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'Geospatial analysis for sustainable development' combined with 'Carbon emissions/removals estimates under Land use, land-use change and forestry (LULUCF) sector'

This combined session aims to provide extensive overview of different methodologies applied to pursue the achievement of one or more Sustainable Development Goals as well as to address issues related to national GHG reporting.

In one part session includes submissions related to global or regional applications of geospatial data analysis techniques to address sustainability challenges (land, energy, water, climate, infrastructure, vegetation, health etc.) and their interactions. Contributions aiming at improving the understanding, planning, and evaluation of technological, environmental and policy solutions pursuing the achievement of one or more Sustainable Development Goals (SDGs) will be considered. The main methodological requirement is the use of GIS data (from earth observation, in-situ collection, or statistical offices) and manipulation tools to develop and apply innovative methodologies leveraging bottom-up, spatially-explicit information and highlighting their benefits vis-à-vis aggregated, top-down analysis. Preference will be given to studies which are broader in geographical scope, and which can be scaled to other contexts.

Also, session will emphasize the importance of LULUCF sector in reaching the long-term climate mitigation objective. Contributions related to national and subnational carbon budget estimates (past, present and future) in different land uses (forests, crops, grasslands, urban areas), using multiple data sources and different calculation methods, will be considered. NFI-based, remote sensing and modelling studies on C stocks and/or fluxes in different ecosystem pools (live biomass, dead wood, litter or soil) are encouraged. Aim is to highlight main issues regarding data integration and model calibration and validation process.

Co-organized by GI6
Convener: Giacomo FalchettaECSECS | Co-conveners: Maša Zorana Ostrogović SeverECSECS, Hrvoje Marjanovic, Anikó Kern, Olha DanyloECSECS, Ahmed HammadECSECS
| Mon, 23 May, 08:30–11:05 (CEST)
Room 0.96/97

Mon, 23 May, 08:30–10:00

Chairpersons: Giacomo Falchetta, Olha Danylo, Ahmed Hammad


Arthur Lutz et al.

The UN Sustainable Development Goals (SDGs) are a powerful concept to drive action towards a more sustainable future. However, the SDGs are formulated in a qualitative and generic way whereas specific and quantitative definitions of targets are required to steer policy and practice.

The Indus river basin is a global hotspot for future climate change and socioeconomic development. The basin has the largest continuous irrigation scheme in the world, and hydropower is developing rapidly with a large hydropower potential still untapped. Therefore, water, food and energy are strongly interlinked in the basin’s water-food-energy nexus. The basin already faces insecurity of water, food and energy in the present situation, and with strong projected climate and socioeconomic change, achieving the SDGs for these three resources in the basin will be challenging.

Here we present a novel approach to translate the global SDGs for water, food and energy (SDGs 2, 6 and 7) to quantitative targets specified for the Indus river basin. Our approach is based on a resource accounting framework operating at sub-basin scale and monthly time step, combining models and geospatial data. The approach uses ensembles of downscaled projections for three climate change scenarios driving water availability and three sets of downscaled projections of socioeconomic drivers, including population and GDP, as main drivers for the demand for water, food and energy. The accounting framework considers dependencies between the three resources and represents scenario-specific exchange of resources between sub-basins in this transboundary river basin. The approach results in scenario-specific quantitative targets for water, food and energy to be realized to achieve the three related SDGs at the river basin scale.

How to cite: Lutz, A., Smolenaars, W., Dhaubanjar, S., Jamil, K., Biemans, H., Ludwig, F., and Immerzeel, W.: From SDGs to IDGs: Translating global Sustainable Development Goals for water, food and energy to river basin specific Indus Development Goals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4378, https://doi.org/10.5194/egusphere-egu22-4378, 2022.

Nandi Moksnes et al.

Energy has been identified as an enabler for several of the Sustainable Development Goals (SDGs). Globally, 759 million people (2019) still lack access to electricity. Energy planning is important to describe the pathway to achieve the nations goals, where energy systems models are important tools to explore scenarios and provide insight. Until recently, modelling energy access with low electrification rate was conducted either at low spatial (e.g. national) or temporal resolution (e.g. annual time slices).  The central grid is often modelled as a black box with approximate optimization methods. This is recognised as unsuitable for understanding integration of technological alternatives to a centralised grid, including distributed generation and mini-grids/renewables. However, methods to model national energy systems at very high spatial and temporal resolutions are data and computation intensive. At the same time increased transparency on the data and code behind these models and insight is important as energy infrastructure is both capital intensive and strategic for the nation.

In this paper we investigate the use of OSeMOSYS, an open-source energy systems model, and increase the spatial resolution while keeping a medium time resolution. OSeMOSYS is a linear programming model and conveniently finds the global optimum in contrast to approximate methods. The approach provides insights into the trade-offs across supply and demand. The model generation is available in an open-source repository where results can be reproduced.

For this paper we use Kenya as our case study where still 16 million people lack access to electricity (2019). We select the spatial resolution to 378 supply cells (40x40km square cells) which leads to 591 demand cells split between electrified and un-electrified. The modelled number of seasons are 12 and the day is split into 3 slices: day, evening, and night, leading to 36 time slices. Specific demand profiles for electrified and un-electrified are assessed in combination with location specific supply options (expansion from the grid, PV, wind, diesel gensets).

Our preliminary results show that the varying un-electrified demand profile, with a high evening peak and low night-time demand, hybrid solutions are preferred with more than one supply option to meet the demand. The expansion of the grid to cells located far away is not motivated due to the low expected consumption, therefore decentralized supply options are required to serve at a high service level.

The results highlight the need for further work to investigate the sensitivity of the spatial and temporal resolutions in combine in energy systems optimization models.

How to cite: Moksnes, N., Howells, M., and Usher, W.: Increasing spatial and temporal resolution in energy system optimization model for energy access – the case of Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12071, https://doi.org/10.5194/egusphere-egu22-12071, 2022.

Srashti Singh and Kamal Jain

Excessive growth in the global human population and eventually urbanisation has become a serious threat to the environment. These situations arise especially in the rapidly developing nations, India being one of them. A higher population naturally poses a high pressure on the environment directly or indirectly, which is a threat for the sustainable development of the country. Most Indian cities face environmental sustainability challenges. Most cities in India are presently going through rapid urbanization and industrialization which leads to environmental degradation of the city. The objective of this study is to analyse the environmental quality of the selected developing cities and also compare the intensity to which they are affected by urbanisation. The study is performed using satellite-based remote sensing data. Initially, Landsat data is used for the years 2001 to 2021 and is utilized for studying the LULC (land use land cover) transformations. MODIS data products are used at 1 km resolution to extract the biophysical indicators (BI) such as normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS data for PM2.5 is also utilised and finally, an index is calculated to represent the comprehensive environmental quality of the selected cities (CEQI). The yearly and decadal changes in the values of this index is mapped. The LULC transformations depicted a phenomenal decay in the greenness and an increase in the urban built-up area of the city. The CEQI variations and temporal trends reveal the significant deterioration of the overall environmental conditions in most of the cities. This is due to the change in gentrification patterns and also the change in urbanization and the greenness of the city. The study suggests that emission control strategies and urban greening can significantly contribute to enhancing urban environmental quality, especially in rapidly developing cities. The measures suggested to improve the environmental quality can help the policy-makers in the sustainable planning of the city.

How to cite: Singh, S. and Jain, K.: A comparative analysis of urban environmental quality of developing cities of India: A geospatial approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13558, https://doi.org/10.5194/egusphere-egu22-13558, 2022.

Khurram Riaz et al.

Climate change has been recognised for decades, and environmental risks related to it are expected to become more common over time as the world's population continues to grow. This tendency is compounded by people congregating in areas such as coastal regions, which are becoming increasingly vulnerable due to climate change. It is demonstrated that overpopulated regions need robust early warning systems representing the region's complex systems to allow all stakeholders to receive the correct information and respond appropriately and quickly under extreme climate events to avoid losing lives and property. The concept of a 'digital twin' is proposed as an accurate virtual representation of the effect of climate events on a specific region, which can be used as a tool to achieve better resilience of cities against extreme events. A digital twin can be created by combining data from various IoT sensors and artificial intelligence with a city model to represent a digital replica of the actual world. This paper presents an up-to-date picture of the GIS-based digital twin technology developed in the last decade for the early warning of extreme climate events worldwide and their integration with the smart city management systems. The findings suggest that GIS-based digital twin technology for severe climate hazard early warning is an emerging method. Yet, it has gained prominence in recent years due to developments in technology, software development, and communication technologies. However, much more research on digital twins is necessary to create a more effective early warning system approach. This paper highlights a potential framework for the development, implementation, and application of GIS-Based digital twins in climate resilience management in coastal regions.

How to cite: Riaz, K., McAfee, M., Anton, I., and Gharbia, S.: Conceptualising the management of climate extreme events through the GIS-based digital twin system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7343, https://doi.org/10.5194/egusphere-egu22-7343, 2022.


Edina Hajdú et al.

Geotourism is a relatively new sector in tourism, in which visitors are offered earth scientific knowledge when visiting spectacular locations (geosites or geotopes) and participating in various organized activities there. Areas and sites with high geological-geomorphological relevance are usually managed by national parks, geoparks or other types of nature reserves. For this reason, research into the assessment of these sites serves not only the purposes of geoscience but also those of these organisations and, through them, tourism.

The aim of our research was to carry out a quantitative geotourism assessment in the NW part of the Gerecse Mts, Hungary, on an area of 180 km2. As this type of assessment determining geotourism potential has not been made here before, the Gerecse Mountains are still undiscovered in terms of quantitative geotourism values. However, this area has great geodiversity due to its earth scientific richness (its various and spectacular geosites are mainly from the Mesozoic, but Eocene, Miocene and Quaternary sediments are also present). It has strong connections to culture and human activities: it is an important source of building stones since Roman times.

We used analogue geological and topographic maps, publications, and databases to identify potential geosites. The selected sites were ranked based on their types (e.g., cliff, quarry, break of slope) and distance from trails. They were visited on site – omitting the least important ones based on the preliminary categorization. Following the fieldwork, the potential geosites were evaluated based on quantitative assessment models that have been used in Hungary several times. We applied the Geosite Assessment Model (GAM, Vujičić et al., 2011) and the Modified Geosite Assessment Model (M-GAM, Tomič & Božić, 2014). Among objective aspects, the latter involves tourists (from other studies) into the evaluation process, thus giving a more realistic image of the geotourism potential of the given geosite. The final score of an object is built up by scientific, infrastructural and this visitor-based values. In the end of the work, each geosite got an analysis on its improvable characteristics, and a group of them were selected as suitable for later geotourism activities and development.

The results (more than 100 evaluated geotopes) contribute to the geosite cadastral of the Gerecse Mts – providing useful data for the management body – the Duna-Ipoly National Park Diretorate. Suitable protection and tourism activity measures of local earth science values can be planned using our results – these two factors are the base of a good balance between nature and society.

EH is supported by the ÚNKP-21-2 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.


Tomić, N., & Božić, S. (2014). A modified Geosite Assessment Model (M-GAM) and its Application on the Lazar Canyon area (Serbia). International Journal of Environmental Research, 8(4), 1041-1052.

Vujičić, M., Vasiljević, D., Marković, S., Hose, T., Lukić, T., Hadžić, O., & Janićević, S. (2011). Preliminary geosite assessment model (GAM) and its application on Fruška Gora Mountain, potential geotourism destination of Serbia. Acta Geographica Slovenica, 51(2), 361-377.

How to cite: Hajdú, E., Albert, G., and Pál, M.: Geotourism assessment of the northwestern part of the Gerecse Mountains, Hungary, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-343, https://doi.org/10.5194/egusphere-egu22-343, 2022.

Dayong Jeong et al.

Jeju island’s unique and diverse species of flora and fauna and well-preserved natural environment earned Jeju the designation as a UNESCO Biosphere Reserve in December 2002(World Heritage Office & Jeju Special Self-Governing Province, 2016). To achieve no net loss and preferably a net gain of this outstanding biodiversity, ‘biodiversity offsets’ can be implemented(BBOP, 2009). Until now, there have been attempts in Korea to introduce the concept of offset, such as the establishment of the ‘Total Natural Resource Conservation’(Lee et al., 2020), but studies on the specific criteria or method of biodiversity offset area are insufficient. It is desirable not to prepare offset area whenever damage occurs, but to select them in consideration of ecological connectivity, environmental functional aspects, and socio-cultural continuity in the planning process(Lee et al., 2020). Therefore, we intend to select the offset area of Jeju Island using the methodology of Pilgrim et al (2012), which derives the relative offsetability in consideration of the biodiversity conservation concern, residual impact magnitude, theoretical offset opportunity, practical offset feasibility. Potential offset area derived from previous studies has already reflected the concept of biodiversity conservation concern, including vulnerability and irreplaceability. Through the Environmental Impact Assessment(EIA) of Jeju Island, the type of development that had a significant impact on biodiversity is selected as an example, and the impact magnitude of the development type is identified. In addition, offset opportunity is derived by considering functional area and natural distribution, and offset feasibility is derived by factors such as developer capacity and financing. Finally, the relative offsetability is evaluated and the offsetability map is established. The characteristics of offset areas are analyzed using the established offsetability map. For instance, the size and patterns of sites with high offsetability can be studied. As a result, the offsetability map is established by evaluating the relative offsetability of potential offset areas. Therefore, it is possible to specifically find where the biodiversity offset is available in Jeju Island, and to identify the offset priority through comparison of the relative offsetability between the selected offset sites. By analyzing the characteristics of the offset area, it is possible to identify what characteristics increase the offsetability, how large it should be to have high offsetability, and what patterns exist between the selected offset areas. This study shows the specific offset area selection process, and through this, it will help to create a roadmap for selecting a site for a biodiversity offset where the biodiversity offset concept was not introduced into the policy. This work was supported by the Korea Environment Industry and Technology Institute (KEITI) through the Decision Support System Development Project for Environmental Impact Assessment, funded by the Korea Ministry of Environment (MOE) (No. 2020002990009). This work was Supported by a Korea University Grant.

How to cite: Jeong, D., Lee, S., Shin, Y., and Jeon, S.: A Study on the Selection of Biodiversity Offset Area in Korea - Focusing on Jeju Island, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3398, https://doi.org/10.5194/egusphere-egu22-3398, 2022.

Yu Jin Shin et al.

Jeju Island, the research area, has been registered as a UNESCO World Natural Heritage Site and has high biodiversity and ecological value, such as designation as a global geopark and biosphere reserve. It also has a beautiful landscape, so it is not only necessary for conservation but also highly demanded as a landscape resource (Kim et al., 2015; Ko, 2011). Accordingly, it is necessary to prioritize the conservation area that can reconcile the conflict between indiscriminate development and nature protection, as well as to establish potential offset sites for ‘No Net Loss’ in order to respond to development impacts. Selecting conservation areas based on biodiversity value can be an effective offset decision-making tool on where and how to prioritize conservation policies (Li et al., 2021; SANBI & UNEP-WCMC, 2016). There have been many studies on biodiversity conservation between excellent ecological value and development pressure in Jeju Island, but there are almost no studies on the implementation conditions of the offset or offset sites. We here aim to map a conservation area map in consideration of the environmental characteristics of Jeju Island and to select a potential offset area that can practically work offset. We will use ‘Zonation’ program, which is a systematic conservation planning-based model. Zonation is a useful land planning tool that can minimize development impact and realize biodiversity offset (Wintle, 2008; Lethomaki & Moilanen, 2013). The biodiversity attributes inputs required for running Zonation are potential habitat data using MaxEnt and environmental variable data. As a result, we will identify the spatial range and location of the potential biodiversity offset area through Zonation Priority Rank Map output. In addition, we can also analyze their spatial and environmental characteristics, and group out the shape of potential offset site composition (size or pattern). This study can be utilized as a basis for feasible offset policy by proposing potential offset areas through selecting conservation areas in Jeju Island. This work was conducted with the support of the Korea Environment Industry & Technology Institute (KEITI) through its Urban Ecological Health Promotion Technology Development Project, and funded by the Korea Ministry of Environment (MOE) (2020002770003).

How to cite: Shin, Y. J., Lee, S., Jeong, D., and Jeon, S.: Mapping the Biodiversity Conservation Value for Potential Offset Area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3421, https://doi.org/10.5194/egusphere-egu22-3421, 2022.

Sonali Sharma et al.

The western Himalaya is one of the most climate-sensitive and ecologically vulnerable ecosystems of the world. In the recent past, the region has undergone rapid alterations owing to climate change and paced urbanization. These alterations have significantly impacted Terrestrial Net Primary Productivity (TNPP) of the region. The present study takes the emerging urbanizing centers: Pithoragarh (Uttarakhand) and Dharamsala (Himachal Pradesh), situated in Indian western Himalaya to estimate TNPP dynamics of various land use classes. The study demonstrates usage of Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) for predicting a high spatio-temporal Normalised Difference Vegetation Index (NDVI) imagery obtained by fusing spatial details of Landsat NDVI and temporal details of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The predicted NDVI showed a good agreement with actual Landsat NDVI (R2=0.64 and 0.89; RMSE: 0.09 and 0.04; p < 0.01 for Dharamsala and Pithoragarh, respectively), therefore was reliable for TNPP estimations. This was assimilated in Carnegie Ames Stanford Approach (CASA) model for TNPP estimation for the years 2001 to 2019. The preliminary results show a net loss in TNPP in both of the urbanizing centers. During the study period 2001-2019, TNPP fluctuated annually and showed a decreasing trend of 1475.77 g C m-2 year-1 and 790.84 g C m-2 year-1 in Dharamsala and Pithoragarh, respectively. Among the forest vegetation classes, Oak the most dominant forest class experienced the highest decline in TNPP accounting for 67.55% and 34.04% of net TNPP loss in Dharamsala and Pithoragarh, respectively. The urban expansion contributed to 14.77% (Dharamsala) and 9.77% (Pithoragarh) decline of net TNPP loss. The results provide a better understanding of spatio-temporal dynamics of TNPP consequent to climatic variability and urbanization and provide a theoretical reference for future urban planning.

How to cite: Sharma, S., Joshi, P. K., and Fürst, C.: Terrestrial net primary productivity dynamics under climatic variability and urban expansion in western Himalaya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11084, https://doi.org/10.5194/egusphere-egu22-11084, 2022.


Donato Amitrano et al.

Satellite remote sensing allows for large scale monitoring with low cost and high revisit time. However, in some applications, it does not provide all the information needed by the analyst for the full characterization of the problem due to, as an example, insufficient resolution or lack of specific measurements. This can lead to inaccuracies in classification and/or detection tasks. Nevertheless, satellite data can be used to guide subsequent discovery, recognition and characterization actions, defining potential areas of interest of limited extension that can be furtherly investigated with on-site strategies. This work presents an innovative framework showing how to use incomplete and inaccurate information extracted by satellite images in order to address on-ground discovery actions aimed to the mapping and the characterization of illegal micro-dumps in Campania Region (Italy). In particular, high-resolution images up to 50 cm resolution are exploited to detect potential micro-dumps by means of a statistical learning method based on spatial features. The detection map is then used to create a priority map based on environmental risk considerations, such as the extension of the area interested by the dump and its proximity to urban settlements, and previous risk mitigation actions. This information is ingested by a planning system in order to allocate and calculate patrolling routes based on the available manpower and vehicles for on-site surveying. The survey is implemented by means of drones equipped with payloads and software allowing for real-time three-dimensional reconstruction of the scene and volumetric estimations. This provides further data to assess the real dangerousness of the site giving to decision makers essential information to plan remediation actions. The system is demonstrated through a case study showing all the stages of the decision process.

How to cite: Amitrano, D., Angelino, C. V., Cicala, L., Gargiulo, F., Gigante, G., Nebula, F., Palumbo, R., Parrilli, S., Pascarella, D., Pigliasco, G., and Tufano, F.: A multiscale approach for discovery of illegal micro-dumps based on satellite detections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11923, https://doi.org/10.5194/egusphere-egu22-11923, 2022.

Daniel Moran et al.

City-level CO2 emissions inventories are foundational for supporting the EU’s decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for all 116,572 municipal and local government units in Europe, containing 108,000 cities at the smallest scale used. The inventory spatially disaggregates the national reported emissions, using 9 spatialization methods to distribute the 167 line items detailed in the National Inventory Reports (NIRs) using the UNFCCC Common Reporting Framework (CRF). The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels, following the region boundaries defined OpenStreetMap, using a new spatialization approach. Project website: openghgmap.net

How to cite: Moran, D., Pichler, P.-P., Zheng, H., Muri, H., Klenner, J., Kramel, D., Többen, J., Weisz, H., Wiedmann, T., Wykmans, A., Strømman, A. H., and Gurney, K. R.: openghgmap.net -  Estimating CO2 Emissions for 108,000 European Cities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5400, https://doi.org/10.5194/egusphere-egu22-5400, 2022.

Ali Mohammadi


Sweden aims to achieve net zero GHG emissions by 2045. To do this, one strategy could be increasing the biomass contribution in energy sector as approximately 75% of total greenhouse gas (GHG) emissions are related to energy consumption. Therefore, it is beneficial to explore efficient ways to upgrade biomass materials into high value-added bioenergy. This study considers the potential of Miscanthus cultivation and its application as biofuel materials in Sweden in terms of carbon sequestration and contribution in climate impact mitigation. Miscanthus, as an energy crop with relatively low maintenance requirements and a high dry matter yield and energy content, can play a major role in the sustainable development of biofuels. Using Miscanthus for energy, results in avoiding fossil fuel combustion and the corresponding GHG emissions. The results of this assessment demonstrated that the Miscanthus cultivation contributes in soil organic carbon sequestration by over one tonne carbon ha−1 yr−1 which results in mitigating a significant amount of soil CO2 fluxes. Therefore, the adaption of Miscanthus biomass, would directly contribute in UN Goal 7, affordable and clean energy, and Goal 13, climate action due to a significant reduction in GHG emissions. The integration of Miscanthus plant into the landscape may stimulate the economy of rural areas in the country and offer more profit than afforestation and reforestation on abandoned and marginal croplands.

Keywords: Energy crops, Climate change, Bioenergy, Soil organic carbon, Ecosystem services

How to cite: Mohammadi, A.: Carbon sequestration potential of Miscanthus application as biofuel source in Sweden, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6413, https://doi.org/10.5194/egusphere-egu22-6413, 2022.

Djamilja Oud et al.

Haiti faces extreme land degradation, making the country prone to natural hazards and poverty, both undeniably linked. The Haitian Red Cross partnered with the Netherlands Red Cross, 510, and Commonland to roll out a long-term landscape restoration program. Over two decades, this program aims to realize 30 ‘Green Pearls. These are areas where best practices on restoration are combined to retrieve healthy landscapes, making communities more resilient and empowering people economically. Landscape restoration happens in small areas through planting trees and bushes. To carefully identify reforestation zones with the highest possible potential success rate, GIS-based site suitability analysis is applied using several indicators: Elevation (Slope), Soil (Soil PH, Soil Texture, Soil Bulk Density), and Climate (Solar Radiation, Temperature, Rainfall). Data on these indicators was obtained from different, often satellite-based data sources. All resulting layers (maps) per indicator are by default processed as equally important. However, the analysis can be tailored to produce different outcomes depending on the reclassification and weights given by experts to specific indicators. For the La Vallée de Jacmel region in the Haiti case, weighting was applied with the help of local experts. The output is a raster map indicating the locations for planting trees divided into five classes (from most suitable to least suitable). Currently, social indicators such as land ownership are not yet included. Our site suitability method is set up as a model using only open data from global datasets and is, therefore, replicable to other areas. The default model has also been applied to a similar case in the Kayes region in Mali. However, local knowledge on the significance of specific indicators remains indispensable input for the reforestation model. Overall, the site suitability method has proven to be a very useful digital support for holistic land restoration.

How to cite: Oud, D., Savchuk, A., Quesseveur, S., Mounkaila Issaka, A. A., and van den Homberg, M.: Green pearls: digital support for reforestation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12962, https://doi.org/10.5194/egusphere-egu22-12962, 2022.


Mon, 23 May, 10:20–11:50

Chairpersons: Maša Zorana Ostrogović Sever, Hrvoje Marjanovic, Anikó Kern

Gilles Erkens et al.

Following the Paris Agreement (2015) that aims to limit climate warming, the Dutch government presented a National Climate Agreement in 2019. This agreement stated the overall ambition of reducing the national greenhouse gas emission by 49% in 2030 (compared to 1990) and allocates this reduction target to different sectors, such as industry, mobility, agriculture or land use. Within the latter sector, the peatland meadows are currently estimated to contribute ~4.6 to 7 Mton per year of CO2 to the national Dutch greenhouse gas emission. In the National Climate Agreement, the aim is to reduce the net CO2 emission from the peatland meadows with 1 Mton per year by 2030. 

To comply with the greenhouse gas emission reduction targets for peatlands, a set of measures that raise groundwater levels are currently being proposed and tested in pilots. The Dutch National Research Programme on Greenhouse Gas Emissions from Peat Meadows (NOBV) investigates the effects of the proposed measures on the greenhouse gas emission balance under different environmental conditions. In the National Climate Agreement, it was decided that annual progress made in reducing greenhouse gas emissions needs to be monitored. The NOBV consortium is developing a registration system for this monitoring and presents it current status and ideas for future development in this contribution.

The registration system SOMERS (Subsurface Organic Matter Emission Registration System) is based on a multi-model ensemble approach. Using numerical models that simulate groundwater and carbon dynamics, the CO2 emission as a result of peat decomposition is calculated. Within SOMERS, existing models are supplemented by two newly developed models for assessing groundwater dynamics and peat decomposition, that require limited data input and have a short runtime. The new models simulate at parcel resolution and together are used to make a multi-model ensemble estimate of annual, national peatland greenhouse gas emissions since 2016 (the reference year). The new models are tested with annual carbon flux estimates. In the long run, we envisage to fully couple the modelling approach with the automated field measurements that are being collected in a new national measurement network.

In this contribution, SOMERS will be introduced, and the calibration and validation approach will be discussed. We present predictions, under idealized average weather conditions, to establish effects of proposed mitigation measures. This directly serves policy development in regional spatial plans for the Dutch peatland meadows. Lastly, a first national peatland CO2 emission budget based on SOMERS is presented, which after some further development may support LULUCF-sector reporting in the Netherlands.

How to cite: Erkens, G., Melman, R., Jansen, S., Boonman, J., van der Velde, Y., Hefting, M., Keuskamp, J., van den Berg, M., van den Akker, J., Fritz, C., Bootsma, H., Aben, R., Hessel, R., Hutjes, R., van Asselen, S., Harpenslager, S. F., Kruijt, B., and consortium, N.: SOMERS: Monitoring greenhouse gas emission from the Dutch peatland meadows on parcel level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12177, https://doi.org/10.5194/egusphere-egu22-12177, 2022.

Giulia De Luca et al.

Long term flux measurements are needed to improve our understanding of the carbon balance of arable lands. The objective of our study was to determine the seasonal dynamics of carbon cycling in a Hungarian cropland and to examine the effect of crop rotation on net ecosystem exchange of CO2 (NEE), furthermore to assess the influences of C outputs and inputs derived from lateral fluxes on soil organic carbon (SOC) stock. In this study we update the results presented in our poster of last year’s conference (EGU21-10977).

The experiment began in 2017 and crop rotation of the measured field consisted of winter wheat (2017-2018 and 2019-2020), rapeseed (2018), sorghum (2019) and sunflower (2021). CO2 fluxes and annual net ecosystem exchange (NEE) of CO2 were measured by a field-scale eddy covariance (EC) station at a Central Hungarian cropland site. Both vertical and lateral C fluxes were taken into account when calculating the net ecosystem carbon budget (NECB).

As presented in our previous study the largest sink activity was observed in the sorghum season (-277 g C m-2 from sowing to harvest). The cropland acted as a source of CO2 during the rapeseed season (140 g C m-2) due to incomplete germination caused by extreme autumnal drought.

We found that during the study period both meteorological variables and lateral carbon fluxes such as C inputs derived from seed and crop residues and outputs (harvest) had significant influence on the C dynamics. The higher temperatures and precipitation amount that characterised the fall of 2019 caused large differences in NEE dynamics for winter wheat when compared to 2017. The impact of climatic factors could be seen in the sunflower period since lack of precipitation in 2021 led to remarkably low carbon uptake.

Fallow periods in total covered a relatively long period of time (approximately 1 year out of the 4 year long study period). These fallow periods had a significant effect on NECB values due to immense C loss. During the four years of our experiment cumulative NEE was -222 g C m-2 and NECB was 726 g C m-2 as carbon loss during fallow periods (437 g C m-2 in total) and carbon export through harvest (964 g C m-2 in total) counterbalanced the crop’s CO2 uptake.

We can conclude that while this Hungarian cropland was a sink of carbon it could not maintain the soil organic carbon content as it was not able to sequester enough carbon to do so. Cover plants and crop residue retention could be a solution to reduce the risk of soil carbon stock depletion but further studies are needed in the field of soil management practices.

How to cite: De Luca, G., Pintér, K., Fóti, S., Nagy, Z., and Balogh, J.: Update on soil carbon balance in Hungarian crop rotation systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2484, https://doi.org/10.5194/egusphere-egu22-2484, 2022.

caterina barrasso et al.

Land cover (LC) is an important indicator to reach several of the targets under the Global Goals. Accurate global LC time-series are thus vital to monitor sustainable development. Although the number and quality of open-access, remotely sensed LC products is increasing, all products have uncertainties due to widespread classification errors. However, the relative magnitude of uncertainties among exiting LC products is largely unknown, which hampers their confident selection and robust use for sustainable development evaluation and planning. To close this gap, we quantified region-, time-period-, and coarse-LC class-specific data uncertainties for the 10 most widely used global LC time-series. To this end, we developed a novel multi-scale validation framework that accounts for differences in mapping resolutions and scale mismatches between the spatial extent of map grid cells and validation samples. We aimed for a fair validation assessment by carefully evaluating the quality of our validation samples with respect to landscape heterogeneity that LC products often fail to classify accurately. To address the issue, we supported the validation assessment with Landsat-based measures of cross-scale spectra similarity. The metric was computed by taking advantage of the full Landsat archive in Google Earth Engine. We base our assessment on more than 1.8 million globally integrated LC validation sites, where we mobilized around 2.8 million samples during the period 1980-2020 composed by hundreds of sampling effort of varied nature, from field surveys to crowdsourcing campaigns. Here, we will present the results of the assessment, providing insights on global and regional patterns of LC uncertainties. We found that no single product is more accurate over the others in mapping all LC classes, regions and time-periods. We will provide recommendations on the selection of fit-for-purpose LC time-series, and discuss future strategies for addressing their uncertainties in sustainable development evaluation and planning.

How to cite: barrasso, C., remelgado, R., and meyer, C.: A global assessment of spatiotemporal uncertainties in Land Cover – a key indicator for monitoring sustainable development, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6287, https://doi.org/10.5194/egusphere-egu22-6287, 2022.

shiqi zhang

The topography of Sichuan Province is extremely complex, with a rich variety of vegetation, and its vegetation shows a clear horizontal and vertical distribution structure. The subtropical evergreen broad-leaved forest is the zonal vegetation of Sichuan. In 1980, according to the field survey data, forestry scientists roughly divided the evergreen broad-leaved forest in Sichuan Province into Erlang Mountain, Daxiangling Mountain, Xiaoliang Mountain or Huangmaogeng. It was divided into a dry evergreen broad-leaved forest in the west and a moist evergreen broad-leaved forest in the east. However, there is no quantitative classification of wet and dry evergreen broad-leaved forests in Sichuan. The traditional forest vegetation survey mainly relies on manual field survey, which has a long period, high cost, and consumes a lot of manpower and material resources. Remote sensing technology, with its wide coverage, large amount of information and short update cycle, brings the possibility of rapid and accurate quantitative classification of wet and dry evergreen broad-leaved forests. In this paper, based on the field survey data of evergreen broad-leaved forests in Sichuan Province, we combined NASADEM_HGT elevation data and Landsat8 images to perform SCS+C topographic correction on remote sensing images of the whole region of Sichuan on the Google earth engine cloud computing platform, and also based on the differences in spectral, textural and temporal characteristics between dry and wet evergreen broad-leaved forests. The experimental results were compared with the field survey data and obtained excellent accuracy, and it provides a strong technical support for vegetation mapping and forestry resources investigation and monitoring, and also lays a certain foundation for the classification of complex mountain forest vegetation.


How to cite: zhang, S.: The division of dry and wet areas of evergreen broad-leaved forest in Sichuan Province, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7143, https://doi.org/10.5194/egusphere-egu22-7143, 2022.

Yuri Shendryk

Accurate mapping of forest aboveground biomass (AGB) is critical for carbon budget accounting, sustainable forest management as well as for understanding the role of forest ecosystem in the climate change mitigation.

In this study, spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data were used in combination with Sentinel-1 synthetic-aperture radar (SAR) and Sentinel-2 multispectral imagery as well as elevation data to produce a wall-to-wall AGB map of Australia that is more accurate and with higher spatial and temporal resolution than what is possible with any one data source alone. Specifically, the AGB density map was produced that covers the whole extent of Australia at 200m spatial resolution for the Austral winter (June-August) of 2020. To produce this map Copernicus Sentinel-1 and Sentinel-2 composites and ALOS World 3D Digital Surface Model (DSM) were trained with samples from the GEDI Level 4A product.

From GEDI Level 4A data available within Australia between June – August 2020, all measurements not meeting the requirements of L4A product quality, and those with degraded state of pointing or positioning information and an estimated relative standard error in GEDI-derived AGB exceeding 50% were rejected. Mean Sentinel-1 composite was generated using thermal noise corrected, radiometrically calibrated and terrain corrected VV- and VH-polarization backscatter imagery. Similarly, median Sentinel-2 composite was generated using cloud and cloud-shadow free Level-2A imagery, and was further used to calculate Normalized Difference Spectral Indices (NDSIs) from all spectral bands. Finally, aspect and slope were calculated from the DSM.

The boosting tree machine learning model was applied to predict wall-to-wall AGB density map. For each 200m × 200m cell the number of available GEDI measurements was calculated and models were built based on average AGB density of cells containing > 5 GEDI measurements.

Up to ≈62000 cells, each 200m × 200m, were used to train predictive machine learning models of AGB density. The predictive performance of models based on Sentinel-2 imagery only (single-data source) and a fusion of Sentinel-2 with Sentinel-1 imagery and elevation data (multi-data source) was compared. Bayesian hyperparameter optimization was used to identify the most accurate Light Gradient Boosting Machine (LightGBM) model using 5-fold cross-validation. 

The single-data source analysis based on Sentinel-2 imagery resulted in AGB density predicted with the coefficient of determination (R2) of 0.74-0.81, root-mean-square error (RMSE) of 40-44 Mg/ha and root-mean-square percentage error (RMSPE) of 45-55%.Model performance improved only marginally with the addition of Sentinel-1 and DSM information: AGB density prediction with R2 of 0.75-0.82, RMSE of 36-41 Mg/ha and RMSPE of 44-48%. Using a SHapley Additive exPlanations (SHAP) approach to explain the output of LightGBM models it was found that Sentinel-2 derived NDSIs using Red Edge and Short-wave Infrared bands were the most important in predicting seasonal AGB density. 

Similar model performance is expected for annual prediction of AGB density at a finer resolution (e.g. 100m) due to higher density of GEDI measurements. This research highlights methodological opportunities for combining GEDI measurements with satellite imagery and other environmental data toward seasonal AGB mapping at the regional scale through data fusion.

How to cite: Shendryk, Y.: Fusing GEDI, Sentinel-1, Sentinel-2, and elevation data for seasonal forest biomass mapping across Australia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2077, https://doi.org/10.5194/egusphere-egu22-2077, 2022.

Luca Foresta et al.

Monitoring carbon release and sequestration is now more important than ever. Not only to confirm that carbon sinks remain intact or vulnerable ecosystems do not further degrade, but also to keep track of our journey towards carbon neutrality, where increasing efforts to offset CO2 emissions have been initiated. Amongst a number of solutions, carbon trading schemes have been introduced, such as the EU Emission Trading System that is used in programs where local smallholder farmers benefit from transitioning towards agroforestry. Critical to the success of such programs is the use of accurate, scalable and transparent remote sensing technologies that objectively monitor the carbon that trees in a given plot of land have removed from the atmosphere.

At Satelligence, we exploit radar and optical satellite data worldwide and at scale to empower clients to combat deforestation and decrease carbon losses, as well as to protect biodiversity and prevent land degradation. In this contribution, we will present our approach to model Aboveground Biomass (AGB) over tropical moist forests at (sub) national scale based on data from several Earth Observation missions (GEDI, Sentinel-1, Sentinel-2, Landsat) and machine learning models. In-situ data, where available, are integrated to improve models at local and regional scales. We will show preliminary results of modeled AGB and carbon sequestration over large areas as well as individual agricultural plots for selected countries in Africa and South America.

How to cite: Foresta, L., Alkema, S., Anders, N., Masselink, R., Schut, V., Takacs, S., and Vrielink, A.: Modeling Aboveground Biomass and carbon sequestration at local and national scale with in-situ and remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9721, https://doi.org/10.5194/egusphere-egu22-9721, 2022.

Tao Zhou et al.

*Corresponding author: Xiaolu Tang (lxtt2010@163.com)

Net primary productivity (NPP) is a key parameter to characterize terrestrial ecological processes. NPP reflects the carbon sequestration capacity of vegetation to absorb atmospheric carbon dioxide, and plays an important role in mitigating atmospheric carbon dioxide content. Currently, the majority of studies focused on the model efficiency total NPP at the global scale. However, whether the model resolution of NPP affects the NPP amount at the global is still uncertainty. To fill this knowledge gap, we first collected 3307 NPP field observations from published literatures, and then model NPP using climate, soil, and vegetation variables using Random Forest (RF) to predicted global NPP at the spatial resolutions of 0.05°, 0.25° and 0.5°. Results showed that RF could well capture the spatial and temporal variability of NPP with the model efficiencies (R2) of 0.55, 0.52 and 0.53 for at the resolution of 0.05°, 0.25° and 0.5°, respectively. Similar spatial patterns were also found for NPP at different spatial resolutions and NPP decreased with increased latitude where the highest NPP was found in the tropical regions and the lowest NPP were distributed in high latitude areas, e.g. alpine tundra. However, a great difference was found for the magnitude of NPP resulting a great difference in total global NPP of 71.5, 78.6, 87.7 Pg C year-1 from 1981 to 2016 for the resolutions of 0.05°, 0.25° and 0.5°, respectively. These findings suggested the challenges to improve modelling accuracies of the global carbon fluxes used appropriate resolutions.

Keywords: Net primary productivity; Different resolutions; Random Forest; Spatial pattern; Appropriate resolution;

Acknowledgment: the study was supported by the National Science Foundation of China (31800365).

How to cite: Zhou, T., Tang, X., Hou, Y., Luo, X., Yang, Z., Lai, Y., Yu, P., Luo, K., and Zhao, R.: Comparing the spatio-temporal differences of global NPP simulation data with different resolutions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3656, https://doi.org/10.5194/egusphere-egu22-3656, 2022.