EO-based Products in Support of Urban Heat Fluxes Estimation

Presently, there is a growing need for information suitable to effectively characterize the Urban Energy Budget (UEB) and, hence, to properly estimate the magnitude of the anthropogenic heat flux QF. Indeed, a precise knowledge of QF - whose implications for urban planners are still prone to large uncertainties - is fundamental for implementing effective strategies to improve thermal comfort and energy efficiency. To address this challenging issue, the Horizon 2020 URBANFLUXES project aims at developing a novel methodology for accurately estimating the different terms of the UEB based on the use of Earth Observation (EO) data and, hence, at reliably characterizing the QF spatiotemporal patterns and its implications on urban climate. In this paper, we aim at giving an overview of the EO-based products which have been identified as the most useful in the framework of the considered study. In particular, the suite which has been implemented so far in the first phase of the project includes biophysical parameters, morphology parameters as well as land-cover map

Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery

Summarization: Being able to quantify land cover changes due to mining and reclamation at a watershed scale is of critical importance in managing and assessing their potential impacts to the Earth system. In this study, a remote sensing-based methodology is proposed for quantifying the impact of surface mining activity and reclamation from a watershed to local scale. The method is based on a Support Vector Machines (SVMs) classifier combined with multi-temporal change detection of Landsat TM imagery. The performance of the technique was evaluated at selected open mining sites located in the island of Milos in Greece. Assessment of the mining impact in the studied areas was based on the confusion matrix statistics, supported by co-orbital QuickBird-2 very high spatial resolution imagery. Overall classification accuracy of the thematic land cover maps produced was reported over 90%. Our analysis showed expansion of mining activity throughout the whole 23-year study period, while the transition of mining areas to soil and vegetation was evident in varying rates. Our results evidenced the ability of the method under investigation in deriving highly and accurate land cover change maps, able to identify the mining areas as well as those in which excavation was replaced by natural vegetation. All in all, the proposed technique showed considerable promise towards the support of a sustainable environmental development and prudent resource management.Presented on: Geocarto Internationa