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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.09530 |
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| _version_ | 1866910705449435136 |
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| author | Clinton, Nicholas Vollrath, Andreas D'annunzio, Remi Liu, Desheng Glick, Henry B. Descals, Adrià Sullivan, Alicia Guinan, Oliver Abramowitz, Jacob Stolle, Fred Goodman, Chris Birch, Tanya Quinn, David Danylo, Olga Lips, Tijs Coelho, Daniel Bihari, Enikoe Cronkite-Ratcliff, Bryce Poortinga, Ate Haghighattalab, Atena Notman, Evan DeWitt, Michael Yonas, Aaron Donchyts, Gennadii Shah, Devaja Saah, David Tenneson, Karis Quyen, Nguyen Hanh Verma, Megha Wilcox, Andrew |
| author_facet | Clinton, Nicholas Vollrath, Andreas D'annunzio, Remi Liu, Desheng Glick, Henry B. Descals, Adrià Sullivan, Alicia Guinan, Oliver Abramowitz, Jacob Stolle, Fred Goodman, Chris Birch, Tanya Quinn, David Danylo, Olga Lips, Tijs Coelho, Daniel Bihari, Enikoe Cronkite-Ratcliff, Bryce Poortinga, Ate Haghighattalab, Atena Notman, Evan DeWitt, Michael Yonas, Aaron Donchyts, Gennadii Shah, Devaja Saah, David Tenneson, Karis Quyen, Nguyen Hanh Verma, Megha Wilcox, Andrew |
| contents | Palm oil production has been identified as one of the major drivers of deforestation for tropical countries. To meet supply chain objectives, commodity producers and other stakeholders need timely information of land cover dynamics in their supply shed. However, such data are difficult to obtain from suppliers who may lack digital geographic representations of their supply sheds and production locations. Here we present a "community model," a machine learning model trained on pooled data sourced from many different stakeholders, to produce a map of palm probability at global scale. An advantage of this method is the inclusion of varied inputs, the ability to easily update the model as new training data becomes available and run the model on any year that input imagery is available. Inclusion of diverse data sources into one probability map can help establish a shared understanding across stakeholders on the presence and absence of a land cover or commodity (in this case oil palm). The model predictors are annual composites built from publicly available satellite imagery provided by Sentinel-1, Sentinel-2, and ALOS-2, and terrain data from Jaxa (AW3D30) and Copernicus (GLO-30). We provide map outputs as the probability of palm in a given pixel, to reflect the uncertainty of the underlying state (palm or not palm). This version of this model provides global accuracy estimated to be 92% (at 0.5 probability threshold) on an independent test set. This model, and resulting oil palm probability map products are useful for accurately identifying the geographic footprint of palm cultivation. Used in conjunction with timely deforestation information, this palm model is useful for understanding the risk of continued oil palm plantation expansion in sensitive forest areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A community palm model Clinton, Nicholas Vollrath, Andreas D'annunzio, Remi Liu, Desheng Glick, Henry B. Descals, Adrià Sullivan, Alicia Guinan, Oliver Abramowitz, Jacob Stolle, Fred Goodman, Chris Birch, Tanya Quinn, David Danylo, Olga Lips, Tijs Coelho, Daniel Bihari, Enikoe Cronkite-Ratcliff, Bryce Poortinga, Ate Haghighattalab, Atena Notman, Evan DeWitt, Michael Yonas, Aaron Donchyts, Gennadii Shah, Devaja Saah, David Tenneson, Karis Quyen, Nguyen Hanh Verma, Megha Wilcox, Andrew Computers and Society Computer Vision and Pattern Recognition Machine Learning Palm oil production has been identified as one of the major drivers of deforestation for tropical countries. To meet supply chain objectives, commodity producers and other stakeholders need timely information of land cover dynamics in their supply shed. However, such data are difficult to obtain from suppliers who may lack digital geographic representations of their supply sheds and production locations. Here we present a "community model," a machine learning model trained on pooled data sourced from many different stakeholders, to produce a map of palm probability at global scale. An advantage of this method is the inclusion of varied inputs, the ability to easily update the model as new training data becomes available and run the model on any year that input imagery is available. Inclusion of diverse data sources into one probability map can help establish a shared understanding across stakeholders on the presence and absence of a land cover or commodity (in this case oil palm). The model predictors are annual composites built from publicly available satellite imagery provided by Sentinel-1, Sentinel-2, and ALOS-2, and terrain data from Jaxa (AW3D30) and Copernicus (GLO-30). We provide map outputs as the probability of palm in a given pixel, to reflect the uncertainty of the underlying state (palm or not palm). This version of this model provides global accuracy estimated to be 92% (at 0.5 probability threshold) on an independent test set. This model, and resulting oil palm probability map products are useful for accurately identifying the geographic footprint of palm cultivation. Used in conjunction with timely deforestation information, this palm model is useful for understanding the risk of continued oil palm plantation expansion in sensitive forest areas. |
| title | A community palm model |
| topic | Computers and Society Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.09530 |