Technological advancements in forest monitoring have led to a new era of forest digitization, revolutionizing the collection and analysis of digital information from various sensors and platforms. Concurrently, direct measurement sensors on tree stems, coupled with IoT technology and data transmission through technology such as LoRa, enable real-time monitoring at individual tree scales. Direct measurement sensors are designed to target specific ecophysiological processes related to tree function and growth. They include sap flow sensors, PAR sensors, Spectrometers and Dendrometers. Here we present a dendrometer to monitor radial stem increment through a linear magnetic encoder based system and operate on the multi sensor IoT platform the Treetalker ®. This study utilizes a commercially available linear magnetic encoder chip from AMS OSRAM GmbH, operating on the Hall effect principle without physical contact. It offers a high resolution, low power input method ideal for long-term monitoring. Design features of the dendrometer include a linear arm, sensor housing, rail and chip braces and magnetic tape. Calibration followed a linear step movement of 0.1mm over a 2mm increment representing 1 dipole imbedded in the magnetic tape. We used a stepper motor to control linear movement with a 100 measurements taken at each 0.1mm increment step. This procedure was repeated 4 times. Results from regression analysis demonstrated a strong correlation between the system and linear movement, R2 0.99 and RMSE 0.05 mm respectively. In addition, we performed a temperature sensitivity analysis under a controlled temperature increment regime from 0-40 C°. Observations suggest minor impact from temperature on the sensor across the selected temperature change regime. To assess sensor performance we installed 4 devices on specimens of Picea abies and Abies alba and compared sensor response to other installed dendrometers. Seasonal patterns of stem radial growth measured by the magnetic encoder dendrometer and D1 dendrometers for example were coherent. Our research demonstrates a high resolution low power input IoT driven dendrometer capable of capturing intra and inter annual trends in radial increment growth which aims to increase the granular nature of in situ ground observations at individual tree level. Such devices may be useful for process based modeling applications for carbon allocation in trees in addition to direct measurement practices such as biomass estimation going forward. Future versions of the device should focus on increasing the measurement precision through more stable design features and a modification of the system to facilitate stem changes in tree stem circumference.

Yates, J.; Belelli Marchesini, L.; Renzi, F.; Gianelle, D.; Valentini, R. (2024). An autonomous IoT operated magnetic driven dendrometer: advancing direct measurement sensors for tree growth monitoring. In: XIV Congresso Nazionale SISEF: Foreste per il futuro: nuove sfide per la gestione multifunzionale e la ricerca, Padova, Italy, 9-12 settembre 2024. Viterbo: SISEF – Società Italiana di Selvicoltura ed Ecologia Forestale: 53. handle: https://hdl.handle.net/10449/88282

An autonomous IoT operated magnetic driven dendrometer: advancing direct measurement sensors for tree growth monitoring

Belelli Marchesini, L.;Gianelle, D.;
2024-01-01

Abstract

Technological advancements in forest monitoring have led to a new era of forest digitization, revolutionizing the collection and analysis of digital information from various sensors and platforms. Concurrently, direct measurement sensors on tree stems, coupled with IoT technology and data transmission through technology such as LoRa, enable real-time monitoring at individual tree scales. Direct measurement sensors are designed to target specific ecophysiological processes related to tree function and growth. They include sap flow sensors, PAR sensors, Spectrometers and Dendrometers. Here we present a dendrometer to monitor radial stem increment through a linear magnetic encoder based system and operate on the multi sensor IoT platform the Treetalker ®. This study utilizes a commercially available linear magnetic encoder chip from AMS OSRAM GmbH, operating on the Hall effect principle without physical contact. It offers a high resolution, low power input method ideal for long-term monitoring. Design features of the dendrometer include a linear arm, sensor housing, rail and chip braces and magnetic tape. Calibration followed a linear step movement of 0.1mm over a 2mm increment representing 1 dipole imbedded in the magnetic tape. We used a stepper motor to control linear movement with a 100 measurements taken at each 0.1mm increment step. This procedure was repeated 4 times. Results from regression analysis demonstrated a strong correlation between the system and linear movement, R2 0.99 and RMSE 0.05 mm respectively. In addition, we performed a temperature sensitivity analysis under a controlled temperature increment regime from 0-40 C°. Observations suggest minor impact from temperature on the sensor across the selected temperature change regime. To assess sensor performance we installed 4 devices on specimens of Picea abies and Abies alba and compared sensor response to other installed dendrometers. Seasonal patterns of stem radial growth measured by the magnetic encoder dendrometer and D1 dendrometers for example were coherent. Our research demonstrates a high resolution low power input IoT driven dendrometer capable of capturing intra and inter annual trends in radial increment growth which aims to increase the granular nature of in situ ground observations at individual tree level. Such devices may be useful for process based modeling applications for carbon allocation in trees in addition to direct measurement practices such as biomass estimation going forward. Future versions of the device should focus on increasing the measurement precision through more stable design features and a modification of the system to facilitate stem changes in tree stem circumference.
Forest digitization
IoT technology
Climate change monitoring
Radial stem increment
Dendrometer
2024
Yates, J.; Belelli Marchesini, L.; Renzi, F.; Gianelle, D.; Valentini, R. (2024). An autonomous IoT operated magnetic driven dendrometer: advancing direct measurement sensors for tree growth monitoring. In: XIV Congresso Nazionale SISEF: Foreste per il futuro: nuove sfide per la gestione multifunzionale e la ricerca, Padova, Italy, 9-12 settembre 2024. Viterbo: SISEF – Società Italiana di Selvicoltura ed Ecologia Forestale: 53. handle: https://hdl.handle.net/10449/88282
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