ABSTRACT

The purpose of this research is to develop a method for estimating the spatially and temporally resolved moisture content of thermally modified Scots pine (Pinus sylvestris) using remote sensing. Hyperspectral time series imaging in the NIR wavelength region (953–2516 nm) was used to gather information about the absorbance of eight thermally modified pine samples each minute as they dried during a period of approximately 20 h. After preprocessing the collected spectral data and identifying an appropriate wavelength selection, partial least squares regression (PLS) was used to map the absorbance data of each pine sample to a distribution of moisture contents within the samples at different time steps during the drying process. To enable separate studying and comparison of the drying dynamics taking place within the early- and latewood regions of the pine samples, the collected images were spatially segmented to separate between early- and latewood pixels. The results of the study indicate that the 1966–2244 nm region of a NIR spectrum, when preprocessed with extended multiplicative scatter correction and first order derivation, can be used to model the average moisture content of thermally modified pine using PLS. The methods presented in this paper allows for estimation and visualization of the intrasample spatial distribution of moisture in thermally modified pine wood.

摘要

这项研究的目的是开发一种方法，用于估算热改性的苏格兰松树（Pinus sylvestris）的时空分辨水分含量。）使用遥感技术。NIR波长区域（953–2516 nm）中的高光谱时间序列成像用于收集大约八小时内干燥的八种热改性松树样品每分钟的吸光度信息。在对收集到的光谱数据进行预处理并确定合适的波长选择之后，使用偏最小二乘回归（PLS）将每个松木样品的吸光度数据映射到干燥过程中不同时间步骤的样品中水分含量的分布。为了能够分别研究和比较松树样品的早木和晚木区域内发生的干燥动力学，将所采集的图像在空间上进行了分割，以区分早木和晚木的像素。研究结果表明，使用扩展的倍增散射校正和一阶导数进行预处理时，可将NIR光谱的1966–2244 nm区域用于PLS对热改性松木的平均水分含量进行建模。本文介绍的方法可以估算和可视化热改性松木中水分的样品内空间分布。