School of GeoSciences, University of Edinburgh, King's Buildings, Alexander Crum Brown Rd, Edinburgh EH9 3FF, United Kingdom.
CREAF, Campus UAB, Cerdanyola del Vallés 08193, Spain.
Tree Physiol. 2024 Aug 3;44(8). doi: 10.1093/treephys/tpae076.
Forest ecosystems face increasing drought exposure due to climate change, necessitating accurate measurements of vegetation water content to assess drought stress and tree mortality risks. Although Frequency Domain Reflectometry offers a viable method for monitoring stem water content by measuring dielectric permittivity, challenges arise from uncertainties in sensor calibration linked to wood properties and species variability, impeding its wider usage. We sampled tropical forest trees and palms in eastern Amazônia to evaluate how sensor output differences are controlled by wood density, temperature and taxonomic identity. Three individuals per species were felled and cut into segments within a diverse dataset comprising five dicotyledonous tree and three monocotyledonous palm species on a wide range of wood densities. Water content was estimated gravimetrically for each segment using a temporally explicit wet-up/dry-down approach and the relationship with the dielectric permittivity was examined. Woody tissue density had no significant impact on the calibration, but species identity and temperature significantly affected sensor readings. The temperature artefact was quantitatively important at large temperature differences, which may have led to significant bias of daily and seasonal water content dynamics in previous studies. We established the first tropical tree and palm calibration equation which performed well for estimating water content. Notably, we demonstrated that the sensitivity remained consistent across species, enabling the creation of a simplified one-slope calibration for accurate, species-independent measurements of relative water content. Our one-slope calibration serves as a general, species-independent standard calibration for assessing relative water content in woody tissue, offering a valuable tool for quantifying drought responses and stress in trees and forest ecosystems.
森林生态系统因气候变化而面临日益增加的干旱暴露，需要准确测量植被含水量来评估干旱胁迫和树木死亡率风险。尽管频域反射计通过测量介电常数提供了一种监测茎含水量的可行方法，但由于与木材特性和物种变异性相关的传感器校准不确定性，限制了其更广泛的应用。我们在亚马孙东部采集了热带森林树木和棕榈树样本，以评估传感器输出差异是如何受到木材密度、温度和分类身份控制的。每个物种采集了三个个体，并在一个多样化的数据集内将其砍伐并切成段，该数据集包含五个双子叶树种和三个单子叶棕榈树种，涵盖了广泛的木材密度范围。使用时间明确的湿涨/干降方法，通过重量法对每个段的含水量进行估计，并检查与介电常数的关系。木质组织密度对校准没有显著影响，但物种身份和温度显著影响传感器读数。在较大的温度差异下，温度伪影具有重要的定量影响，这可能导致以前研究中对每日和季节性水分含量动态的显著偏差。我们建立了第一个热带树木和棕榈树校准方程，该方程可很好地估算水分含量。值得注意的是，我们证明了灵敏度在物种间保持一致，从而可以创建简化的单斜率校准，用于准确、独立于物种的相对水分含量测量。我们的单斜率校准可作为评估木质组织相对水分含量的通用、独立于物种的标准校准，为量化树木和森林生态系统的干旱响应和胁迫提供了有价值的工具。
