2.
Slagter, Bart; Reiche, Johannes; Marcos, Diego; Mullissa, Adugna; Lossou, Etse; Peña-Claros, Marielos; Herold, Martin
Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and -2 data Journal Article
In: Remote Sensing of Environment, vol. 295, pp. 113655, 2023, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: deep learning, Deforestation, Driver attribution, Forest degradation, Near real-time monitoring, Small-scale forest disturbance, Smallholder agriculture, tropical forests
@article{nokey,
title = {Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and -2 data},
author = {Bart Slagter and Johannes Reiche and Diego Marcos and Adugna Mullissa and Etse Lossou and Marielos Peña-Claros and Martin Herold},
url = {https://www.sciencedirect.com/science/article/pii/S0034425723002067},
doi = {10.1016/j.rse.2023.113655},
issn = {0034-4257},
year = {2023},
date = {2023-09-01},
journal = {Remote Sensing of Environment},
volume = {295},
pages = {113655},
abstract = {Advancements in satellite-based forest monitoring increasingly enable the near real-time detection of small-scale tropical forest disturbances. However, there is an urgent need to enhance such monitoring methods with automated direct driver attributions to detected disturbances. This would provide important additional information to make forest disturbance alerts more actionable and useful for uptake by different stakeholders. In this study, we demonstrate spatially explicit and near real-time methods to monitor direct drivers of small-scale tropical forest disturbance across a range of tropical forest conditions in Suriname, the Republic of the Congo and the Democratic Republic of the Congo. We trained a convolutional neural network with Sentinel-1 and Sentinel-2 data to continuously classify newly detected RAdar for Detecting Deforestation (RADD) alerts as smallholder agriculture, road development, selective logging, mining or other. Different monitoring scenarios were evaluated based on varying sensor combinations, post-disturbance time periods and confidence levels. In general, the use of Sentinel-2 data was found to be most accurate for driver classifications, especially with data composited over a period of 4 to 6 months after the disturbance detection. Sentinel-1 data showed to be valuable for more rapid classifications of specific drivers, especially in areas with persistent cloud cover. Throughout all monitoring scenarios, smallholder agriculture was classified most accurately, while road development, selective logging and mining were more challenging to distinguish. An accuracy assessment throughout the full extent of our study regions revealed a Macro-F1 score of 0.861 and an Overall Accuracy of 0.897 for the best performing model, based on the use of 6-month post-disturbance Sentinel-2 composites. Finally, we addressed three specific monitoring use cases that relate to rapid law enforcement against illegal activities, ecological impact assessments and timely carbon emission reporting, by optimizing the trade-off in classification timeliness and confidence to reach required accuracies. Our findings demonstrate the strong capacities of high spatiotemporal resolution satellite data for monitoring direct drivers of small-scale forest disturbance, considering different user interests. The produced forest disturbance driver maps can be accessed via: https://bartslagter94.users.earthengine.app/view/forest-disturbance-drivers.},
keywords = {deep learning, Deforestation, Driver attribution, Forest degradation, Near real-time monitoring, Small-scale forest disturbance, Smallholder agriculture, tropical forests},
pubstate = {published},
tppubtype = {article}
}
Advancements in satellite-based forest monitoring increasingly enable the near real-time detection of small-scale tropical forest disturbances. However, there is an urgent need to enhance such monitoring methods with automated direct driver attributions to detected disturbances. This would provide important additional information to make forest disturbance alerts more actionable and useful for uptake by different stakeholders. In this study, we demonstrate spatially explicit and near real-time methods to monitor direct drivers of small-scale tropical forest disturbance across a range of tropical forest conditions in Suriname, the Republic of the Congo and the Democratic Republic of the Congo. We trained a convolutional neural network with Sentinel-1 and Sentinel-2 data to continuously classify newly detected RAdar for Detecting Deforestation (RADD) alerts as smallholder agriculture, road development, selective logging, mining or other. Different monitoring scenarios were evaluated based on varying sensor combinations, post-disturbance time periods and confidence levels. In general, the use of Sentinel-2 data was found to be most accurate for driver classifications, especially with data composited over a period of 4 to 6 months after the disturbance detection. Sentinel-1 data showed to be valuable for more rapid classifications of specific drivers, especially in areas with persistent cloud cover. Throughout all monitoring scenarios, smallholder agriculture was classified most accurately, while road development, selective logging and mining were more challenging to distinguish. An accuracy assessment throughout the full extent of our study regions revealed a Macro-F1 score of 0.861 and an Overall Accuracy of 0.897 for the best performing model, based on the use of 6-month post-disturbance Sentinel-2 composites. Finally, we addressed three specific monitoring use cases that relate to rapid law enforcement against illegal activities, ecological impact assessments and timely carbon emission reporting, by optimizing the trade-off in classification timeliness and confidence to reach required accuracies. Our findings demonstrate the strong capacities of high spatiotemporal resolution satellite data for monitoring direct drivers of small-scale forest disturbance, considering different user interests. The produced forest disturbance driver maps can be accessed via: https://bartslagter94.users.earthengine.app/view/forest-disturbance-drivers.
1.
Milenkovic, Milutin
Big EO Data Processing Approaches for Monitoring Tropical Forest Disturbance with Sentinel-1 Data Presentation
15.11.2022.
Abstract | BibTeX | Tags: Earth Observations, Presentation, Sentinel-1, tropical forests
@misc{nokey,
title = {Big EO Data Processing Approaches for Monitoring Tropical Forest Disturbance with Sentinel-1 Data},
author = {Milutin Milenkovic},
year = {2022},
date = {2022-11-15},
abstract = {An increase in the disturbance of tropical forests, mainly caused by human-driven deforestation, drouths, and forest fires, has been constantly reported over the last decades. Those stressors are pushing tropical ecosystems to their limits, and it is urgently needed to understand how far the ecosystems are from their point of no return. Novel global and free Earth Observation (EO) data, such as Sentinel-1 satellite radar images from the EU’s Copernicus programme, have high spatial (20 m) and temporal (6 or more days) resolution, providing an excellent possibility for monitoring tropical ecosystem change. However, those terabytes of data also require novel processing ways. In this talk, I will present three different big data approaches utilized to detect Sentinel-1 signal disturbances and recoveries over tropical forests in the Amazon and Congo basins. The presentation will give a user perspective and experience on the Sentinel-1 processing on local servers, the ESA openEO platform, and a national computing infrastructure used within the C-SCALE project. A novel EU-funded project on big EO data processing, the open Earth monitor cyberinfrastructure, will also be introduced.
Event: Enhancing Satellite Imagery and Geospatial Information Capabilities in Support of Nuclear Safeguards
Place: UN, IAEA Headquarters, Vienna, Austria
Date: 15–17 November 2022},
keywords = {Earth Observations, Presentation, Sentinel-1, tropical forests},
pubstate = {published},
tppubtype = {presentation}
}
An increase in the disturbance of tropical forests, mainly caused by human-driven deforestation, drouths, and forest fires, has been constantly reported over the last decades. Those stressors are pushing tropical ecosystems to their limits, and it is urgently needed to understand how far the ecosystems are from their point of no return. Novel global and free Earth Observation (EO) data, such as Sentinel-1 satellite radar images from the EU’s Copernicus programme, have high spatial (20 m) and temporal (6 or more days) resolution, providing an excellent possibility for monitoring tropical ecosystem change. However, those terabytes of data also require novel processing ways. In this talk, I will present three different big data approaches utilized to detect Sentinel-1 signal disturbances and recoveries over tropical forests in the Amazon and Congo basins. The presentation will give a user perspective and experience on the Sentinel-1 processing on local servers, the ESA openEO platform, and a national computing infrastructure used within the C-SCALE project. A novel EU-funded project on big EO data processing, the open Earth monitor cyberinfrastructure, will also be introduced.
Event: Enhancing Satellite Imagery and Geospatial Information Capabilities in Support of Nuclear Safeguards
Place: UN, IAEA Headquarters, Vienna, Austria
Date: 15–17 November 2022
Event: Enhancing Satellite Imagery and Geospatial Information Capabilities in Support of Nuclear Safeguards
Place: UN, IAEA Headquarters, Vienna, Austria
Date: 15–17 November 2022