Marco Ottinger

Mangrove ecosystems are among the most productive and valuable environments on Earth, delivering essential ecological and socio-economic benefits including carbon sequestration, coastal protection, and habitat provision for diverse marine species. However, mangroves face increasing pressures from human activities, with rapid expansion of pond aquaculture emerging as a main driver of mangrove deforestation, especially in Asia, which hosts nearly 40% of the world’s mangroves.This study presents a comprehensive continental-scale assessment of mangrove loss attributable to aquaculture pond expansion across Asia’s coastal zones, with a focus on Southeast Asia, where mangrove conversion is most severe. Utilizing satellite-based Earth observation data, including an object-based, single-feature inventory of aquaculture pond dynamics derived from Sentinel-1/-2 optical and radar time series and Landsat archive imagery, alongside the Global Mangrove Watch (GMW) dataset, we quantified spatial and temporal relationships between pond presence and mangrove forest decline.By integrating these datasets within a harmonized time-indexing framework, we directly associate pond activation events with subsequent mangrove decline to attribute deforestation to aquaculture expansion. Our results reveal strong spatial-temporal correlations: aquaculture ponds predominantly cluster in coastal river deltas, overlapping with mangrove loss hotspots, while pond activation frequently coinciding with or directly following significant mangrove loss. Across Asia, mangrove cover declined by approximately 7.2 percent (2,284 km²) in Indonesia and up to 22.2 percent in Pakistan over the study period from 1996-2019. Key hotspots of aquaculture-driven mangrove degradation were identified primarily in Indonesia, Myanmar, and Vietnam, with Indonesia alone accounting for over 13,000 hectares of mangrove loss between 1996 and 2007 due to pond expansion.Overall, this study underscores the substantial environmental footprint of pond aquaculture on Asia’s coastal ecosystems, demonstrating that aquaculture expansion is a principal driver of mangrove loss in critical regions. By leveraging advanced satellite Earth observation technologies, this research demonstrates the potential of remote sensing data to accurately quantify and monitor mangrove loss at large scales, providing timely, spatially detailed insights into ecosystem changes. Such capabilities are essential for deepening our understanding of the increasing pressures blue carbon ecosystems face from anthropogenic and climatic changes.

Cultural heritage (CH) sites are frequently exposed to natural elements, and their exposure becomes particularly precarious with the onset of climate change. This increased vulnerability places these sites at risk of deterioration or complete destruction. Risks such as land deformation, floods, acid rain, and erosion significantly threaten historic monuments, while water-related hazards, significantly influenced by both climate change and human activities, present a particularly grave risk to these invaluable sites. Considerable research efforts have focused on safeguarding CH sites. However, there remains a deficiency in systemic approaches towards identifying and mitigating risks for CH sites. The TRIQUETRA project proposes a technological toolbox and a methodological framework for tackling climate change risks and natural hazards threatening CH in the most efficient way possible. It aims at creating an evidence-based assessment platform allowing precise risk stratification as well as a database of available mitigation measures and strategies, acting as a Decision Support System (DSS) towards efficient risk mitigation and site remediation. TRIQUETRA is a European project that brings together a diverse group of researchers with varied expertise, encompassing university research groups, research institutes, public entities, as well as small and medium-sized enterprises. In this article, TRIQUETRAs overall methodology is presented, and preliminary results concerning risk identification, TRIQUETRAs knowledge base, as well as novel sensors and coatings, are discussed.

Asia dominates the world’s aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.

Asia is the world’s most important region for aquaculture and generates almost 90 percent of the total production. The farming of fish and shrimp in land-based aquaculture systems expanded mainly along the shorelines of South Asia, Southeast Asia, and East Asia, and is a primary protein source for millions of people. The production of fish and shrimp in freshwater and brackish water ponds in coastal regions of Asia has increased rapidly since the 1990s due to the rising demand for protein-rich foods from a growing (world) population. The aquaculture sector generates income, employment and contributes to food security, has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources and human health.With free and open access to the rapidly growing volume of data from the European Sentinel satellites as well as using machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental-scale. We present a multi-sensor approach which utilizes Earth Observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as a buffer of 200km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at single pond level based on temporal features derived from high spatial resolution SAR and optical satellite acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth observation derived aquaculture dataset to investigate spatial distribution and to identify production hotspots in various administrative units at regional, national, and sub-national scale.

Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018.

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.

Given the growing world population and rising demand for fish and seafood, aquaculture is becoming the main source of aquatic food in human consumption and a primary protein source for millions of people. Since 1990, the world aquaculture production increased from 13 to over 80 million tonnes and is currently valued at USD 231 billion. The cultivation of shrimp species in land-based ponds is one of the fastest growing food production economies and became an important industry in coastal regions, generates income and employment and contributes to food security. Shrimp farms are mainly found in low-lying coastal regions such as estuaries, bays and river deltas along the shorelines of Asia and America. Shrimp farming expanded rapidly in recent years and led to environmental degradation and conversion of valuable wetlands such as mangroves and other coastal forests. The loss of mangroves poses a major threat to coastal ecosystems and population, as mangroves provide valuable flood and coastal protection, as well as risk reduction benefits with regard to global climate change induced effects.In this research, we use image segmentation for temporal features derived from space-borne, high-resolution synthetic aperture radar (SAR) data to extract shrimp farming ponds in coastal mangrove forest areas in Ecuador, South America. An automatic object-based image processing approach aims for the detection of rectangular shaped pond objects utilizing per-pixel median images calculated from C-band Sentinel-1 and L-band ALOS-Palsar SAR time series data. An open source connected component segmentation algorithm was used to extract and locate rectangular shrimp farms in coastal areas based on backscatter intensity and shape features. This study illustrates the opportunities by earth observation for area-wide assessments of shrimp farming activities in mangrove areas to gain more knowledge on land use dynamics with regard to global change and food security. Earth observation can effectively support the planning and management of aquaculture practices and support stakeholders, politicians, and conservationists in implementing appropriate measures in order to protect coastal environments and foster sustainable development in the coastal zone.

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%.

Asia is the major contributor to global aquaculture production in quantity, accounting for almost 90%. These practices lead to extensive land-use and land-cover changes in coastal areas, and thus harm valuable and sensitive coastal ecosystems. Remote sensing and GIS technologies contribute to the mapping and monitoring of changes in aquaculture, providing essential information for coastal management applications. This study aims to investigate aquaculture expansion and spatio-temporal dynamics in two Chinese river deltas over three decades: the Yellow River Delta (YRD) and the Pearl River Delta (PRD). Long-term patterns of aquaculture change are extracted based on combining a reference layer on existing aquaculture ponds for 2015 derived from Sentinel-1 data with annual information on water bodies extracted from the long-term Landsat archive. Furthermore, the suitability of the proposed approach to be applied on a global scale is tested based on exploiting the Global Surface Water (GSW) dataset. We found enormous increases in aquaculture area for the investigated target deltas: an 18.6-fold increase for the YRD (1984–2016), and a 4.1-fold increase for the PRD (1990–2016). Furthermore, we detect hotspots of aquaculture expansion based on linear regression analyses for the deltas, indicating that hotspots are located in coastal regions for the YRD and along the Pearl River in the PRD. A comparison with high-resolution Google Earth data demonstrates that the proposed approach can detect spatio-temporal changes of aquaculture at an overall accuracy of 89%. The presented approach has the potential to be applied to larger spatial scales covering a time period of more than three decades. This is crucial to define appropriate management strategies to reduce the environmental impacts of aquaculture expansion, which are expected to increase in the future.