Earth observation for exposome mapping of Germany: analyzing environmental factors relevant to non-communicable diseases

Non-communicable diseases - NCDs - (e.g., asthma, cancer, or diabetes) are a major concern for society and medicine. According to the World Health Organization, NCDs are responsible for > 70 % of global premature deaths. Apart from increasing mortality, these diseases strain one’s immune system which leads to higher susceptibility to transmittable diseases. NCD-susceptibility depends on the genome (genetic predisposition), behavior (lifestyle), and exposome of a person. The exposome is a composition of environmental parameters such as exposure to air pollution, noise, extreme temperatures, or surrounding greenness. Using Earth Observation data, the majority of factors making up the exposome can be monitored over long periods of time at high resolution and with nearly global coverage. Still, exposome maps and products communicating NCD risk are not widely available. In this study, we utilize eight land surface datasets (distance to green spaces, distance to blue spaces, temperature, noise from industry, as well as road, rail, and air traffic, and light pollution) as well as two air pollution datasets (PM2.5 and NO2) to map health-relevant environmental exposure. We use an established cumulative approach and incorporate exposure-response relationships from scientific literature to map environments that impact public health for the complete area of Germany. We present results communicating exposure relevant to myocardial infarction risk. The methodology is transferable to other NCDs and other areas of interest. In the context of the global health burden from NCDs and ongoing global change, this approach supplies findings for communicating health-relevant exposure

Earth Observation Data Supporting Non-Communicable Disease Research: A Review

A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common-environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries economy due to healthcare costs and missing work force. Additionally, they affect the individual’s immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor >= 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases

Remote Sensing of Surface Water Dynamics in the Context of Global Change - A Review

Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution

Remote Sensing for large-scale agricultural investment areas in Ethiopia – agricultural monitoring based on Earth observation time-series

Ethiopia is known to be currently food insecure and suffering from considerable food deficits. The Government of Ethiopia strives to increase the agricultural production and its efficiency. Therefore, Ethiopia has been promoting large-scale agricultural investment (LSAI) to transform the agricultural sector. However, the progress by agricultural development has been limited. Investors only developed a small fraction of the transferred land. Therefore, there is a great need for monitoring of the implementation and actual state of land use of every LSAI project. The use of remote sensing can substantially support agricultural monitoring. In this study, Earth observation time series are analyzed to examine the land used for agricultural production and to differentiate crop types grown within the three study areas. Current land use/land cover (LULC) is analyzed using Sentinel-2 time series to identify cropland areas. In a second step, remote-sensing time-series of Sentinel-1 and Sentinel-2 are used to differentiate among 20 different crop types grown in the region. The developed classification methods have been applied to derive information products for three study regions in Ethiopia including the LSAI areas within the provinces of Amhara, Benishangul, and Gambella. The methods and derived information products on LULC and crop types will be made available to GIZ and regional experts to support agricultural monitoring of developed land in Ethiopia

The hazard of locust outbreaks - Examples from EO to support locust management

The goal of this research was to explore whether ecological niche modelling (ENM) and a habitat suitability index (HSI) model can be combined to refine results for actual breeding areas of three different locust pests. With the application of ENM as part of HSI, the information value based on climatic and soil preference components defining locust species’ ecological niche are maintained. In addition, up to date land surface parameters, vegetation development and other species relevant environmental parameters were incorporated in the HSI model. Moreover, human interaction and actual land surface dynamics play a crucial role for locust outbreaks and influence and define suitable breeding areas. Therefore, modelling based only on climatic and edaphic variables provides only the ecological niche of a species without considering actual changes of the landscape or situation. Here, we demonstrated a way to account for this issue by implementing different variables derived from Sentinel-2 time-series analysis, which describe the actual state of the land and in this way further narrow suitable breeding areas within an HSI model. The presented approach for mapping egg-pod incubation and breeding suitability was tested for Italian locust in Pavlodar oblast (Kazakhstan), for Moroccan locust in Turkistan oblast (Kazakhstan), and for desert locust in the Awash river basin (Ethiopia, Djibouti, Somalia)

Earth Observation for Exposome Mapping – Proof of Concept and Case Study in Augsburg, Germany

Non-communicable diseases – NCDs – (asthma, cancer, or diabetes, for example) are a major concern for modern medicine. According to the World Health Organization, NCDs are responsible for over 70% of global premature deaths. Apart from the mortality directly related to these chronic diseases, they constantly strain the individual’s immune system, increasing susceptibility to transmittable diseases. Thus, mitigating the effects of NCDs is one of the most pressing issues of modern medicine, healthcare, and governments in general. NCD-susceptibility is dependent on the Genome and the Exposome. The latter of which is principally made up of environmental parameters such as pollution and radiation. Exposure to environmental stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to NCDs. The contribution of Earth Observation (EO) products to NCD-research is on the rise. This is especially true for remote sensing-derived geoinformation products on land surface dynamics. But while numerous reviewed works have provenly associated environmental parameters with significant health impacts, exposome maps and products that communicate NCD-risk are still not widely available. This thesis introduces a novel methodological framework for EO-based, area-wide exposure and NCD-risk analyses based on insights gathered from review results. Using freely available datasets, exemplary calculations are performed for exposure intensity analyses on three varying levels of complexity: ranging from one index including six environmental parameters, such as the NDVI and basic air pollution parameters to more complex indices that include up to twelve environmental parameters and account for specific stressors such as traffic-induced noise pollution. Further, NCDrisk is assessed by accounting for demographic and socioeconomic susceptibility to environmental stressors. In the frame of this thesis, the city region of Augsburg in Southern Germany was chosen as study area. However, all used datasets are available at least nationwide