Ciaran Robb
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View article: Digital mapping of peat thickness and carbon stock of global peatlands
Digital mapping of peat thickness and carbon stock of global peatlands Open
Peatlands, occupying merely 5% of the Earth's land surface, are an important carbon sink, storing up to double the carbon of the world's forests. The quantification of global peatlands carbon stock and their spatial distribution, however, …
View article: Soil Property, Carbon Stock and Peat Extent Mapping at 10 m Resolution in Scotland Using Digital Soil Mapping Techniques
Soil Property, Carbon Stock and Peat Extent Mapping at 10 m Resolution in Scotland Using Digital Soil Mapping Techniques Open
The estimation of soil carbon stocks is an important component in environmental planning, policy and land management, particularly in the context of climate change mitigation. The following work presents national‐scale soil property mappin…
View article: PEATGRIDS: Mapping thickness and carbon stock of global peatlands via digital soil mapping
PEATGRIDS: Mapping thickness and carbon stock of global peatlands via digital soil mapping Open
Peatlands, which only cover 3 to 5 percent of the global land area, can store up to twice the amount of carbon as the world’s forests. Although recognised for their significant role in the global carbon cycle, discovering the global extent…
View article: Supplementary material to "PEATGRIDS: Mapping thickness and carbon stock of global peatlands via digital soil mapping"
Supplementary material to "PEATGRIDS: Mapping thickness and carbon stock of global peatlands via digital soil mapping" Open
BD), and carbon content (CC) across the globe.
View article: A deep learning approach for high‐resolution mapping of Scottish peatland degradation
A deep learning approach for high‐resolution mapping of Scottish peatland degradation Open
Peat makes up approximately a quarter of Scotland's soil by area. Healthy, undisturbed, peatland habitats are critical to providing resilient biodiversity and habitat support, water management, and carbon sequestration. A high and stable w…
View article: Using Deep Learning and High-Resolution Imagery to Map the Condition of Scotland’s Peatland Resource.
Using Deep Learning and High-Resolution Imagery to Map the Condition of Scotland’s Peatland Resource. Open
Peat makes up roughly 28% of Scotland’s soil and is critical in many areas, including biodiversity and habitat support, water management, and carbon sequestration. The latter is only possible in healthy, undisturbed peatland habitats…
View article: Soil property, carbon stock and peat extent mapping at 10m resolution in Scotland using digital soil mapping techniques
Soil property, carbon stock and peat extent mapping at 10m resolution in Scotland using digital soil mapping techniques Open
The estimation of soil carbon stocks is an important component in environmental planning, policy and land management, particularly in the context of climate change mitigation. The following work presents national-scale soil property mappin…
View article: Land degradation neutrality: Testing the indicator in a temperate agricultural landscape
Land degradation neutrality: Testing the indicator in a temperate agricultural landscape Open
Land degradation directly affects around 25% of land globally, undermining progress on most of the UN Sustainable Development Goals (SDG), particularly target 15.3. To assess land degradation, SDG indicator 15.3.1 combines sub-indicators o…
View article: Peat Drainage Ditch Mapping from Aerial Imagery Using a Convolutional Neural Network
Peat Drainage Ditch Mapping from Aerial Imagery Using a Convolutional Neural Network Open
This study trialled a convolutional neural net (CNN)-based approach to mapping peat ditches from aerial imagery. Peat ditches were dug in the last century to improve peat moorland for agriculture and forestry at the expense of habitat heal…
View article: Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning
Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning Open
Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global proble…
View article: Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests
Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests Open
The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cov…
View article: Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture
Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture Open
We present an image processing method for accurately segmenting crop plots from Unmanned Aerial System imagery (UAS). The use of UAS for agricultural monitoring has increased significantly, emerging as a potentially cost effective alternat…