Photo credit: www.sciencedaily.com
Forests represent crucial ecosystems that are continually changing; however, their monitoring often lags behind these transformations, according to Rytis Maskeliūnas, a professor at Kaunas University of Technology (KTU). With the accelerating impacts of climate change, pest infestations, and human activities, shifts in forest ecosystems can occur so rapidly that the signs often appear only after significant damage has been done.
To address this pressing issue, researchers at KTU are introducing advanced technological innovations, including a novel forest regeneration model and a sound analysis system designed to monitor forest conditions in real-time and identify environmental changes as they happen.
Recent environmental shifts pose significant challenges for forest management, particularly in sensitive areas like Lithuania. “Rising winter temperatures are adversely affecting forests. Factors such as this lead to weaker trees that are increasingly susceptible to pests,” Maskeliūnas noted.
Traditional methods of monitoring, including visual inspections and trap-based strategies, are now considered inadequate. “We cannot rely solely on manpower to keep an eye on forest ecosystems continuously,” he said.
To bolster forest protection measures, KTU researchers are deploying artificial intelligence (AI) alongside data analytics. These technologies not only facilitate real-time monitoring but also provide predictive insights that enable proactive responses to environmental alterations.
Impact on Spruce Trees
A crucial component of their strategy is the development of a forest regeneration dynamics model, which predicts the growth and developmental changes in forests over time. This model assesses various age groups of trees and calculates transition probabilities among these groups by examining growth and mortality rates.
Robertas Damaševičius, an expert in data analysis and head of the Real-Time Computer Center (RLKSC), highlights the model’s primary benefits, which include identifying suitable tree species that can thrive in various environments and guiding replanting initiatives to promote forest resilience against climate change. “By predicting areas susceptible to pest vulnerabilities, the model enhances preventive strategies, supports biodiversity, and optimizes financial resources for forest conservation,” emphasizes Maskeliūnas.
The model employs advanced statistical techniques, specifically a Markov chain model, which delineates how forests shift states based on current conditions and statistical growth and mortality probabilities. “This allows us to forecast survival rates for young trees and the risks they face from diseases or pests, leading to informed management decisions,” Maskeliūnas added, referring to his role at KTU’s Faculty of Informatics.
A multidirectional time series decomposition also distinguishes long-term growth trends from seasonal fluctuations or unexpected disturbances like droughts or pest invasions. This integrated approach offers a richer understanding of forest dynamics, thus improving forecasting accuracy amid varying environmental scenarios.
When applied to Lithuania’s forests, the model reveals precarious conditions for spruce trees, which are increasingly challenged by climate change effects, particularly longer dry spells during summer and milder winters. “Although young spruce grow quickly, their mortality rises significantly later in life due to diminished resilience against environmental stresses,” says Maskeliūnas.
Acoustic Monitoring of Ecosystem Health
In addition to the regeneration model, KTU researchers have created a sound analysis system capable of picking up natural forest sounds and identifying anomalies that could indicate disturbances in the ecosystem or human interference. This sound analysis marks a significant advancement in forest digitization, facilitating quicker responses to potential ecological threats.
The innovative model, developed by KTU RLKSC PhD student Ahmad Qurthobi, merges convolutional neural networks (CNN) with bi-directional long short-term memory (BiLSTM) techniques. “While CNN effectively captures sound features, understanding how these sounds evolve over time requires BiLSTM’s analysis of temporal sequences,” explains Maskeliūnas.
This hybrid approach not only enables precise detection of static sounds, such as bird calls, but also recognizes dynamic changes, like the sounds associated with deforestation or variations in wind strength.
“Birdsong can offer vital insights into species activity, diversity, and migratory habits. Abrupt changes in these sounds can indicate underlying ecological issues,” Maskeliūnas points out. Additionally, sounds generated by trees, such as those related to wind or broken branches, can reflect the stress levels experienced by the trees themselves.
Researchers suggest that the model could extend its utility to monitor a broader range of environmental factors, detecting animal sounds like wolf howls or deer calls, thus helping track animal movements and behaviors. Urban applications may include monitoring noise pollution levels.
Importantly, this solution represents more than theoretical advances; the sound analysis system seamlessly integrates with KTU’s smart forest Internet of Things (IoT) framework dubbed Forest 4.0. “The devices within Forest 4.0 act as vigilant guardians of our ecosystems, continuously analyzing the health of our forests and fostering a future where technology listens to the natural world,” adds IoT expert Prof. Egidijus Kazanavičius.
Many existing models used by foresters tend to oversimplify ecological complexities, often overlooking species competition, environmental responses, and climate variations. This has made predictive modeling of forest reactions challenging. “This is why embracing these advanced technologies is pivotal for the future of forest management,” concludes Maskeliūnas.
Source
www.sciencedaily.com