Dr. David T. Milodowski Profile

Dr. David T. Milodowski

at Univ of Edinburgh

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Publications (3)

Proceedings Article | 12 October 2021 Presentation

Mapping fragmented woodlands in cloudy landscapes using Sentinel satellite data in NW Scotland

David Milodowski, Alex Merrington, Nicholas Ross, Dave McKay, Paula McGregor, Mathew Williams

Proceedings Volume 11888, 118880B (2021) https://doi.org/10.1117/12.2601009

KEYWORDS: Satellites, Associative arrays, Spatial resolution, Remote sensing, Backscatter, Vegetation, Satellite imaging, Radar, Modulation, LIDAR

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Expansion of forests and woodlands are key elements of global strategies to capture carbon from the atmosphere and therefore mitigate climate change. Fundamental to the successful planning and management of woodland conservation and restoration is the ability to map accurately the spatial extent and character of woodland ecosystems. Satellite remote sensing increasingly provides a powerful tool to facilitate these monitoring efforts at scale. However, woodland cover in Scotland is highly fragmented, with marked differences in structure between the remnant native woodlands and more common commercial plantation systems. In addition, the landscape is topographically complex and frequently shrouded in cloud. These factors pose challenges for satellite remote sensing. Therefore, the capacity to characterise fragmented woodland cover accurately in such landscapes remains uncertain. In this contribution, we assess the extent to which trees can be mapped at 10m spatial resolution using a combination of openly available Sentinel 1 C-band radar backscatter data and Sentinel 2 multi-spectral imagery in fragmented woodland landscapes in NW Scotland. We assess the predictive accuracy of the resultant classifiers using a spatially rigorous site-level cross-validation across six sites of varied woodland cover, and explore the role of canopy characteristics and topography in modulating the accuracy with which trees are detected. We find that the accuracy with which we can detect trees is strongly dependent on the stand structure. Trees are mapped more accurately in dense woodland (≥50% tree cover) than more open woodlands (≥20% tree cover), and especially compared to isolated trees. Accuracy also varied with topography, with highest accuracies in flat terrain and reduced accuracies on steeper slopes. These results demonstrate clear potential for integrating Sentinel satellite monitoring systems within woodland management frameworks, while highlighting the importance of reporting context-dependent accuracy statistics with remotely sensed maps of tree or forest cover.

Proceedings Article | 12 October 2021 Presentation + Paper

Optimising remotely sensed land cover classification for habitat mapping in complex Scottish upland landscapes

Alexander Merrington, David Milodowski, Mathew Williams

Proceedings Volume 11888, 118880G (2021) https://doi.org/10.1117/12.2600869

KEYWORDS: Image segmentation, Spatial resolution, Vegetation, Data modeling, Image classification, Remote sensing, Classification systems, Satellites, Ecosystems

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Habitat mapping is key to meeting land management and conservation objectives, from supporting estimation of natural capital in a landscape to monitoring habitat change over time. Current land management challenges, including conserving rare species and habitats, illustrate a growing need for spatially extensive and rapidly updatable biodiversity information; a need that can best be met through remotely sensed imagery combined with the remarkable data processing potential of machine learning. We assess the potential for optical satellite data to classify complex upland habitat types in the Scottish Highlands, following two UK national habitat classification frameworks. We explore how the differences in spatial and spectral resolution of satellite sensors affects the accuracy of derived habitat maps. Specifically, we contrast the performance of open-source Sentinel-2 data (20 m spatial resolution) against higher spatial-resolution data from the commercial Worldview-2 satellite (0.5 m resolution). We then compare the results produced with these satellite datasets against equivalent results obtained with high-resolution (25 cm) colour airborne photographs, to better inform users on the utility of available optical data before subsequent analysis, e.g. natural capital assessments, in comparable settings. We demonstrate that high-fidelity habitat maps (93% overall accuracy) can be produced using high resolution optical satellite data (Worldview-2). This level of accuracy exceeded that of maps derived from airborne surveys (~75%) and is deemed sufficient to be useful to ecologists in-situ. In contrast, the capacity of Sentinel-2 data was considerably more limited (~50% overall accuracy). This highlights the importance of spatial resolution for characterising habitat mosaics at scale, especially in settings such as upland Scotland where shifts in habitat and species composition of importance to land managers may occur at relatively fine length scales (<10m). Provided high spatial resolution optical data is available, the framework developed is scalable to a national scale, therefore, facilitating effective land management strategies

Proceedings Article | 18 October 2016 Paper

A webmapping platform for publishing, sharing, and managing EO-derived data for forest protection

Karin Viergever, Pedro Andrade, Manoel Cardoso, Miguel Castillo, Jean-François Exbrayat, Sarah Middlemiss, David Milodowski, Edward T. A. Mitchard, Jean Ometto, Veronique Morel, Richard Tipper, Mathew Williams

Proceedings Volume 10005, 1000511 (2016) https://doi.org/10.1117/12.2242030

KEYWORDS: Data modeling, Associative arrays, Ecosystems, Earth observing sensors, Carbon, Atmospheric modeling, Vegetation, Climatology, Systems modeling, Computer simulations

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