Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario
Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, w...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2024.2445836 |
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| author | Mitchell T. Bonney Yuhong He |
| author_facet | Mitchell T. Bonney Yuhong He |
| author_sort | Mitchell T. Bonney |
| collection | DOAJ |
| description | Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978–1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales. |
| format | Article |
| id | doaj-art-02656d4b2b06492f807eb83a6c243058 |
| institution | Kabale University |
| issn | 1712-7971 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj-art-02656d4b2b06492f807eb83a6c2430582025-02-05T12:46:13ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712025-12-0151110.1080/07038992.2024.24458362445836Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern OntarioMitchell T. Bonney0Yuhong He1Department of Geography, Geomatics and Environment, University of Toronto MississaugaDepartment of Geography, Geomatics and Environment, University of Toronto MississaugaCanadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978–1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales.http://dx.doi.org/10.1080/07038992.2024.2445836 |
| spellingShingle | Mitchell T. Bonney Yuhong He Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario Canadian Journal of Remote Sensing |
| title | Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario |
| title_full | Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario |
| title_fullStr | Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario |
| title_full_unstemmed | Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario |
| title_short | Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario |
| title_sort | using landsat time series to investigate nearly 50 years of tree canopy cover change across an urban rural landscape in southern ontario |
| url | http://dx.doi.org/10.1080/07038992.2024.2445836 |
| work_keys_str_mv | AT mitchelltbonney usinglandsattimeseriestoinvestigatenearly50yearsoftreecanopycoverchangeacrossanurbanrurallandscapeinsouthernontario AT yuhonghe usinglandsattimeseriestoinvestigatenearly50yearsoftreecanopycoverchangeacrossanurbanrurallandscapeinsouthernontario |