Natural ecosystems, especially primary forests, are impacted by the rapid expansion of human land use and global climate change, putting the most bio-diverse areas of our planet under threat. Large amounts of Earth Observation and analysis-ready data sets are made available (almost) for free. Yet, the usage of such data in conservation finance and policy making does currently not live up to its full potential. It is a complex endeavor to access relevant portions of Big Geospatial Datasets efficiently due to the high number of different data providers, formats and interfaces. Even more important, we need to generate information in an open and reproducible way to take informed decisions to allocate funds responsibly and maximize public goods and benefits

MAps for Planning, Monitoring and Evaluation (MAPME) is an collaborative initiative based on OpenScience principles to leverage the potential of geospatial data for relevant actors in the development cooperation sector. The initiative is driven by Free and Open Software (FOSS) enthusiasts within German (KfW, GIZ) and French (AFD, IRD) development institutions. Together with our partner countries we are key decision makers in the allocation of the so-called Official Development Assistance (ODA). To bridge the “last-mile” gap between vast amounts of openly available geospatial data sets and productive monitoring applications, we have developed an OpenSource software used within our institutions.

The software is written in R and relies on the Geospatial Data Abstraction Library (GDAL) bindings provided by the `sf` and `terra` packages. It allows efficient analysis of large data collections on deforestation and greenhouse gas emissions such as Global Forest Watch (GFW). Focusing on expandability, everyone can include new in-house or open data sets, and custom analysis routines. Thus, the functionality can be extended to other sectors beyond forest monitoring. It opens the way to deliver crucial information on the state of ecosystems around the globe in a timely and reproducible way, allowing our institutions to make better allocation decisions.

We will present the MAPME Initiative and shed a light on our approach to developing applications based on FOSS. We will showcase first data solutions build by our partners on top of the framework, such as a geospatial impact evaluation of preventing deforestation and a dashboard for continuous monitoring of protected areas of the German development cooperation portfolio.

How to cite: Görgen, D. and Schielein, J.: MAPME – Versatile analysis tool for big geospatial data in the context of sustainable development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18584, https://doi.org/10.5194/egusphere-egu24-18584, 2024.

Forests are the major component of the terrestrial ecosystem and provide an essential source of livelihood to local communities. Nevertheless, forests worldwide are increasingly threatened by natural and human-driven activities, such as extensive logging for the extractive industries, severe weather, pests and wildfires. A responsible forest management substantially contribute to the protection and conservation of forest ecosystem and services. The United Nations’ Sustainable Development Goals (UN SDGs) 15 “Life on land” – and specifically indicator 15.1.1 “Forest area as a proportion of total land area” – is concerned with mapping and protecting forest ecosystems.  At the European Union (EU) level, the UN indicator 15.1.1 is translated into EUROSTAT indicator “Share of forest area”.  Monitoring of this indicator enhance compliance with EU policies of land use and land cover, supporting the EU forest strategy for 2030 and helping to implement the regulation on deforestation-free products.

The SDGs-EYES project is a major EU-wide initiative aiming at exploiting data and information coming from the European Copernicus Programme to develop, implement and deploy a new service for monitoring SDG targets. It will provide novel and robust workflows to consistently assess SDG indicators across EU countries, with potential for global upscaling. In recent years, the release of frequent and high-resolution satellite data from the Copernicus Sentinel missions has opened new frontiers for consistently mapping global forest cover.  Nevertheless, detecting small-scale forest disturbance - also known as forest degradation - remains a challenging task. Studies aiming at quantifying the carbon emissions and extent of forest degradation show that it affects land portions similar to, or even larger, than deforestation. It is clear that accurate forest cover maps are urgently needed to avoid underestimating the loss of forest habitats, thereby preventing further carbon emissions, land degradation and biodiversity decline.

In this study, we apply a cumulative sum change detection algorithm on Sentinel-1 and Sentinel-2 time-series data to estimate forest cover and forest cover change in the Olt River basin, Romania, for the 2020-2022 period. Romania hosts the largest share (218,000 ha) of the EU's temperate primary and old-growth forests, many of which have been logged, both legally and illegally, although officially under protection by national parks or Natura 2000 sites. Through the integration of multi-sensor information (e.g. Sentinel-1 and 2, ESA CCI WorldCover), the resulting maps are able to detect hotspots of forest cover change at 20 m resolution, while also providing exact timing of the disturbance events. The suggested approach, hosted on the SDGs-EYES platform, provides a scalable methodology that can be systematically used in other geographical areas and for selected periods of interest. In this way, we enhance monitoring and evaluation of indicator 15.1.1, in agreement with the UN and EU indicators while improving the current weaknesses of the two frameworks.

How to cite: Aquino, C., Balzarolo, M., Chiriacò, M. V., and Santini, M.: Advancing Forest Cover and Forest Cover Change Mapping for SDG 15: A Novel Approach Using Copernicus Data Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15509, https://doi.org/10.5194/egusphere-egu24-15509, 2024.

During 2022, Bohemian Switzerland NP was affected by the largest wildfire in the Czech Republic throughout its modern history. The NP is formed by sandstone towers, deep narrow valleys and dense forests. From the 19th century onwards, Norway spruce and non-native Weymouth pine were massively planted here. A series of weather extremes in the last years caused an exceptional drought and consequent massive bark beetle outbreak and spruce die off, followed by the catastrophic event. Wildfires of such a dimension are rather uncommon in Central Europe, and this event therefore serves as a perfect model situation to study the role of species composition, bark beetle and water availability on the fire dynamics as well as the changes in biodiversity and natural succession after the disaster. Before the fire, the area was dominated by conifers, mostly standing dry after the bark beetle attack except along the water courses, and further formed of clear cuts, healthy deciduous beech forests and rocky outcrops. 

Pre-fire vegetation state, fire severity and post-fire regeneration were assessed using a combination of remote sensing sources. In particular, we used pre- and post-fire series of Sentinel-2 satellite MSS imagery, and acquisition of multispectral (MSS) and LIDAR data. The whole area was sampled from small aircraft TL232 Condor by three sensors - photogrammetric camera Hasselblad A6D-100c (ground sampling distance - GSD - 5 cm), MSS sensor MicaSense Altum (GSD 32 cm) and LIDAR RIEGL VUX 1-LR (13 points/m), and detailed sites were sampled using drone mounted sensors - MSS (MicaSense Altum, GSD 5 cm) and LIDAR (DJI L1). Forest composition and changes in health status were derived using a range of spectral indices and supervised classification. Fire severity and forest structure were derived using a combination of Lidar and optical point cloud, fisheye camera, ground sampling, and analysis of optical data (supervised classification, vegetation indices). 

Our research revealed that fire disturbance was low or none at native deciduous tree stands and waterlogged sites. On the opposite, it was more severe at dry bark-beetle clearings covered by a thick layer of litter as compared to standing dead spruce. We can infer that in places where many stems were only partly burned or the trees postponed the die-off, the fire went faster and the severity of disturbance was lower. In some cases, we could see patterns formed by ground fire, such as burned circles around trees or tree stools surrounded by unburned areas. Post-fire regeneration is very fast, and even after one year, vegetation growth can be detected using LIDAR and photogrammetric point clouds. Derived information on fire severity, detailed 3D stand structure and health status are to be used as a proxy of the fire disturbance impact on biodiversity and patterns of regeneration.

How to cite: Müllerová, J., Pacina, J., Adámek, M., Brétt, D., and Přemysl, B.: Wildfire as an interplay between water deficiency, manipulated tree species composition and bark beetle. A remote sensing approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18869, https://doi.org/10.5194/egusphere-egu24-18869, 2024.

The provision of ecosystem services (ES) by agricultural systems is a shared objective of agricultural policies in most developed countries in response to an increasing demand from society. Sustainable management of grassland ecosystems leads to enhanced soil fertility, ensures food security, acts as natural filters and purifiers of water, and functions as carbon sinks, sequestering carbon dioxide and mitigating climate change. All of these goals are deeply interconnected with several SDGs. The Common Agricultural Policy (CAP) of the European Union is environmentally oriented. However, a broad consensus indicates that the current policy instruments are not effectively promoting the provision of ES. Thus, it is essential to develop efficient and innovative policy instruments to enhance ES's agricultural provision. One of the challenges for applying new policy instruments, such as results-based payments (OECD, 2015), is the quantification of ES supply, usually involving intensive and specialized field data. Therefore, there is a need to create quantitative indicators for ES based on reliable and affordable data. Remote sensing data can be an effective tool, especially if the data are easily accessible, available at an appropriate scale, and provided free of cost.

Olive groves and Mediterranean oak savanna were used in this work as case studies to examine the herbaceous layer's contribution to the provision of ecosystem services. In both ecosystems, grasslands play a relevant role in supplying provisioning (such as forage, freshwater or genetic library), regulating (carbon sequestration, soil conservation, climate, and air quality regulation) and cultural services (aesthetic appreciation, cultural identity). The biomass or above-ground net primary production (ANPP) and biodiversity are essential integrators of ecosystem functioning. Biomass is responsible for the input level of various ecosystem services, and it is directly connected to carbon sequestration and soil conservation. Biodiversity, on the other hand, contributes to the processes that underpin other ecosystem services and constitutes an ecosystem good that humans directly value. This work describes the general scheme to measure several grassland ES (GES) in olive groves and oak savannas, including ANPP, biodiversity, carbon sequestration, and aesthetic appreciation, and preliminary results about the ANPP and biodiversity are presented. 

How to cite: González-Dugo, M. P., Muñoz-Gómez, M. J., Gálvez, C., Blázquez-Carrasco, Á., Carbonero, M. D., Tortosa, F. S., Castro, J. C., Guerrero-Casado, J., Castro, J., Colombo, S., Arriaza, M., and Villanueva, A.: Remote-sensing based tools to monitor grassland ecosystem services, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19821, https://doi.org/10.5194/egusphere-egu24-19821, 2024.

Accounting of the hydrologic process of evapotranspiration (ET) or consumptive use of water is important for water resources allocation, irrigation management, drought early warning, climate change impact assessment as well as in agro-water-climate nexus modeling. In fact, monitoring the United Nations' sustainable development goals (SDGs) that emphasize on improved food security, access to clean water, promotion of sustainable habitats and mitigation of natural disasters (droughts) hinge upon access to better quality data of ET. Though numerous studies have targeted accurate estimation of potential evapotranspiration (PET) using earth observation (EO) data; hydrologists are yet to reach consensus on the best set of predictor variables that can be used irrespective of spatio-temporal scale. This can be attributed to the nonlinear and complex nature of the process of ET. When it comes to the estimation of actual ET (AET), studies employing Eddy Covariance (EC) towers have been successful in different regions of the world. However, the developing countries of the world lack access to EC observations, requiring viable economical methods for accurate ET measurement, even using reliable estimates of PET. The proposed study explored fusion of regional climate reanalysis data, EO data, and machine learning techniques for high-resolution PET estimation. In this analysis, owing to the documented success of data-driven models in hydrological studies, performance of two machine learning models- tree based Random Forest (RF) and regressor Multivariate Adaptive Regression Splines (MARS), are evaluated for estimating monthly PET. A suite of input predictors are chosen to describe three model categories: meteorological-, EO- and hybrid-based predictor models. There are about 10 input combinations that can be generated for the PET model development, particularly for an agriculture-dominated study region - Dhenkanal district, located in Odisha in eastern part of India. In this study, reanalysis-based (meteorological) inputs at a grid resolution of 0.12° and Sentinel 2A (EO) products at spatial resolution of 20 m have been used. Results of the analysis indicate that solar radiation is the most important meteorological variable that controls PET estimation. Among the vegetation indices obtained from remote sensing data, we find that the Normalized Difference Water Index (NDWI) that represents availability of water in plants and soil, is particularly useful. The best PET estimation model that uses only solar radiation and few vegetation indices (NDVI, NDWI) gave coefficient of determination (R²) 0.88 and root mean square error (RMSE) of 0.14 during validation stage, whereas the use of hybrid predictor model that utilize temperature and vegetation indices information further reduced the error and increased the prediction accuracy (6.86%). When the meteorological inputs: precipitation and wind speed are only used, model did not perform well. Mapping the ET using the proposed models can facilitate reporting of progress in SDG with regard to water use, crop water stress, adaptation to agricultural droughts and food security. In this context, the Evaporative Demand Drought Index (EDDI) is computed across the study region to understand the drought patterns in the region.

Keywords: Potential Evapotranspiration, Agricultural Drought, Food Security, EO Data, Random Forest, Machine Learning, Vegetation Indices

How to cite: Tripathy, S. S. and Ramadas, M.: Data Fusion of Regional Reanalysis- and Sentinel (Earth Observation)-based Products with Machine Learning Tools for Monitoring Evapotranspiration and Drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15184, https://doi.org/10.5194/egusphere-egu24-15184, 2024.

The challenges of climate change in West Africa are closely linked to food security in the region. Rising temperatures and increasingly variable precipitation threaten traditional rain-fed agriculture relying on the rainy season. Climate change is affecting the rainy season in West Africa in multiple ways, e.g., by shifting the onset, shortening its duration and increasingly interrupting the growing period by dry spells. An increase of extreme weather events such as heavy precipitation or storms add another risk to agriculture. The risk of crop failures hits an already vulnerable system. Since a large portion of food is imported the West African countries are vulnerable to external economic shocks. Furthermore, West Africa has one of the highest population growth rates in the world, its population will increase to 1.2 billion people by 2050. To guarantee sufficient food supply and to achieve the Sustainable Development Goals (SDG), a sustainable intensification of agriculture is needed (i.e., increasing yields without additional land consumption and without adverse effects on climate change) and mitigation and adaption strategies against the negative effects of climate change are required. Remote sensing has proven to be a suitable instrument to measure and evaluate both, mitigation and adaptation actions in a reliable and cost-effective way. Depending on the method of cultivation, agriculture causes different amounts of greenhouse gas (GHG) emissions. Remote sensing can provide information about biophysical development as input and reference data for land surface models to assess the produced GHG under different cultivation practices. Since the negative impact of climate change on agriculture is already measurable and visible, adaptation measures are highly important. They differ in terms of their complexity, their technical feasibility and their costs. Adaptation measures can be for example a change in land management, the choice of crop variety or technical innovation like weather forecast or irrigation systems. In various interdisciplinary research projects (CONCERT, COINS, AgRAIN), we selected adaptation measures of varying complexity and monitor and evaluate them using remote sensing-based analysis, mainly on Sentinel-1, Sentinel-2 and Planet data. The analyses range from land cover and land use mapping to crop classification, crop suitability modeling, field boundary delineation, identification of management events, and site-specific productivity measurements. We employ a range of methodologies, including random forest regression, convolutional neural networks (CNN), fuzzy logic approaches, and time series analysis. The results serve as a basis for local stakeholders and decision-makers, enabling the implementation of proven adaption measures to enhance resilience against climate change and promote sustainable agricultural intensification.

How to cite: Meier, J., Thonfeld, F., Huber Garcia, V., Aidoo, K., Heiss, N., and Gessner, U.: The Potential of Remote Sensing for Enhancing a Sustainable Agricultural Intensification under a Changing Climate in West Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16115, https://doi.org/10.5194/egusphere-egu24-16115, 2024.

Many countries have goals and strategies to reduce soil sealing of agricultural land to preserve food production capacity. This is essential in relation to Sustainable Development Goal 2: Zero Hunger. To monitor progress, reliable data are needed to quantify soil sealing and changes over time. We examined the potential of the Copernicus High Resolution Layer Imperviousness Density (HRL IMD) to assess soil sealing in agricultural areas in Poland and Norway.

We quantified the accuracy and reliability of the products Imperviousness Classified Change (IMCC) for the period 2015-2018 and Imperviousness degree (IMD) for the reference year 2018. We found a very high overall accuracy of IMCC 2015-2018 in both Poland and Norway. However, this was mainly due to the dominance of area with no change.  When we focused on the small areas where change does occur, we found low user accuracy, with an overestimation of soil sealing. The producer accuracy was generally much higher, meaning that real cases of soil sealing were captured. This is a much better result than if IMCC had under-estimated soil sealing. It suggests that IMCC can play a valuable role in detecting soil sealing, by highlighting areas where soil sealing may have occurred, allowing the user to carry out a further control of this much smaller area, without having to assess the great expanse of unchanged area.

We conclude that the datasets provide useful information for Europe. They are standardised and comparable across countries, which can enable comparison of the effects of policies intended to prevent soil sealing of agricultural land. We advise caution in using older versions of the change data. In particular, it is advisable to merge the closely related classes “1: new cover” and “11: increased cover” and the same for “2: loss of cover” and “12: decreased cover”. These distinctions are not reliable, but the general information about increase or decrease is much better. The transition to finer resolution (10 x 10 m) in the newer datasets represents a great improvement and will make the change data more reliable and useful in future versions.

How to cite: Fjellstad, W., Krøgli, S. O., Rizzi, J., and Hościło, A.: Using Copernicus High Resolution Layer Imperviousness Density to monitor soil sealing in agricultural areas (SDG 2: Zero Hunger), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20511, https://doi.org/10.5194/egusphere-egu24-20511, 2024.

Oceanic uptake of anthropogenic CO₂ is altering the seawater chemistry of the oceans, leading to a decrease in pH and thus to ocean acidification (OA). This has multiple consequences not only for marine biogeochemistry, but also for marine biota and ecosystems. Therefore, the Sustainable Development Goal SDG Target 14.3 addresses OA and the SDG 14.3.1 calls for the average marine acidity (pH) and on guidance on monitoring and reporting OA data.

Here we want to present the international collaboration between the European Marine Observation and Data Network (EMODnet Chemistry), NOAA and UNESCO on how to observe and report OA data, following the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The final aim is to enable global comparisons of the changes in ocean chemistry and to provide a unified, globally coordinated, sustained, long-term observation network and database. Detailed vocabularies and the according metadata will guarantee the correct description of the carbonate system and thus also the long term usability of the data, including reliable trend calculations.

This global collaboration will provide more accurate and detailed OA data and will help policy and decision makers to communicate more clearly and precisely about the impacts of climate change on marine ecosystems and resources, enabling holistic approaches.

How to cite: Kubin, E., Lipizer, M., Molina Jack, M. E., French, M. A., and Giorgetti, A.: Ocean Acidification: Weaves to be tied on European and global scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19568, https://doi.org/10.5194/egusphere-egu24-19568, 2024.

This study scrutinizes the impact of an anomalous early summer Land Surface Temperature (LST) surge on food security, energy dynamics, and human health in India's National Capital Region (NCR), and its implications for Sustainable Development Goals (SDGs). By analyzing MODIS images and employing Standard Anomaly (StA), monthly diurnal LST ranges were assessed. Results reveal March temperatures peaking from 23.11 to 41.57 °C, 3.5 °C above the average 21.78 to 39.41 °C range. Notably, contrary to conventional patterns, prolonged rain deficits drive this early summer warming rather than Sea Surface Temperature (SST). This warming adversely affects SDGs, significantly reducing crop yields, jeopardizing SDG-2's Zero Hunger target, impeding indicator SDG-2.4.1, and disrupting target 3.4.1 for health. Moreover, heightened energy consumption due to early summer warming disrupts SDG-6 on clean energy, directly impacting target 7.1 for electricity access. The findings underscore the urgency of addressing early summer warming's impact to progress toward achieving SDGs in India's NCR. Understanding and mitigating these effects are imperative for sustainable development initiatives in the region.

How to cite: Mahato, S. and Joshi, P. K.: Rising Temperatures, Rising Concerns: Early Summer and Sustainable Development in National Capital Regions of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1207, https://doi.org/10.5194/egusphere-egu24-1207, 2024.

Solar energy shall be an indispensable part in India’s clean energy transition. As renewable energy requires large amount of space considerations, policy makers often question the land based targets for deploying solar parks. A robust geospatial information on existing solar parks shall be crucial for both the governments and policy makers.

This study presents a novel method to detect solar parks using a synergy of satellite imagery from Sentinel-2 and convolutional neural networks (CNN). For the work, a total of nearly 2000 satellite images from Sentinel-2 were chosen over ten number of solar parks situated in India. Case study results are presented for the solar parks in India namely Bhadla Solar Park, Rajasthan, and Pavagada Solar Park, Karnataka. This dataset measures solar footprint over India and examines environmental impacts of solar parks over nearby ecosystem.

How to cite: Basant, S., Indu, J., and Banerjee, B.: Solar Park Detection Based On Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4584, https://doi.org/10.5194/egusphere-egu24-4584, 2024.