Mapping and quantifying land cover dynamics using dense remote sensing time series with the user-friendly pyNITA software

The study of land change within social-ecological systems (SES) is of great interest and increasingly makes use of remote sensing (RS) imagery to scale inferences up through space and time. However, spatial analysis using dense time series of RS data poses technical hurdles for non-expert users. To broaden the community of SES researchers using RS, we present a simple tool for mapping land change at local to regional scales. The Python implementation of the Noise Insensitive Trajectory Algorithm (pyNITA) is accessed through a streamlined graphical user interface and requires minimal user parameterization to generate long-term trends and identify key dates of significant change (i.e., disturbance events) based on time series of Landsat or Sentinel-2 data. In this paper, we introduce the pyNITA software, explain the underlying algorithm, analyze key parameter sensitivities, and summarize methods and results from three SES case studies of land change.