TerraTrace: Temporal Signature Land Use Mapping System

TL;DR

TerraTrace leverages unique temporal NDVI signatures to improve land use classification, distinguishing farms from forests and tracking agricultural practices over time with a novel dataset and user-friendly platform.

Contribution

The paper introduces a new longitudinal NDVI dataset and a comprehensive platform that classifies land use based on temporal signatures, enhancing remote sensing capabilities.

Findings

NDVI curves are consistent with agricultural practices

NDVI signatures are unique to each crop

The platform effectively differentiates farms from forests

Abstract

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.