Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series
The advent of CubeSat constellations is revolutionizing the ability to observe Earth systems through time. The improved spatial and temporal resolutions from these data could assist in tracking forest harvesting by forest management companies or government organizations interested in monitoring the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-01-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2022.2154598 |
| _version_ | 1797366112747257856 |
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| author | Levi Keay Christopher Mulverhill Nicholas C. Coops Grant McCartney |
| author_facet | Levi Keay Christopher Mulverhill Nicholas C. Coops Grant McCartney |
| author_sort | Levi Keay |
| collection | DOAJ |
| description | The advent of CubeSat constellations is revolutionizing the ability to observe Earth systems through time. The improved spatial and temporal resolutions from these data could assist in tracking forest harvesting by forest management companies or government organizations interested in monitoring the sustainable management of forest resources. However, differing characteristics of individual satellites in each constellation requires study into geometric and radiometric normalization of the imagery and tuning parameters for change detection algorithms. In this study, a method for the spatial and temporal detection of forest harvest operations using images from the PlanetScope constellation was developed and implemented for a managed forest in Ontario, Canada. Temporal smoothing was applied on Landsat-normalized PlanetScope values of the Normalized Differential Vegetation Index (NDVI), and change points were detected based on the first derivative of the NDVI trend. Detected changes were compared to known locations of harvesting machines. Results indicate that 80–90% of harvested areas were detected, with temporal errors of approximately 9–10 days for two sites. Overall, this study demonstrated that forest harvesting can be detected with relative accuracy, deriving previously unavailable levels of spatial and temporal detail and enhancing the ability of forest stakeholders to monitor the sustainable use of forest resources. |
| first_indexed | 2024-03-08T16:59:42Z |
| format | Article |
| id | doaj.art-2f375fd03dbc4231ad2ac6de587bc6a1 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-08T16:59:42Z |
| publishDate | 2023-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-2f375fd03dbc4231ad2ac6de587bc6a12024-01-04T15:59:06ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712023-01-0149110.1080/07038992.2022.21545982154598Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time SeriesLevi Keay0Christopher Mulverhill1Nicholas C. Coops2Grant McCartney3Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British ColumbiaIntegrated Remote Sensing Studio, Department of Forest Resources Management, University of British ColumbiaIntegrated Remote Sensing Studio, Department of Forest Resources Management, University of British ColumbiaForsite Consultants LtdThe advent of CubeSat constellations is revolutionizing the ability to observe Earth systems through time. The improved spatial and temporal resolutions from these data could assist in tracking forest harvesting by forest management companies or government organizations interested in monitoring the sustainable management of forest resources. However, differing characteristics of individual satellites in each constellation requires study into geometric and radiometric normalization of the imagery and tuning parameters for change detection algorithms. In this study, a method for the spatial and temporal detection of forest harvest operations using images from the PlanetScope constellation was developed and implemented for a managed forest in Ontario, Canada. Temporal smoothing was applied on Landsat-normalized PlanetScope values of the Normalized Differential Vegetation Index (NDVI), and change points were detected based on the first derivative of the NDVI trend. Detected changes were compared to known locations of harvesting machines. Results indicate that 80–90% of harvested areas were detected, with temporal errors of approximately 9–10 days for two sites. Overall, this study demonstrated that forest harvesting can be detected with relative accuracy, deriving previously unavailable levels of spatial and temporal detail and enhancing the ability of forest stakeholders to monitor the sustainable use of forest resources.http://dx.doi.org/10.1080/07038992.2022.2154598 |
| spellingShingle | Levi Keay Christopher Mulverhill Nicholas C. Coops Grant McCartney Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series Canadian Journal of Remote Sensing |
| title | Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series |
| title_full | Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series |
| title_fullStr | Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series |
| title_full_unstemmed | Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series |
| title_short | Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series |
| title_sort | automated forest harvest detection with a normalized planetscope imagery time series |
| url | http://dx.doi.org/10.1080/07038992.2022.2154598 |
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