Customise Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.

No cookies to display.

EcoSentience

Pioneering AI for forest conservation

Introducing EcoSentience: A Collaborative Journey With AI To Revolutionise Woodland Conservation

We’re excited to introduce EcoSentience, a unique collaboration between myself, a woodland owner, and GPT-4, a cutting-edge AI language model. Together, we aim to change woodland management by combining AI technology with traditional conservation methods. Our goal is to watch over, preserve, and restore our woodlands for future generations

Our partnership has inspired many great ideas for improving woodland health and sustainability. We’re using AI-driven monitoring systems, data analysis, trail cameras, and digital connectivity. These innovative approaches help us manage woodlands effectively. As a result, we can protect our woodlands and help them thrive.

We invite you to join our journey and become part of the EcoSentience community. We welcome researchers, woodland management experts, and local community members who care about the environment. We’re especially eager to involve other woodland owners in our mission. Visit our discussion forum to share ideas, insights, and collaborate with like-minded people. Together, we can make a lasting impact on our woodlands and ensure their health and vitality for years to come.

In a recent study titled “Unmanned Aerial Vehicle and Artificial Intelligence Revolutionizing Efficient and Precision Sustainable Forest Management” by Tiedong Liu et al., the authors explored the use of Unmanned Aerial Vehicle-Structure from Motion (UAV-SfM) technology and Convolutional Neural Network (CNN) method for evaluating tropical forest biomass distribution and biodiversity in water conservation districts. The study addressed the challenges of monitoring tropical forests, such as high forest density, complex forest structure, and difficult access. By combining 3D point cloud reconstruction using UAV-SfM technology and forest type classification with CNN, they were able to accurately assess the forest biomass and biodiversity in these areas. This innovative integration of UAV and artificial intelligence technology could potentially solve practical problems faced by sustainable forest management, offering an important foundation for assessing the sustainability of forest ecosystems. This research could be particularly relevant to our work through EcoSentience, as it demonstrates the potential for advanced technology to enhance forest monitoring and management.

Liu, T., Sun, Y., Wang, C., Zhang, Y., Qiu, Z., Gong, W., Lei, S., Tong, X., & Duan, X. (2021). Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management. Journal of Cleaner Production, 313, 127546. https://doi.org/10.1016/j.jclepro.2021.127546

Leave a Comment

Your email address will not be published. Required fields are marked *