ITS3.3/ESSI4.1 | Earth Observations for Assessing and Monitoring the UN Sustainable Development Goals
Earth Observations for Assessing and Monitoring the UN Sustainable Development Goals
Convener: Monia Santini | Co-conveners: M. Miguel-Lago, Francesca Piatto, Manuela Balzarolo
Orals
| Thu, 18 Apr, 08:30–10:15 (CEST)
 
Room 2.17
Posters on site
| Attendance Thu, 18 Apr, 10:45–12:30 (CEST) | Display Thu, 18 Apr, 08:30–12:30
 
Hall X2
Orals |
Thu, 08:30
Thu, 10:45
The United Nations (UN) 2030 Agenda for Sustainable Development set a milestone in the evolution of society's efforts towards sustainable development which must combine social inclusion, economic growth, and environmental sustainability. The definition of the Sustainable Development Goals (SDGs) and the associated Global Indicator Framework represent a data-driven framework helping countries in evidence-based decision-making and development policies.

Earth observation (EO) data, including satellite and in-situ networks, and EO data analytics and machine learning plays a key role in assessing progress toward meeting the SDGs, since it can make the 2030 Agenda monitoring and reporting viable, technically and financially and be beneficial in making SDG indicators' monitoring and reporting comparable across countries.

This session invites contributions on how to make use of Earth Observations data to address SDG monitoring and reporting, in particular welcomes presentations about EO-driven scientific approaches, EO-based tools, and EO scientific initiative and projects to build, assess and monitor UN SDGs indicators.

Orals: Thu, 18 Apr | Room 2.17

Chairpersons: Manuela Balzarolo, Weronika Borejko
08:30–08:35
08:35–08:45
|
EGU24-18584
|
ITS3.3/ESSI4.1
|
ECS
|
On-site presentation
Darius Görgen and Johannes Schielein

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.

08:45–08:55
|
EGU24-15509
|
ITS3.3/ESSI4.1
|
ECS
|
Highlight
|
On-site presentation
Chiara Aquino, Manuela Balzarolo, Maria Vincenza Chiriacò, and Monia Santini

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.

08:55–09:05
|
EGU24-18869
|
ITS3.3/ESSI4.1
|
On-site presentation
|
|
Jana Müllerová, Jan Pacina, Martin Adámek, Dominik Brétt, and Bobek Přemysl

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.

09:05–09:15
|
EGU24-19821
|
ITS3.3/ESSI4.1
|
On-site presentation
Maria P. González-Dugo, Maria José Muñoz-Gómez, Cristina Gálvez, Ángel Blázquez-Carrasco, M. Dolores Carbonero, Francisco S. Tortosa, Juan Carlos Castro, José Guerrero-Casado, Juan Castro, Sergio Colombo, Manuel Arriaza, and Anastasio Villanueva

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.

09:15–09:25
|
EGU24-15184
|
ITS3.3/ESSI4.1
|
ECS
|
Highlight
|
Virtual presentation
Subham Saroj Tripathy and Meenu Ramadas

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.

09:25–09:35
|
EGU24-16115
|
ITS3.3/ESSI4.1
|
ECS
|
On-site presentation
Jonas Meier, Frank Thonfeld, Verena Huber Garcia, Kenneth Aidoo, Niklas Heiss, and Ursula Gessner

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.

09:35–09:45
|
EGU24-20511
|
ITS3.3/ESSI4.1
|
On-site presentation
Wendy Fjellstad, Svein Olav Krøgli, Jonathan Rizzi, and Agata Hościło

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.

09:45–09:55
|
EGU24-19568
|
ITS3.3/ESSI4.1
|
ECS
|
On-site presentation
Elisabeth Kubin, Marina Lipizer, Maria Eugenia Molina Jack, Megan Anne French, and Alessandra Giorgetti

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.

09:55–10:05
|
EGU24-1207
|
ITS3.3/ESSI4.1
|
ECS
|
On-site presentation
Susanta Mahato and Pawan K. Joshi

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.

10:05–10:15
|
EGU24-4584
|
ITS3.3/ESSI4.1
|
On-site presentation
Shivam Basant, Jayaluxmi Indu, and Biplab Banerjee

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.

Posters on site: Thu, 18 Apr, 10:45–12:30 | Hall X2

Display time: Thu, 18 Apr 08:30–Thu, 18 Apr 12:30
Chairperson: Manuela Balzarolo
X2.44
|
EGU24-7161
|
ITS3.3/ESSI4.1
|
ECS
|
Highlight
Shanshan Du, Weina Duan, and Liangyun Liu

Several satellite-based solar-induced chlorophyll fluorescence (SIF) products have progressively emerged and have been developed in recent years. However, till date, no direct validation has been conducted on existing satellite-based SIF products. In this study, validation of two groups of TROPOspheric Monitoring Instrument (TROPOMI) SIF products, namely TROPOSIFCaltech (containing far-red and red TROPOSIFCaltech datasets) and TROPOSIFESA (containing TROPOSIF735 and TROPOSIF743 datasets that are retrieved from two different retrieval windows), was conducted using tower-based SIF measurements over seven sites. Several issues and potential obstacles emerged while matching satellite-based and in situ SIF retrievals, including spatial scale mismatch. To overcome the spatial scale mismatch between the satellite data and ground observations, a near-infrared reflectance of vegetation (NIRv)-scaled approach was employed to mitigate the spatial difference between the locations of specific sites and the matched TROPOSIF samples using Sentinel-2 imagery. Other issues related to retrival methods and instrument differences were examined. Subsequently, the 3FLD retrieval method was chosed for the in situ data. The validation results showed that the three far-red TROPOSIF datasets exhibit slightly different performances in terms of the validation accuracy; the R2 for TROPOSIFCaltech, TROPOSIF735, and TROPOSIF743 was 0.43, 0.33 and 0.40, respectively, which is asociated with root-mean-square error(RMSE) values of 0.59, 0.42 and 0.57 mW m−2 sr−1 nm−1, respectively. However, red TROPOSIFCaltech exhibited no significant correlation with tower-based SIF with R2 of 0.02 and RMSE of 0.34 mW m−2 sr−1 nm−1. Furthermore, the validation results at different sites varied, with R2 ranging from 0.01 to 0.70. Uncertainties still exist in the validation of the four TROPOSIF datasets, which are attributed to some unresolved issues, such as the limited quality of in situ SIF retrievals and the spatial scaling difference. Thus, to fully utilize satellite-based SIF products for wide ranging applications, further improvements in SIF product quality are urgently required at both ground and satellite scales.

How to cite: Du, S., Duan, W., and Liu, L.: Addressing validation challenges for TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements and an NIRv-scaled approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7161, https://doi.org/10.5194/egusphere-egu24-7161, 2024.

X2.45
|
EGU24-8575
|
ITS3.3/ESSI4.1
|
ECS
|
|
damir akhmetshin, Owen Naughton, Leon Cavanagh, and Dean Callaghan

The use of unmanned aerial vehicles (UAVs) with off-the-shelf RGB and multispectral sensors has expanded for environmental monitoring. While multispectral data enables analysis impossible with RGB, visible range cameras have benefits for large-scale habitat mapping. This research compared RGB, multispectral, and fused RGB-multispectral data from UAVs for seaweed mapping along the Irish coast. Three classification algorithms – Random Forest, Maximum Likelihood Classifier and Support Vector Machines – were tested on the three datasets to compare accuracies for seaweed species delineation and percent cover estimation. The RGB sensor effectively classified broad intertidal classes, but struggled differentiating some seaweed species. Multispectral data significantly improved species-level classification accuracy but tended to overestimate the presence of red and green algae. Fusing the RGB and multispectral data improved species classification accuracy over multispectral and RGB images. The results demonstrate the benefits of RGB sensors for broad habitat mapping and cover estimation, and multispectral for detailed species delineation. Fusion of the two sensor types enhances the strengths of both. This highlights the potential for UAVs paired with off-the-shelf visible range and multispectral cameras to provide detailed, accurate, and affordable change monitoring of intertidal seaweed habitats.

How to cite: akhmetshin, D., Naughton, O., Cavanagh, L., and Callaghan, D.: Optimizing UAV seaweed mapping through algorithm comparison across RGB, multispectral, and combined datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8575, https://doi.org/10.5194/egusphere-egu24-8575, 2024.

X2.46
|
EGU24-1074
|
ITS3.3/ESSI4.1
|
ECS
|
Ravi Verma and Pradeep Kumar Garg

Urban structures in any city needs to be analyzed in conjunction to Urban Green Spaces (UGS). The relations between spatial attributes of built-up and UGS Land use/ Land Cover (LULC) can help analyzing various ecosystem services like micro-climate problems in aspects of increasing Land Surface Temperature (LST) patterns causing Urban Heat Island (UHI) inside the city. These relations between both LULC can also improve aesthetic structure of city. India, a magnanimous country comprising of 36 administrative boundaries, shows a range of diversity in population and culture inhabited by its dwellers. These large population centres have different settlement characteristics at different administrative levels (States/Union Territories, Districts, Sub-Districts, Villages/Towns and Wards/Blocks etc.) of India. These settlements can affect climate and development of country in longer duration. As such spatio-temporal analysis of urban population dynamics over different constituent land use/land cover (LU/LC) is performed in this study using open source data and software programs only. The study derives a pattern of Landscape Metrics (LSM) of built-up LULC over a period of 30 years in 7 zones of India comprising of 694 districts in total of various 28 states and 8 UTs. Landscape Metrics are one of the efficient ways to analyze the patterns of LULC in a study area. Publically available data such as Pan India Decadal LULC by ORNL DAAC for year 2005 and Copernicus Global Land service LULC for year 2015 at 100m resolution has been used as classified maps in study. These decadal LULC maps are predominantly classified using multi-temporal Landsat series data for Pan India coverage giving annual LULC classification maps consisting 19 classes with overall classification accuracy of 0.94 for all 3 year data. Built-up class present in both classified maps are used for analysis as urban patches. Landscape metric analysis is done through landscapemetrics library in RStudio® and 34 of the class level landscape metrics were calculated for urban area using multi-patch analysis for multi-year data. Significance of metrics was determined through calculation of coefficient of determination and establishment of variable importance between all 34 landscape metrics for urban and Population averaged over states and UTs containing 694 districts units of India. "Number of Patches (NP)","Total Class Area (CA)", "Total Core Area (TCA)" and "Total Edge (TE)" stood out as most viable metrics showing relation as high as R2 of 0.82 between spatial attributes of urban patches and population in the Indian administrative units. Spatial relation in terms of zones of India is much more existent than temporal as yearly variation for relation between urban patches and population. North, West and North East Zone of India are showing most consistent and highest values of correlation whereas South zone and UTs lowest with Central zone being most inconsistent. Such high relations between spatial patterns of urban patches and population suggest a significant need to prioritize configuration and optimization of population in cities, which can not only affect urbanization pattern inside the city boundary but also help achieving the sustainability causes of ecosystem services in city boundary.

How to cite: Verma, R. and Garg, P. K.: Pan-India analysis of relationship between Spatial Attributes of Urban Area and Population, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1074, https://doi.org/10.5194/egusphere-egu24-1074, 2024.

X2.47
|
EGU24-12810
|
ITS3.3/ESSI4.1
|
ECS
Chengxiu Li and Le Yu

Globally, 1.2 billion urban dwellers live in slums facing essential service deficiencies and heightened vulnerability, thereby challenging the United Nations' commitment to "Leave no one behind" in achieving Sustainable Development Goals (SDGs). We investigated availability of key urban services (water, sanitation, housing, living spaces) that define slums, revealing that 58.9% of households in 27 African countries lack access to at least one of above service based on household surveys, leading to their categorization as slums households. While slum proportion has decreased over the past two decades, however inequality has rose in countries with a high prevalence of slums.

Through the integration of household surveys, geospatial data, and machine learning algorithms, we estimated the wealth level and key service availability across sub-Saharan Africa. This approach revealed that 53.4% of urban population resides in slums, surpassing the UN's estimate of 44.9%. This study revealed that poor urban service in slums exacerbate inequality, however current aggregated statistics underestimate the extent of under-serviced urban slums, leading to ineffective efforts in building prosperity for all.

How to cite: Li, C. and Yu, L.: Urban deprivation and enhanced inequality in sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12810, https://doi.org/10.5194/egusphere-egu24-12810, 2024.

X2.48
|
EGU24-4493
|
ITS3.3/ESSI4.1
|
ECS
|
|
Ritu Yadav, Andrea Nascetti, and Yifang Ban

With the rapid shift of urban population to cities, urbanization monitoring has become essential to ensure sustainable development. In the last decade, 2D urban monitoring such as building footprint extraction has received considerable attention resulting in multiple high and low-resolution products. But despite being the essential component of urbanization, the vertical dimension (height) has not been studied at a large scale. Accurate estimation of building height plays an important role in urban planning, as it is correlated with energy consumption, population, transportation, city planning, urban climate and many other monitoring and planning required for sustainable development.

Airborne LiDAR or high-resolution orthophotos can be used for accurate building height estimation but for large-scale monitoring applications, the data collection itself is extremely expensive. With a compromise of resolution, Earth observation data, especially free-of-cost data can be used for large-scale monitoring. Existing large-scale building height estimation methods operate at low resolution (1km to 100m). A few of the recent studies improved the resolution to 10m while operating in a few cities to few states of the country. In this study, we estimate building heights across four countries. We propose a DL model that operates on a time series of Sentinel-1 SAR and Sentinel-2 MSI data and estimates building height at 10m spatial resolution. Our model estimates building height with 1.89m RMSE (Root Mean Square Error) surpassing the best score of 3.73m reported in previous studies. 

To demonstrate the effectiveness of our approach, we tested it on data from four countries and compared it with a baseline and four recent DL networks. We evaluate the impact of time series input and individual input modality i.e., SAR and optical data on the performance of the proposed model. The model is also tested for generalizability. Furthermore, the predicted building heights are downsampled and compared with GHSL-Built-H R2023A, a state-of-the-art product at 100m spatial resolution. The results show an improvement of 0.3m RMSE.

References

[1] Yadav, R., Nascetti, A., & Ban, Y. (2022). BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 1377–1383. https://doi.org/10.5194/isprs-archives-xliii-b3-2022-1377-2022

[2] Yang, C., &; Zhao, S. (2022). A building height dataset across China in 2017 estimated by the spatially-informed approach. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01192-x

[3] Cai, B., Shao, Z., Huang, X., Zhou, X., & Fang, S. (2023). Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, 122, 103399. https://doi.org/10.1016/j.jag.2023.103399

[4] Yadav, Ritu, Andrea Nascetti, and Yifang Ban. "A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series." arXiv preprint arXiv:2307.01378 (2023)

[5] Pesaresi, M., and P. Politis. "GHS-BUILT-H R2023A—GHS Building Height, Derived from AW3D30, SRTM30, and Sentinel2 Composite (2018)." (2018)

 

How to cite: Yadav, R., Nascetti, A., and Ban, Y.: Building Height Estimation at 10m across multiple countries using Sentinel Time-Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4493, https://doi.org/10.5194/egusphere-egu24-4493, 2024.

X2.49
|
EGU24-12274
|
ITS3.3/ESSI4.1
|
ECS
Sandra Lorenz, Moritz Kirsch, René Booysen, and Richard Gloaguen

The transition towards a green economy has led to an increased demand for raw materials, which are mainly sourced by mining. Mining activities generate residues such as rock wastes, tailings and stockpiles. These materials are associated with environmental and safety risks that need to be carefully managed throughout their life cycle, with an emphasis on stability and the prevention of water and soil pollution. Earth-observation (EO)-based techniques are seldom used for monitoring these deposits, and multi-sensor field data is commonly not integrated despite recent technological advances. We will develop holistic, full-site services for the geotechnical and environmental monitoring as well as valorisation of mining-related deposits based on a combination of EO and in situ geophysical data. The work will be accomplished under the “Multiscale Observation Services for Mining related deposits” project (MOSMIN for short), and funded by the European Union Agency for the Space Programme (EUSPA) with project number 101131740. MOSMIN services will use Copernicus EO data for time-resolved, spatially extensive, remote monitoring of ground deformation and surface composition. Innovative change detection algorithms will highlight displacements and identify environmental hazards. Satellite data will be integrated with real-time, high-resolution data obtained from unoccupied aerial vehicles and sensors installed at the site, leveraging the power of machine learning for fusion and resolution enhancement of multi-scale, multi-source data. Novel, non-invasive geophysical techniques such as distributed fibre-optic sensing will provide subsurface information to identify potential risks such as internal deformation and seepage. In collaboration with international mining companies, MOSMIN will use pilot sites in the EU, Chile and Zambia to develop and trial comprehensive monitoring services, which are calculated to have a Total Available Market of €1.2bn and expect to be commercialised shortly after project completion by three industry partners. The MOSMIN integrative service and tools will improve the efficiency and reliability of monitoring, maximise resource utilisation and help mitigate environmental risks and the impact of mining operations. - On behalf of the MOSMIN consortium.

How to cite: Lorenz, S., Kirsch, M., Booysen, R., and Gloaguen, R.: MOSMIN: Multiscale observation services for mining related deposits, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12274, https://doi.org/10.5194/egusphere-egu24-12274, 2024.