Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery

The increasing availability and diversity of remotely sensed imagery, particularly high-resolution imagery has seen a rapid evolution in the use of this technology for generating detailed land cover mapping for protected areas. With a growing selection of different image products available, the choi...

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Bibliographic Details
Main Author: Green, Peter
Other Authors: Watmough, Gary
Format: Master Thesis
Language:English
Published: The University of Edinburgh 2019
Subjects:
Online Access:https://hdl.handle.net/1842/36251
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spelling ftunivedinburgh:oai:era.ed.ac.uk:1842/36251 2023-07-30T04:02:59+02:00 Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery Green, Peter Watmough, Gary 2019-11-26 application/pdf https://hdl.handle.net/1842/36251 en eng The University of Edinburgh https://hdl.handle.net/1842/36251 Remote Sensing Sentinel-2 WorldView-2 Image Classification Landcover Mapping Geographic Object Based Image Analysis Random Forest Protected Areas 100 Wild Islands Conservation Area Thesis or Dissertation Masters MSc Master of Science 2019 ftunivedinburgh 2023-07-09T20:32:57Z The increasing availability and diversity of remotely sensed imagery, particularly high-resolution imagery has seen a rapid evolution in the use of this technology for generating detailed land cover mapping for protected areas. With a growing selection of different image products available, the choice of imagery will impact the level of detail, accuracy and utility of landcover mapping. Taking into consideration spatial, spectral and temporal resolution, geographical scale, target features, and cost, the choice is often a compromise between these characteristics. This study compares the performance and effectiveness of 10-metre spatial resolution Sentinel-2 and 2-metre spatial resolution WorldView-2 imagery for the identification and mapping of landcover within the 100 Wild Islands Conservation Area, Canada. A reproducible two-phase hybrid image classification workflow incorporating the strengths of object-based image analysis and a random forest machine learning algorithm was used to generate an eleven-class landcover map. Statistical assessment of the results shows that overall WorldView-2 was 16.2% higher in terms of classification accuracy than Sentinel-2. A visual assessment shows that WorldView-2 more accurately and effectively identified the boundaries of landcover patches, particularly small, narrow patches important for understanding habitat distribution and connectivity, as well as patch level metrics. In the case of rockweed (Fucus vesiculosus), a commercially harvested species important in the life cycle of common eider, WorldView-2 more effectively discriminated the size and distribution of patches, providing information critical for informed and effective management of this habitat. While the results of this study show that WorldView-2 imagery is more suitable for landcover mapping within the 100 Wild Islands Conservation Area, for larger protected areas, high-resolution imagery may be impractical when considering cost, availability and processing time. However, a strategic combination of imagery ... Master Thesis Common Eider Edinburgh Research Archive (ERA - University of Edinburgh) Canada
institution Open Polar
collection Edinburgh Research Archive (ERA - University of Edinburgh)
op_collection_id ftunivedinburgh
language English
topic Remote Sensing
Sentinel-2
WorldView-2
Image Classification
Landcover Mapping
Geographic Object Based Image Analysis
Random Forest
Protected Areas
100 Wild Islands Conservation Area
spellingShingle Remote Sensing
Sentinel-2
WorldView-2
Image Classification
Landcover Mapping
Geographic Object Based Image Analysis
Random Forest
Protected Areas
100 Wild Islands Conservation Area
Green, Peter
Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery
topic_facet Remote Sensing
Sentinel-2
WorldView-2
Image Classification
Landcover Mapping
Geographic Object Based Image Analysis
Random Forest
Protected Areas
100 Wild Islands Conservation Area
description The increasing availability and diversity of remotely sensed imagery, particularly high-resolution imagery has seen a rapid evolution in the use of this technology for generating detailed land cover mapping for protected areas. With a growing selection of different image products available, the choice of imagery will impact the level of detail, accuracy and utility of landcover mapping. Taking into consideration spatial, spectral and temporal resolution, geographical scale, target features, and cost, the choice is often a compromise between these characteristics. This study compares the performance and effectiveness of 10-metre spatial resolution Sentinel-2 and 2-metre spatial resolution WorldView-2 imagery for the identification and mapping of landcover within the 100 Wild Islands Conservation Area, Canada. A reproducible two-phase hybrid image classification workflow incorporating the strengths of object-based image analysis and a random forest machine learning algorithm was used to generate an eleven-class landcover map. Statistical assessment of the results shows that overall WorldView-2 was 16.2% higher in terms of classification accuracy than Sentinel-2. A visual assessment shows that WorldView-2 more accurately and effectively identified the boundaries of landcover patches, particularly small, narrow patches important for understanding habitat distribution and connectivity, as well as patch level metrics. In the case of rockweed (Fucus vesiculosus), a commercially harvested species important in the life cycle of common eider, WorldView-2 more effectively discriminated the size and distribution of patches, providing information critical for informed and effective management of this habitat. While the results of this study show that WorldView-2 imagery is more suitable for landcover mapping within the 100 Wild Islands Conservation Area, for larger protected areas, high-resolution imagery may be impractical when considering cost, availability and processing time. However, a strategic combination of imagery ...
author2 Watmough, Gary
format Master Thesis
author Green, Peter
author_facet Green, Peter
author_sort Green, Peter
title Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery
title_short Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery
title_full Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery
title_fullStr Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery
title_full_unstemmed Mapping landcover within the 100 Wild Islands Conservation Area, Nova Scotia using a hybrid image classification approach: A comparison of Sentinel-2 and WorldView-2 satellite imagery
title_sort mapping landcover within the 100 wild islands conservation area, nova scotia using a hybrid image classification approach: a comparison of sentinel-2 and worldview-2 satellite imagery
publisher The University of Edinburgh
publishDate 2019
url https://hdl.handle.net/1842/36251
geographic Canada
geographic_facet Canada
genre Common Eider
genre_facet Common Eider
op_relation https://hdl.handle.net/1842/36251
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