9.
Domenico, Vitale; Gerardo, Fratini; Carol, Helfter; Lukas, Hortnagl; Kukka-Maaria, Kohonen; Ivan, Mammarella; Eiko, Nemitz; Giacomo, Nicolini; Corinna, Rebmann; Simone, Sabbatini; Dario, Papale
A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems Journal Article
In: Springer Link, vol. 31, pp. 219–244, 2024, ISSN: 1573-3009.
Abstract | Links | BibTeX | Tags: Open Access
@article{nokey,
title = {A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems},
author = {Vitale Domenico and Fratini Gerardo and Helfter Carol and Hortnagl Lukas and Kohonen Kukka-Maaria and Mammarella Ivan and Nemitz Eiko and Nicolini Giacomo and Rebmann Corinna and Sabbatini Simone and Papale Dario },
url = {https://link.springer.com/article/10.1007/s10651-024-00615-9},
doi = {https://doi.org/10.1007/s10651-024-00615-9},
issn = {1573-3009},
year = {2024},
date = {2024-04-21},
journal = {Springer Link},
volume = {31},
pages = {219–244},
abstract = {The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than
) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.},
keywords = {Open Access},
pubstate = {published},
tppubtype = {article}
}
The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than
) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.
) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.
8.
Reiche, Johannes; Balling, Johannes; Pickens, Amy Hudson; Masolele, Robert N; Berger, Anika; Weisse, Mikaela J; Mannarino, Daniel; Gou, Yaqing; Slagter, Bart; Donchyts, Gennadii
Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence Journal Article
In: Environmental Research Letters, vol. 19, no. 5, 2024.
Abstract | Links | BibTeX | Tags: Open Access
@article{nokey,
title = {Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence},
author = {Johannes Reiche and Johannes Balling and Amy Hudson Pickens and Robert N Masolele and Anika Berger and Mikaela J Weisse and Daniel Mannarino and Yaqing Gou and Bart Slagter and Gennadii Donchyts},
url = {https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82},
doi = {10.1088/1748-9326/ad2d82},
year = {2024},
date = {2024-04-16},
journal = {Environmental Research Letters},
volume = {19},
number = {5},
abstract = {Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based forest disturbance alerts, each with varying detection capabilities depending mainly on the satellite sensor used, poses a challenge for users in selecting the most suitable system for their monitoring needs and workflow. Integrating multiple alerts holds the potential to address the limitations of individual systems. We integrated radar-based RAdar for Detecting Deforestation (RADD) (Sentinel-1), and optical-based Global Land Analysis and Discovery Sentinel-2 (GLAD-S2) and GLAD-Landsat alerts using two confidence rulesets at ten 1° sites across the Amazon Basin. Alert integration resulted in faster detection of new disturbances by days to months, and also shortened the delay to increased confidence. An increased detection rate to an average of 97% when combining alerts highlights the complementary capabilities of the optical and cloud-penetrating radar sensors in detecting largely varying drivers and environmental conditions, such as fires, selective logging, and cloudy circumstances. The most improvement was observed when integrating RADD and GLAD-S2, capitalizing on the high temporal observation density and spatially detailed 10 m Sentinel-1 and 2 data. We introduced the highest confidence class as an addition to the low and high confidence classes of the individual systems, and showed that this displayed no false detection. Considering spatial neighborhood during alert integration enhanced the overall labeled alert confidence level, as nearby alerts mutually reinforced their confidence, but it also led to an increased rate of false detections. We discuss implications of this study for the integration of multiple alert systems. We demonstrate that alert integration is an important data preparation step to make use of multiple alerts more user-friendly, providing stakeholders with reliable and consistent information on new forest disturbances in a timely manner. Google Earth Engine code to integrate various alert datesets is made openly available.},
keywords = {Open Access},
pubstate = {published},
tppubtype = {article}
}
Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based forest disturbance alerts, each with varying detection capabilities depending mainly on the satellite sensor used, poses a challenge for users in selecting the most suitable system for their monitoring needs and workflow. Integrating multiple alerts holds the potential to address the limitations of individual systems. We integrated radar-based RAdar for Detecting Deforestation (RADD) (Sentinel-1), and optical-based Global Land Analysis and Discovery Sentinel-2 (GLAD-S2) and GLAD-Landsat alerts using two confidence rulesets at ten 1° sites across the Amazon Basin. Alert integration resulted in faster detection of new disturbances by days to months, and also shortened the delay to increased confidence. An increased detection rate to an average of 97% when combining alerts highlights the complementary capabilities of the optical and cloud-penetrating radar sensors in detecting largely varying drivers and environmental conditions, such as fires, selective logging, and cloudy circumstances. The most improvement was observed when integrating RADD and GLAD-S2, capitalizing on the high temporal observation density and spatially detailed 10 m Sentinel-1 and 2 data. We introduced the highest confidence class as an addition to the low and high confidence classes of the individual systems, and showed that this displayed no false detection. Considering spatial neighborhood during alert integration enhanced the overall labeled alert confidence level, as nearby alerts mutually reinforced their confidence, but it also led to an increased rate of false detections. We discuss implications of this study for the integration of multiple alert systems. We demonstrate that alert integration is an important data preparation step to make use of multiple alerts more user-friendly, providing stakeholders with reliable and consistent information on new forest disturbances in a timely manner. Google Earth Engine code to integrate various alert datesets is made openly available.
7.
Pondi, Brian; Appel, Marius; Pebesma, Edzer
OpenEOcubes; an open-source and lightweight R-based RESTful web service for analyzing earth observation data cubes Journal Article
In: Earth Science Informatics, vol. 17, pp. 1809–1818, 2024, ISBN: 1865-0481.
Abstract | Links | BibTeX | Tags: Open Access
@article{nokey,
title = {OpenEOcubes; an open-source and lightweight R-based RESTful web service for analyzing earth observation data cubes},
author = {Brian Pondi and Marius Appel and Edzer Pebesma},
url = {https://link.springer.com/article/10.1007/s12145-024-01249-y},
doi = {https://doi.org/10.1007/s12145-024-01249-y},
isbn = {1865-0481},
year = {2024},
date = {2024-02-19},
urldate = {2024-02-19},
journal = {Earth Science Informatics},
volume = {17},
pages = { 1809–1818},
abstract = {In recent decades, Earth Observation (EO) systems have seen remarkable technological advancements, leading to a surge in Earth-orbiting satellites capturing EO data. Cloud-based storage solutions have been adopted to manage the increasing data volume. Although numerous EO data management and analysis platforms have emerged to accommodate this growth, many suffer from limitations like closed-source software, leading to platform lock-in and restricted functionalities, restricting the scientific community from conducting open and reproducible research. To tackle these issues, we present OpenEOcubes, a lightweight EO data cubes analysis service that embraces open-source tools, standardized APIs, and containerized deployment, we demonstrate the service’s capabilities in two user scenarios: performing vegetation analysis in Amazonia, Brazil for one year, and detecting changes in a forested area in Brandenburg, Germany based on five years of EO data.OpenEOcubes is an easy-to-deploy service that empowers the scientific community to reproduce small and medium-sized EO scientific analysis while aggregating over a potentially huge amount of data. It enables the extension of functionalities and validation of analysis carried out on different EO data processing platforms.},
keywords = {Open Access},
pubstate = {published},
tppubtype = {article}
}
In recent decades, Earth Observation (EO) systems have seen remarkable technological advancements, leading to a surge in Earth-orbiting satellites capturing EO data. Cloud-based storage solutions have been adopted to manage the increasing data volume. Although numerous EO data management and analysis platforms have emerged to accommodate this growth, many suffer from limitations like closed-source software, leading to platform lock-in and restricted functionalities, restricting the scientific community from conducting open and reproducible research. To tackle these issues, we present OpenEOcubes, a lightweight EO data cubes analysis service that embraces open-source tools, standardized APIs, and containerized deployment, we demonstrate the service’s capabilities in two user scenarios: performing vegetation analysis in Amazonia, Brazil for one year, and detecting changes in a forested area in Brandenburg, Germany based on five years of EO data.OpenEOcubes is an easy-to-deploy service that empowers the scientific community to reproduce small and medium-sized EO scientific analysis while aggregating over a potentially huge amount of data. It enables the extension of functionalities and validation of analysis carried out on different EO data processing platforms.
6.
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: Science Direct, vol. 554, 2024.
Abstract | Links | BibTeX | Tags: Open Access
@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.sciencedirect.com/science/article/pii/S0378112723008861?via%3Dihub},
doi = {https://doi.org/10.1016/j.foreco.2023.121652},
year = {2024},
date = {2024-02-15},
journal = {Science Direct},
volume = {554},
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 = {Open Access},
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.
5.
Qiu, Junliang; Zhao, Wei; Brocca, Luca; Tarolli, Paolo
Storm Daniel revealed the fragility of the Mediterranean region Journal Article
In: The Innovation Geoscience, 2023, ISSN: 2959-8753.
Abstract | Links | BibTeX | Tags: Open Access
@article{nokey,
title = {Storm Daniel revealed the fragility of the Mediterranean region},
author = {Junliang Qiu and Wei Zhao and Luca Brocca and Paolo Tarolli},
url = {https://www.the-innovation.org/article/doi/10.59717/j.xinn-geo.2023.100036},
doi = {https://doi.org/10.59717/j.xinn-geo.2023.100036},
issn = {2959-8753},
year = {2023},
date = {2023-12-12},
urldate = {2023-12-12},
journal = {The Innovation Geoscience},
abstract = {Over the past two years, the world has witnessed a surge in extreme events, including record-breaking droughts, heatwaves, forest fires, floods, ocean warming, and sea ice melting. These events have affected large regions, with devastating droughts striking Europe, East Africa, Asia, and South America, historic floods hitting Pakistan, and unprecedented heatwaves scorching western North America. Major wildfires have ravaged areas in Algeria, southern Turkey, Greece, and Spain, while the Arctic and Antarctic continue to experience alarming sea ice melt.},
keywords = {Open Access},
pubstate = {published},
tppubtype = {article}
}
Over the past two years, the world has witnessed a surge in extreme events, including record-breaking droughts, heatwaves, forest fires, floods, ocean warming, and sea ice melting. These events have affected large regions, with devastating droughts striking Europe, East Africa, Asia, and South America, historic floods hitting Pakistan, and unprecedented heatwaves scorching western North America. Major wildfires have ravaged areas in Algeria, southern Turkey, Greece, and Spain, while the Arctic and Antarctic continue to experience alarming sea ice melt.
4.
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
In: vol. 298, 2023.
Abstract | Links | BibTeX | Tags: Open Access
@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 = {https://doi.org/10.1016/j.rse.2023.113799},
year = {2023},
date = {2023-12-01},
volume = {298},
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 = {Open Access},
pubstate = {published},
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.
3.
Laurin, Gaia Vaglio; Cotrina-Sanchez, Alexander; Belelli-Marchesini, Luca; Tomelleri, Enrico; Battipaglia, Giovanna; Cocozza, Claudia; Niccoli, Francesco; Kabala, Jerzy Piotr; Gianelle, Damiano; Vescovo, Loris; Ros, Luca Da; Valentini, Riccardo
Comparing ground below-canopy and satellite spectral data for an improved and integrated forest phenology monitoring system Journal Article
In: ScienceDirect, vol. 158, 2023.
Abstract | Links | BibTeX | Tags: Open Access
@article{nokey,
title = {Comparing ground below-canopy and satellite spectral data for an improved and integrated forest phenology monitoring system},
author = {Gaia Vaglio Laurin and Alexander Cotrina-Sanchez and Luca Belelli-Marchesini and Enrico Tomelleri and Giovanna Battipaglia and Claudia Cocozza and Francesco Niccoli and Jerzy Piotr Kabala and Damiano Gianelle and Loris Vescovo and Luca Da Ros and Riccardo Valentini},
url = {https://www.sciencedirect.com/science/article/pii/S1470160X2301470X?via%3Dihub},
doi = {https://doi.org/10.1016/j.ecolind.2023.111328},
year = {2023},
date = {2023-11-30},
journal = {ScienceDirect},
volume = {158},
abstract = {Phenology monitoring allows a better understanding of forest functioning and climate impacts. Satellite indicators are used to upscale ground phenological observations, but often differential responses are observed, and data availability can be limited. In view of climate impacts, new tools capable to detect rapid phenological changes and to work at single species level are needed. This research compares indices derived by the TreeTalker© (TT + ) below canopy upward-looking spectral data and Sentinel 2 satellite data, used to assess the phenological behavior and changepoints in several European beech forests. Overall, a mismatch between the information derived by the two sensor types is evidenced, with main differences in: start/end and length of season and phenology changepoints; larger variability captured by TT + with respect to Sentinel 2 especially in the leaf on period; mixed signal response from multiple vegetation layers in Sentinel 2 data. The complementarity of satellite and TT + indices allow exploring the phenological responses from different vegetation layers. TT + higher temporal resolution demonstrates precision in capturing the phenological changepoints in beech forests, especially if satellite image availability is limited by cloud cover and leads to miss critical phenological dates. The best settings for TT + data collection and the advantages to have two spectral data sources for improved forest phenology monitoring are also commented. The TT+, collecting additional tree parameters, can be a valuable tool for an integrated monitoring system based on spectral signals from above and below the canopy, at high temporal frequency and high spatial resolution.},
keywords = {Open Access},
pubstate = {published},
tppubtype = {article}
}
Phenology monitoring allows a better understanding of forest functioning and climate impacts. Satellite indicators are used to upscale ground phenological observations, but often differential responses are observed, and data availability can be limited. In view of climate impacts, new tools capable to detect rapid phenological changes and to work at single species level are needed. This research compares indices derived by the TreeTalker© (TT + ) below canopy upward-looking spectral data and Sentinel 2 satellite data, used to assess the phenological behavior and changepoints in several European beech forests. Overall, a mismatch between the information derived by the two sensor types is evidenced, with main differences in: start/end and length of season and phenology changepoints; larger variability captured by TT + with respect to Sentinel 2 especially in the leaf on period; mixed signal response from multiple vegetation layers in Sentinel 2 data. The complementarity of satellite and TT + indices allow exploring the phenological responses from different vegetation layers. TT + higher temporal resolution demonstrates precision in capturing the phenological changepoints in beech forests, especially if satellite image availability is limited by cloud cover and leads to miss critical phenological dates. The best settings for TT + data collection and the advantages to have two spectral data sources for improved forest phenology monitoring are also commented. The TT+, collecting additional tree parameters, can be a valuable tool for an integrated monitoring system based on spectral signals from above and below the canopy, at high temporal frequency and high spatial resolution.
2.
Mo, Lidong; Zohner, Constantin M; Reich, Peter B; Liang, Jingjing; Miguel, Sergio De; Nabuurs, Gert-Jan; Renner, Susanne S; van den Hoogen, Johan; Araza, Arnan; and, Martin Herold
Integrated global assessment of the natural forest carbon potential Journal Article
In: Nature, vol. 624, pp. 92–101, 2023.
Abstract | Links | BibTeX | Tags: Open Access
@article{nokey,
title = {Integrated global assessment of the natural forest carbon potential},
author = {Lidong Mo and Constantin M Zohner and Peter B Reich and Jingjing Liang and Sergio De Miguel and Gert-Jan Nabuurs and Susanne S Renner and Johan van den Hoogen and Arnan Araza and Martin Herold and et al.},
url = {https://www.nature.com/articles/s41586-023-06723-z},
doi = {https://doi.org/10.1038/s41586-023-06723-z},
year = {2023},
date = {2023-11-13},
journal = {Nature},
volume = {624},
pages = {92–101},
abstract = {Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.},
keywords = {Open Access},
pubstate = {published},
tppubtype = {article}
}
Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.
1.
Mosaffa, Hamidreza; Filippucci, Paolo; Massari, Christian; Ciabatta, Luca; Brocca, Luca
SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies Bachelor Thesis
2023.
Abstract | Links | BibTeX | Tags: Open Access
@bachelorthesis{nokey,
title = {SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies},
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},
doi = {https://doi.org/10.1038/s41597-023-02654-6},
year = {2023},
date = {2023-10-31},
journal = {Nature},
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 = {Open Access},
pubstate = {published},
tppubtype = {bachelorthesis}
}
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.