32.
Mullissa, Adugna; Reiche, Johannes; Herold, Martin
Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping Journal Article Forthcoming
In: Forthcoming.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping},
author = {Adugna Mullissa and Johannes Reiche and Martin Herold},
url = {https://www.sciencedirect.com/science/article/pii/S0034425723003504?via%3Dihub},
doi = {10.1016/j.rse.2023.113799},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
abstract = {The advent of temporally dense radar data such as the Sentinel-1 SAR have opened the door for rapid forest disturbance detection in the humid tropics. Tropical dry forest disturbance detection, however, were challenged by seasonality and more open canopy characteristics. In this manuscript, we proposed a Sentinel-1 SAR and deep learning based rapid forest disturbance detection approach for tropical dry forests. We demonstrated a weakly supervised method for reference label harvesting based on medium resolution globally available forest and forest disturbance maps. We trained a deep neural network model to derive forest and forest disturbance probabilities from Sentinel-1 images in the first step. We then implemented a probabilistic disturbance detection and refinement method to map forest disturbances in near real-time in two test regions in Paraguay and Mozambique. We mapped new forest disturbances in an emulated near real-time scenario for 2020 and 2021 and evaluated the spatial accuracy of the disturbance alerts by generating area adjusted precision, recall and F-1 score. We also evaluated the improvement in timeliness of disturbance detection by estimating mean time difference of disturbance events detection with that of Landsat-based GLAD alerts. The generated alerts in Paraguay and Mozambique achieved a precision, recall and F-1 score of 0.99, 0.61, 0.75 and 0.97, 0.51, 0.66, respectively. The proposed method detected disturbances with a mean of 21 days (
18 days) earlier in Paraguay and 18 days (
18 days) earlier in Mozambique than the Landsat-based GLAD alerts. These results demonstrated the efficacy of the proposed method and its viability to be used in an operational setting to generate large area rapid near real-time disturbance alerts in the dry tropics.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
The advent of temporally dense radar data such as the Sentinel-1 SAR have opened the door for rapid forest disturbance detection in the humid tropics. Tropical dry forest disturbance detection, however, were challenged by seasonality and more open canopy characteristics. In this manuscript, we proposed a Sentinel-1 SAR and deep learning based rapid forest disturbance detection approach for tropical dry forests. We demonstrated a weakly supervised method for reference label harvesting based on medium resolution globally available forest and forest disturbance maps. We trained a deep neural network model to derive forest and forest disturbance probabilities from Sentinel-1 images in the first step. We then implemented a probabilistic disturbance detection and refinement method to map forest disturbances in near real-time in two test regions in Paraguay and Mozambique. We mapped new forest disturbances in an emulated near real-time scenario for 2020 and 2021 and evaluated the spatial accuracy of the disturbance alerts by generating area adjusted precision, recall and F-1 score. We also evaluated the improvement in timeliness of disturbance detection by estimating mean time difference of disturbance events detection with that of Landsat-based GLAD alerts. The generated alerts in Paraguay and Mozambique achieved a precision, recall and F-1 score of 0.99, 0.61, 0.75 and 0.97, 0.51, 0.66, respectively. The proposed method detected disturbances with a mean of 21 days (
18 days) earlier in Paraguay and 18 days (
18 days) earlier in Mozambique than the Landsat-based GLAD alerts. These results demonstrated the efficacy of the proposed method and its viability to be used in an operational setting to generate large area rapid near real-time disturbance alerts in the dry tropics.
18 days) earlier in Paraguay and 18 days (
18 days) earlier in Mozambique than the Landsat-based GLAD alerts. These results demonstrated the efficacy of the proposed method and its viability to be used in an operational setting to generate large area rapid near real-time disturbance alerts in the dry tropics.
31.
Balling, Johannes; Herold, Martin; Reiche, Johannes
How textural features can improve SAR-based tropical forest disturbance mapping Journal Article
In: vol. 124, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {How textural features can improve SAR-based tropical forest disturbance mapping},
author = {Johannes Balling and Martin Herold and Johannes Reiche},
url = {https://doi.org/10.1016/j.jag.2023.103492},
doi = {10.1016/j.jag.2023.103492},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
volume = {124},
abstract = {Spatially and timely accurate information about tropical forest disturbances is crucial for tracking critical forest changes, supporting forest management, and enabling law enforcement activities. In recent years, forest disturbance monitoring and alerting using cloud-penetrating Synthetic Aperture Radar (SAR) imagery has proven effective at national and pan-tropical scales. Related detection approaches mostly rely on detecting post-disturbance altered backscatter values in C or L-band SAR backscatter time series. Some disturbances are characterized by post-disturbance tree remnants or debris. For the time periods where these kinds of remnants remain present at the surface, the SAR backscatter values can be similar to those of stable forest. This can cause omission errors and delayed detection and it is considered a key shortcoming of current backscatter-based approaches. We hypothesized that despite fairly stable backscatter values, different orientation and arrangement of tree remnants leads to an altered heterogeneity of neighboring pixel values and that this can be quantified by textural features. We assessed six uncorrelated Gray-Level Co-Occurrence Matrix (GLCM) textural features using dense Sentinel-1C-band SAR time series. Forest disturbances, based on each GLCM feature using a pixel-based probabilistic change detection algorithm, were compared against results from forest disturbances mapped based only on backscatter data. We studied the impact of speckle-filtering on GLCM features and GLCM kernel sizes. We developed a method to combine backscatter and GLCM features, and we evaluated its robustness for a variety of natural and human-induced forest disturbance types across the Amazon Biome. Out of the six tested GLCM features GLCM Sum Average (SAVG) performed best. GLCM features derived from non-speckle filtered and speckle-filtered backscatter data did not show a noticeable impact on accuracy. A combination of backscatter and GLCM SAVG resulted in a reduced omission error of up to 36% and an improved timeliness of detections by average of to 30 days, with individual detection showing even higher improvements on a pixel level. The method was found to be robust across a variety of forest disturbance types. The largest reduction of omission errors and greatest improvement of timeliness was evident for sites with large unfragmented disturbance patches (e.g., large-scale clearings, fires and mining). For increasing GLCM kernel sizes, we observed a trade-off between reduced omission errors combined with improved timeliness and increasing commission errors. A kernel size of 5 was found to provide the best trade-off for reducing omission errors and improving timeliness while not introducing commission errors. The results emphasize that combining SAR-based textural features and backscatter can overcome omission errors caused by post-disturbance tree remnants or debris. This can help to improve the consistency and timelines of short (C-band) and long wavelength (L-band) based operational SAR disturbance monitoring and alerting. Result maps can be visualized via: https://johannesballing.users.earthengine.app/view/forest-disturbance-texture.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spatially and timely accurate information about tropical forest disturbances is crucial for tracking critical forest changes, supporting forest management, and enabling law enforcement activities. In recent years, forest disturbance monitoring and alerting using cloud-penetrating Synthetic Aperture Radar (SAR) imagery has proven effective at national and pan-tropical scales. Related detection approaches mostly rely on detecting post-disturbance altered backscatter values in C or L-band SAR backscatter time series. Some disturbances are characterized by post-disturbance tree remnants or debris. For the time periods where these kinds of remnants remain present at the surface, the SAR backscatter values can be similar to those of stable forest. This can cause omission errors and delayed detection and it is considered a key shortcoming of current backscatter-based approaches. We hypothesized that despite fairly stable backscatter values, different orientation and arrangement of tree remnants leads to an altered heterogeneity of neighboring pixel values and that this can be quantified by textural features. We assessed six uncorrelated Gray-Level Co-Occurrence Matrix (GLCM) textural features using dense Sentinel-1C-band SAR time series. Forest disturbances, based on each GLCM feature using a pixel-based probabilistic change detection algorithm, were compared against results from forest disturbances mapped based only on backscatter data. We studied the impact of speckle-filtering on GLCM features and GLCM kernel sizes. We developed a method to combine backscatter and GLCM features, and we evaluated its robustness for a variety of natural and human-induced forest disturbance types across the Amazon Biome. Out of the six tested GLCM features GLCM Sum Average (SAVG) performed best. GLCM features derived from non-speckle filtered and speckle-filtered backscatter data did not show a noticeable impact on accuracy. A combination of backscatter and GLCM SAVG resulted in a reduced omission error of up to 36% and an improved timeliness of detections by average of to 30 days, with individual detection showing even higher improvements on a pixel level. The method was found to be robust across a variety of forest disturbance types. The largest reduction of omission errors and greatest improvement of timeliness was evident for sites with large unfragmented disturbance patches (e.g., large-scale clearings, fires and mining). For increasing GLCM kernel sizes, we observed a trade-off between reduced omission errors combined with improved timeliness and increasing commission errors. A kernel size of 5 was found to provide the best trade-off for reducing omission errors and improving timeliness while not introducing commission errors. The results emphasize that combining SAR-based textural features and backscatter can overcome omission errors caused by post-disturbance tree remnants or debris. This can help to improve the consistency and timelines of short (C-band) and long wavelength (L-band) based operational SAR disturbance monitoring and alerting. Result maps can be visualized via: https://johannesballing.users.earthengine.app/view/forest-disturbance-texture.
30.
Mosaffa, Hamidreza; Filippucci, Paolo; Massari, Christian; Ciabatta, Luca; Brocca, Luca
SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studie Journal Article
In: Scientific Data, vol. 10, no. 749, 2023.
Abstract | Links | BibTeX | Tags: Climate Science, Hydrology
@article{nokey,
title = {SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studie},
author = {Hamidreza Mosaffa and Paolo Filippucci and Christian Massari and Luca Ciabatta and Luca Brocca },
url = {https://www.nature.com/articles/s41597-023-02654-6#citeas},
doi = {https://doi.org/10.1038/s41597-023-02654-6},
year = {2023},
date = {2023-10-31},
urldate = {2023-10-31},
journal = {Scientific Data},
volume = {10},
number = {749},
abstract = {A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products’ high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998–2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend.},
keywords = {Climate Science, Hydrology},
pubstate = {published},
tppubtype = {article}
}
A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products’ high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998–2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend.
29.
Hacklander, Julia; Parente, Leandro; Ho, Yu-Feng; Hengl, Tomislav; Simoes, Rolf; Consoli, Davide; Sahin, Murat; Tian, Xuemeng; Jung, Martin; Herold, Martin; Duveiller, Gregory; Weynants, Melanie; and Ichsani Wheeler,
Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution},
author = {Julia Hacklander and Leandro Parente and Yu-Feng Ho and Tomislav Hengl and Rolf Simoes and Davide Consoli and Murat Sahin and Xuemeng Tian and Martin Jung and Martin Herold and Gregory Duveiller and Melanie Weynants and and Ichsani Wheeler},
url = {https://www.researchsquare.com/article/rs-3415685/v1},
doi = {10.21203/rs.3.rs-3415685/v1},
year = {2023},
date = {2023-10-08},
urldate = {2023-10-08},
abstract = {The paper presents results of using remote sensing time series and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Monthly aggregated FAPAR time series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8–day GLASS FAPAR V6 product for 2000–2021 and used to determine long–term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio–physical variables representing climate, terrain, land form, and vegetation cover, as well as several variables related to human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (Extremely Randomized Trees, Gradient Descended Trees and Artificial Neural Network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs potential FAPAR and long–term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes urban, sparse vegetation and rainfed cropland. On the other hand, classes irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The paper presents results of using remote sensing time series and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Monthly aggregated FAPAR time series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8–day GLASS FAPAR V6 product for 2000–2021 and used to determine long–term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio–physical variables representing climate, terrain, land form, and vegetation cover, as well as several variables related to human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (Extremely Randomized Trees, Gradient Descended Trees and Artificial Neural Network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs potential FAPAR and long–term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes urban, sparse vegetation and rainfed cropland. On the other hand, classes irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
28.
Chu, Housen; Christianson, Danielle S.; Cheah, You-Wei; Pastorello, Gilberto; O’Brien, Fianna; Geden, Joshua; Ngo, Sy-Toan; Hollowgrass, Rachel; Leibowitz, Karla; Beekwilder, Norman F.; Sandesh, Megha; Dengel, Sigrid; Chan, Stephen W.; Santos, André; Delwiche, Kyle; Yi, Koong; Buechner, Christin; Baldocchi, Dennis; Papale, Dario; Keenan, Trevor F.; Biraud, Sébastien C.; Agarwal, Deborah A.; Torn, Margaret S.
AmeriFlux BASE data pipeline to support network growth and data sharing Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {AmeriFlux BASE data pipeline to support network growth and data sharing},
author = {Housen Chu and Danielle S. Christianson and You-Wei Cheah and Gilberto Pastorello and Fianna O’Brien and Joshua Geden and Sy-Toan Ngo and Rachel Hollowgrass and Karla Leibowitz and Norman F. Beekwilder and Megha Sandesh and Sigrid Dengel and Stephen W. Chan and André Santos and Kyle Delwiche and Koong Yi and Christin Buechner and Dennis Baldocchi and Dario Papale and Trevor F. Keenan and Sébastien C. Biraud and Deborah A. Agarwal and Margaret S. Torn },
url = {https://www.nature.com/articles/s41597-023-02531-2#article-info},
doi = {10.1038/s41597-023-02531-2},
year = {2023},
date = {2023-09-11},
urldate = {2023-09-11},
abstract = {AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.
27.
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.
26.
Hengl, Tomislav; Consoli, Davide; Bagić, Matej; Brocca, Lucca; Herold, Martin
AI technology: what it is and what it’s not, and how it can (potentially) help us solve the climate crisis Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {AI technology: what it is and what it’s not, and how it can (potentially) help us solve the climate crisis},
author = {Tomislav Hengl and Davide Consoli and Matej Bagić and Lucca Brocca and Martin Herold},
url = {https://zenodo.org/records/8300534},
doi = {10.5281/zenodo.8300534},
year = {2023},
date = {2023-08-30},
urldate = {2023-08-30},
abstract = {AI (Artificial Intelligence) technology, with the launch of OpenAI’s ChatGPT (the fastest growing app ever) and similar, is now a buzz: a new technological jump of the human race, but potentially a Pandora box for information manipulation and misuse. AI could soon replace thousands of jobs and revolutionize how we travel (self-driving cars), purchase items, do admin/office work, communicate with computers (and people), but also how governments fight wars and control people. AI is making a lot of people enthusiastic, but even more nervous. We review the potentials and perils of AI tech; how it can also help us with extremely important things such as solving the climate crisis and better monitoring and conservation of natural resources. Links and references are extensive and hopefully will motivate you to read more on the topic.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
AI (Artificial Intelligence) technology, with the launch of OpenAI’s ChatGPT (the fastest growing app ever) and similar, is now a buzz: a new technological jump of the human race, but potentially a Pandora box for information manipulation and misuse. AI could soon replace thousands of jobs and revolutionize how we travel (self-driving cars), purchase items, do admin/office work, communicate with computers (and people), but also how governments fight wars and control people. AI is making a lot of people enthusiastic, but even more nervous. We review the potentials and perils of AI tech; how it can also help us with extremely important things such as solving the climate crisis and better monitoring and conservation of natural resources. Links and references are extensive and hopefully will motivate you to read more on the topic.
25.
Orduna-cabrera, Fernando; Sandoval-Gastelum, Marcial; MCCALLUM, Ian; SEE, Linda; Fritz, Steffen; Karanam, Santosh; Sturn, Tobias; Javalera-Rincon, Valeria; González-Navarro, F. Fernando
Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information},
author = {Fernando Orduna-cabrera and Marcial Sandoval-Gastelum and Ian MCCALLUM and Linda SEE and Steffen Fritz and Santosh Karanam and Tobias Sturn and Valeria Javalera-Rincon and F. Fernando González-Navarro},
url = {https://www.mdpi.com/2673-7086/3/3/29},
doi = {10.3390/geographies3030029},
year = {2023},
date = {2023-08-30},
urldate = {2023-08-30},
abstract = {The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.
24.
Bonannella, Carmelo; Parente, Leandro; de Bruin, Sytze; Herold, Martin
Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift},
author = {Carmelo Bonannella and Leandro Parente and Sytze de Bruin and Martin Herold},
url = {https://www.researchsquare.com/article/rs-3288937/v1},
doi = {10.21203/rs.3.rs-3288937/v1},
year = {2023},
date = {2023-08-24},
urldate = {2023-08-24},
abstract = {Climate change poses a significant threat to the distribution and composition of forest tree species worldwide. European forest tree species' range is expected to shift to cope with the increasing frequency and intensity of extreme weather events, pests and diseases caused by climate change. Despite numerous regional studies, a continental scale assessment of current changes in species distributions in Europe is missing due to the difficult task of modeling a species realized distribution and to quantify the influence of forest disturbances on each species. In this study we conducted a trend analysis on the realized distribution of 6 main European forest tree species (Abies alba Mill., Fagus sylvatica L., Picea abies L. H. Karst., Pinus nigra J. F. Arnold, Pinus sylvestris L. and Quercus robur L.) to capture and map the prevalent trends in probability of occurrence for the period 2000–2020. We also analyzed the impact of forest disturbances on each species' range and identified the dominant disturbance drivers. Our results revealed an overall trend of stability in species' distributions (85% of the pixels are considered stable by 2020 for all species) but we also identified some hot spots characterized by negative trends in probability of occurrence, mostly at the edges of each species' latitudinal range. Additionally, we identified a steady increase in disturbance events in each species' range by disturbance (affected range doubled by 2020, from 3.5% to 7% on average) and highlighted species-specific responses to forest disturbance drivers such as wind and fire. Overall, our study provides insights into distribution trends and disturbance patterns for the main European forest tree species. The identification of range shifts and the intensifying impacts of disturbances call for proactive conservation efforts and long-term planning to ensure the resilience and sustainability of European forests.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Climate change poses a significant threat to the distribution and composition of forest tree species worldwide. European forest tree species' range is expected to shift to cope with the increasing frequency and intensity of extreme weather events, pests and diseases caused by climate change. Despite numerous regional studies, a continental scale assessment of current changes in species distributions in Europe is missing due to the difficult task of modeling a species realized distribution and to quantify the influence of forest disturbances on each species. In this study we conducted a trend analysis on the realized distribution of 6 main European forest tree species (Abies alba Mill., Fagus sylvatica L., Picea abies L. H. Karst., Pinus nigra J. F. Arnold, Pinus sylvestris L. and Quercus robur L.) to capture and map the prevalent trends in probability of occurrence for the period 2000–2020. We also analyzed the impact of forest disturbances on each species' range and identified the dominant disturbance drivers. Our results revealed an overall trend of stability in species' distributions (85% of the pixels are considered stable by 2020 for all species) but we also identified some hot spots characterized by negative trends in probability of occurrence, mostly at the edges of each species' latitudinal range. Additionally, we identified a steady increase in disturbance events in each species' range by disturbance (affected range doubled by 2020, from 3.5% to 7% on average) and highlighted species-specific responses to forest disturbance drivers such as wind and fire. Overall, our study provides insights into distribution trends and disturbance patterns for the main European forest tree species. The identification of range shifts and the intensifying impacts of disturbances call for proactive conservation efforts and long-term planning to ensure the resilience and sustainability of European forests.
23.
Araza, Arnan; de Bruin, Sytze; Hein, Lars; Herold, Martin
Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting},
author = {Arnan Araza and Sytze de Bruin and Lars Hein and Martin Herold },
url = {https://www.nature.com/articles/s41598-023-38935-8#article-info},
doi = {10.1038/s41598-023-38935-8},
year = {2023},
date = {2023-08-05},
urldate = {2023-08-05},
abstract = {Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010–2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6–47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9–39 Mg C
and
=0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010–2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6–47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9–39 Mg C
and
=0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.
and
=0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.
22.
Masó, Joan
OGC Cloud Optimized GeoTIFF Standard Technical Report
2023.
Abstract | Links | BibTeX | Tags: cloud optimised, GeoTIFF
@techreport{nokey,
title = {OGC Cloud Optimized GeoTIFF Standard},
author = {Joan Masó},
url = {https://docs.ogc.org/is/21-026/21-026.html },
year = {2023},
date = {2023-07-14},
urldate = {2023-07-14},
journal = {OGC Cloud Optimized GeoTIFF Standard},
abstract = {The Cloud Optimized GeoTIFF (COG) relies on two characteristics of the TIFF v6 format (tiles and reduced resolution subfiles), GeoTIFF keys for georeference, and the HTTP range, which allows for efficient downloading of parts of imagery and grid coverage data on the web and to make fast data visualization of TIFF or BigTIFF files and fast geospatial processing workflows possible. COG-aware applications can download only the information they need to visualize or process the data on the web. Numerous remote sensing datasets are available in cloud storage facilities that can benefit from optimized visualization and processing. This standard formalizes the requirements for a TIFF file to become a COG file and for the HTTP server to make COG files available in a fast fashion on the web.
The key work for crafting this OGC Standard was undertaken in the Open-Earth-Monitor Cyberinfrastructure (OEMC) project, which received funding from the European Union’s Horizon Europe research and innovation program under grant agreement number 101059548 and in the All Data 4 Green Deal - An Integrated, FAIR Approach for the Common European Data Space (AD4GD) project, which received funding from the European Union’s Horizon Europe research and innovation program under grant agreement number 101061001.},
keywords = {cloud optimised, GeoTIFF},
pubstate = {published},
tppubtype = {techreport}
}
The Cloud Optimized GeoTIFF (COG) relies on two characteristics of the TIFF v6 format (tiles and reduced resolution subfiles), GeoTIFF keys for georeference, and the HTTP range, which allows for efficient downloading of parts of imagery and grid coverage data on the web and to make fast data visualization of TIFF or BigTIFF files and fast geospatial processing workflows possible. COG-aware applications can download only the information they need to visualize or process the data on the web. Numerous remote sensing datasets are available in cloud storage facilities that can benefit from optimized visualization and processing. This standard formalizes the requirements for a TIFF file to become a COG file and for the HTTP server to make COG files available in a fast fashion on the web.
The key work for crafting this OGC Standard was undertaken in the Open-Earth-Monitor Cyberinfrastructure (OEMC) project, which received funding from the European Union’s Horizon Europe research and innovation program under grant agreement number 101059548 and in the All Data 4 Green Deal - An Integrated, FAIR Approach for the Common European Data Space (AD4GD) project, which received funding from the European Union’s Horizon Europe research and innovation program under grant agreement number 101061001.
The key work for crafting this OGC Standard was undertaken in the Open-Earth-Monitor Cyberinfrastructure (OEMC) project, which received funding from the European Union’s Horizon Europe research and innovation program under grant agreement number 101059548 and in the All Data 4 Green Deal - An Integrated, FAIR Approach for the Common European Data Space (AD4GD) project, which received funding from the European Union’s Horizon Europe research and innovation program under grant agreement number 101061001.
21.
Boogaard, Hendrik; Pratihast, Arun Kumar; Juan Carlos Laso Bayas; Santosh Karanam,; Steffen Fritz,; Kristof Van Tricht,; Jeroen Degerickx,; Gilliams, Sven
Building a community-based open harmonised reference data repository for global crop mapping Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Building a community-based open harmonised reference data repository for global crop mapping},
author = {Hendrik Boogaard and Arun Kumar Pratihast and ,Juan Carlos Laso Bayas, and Santosh Karanam, and Steffen Fritz, and Kristof Van Tricht, and Jeroen Degerickx, and Sven Gilliams},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0287731},
doi = {10.1371/journal.pone.0287731},
year = {2023},
date = {2023-07-13},
urldate = {2023-07-13},
abstract = {Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.
20.
Orduna-cabrera, Fernando; Sandoval-Gastelum, Marcial; MCCALLUM, Ian; SEE, Linda; Fritz, Steffen; Karanam, Santosh; Sturn, Tobias; Javalera-Rincon, Valeria; González-Navarro, F. Fernando
Crop-Type Recognition in Street-level Images with Convolutional Neural Networks Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Crop-Type Recognition in Street-level Images with Convolutional Neural Networks},
author = {Fernando Orduna-cabrera and Marcial Sandoval-Gastelum and Ian MCCALLUM and Linda SEE and Steffen Fritz and Santosh Karanam and Tobias Sturn and Valeria Javalera-Rincon and F. Fernando González-Navarro},
url = {https://www.preprints.org/manuscript/202307.0724/v1},
doi = {10.20944/preprints202307.0724.v1},
year = {2023},
date = {2023-07-11},
urldate = {2023-07-11},
abstract = {The creation of crop-type maps from satellite data has proven challenging, often impeded by a lack of accurate in-situ data. This paper aims to demonstrate a method for crop-type (ie. Maize, Wheat and Other) recognition based on Convolutional Neural Networks using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery. Classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for other. Given that wheat and maize are the two most common food crops globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved crop-type monitoring globally. Challenges remain in addressing the noisy aspect of street-level imagery (ie. buildings, hedgerows, automobiles, etc.), where a variety of different objects tend to restrict the view and confound the algorithms
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The creation of crop-type maps from satellite data has proven challenging, often impeded by a lack of accurate in-situ data. This paper aims to demonstrate a method for crop-type (ie. Maize, Wheat and Other) recognition based on Convolutional Neural Networks using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery. Classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for other. Given that wheat and maize are the two most common food crops globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved crop-type monitoring globally. Challenges remain in addressing the noisy aspect of street-level imagery (ie. buildings, hedgerows, automobiles, etc.), where a variety of different objects tend to restrict the view and confound the algorithms
19.
Fraisl, Dilek; See, Linda; Campbell, Jillian; Danielsen, Finn; Andrianandrasana, Herizo T.
The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks Bachelor Thesis
2023.
Abstract | Links | BibTeX | Tags:
@bachelorthesis{nokey,
title = {The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks},
author = {Dilek Fraisl and Linda See and Jillian Campbell and Finn Danielsen and Herizo T. Andrianandrasana},
url = {https://theoryandpractice.citizenscienceassociation.org/articles/10.5334/cstp.643#funding-information},
doi = {10.5334/cstp.643},
year = {2023},
date = {2023-06-27},
volume = {8},
issue = {1},
pages = {27},
abstract = {The United Nations (UN) Sustainable Development Goals (SDGs) are a series of global development targets that were adopted by all UN member states in 2015 to address the world’s most pressing societal, environmental, and economic challenges by 2030 (UN 2015). They include 17 goals and 169 targets covering a wide range of topics from inclusive and equitable education to climate change. The achievement of the SDGs depends on our ability to accurately measure progress towards these topics using timely, relevant, and reliable data (Dang and Serajuddin 2020). To help in the development and implementation of such data and of monitoring mechanisms, a Global Indicator Framework was adopted by the UN General Assembly in 2017, which currently includes 231 unique indicators as a set of metrics to deliver information on the status and trends in each SDG target (UN 2017). However, the lack of resources and institutional capacity makes the monitoring of these indicators very challenging for the producers and users of official statistics, including National Statistical Offices (NSOs), line ministries, UN agencies and others that are responsible for compiling and disseminating official statistics (Fraisl et al. 2022).},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The United Nations (UN) Sustainable Development Goals (SDGs) are a series of global development targets that were adopted by all UN member states in 2015 to address the world’s most pressing societal, environmental, and economic challenges by 2030 (UN 2015). They include 17 goals and 169 targets covering a wide range of topics from inclusive and equitable education to climate change. The achievement of the SDGs depends on our ability to accurately measure progress towards these topics using timely, relevant, and reliable data (Dang and Serajuddin 2020). To help in the development and implementation of such data and of monitoring mechanisms, a Global Indicator Framework was adopted by the UN General Assembly in 2017, which currently includes 231 unique indicators as a set of metrics to deliver information on the status and trends in each SDG target (UN 2017). However, the lack of resources and institutional capacity makes the monitoring of these indicators very challenging for the producers and users of official statistics, including National Statistical Offices (NSOs), line ministries, UN agencies and others that are responsible for compiling and disseminating official statistics (Fraisl et al. 2022).
18.
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: vol. 295, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokeyk,
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?via%3Dihub},
doi = {10.1016/j.rse.2023.113655},
year = {2023},
date = {2023-06-21},
volume = {295},
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},
keywords = {},
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
17.
Camara, Gilberto; Simoes, Rolf; Ruivo, Heloisa M; Andrade, Pedro R; Soterroni, Aline C; Ramos, Fernando M; Ramos, Rafael G; Scarabello, Marluce; Almeida, Claudio; Sanches, Ieda; Maurano, Luis; Coutinho, Alexandre; Esquerdo, Julio; Antunes, João; Venturieri, Adriano; Adami, Marcos
Impact of land tenure on deforestation control and forest restoration in Brazilian Amazonia Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokeyj,
title = {Impact of land tenure on deforestation control and forest restoration in Brazilian Amazonia},
author = {Gilberto Camara and Rolf Simoes and Heloisa M Ruivo and Pedro R Andrade and Aline C Soterroni and Fernando M Ramos and Rafael G Ramos and Marluce Scarabello and Claudio Almeida and Ieda Sanches and Luis Maurano and Alexandre Coutinho and Julio Esquerdo and João Antunes and Adriano Venturieri and Marcos Adami},
url = {https://iopscience.iop.org/article/10.1088/1748-9326/acd20a},
doi = {10.1088/1748-9326/acd20a},
year = {2023},
date = {2023-05-12},
abstract = {This study examines how land tenure constrains Brazil's ability to meet its deforestation control and forest restoration goals in its Amazonia biome. Our findings are based on an updated assessment of land tenure and land use in the region. Between 2019 and 2021, 44% of deforestation in Amazonia occurred in private lands, while forest removal in settlements ranged from 31% to 27% of the total. Deforestation in undesignated public lands increased from 11% in 2008 to 18% in 2021. Deforestation is highly concentrated, with 1% of properties accounting for 82.5% of forest cuts in 2021. In Amazonia, there is considerable non-compliance with the legal reserve provisions set by Brazil's Forest Code. Legal reserve deficits in private lands sum up to 18.17 Mha (million hectares), compared with 12.49 Mha of legal reserve surpluses. Even if all forest surpluses are offered in the forest credits market set in the Forest Code, farmers still need to restore 5.67 Mha to comply with the law. Large-scale cattle ranchers have a legal reserve deficit of 10.35 Mha (34% of their area). Most crop farming occurs in medium and large properties (4.63 Mha) with a large proportion of legal reserve deficits (45%). Given the political power and financial resources of large ranchers and crop producers, Brazil faces major challenges in inducing these farmers to meet their legal obligations. Therefore, Brazil needs to combine robust command-and-control strategies with market-based policies to achieve its deforestation and forest restoration goals. The government should tailor forest protection and restoration policies to the needs of different landowners, considering their land use practices, technical capacity, and financial resources.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study examines how land tenure constrains Brazil's ability to meet its deforestation control and forest restoration goals in its Amazonia biome. Our findings are based on an updated assessment of land tenure and land use in the region. Between 2019 and 2021, 44% of deforestation in Amazonia occurred in private lands, while forest removal in settlements ranged from 31% to 27% of the total. Deforestation in undesignated public lands increased from 11% in 2008 to 18% in 2021. Deforestation is highly concentrated, with 1% of properties accounting for 82.5% of forest cuts in 2021. In Amazonia, there is considerable non-compliance with the legal reserve provisions set by Brazil's Forest Code. Legal reserve deficits in private lands sum up to 18.17 Mha (million hectares), compared with 12.49 Mha of legal reserve surpluses. Even if all forest surpluses are offered in the forest credits market set in the Forest Code, farmers still need to restore 5.67 Mha to comply with the law. Large-scale cattle ranchers have a legal reserve deficit of 10.35 Mha (34% of their area). Most crop farming occurs in medium and large properties (4.63 Mha) with a large proportion of legal reserve deficits (45%). Given the political power and financial resources of large ranchers and crop producers, Brazil faces major challenges in inducing these farmers to meet their legal obligations. Therefore, Brazil needs to combine robust command-and-control strategies with market-based policies to achieve its deforestation and forest restoration goals. The government should tailor forest protection and restoration policies to the needs of different landowners, considering their land use practices, technical capacity, and financial resources.
16.
Sabbatini, Simone; Nicolini, Giacomo; Gielen, Bert; de Beeck, Maarten Op; Michilsens, Fana; Iserbyt, Arne; Loustau, Denis; Lafont, Sébastien; Loubet, Benjamin; Canfora, Eleonora; Polidori, Diego; Ribeca, Alessio; Papale, Dario
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {High-precision datasets from monitoring stations based on eddy covariance measurements: what six years of quality evaluation process of ICOS ecosystem stations have to tell},
author = {Simone Sabbatini and Giacomo Nicolini and Bert Gielen and Maarten Op de Beeck and Fana Michilsens and Arne Iserbyt and Denis Loustau and Sébastien Lafont and Benjamin Loubet and Eleonora Canfora and Diego Polidori and Alessio Ribeca and Dario Papale},
url = {https://meetingorganizer.copernicus.org/EGU23/EGU23-16343.html},
doi = {10.5194/egusphere-egu23-16343},
year = {2023},
date = {2023-04-24},
abstract = {ICOS (Integrated Carbon Observation System) is a Research Infrastructure aiming at getting a deeper understanding of the European Carbon balance by means of a network of monitoring stations, based on eddy covariance (EC) technique, spread out all over the European Continent, and continuously expanding. The Ecosystem Thematic Centre (ETC) coordinates the activities of ecosystem stations to ensure high-precision datasets and standardisation. The so-called Labelling procedure is made of two steps, conceived to guide the candidate stations to get the official ICOS label: the Step 1 is focused on the sensors’ setup and is structured as a discussion between the ETC and the station teams, while the Step 2 concerns the practical build-up of the station and the data evaluation. For stations with the stricter standards (so-called Class 1 and Class 2), some quality tests on the data are included: one on the EC data quality, two on the representativeness of the measured EC fluxes and one on the representativeness of the ancillary plots.
Currently 58 of 86 candidate stations completed the labelling procedure, of which 30 Class 1 and 2. The more common fixes agreed in Step 1 are changes in sonic orientation and height or location, to better deal with fetch and canopy inhomogeneities. In Step 2, apart from increasing the signal resolution and fixing some metadata, a further correction of the location/height of the sensors led to solving the remaining problems. Overall, two thirds of the stations passed the three EC tests at the first try (all the wetlands, 74% of the forests, 33% of the crops), pointing at the efficiency of the Step 1 evaluations, while the remaining ten didn’t pass one or more of the two other EC tests, testifying that some issues are only discoverable from proper data analysis. About one third of the stations didn’t pass the ancillary representativeness test, all of them over forests: the most common solution was to add or move one or more plots.
The results support the common knowledge that more complex ecosystems - not uniform canopy geometries, fast growing vegetation - are more likely to be affected by some data quality issue. This constitutes a crucial warning to researchers and technicians in the direction of properly considering the station characteristics when planning its setup and sampling design, as well as continuously checking the data produced, to ensure the production of high-precision datasets.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ICOS (Integrated Carbon Observation System) is a Research Infrastructure aiming at getting a deeper understanding of the European Carbon balance by means of a network of monitoring stations, based on eddy covariance (EC) technique, spread out all over the European Continent, and continuously expanding. The Ecosystem Thematic Centre (ETC) coordinates the activities of ecosystem stations to ensure high-precision datasets and standardisation. The so-called Labelling procedure is made of two steps, conceived to guide the candidate stations to get the official ICOS label: the Step 1 is focused on the sensors’ setup and is structured as a discussion between the ETC and the station teams, while the Step 2 concerns the practical build-up of the station and the data evaluation. For stations with the stricter standards (so-called Class 1 and Class 2), some quality tests on the data are included: one on the EC data quality, two on the representativeness of the measured EC fluxes and one on the representativeness of the ancillary plots.
Currently 58 of 86 candidate stations completed the labelling procedure, of which 30 Class 1 and 2. The more common fixes agreed in Step 1 are changes in sonic orientation and height or location, to better deal with fetch and canopy inhomogeneities. In Step 2, apart from increasing the signal resolution and fixing some metadata, a further correction of the location/height of the sensors led to solving the remaining problems. Overall, two thirds of the stations passed the three EC tests at the first try (all the wetlands, 74% of the forests, 33% of the crops), pointing at the efficiency of the Step 1 evaluations, while the remaining ten didn’t pass one or more of the two other EC tests, testifying that some issues are only discoverable from proper data analysis. About one third of the stations didn’t pass the ancillary representativeness test, all of them over forests: the most common solution was to add or move one or more plots.
The results support the common knowledge that more complex ecosystems - not uniform canopy geometries, fast growing vegetation - are more likely to be affected by some data quality issue. This constitutes a crucial warning to researchers and technicians in the direction of properly considering the station characteristics when planning its setup and sampling design, as well as continuously checking the data produced, to ensure the production of high-precision datasets.
Currently 58 of 86 candidate stations completed the labelling procedure, of which 30 Class 1 and 2. The more common fixes agreed in Step 1 are changes in sonic orientation and height or location, to better deal with fetch and canopy inhomogeneities. In Step 2, apart from increasing the signal resolution and fixing some metadata, a further correction of the location/height of the sensors led to solving the remaining problems. Overall, two thirds of the stations passed the three EC tests at the first try (all the wetlands, 74% of the forests, 33% of the crops), pointing at the efficiency of the Step 1 evaluations, while the remaining ten didn’t pass one or more of the two other EC tests, testifying that some issues are only discoverable from proper data analysis. About one third of the stations didn’t pass the ancillary representativeness test, all of them over forests: the most common solution was to add or move one or more plots.
The results support the common knowledge that more complex ecosystems - not uniform canopy geometries, fast growing vegetation - are more likely to be affected by some data quality issue. This constitutes a crucial warning to researchers and technicians in the direction of properly considering the station characteristics when planning its setup and sampling design, as well as continuously checking the data produced, to ensure the production of high-precision datasets.
15.
Calders, Kim; Brede, Benjamin; Newnham, Glenn; Culvenor, Darius; Armston, John; Bartholomeus, Harm; Griebel, Anne; Hayward, Jodie; Junttila, Samuli; Lau, Alvaro; Levick, Shaun; Morrone, Rosalinda; Origo, Niall; Pfeifer, Marion; Verbesselt, Jan; Herold, Martin
StrucNet: a global network for automated vegetation structure monitoring Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokeyd,
title = {StrucNet: a global network for automated vegetation structure monitoring},
author = {Kim Calders and Benjamin Brede and Glenn Newnham and Darius Culvenor and John Armston and Harm Bartholomeus and Anne Griebel and Jodie Hayward and Samuli Junttila and Alvaro Lau and Shaun Levick and Rosalinda Morrone and Niall Origo and Marion Pfeifer and Jan Verbesselt and Martin Herold},
url = {https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.333#pane-pcw-figures},
doi = {10.1002/rse2.333},
year = {2023},
date = {2023-04-14},
urldate = {2023-04-14},
abstract = {Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.
14.
Araza, Arnan; Herold, Martin; de Bruin, Sytze; Ciais, Philippe; Gibbs, David A.; Harris, Nancy; Santoro, Maurizio; Wigneron, Jean-Pierre; Yang, Hui; Málaga, Natalia; Nesha, Karimon; Rodriguez-Veiga, Pedro; Brovkina, Olga; Brown, Hugh C. A.; Chanev, Milen; Dimitrov, Zlatomir; Filchev, Lachezar; Fridman, Jonas; García, Mariano; Gikov, Alexander; Govaere, Leen; Dimitrov, Petar; Moradi, Fardin; Muelbert, Adriane Esquivel; Novotný, Jan; Pugh, Thomas A. M.; Schelhaas, Mart-Jan; Schepaschenko, Dmitry; Stereńczak, Krzysztof; Hein, Lars
Past decade above-ground biomass change comparisons from four multi-temporal global maps Journal Article
In: vol. 118, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokeyl,
title = {Past decade above-ground biomass change comparisons from four multi-temporal global maps},
author = {Arnan Araza and Martin Herold and Sytze de Bruin and Philippe Ciais and David A. Gibbs and Nancy Harris and Maurizio Santoro and Jean-Pierre Wigneron and Hui Yang and Natalia Málaga and Karimon Nesha and Pedro Rodriguez-Veiga and Olga Brovkina and Hugh C.A. Brown and Milen Chanev and Zlatomir Dimitrov and Lachezar Filchev and Jonas Fridman and Mariano García and Alexander Gikov and Leen Govaere and Petar Dimitrov and Fardin Moradi and Adriane Esquivel Muelbert and Jan Novotný and Thomas A.M. Pugh and Mart-Jan Schelhaas and Dmitry Schepaschenko and Krzysztof Stereńczak and Lars Hein},
url = {https://www.sciencedirect.com/science/article/pii/S1569843223000961?via%3Dihub},
doi = {10.1016/j.jag.2023.103274},
year = {2023},
date = {2023-04-04},
volume = {118},
abstract = {Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (
AGB) products has increased in recent years. Here we assessed the past decade net
AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local
AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels
10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall
AGB map cross-correlation between maps varied in the range 0.11–0.29 (r). Reported
AGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that
AGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently,
AGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (
AGB) products has increased in recent years. Here we assessed the past decade net
AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local
AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels
10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall
AGB map cross-correlation between maps varied in the range 0.11–0.29 (r). Reported
AGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that
AGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently,
AGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.
AGB) products has increased in recent years. Here we assessed the past decade net
AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local
AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels
10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall
AGB map cross-correlation between maps varied in the range 0.11–0.29 (r). Reported
AGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that
AGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently,
AGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.
13.
Camara, Gilberto; Simoes, Rolf; Ruivo, Heloisa; Andrade, Pedro; Soterroni, Aline; Ramos, Fernando; Ramos, Rafael; Scarabello, Marluce; Almeida, Claudio; Sanches, Ieda; Maurano, Luis; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Venturieri, Adriano; Adami, Marcos
Forest restoration challenges in Brazilian Amazonia Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Forest restoration challenges in Brazilian Amazonia},
author = {Gilberto Camara and Rolf Simoes and Heloisa Ruivo and Pedro Andrade and Aline Soterroni and Fernando Ramos and Rafael Ramos and Marluce Scarabello and Claudio Almeida and Ieda Sanches and Luis Maurano and Alexandre Coutinho and Julio Esquerdo and Joao Antunes and Adriano Venturieri and Marcos Adami },
url = {https://www.preprints.org/manuscript/202303.0365/v1},
doi = {10.20944/preprints202303.0365.v1 },
year = {2023},
date = {2023-03-21},
urldate = {2023-03-21},
abstract = {In its Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change, Brazil committed to reducing greenhouse gas emissions and restoring its forests. This study examines the challenges of fulfilling these commitments in Brazilian Amazonia. We carry out a detailed assessment of the current status of land tenure in the region and its relation to deforestation. After dealing with conflicts and overlaps between data from various sources, we produce a new map of public and private land tenure in Amazonia. Combining this map with Brazil's official data on deforestation, we find out how much natural vegetation has been preserved in each public or private area. The result is used to estimate how much deforestation is illegal. We also establish how much deforestation is associated with each land tenure type. Our results show that most deforestation inside rural properties is done by a few landowners, a finding that has important consequences for law enforcement. We then assess the challenges for reforestation in detail. To do so, we consider how much forest needs to be rehabilitated according to Brazil's Forest Code. Our analysis provides a comprehensive appraisal of the potential opportunity costs for forest restoration in the biome, considering farm size and land use. This analysis provides insights into targeted land use policies that can meet Brazil’s forest restoration goals.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In its Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change, Brazil committed to reducing greenhouse gas emissions and restoring its forests. This study examines the challenges of fulfilling these commitments in Brazilian Amazonia. We carry out a detailed assessment of the current status of land tenure in the region and its relation to deforestation. After dealing with conflicts and overlaps between data from various sources, we produce a new map of public and private land tenure in Amazonia. Combining this map with Brazil's official data on deforestation, we find out how much natural vegetation has been preserved in each public or private area. The result is used to estimate how much deforestation is illegal. We also establish how much deforestation is associated with each land tenure type. Our results show that most deforestation inside rural properties is done by a few landowners, a finding that has important consequences for law enforcement. We then assess the challenges for reforestation in detail. To do so, we consider how much forest needs to be rehabilitated according to Brazil's Forest Code. Our analysis provides a comprehensive appraisal of the potential opportunity costs for forest restoration in the biome, considering farm size and land use. This analysis provides insights into targeted land use policies that can meet Brazil’s forest restoration goals.