On the relationship between the circumference of rubber trees and L-band waves

Trisasongko, B, Paull, D, Griffin, A, Jia, X and Panuju, D 2019, 'On the relationship between the circumference of rubber trees and L-band waves', International Journal of Remote Sensing, vol. 40, no. 16, pp. 6395-6417.

Document type: Journal Article
Collection: Journal Articles

Title On the relationship between the circumference of rubber trees and L-band waves
Author(s) Trisasongko, B
Paull, D
Griffin, A
Jia, X
Panuju, D
Year 2019
Journal name International Journal of Remote Sensing
Volume number 40
Issue number 16
Start page 6395
End page 6417
Total pages 23
Publisher Taylor & Francis
Abstract Despite substantial research conducted within the forestry domain, detailed assessments to monitor plantations and support their sustainable management have been understudied. This article attempts to fill this gap through coupling fully polarimetric L-band data and contemporary data mining methods for the estimation of tree circumference as: (1) a primary dataset for biomass accumulation studies; and, (2) critical information for operational management in rubber plantations. We used two rubber plantation sites in Subang (West Java) and Jember (East Java), Indonesia, to evaluate the capability of L-band radar data. Although polarimetric features derived from polarimetric decomposition theorems have been advocated by others, we show that backscatter coefficients, especially HV polarization, remain an important dataset for this research domain. Using Subang data to build the model, we found that modern machine learning methods do not always deliver the best performance. It appears that the data being ingested plays a significant role in obtaining a good model, hence careful selection of datasets from multiple forms of polarimetric SAR data needs to be further considered. The highest coefficient of determination (R 2 = 0.79) was achieved by Yamaguchi decomposition features with the aid of partial least squares regression. Nonetheless, we note that the R 2 gap was insignificant to the backscatter coefficient when random forests regression was used (R 2 = 0.78). Overall, only the backscatter coefficient dataset delivered fairly consistent results with any regression model, with the average R 2 being about 0.67. When tuning parameters were not assessed, random forests consistently outweighed support vector regressions in all forms of datasets. The latter generated a substantial increase in R 2 when a linear kernel was used instead of the popular radial basis function. The issue of transferability of the model is also addressed in this article. It appears that similarity o
Subject Forestry Management and Environment
Photogrammetry and Remote Sensing
Keyword(s) support vector machines
random forest
scattering model
pine plantation
carbon stocks
oil palm
DOI - identifier 10.1080/01431161.2019.1591650
Copyright notice © 2019 Informa UK Limited, trading as Taylor & Francis Group
ISSN 0143-1161
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