Ms. Claire Horton Profile

Claire Horton

at Welsh Assembly Government

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Author

Publications (2)

Proceedings Article | 20 September 2020 Presentation + Paper

Living Wales: automatic and routine environmental monitoring using multi-source Earth observation data

Carole Planque, Suvarna Punalekar, Richard Lucas, Sébastien Chognard, Chris Owers, Daniel Clewley, Peter Bunting, Helena Sykes, Claire Horton

Proceedings Volume 11534, 115340C (2020) https://doi.org/10.1117/12.2573763

KEYWORDS: Environmental monitoring, Classification systems, Ecosystems, Sensors, Agriculture, Taxonomy, Earth sciences, Environmental sensing, Radar, LIDAR

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Increasing awareness of the adverse impacts of human-induced environmental change have prompted the need for more sustainable development and proactive planetary restoration. An essential component is to equip stakeholders with timely and reliable data that provide informed understanding of landscape change across varying spatial and temporal scales. The Earth Observation Data for Ecosystem Monitoring (EODESM), which is based on the Food and Agriculture Organisation’s (FAO) Land Cover Classification System (LCCS), is an open source system allowing routine and automatic generation of land cover and change maps from Earth Observation (EO) data. It is currently being developed and implemented at national scales through the Living Wales project (https://wales.livingearth.online) using multi-source freely available EO data, including those provided by the Sentinel-1 and Sentinel-2 sensors. Airborne LiDAR, Open Street Map, Copernicus High Resolution Layers, and National Forest Inventory data have also been integrated. These EO data are transformed into Environmental Descriptors (EDs) which are then combined in EODESM to generate land cover maps. From those maps, changes are detected in the landscape using the evidence-based change module. The system allowed generation of nationally consistent land cover maps for Wales (UK) at 10 m spatial resolution. Using the evidence-based change module, 2017-2019 multi-year forest clearcutting as well as daily changes in water extent associated with flooding were identified and described. As the system is independent of temporal and spatial scale, EODESM has the capacity to classify diverse landscape changes across multiple time frames (e.g., localised episodic events or decadal processes) and provides robust, consistent and interpretable classifications. Furthermore, additional EDs can be ingested, which provides a logical and simple approach to tailoring user requirements. EODESM shows considerable promise for directing short to long-term restoration and enhancing natural resource management in support of greater ecosystem resilience.

Proceedings Article | 20 September 2020 Presentation

Mapping dominant genus/species types in natural and seminatural landscapes across Wales through application of Sentinel-2 time-series data

Suvarna Punalekar, Carole Planque, Patryk Poslajko, Richard Lucas, Sebastien Chognard, Christopher Owers, Pete Bunting, Daniel Clewley, Claire Horton

Proceedings Volume 11528, 1152805 (2020) https://doi.org/10.1117/12.2574005

KEYWORDS: Associative arrays, Satellites, Taxonomy, Optical resolution, Radar, Standards development, Satellite imaging, Earth observing sensors, Agriculture, Classification systems

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The unprecedented loss of habitats and biodiversity in the UK has been particularly evident during the past few decades and strongly emphasises the need for monitoring habitat states and dynamics on a more regular and consistent basis. Timely availability of free, high resolution Sentinel optical and radar data offers an opportunity to develop capacity for monitoring and informing natural resource management and interventions. This study demonstrates a workflow to utilise the full potential of Sentinel-2 datasets through a systematic, scalable approach for routinely mapping dominant vegetation genus/species in the natural and semi-natural habitats across the landscape. The present study is part of the Living Wales project which is developing a landcover monitoring framework for Wales with operational capabilities. The project utilizes the Food and Agriculture Organisation Land Cover Classification System (LCCS) as the base taxonomy. The LCCS provides capability to integrate Essential Environmental Descriptors (EEDs) of the environment retrieved from Earth observation (EO) data to generate the land cover maps. A precursor classification step first involved identification and separation of natural/semi-natural vegetated and cultivated areas across Wales into woody (trees and shrubs) and herbaceous lifeforms. Within the natural/semi-natural lifeform category, spectrally distinct genus or species were separated first, followed by further segregation of groups with similar spectral response. A machine learning algorithm was optimized by identifying appropriate spectral variables and time windows for the stepwise separations. This study generates most recent dominant species/genus map for Wales with validation and pixelwise probability statistics. The map represents a composite period of four years (2015-19), with capacity for routinely update (e.g., for 2016-20), thereby ensuring temporal consistency. The method demonstrates a systematic EO-based approach for genus/species mapping by efficiently utilising dense time-series information from ~10 m resolution Sentinel-2 data.

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