Scientific publications

@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}

}

@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}

}

@article{nokey,

title = {Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution},

author = {Julia Hackländer​ and Leandro Parente and Yu-Feng Ho and Tomislav Hengl and Rolf Simoes and Davide Consoli and Murat Şahin and Xuemeng Tian and Martin Jung and Martin Herold and Gregory Duveiller and Melanie Weynants, Ichsani Wheeler},

url = {https://peerj.com/articles/16972/},

doi = {https://doi.org/10.7717/peerj.16972},

issn = {2167-8359},

year  = {2024},

date = {2024-03-13},

journal = {PeerJ},

abstract = {The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. 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, landform, and vegetation cover, as well as several variables representing 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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}