In the last years, several studies were published evaluating aerosol-cloud-precipitation interactions. These studies improved the knowledge and reduced the uncertainties in the quantification of the aerosol aerosol-cloud interactions. However, there were only very few attempts to describe how clouds modify the aerosol properties. The main goal of this study is to evaluate the effect of weather events on the Particle Size Distribution (PSD) at the Amazon Tall Tower Observatory (ATTO). This research combines different types of datasets, all co-located at the ATTO towers. Basically, the data were obtained from the new generation of GOES satellites, GOES-16, the SIPAM S Band radar and two Scanning Mobility Particle Sizers (SMPS) installed at the heights of 60 and 325 m from 2017 to 2020. In addition, the LAP 3000 radar wind profile recently installed at the ATTO- Campina site was employed to evaluate the vertical distribution of the vertical velocity. The combination of these datasets allows to explore changes in PSD due to the different meteorological processes. The diurnal cycle shows an increase of nucleation particles and decrease in Aitken and accumulation modes during the night. The early morning is the time of minimum mass concentration. From the early morning to the middle of the afternoon, a contrary behaviour is observed, where the concentration of nucleation particles decreases and Aitken and accumulation mode increase, characterizing a typical particle growth process. In the late afternoon, when rain starts, PSD begin to have the night behaviour described above. Composite studies were computed to evaluate how the PSD evolve during rainfall events. The composite from lighting density shows a large increase in nucleation particles from around 100 minutes before the maximum lighting density, reaching maximum values nearly 200 minutes later. The nucleation particles growth rate increases exponentially with the thunderstorm intensity. Aitken and accumulation modes have a different behaviour, with decreasing number concentration from around 100 minutes before the maximum lighting activity and reaching the minimum concentration at the time of maximum lighting activity. This effect could be related to the more intense downdraft in thunderstorms that intensify the transport of ultrafine particles from the upper atmosphere as described in recent studies using GoAmazon and ACRIDICON-CHUVA data. Another possibility could be the transport of O₃ and NO₂ column densities during thunderstorms events, helping the oxidation of volatile organic component forming secondary organic aerosol at the surface. This is an open question and needs further studies specifically designed to understand the chemical processes occurring near-surface during intense rainfall events. The first data from the radar wind profile installed at the ATTO-Campina site was employed to compute the vertical distribution of the vertical velocity. The downdrafts are mainly located below 10km, but the layer of maximum concentration of ultrafine particles is mainly above 10km. In addition, the number concentration of nucleation particles at 60m is around twice the value at 325 m, in contrast to former studies showing an increase in ultrafine particles with height. CAFE-Brazil, scheduled for 2022, will be an opportunity to study these open questions.

How to cite: T. Machado, L. A., Pöhlker, M., Artaxo, P., Cecchini, M., Ditas, F., M. Franco, M. A., Kremper, L., Andreae, M. O., Saraiva, I., Pöschl, U., and Pöhlker, C.: How Weather Events Modify Amazonian Surface Aerosol Particle Size Distributions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11283, https://doi.org/10.5194/egusphere-egu21-11283, 2021.

The vegetated canopy plays a key role in regulating the surface fluxes and, therefore, the global energy, water and carbon cycles. In particular, vulnerable ecosystems like the Amazonia basin can be very sensitive to changes in vegetation that exert subsequent shifts in the partition of the energy, water and carbon in and above the canopy. Despite this relevance, most 3D atmospheric models represent the vegetated canopy as a flat 2D layer with, at most, a rough imitation of its effect in the atmospheric boundary layer through a modified roughness length. Thus, the representations often describe quite crudely the surface fluxes. In this work, particular emphasis is placed in the biophysical processes that take place within the canopy and its impact above. Our approach is to represent the coupling of the flow between the canopy and the atmosphere including the following processes: radiative transfer, photosynthesis, soil evaporation and CO2 respiration, combined with the mostly explicit atmospheric turbulence within and above the canopy. To this end, we implemented in LES a detailed multi-layer canopy model that solves the leaf energy balance for sunlit and shaded leaves independently, regulating the exchange of heat, moisture and carbon between the leaves and the air around. This allows us to connect the mechanistically represented processes occurring at the leaf level and strongly regulated by the transfer of diffuse and direct radiation within the canopy to the turbulent mixing explicitly resolved at the meter scale.

We test and validate this combined photosynthesis-turbulence-canopy model by simulating a representative clear day transitioning to shallow cumulus. We based our evaluation on observations by the GoAmazon2014/5 campaign in Brazil in 2014. More specifically, we systematically validate the in-canopy radiation profiles; sources, sinks and turbulent fluxes of moisture, heat and CO2, and main state variables within the canopy, and also study the effects of these in the air above. Preliminary results show an encouraging satisfactory match to the observed evolution of the profiles. As a first exploration and demonstration of the capabilities of the model, we test the effects of a coarser in-canopy resolution, a different radiation scheme and the use of a more simple 2D canopy representation.

How to cite: Pedruzo-Bagazgoitia, X., Moene, A. F., Ouwersloot, H., Gerken, T., Machado, L. A. T., Martin, S. T., Patton, E. G., Sörgel, M., Stoy, P. C., Yamasoe, M. A., and Vilà-Guerau de Arellano, J.: Understanding in and above canopy-atmosphere interactions by combining large-eddy simulations with a comprehensive observational set, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12869, https://doi.org/10.5194/egusphere-egu21-12869, 2021.

Mauritia flexuosa palm swamp, the prevailing Peruvian Amazon peatland ecosystem, is

extensively threatened by degradation. The unsustainable practice of cutting whole

palms for fruit extraction modifies forest's structure and composition and eventually

alters peat-derived greenhouse gas (GHG) emissions. We evaluated the spatio-temporal

variability of soil N₂O and CH₄ fluxes and environmental controls along a palm swamp

degradation gradient formed by one undegraded site (Intact), one moderately degraded

site (mDeg) and one heavily degraded site (hDeg). Microscale variability differentiated

hummocks supporting live or cut palms from surrounding hollows. Macroscale analysis

considered structural changes in vegetation and soil microtopography as impacted

by degradation. Variables were monitored monthly over 3 years to evaluate intra- and

inter-annual variability. Degradation induced microscale changes in N₂O and CH₄ emission

trends and controls. Site-scale average annual CH₄ emissions were similar along the

degradation gradient (225.6 ± 50.7, 160.5 ± 65.9 and 169.4 ± 20.7 kg C ha⁻¹ year⁻¹ at

the Intact, mDeg and hDeg sites, respectively). Site-scale average annual N₂O emissions

(kg N ha⁻¹ year⁻¹) were lower at the mDeg site (0.5 ± 0.1) than at the Intact (1.3 ± 0.6) and

hDeg sites (1.1 ± 0.4), but the difference seemed linked to heterogeneous fluctuations

in soil water-filled pore space (WFPS) along the forest complex rather than to degradation.

Monthly and annual emissions were mainly controlled by variations in WFPS, water

table level (WT) and net nitrification for N₂O; WT, air temperature and net nitrification

for CH₄. Site-scale N₂O emissions remained steady over years, whereas CH₄ emissions

rose exponentially with increased precipitation. While the minor impact of degradation

on palm swamp peatland N₂O and CH₄ fluxes should be tested elsewhere, the evidenced

large and variable CH₄ emissions and significant N₂O emissions call for improved modeling

of GHG dynamics in tropical peatlands to test their response to climate changes.

How to cite: Hergoualc’h, K., Dezzeo, N., Verchot, L., Martius, C., van Lent, J., Del Aguila Pasquel, J., and Lopez, M.: Spatial and temporal variability of soil N2O and CH4 fluxes along a degradation gradient in a palm swamp peat forest in the Peruvian Amazon, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-831, https://doi.org/10.5194/egusphere-egu21-831, 2021.

Overall, global forests are expected to contribute about a quarter of pledged mitigation under the Paris Agreement, by limiting deforestation and by encouraging forest regrowth.

Secondary Forests in the Neo-tropics have a large climate mitigation potential, given their ability to sequester carbon up to 20 times faster than old-growth forests. However, this rate does not account for the spatial patterns in secondary forest regrowth influenced by regional and local-scale environmental and anthropogenic disturbance drivers.

Secondary Forests in the Brazilian Amazon are expected to play a key role in achieving the goals of the Paris Agreement, however, the Amazon is a large and geographically complex region such that regrowth rates are not uniform across the biome.

To understand the impact of key drivers we used a multi-satellite data approach with the aim of understanding the spatial variations in secondary forest regrowth in the Brazilian Amazon. We mapped secondary forest area and age using a land-use-land-cover dataset – MapBiomas – and, combined with the European Space Agency Aboveground Carbon dataset, constructed regional regrowth curves for the year 2017.

We found large variations in the regrowth rates across the Brazilian Amazon due to large-scale environmental drivers such as rainfall and shortwave-radiation. Regrowth rates are similar to previous pan-Amazonian estimates in the North-West (3 ±1.0 MgC ha^-1 yr^-1), which are double than those in the North-East Amazon (1.3 ±0.3 MgC ha^-1 yr^-1). The impact of anthropogenic disturbances, namely fire and repeated deforestation prior to the most recent regrowth only reduces the regrowth by 20% in the North-West (2.4 ±0.8 MgC ha^-1 yr^-1) compared to 55% in the North-East (0.8 ±0.8 MgC ha^-1 yr^-1). Overall, secondary forest carbon stock of 294 TgC in the year 2017, could have been 8% higher with avoided fires and repeat deforestation. We found that the 2017 area of secondary forest, which occupies only ~4% of the Brazilian Amazon biome, can contribute significantly (~5.5%) to Brazil’s net emissions reduction targets, accumulating ~19.0 TgC yr^-1until 2030 if the current area of secondary forest is maintained (13.8 Mha). However,this value reduces rapidly to less than 1% if only secondary forests older than 20 years are preserved (2.2 Mha).

Preserving the remaining old-growth forest carbon stock and implementing legal mechanisms to protect and expand secondary forest areas are key to realising the potential of secondary forest as a nature-based climate change mitigation solution.

How to cite: Heinrich, V., Dalagnol, R., Cassol, H., Rosan, T., Torres de Almeida, C., Silva Junior, C., Campanharo, W., House, J., Sitch, S., Hales, T., Adami, M., Anderson, L., and Aragão, L.: Quantifying the spatial patterns of Secondary Forest carbon sequestration potential in the Brazilian Amazon, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7956, https://doi.org/10.5194/egusphere-egu21-7956, 2021.

Forest - savanna transition is the most widespread and perhaps more dynamic ecotone in the tropics, and extremely sensitive to climate and environmental change. Both kinds of tropical ecosystems are globally strategic and their presence and dynamics have important ecological, climatic and biogeochemical implications, even at the global scale. However, the processes and mechanisms that control this transition vary among regions and remain not fully understood. In general, this transition is influenced by multiple interactions between vegetation and environmental factors such as climate, soil properties, fire, and herbivory. However, the magnitude of these effects can vary substantially across continents, which can result in different responses to environmental change. For this reason, more regional studies are needed to describe and understand the factors and interactions that control forest - savanna transition, particularly in Northern South America, where climate alone has failed to explain this transition. Based on a combination of LiDAR and satellite-derived data, we developed a statistical analysis on the interactive effects of rainfall, soil properties, and fire on the forest - savanna transition in Northern South America, in the savanna region between Colombia and Venezuela, using tree cover as an indicator variable that differentiates forest from savanna. Specifically, we analyze the relationships of tree cover (from GEDI) with soil sand content (from SoilGrids), fire frequency (from Fire_CCI v5.1) as well as three rainfall variability components (from CHIRPS): mean dry-season rainfall, length of the dry season, and frequency of rainy days within the dry season. Our results show that tree cover increased with mean dry-season rainfall and frequency of rainy days within the dry season, whereas it decreased with increased fire frequency. In particular, mean dry-season rainfall followed by fire frequency are the most important predictors of tree cover gradient in the transition. Importantly, our results also suggest that areas with high annual rainfall (2000 to 2800 mm) have low tree cover (i.e. savanna) if the local rainfall climatology consists of infrequent (< 0.35) and low total rainfall (< 650 mm) in the dry season. This highlights the role of water availability and fire disturbance in determining the limits between forest and the second largest area of savanna in South America. Further, our results support that future projections for forest - savanna transition should include not only changes in mean annual rainfall but also changes in rainfall variability, which is expected to be more impacted by climate change. 

How to cite: Valencia, S., Villegas, J. C., and Salazar, J. F.: Analysis of determinants of forest - savanna transition in the northern South America, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8937, https://doi.org/10.5194/egusphere-egu21-8937, 2021.

Tropospheric ozone is a greenhouse gas, and high tropospheric ozone levels can directly impact plant growth and human health. In the Congo basin, simulations predict high ozone concentrations, induced by high ozone precursor (VOC and NOx) concentrations and high solar irradiation, which trigger the chemical reactions that form ozone. Additionally, biomass burning activities are widespread on the African continent, playing a crucial role in ozone precursor production. How these potentially high ozone levels impact tropical forest primary productivity remains poorly understood, and field-based ozone monitoring is completely lacking from the Congo basin. This study intends to show preliminary results from the first full year of in situ measurements of ozone concentration in the Congo Basin (i.e., Yangambi, Democratic Republic of the Congo). We show the relationships between meteorological variables (temperature, precipitation, radiation, wind direction and speed), fire occurrence (derived from remote sensing products) and ozone concentrations at a new continuous monitoring station in the heart of the Congo Basin. First results show higher daily mean ozone levels (e.g. 43 ppb registered in January 2020) during dry season months (December-February). We identify a strong diurnal cycle, where minimum values of ozone (almost near zero) are registered during night hours, and maximum values (near 100 ppb) are registered during the daytime. We also verify that around 2.5% of the ozone measurements exceeds a toxicity level (potential for ozone to damage vegetation) of 40 ppb. In the longer term, these measurements should improve the accuracy of future model simulations in the Congo Basin and will be used to assess the impact of ozone on the tropical forest’s primary productivity.

How to cite: Vieira, I., Verbeeck, H., Meunier, F., Peaucelle, M., Lefevre, L., Cheesman, A., Sitch, S., Mbifo, J., Boeckx, P., and Bauters, M.: Meteorological and fire impacts on tropospheric ozone concentration over tropical forest in the Congo Basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13301, https://doi.org/10.5194/egusphere-egu21-13301, 2021.

Oil palm (Elaeis guineensis) is the most important oil crop in the world, with more than 85% of the global production coming from Indonesia and Malaysia. However, knowledge of country-wide past, current and likely future greenhouse gas (GHG) footprints from palm oil production remains largely incomplete. Over the past year, first studies reporting measurements of net ecosystem carbon dioxide (CO₂) fluxes in oil palm plantations of different ages and on different soil types became available. Combining the recent CO₂ flux estimates with existing measurements on methane and nitrous oxide fluxes allows for a refined quantification of the GHG footprint of palm oil production over the whole plantation life cycle.

To derive country-wide GHG emissions from palm oil production for both Indonesia and Malaysia, we applied the refined GHG footprint estimates to oil palm area extents. Therein, we differentiated between mineral and peat soils, second- and first-generation plantations and within the latter category also among previous land-use systems from which conversion to oil palm likely occurred. For deriving the current (2020) proportions for each category, we combined FAO data with existing remotely sensed maps on oil palm extent and tree density as well as peatland and intact forest layers. These area proportions were then applied to available historic (1970 – 2010) and future (2030 – 2050) oil palm extent estimates as a business-as-usual scenario (BAU), complemented by alternative scenarios. GHG footprint estimates comprise all GHG emissions from palm oil production, i.e. from land-use change, cultivation, milling and use.

Our refined approach estimates the 2020 GHG emissions from palm oil production at 1011 Tg CO₂-eq. yr^-1 for Indonesia and at 261 Tg CO₂-eq. yr^-1 for Malaysia. Our results show that while plantations on peatland only represented 17% and 15% of the total plantation area in 2020 for Indonesia and Malaysia, they accounted for 73% and 72% of the total GHG emissions from palm oil production. Emissions in 1980 and 2000 were estimated to be only 1% and 14% of the 2020 palm oil emissions for Indonesia, but already 24% and 96% for Malaysia due to the earlier oil palm expansion. Projected emissions for 2050, assuming further oil palm expansion on suitable land and constant yields from 2020 on, represent 64% of the 2020 value for Indonesia and 97% for Malaysia under a BAU expansion scenario. These lower or constant GHG emissions for future scenarios despite assumed increases in cultivated area are the consequence of lower GHG emissions in second and subsequent rotation cycles. For both countries, the 2050 BAU emissions could be reduced by more than 50% by halting all conversion of peatlands and forests to oil palm from 2020 on, and by more than 75% when additionally restoring all peatlands currently under oil palm to forest until 2050. Closing yield gaps could potentially lead to further emissions savings.   

How to cite: Meijide, A., de la Rúa, C., Ehbrecht, M., and Röll, A.: Refined past, current and future greenhouse gas footprints of palm oil production, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15996, https://doi.org/10.5194/egusphere-egu21-15996, 2021.

The impact of soil age on geochemical properties and carbon cycling has been studied via chronosequences. However, only few studies have addressed how land-use and soil age might interactively shape properties of Andosols and in turn their capability to retain organic carbon (OC). Geochemical soil analyses and laboratory incubation experiments were carried out to assess soil characteristics and mineralization of soil organic carbon (SOC) in Indonesian soils with two contrasting land uses, viz. pine forest and horticulture. Both of these land uses are the results of conversion of primary forest which had similar parent materials, soil age, as well as weathering intensity. Results showed that intensive agricultural practices (+ 40-50 years) did not result in a significant loss of SOC or the increase of bulk density compared to forest. On the other hand, they were found to increase pH, exchangeable cations, base saturation, and most strikingly non-crystalline materials (i.e. Al_o + ½ Fe_o) leading to phenotype formation in agricultural soils. Positive correlations were found between non-crystalline materials with properties such as soil specific surface area and micropores volume, and it was also positively correlated with SOC, particularly in the subsoil. This study highlighted the resilience of Andosols to soil degradation under agricultural practices and its ability to stabilize SOC.

How to cite: Anindita, S., Sleutel, S., and Finke, P.: Agriculture effects on geochemical soil properties and stability of soil organic carbon on tropical Andosols, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11776, https://doi.org/10.5194/egusphere-egu21-11776, 2021.

The Philippines is one of the world’s leading producers of pineapples, wherein production is comprised mostly of small family farms that are less than 2 hectares in size. As by-product, they generate a large amount of plant residues (e.g., crowns and stems) that are commonly left at the edge of the field. This practice releases substantial amount of greenhouse gas (GHG) emissions and neglects the potential value of pineapple residue. Enabling a waste treatment by returning them to the field through incorporation or mulching holds the potential to maintain soil fertility, reduce climate impact, secure yield stability, and achieving a high resource efficiency by closing material cycles locally. It may also increase soil organic carbon stock (SOC) and reduce greenhouse gas (GHG) emissions. To date, however, the knowledge about this is still very sparse.

The rePRISING project aims to demonstrate that returning pineapple residue either through mulching or incorporation to the field may help promote the closing of nutrient-cycles (C/N/P/K) locally, thus helping to increase soil fertility and soil C sequestration, while reducing GHG emissions. Within the project, the recycling of pineapple residue together with various local organic and inorganic amendments will be studied during a two-year field experiment using the manual closed chamber method. The field study will be supplemented by pot-scale greenhouse and incubation experiments, used inter alia to determine baseline GHG emissions and carbon budgets of pineapple cultivation systems and residue treatments.

Here we present first results of a pot experiment performed during winter 2020-2021 used to develop a suitable procedure for the in-situ determination of dynamic net ecosystem C balances (NECB) for pineapple cultivation systems. This will be further utilized for upcoming field study. This is challenging in so far as pineapple plants use the Crassulacean acid metabolism (CAM photosynthesis) and the manual closed chamber method has not yet been applied to determine NECB from CAM plants.

Keywords: nutrient-cycling, carbon sequestration, greenhouse gas (GHG) emissions, pineapple residue, climate change mitigation

How to cite: Macagga, R., Vaidya, S., Antonijevic, D., Schmidt, M., Lueck, M., Jancsó, M., Augustin, J., Sanchez, P., and Hoffmann, M.: Reuse of Pineapple Residue in Philippine Agriculture: determination of the net ecosystem C balance for a CAM plant in a pot-scale experiment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8240, https://doi.org/10.5194/egusphere-egu21-8240, 2021.

The carbon fluxes in rivers plays a critical role in the global carbon cycle but its role is always understated. The tropical rivers alone accounts for about 70% of global riverine carbon fluxes due to their large areal extent, varying climatic conditions and land use. Studies on the dissolved carbon fluxes in non-perennial tropical rivers are limited, but it holds much importance as that of perennial rivers. Hence, the present study was carried out with an objective to understand about the inorganic and organic carbon fluxes in a large non-perennial tropical river of Southern India. The samples were collected from 28 locations along the river thrice in a year from 2013-2020 and were analysed for major ions, DIC and DOC. The concentration of DIC in the river water in most of the locations is greater than that of DOC. The DOC concentration is greater at pristine locations thereby decreasing along the flow direction of the river, whereas the DIC concentration increases along the flow direction. The spatial and temporal variability in DOC and DIC concentrations is attributed due to the changes in the rainfall, river flow, climate, lithology, land use patterns, in the catchment. The DIC concentration was found to be majorly governed by silicate and carbonate weathering along with biogenic process, mineralisation and other river process, whereas the primary production, microbial process along with soil organic carbon influences the DOC concentration in the rivers. Thus, this study identifies the sources of DIC and DOC in rivers and the processes which influences the carbon export to the sea.

How to cite: Ramesh, R. and Lakshmanan, E.: Assessment of the dissolved inorganic and organic carbon flux in Cauvery, a tropical river of Southern India., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16162, https://doi.org/10.5194/egusphere-egu21-16162, 2021.

Land cover and land management (LCLM) changes have been highlighted for their critical role in low-end warming scenarios, both in terms of global mitigation and local adaptation. Yet the overall potential of LCLM options and their combination is still poorly understood. Here we model the climatic effects of four LCLM options using three state-of-the-art Earth system models, including the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). The considered LCLM options represent idealized conditions:(i) a fully afforested world, (ii) a fully deforested world, (ii) a fully afforested world with extensive wood harvesting, and (iv) a fully deforested world with extensive irrigation. In these idealized sensitivity experiments, ran under present-day climate conditions, the effects of the different LCLM strategies represent an upper bound of the potential for global mitigation and local adaptation. To disentangle the local and non-local effects from the LCLM changes, a checkerboard perturbation, as proposed by Winckler et al. (2017) is applied.

Our first results show that deforestation leads to a pronounced warming in 2m air temperature in CESM over most regions, being most pronounced in the tropics (up to 4°C). In contrast, in the boreal regions of North America and Asia, deforestation causes a ~1°C cooling in 2m air temperature. In CESM, the local effect seems to dominate the temperature response from deforestation, while the resulting non-local effect overall has a smaller magnitude. This contrasts to the effect from afforestation, of which the non-local component dominates the 2m air temperature signal. Afforestation indeed shows a strong local cooling in the tropics and a slight local warming in the temperate and boreal regions, yet, the local cooling is regionally offset by  a global, non-local warming of up to 2 °C. In a next step, we will extend this analysis to the ensemble of Earth system models and increase our process-based understanding of these results and their implications on hot extremes as well as the effects on other temperature metrics (surface temperature and temperature of the lowest level of atmospheric column). Finally, we will perform a subgrid-scale comparison of the effects of LCLM on temperature.

References:

Winckler, J., Reick, C.H., Pongratz, J., 2017. Robust identification of local biogeophysical effects of land-cover change in a global climate model, American Meteorological society, 30(2), DOI: 10.1175/JCLI-D-16-0067.1

How to cite: De Hertog, S., Vanderkelen, I., Havermann, F., Guo, S., Pongratz, J., Manola, I., Coumou, D., Davin, E., Seneviratne, S., Lejeune, Q., Menke, I., Schleussner, C.-F., and Thiery, W.: Biogeophysical effects of idealised land cover and land management changes on the climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2818, https://doi.org/10.5194/egusphere-egu21-2818, 2021.

Land use and land cover change (LULCC) is one of the most important forcings affecting climate in the past century. This study evaluates the global biogeophysical LULCC impacts in 1950–2015 by employing an annually updated LULCC map in a coupled land–atmosphere–ocean model. The difference between LULCC and control experiments shows an overall land surface temperature (LST) increase by 0.48 K in the LULCC regions and a widespread LST decrease by 0.18 K outside the LULCC regions. A decomposed temperature metric (DTM) is applied to quantify the relative contribution of surface processes to temperature changes. Furthermore, while precipitation in the LULCC areas is reduced in agreement with declined evaporation, LULCC causes a southward displacement of the intertropical convergence zone (ITCZ) with a narrowing by 0.5°, leading to a tripole anomalous precipitation pattern over the warm pool. The DTM shows that the temperature response in LULCC regions results from the competing effect between increased albedo (cooling) and reduced evaporation (warming). The reduced evaporation indicates less atmospheric latent heat release in convective processes and thus a drier and cooler troposphere, resulting in a reduction in surface cooling outside the LULCC regions. The southward shift of the ITCZ implies a northward cross-equatorial energy transport anomaly in response to reduced latent/sensible heat of the atmosphere in the Northern Hemisphere, where LULCC is more intensive. Tropospheric cooling results in the equatorward shift of the upper-tropospheric westerly jet in both hemispheres, which, in turn, leads to an equatorward narrowing of the Hadley circulation and ITCZ.

How to cite: Huang, H., Xue, Y., Chilukoti, N., Liu, Y., Chen, G., and Diallo, I.: Assessing global biogeophysical effects of reconstructed land use and land cover change on climate since 1950 using a coupled land–atmosphere–ocean model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3552, https://doi.org/10.5194/egusphere-egu21-3552, 2021.

It is widely accepted in the scientific, business and policy communities that meeting the Paris Agreement targets will require a large-scale deployment of negative emission technologies and practices. As a result, nature-based climate solutions, including carbon sequestration (Cseq) in soils and forests, have received much attention in the literature recently. Several national and global assessments have identified considerable potential for terrestrial Cseq, while other studies have raised doubts regarding its practical limits in the face of the likely future pressures from climate change and land use change. In general, the existing Cseq assessments lack sensitivity to climate change, perspective on local land use and nutrient limitations. Accounting for these factors requires process-based modelling, and is feasible only at national to regional scales at present, underpinned by a wide body of local evidence. Here, we apply an integrated terrestrial C-N-P cycle model (N14CP) with representative ranges of high-resolution climate and land use scenarios to estimate Cseq potential in temperate regions, using the UK as a national-scale example. Meeting realistic UK targets for grassland restoration and forestation over the next 30 years is estimated to sequester an additional 120 TgC by 2100 (similar to current annual UK greenhouse gas emissions), conditional on climate change of <2°C. Conversely, UK arable expansion would reduce Cseq by a similar magnitude, while alternative arable management practices such as extensive rotations with grass leys would have a comparatively small effect on country-wide Cseq outcomes. Most importantly, the simulations suggest that warmer climates will cause net reductions in Cseq as soil carbon losses exceed gains from increased plant productivity. Our analysis concludes that concerted land use change can make a moderate contribution to Cseq, but this is dependent on us cutting emissions from fossil fuels, soil degradation and deforestation in line with a <2°C pathway.

How to cite: Yumashev, D., Janes-Bassett, V., Redhead, J., Rowe, E., and Davies, J.: Carbon storage in soils and plant biomass under future climate and land use pressures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6002, https://doi.org/10.5194/egusphere-egu21-6002, 2021.

The deforestation rate in the Maritime Continent (MC) has been accelerating during the past several decades. Understanding the changes in local hydro-climatological cycles as deforestation takes place is essential because the MC is suffering from frequent and extreme droughts and fires, which often occur during the dry season and are more severe during El Niños. Therefore, this study explores how deforestation affects the hydrological cycle and precipitation in the MC during El Niños, focusing on the boreal autumn season and using the coupled atmosphere-land model simulations. It is found that the precipitation over the MC increases in the deforestation experiments, and the precipitation responses can be magnified during El Niño events. A strong subsidence anomaly associated with El Niño does not prevent enhanced convection associated with local deforestation. Instead, the subsidence reduces the cloud cover in the MC region during El Niño, which increases the incoming solar radiation and increases surface temperatures. Under a warmer environment induced by El Niño, the nonlinear biogeophysical feedbacks associated with deforestation also play a critical role in more substantial land surface warming. A warmer land surface induces a more unstable atmospheric environment associated with a tendency toward enhanced local convection and lateral moisture convergence. This study highlights how the different mean climate states may modulate the impact of local land-use changes on hydroclimatological cycles in the Maritime Continent, and sheds light on the state of our knowledge of interactions between the local land surface and remote large-scale atmospheric circulations.

How to cite: lee, T. and lo, M.: The Role of El Niño in Modulating the Effects of Deforestation in the Maritime Continent, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7085, https://doi.org/10.5194/egusphere-egu21-7085, 2021.

Mountains are some of the most inaccessible regions, where not many weather stations located due to the high altitudes. Thus, the amount of available mountain meteorological data is limited. One of the modern solutions to data insufficiency is modelling. However, it remains challenging to assess how well a model simulates local climate conditions.

The main goal of this study was to check the model accuracy by comparing its results to observed data, with a focus on the radiation budget.

The Community Land Model 4.5 (CLM4.5) provided by the University of Oslo was used. It is a one-dimensional model and the default land component in the Community Earth System Model 1.2. CLM4.5 simulates various biogeophysical and biogeochemical processes based on surface energy, water, and carbon balances [Oleson et al. 2013]. Here, the model was run from 1901 to 2014 in the offline mode, meaning it was getting input from a pre-existing dataset. Modelled fluxes from the radiation budget, such as incoming (K_in) and outgoing shortwave (K_out) radiation, incoming (L_in) and outgoing (L_out) longwave radiation, net all-wave (Q*), net shortwave (K*) and net longwave (L*) radiation, were used for compassion with observations.

A 2.5×0.2 km site on Mount Imingfjell (1191 m) in southern Norway was selected as the study object. Different microclimatic parameters, including radiation fluxes, were measured separately over lichens and shrubs for 44 days in the 2018-2019 summers [Aartsma et al. 2020]. These vegetation types were chosen to understand the differences between them and see the potential impact of “shrubification” on surface albedo. Since there was no time overlap between modelled and observed data, we had to make datasets more comparable. 44 days from field data were used to create composite datasets that represent three temperature regimes based on data from the nearest weather station: “cold”, “normal” and “warm”. Each observation was assigned to one of these temperature regimes. In CLM4.5, recently available years were analysed to find ones with average summer temperatures closest to the stated temperature regimes. Statistical analysis, such as a two-sample t-test, was performed to see if there were any significant differences between the datasets.

T-tests showed that modelled K_in, L_in and K* were always similar to measurements, except for L_in and K* in “cold” conditions. CLM4.5 K_out differed from observed ones in almost all regimes. Simulated L*, Q* and L_out varied between temperature conditions and vegetation types. Still, about 70% of the modelled fluxes closely resembled the shrub ones, while only around 50% resembled lichens. Modelled albedo was also closer to shrub albedo.

In conclusion, CLM4.5 most likely modelled credible values for radiation fluxes, but further research is needed for greater clarity.

References

1. Aartsma, P., Asplund, J., Odland, A., Reinhardt, S., & Renssen, H. (2020). Surface albedo of alpine lichen heaths and shrub vegetation. Arctic, Antarctic, and Alpine Research, 52(1), 312-322.

2. Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., . . . Yang, Z.-L. (2013). Technical description of version 4.5 of the Community Land Model (CLM).

How to cite: Vasiakina, A., Renssen, H., and Aartsma, P.: Comparison of modelled radiation budgets with observations over lichens and shrubs at Mount Imingfjell, Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8053, https://doi.org/10.5194/egusphere-egu21-8053, 2021.

Land cover and land management (LCLM) changes are important sources and sinks for anthropogenic CO₂ fluxes. Current earth system models (ESMs) are capable to simulate the globally most sensitive LCLM changes (strong effects or large spatial extent in the earth system) such as de- and afforestation, wood harvest and irrigation, however, a comprehensive analysis between these ESMs is still absent. The present study aims to quantify the biogeochemical effects of forest cover changes, wood harvesting and irrigation of croplands on the global carbon cycle.

Therefore, we conducted coupled atmosphere-ocean-land experiments of idealized global deforestation with and without cropland irrigation as well as global re-/afforestation with and without wood harvest over a 150-year period under present day solar and trace gas forcing. All experiments were simulated by three different ESMs (MPI-ESM, EC-EARTH and CESM) to quantify inter-model uncertainty and potentially uncover specific model biases. The analysis focuses on the transient response of land carbon fluxes and pools after an abrupt LCLM practice change, in order to track the emergence of signals that could potentially mitigate climate change. Additionally, we want to unravel model differences concerning the temporal dynamics of LCLM change effects. Since greenhouse gases (GHG) concentration is kept constant at present-day level, the climate changes here arise from the biogeophysical effects of LCLM changes. We use a checkerboard approach to separate local and non-local components of the climate changes as proposed by Winckler et al., 2017, i.e. we separate the changes in climate induced locally by the LCLM changes from those induced remotely by advection and changes in atmospheric circulation.

First results with the MPI-ESM show that immediate global deforestation starting from present-day land-use distribution causes a 824 GtC loss of the total land carbon pool throughout the simulation period of 150 years, about 46% of which stem from tropical regions (17°S–17°N). Land carbon stocks are not balanced until the end of the simulation, which indicates that the land will continue to emit CO₂ to the atmosphere and a long-term commitment by deforestation for climate change. Non-local effects lead to a loss of 26 GtC from land, again with largest losses found for the tropical regions. Even though it is a small part compared to the total loss (local plus non-local effect), it reveals potentially substantial consequences that LCLM changes at large scale can have unintendedly on other regions, including remote pristine ones, through biogeophysical climate change.

How to cite: Guo, S., Pongratz, J., Havermann, F., De Hertog, S., Thiery, W., Manola, I., Coumou, D., Lejeune, Q., and Schleussner, C.-F.: Simulated biogeochemical effects of idealized land cover and land management changes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9826, https://doi.org/10.5194/egusphere-egu21-9826, 2021.

The use of a dynamic vegetation model, CARAIB, to estimate carbon sequestration from land-use and land-cover change (LULCC) offers a new approach for spatial and temporal details of carbon sink and for terrestrial ecosystem productivity affected by LULCC. Using the remote sensing satellite imagery (Landsat) we explore the role of land use land cover change (LULCC) in modifying the terrestrial carbon sequestration. We have constructed our LULCC data over Wallonia, Belgium, and compared it with the ground-based statistical data. However, the results from the satellite base LULCC are overestimating the forest data due to the single isolated trees. We know forests play an important role in mitigating climate change by capturing and sequestering atmospheric carbon. Overall, the conversion of land and increase in urban land can impact the environment. Moreover, quantitative estimation of the temporal and spatial pattern of carbon storage with the change in land use land cover is critical to estimate. The objective of this study is to estimate the inter-annual variability in carbon sequestration with the change in land use land cover. Here, with the CARAIB dynamic vegetation model, we perform simulations using remote sensing satellite-based LULCC data to analyse the sensitivity of the carbon sequestration. We propose a new method of using satellite and machine learning-based observation to reconstruct historical LULCC. It will quantify the spatial and temporal variability of land-use change during the 1985-2020 periods over Wallonia, Belgium at high resolution. This study will give the space to analyse past information and hence calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2070). Further, we will also analyse the change in other climate variables, such as CO₂, temperature, etc. Overall, this study allows us to understand the effect of changing land-use patterns and to constrain the model with an improved input dataset which minimizes the uncertainty in model estimation.

How to cite: Verma, A., Francois, L., Jacquemin, I., Tölle, M., Zhang, H., and Lanssens, B.: Assessing the effects of climate and land use land cover changes on recent carbon storage in terrestrial ecosystem using model-satellite approach over Wallonia, Belgium, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11207, https://doi.org/10.5194/egusphere-egu21-11207, 2021.

The aim of this study is to estimate the net carbon fluxes from agriculture-related land-use and land cover change (LULCC) activities, which are referred to as emissions from the land due to human activities. These include land use (LU, e.g., farmland for food and feed production, including management) and land cover changes (LCC, e.g., deforestation for and reforestation of agricultural land, and conversion of grasslands and pastureland to agriculture land or vice versa). Agriculture land-use practices could be a source of atmospheric CO2. However, the management of agricultural practices may reduce carbon emissions and increase soil carbon sequestration. Simultaneously, land-cover change activities clear existing ecosystems, their biomass and disturb the soil, generating carbon emissions. Previous earth system models usually have a simple or no representation of land agriculture practices, such as planting crops, fertilization, irrigation, harvesting grains for food and livestock-feed, recovering crop residue for feed and other usages, and grazing, livestock-feed, and manure cycle. This study uses a land surface model with spatially heterogeneous representations of such agricultural land use activities, in addition to land cover change, such as the change from forest to agricultural land. Our study shows the net agricultural land area increase of 0.11 million hectares/yr during 2007-2013, including 2.12 million hectares/yr of other land converted to agricultural land and 2.01 million hectares/yr of agricultural land converted to other lands. The results show that global net carbon flux due to agriculture-related LULCC is 2.26 Pg C/yr (net emission), consisting of 38% due to land-use activities and 62% due to land cover change. South America (22%), North America (19%), and South and Southeast Asia (13%) are the top contributing regions for net carbon flux induced by LULCC. South America has contributed the most flux from land cover change (18%), while North America has generated the most carbon flux due to land-use activities (12%) among all macro geopolitical regions.  By quantifying the carbon fluxes induced by different agriculture activities this study provides a complete estimate of the yearly carbon cycle in the agriculture system at the spatial scale, which may improve the representations of agriculture land use activities in Earth System Models.

How to cite: Jain, A., Xu, X., and Shu, S.: Global Carbon Fluxes Induced by Agriculture-Related Land-Use and Land Cover Change Activities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13361, https://doi.org/10.5194/egusphere-egu21-13361, 2021.

Continued scientific study has revealed that land use and land cover change play a key role in climate and that the application of irrigation is an important biogeophysical contributor to climate modification across spatial scales. The Great Plains Irrigation Experiment (GRAINEX) was conducted in the spring and summer of 2018 to investigate Land-Atmosphere interactions just prior to and through the growing season across adjacent, but distinctly unique, soil moisture regimes (contrasting irrigated and rainfed fields). GRAINEX was uniquely designed for the development and analysis of an extensive observational dataset for comprehensive process studies of Land-Atmosphere interactions, by focusing on irrigated and rainfed croplands in a ~100 x 100 km domain in southeastern Nebraska. Observation platforms included multiple NCAR EOL Integrated Surface Flux Systems and Integrated Sounding Systems, NCAR CSWR Doppler Radar on Wheels, 1200 radiosonde balloon launches from 5 sites, the NASA GREX airborne L-Band radiometer, and 75 University of Alabama-Huntsville Environmental Monitoring Economic Monitoring Sensor Hubs (EMESH mesonet stations). The presentation will provide an overview of the field campaign, the dataset collected, and investigate the contrast of L-A intractions across an irrigation gradient through observations and mesoscale/microscale modeling on timescales ranging from the diurnal to the seasonal. Attention will be given to how variations in the land surface state, as a function of irrigation fraction, impacts near-surface meteorology and atmospheric boundary layer evolution at local and regional scales.

How to cite: Rappin, E., Mahmood, R., Udaysankar, N., and Pielke Sr., R.: Observational and Modeling Analysis of Land-Atmosphere Interactions over Adjacent Irrigated and Rainfed Cropland During the GRAINEX Field Campaign., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13385, https://doi.org/10.5194/egusphere-egu21-13385, 2021.

Lake sediment records offer the opportunity to quantify past changes in catchment P exports, information essential if we are to understand the long-term drivers that control P cycling. However, the interpretation of such records generally depends on the assumption that sediment P concentration profiles remain intact after burial. This assumption appears to be in conflict with the phenomenon of internal P loading, whereby P is exported from sediment to the water column. Here we apply a simple long-term mass balance model to published sediment record data from Søbygaard, a site that has an exceptionally high internal P loading, and an exceptionally well-studied sediment P record (Søndergaard and Jeppesen, 2019). Repeat cores collected from 1985 to 2004 constrain the temporal evolution of a sediment P peak arising from past sewage inflows, providing a critical test of our modelling approach. We find that useful sediment inference of long-term mean lake water TP is preserved in the sediment record, and predict also useful inference of long-term mean external P loading. Limitation on temporal resolution of the records is examined.

How to cite: Boyle, J. and Moyle, M.: Using lake sediment P records to estimate internal and external P loading and historic long-term lake water mean TP: a case study using published records from Søbygaard Sø, Denmark., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13278, https://doi.org/10.5194/egusphere-egu21-13278, 2021.

Phosphorus (P) is the key and limiting nutrient in the eutrophication of freshwater resources. Modeling P retention in lakes using steady-state mass balance models (i.e. Vollenweider-type models) provides insights into the lake P management and a simple method for large-scale assessments of P in lakes. One of the basic problems in the mass balance modeling of P in lakes is the removal of P from the lake water column by settling. A fraction of the incoming P into the lake from the watershed is associated with fast-settling particles (e.g. sediment particles) that result in the removal of that fraction of P quickly at the lake entrance. However, existing models considering a constant fraction of fast-settling TP for all lakes are shown to result in overestimation of the retention of P in lakes with short hydraulic residence time. In this study, we combine a hypothesis of the fast- and slow-settling P fractions into the steady-state mass balance models of P retention in lakes. We use a large database of lakes to calibrate the model and evaluate the hypothesis. The results of this work can be used for the improvement of the prediction power of P retention models in lakes and help to better understand the processes of P cycling in lakes.

How to cite: Khorasani, H. and Zhu, Z.: Investigating Fast- and Slow-settling Phosphorus Fractions in Lakes using Steady-state Modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13910, https://doi.org/10.5194/egusphere-egu21-13910, 2021.

The dynamics of sediment phosphorus (P) remobilization and recycling in many polymictic systems due to distinct external and internal loading conditions are poorly understood. Here we used a multifaceted approach of quantifying sediment P binding forms and corresponding metal contents in sediment cores down to 30 cm from 8 different locations at Lake of Woods (LOW) in different seasons. We also measured pH, redox potential and dissolved oxygen uptake across the sediment-water interface and the concentration of nutrient and metals in pore water at different depths. Additionally, we applied a reaction-transport diagenetic model to construct the spatial and temporal trend of internal P loading in response to environmental variations. The summer diffusive fluxes of P ranged between 3 and 83 µmol m^-2 d^-1 whereas the winter fluxes were lower ranged from 0.1 to 0.35 µmol m^-2 d^-1. P recycling efficiency were 13% to 77%. P bound to redox sensitive iron (Fe)-P binding forms in sediments were the major source of P release in all stations, while P immobilization is controlled by redox-insensitive calcium (Ca)-P phases. The modeling results supported the notion that P release was mostly driven by the diagenetic recycling of redox sensitive and organic bound P.

How to cite: Alam, M. S., Zastepa, A., and Dittrich, M.: Phosphorus recycling and retention in Lake of the Woods: Reactive-transport diagenetic modeling across spatial and temporal Scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14068, https://doi.org/10.5194/egusphere-egu21-14068, 2021.

Phosphate (P) as an essential resource for food production is becoming scarce. Its uncontrolled loss from agricultural areas is in conflict with the principles of a circular economy. Enhanced loading of surface waters with P is the main cause for eutrophication and presents a key challenge in meeting the objectives of the EU Water Framework Directive. Understanding and controlling environmental P fluxes therefore is key to target both problems, to develop new methods and approaches to manage environmental P fluxes, and to improve surface water quality.

In March 2019 the EU Marie Sklodowska-Curie Innovative Training Network P-TRAP has been launched. P-TRAP establishes a framework of partners from multiple science and engineering disciplines. Integration of non-academic partners from various stakeholder groups into the P-TRAP consortium paves the way for direct implementation of the acquired knowledge. The project is targeting the diffuse flux of phosphate (P) into surface waters, i.e. the problems of understanding and controlling environmental P fluxes. P-TRAP aims to develop new methods and approaches to trap P in drained agricultural areas and in the sediments of eutrophic lakes. Trapping of P involves the application of iron(Fe)-containing by-products from drinking water treatment. P-TRAP aspires the ideas of a circular economy and aims at recovering the retained P in agricultural systems. Novel microbial technologies will be developed to convert P-loaded Fe-minerals into marketable fertilizers whose suitability will be evaluated. The P-TRAP technologies have in common that they rely on the naturally strong connection between P and Fe and the innovative P-TRAP strategies will be underpinned by process-orientated investigations on the behaviour of P during the transformation of Fe minerals. The latter are key in trapping and recycling of P in agricultural systems and lakes. Here we will present the structure and the planned research of the project, including a first overview of achievements of the first two years. 

How to cite: Behrends, T. and Walter, S.: P-TRAP – Reducing diffuse phosphorus input to surface waters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3317, https://doi.org/10.5194/egusphere-egu21-3317, 2021.

Sulfidation of Fe(III) (hydr)oxides plays an important role in the phosphate(P) cycle in oceans and lakes. P has a strong affinity to Fe(III) (hydr)oxides and can either become incorporated via coprecipitation or adsorb onto the solid’s surface. Consequently, P enters aquatic sediments often associated with Fe(III) (hydr)oxides. In the sediments, when sulfidic conditions are prevalent, the reaction of Fe(III) (hydr)oxides with sulfide can lead to the formation of Fe(II) sulfides and P release. The released P can, in turn, diffuse upwards into the overlying water and thus aggravate eutrophication in water bodies.  Although it is generally expected that P is released during the sulfidation of P containing Fe(III) (hydr)oxides, questions remain whether part of the P could be re-adsorbed onto the products of the sulfidation reaction, or trigger the formation of vivianite ^[1] (Fe₃(PO₄)₂ · 8H₂O). Furthermore, it is still unclear how the rates of P release are related to the progress of the sulfidation reaction.

In order to study the P dynamics during sulfidation, we performed experiments in flow-through reactors with P-bearing lepidocrocite (γ-FeOOH). The inflow solution contained sulfide and we monitored P, dissolved S(-II) and Fe(II) in the outflow to follow the progress of sulfide consumption and P release. Sulfide concentrations in the outflow of reactors containing lepidocrocite with adsorbed P tended to be lower than in the outflow of reactors with lepidocrocite but no P. Consequently, the preliminary results indicate that consumption rates of sulfide by the reaction with lepidocrocite were lower when P was present, implying that adsorbed P reduced the rates of sulfidation. At the beginning of the experiment, P concentrations in the outflow remained low, then started to increase and reached a steady state after passing several reactor volumes. This indicates that P was not instantaneously released upon sulfide adsorption but only as lepidocrocite sulfidation progressed. At the end of the experiment, the fraction of P released from the reactor was significantly lower than the fraction of lepidocrocite that had reacted with sulfide(calculated from cumulative sulfide consumption and solid phase characterization). This implies that part of the P has been retained in the solid phase despite the reductive transformation of lepidocrocite. The underlying mechanisms of P retention and the complex relationship between the rates of sulfide consumption and P release will be discussed.

References

[1] Jilbert and Slomp, 2013. Geochimica et Cosmochimica Acta 107, 155-169.

How to cite: Ma, M., Voegelin, A., and Behrends, T.: Kinetics and mechanism of phosphate release upon sulfidation of phosphate-containing lepidocrocite, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13645, https://doi.org/10.5194/egusphere-egu21-13645, 2021.

The cycling of phosphorus in terrestrial and aquatic systems is tightly coupled to the redox-cycling of iron (Fe). The oxidation of dissolved Fe(II) in natural waters leads to the precipitation of amorphous to poorly crystalline Fe(III)-solids that can bind phosphate (P) and other nutrients as well as toxic compounds. The EU project P-TRAP is aimed at developing methods to reduce diffuse P inputs into surface waters to mitigate eutrophication, by using Fe-rich byproducts from water treatment (https://h2020-p-trap.eu/). Within this project, we study mechanistic aspects of the formation and transformation of P-containing Fe(III)-precipitates and their implications for P retention in soils and water filters.

Freshly formed Fe(III)-precipitates are metastable and can transform into more stable phases over time. This may lead to the release of co-precipitated P. In laboratory experiments, we assessed how Ca, Mg, silicate (Si) and P impact on the formation and transformation of Fe oxidation products (at 0.5 mM Fe) and their P retention in synthetic bicarbonate-buffered groundwater. The time-resolved experiments were performed in electrolyte solutions containing Na, Ca, or Mg as electrolyte cation, without or with Si (at molar Si/Fe of 1), and P (P/Fe of 0.3 and 0.05). Changes in dissolved element concentrations over time were linked to changes in the structure and composition of the Fe(III)-solids; with Fe coordination probed by X-ray absorption spectroscopy, mineralogy by X-ray diffraction, and nano-scale morphology and composition heterogeneity by transmission electron microscopy with energy-dispersive X-ray detection.

The freshly-formed Fe(III)-precipitates were mixtures of amorphous Fe(III)-phosphate with either poorly-crystalline lepidocrocite (without Si) or Si-containing ferrihydrite (with Si). Increases in dissolved P during aging were largest in Na electrolytes without Ca, Mg or Si, and were linked to the transformation of amorphous Fe(III)-phosphate into lepidocrocite with a lower P retention capacity than Fe(III)-phosphate. In Ca- and to a lesser extent Mg-containing electrolytes, the Ca or Mg stabilized the amorphous Fe(III)-phosphate and thereby reduced P release over time. The presence of Si increased initial P uptake and inhibited P release during aging by causing the formation of Si-ferrihydrite with higher P sorption capacity than lepidocrocite formed in the absence of Si. In conclusion, the extents to which P is trapped by fresh Fe(III)-precipitates and released during aging can be attributed to the individual and coupled impacts of Ca, Mg and Si on Fe(III)-precipitate structure, stability and transformation.

In continuing work, we aim to expand our work to study how organic compounds impact on the formation and colloidal stability of Fe(III)-precipitates and P retention.

How to cite: Nenonen, V., Kaegi, R., Hug, S. J., Mangold, S., Göttlicher, J., Winkel, L., and Voegelin, A.: Effects of aging and transformation of Fe(III)-precipitates on the retention of co-precipitated phosphate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13281, https://doi.org/10.5194/egusphere-egu21-13281, 2021.

The vital element phosphorus (P) invokes two extremes in the environment; (i) scarcity, as a non-renewable resource and as a poorly bioavailable limiting nutrient for plants, and (ii) excess, as cause of eutrophication in surface waters. To tackle both these problems, the inter-relationship between the P and the iron (Fe) cycle is widely discussed with a special interest in the ferrous iron-phosphate mineral vivianite (Fe(II)₃(PO₄)₂*8H₂O). Vivianite forms naturally in sub-/anoxic environments with high Fe(II) and PO₄ concentrations, and is a sink to the dissolved P concentration. On the other hand, vivianite has been proposed as a P source through application as a slow-release Fe-P fertilizer prepared from recycled P. However, vivianite is a metastable mineral under oxic conditions; it readily oxidizes, notably changing color from white to dark blue/purple. This transformation changes the properties of the mineral (surface), and thus its suitability as a fertilizer.

We investigated the oxidation and dissolution of vivianite under different environmental conditions with the aim of developing a mechanistic and kinetic model that relates the oxidation process with dissolution rates.  Moreover, the effect of secondary mineral precipitation on the ‘net’ availability of P and Fe for soil organisms was also studied. Quantifying dissolution rates and secondary mineral formation under environmentally relevant conditions provides the fundamental knowledge needed to assess the suitability of vivianite as Fe and P fertilizer. This information is also paramount to the idea of a circular economy concept: starting with the reduction of P loads of (waste) waters and using the byproduct vivianite as P source for fertilization.

How to cite: Metz, R., Kumar, N., Schenkeveld, W., and Kraemer, S.: Biogeochemical mechanisms influencing the bioavailability of P and Fe from vivianite, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12798, https://doi.org/10.5194/egusphere-egu21-12798, 2021.

How to cite: Mongol, N.: Phosphorus solubilisation with varying drying and rewetting stresses under four contrasting soils from different regions of China , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13596, https://doi.org/10.5194/egusphere-egu21-13596, 2021.

Phosphorus is closely linked to other nutrient cycles, notably carbon and nitrogen, therefore, to understand potential risks to food production models are required that simulate integrated nutrient cycling over long timescales. The soil-plant system model N14CP meets these requirements and simulates both semi-natural and agricultural environments. N14CP has been validated both spatially and temporally across a range of long-term agricultural experimental sites comparing soil C, N and P, and crop yields, and in most instances performs well. However, under experimental conditions where N is applied in the absence of P, the model indicates exhaustion of P reserves and a decline in yields that is not observed at these sites, highlighting a gap in the model process representation. Potential sources of this ‘missing P’ such as enhanced atmospheric deposition, weathering and flexible plant stoichiometries were explored yet cannot account for this deficit. We hypothesise that access of organic P through other mechanisms not fully represented within the model, such as phosphatase enzymes, could be part of this explanation.

In order to test this, we conducted a meta-analysis of phosphatase enzyme activity in agricultural settings, comparing response to P sufficient and deficient conditions. Results suggest phosphatase enzyme activity is higher in P deficient conditions compared to inorganic P addition, yet lower compared to organic P addition. Meta-regression analysis indicates magnitude of P addition and pH of substrate are significant factors influencing enzyme response. However, due to numerous additional processes and adaption strategies in response to P deficiency and the difficulty isolating the role of phosphatase enzymes it is not possible to determine the degree to which this mechanism alone accounts for the missing P. We discuss the continuing need for additional empirical evidence to understand the cycling of organic P, and the development of models to include these processes to inform sustainable land management and ensure long-term food security.

How to cite: Janes-Bassett, V., Haygarth, P., Blackwell, M., Mezeli, M., Stewart, G., Blair, G., and Davies, J.: Integrated carbon-nitrogen-phosphorus cycling for sustainable agriculture – a knowledge gap and investigation of the role of phosphatase enzymes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3037, https://doi.org/10.5194/egusphere-egu21-3037, 2021.

Gas-charged sediments of shallow water bodies are significant sources of atmospheric methane, an important greenhouse gas. Past accounts of gas bubbles developed in shallow aquatic sediments (and in their surrogates) have reported a controversial occurrence of vertical as well as horizontal bubbles topologies. Within the framework of tensile fracturing of muddy sediment produced by the growing bubbles, the vertical orientation of bubbles is well understood, however factors controlling horizontal bubble growth are largely unclear. This study is conducted by employing a mechanical/reaction–transport numerical model, which couples diffusion-led expansion of gas bubble and elastic-fracture mechanical response of sediment to its growth. Muddy sediment is assumed to exhibit a transverse anisotropy in fracture toughness (a property describing an easiness of breaking the inter particle bonds), attributed to partial or full alignment of plate-like clay particles. Our results demonstrate that bubbles growing in isotropic sediment develop a vertically oriented topology and start their ascent once reaching their mature sizes. Under an increasing measure of anisotropy, the bubbles grow horizontally at the initial stages, however at later stages they start evolving in vertical direction as well, under influence of gravity, and eventually initiate their vertical ascent as well. Our results suggest an explanation of apparent conundrum about preferred orientations of bubbles in muddy sediments. Laterally growing bubbles produced in anisotropic sediment are able to coalesce with neighboring ones and form interconnected permeable horizontal gas networks, as observed in some lab experiments. For the first time, our results reveal that anisotropy-led initial lateral bubble growth can also play a crucial role in accumulating gas reserve from long distances around large and small scale seeps and outlets, at continental margins and inland water bodies sediments. Additionally, horizontal bubbles tend to be stationary (in contrast to the vertical bubbles) thus being responsible for high gas storage (or retention) capability of aquatic sediments.

How to cite: Painuly, A. and Katsman, R.: Influence of anisotropy in sediment mechanical properties on CH4 bubble growth topology and migration pattern in muddy aquatic sediments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-583, https://doi.org/10.5194/egusphere-egu21-583, 2021.

Large amounts of methane hydrate locked up within marine sediments are associated to mud volcanoes. We have investigated by means of mathematical modeling the unsteady process of accumulation of gas hydrates associated with the processes of mud volcanism. A mathematical model has been developed. The system of equations of the model describes the interrelated processes of filtration of gas-saturated fluid, thermal regime and pressure, and accumulation of gas hydrates in the seabed in the zone of thermobaric stability of gas hydrates. The numerical simulation of the accumulation of gas hydrates in the seabed in the deep structures of underwater mud volcanoes has been carried out using the realistic physical parameters values. The influence of the depth of the feeding reservoir and the pressure in it on the evolution of gas hydrate accumulations associated with deep-sea mud volcanoes is quantitatively analyzed. Modeling quantitatively showed that the hydrate saturation in the zones of underwater mud volcanoes is variable and its evolution depends on the geophysical properties of the bottom environment (temperature gradient, porosity, permeability, physical properties of sediments) and the depth of the mud reservoir and pressure in it. The volume of accumulated gas hydrates depends on the duration of the non-stationary process of accumulation between eruptions of a mud volcano. The rate of hydrate accumulation is tens and hundreds times the rate of hydrate accumulation in sedimentary basins of passive continental margins.

How to cite: Sobisevich, A. L., Suetnova, E. I., and Zhostkov, R. A.: Mathematical model of a non-stationary process of accumulation of gas hydrates, confined to deep-water mud volcanoes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1807, https://doi.org/10.5194/egusphere-egu21-1807, 2021.

Existence of strong seabed sources of methane, including gas hydrates, in the Arctic and sub-Arctic seas with proven oil/gas deposits is well documented. Enhanced concentrations of dissolved methane in deep layers are widely observed. Many of marine sources are highly sensitive to climate change; however, the Arctic methane sea-to-air flux remains poorly understood: harsh natural conditions prevent in-situ measurements during winter. Satellite remote sensing, based on terrestrial outgoing Thermal IR radiation measurements, provides a novel alternative to those efforts. We present year-round methane data from 3 orbital sounders since 2002. Those data confirm that negligible amounts of methane are fluxed from the seabed to the atmosphere during summer. In summer, the water column is strongly stratified from sea-ice melt and solar warming. As a result, ~90% of dissolved methane is oxidized by bacteria. Conversely, some marine areas are characterized by positive atmospheric methane anomalies that begin in November. During winter, ocean stratification weakens, convection and winter storms mix the water column efficiently. We also find that the amplitudes of the seasonal cycles over Kara and Okhotsk Seas have increased during last 18 years due to winter concentration growth. There may be several factors responsible for sea-air flux: growing emission from clathrates due to warming, changes in methane transport from the seabed to the surface, changes in microbial oxidation, ice cover, etc. Finally, methane remote sensing results are compared to available observations of temperature in deep ocean layers, estimates of Mixed Layer Depth, and satellite microwave sea-ice cover measurements.

How to cite: Yurganov, L., Carroll, D., Pnyushkov, A., Polyakov, I., and Zhang, H.: Wintertime Methane Emission From the Barents and Kara Seas and Sea of Okhotsk: Satellite Evidence., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5628, https://doi.org/10.5194/egusphere-egu21-5628, 2021.

Hydrocarbon escape systems can be regionally active on multi-million-year timescales. However, reconstructing the timing and evolution of repeated escape events can be challenging because their expression may overlap in time and space. In the northern Levant Basin, eastern Mediterranean, distinct fluid escape episodes from common leakage points formed discrete, cross-evaporite fluid escape pipes, which are preserved in the stratigraphic record due to the coeval Messinian salt tectonics.

The pipes consistently originate at the crest of prominent sub-salt anticlines, where thinning and hydrofracturing of overlying salt permitted focused fluid flow. Sequential pipes are arranged in several kilometers-long trails that were progressively deformed due to basinward gravity-gliding of salt and its overburden. The correlation of the oldest pipes within 12 trails suggests that margin-wide fluid escape started in the Late Pliocene/Early Pleistocene, coincident with a major phase of uplift of the Levant margin. We interpret that the consequent transfer of overpressure from the deeper basin areas triggered seal failure and cross-evaporite fluid flow. We infer that other triggers, mainly associated with the Messinian Salinity Crisis and compressive tectonics, played a secondary role in the northern Levant Basin. Further phases of fluid escape are unique to each anticline and, despite a common initial cause, long-term fluid escape proceeded independently according to structure-specific characteristics, such as the local dynamics of fluid migration and anticline geometry.

Whereas cross-evaporite fluid escape in the southern Levant Basin is mainly attributed to the Messinian Salinity Crisis and compaction disequilibrium, we argue that these mechanisms do not apply to the northern Levant Basin; here, fluid escape was mainly driven by the tectonic evolution of the margin. Within this context, our study shows that the causes of cross-evaporite fluid escape can vary over time, act in synergy, and have different impacts in different areas of large salt basins.

How to cite: Oppo, D., Evans, S., Jackson, C. A.-L., Iacopini, D., Kabir, S. M., and Maselli, V.: Leaky salt: pipe trails record the history of cross-evaporite fluid escape in the northern Levant Basin, Eastern Mediterranean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2545, https://doi.org/10.5194/egusphere-egu21-2545, 2021.

In the Mediterranean Basin, gas hydrate bottom simulating reflectors (BSR) are absent, with very few and spatially limited exceptions occurring in Eastern Mediterranean mud volcanoes and in the Nile deep sea fan. This is in spite of widespread occurrence of hydrocarbon gases in the subsurface, mainly biogenic methane, from a wide range of stratigraphic intervals.
In this study we model the methane hydrate stability field using all available information on DSDP and ODP boreholes in the Western Mediterranean and in the Levant Basin, including the downhole changes of pore water salinity. The models take into account the consequent pore water density changes and use known estimates of geothermal gradient. None of the drilled sites were located on seismic profiles in which a BSR is present.
The modelled base of the stability field of methane hydrates is located variably within, below, or even above the drilled sedimentary section (the latter case implies that it is located in the water column). We discuss the results in terms of geodynamic environments, areal distribution of Messinian evaporites, upward ion diffusion from Messinian evaporites, organic carbon content, and the peculiar thermal structure of the Mediterranean water column.
We conclude that the cumulative effects of geological and geochemical environments make the Mediterranean Basin a region that is unfavorable to the existence of BSRs in the seismic record, and most likely to the existence of natural gas hydrates below the seabed.

How to cite: Corradin, C., Camerlenghi, A., Giustiniani, M., Tinivella, U., and Bertoni, C.: On the lack of widespread bottom simulating reflectors in the Mediterranean Basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10216, https://doi.org/10.5194/egusphere-egu21-10216, 2021.

Over the last 25 years, the world’s forests have undergone substantial changes. Deforestation and forest degradation in particular contribute greatly to biodiversity loss through habitat destruction, soil erosion, terrestrial water cycle disturbances and anthropogenic CO2 emissions. In certain regions and countries, the changes have been more rapid, which is the case in the Greater Mekong sub-region recognized as deforestation hotspot (FAO, 2020). In this region, illegal and unsustainable logging and conversion of forests for agriculture, construction of dams and infrastructure are the direct causes of deforestation. Effective tools are therefore urgently needed to survey illegal logging operations which cause widespread concern in the region.

Monitoring systems based on optical data, such as the UMD/GLAD Deforestation alerts implemented on the Global Forest Watch platform, are limited by the important cloud cover which causes delays in the detections. However, it has been demonstrated in the last few years that forest losses can be timely monitored using dense time series of (synthetic aperture) radar data acquired by Sentinel-1 satellites, developed in the frame of the European Union’s Earth observation Copernicus programme. Ballère et al. (2021) showed for example that 80% of the forest losses due to gold mining in French Guiana are detected first by Sentinel-1-based forest loss detection methods compared with optical-based methods, sometimes by several months. Methods based on Sentinel-1 have been successfully applied at the local scale (Bouvet et al., 2018, Reiche et al., 2018) and can be adapted and tested at the national scale (Ballère et al., 2020).

We show here the main results of the SOFT project funded by ESA in the frame of the EO Science for Society open calls. The overall SOFT project goal is to provide validated forest loss maps every month over Vietnam, Cambodia and Laos with a minimum mapping unit of 0.04 ha, using Sentinel-1 data. The results confirm the analysis of the deforestation fronts published recently by the WWF (Pacheco et al., 2021), showing that Eastern Cambodia, and Southern and Northern Laos are currently forest disturbances hotspots.

References:

Ballère et al., (2021). SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery. Remote Sensing of Environment, 252, 112159.

Bouvet et al., (2018). Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. Remote Sensing, 10(8), 1250.

FAO. Global Forest Resources Assessment; Technical Report; Food and Agriculture Association of the United-States: Rome, Italy, 2020.

Pacheco et al., 2021. Deforestation fronts: Drivers and responses in a changing world. WWF, Gland, Switzerland

How to cite: Mermoz, S., Bouvet, A., Ballère, M., Koleck, T., and Le Toan, T.: Forest disturbances detection in Vietnam, Cambodia and Laos using Sentinel-1 data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16177, https://doi.org/10.5194/egusphere-egu21-16177, 2021.

The assessment of forest fire risk has recently gained interest in countries of Central Europe and the alpine region since the occurrence of forest fires is expected to increase with a changing climate. Information on forest fuel structure, which is related to forest structure, is a key component in such assessments. Forest structure information can be derived from airborne laser scanning (ALS) data, whose value for the derivation of respective metrics at a high accuracy level has been demonstrated in numerous studies over the last years.

Yet, the temporal resolution of ALS data is low as flight missions are typically carried out in time intervals of five to ten years in Central Europe. ALS-derived forest structure descriptors for fire risk assessments, therefore, are often outdated. Open access earth observation data offer the potential to fill these information gaps. Data provided by synthetic aperture radar (SAR) sensors, in particular, are of interest in this context since this technology has a known sensitivity to the vegetation structure and acquires data independent of weather or daylight conditions.

In our study, we investigate the potential to derive forest structure descriptors from time series of Sentinel-1 (S-1) SAR data for a deciduous forest site in the Eastern part of Austria. We focus on forest stand height and fractional cover, which is a measure for forest density, as both of these components impact forest fire propagation and ignition. The two structure metrics are estimated using a random forest (RF) model, which takes a total of 36 predictors as input, which we compute from the S-1 time series. The model is trained using ALS-derived structure metrics acquired during the same year as the S-1 data.

We estimated stand height with a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m at 100 m resolution, while the RMSE for the fractional cover estimation is 0.08 with a bias of zero at the same resolution. The spatial comparison of the structure predictions with the ALS reference further shows that the general structure is well reproduced. Yet, fine scale variations cannot be completely reproduced by the S1-derived structure products, and the height of tall stands and very dense canopy parts are underestimated. Due to the high correlation of the predicted values to the reference (Pearson’s R of 0.88 and 0.94 for the stand height and the fractional cover, respectively), we consider S-1 time series in combination with ALS data with low temporal resolution and machine learning techniques to be a reliable data source and workflow for regularly (e.g. < yearly) updating ALS structure information in an operational way.

How to cite: Bruggisser, M., Dorigo, W., Dostálová, A., Hollaus, M., Navacchi, C., Schlaffer, S., and Pfeifer, N.: Derivating forest structure from Sentinel-1 time series to assist forest fire risk assessments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9033, https://doi.org/10.5194/egusphere-egu21-9033, 2021.

Deforestation and degradation are two major threats to the global forest that jeopardize their functions to store carbon and mitigate climate change. Forest degradation undermines the health and functions of the forest to perform ecosystem services and is a stepping stone to deforestation. However, forest degradation has not been sufficiently monitored and quantified due to the varying intensity of disturbance and usually inconsistent spectral signals reflected in optical remote sensing. Drivers of forest degradation can be natural and/or human-related, and charcoal production is a key driver of forest degradation in sub-Saharan Africa due to the high demands for charcoal for energy consumption and the increasing rate of population growth and urbanization. In this study, we focus on charcoal production-driven forest degradation that occurred at the Mabalane district in Southern Mozambique from 2008 to 2018. We intend to demonstrate the potential of combining Global Ecosystem Dynamics Investigation (GEDI) data and Landsat time stacks for inspecting the changes in forest structure and aboveground biomass (AGB). To do so, we categorize the degraded forest by the year of disturbance based on a disturbance map produced for the study area for 2008-2018 by Sedano et al. (2019) and analyze the first year of publicly-released GEDI data to characterize forest structure and AGB at different disturbance classes. We also compare the GEDI L4A biomass with three other global and continental AGB products to understand the pre-disturbance biomass storage and the degradation patterns. Lastly, we build an empirical model between GEDI biomass and Landsat spectral bands and vegetation indices to quantify the biomass removal and regrowth from 10-year charcoal production. Uncertainties from the GEDI-Landsat models are estimated using Monte Carlo Simulations to propagate errors. The study improves the current understanding of forest degradation and carbon dynamics associated with it in tropical dry forests of sub-Saharan Africa. It also demonstrates the potential of combining spaceborne lidar missions and Landsat archives to facilitate accurate mapping of forest structural and AGB change in the degraded forest at a local scale. 

How to cite: Liang, M., Duncanson, L., and Sedano, F.: Quantifying Local-scale Forest Degradation Intensity from Charcoal Production Using a Fusion of GEDI and Landsat Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3147, https://doi.org/10.5194/egusphere-egu21-3147, 2021.

Multispectral imaging using unmanned aerial systems (UAS) enables rapid and accurate detection of pest insect infestations, which are an increasing threat to midlatitude natural forests. Pest detection at the level of an individual tree is of particular importance in mixed forests, where it enables a sensible forest management approach. Moreover, urban forests may be affected more seriously because an urban environment produces additional stressors. The stressors include changes in forest soil properties, tree species diversity, higher temperatures, and carbon dioxide content. The stressed trees are then optimal material for a bark beetle feeding. Therefore, it is necessary to use an appropriate method for the detection of individual infested trees.

In this contribution, we present a novel method for individual tree crown delineation (ITCD) followed by feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud (PPC) and a multispectral orthomosaic. An excess green index (ExG) threshold mask was applied before the ITCD to separate targeted coniferous trees from deciduous trees and backgrounds. The individual crowns of conifer trees were automatically delineated as (i) a full tree crown using marker-controlled watershed segmentation (MCWS), Dalponte2016, and Li 2012 region growing algorithms or (ii) a buffer around a treetop from the masked PPC.

We statistically compared selected spectral and elevation features extracted from automatically delineated crowns of each method to reference tree crowns to distinguish between the forest disturbance classes and two tree species. Moreover, the effect of PPC density on the ITCD accuracy and feature extraction was investigated. The ExG threshold mask application resulted in the excellent separability of targeted conifer trees and the increasing shape similarity of automatically delineated crowns compared to reference tree crowns. The results revealed a strong effect of PPC density on treetop detection and ITCD. If the PPC density is sufficient (> 10 points/m²), the automatically delineated crowns produced by Dalponte2016, MCWS, and Li 2012 methods are comparable, and the extracted feature statistics insignificantly differ from reference tree crowns. The buffer method is less suitable for detecting a bark beetle disturbance in the mixed forest because of the simplicity of crown delineation. It caused significant differences in extracted feature statistics compared to reference tree crowns. Therefore, the point density was found to be more significant than the algorithm used.

We conclude that the automatic methods may constitute a reliable substitute for the time-consuming manual tree crown delineation in tree-based bark beetle disturbance detection and sanitation of individual infested trees using the suggested methodology and high-density (>20 points/m², 10 points/m² minimum) PPC.

How to cite: Minařík, R., Langhammer, J., and Lendzioch, T.: Automatic Tree Crown Feature Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in an Urban Forest, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8297, https://doi.org/10.5194/egusphere-egu21-8297, 2021.

In the past few decades, the occurrence of shrub forest dominated by the two species Green alder (Alnus viridis) and Dwarf mountain pine (Pinus mugo) has increased in the Swiss Alps. Up-to-date and area-wide information on its distribution is required for countrywide forest reporting (5 % of Swiss forest consists of shrub forest) and of great interest to the forestry sector. Such information helps to better understand forest succession and supports the evaluation and management of protection forests. Until now, this information has been based on estimates from the Swiss National Forest Inventory (NFI). Due to their sampling scheme that uses a regular grid, these data are not area-wide maps. However, new developments in remote sensing techniques in combination with high spatial and temporal resolution data have facilitated the production of maps over large areas, e.g. the whole of Switzerland (41’285 km²).

To map the shrub forest areas, we developed an approach that uses a Random Forest (RF) model, active learning techniques and data from multiple remote sensing sources. The training data was produced via aerial image interpretation of areas covered by shrub forest. We used predictor data from different sensors and technologies, complementing each other by their diverse sensitivity to properties of shrub forests. These data included airborne Digital Terrain (DTM) and Vegetation Height Models (VHM), and spaceborne Synthetic Aperture Radar (SAR) backscatter from the Sentinel-1 constellation and multispectral imagery from Sentinel-2. To improve mapping quality, an iterative and semi-automatic active learning technique was used to generate further training data.

The above outlined workflow enabled the production of a shrub forest map for the whole of Switzerland with a spatial resolution of 10 m. An accuracy assessment was performed using independent validation data of a total of 7’640 regularly distributed NFI plots. Mean shrub forest cover per plot (50 m x 50 m) was slightly underestimated by 1.5 % with a root mean square error of 10 %. The influence of the active learning was observed and revealed higher accuracies after each additional iteration of training data production. The proposed approach underscores the potential of multi-sensor data combined with active learning techniques to provide cost-effective and area-wide information on the occurrence of shrub forest in a manner complementary to the NFI measurements.

How to cite: Rüetschi, M., Weber, D., Koch, T. L., Small, D., and Waser, L. T.: Wide-area shrub forest map based on multi-sensor data and active learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11209, https://doi.org/10.5194/egusphere-egu21-11209, 2021.

Although forests cover about one third of global land surface, forests act as important biophysical, biogeochemical, hydrological, economic and cultural roles in the Earth systems. Forests contribute up to 75% of terrestrial gross primary production and store more carbon in forest biomass and soil compared to the atmosphere. Forest aboveground biomass (AGB) plays a crucial role in regional and global ecological balance. However, due to the difficulties in measuring forest biomass in the field at regional scales, a quantitative estimation with high accuracy of forest AGB by linking remote sensing is still a challenge, particularly in mountainous region. Thus, we combined the Landsat 8 OLI and Sentinel-2B data to estimate subalpine forest AGB using linear regression (LR), and two machine learning approaches - random forest (RF) and extreme gradient boosting (XGBoost), with the linkage of field observations in Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. A 10-fold cross validation (CV) method was used to evaluate the model accuracy, and then the proximity between the predicted value and the actual value was compared. The model efficiency (pseudo R²) and root mean square error (RMSE) were used as the accuracy evaluation criteria. Based on 54 field observations, results showed that mean forest AGB was 180.6 Mg ha^-1with a strong spatial variability from 61.7 to 475.1 Mg ha^-1. AGB varied significantly among forest types that AGB in coniferous forests was significantly higher than coniferous mixed forests and broad-leaved forests. Landsat 8 OLI and Sentinel-2B imagery were successfully applied to estimate AGB separately or combined. Integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency for different modelling approaches. For the regression algorithms, machine learning method outperformed the linear regression. Among LR, RF and XGBoost approaches, XGBoost performed best with a model efficiency (R²) of 0.71 and root mean square error values of 46 Mg ha^-1 and subsequently used for spatial modelling. Modelled results indicated a strong spatial variability in AGB, with a total 6.6×10⁶ Mg across the study area. AGB distribution in the study area had obvious spatial characteristics, which was closely related to the elevation. It was mainly concentrated in the north and central areas, while in the southern region the AGB was relatively low, which was contrary to the trend of the elevation variation in the study area where the terrain was high in the south and low in the north. Our study highlighted a potential way to improve the estimate accuracy of forest AGB in mountainous region by integrating the Landsat 8 OLI and Sentinel-2B data using machine learning algorithms.

How to cite: Luo, K., Tang, X., Liu, L., Luo, X., and Li, J.: Machine learning based estimation of aboveground biomass in subalpine forests using Landsat 8 OLI and Sentinel-2B images in Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4616, https://doi.org/10.5194/egusphere-egu21-4616, 2021.

Aboveground plant water storage (APWS), the total of water storing in aboveground parts of plant, has the function of sustaining the balance between water loss by transpiration and water gain of root uptake. APWS is also essential for plants and hydrological cycle, particularly for semi-arid areas, where water availability is limited. However, APWS varies spatially due to the heterogeneity of natural areas that are composed of a large variety of vegetation types, and studies on the spatial variability of APWS are quite limited in semi-arid areas. To fill this knowledge gap, we established 55 inventory plots with 36 plots in forests and 19 plots in shrubs to detect the spatial variability of APWS using a Random Forest (RF) algorithm and Sentinel-2 images in Mao County, China. Field observations indicated that APWS varied significantly with ecosystems, with the highest APWS in forests. Regardless of ecosystem type, mean APWS in Mao Country was 117.63 Mg ha-1. 10-fold cross-validation suggested that the RF model could reasonably predict APWS (model efficiency = 0.68, root mean square error = 54 Mg ha-1), enabling to capture the spatial variability of APWS. A robust spatial variability of APWS was observed with the highest APWS in forests located high altitude areas, while the lowest APWS was found in shrubs located in low altitude areas. Total APWS was 3.39×107 Mg across the whole study area, which could be used as a valuable natural resource for the semi-arid area. Our study successfully explored the spatial variability of APWS, suggesting the capability of detecting APWS using Sentinel-2 and providing essential data evidence for environmental protection for semi-arid areas.

How to cite: Liu, L., Li, S., Luo, X., Yang, W., Zhang, Y., Lei, J., Chen, G., and Tang, X.: Estimation of aboveground plant water storage using Sentinel-2 images in a semi-arid area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5111, https://doi.org/10.5194/egusphere-egu21-5111, 2021.

Remote sensing (RS) techniques have great potentials for earth surface monitoring. Nevertheless, for most low to moderate resolution satellites, the problem of mixed pixels with information from the vegetation of interest and the background surfaces can cause large biases in signals and also in their interpretations. This is especially so in low-density forests and semi-arid ecosystems. Ground-level multispectral instruments reduce these effects by measuring at close range to the canopy. However, little work has been published on partitioning the contributions from vegetation and the background elements for both approaches.

This work was motivated by the observed mismatch between data for the same ecosystem from Landsat 8 satellite and Skye radiometer installed on a flux tower in a low-density semi-arid pine forest from 2013-2019. Data from both sources showed similar seasonal patterns in NDVI, but large differences in the reflectance bands. This was most prominent in the NIR reflectance, which showed an opposite seasonal cycle in the two sensors. Thus, similar changes in NDVI were produced by different signals. We hypothesized that the different contributions of the surface components (canopy, shaded areas, and exposed soil) in the footprint areas of the two sensors can explain, and can help correct, these differences.  

Multispectral images with a spatial resolution of 5 cm were captured monthly using an Unmanned Aerial Vehicle (UAV) from April 2018 to November 2019. Reflectance-based algorithms were developed to identify and estimate the fraction and reflectance from the canopy, shaded areas, and open soil. This information was, in turn, applied in the equivalent nadir-viewing satellite pixel. For the tower-based Skye footprint, the same quantities were calculated from its 90° angle of view and the 3D canopy data.

The results showed a canopy fraction of 45% and 95% in the Landsat 8 and Skye footprints. The remaining soil fraction showed a similar seasonal cycle in NDVI as the canopy, but different in the NIR reflectance. The partition between exposed and shaded soil was related to the sun angle, with the exposed soil having a NIR seasonal cycle opposite to that of the vegetation (correlating with soil moisture), and shaded soil having a weak NIR signal variably diluting the overall pixel NIR signal. Differences in the red reflectance were smaller with less effects on the seasonal NDVI cycles.

The results demonstrated firstly, that accounting for the fractional contributions of the surface components can reconcile differences between satellite and ground-based RS. Secondly, vegetation indices such as NDVI obtained by satellite RS in low-density forests can provide misleading information, despite its apparent correlation with certain vegetation variables.

How to cite: Wang, H., Muller, J., Tatarinov, F., Rotenberg, E., and Yakir, D.: Using high-resolution UAV spectral images to disentangle soil, shade, and tree contributions to satellite vegetation indices in sparse dry forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9189, https://doi.org/10.5194/egusphere-egu21-9189, 2021.

Shifts in climate driven by anthropogenic land use and land cover change are expected to alter various land–atmosphere interactions. Evapotranspiration (ET) is one of these processes and plays a fundamental role in the hydrologic cycle. Using gridded reanalysis and remote sensing data, we investigated the spatiotemporal trends of precipitation, temperature, and ET for croplands and forest areas in the Baltic states where these land cover type had not changed from 2000 to 2018. We focused on ET but investigated the spatiotemporal trends for the three variables at monthly, seasonal, and annual time scales during this period to quantify trade-offs among months and seasons. We used the Mann-Kendall test and Sen’s slope to calculate the trends and rate of change for the three variables. Although precipitation showed fewer statistically significant increasing and decreasing trends due to its high variability, temperature showed only increasing trends in all time scales. The increasing trends were concentrated in late spring (May, +0.14ºC per year), summer (June and August, +0.10ºC), and early autumn (September, +0.13ºC). For unchanged forest and cropland areas, we found no statistically significant ET trends. However, Sen’s slope indicated increasing ET in April, May, June, and September for forest areas and in May and June for cropland. Our results indicate that during the study period, the temperature changes may have lengthened the growing season, which affected the ET patterns of forest and cropland areas. The results also provide important insights into the regional water balance, specially for critical periods where the ET rates increase while precipitation decrease (May, June. and July). Moreover, our study also complements the findings of other studies over the Baltic states.

How to cite: Montibeller, B., Jaagus, J., Mander, Ü., and Uuemaa, E.: Evapotranspiration intensification over unchanged temperate vegetation in the Baltic states is being driven by climate shifts , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11098, https://doi.org/10.5194/egusphere-egu21-11098, 2021.

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.

We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km² contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).

Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm) (Viinikka et al. 2020; Mäyrä et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.

How to cite: Kumpula, T., Mäyrä, J., Kuzmin, A., Viinikka, A., Kivinen, S., Tanhuanpää, T., Hurskainen, P., Keski-Saari, S., Kullberg, P., Poikolainen, L., Korpelainen, P., Ritakallio, A., Tuominen, S., and Vihervaara, P.: Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16273, https://doi.org/10.5194/egusphere-egu21-16273, 2021.

The relative concentrations of photosynthetic and photoprotective pigments provide important information about the physiological state of the plant and are determined, among other things, by the lighting regime and the presence of nutrients. Relative composition of the pigments is depending on the physiological response of the plant to external influences. In most cases, when an on-line in-situ analysis is required, only the main pigments are measured: Chla, Chlb and a rough estimate of the "total carotenoids" in higher plants, but such an estimate may not always be reliable. Differential Optical Absorption Spectroscopy (DOAS) is known for its applications for the trace gases measurements in the atmosphere sciences; however, no application has been found for the determination of color pigments for plant extracts. For the correct application of the DOAS method, it is necessary to determine the appropriate optical thickness of the sample under study, the fitting intervals for analysis, as well as a set of absorption cross sections for the target pigments.

Purpose of the work is to determine the appropriate settings for the retrieval of concentrations of colored pigments employing the DOAS method by investigating the sample of pine and spruce needles extraction. The relevance of the work consists in the development of a new method for analyzing transmission spectra, which does not require the creation of specialized software, since programs for analyzing spectra by the DOAS method are available.

For the spectra registration, Solar M150 spectrometer with Hamamatsu S7031-1006S detector has been used, the transmission spectra recorded in the 330 - 750 nm range, and pure acetone employed as a solvent. The paper presents the results of DOAS-analysis of extracts of various coniferous samples, from which it was possible to retrieve the contents of Cha, Chb, B,b-carotene, B,e-carotene, and small amounts of Phaeophytin-a, Neoxanthin. Optimal settings for the DOAS-analysis and experimental setup details for photosynthetic and photoprotective pigments retrieval are discussed.

How to cite: Bruchkouski, I., Siliuk, V., Guliaeva, S., and Litvinovich, H.: Quantitative measurements of pigments in coniferous by the method of differential optical absorption spectroscopy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12980, https://doi.org/10.5194/egusphere-egu21-12980, 2021.

Leaf phenology, the timing of leaf life cycle events, is a vital indicator of terrestrial biosphere function. The influence of global change upon leafing phenology in mid to high latitude regions is uncertain due to a complex interaction of drivers and lack of temporally and spatially resolved baseline data.  Leaf phenology has been observed manually for millennia, and through satellite platforms for decades. A novel technique of monitoring leaf phenology known as near remote sensing employing time-lapse photography at the canopy level (or phenocams) allows for objective observations with high temporal and spatial resolution. We deployed 13 solar-powered time-lapse camera stations across a climate gradient in Nova Scotia, Canada to observe leaf phenology of locally abundant species including more than 300 individuals over the 2019 and 2020 growing seasons. To examine the influence of thermal, photoperiodic, and genetic drivers, our remote phenology monitoring stations were situated in comparative edaphic and topographic contexts and complemented with relative humidity and ambient temperature sensors. We observed variability in the timing of leaf budburst, peak of season greenness, redness, senescence, and abscission between and within species, despite similar degrees of environmental forcing. Moving forward, we will apply our insights to develop species specific process based models of leaf phenology, and test the wider application of our techniques to observational records from other regions. This work demonstrates the complexity of environmental influence upon leaf phenology, as well as the utility of phenocams in monitoring leafing phenology in remote regions of Maritime Canada.

How to cite: Spafford, L. and MacDougall, A. H.: Heterogenous Patterns in Leaf Phenology Across a Climate Gradient in Maritime Canada Observed through Phenocams, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-83, https://doi.org/10.5194/egusphere-egu21-83, 2021.

Sentinel-2 time series provide large amounts of data and information which can be easily used to classify tree species with machine learning algorithms. In addition to the original Sentinel-2 bands, further data such as indices, phenological metrics or even synthetical images can be derived. While tree species classifications highly benefit from such additional data resulting in improved prediction accuracy, severe drawbacks have to be considered - For large data sets, large storage is needed, the computation time expands and a linkage to ecological or phenological reasons behind the usage of these variables can hardly be drawn. Therefore, the implemented variables should be limited to the ones, which are meaningful and on the same time providing the best prediction accuracy. To identify meaningful variables from original Sentinel-2 images and the additionally calculated data first we used basic correlation analyses and subsequently feature selection methods in combination with the commonly used Random Forest algorithm. We classified the most common forest tree species in the Swiss canton of Grisons, which is mountainous and characterized by diverse landscapes. The presented approach will lead to higher efficiency for classifying tree species and additionally provides potential conclusions regarding ecological patterns beyond the distinction of tree species by remote sensing data. Moreover, the proposed approach can also be used to improve classifications or predictions of other outcome variables for vegetated areas with Sentinel-2.

How to cite: Koch, T. L., Rüetschi, M., and Waser, L. T.: The identification of meaningful variables from Sentinel-2 time series data for effective tree species classification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14341, https://doi.org/10.5194/egusphere-egu21-14341, 2021.

With consumer-grade unmanned aerial vehicles (UAVs) on the rise, which enable easy, time-flexible, and cost-effective acquisition of very high-resolution RGB data, the mapping of forest tree species using solely RGB imagery is of high interest, as it does not rely on sophisticated sensors, does not require extensive calibration and preprocessing and, therefore, enables the application by a wide audience. In combination with convolutional neural networks (CNNs), which particularly exploit spatial patterns and, therefore, highly benfit from very high-resolution remote sensing, this offers great potential for accurately mapping forest tree species.

Here, we present the findings of our recent study, in which we used very high-resolution RGB imagery from UAVs in combination with CNNs for the mapping of forest tree species. In this study, we used multicopter UAVs to obtain very high-resolution (<2 cm) RGB imagery over 51 ha of temperate forests in the Southern Black Forest region, and the Hainich National Park in Germany. To fully harness the end-to-end learning capabilities of CNNs, we used a semantic segmentation approach (U-net) that concurrently segments and classifies tree species from imagery. With a diverse dataset in terms of study areas, site conditions, illumination properties, and phenology, we accurately mapped nine tree species, three genus-level classes, deadwood, and forest floor (mean F1-score 0.73). We found that a coarser spatial resolution substantially reduced the model accuracy (mean F1-score of 0.26 at 32 cm resolution) and that larger tile sizes during CNN training negatively affected the model accuracies for underrepresented classes. Additional height information from normalized digital surface models slightly increased the model accuracy but simultaneously increased computational complexity and data requirements. Our results highlight the key role that UAVs can play in the mapping of forest tree species, given that air- and spaceborne remote sensing currently does not provide comparable spatial resolutions. Given the end-to-end learning capabilites of CNNs extensive preprocessing becomes partly obsolete, whereas the use of a large and diverse dataset facilitates a high degree of generalization of the CNN, thus fostering transferability. The synergy of high-resolution UAV imagery and CNN provide a fast and flexible yet accurate means of mapping forest tree species.

In this contribution, we will give an outlook on how the combination of UAV imagery and CNNs can be integrated with multitemporal satellite imagery (Sentinel-1 and Sentinel-2) in order to extrapolate the UAV-based tree species maps to larger areas.

How to cite: Schiefer, F., Kattenborn, T., Frick, A., Frey, J., Schall, P., Koch, B., and Schmidtlein, S.: Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12957, https://doi.org/10.5194/egusphere-egu21-12957, 2021.

Drying out of coniferous trees (Picea abies) due to bark beetle infestation and other diseases leads to a high rate of conifers mortality. The coniferous forests in Belarus are largely exposed to damage by the bark beetle, the early symptoms of which are the changes in the color and loss of shine of the needles.  

Purpose of the work is to identify drying out stages combining the TripleSat multispectral satellite data (spatial resolution 3.2 m MS, 0.8 m PAN, bands R, G, B, NIR) for the test coniferous forest area in Belarus (53.65419º N, 27.640213º E) with quasi-synchronous airborne photo-spectral measurements which have been used as a reference data. Airborne measurements of reflectance coefficient function of underlying coniferous trees have been carried out by employing two spectrometers (wavelength range 400-900 nm, spectral resolution 4.3 nm) and photo-camera (visible range, FOV 50º) mounted on board of Diamond DA40NG aircraft in nadir geometry.  

Airborne RGB-images have been used for visual identification of the type of underlying surface and for subsequent training data set formation. Training data consist of several sets (10 – 20) of vegetation indexes for each type of underlying surface. The linear discriminant analysis (LDA) classification algorithm has been applied in this study for distinguishing the conifers drying out stages. A set of vegetation indices evaluated for each reflectance coefficient function has been applied as input data for LDA classification algorithm.

LDA classification algorithm has been employed to the TripleSat image for identification drying out stages of coniferous trees. The reference data for LDA classification algorithm of the TripleSat image included the combination of coordinates and corresponding types of underlying surface obtained from the results of the airborne experiment classification. A set of vegetation indices has been derived for each pixel of the image and used as input data for LDA algorithm; also vegetation indices calculated for the reference pixels have been applied for training data set formation.

The classification accuracy of three conifers drying out stages based on the airborne experiment is estimated to be in a range of 27 - 74%. The verification of TripleSat classification results has been performed by visual comparison with high resolution aerial images.

How to cite: Litvinovich, H., Guliaeva, S., Bruchkouski, I., Siliuk, V., and Katkouski, L.: Drying out conifers classification employing TripleSat satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15269, https://doi.org/10.5194/egusphere-egu21-15269, 2021.

LiDAR-based forest inventories focusing on estimating and mapping structure-related forest inventory variables across large areas have reached operationality. In the commonly applied area-based approach, a set of field-measured inventory plots is combined with spatially co-located airborne laserscanning data to train empirical models that can then be used to predict the target metric over the entire area covered by LiDAR data.

The area-based approach was found to produce reliable estimates for structure-related forest inventory metrics such as wood volume and biomass across many forest types. However, the current workflows still leave space for improvement that may result in cost-reduction with respect to data acquisition or improved accuracies. This is particularly relevant as the area-based approach is increasingly used in operational forestry settings. To further optimize existing workflows, experiments are required that need large amounts of forest inventory data (e.g., to examine the effect of sample size or the field inventory design on the model performances) or multiple LiDAR acquisitions (e.g., to identify optimal/cost-efficient acquisition settings). The acquisition of these types of data is cost-intensive and is hence often limited to small extents within scientific experiments.

Here, we present the ”GeForse - Generating Synthetic Forest Remote Sensing Data” approach to create synthetic LiDAR datasets suitable for such optimization studies. GeForse combines a database of single-tree models consisting of point clouds extracted from real LiDAR data with the outputs of a spatially explicit, single tree-based forest growth simulator (in this case SILVA). For each simulated tree, we insert a real point-cloud tree with properties (species, crown diameter, height) matching the properties of the simulated tree. This results in a synthetic 3D forest with a realistic 3D-structure where the inventory metrics of each tree are known. This 3D forest then serves as input to the “Heidelberg LiDAR Operations Simulator” (HELIOS++, https://github.com/3dgeo-heidelberg/helios) and thereby enables the simulation of LiDAR acquisition flights with varying acquisition settings and flight trajectories. In combination with the “full inventory” of all trees in the simulated forest, this enables a wide variety of sensitivity analyses.

In this contribution, we give an overview of the complete GeForse approach from extracting the tree models, to generating the 3D forest and simulating LiDAR flights over the 3D forest using HELIOS++. Further, we present a brief case-study where this approach was applied to optimize certain aspects of area-based forest inventory approaches using LiDAR data from a forest area in central Europe. Finally, we provide an outlook on future application fields of the GeForse approach.

How to cite: Fassnacht, F. E., Schäfer, J., Weiser, H., Winiwarter, L., Krašovec, N., Latifi, H., and Höfle, B.: Presenting the GeForse approach to create synthetic LiDAR data from simulated forest stands to optimize forest inventories, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9197, https://doi.org/10.5194/egusphere-egu21-9197, 2021.

Virtual laser scanning (VLS) is a valuable method to complement expensive laser scanning data acquisition in the field. VLS refers to the simulation of LiDAR to create 3D point clouds from models of scenes, platforms and sensors mimicking real world acquisitions. In forestry, this can be used to generate training and testing data with complete ground truth for algorithms performing essential tasks such as tree detection or tree species classification. Furthermore, VLS allows for the in-depth investigation of the influence of different acquisition parameters on the point clouds and thus also the behaviour of algorithms, which is important when relating point cloud metrics to forest inventory variables. Finally, VLS can be used for acquisition planning and optimisation, as different configurations can be tested regarding their ability to create data of the required quality with minimal effort. For these purposes, we developed the open source Heidelberg LiDAR Operations Simulator HELIOS++ (written in C++) which is available on GitHub (https://github.com/3dgeo-heidelberg/helios), as a precompiled command line tool, and as Python package (pyhelios). HELIOS++ provides a high-fidelity framework for full 3D laser scanning simulations with multiple platforms and a flexible system to represent the scene. HELIOS++ models the beam divergence and supports the recording of the full waveform.

One important premise for the usefulness of VLS data is the use of an adequate 3D scene in the simulation. In this context, we conducted a study investigating point clouds simulated based on opaque voxel-based forest models computed from terrestrial laser scanning data using different voxel sizes. Coupling the LiDAR simulation with a database containing point clouds of single trees from terrestrial, UAV-borne and airborne acquisitions, allowed us to compare metrics derived from real and simulated data. Furthermore, by including the tree neighbourhood in the scene, we were able to consider occlusion effects between the trees.

We found that the voxel size is an important parameter, where values of e.g. 0.25 m lead to unrealistic occlusion effects of the mid- and understory, as only few gaps remain in the forest models through which the laser beam can pass. This results in fewer multiple returns, the vertical point distribution is shifted upwards, and tree metrics such as crown projection area and crown base height are estimated poorly. Smaller voxel sizes are therefore preferable, though the appropriate voxel size depends on the resolution of the input point cloud. With very small voxels, the voxel model may become too transparent. To achieve realistic simulations without the need for a high number of voxels we suggest variable downscaling of voxel cubes based on appropriate local metrics such as the plant area density. This approach decreases the computational requirements for the simulation, as fewer primitives are present in the scene. In our study, the use of such scaled voxels derived for a grid size of 0.25 m achieves equally and partly more reliable estimates of point cloud and tree metrics than regular voxels at fixed side lengths of 0.05 and 0.02 m.

How to cite: Weiser, H., Winiwarter, L., Schäfer, J., Fassnacht, F. E., Anders, K., Esmorís Pena, A. M., and Höfle, B.: Virtual laser scanning (VLS) in forestry – Investigating appropriate 3D forest representations for LiDAR simulations with HELIOS++, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9178, https://doi.org/10.5194/egusphere-egu21-9178, 2021.

Trees supply a multitude of ecosystem services (e.g. carbon storage, suppression of air pollution, oxygen, shade, recreation etc.) not only in forested areas but also in urban landscapes. Many of these services are positively correlated with tree size and structure. The assessment of carbon storage potential via the quantification of above ground biomass (AGB) is of special importance. However, quantification of AGB is difficult and applied allometries are often based on forest trees, which are subject to very different growing conditions, competition and form compared to urban trees. In this contribution, we highlight the potential of terrestrial laser scanning (TLS) techniques to extract high detailed information on tree structure and AGB with a focus on urban trees.

A total of 55 urban trees distributed over eight cities in Switzerland were measured using TLS and traditional forest inventory techniques before they were felled and weighted. Tree structure, volumes and AGB from the TLS point clouds were extracted using Quantitative Structure Modelling (QSM). TLS derived AGB estimates were compared to allometric estimates dependent on diameter at breast height only. The allometric models were established within the Swiss National Forest Inventory and are therefore optimised for forest trees.

TLS derived AGB estimates showed good performance when compared to destructively harvested references with an R² of 0.954 (RMSE = 556 kg), compared to an R² of 0.837 (RMSE = 1159 kg) for allometrically derived AGB estimates. A correlation analysis showed that different TLS derived wood volume estimates as well as trunk diameters and tree crown metrics show high correlation in describing total wood AGB.

The presented results show that TLS based wood volume estimates show high potential to estimate tree AGB independent of tree species, size and form. This allows us to retrieve highly accurate, non-destructive AGB estimates that could be used to establish new allometric equations without the need of extensive destructive harvest.

How to cite: Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L.: Above Ground Biomass References for Urban Trees from Terrestrial Laser Scanning Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5399, https://doi.org/10.5194/egusphere-egu21-5399, 2021.

Fuel management is a crucial action to maintain wildland fires under the threshold of manageability; hence, in order to allocate resources in the best way, wildland fuel mapping is regarded as a necessary tool by land managers. Several studies have used Aerial Laser Scanner (ALS) data to estimate forest fuels characteristics at plot level, but few have extended such estimates at a zonal level.

In the context of the EU Interreg Project CROSSIT SAFER, a test of the possibilities of ALS data to predict fuels attributes has been performed in three different areas: an alpine basin, a coastal wildland-urban interface and a karstic highland. Eighteen sampling plots have been laid out over 6 forest categories, with a special focus on Pinus nigra J. F. Arnold artificial forests. Low density (average 4 points/m²) discrete return LiDAR data has been analysed with FUSION, a free point cloud analysis software tailored to forestry purposes; field and remote sensing data have been connected with simple statistical modelling and results have been spatialised over the case study areas to provide wall-to-wall inputs for FLAMMAP fire behaviour simulation software.

Resulting maps can be of relevance for land managers to better highlight the most vulnerable or fire prone areas at a mesoscale administrative level. Limitations and room for improvement are pointed out, in the view that land management should keep updated with the latest technology available.

How to cite: Taccaliti, F., Venturini, L., Marchi, N., and Lingua, E.: Forest fuel assessment by LiDAR data. A case study in NE Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12755, https://doi.org/10.5194/egusphere-egu21-12755, 2021.