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

Abstract

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 (R2 = 0.79) was achieved by Yamaguchi decomposition features with the aid of partial least squares regression. Nonetheless, we note that the R2 gap was insignificant to the backscatter coefficient when random forests regression was used (R2 = 0.78). Overall, only the backscatter coefficient dataset delivered fairly consistent results with any regression model, with the average R2 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 R2 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 of terrain characteristics substantially influences the model’s performance. Models developed in Subang, which has gentle slopes, seem valid only in plantations with similar terrain. Validation attempts in very flat terrain within two plantation sectors in Jember delivered a poor result, although they have similar elevations to the Subang site. In contrast, validation in a plantation sector with similar, gently sloping terrain achieved an R2 of about 0.6 using some datasets.