The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for enhancing forest monitoring workflows. Two central challenges in this domain are pixel-level change detection and semantic change interpretation, particularly for complex forest dynamics. While large language models (LLMs) are increasingly adopted for data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored, especially beyond urban environments. We introduce Forest-Chat, an LLM-driven agent designed for integrated forest change analysis. The proposed framework enables natural language querying and supports multiple RSICI tasks, including change detection, change captioning, object counting, deforestation percentage estimation, and change reasoning. Forest-Chat builds upon a multi-level change interpretation (MCI) vision-language backbone with LLM-based orchestration, and incorporates zero-shot change detection via a foundation change detection model together with an interactive point-prompt interface to support fine-grained user guidance. To facilitate adaptation and evaluation in forest environments, we introduce the Forest-Change dataset, comprising bi-temporal satellite imagery, pixel-level change masks, and multi-granularity semantic change captions generated through a combination of human annotation and rule-based methods. Experimental results demonstrate that Forest-Chat achieves strong performance on Forest-Change and on LEVIR-MCI-Trees, a tree-focused subset of LEVIR-MCI, for joint change detection and captioning, highlighting the potential of interactive, LLM-driven RSICI systems to improve accessibility, interpretability, and analytical efficiency in forest change analysis.

翻译：高分辨率卫星影像的日益普及与深度学习的进步相结合，为增强森林监测工作流程创造了新的机遇。该领域的两个核心挑战是像素级变化检测和语义变化解释，特别是针对复杂的森林动态。尽管大型语言模型（LLMs）越来越多地被用于数据探索，但其与视觉-语言模型（VLMs）在遥感图像变化解释（RSICI）中的集成仍未得到充分探索，尤其是在城市环境之外。我们提出了Forest-Chat，一个专为集成森林变化分析设计的LLM驱动智能体。该框架支持自然语言查询，并能够执行多种RSICI任务，包括变化检测、变化描述、目标计数、森林砍伐百分比估计以及变化推理。Forest-Chat构建于一个多级变化解释（MCI）视觉-语言骨干网络之上，采用基于LLM的编排机制，并集成了通过基础变化检测模型实现的零样本变化检测功能，以及一个交互式点提示界面，以支持细粒度的用户引导。为了促进在森林环境中的适配与评估，我们引入了Forest-Change数据集，该数据集包含双时相卫星影像、像素级变化掩码以及通过人工标注与基于规则方法相结合生成的多粒度语义变化描述。实验结果表明，Forest-Chat在Forest-Change数据集以及LEVIR-MCI-Trees（LEVIR-MCI的一个以树木为重点的子集）上，对于联合变化检测与描述任务均取得了强劲的性能，凸显了交互式、LLM驱动的RSICI系统在提升森林变化分析的可访问性、可解释性和分析效率方面的潜力。