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This paper introduces a novel framework, Tree-GPT, which incorporates LargeLanguage Models (LLMs) into the forestry remote sensing data workflow, therebyenhancing the efficiency of data analysis. Currently, LLMs are unable toextract or comprehend information from images and may generate inaccurate textdue to a lack of domain knowledge, limiting their use in forestry dataanalysis. To address this issue, we propose a modular LLM expert system,Tree-GPT, that integrates image understanding modules, domain knowledge bases,and toolchains. This empowers LLMs with the ability to comprehend images,acquire accurate knowledge, generate code, and perform data analysis in a localenvironment. Specifically, the image understanding module extracts structuredinformation from forest remote sensing images by utilizing automatic orinteractive generation of prompts to guide the Segment Anything Model (SAM) ingenerating and selecting optimal tree segmentation results. The system thencalculates tree structural parameters based on these results and stores them ina database. Upon receiving a specific natural language instruction, the LLMgenerates code based on a thought chain to accomplish the analysis task. Thecode is then executed by an LLM agent in a local environment and . Forecological parameter calculations, the system retrieves the correspondingknowledge from the knowledge base and inputs it into the LLM to guide thegeneration of accurate code. We tested this system on several tasks, includingSearch, Visualization, and Machine Learning Analysis. The prototype systemperformed well, demonstrating the potential for dynamic usage of LLMs inforestry research and environmental sciences.

Tree-GPT: Modular Large Language Model Expert System for Forest Remote Sensing Image Understanding and Interactive Analysis