Nesha, K., Herold, M., De Sy, V., de Bruin, S., Araza, A., Málaga, N., Gamarra, J. G., Hergoualc'h, K., Pekkarinen, A., Ramirez, C., Morales-Hidalgo, D., Tavani, R. (2022): Exploring characteristics of national forest inventories for integration with global space-based forest biomass data. - Science of the Total Environment, 157788. https://doi.org/10.1016/j.scitotenv.2022.157788

Abstract: National forest inventories (NFIs) are a reliable source for national forest measurements. However, they are usually not developed for linking with remotely sensed (RS) biomass information. There are increasing needs and opportunities to facilitate this link towards better global and national biomass estimation. Thus, it is important to study and understand NFI characteristics relating to their integration with space-based products; in particular for the tropics where NFIs are quite recent, less frequent, and partially incomplete in several countries. Here, we (1) assessed NFIs in terms of their availability, temporal distribution, and extent in 236 countries from FAO's Global Forest Resources Assessment (FRA) 2020; (2) compared national forest biomass estimates in 2018 from FRA and global space-based Climate Change Initiative (CCI) product in 182 countries considering NFI availability and temporality; and (3) analyzed the latest NFI design characteristics in 46 tropical countries relating to their potential integration with space-based biomass datasets. We observed significant NFI availability globally and multiple NFIs were mostly found in temperate and boreal countries while most of the single NFI countries (94 %) were in the tropics. The latest NFIs were more recent in the tropics and many countries (35) implemented NFIs from 2016 onwards. The increasing availability and update of NFIs create new opportunities for integration with space-based data at the national level. This is supported by the agreement we found between country biomass estimates 2018 from FRA and CCI product, with a significantly higher correlation in countries with recent NFIs. We observed that NFI designs varied greatly in tropical countries. For example, the size of the plots ranged from 0.01 to 1 ha and more than three-quarters of the countries had smaller plots of ≤0.25 ha. The existing NFI designs could pose specific challenges for statistical integration with RS data in the tropics. Future NFI and space-based efforts should aim towards a more integrated approach taking advantage of both data streams to improve national estimates and help future data harmonization efforts. Regular NFI efforts can be expanded with the inclusion of some super-site plots to enhance data integration with currently available space-based applications. Issues related to cost implications versus improvements in the accuracy, timeliness, and sustainability of national forest biomass estimation should be further explored.

Angelopoulos, M., Damm, E., Simões Pereira, P., Abrahamsson, K., Bauch, D., Bowman, J., Castellani, G., Creamean, J., Divine, D. V., Dumitrascu, A., Fons, S. W., Granskog, M. A., Kolabutin, N., Krumpen, T., Marsay, C., Nicolaus, M., Oggier, M., Rinke, A., Sachs, T., Shimanchuk, E., Stefels, J., Stephens, M., Ulfsbo, A., Verdugo, J., Wang, L., Zhan, L., Haas, C. (2022 online): Deciphering the Properties of Different Arctic Ice Types During the Growth Phase of MOSAiC: Implications for Future Studies on Gas Pathways. - Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.864523

Abstract: The increased fraction of first year ice (FYI) at the expense of old ice (second-year ice (SYI) and multi-year ice (MYI)) likely affects the permeability of the Arctic ice cover. This in turn influences the pathways of gases circulating therein and the exchange at interfaces with the atmosphere and ocean. We present sea ice temperature and salinity time series from different ice types relevant to temporal development of sea ice permeability and brine drainage efficiency from freeze-up in October to the onset of spring warming in May. Our study is based on a dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 and 2020. These physical properties were used to derive sea ice permeability and Rayleigh numbers. The main sites included FYI and SYI. The latter was composed of an upper layer of residual ice that had desalinated but survived the previous summer melt and became SYI. Below this ice a layer of new first-year ice formed. As the layer of new first-year ice has no direct contact with the atmosphere, we call it insulated first-year ice (IFYI). The residual/SYI-layer also contained refrozen melt ponds in some areas. During the freezing season, the residual/SYI-layer was consistently impermeable, acting as barrier for gas exchange between the atmosphere and ocean. While both FYI and SYI temperatures responded similarly to atmospheric warming events, SYI was more resilient to brine volume fraction changes because of its low salinity (<< 2). Furthermore, later bottom ice growth during spring warming was observed for SYI in comparison to FYI. The projected increase in the fraction of more permeable FYI in autumn and spring in the coming decades may favor gas exchange at the atmosphere-ice interface when sea ice acts as a source relative to the atmosphere. While the areal extent of old ice is decreasing, so is its thickness at the onset of freeze-up. Our study sets the foundation for studies on gas dynamics within the ice column and the gas exchange at both ice interfaces, i.e. with the atmosphere and the ocean.

Yuan, K., Zhu, Q., Li, F., Riley, W. J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K., Wu, H., Baldocchi, D., Ma, H., Desai, A. R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., Tuittila, E.-S., Jurasinski, G., Koebsch, F., Campbell, D., Schmid, H. P., Lohila, A., Goeckede, M., Nilsson, M. B., Friborg, T., Jansen, J., Zona, D., Euskirchen, E., Ward, E. J., Bohrer, G., Jin, Z., Liu, L., Iwata, H., Goodrich, J., Jackson, R. (2022): Causality guided machine learning model on wetland CH4 emissions across global wetlands. - Agricultural and Forest Meteorology, 324, 109115. https://doi.org/10.1016/j.agrformet.2022.109115

Abstract: Wetland CH₄ emissions are among the most uncertain components of the global CH₄ budget. The complex nature of wetland CH₄ processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH₄ emissions. In this study, we used the flux measurements of CH₄ from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH₄ emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH₄ emissions in all studied wetland types. Ecosystem respiration (CO₂) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH₄ emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH₄ emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH₄ emissions within earth system land models.

Liang, J., Gamarra, J.G.P., Picard, N. et al. Co-limitation towards lower latitudes shapes global forest diversity gradients. Nat Ecol Evol

 (2022). doi.org/10.1038/s41559-022-01831-x

Abstract: The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.

Benjamin Brede, Loise Terryn, Nicolas Barbier, Harm M. Bartholomeus, Renée Bartolo, Kim Calders, Géraldine Derroire, Sruthi M. Krishna Moorthy, Alvaro Lau, Shaun R. Levick, Pasi Raumonen, Hans Verbeeck, Di Wang, Tim Whiteside, Jens van der Zee, Martin Herold; https://doi.org/10.1016/j.rse.2022.113180

Abstract: Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100 ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB

Am 18. Juli geben wir gemeinsam mit allen Partnern den Startschuss für unser Open-Earth-Monitor-Projekt! Dieses Projekt hat zum Ziel, die Nutzung von Open-Source-Umweltdaten (erfasst durch z.B. Fernerkundung und in-situ Messungen) signifikant zu steigern und Anwendungen zu entwickeln, um das Monitoring und die Zustandserfassung von kritischen Regionen der Erde deutlich zu verbessern. Dafür wird eine Open-Source-Cyberinfrastruktur aufgebaut, welche die Nutzung von Umweltinformationen erheblich beschleunigt und den Aufbau von Nutzergemeinschaften auf europäischer und globaler Ebene unterstützt. Damit unterstützt dieses Projekt direkt die Open-Science-Ziele des GFZ.

Anlässlich des Projektstarts wird am 19. Juli ein erster öffentlicher Workshop mit interaktiven Debatten, Feedback-Runden und Demo-Sessions stattfinden. Mit dabei sind prominente Vertretende aus den Bereichen Erdbeobachtung, Datenpolitik, Wirtschaft und Wissenschaft. Hier geht es zur kostenfreien Registrierung: https://bit.ly/3ArlA9y

Das Open-Earth-Monitor Projekt wird von der OpenGeoHub Foundation koordiniert, einer unabhängigen, gemeinnützigen Forschungsstiftung zur Förderung von Open Source- und Open Data-Lösungen.

In den letzten Jahrzehnten hat die Erdbeobachtungstechnologie weltweit einen rasanten Aufschwung erlebt und wird von Vielen inzwischen als eines der wichtigsten Instrumente zur grenzüberschreitenden Bewältigung von Umwelt- und Klimakrisen angesehen. Dies hat jedoch seinen Preis: Massive und inkonsistente Datenmengen, die von Erdbeobachtungssensoren und Bodenüberwachungsnetzen erzeugt werden, überfordern inzwischen die Forschungsnetze; Umweltinformationen werden oft nur unzureichend genutzt, da sie ein hohes Maß an Fachwissen und Rechenkapazität erfordern. Damit sind Umweltdaten und digitale Lösungen noch kein Allgemeingut für Landwirte, Landnutzer, Praktiker der Ökosystemregeneration, Institutionen und politische Entscheidungsträger geworden.

Impact:

Das Open-Earth-Monitor-Konsortium besteht aus 23 Partnerorganisationen innerhalb und außerhalb Europas. Folgende Aktivitäten werden wir gemeinsam umsetzen:

• Inventarisierung von Nutzerbedarfen, verfügbaren Open-Source-Daten und existierendem Knowhow.
• Erleichterung des Zugangs zu existierenden europäischen und globalen Erdbeobachtungsdaten und Umweltinformationen sowie die Reduzierung der Datenkomplexität für alle Stakeholder.
• Entwicklung einer Reihe von intuitiven Applikationen um die Zustandserhebung und das Monitoring naturräumlicher Ressourcen auf europäischer und globaler Ebene zu vereinfachen und damit nachhaltige und ökologische Business-2-Business-Lösungen zu fördern.
• Aufbau einer umfassenden und systematischen Plattform zur Verbesserung der FAIRness (Auffindbarkeit, Zugänglichkeit, Interoperabilität und Wiederverwendbarkeit) von Umweltinformationen, insbesondere zur Erfüllung der Anforderungen des europäischen AI-Gesetzes und des europäischen GDPR-Gesetzes.

Etablierung einer dauerhaften und nachhaltigen Lösung zur operativen Verarbeitung und Bereitstellung von Erdbeobachtungsdaten, In-situ-Umweltdaten, künstlicher Intelligenz, maschinellem Lernen und HPC-Modellen (OEMC-Computing-Engine).

Das GFZ unterstützt das Open-Earth-Monitor Projekt im Wesentlichen durch folgende Aktivitäten:

• Initiierung und Pflege eines aktiven Austausches mit Nutzenden, um deren Bedarfe und Anforderungen zu verstehen und gemeinsam ein bedarfsorientiertes und nutzerfreundliches Systemdesign sowie FAIR-Daten-Workflows zu definieren.
• Entwicklung von Methoden und Applikationen zur Schätzung der Biomasse sowie die Zusammenführung und Verarbeitung vorhandener Biomassedaten auf europäischer und globaler Ebene. Dies beinhaltet die Bewertung und Verarbeitung verfügbarer Biomassedatensätze (weltraumgestützt, in-situ) sowie die Implementierung eines Open Source Tools zur Waldbiomasseschätzung aus kombinierten satelliten- und bodengestützten Datenquellen.
• Darüber hinaus wird das GFZ die Entwicklung eines globalen Wald-THG-Emissionsmonitors leiten, der mit den verschiedenen Rohstoffen und Landnutzungen (z.B. Ölpalmen, Tropenholz, Soja, Rindfleisch und Kakao) verknüpft werden kann.