Assessment of Hyperspectral and SAR Remote Sensing for Solid Waste Landfill Management

Globally, waste management is one of the most critical environmental concerns that modern society is facing. Controlled disposal to land (landfill) is currently important, and due to the potentially harmful effects of gas emissions and leachate land contamination, the monitoring of a landfill is inherent in all phases of the site’s life cycle. Data from satellite platforms can provide key support to a number of landfill management and monitoring practices, potentially reducing operational costs and hazards, and meeting the challenges of the future waste management agenda. The few previous studies performed show the value of EO data for mapping landcover around landfills and monitoring vegetation health. However, these were largely qualitative studies limited to single sensor types. The review of these studies highlights three key aspects. Firstly, with regard to leachate and gas monitoring, space-borne remote sensing has not proved to be a valid tool for an accurate quantitative analysis, it can only support ground remediation efforts based on the expertise of the visual interpreter and the knowledge of the landfill operator. Secondly, the additional research that focuses on landfill detection concentrates only on the images’ data dimension (spatial and spectral), paying less attention to the sensor-independent bioand geo-physical variables and the modelling of remote sensing physical principles for both active and restored landfill sites. These studies show some ambiguity in their results and additional aerial images or ground truth visits are always required to support the results. Thirdly, none of the studies explores the potential of Synthetic Aperture Radar (SAR) remote sensing and SAR interferometric processing to achieve a more robust automatic detection algorithm and extract additional information and knowledge for landfill management. Based on our previous work with ERS radar images and SAR interferometry, expertise in the waste management sector, and practical knowledge of landfill management practices, we propose to evaluate the use of hyperspectral and radar images for landfill monitoring and management. CHRIS offers hyperspectral data of commensurate spatial resolution with Envisat radar images and thus appears ideally suited for studies using multi-sensor data fusion. The goal of the research is to identify practical ways in which EO data can support landfill management and monitoring, providing quantitative data where possible. Our objectives (based on fieldwork in UK landfills) are (1) to develop robust methods of detecting and mapping landfill sites, (2) to correlate EO data with on-site operational procedures, and (3) to investigate data fusion techniques based on our findings with the separate sensors. Dissemination of the findings will be through scientific journals, professional waste management publications and workshops. It is expected that the research will help the development of techniques which could be applied to monitor waste disposal to land beyond the UK scope of this study, including global monitoring.