用植被时序特征区分农田与森林，提升土地利用监测精度
TerraTrace: Temporal Signature Land Use Mapping System
- 基于多年NDVI变化曲线识别作物种植周期和农业活动
- 构建2020-2023年加州500米分辨率超7000万点时序数据集
- 支持自然语言查询的可视化平台，适合环境研究者使用
理解土地利用随时间的变化对追踪气候变化事件（如毁林）至关重要。然而，现有基于卫星遥感的监测工具难以区分农田、果园与森林的植被类型。我们发现，基于植物光合作用的归一化差分植被指数（NDVI）具有独特的时序特征，能反映农业实践与季节周期。通过对20个农场10种不同作物的年度NDVI变化分析，结果表明：NDVI曲线与农业活动一致，作物间特征独特，在全球范围内具有一致性，且可有效区分农田与森林。基于此，我们构建了2020–2023年加州高分辨率（500米）纵向NDVI数据集，包含超过7000万条记录，并开发出TerraTrace平台——一个端到端的土地利用分类系统，支持通过大语言模型聊天机器人和图形界面进行交互式查询。
原文摘要 · Abstract (English)
Understanding land use over time is critical to tracking events related to climate change, like deforestation. However, satellite-based remote sensing tools which are used for monitoring struggle to differentiate vegetation types in farms and orchards from forests. We observe that metrics such as the Normalized Difference Vegetation Index (NDVI), based on plant photosynthesis, have unique temporal signatures that reflect agricultural practices and seasonal cycles. We analyze yearly NDVI changes on 20 farms for 10 unique crops. Initial results show that NDVI curves are coherent with agricultural practices, are unique to each crop, consistent globally, and can differentiate farms from forests. We develop a novel longitudinal NDVI dataset for the state of California from 2020-2023 with 500~m resolution and over 70 million points. We use this to develop the TerraTrace platform, an end-to-end analytic tool that classifies land use using NDVI signatures and allows users to query the system through an LLM chatbot and graphical interface.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。