Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country
Long-term monitoring of Land Use/Land Cover (LULC) dynamics is fundamental for implementing effective policy and mitigating the effects of climate change. In the UK, the Scottish Flow Country represents an area of ~4000km2 spanning Caithness and Sutherland, encompassing 25% of global blanket bogs. T...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2021
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| Online Access: | https://eprints.nottingham.ac.uk/67186/ |
| _version_ | 1848800396088180736 |
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| author | Sutherland, Neil |
| author_facet | Sutherland, Neil |
| author_sort | Sutherland, Neil |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Long-term monitoring of Land Use/Land Cover (LULC) dynamics is fundamental for implementing effective policy and mitigating the effects of climate change. In the UK, the Scottish Flow Country represents an area of ~4000km2 spanning Caithness and Sutherland, encompassing 25% of global blanket bogs. There is a need to understand these peatland ecosystems in a broader context, appreciating their importance within evolving landscapes. Frequent advances in remote sensing (RS) have provided a means for large-scale LULC mapping to be executed with increasing temporal and spatial resolutions. In addition, cloud-computing services such as Google Earth Engine (GEE) have enabled the processing and analysis of geospatial data, allowing various stakeholders to address challenges with the assistance of “Geo Big Data”. This study looks to assess how the LULC mapping can take advantage of geospatial data, cloud-computing and machine learning for the monitoring of peatland ecosystems within a broader economic and environmental policy-driven context. The following objectives were defined: (1) determine the optimal combination of optical, radar and topographic data for LULC mapping of the Scottish Land Use Strategy; (2) assess their application in GEE; and (3) evaluate Random Forest for classification of LULC classes. Results suggest a combination of optical, radar and topographic features is necessary for comprehensive LULC mapping (LUSTOR OA=0.823 and KA=0.792), particularly when delineating ecologically, hydrologically and geomorphologically heterogenous landscapes. Finally, RF performance was evaluated, future improvements were outlined and the effectiveness of LULC mapping for policy assessments is discussed. |
| first_indexed | 2025-11-14T20:50:53Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-67186 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:50:53Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-671862021-12-08T04:41:07Z https://eprints.nottingham.ac.uk/67186/ Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country Sutherland, Neil Long-term monitoring of Land Use/Land Cover (LULC) dynamics is fundamental for implementing effective policy and mitigating the effects of climate change. In the UK, the Scottish Flow Country represents an area of ~4000km2 spanning Caithness and Sutherland, encompassing 25% of global blanket bogs. There is a need to understand these peatland ecosystems in a broader context, appreciating their importance within evolving landscapes. Frequent advances in remote sensing (RS) have provided a means for large-scale LULC mapping to be executed with increasing temporal and spatial resolutions. In addition, cloud-computing services such as Google Earth Engine (GEE) have enabled the processing and analysis of geospatial data, allowing various stakeholders to address challenges with the assistance of “Geo Big Data”. This study looks to assess how the LULC mapping can take advantage of geospatial data, cloud-computing and machine learning for the monitoring of peatland ecosystems within a broader economic and environmental policy-driven context. The following objectives were defined: (1) determine the optimal combination of optical, radar and topographic data for LULC mapping of the Scottish Land Use Strategy; (2) assess their application in GEE; and (3) evaluate Random Forest for classification of LULC classes. Results suggest a combination of optical, radar and topographic features is necessary for comprehensive LULC mapping (LUSTOR OA=0.823 and KA=0.792), particularly when delineating ecologically, hydrologically and geomorphologically heterogenous landscapes. Finally, RF performance was evaluated, future improvements were outlined and the effectiveness of LULC mapping for policy assessments is discussed. 2021-12-08 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/67186/1/MRes_20315184_20_08_2021.pdf Sutherland, Neil (2021) Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country. MRes thesis, University of Nottingham. Google Earth Engine (GEE) Random Forest (RF) Sentinel Peatland Land Use Land Cover Mapping (LULC) Pixel-Based Image Classification |
| spellingShingle | Google Earth Engine (GEE) Random Forest (RF) Sentinel Peatland Land Use Land Cover Mapping (LULC) Pixel-Based Image Classification Sutherland, Neil Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country |
| title | Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country |
| title_full | Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country |
| title_fullStr | Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country |
| title_full_unstemmed | Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country |
| title_short | Multi-temporal, multi-sensor land use/land cover mapping: Google Earth Engine and Random Forest for the classification of the Scottish flow country |
| title_sort | multi-temporal, multi-sensor land use/land cover mapping: google earth engine and random forest for the classification of the scottish flow country |
| topic | Google Earth Engine (GEE) Random Forest (RF) Sentinel Peatland Land Use Land Cover Mapping (LULC) Pixel-Based Image Classification |
| url | https://eprints.nottingham.ac.uk/67186/ |