The Tracking and Frequency Measurement of the Sway of Leafless Deciduous Trees by Adaptive Tracking Window Based on MOSSE

The tree sway frequency is an important part of the dynamic properties of trees. In order to obtain trees sway frequency in wind, a method of tracking and measuring the sway frequency of leafless deciduous trees by adaptive tracking window based on MOSSE was proposed. Firstly, an adaptive tracking window is constructed for the observed target. Secondly, the tracking method based on Minimum Output Sum Of Squared Error Filter (MOSSE) is used to track tree sway. Thirdly, Fast Fourier transform was used to analyze the horizontal sway velocity of the target area on the trees, and the sway frequency was determined. Finally, comparing the power spectral densities (PSDs) of the x axis acceleration measured by the accelerometer and PSDs of the x axis velocity measured by the video, the fundamental sway frequency measured by the accelerometer is equal to the fundamental sway frequency measured by video. The results show that the video-based method can be used successfully for measuring the sway frequency of leafless deciduous trees.

Wind speed inference from environmental flow–structure interactions. Part 2. Leveraging unsteady kinematics

This work explores the relationship between wind speed and time-dependent structural motion response as a means of leveraging the rich information visible in flow–structure interactions for anemometry. We build on recent work by Cardona, Bouman and Dabiri (Flow, vol. 1, 2021, E4), which presented an approach using mean structural bending. Here, we present the amplitude of the dynamic structural sway as an alternative signal that can be used when mean bending is small or inconvenient to measure. A force balance relating the instantaneous loading and instantaneous deflection yields a relationship between the incident wind speed and the amplitude of structural sway. This physical model is applied to two field datasets comprising 13 trees of 4 different species exposed to ambient wind conditions. Model generalization to the diverse test structures is achieved through normalization with respect to a reference condition. The model agrees well with experimental measurements of the local wind speed, suggesting that tree sway amplitude can be used as an indirect measurement of mean wind speed, and is applicable to a broad variety of diverse trees.

Water stress detection using radar

Vegetation is a crucial part of the water and carbon cycle. Through photosynthesis carbon is assimilated for biomass production, and oxygen is released into the atmosphere. During this process, water is transpired through the stomata, and is redistributed in the plant. Transpired water is refilled by uptake of water from the root zone in the subsurface. Transpiration by vegetation accounts for most of the total evaporation from land on a global scale. In some ecosystems, such as tropical rainforests, transpiration even makes up more than 70% of total evaporation. Periods of low water availability, water stress, leads to irreversible damage to plants, and can eventually lead to plant death. To prevent this, various mechanisms are activated by the vegetation to survive. Transpiration is reduced as a result of vegetation water stress, which can affect the water and carbon cycle on local, regional, and even global scales. Additionally, water stress in crops is one of the major reasons for harvest losses, threatening food security. However, many effects of vegetation water stress on crops and tropical forests remains poorly understood.New satellite observations provide opportunities for better detection and understanding of vegetation water stress. Recent research suggests that radar remote sensing might yield valuable insights into vegetation water content. Radar backscatter is sensitive to vegetation because of direct backscatter from the canopy, and through two-way attenuation of the signal as it travels through the vegetation layer. The degree of interaction of radar waves with the vegetation is mainly a function of the vegetation dielectric constant, which is in turn primarily influenced by vegetation water content. Over the last years, various studies have reported links between anomalies in radar backscatter and vegetation water stress. This has led to the hypothesis that radar backscatter is sensitive to vegetation water stress. Additional field measurements of vegetation water content and dielectric constant, in combination with radar backscatter are necessary to test this hypothesis. This is what inspired this thesis. Based on a combination of field measurements using new sensors, models, and radar backscatter, this thesis focuses on understanding the effects of water stress on plant dynamics, identifying early signatures of vegetation water stress, and exploring the opportunities of early water stress detection using radar remote sensing. This thesis studies the effects of vegetation water stress across scales, from individual leaves to rainforests. A new method is presented that allows measurements of leaf dielectric properties on living plants. First, the method is tested on tomato plants in a controlled environment. By measuring tomato plants with and without water stress, it is demonstrated that there is a significant difference in the leaf dielectric properties of stressed and unstressed tomato plants. Second, this same method is used under field conditions. Using data sets of corn plants with and without water stress, it is demonstrated that water stress changes plant water content, resulting in significant changes of leaf dielectric properties. Using the field data from the stressed corn field, a modeling study was done to investigate the sensitivity of radar backscatter to water stress. Here, it is shown that total and leaf water content can change considerably during the day, leading to observable differences in radar backscatter.To study the effects of water stress in tropical rainforests, accelerometers were placed on trees in the Brazilian Amazon to measure tree sway. Tree sway depends on various tree properties, and this thesis demonstrates that the measured tree acceleration is sensitive to tree mass, intercepted rainfall, and tree-atmosphere interactions. Using five months of acceleration data from 19 trees, an effect of the transition from the wet to the dry season was found. This thesis hypothesizes that this was caused by water related changes in tree mass, or leaf fall in response to increased tree water deficit.Finally, coinciding field data on tree water content and tree water deficit, and radar backscatter, were used to demonstrate the sensitivity of radar backscatter to increased water stress. During the transition from wet to dry season, a strong drop was found in radar backscatter, which is explained by a rapid increase in measured tree water deficit.For years, the hypothesis that radar backscatter is sensitive to vegetation water stress has been discussed. Yet, a lack of observations withheld this hypothesis to be tested. This thesis uses field data of crops, and trees in tropical forests, and modeling approaches to finally demonstrate that vegetation water stress results in significant changes in plant water status, which lead to observable variations in radar backscatter.