Abstract

Environmental Science and Management

Biochar soil amendment is globally recognized as an emerging approach to mitigate CO2 emissions and increase crop yield. Because the durability and changes of biochar may affect its long term functions, it is important to quantify biochar in soil after application. In this chapter, an automatic soil biochar estimation method is proposed by analysis of hyperspectral images captured by cameras that cover both visible and infrared light wavelengths. The soil image is considered as a mixture of soil and biochar signals, and then hyperspectral unmixing methods are applied to estimate the biochar proportion at each pixel. The final percentage of biochar can be calculated by taking the mean of the proportion of hyperspectral pixels. Three different models of unmixing are described in this chapter. Their experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil.

Details

Title: Automatic estimation of soil biochar changes via hyperspecral unmixing
Authors: L Tong (Author) - Griffith University
J Zhou (Author) - Griffith University
Shahla Hosseini Bai (Author) - Griffith University
Cheng Yuan Xu (Author) - Griffith University
Y Qian (Author) - Zhejiang University, China
Y Gao (Author) - Griffith University
Z Xu (Author) - Griffith University
Contributors: Jun Zhou (Editor)
Bai Xiao (Editor)
Terry Caelli (Editor)
Publication details: Computer vision and pattern recognition in environmental informatics, pp.220-247
Publisher: Information Science Reference
Date published: 2016
DOI: 10.4018/978-1-4666-9435-4.ch011; 10.4018/978-1-4666-9435-4
ISBN: 9781466694361
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99449261302621
Output Type: Book chapter

Metrics

993 Record Views