A Latent Analysis of A Super-Resolved Sentinel-2 Data Cube For Green Urban Infrastructure Health Monitoring

Abstract

In the context of accelerated urbanization, metropolitan green infrastructure is considered a strategic approach to ensure healthy and sustainable living environments. Earth Observation (EO) offers the right means for large scale and long term assessment and monitoring of such green areas and the entire urban environment. The methodology presented in this paper leverages one of the most common satellite missions for vegetation assessment, the Sentinel-2 mission, applies super-resolutions techniques to increase the image spatial resolution and quantifies the spectral radiation reflected by the ground in order to map the Earth’s biophysical properties. By considering multiple acquisitions over the same area, time series of spectral indices are generated and processed using LDA, a generative model well known for hierarchical latent information extraction in both text and image analysis. The resulting temporal signature of each topic is further correlated with the evidence of environmental indicators to underline the vegetation vulnerability and specificity of the species. A use case centered for the Bucharest city in Romania, was included.