TerraTrace: Temporal Signature Land Use Mapping System
This addresses the challenge of accurate land use mapping for climate change tracking, such as deforestation monitoring, by providing a tool to distinguish farms from forests, though it is incremental as it builds on existing NDVI methods.
The paper tackled the problem of differentiating agricultural land from forests in satellite-based land use monitoring by analyzing temporal NDVI signatures of crops, showing that these signatures are unique per crop and consistent globally, enabling farm-forest differentiation.
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.