AI Augmentation to Remote Sensing Imagery in Forestry Conservation & Restoration for Increased Responsive Capabilities

Responsive forestry management is critical to carbon management and climate change mitigation. The United Nation's Intergovernmental Panel on Climate Change (IPCC) Special Report stated that mitigation measures in forests from 2010-2019 have delivered approximately 80% of carbon mitigation from Land Use sectors. Advancements in Remote Sensing (RS) and Artificial Intelligence (AI) technologies hold significant potential to increase the necessary coverage and speed of forestry management; however, there is a delay in the current capability integration due to technical, cost, and human-factor constraints that prevent adoption and deployment. This research details the formation of the ART3MIS-AI (Augmented Real-Time 3D Mapping with Intelligent Sensing AI) system framework, an AI-augmentation framework design that minimizes these constraints. It is tailored for responsive deployment in forestry agencies and provides long-term robustness and extensibility for all four functions of forestry management. Our research analyzes AI capabilities in forestry RS from 75 research papers and assesses the potential integration pathways of these capabilities into the post-processing infrastructures of RS within forestry management for augmented-data transformation and critical metric extraction. The ART3MIS-AI system framework optimizes these observed capabilities for additional high levels of interpretability and extensibility given the technical and sensing platform constraints of forestry agencies. We generated a set of objectives and guidelines to facilitate the responsive deployment and integration of AI-augmentation frameworks within forestry agencies. The guidelines are based on established work in Human Machine Teaming, AI-Assurance, and prior research, in addition to consultation from our forestry professional partners in United States forestry agencies. The proposed ART3MIS-AI system framework is a detailed four-phase, automated and pipelined system implemented in Python and C++. It transforms RS imagery into "Smart" Point Clouds with augmented hyperspectral information, vegetation classification, and structural mission metrics to an individual tree scale (canopy height/width, timber volume/fuel loading, species identification, etc.). The ART3MIS-AI system framework accelerates structured tactical data delivery to forestry professionals and provides capabilities in tailored virtual planning. The system framework design, integration analysis, and additional guidelines within this paper provide the foundations for the large-scale and methodological integration of AI-augmentation into RS forestry conservation and restoration capabilities.