Lessons in New Measurement Technologies: From Instrumenting Trees to the Trans-African Hydrometeorological Observatory

Abstract

Ecohydrological monitoring technology is experiencing unprecedented expansion in capacity at ever lower costs. This allows for monitoring of systems at new scales spatially and allows for completely new strategies in observation. To represent the scale of this transformation, we present the framework for establishing a novel ecohydrological observation platform across the African continent (addressing the transformative opportunities made possible by wide-scale GPRS communication systems combined with solid-state sensing technology), as well as a strategy to leverage newly available accelerometer systems to monitor the dynamics of aboveground tree mass. The African observations are organized under the Trans-African Hydrometeorological Observatory (TAHMO.org), currently with about 500 installed stations across 20 African countries. Specific sensor technologies also open completely new approaches to measure key environmental variables. Aboveground mass of trees reflects, among other processes, the interception of rain, fog and snow, delivery of sap, addition of leaves, and loss of stem water. We demonstrate that passive sensing of tree acceleration due to wind can be used to evaluate change in mass caused by events such as leafing out or loss of leaves. We conclude by exploring the implications of ecohydrological observation at ever greater resolution and richness of variables.

Appendix: Additional Details on Methods

Accelerometer, Power Supply, Memory

We used the X16-1C 3-axis USB accelerometer with 15-bit resolution made by Gulf Coast Data Concepts, LLC. This is a low cost and low power device. The USB-Accelerometer acts as a USB mass storage device with a removable Micro SD memory card (here used with a 2 GB card).

Experiments

We performed the experiment relating tree water content to resonance on a sugar maple tree in a residential area in Cedar Rapids, Iowa. The tree dimensions are approximately 12 m high and 5 m wide. The accelerometer was glued in a waterproof enclosure constructed from PVC pipe fittings. The accelerometer was tightly strapped to the tree at 4.6 m high on the main stem. It was located immediately below the point where the main stem divided into multiple branches. We performed the oak tree experiment on a white oak tree in Corvallis, Oregon, with the same type of accelerometer mounted 5.5 m above the ground. The tree is approximately 8.2 m high and 5.3 m wide at the widest part of the tree. The accelerometer was encased in a waterproof bottle stuffed tightly with cotton and strapped to the tree at approximately one half the height of the tree. Acceleration was measured for windy periods before and after leaf-out.

Accelerometer Configuration and Data Collection

For the sugar maple experiment, we configured the accelerometer with a sampling rate of 1.6 kHz. While the commercial specifications for that accelerometer recommend usage at several hundred Hz, we contacted the manufacturer and they said it can be used up to 1.6 kHz. We measured three-axis acceleration set to the most sensitive acceleration range of 2 g recorded the first 10 min of each hour. All three axes were combined into a single acceleration vector for the analysis. A 10-minute sample window was chosen to conserve battery life and sensor memory; 528 h of acceleration data were collected, though not all data were usable. Data files created when the temperature dropped below freezing were discarded. Additionally, some data files exceeded size limits of the device or were missing lines of data due to write-errors of the accelerometer. These issues were largely resolved by the end of the experimental effort. For the oak tree experiment, the accelerometer sampling rate was set to 10 Hz. This sampling rate differed from the above-described experiment because the objective differed. In this case, we were looking for the resonant frequency before and after leaf-out rather than the diurnal change in resonant frequency, which otherwise would require more battery power and a higher sampling rate for shorter periods (on the hour). Data were collected during windy weather before leaf-out (or 178,200 data points; about 5 h) and after leaf-out (or 12,000 points; about 20 min). One of the three acceleration axes was found to yield the highest magnitude data both before and after leaf-out, and those data were used. While we used data from windy periods for the oak tree experiment, all data were used for the maple experiment regardless of the presence of wind.

TDR Probe

To verify that the accelerometer was able to capture the diurnal cycle in water content of the maple, we installed a Time Domain Reflectometer (model CS616, Campbell Scientific, Inc.) adjacent to the accelerometer. The volumetric water content of the tree was recorded every 15 min. The probe was inserted into holes that were drilled 30.5 cm into the main trunk on the tree. These measurements were averaged by the hour.

Data processing and spectral analysis to derive resonant frequency

For the maple tree experiment, in order to find the dominant resonant frequency induced by the wind, we used the Welch periodogram (50% overlap) and the Multiple Signal Classification (MUSIC) (Schmidt 1986) spectral estimation methods, as implemented in the pmusic function of the software MATLAB R2012a. The spectra showed similar peaks and overall congruence with spectral estimation provided by Fourier analysis that employs smooth windowing. For the oak tree experiment, we used a Fourier spectral analysis to generate the spectra for the leaf-out experiment in Fig. 6.2. The results provided by the Fourier analysis were corroborated when checked against MUSIC.

Derivation of mass from frequency

We assumed that the tree acts as a simple mass-spring oscillator and the relationship between mass and frequency is:

$$ m\propto \frac{E}{f^2} $$

where m is an estimate of mass of the tree, f is dominant resonant frequency of tree, and E is Young’s modulus. We assumed that we may neglect the effect of damping given the following. Consider the damping ratio as ζ = c/(2mω_n) and the damped natural frequency as ω_d = ω_n (1-ζ²)^1/2, where ω_n is the resonant frequency without damping in rad per second (OMEGA = 2 ∗ PI ∗ f), and c is the damping coefficient. As c increases, ζ increases. As ζ increases, damping will affect the damped resonant frequency ω_n (1-ζ²)^1/2. Thus, the damping value, c, must be very large to substantially affect the resonant frequency. Current estimates of c for trees are around 0.5 (Kooreman 2013), which would yield a very small effect on the resonant frequency compared to the effect of mass.

We derived E individually for each time point by employing eq. 4-3 on page 4–34 of Green et al. (1999), where E is a function of: volumetric water content (measured), the modulus of elasticity of green wood of sugar maple from Table 4-3a in Green et al. (1999), the modulus of elasticity of sugar maple at 12% water content from Table 4-3a in Green et al. (1999), and the point at which the moisture-strength line of green wood meets that of dry wood assumed to be 25 following Green et al. (1999), page 4–34. We discovered that accounting for variation in volumetric water content through time mattered little in the estimate of E and E/f ² (Fig. 6.6).

Fig. 6.6

Accelerometer and waterproof enclosure installed on sugar maple tree (top left), X-16C accelerometer with U.S. quarter shown for scale (bottom left), and full-sized maple tree employed in these experiments (right). The TDR probe is adjacent to the accelerometer and above it approximately 0.35 m, although not pictured