A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium

Abstract

Urban planning is a challenge, especially when it comes to limiting land take. In former industrial regions such as Wallonia, the presence of a large number of brownfields, here called “redevelopment sites”, opens up new opportunities for sustainable urban planning through their revalorization. The Walloon authorities are currently managing an inventory of more than 2200 sites, which requires a significant amount of time and resources to update. In this context, the Sentinel satellites and the Terrascope platform, the Sentinel Collaborative Ground Segment for Belgium, enabled us to deploy SARSAR, an Earth observation service used for the automated monitoring of redevelopment sites that generates regular and automatic change reports that are directly usable by the Walloon authorities. In this paper, we present the methodological aspects and implementation details of the service, which combines two well-known and robust methods: the Pruned Exact Linear Time method for change point detection and threshold-based classification, which assigns the detected changes to three different classes (vegetation, building and soil). The overall accuracy of the system is in the range of 70–90%, depending on the different methods and classes considered. Some remarks on the advantages and possible drawbacks of this approach are also provided.

Brownfield-related studies in the context of climate change: A comprehensive review and future prospects

“…Neural network models, on the other hand, exhibit attributes of “high data processing efficiency + accurate trend prediction + broad coverage of influencing factors”. For instance, in the temporal dimension, Sophie [ 98 ] utilized Earth observation service data from Sentinel-1 and Sentinel-2 time series to monitor seasonal variations in newly developed brownfield site environments. Navrátil [ [99] , [100] ] constructed an agricultural brownfield identification database based on land use image data for analyzing agricultural brownfield changes over time.…”

Classification of Potential Wetlands using the Random Forest in Google Earth Engine in Geomorphological Units - Rio Grande do Sul, Brazil

“…Sentinel-1 imagery was used to assist in the identification of PW due to its sensitivity to herbaceous and wetland vegetation features, as well as the change from exposed soil to vegetation, wetlands, and water due to backscatter (VALENTI et al, 2020;JAMALI et al, 2021). In addition, Sentinel-1 images detect vegetation features on cloudy days or nights (PETIT et al, 2022).…”