Summary Draft #2: Artificial Intelligence

Thesis statement:

The sustainability of forests in Finland can be transformed with the help of advanced technologies such as robots and artificial intelligence. Robots can autonomously maintain the forest while artificial intelligence can predict information regarding forests, thus allowing stakeholders to better take care of them.

Summary:

The article “How Artificial Intelligence, Robots Enhance Forest Sustainability in Finland”, McQueen (2019) reported about The Finnish Forest Centre’s implementation of advanced technologies to improve forest sustainability. 

As almost 75 percent of Finland is covered by forests, data can be collected using aerial and lidar imagery technologies. Robots will then use the analysed data from a geographic information system.

With robots and automation, forest management in private forests can be simplified, maximising carbon sequestration of trees, while filling up the employment gap in forestry jobs.

To balance economic growth and forest preservation, Finland adopted environmental regulations banning practises such as draining bogs and herbicide application. With this legislation, landowners are encouraged to be involved with their land while the centre gives recommendations on how to take care of them.

The centre also plans to create an online marketplace where forest owners can connect with forestry professionals for services, with collected data. The portal gathers data using laser scanning, aerial photography, sample plot measurements and site visits.

Lastly, artificial intelligence can assist in obtaining more accurate data. The combined data can produce precise measurements of the forests and better predict forest inventory. Additionally, this can also improve logistics and the supply chain.

Determining the science can push change in forestry legislation while discovering ways to maximise carbon sequestration of trees.

3 supporting articles:

1)

Liu et al. (2021). Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management. Journal of Cleaner Production, Volume 311, 2021, 127546, ISSN 0959-6526. Retrieved from https://doi.org/10.1016/j.jclepro.2021.127546

2)

Holloway, J., & Mengersen, K. (2018). Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sensing, 10(9), 1365. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs10091365

3)

Kocer, Ho, B., Zhu, X., Zheng, P., Farinha, A., Xiao, F., Stephens, B., Wiesemuller, F., Orr, L., & Kovac, M. (2021). Forest Drones for Environmental Sensing and Nature Conservation. 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO), 1–8. https://doi.org/10.1109/AIRPHARO52252.2021.9571033