Georeferencing accuracy assessment of high-resolution satellite images using figure condition method

In the case of sensor-independent georeferencing, accuracy of the used model is commonly assessed by misfits separately obtained from ground control points and independent check points. However, applying only this approach has some disadvantages. This paper proposes using the figure condition method to support the common approach. Applying the figure condition process, a more rigorous analysis of accuracy for the used models can be conducted, and one can decide whether the used model is proper or not. In this contribution, a case study is carried out using affine and extended affine models for high-resolution IKONOS Geo, OrbView-3 Basic, and QuickBird OrthoReady Standard images. The results obtained are subjected to the analysis of figure condition. © 2006 IEEE

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Spatiotemporal Dynamics in Regulating Ecosystem Services of Urban Green-blue Infrastructure

Synoptic citywide maps of green-blue infrastructure (GBI) and associated regulating ecosystem services (RES) can indicate priority locations for GBI investment to build urban resilience to future climate stressors. However, current approaches are typically static in view, and may fail to consider change in services over different temporal cycles. Planned GBI investment may not offer optimal RES solutions when considering seasonal fluctuations in climate and ecological conditions, or environmental change due to future urban development. In response, this thesis aimed to develop a range of spatiotemporal analysis methods to improve the usefulness of current RES map information. The city of Manchester, UK, is the study area, as the environmental impacts of considerable urban development, since the turn of the century, is currently poorly understood by local planning stakeholders. Overall, findings indicate that seasonal variation in RES is a limited concern for the city. Incorporation of seasonally adjusted indicators for temperature regulation and stormwater storage RES, against typical assumptions of static year-round RES functions, result in less than 5% discrepancy in identified RES deprived areas. In contrast, environmental change is more evident over an inter-year period (2000 – 2017). The city lost approximately 11% of existing GBI, although net GBI increases were recorded in a minority of areas. GBI declines were recorded for most land uses, with losses of between 5.7% and 28.3% a concern for residential land uses where residents live and consume RES. In response, scenario analysis indicates that concerted land use targeted GBI conservation (i.e. street tree and residential gardens) policies are the minimum action required to prevent significant future declines in GBI and RES. Overall, the thesis provides a multi-stage analysis workflow to investigate various GBI and RES management scenarios within the context of planned and unplanned urban development. GBI loss is a common urban trend across the globe, whilst cyclical variation in RES may prove more important for cities with greater seasonal extremes in climate conditions. The ecological modelling, map classification and change analysis methods here work with accessible research data and are therefore theoretically adaptable to a range of urban conditions. Indicators are mapped at scales (100m grid) suitable to investigate GBI retrofits of existing built infrastructure and can accommodate different data assumptions regarding proxy model parameterisation