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All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

A Development Emergency: the title of this year's Global Monitoring Report, the sixth in an annual series, could not be more apt. The global economic crisis, the most severe since the Great Depression, is rapidly turning into a human and development crisis. No region is immune. The poor countries are especially vulnerable, as they have the least cushion to withstand events. The crisis, coming on the heels of the food and fuel crises, poses serious threats to their hard-won gains in boosting economic growth and reducing poverty. It is pushing millions back into poverty and putting at risk the very survival of many. The prospect of reaching the Millennium Development Goals (MDGs) by 2015, already a cause for serious concern, now looks even more distant. A global crisis must be met with a global response. The crisis began in the financial markets of developed countries, so the first order of business must be to stabilize these markets and counter the recession that the financial turmoil has triggered. At the same time, strong and urgent actions are needed to counter the impact of the crisis on developing countries and help them restore strong growth while protecting the poor. Global Monitoring Report 2009, prepared jointly by the staff of the World Bank and the International Monetary Fund, provides a development perspective on the global economic crisis. It assesses the impact on developing countries, their growth, poverty reduction, and other MDGs. And it sets out priorities for policy response, both by developing countries themselves and by the international community. This report also focuses on the ways in which the private sector can be better mobilized in support of development goals, especially in the aftermath of the crisis.

Although only a small percentage of global land cover, urban areas significantly alter climate, biogeochemistry, and hydrology at local, regional, and global scales. To understand the impact of urban areas on these processes, high quality, regularly updated information on the urban environment—including maps that monitor location and extent—is essential. Here we present results from efforts to map the global distribution of urban land use at 500m spatial resolution using remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach uses a supervised decision tree classification algorithm that we process using region-specific parameters. An accuracy assessment based on sites from a stratified random sample of 140 cities shows that the new map has an overall accuracy of 93% (k = 0.65) at the pixel level and a high level of agreement at the city scale (R2 = 0.90). Our results (available at http://sage.wisc.edu/urbanenvironment.html) also reveal that the land footprint of cities occupies less than 0.5% of the Earth’s total land area.

Background To tackle noncommunicable disease (NCD) burden globally, two sets of NCD surveillance indicators were established by the World Health Organization: 25 Global Monitoring Framework (GMF) indicators and 10 Progress Monitoring Indicators (PMI). This study aims to assess the data availability of these two sets of indicators in six ASEAN countries: Cambodia, Lao PDR, Malaysia, Myanmar, Thailand, and Vietnam. Methods As data on policy indicators were straightforward and fully available, we focused on studying 25 non-policy indicators: 23 GMFs and 2 PMIs. Gathering data availability of the target indicators was conducted among NCD surveillance experts from the six selected countries during May-June 2020. Our research team found information regarding whether the country had no data at all, was using WHO estimates, was providing ‘expert judgement’ for the data, or had actual data available for each target indicator. We triangulated their answers with several WHO data sources, including the WHO Health Observatory Database and various WHO Global Reports on health behaviours (tobacco, alcohol, diet, and physical activity) and NCDs. We calculated the percentages of the indicators that need improvement by both indicator category and country. Results For all six studied countries, the health-service indicators, based on responses to the facility survey, are the most lacking in data availability (100% of this category’s indicators), followed by the health-service indicators, based on the population survey responses (57%), the mortality and morbidity indicators (50%), the behavioural risk indicators (30%), and the biological risk indicators (7%). The countries that need to improve their NCD surveillance data availability the most are Cambodia (56% of all indicators) and Lao PDR (56%), followed by Malaysia (36%), Vietnam (36%), Myanmar (32%), and Thailand (28%). Conclusion Some of the non-policy GMF and PMI indicators lacked data among the six studied countries. To achieve the global NCDs targets, in the long run, the six countries should collect their own data for all indicators and begin to invest in and implement the facility survey and the population survey to track NCDs-related health services improvements once they have implemented the behavioural and biological Health Risks Population Survey in their countries.

Recognizing the risk of pandemic and the importance of monitoring and data sharing, we highlight the importance of establishing a global wastewater surveillance consortium, particularly under the umbrella of an international organization such as WHO, to strengthen future pandemic preparedness.

Objectives This study aims to provide real-time surveillance of epidemiological outbreaks of avian influenza in humans and mammals. The primary objective is to understand and track the dynamics of outbreaks as they develop, facilitating timely interventions and informed public health decisions. The data collection is part of a broader initiative focused on improving preparedness and response capabilities to emerging health threats. Data description The dataset includes comprehensive and up-to-date information on epidemiological patterns, including geographic spread, incidence rates, and demographic factors. Collected through systematic monitoring and reporting systems, this dataset is invaluable to researchers seeking to understand the evolving nature of avian influenza outbreaks in the global context. By sharing these data, we aim to contribute to the collective knowledge base by supporting evidence-based strategies for effective public health management and intervention.

Global monitoring of sun-induced chlorophyll fluorescence (SIF) is improving our knowledge about the photosynthetic functioning of terrestrial ecosystems. The feasibility of SIF retrievals from spaceborne atmospheric spectrometers has been demonstrated by a number of studies in the last years. In this work, we investigate the potential of the upcoming TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite mission for SIF retrieval. TROPOMI will sample the 675–775 nm spectral window with a spectral resolution of 0.5 nm and a pixel size of 7 km × 7 km. We use an extensive set of simulated TROPOMI data in order to assess the uncertainty of single SIF retrievals and subsequent spatio-temporal composites. Our results illustrate the enormous improvement in SIF monitoring achievable with TROPOMI with respect to comparable spectrometers currently in-flight, such as the Global Ozone Monitoring Experiment-2 (GOME-2) instrument. We find that TROPOMI can reduce global uncertainties in SIF mapping by more than a factor of 2 with respect to GOME-2, which comes together with an approximately 5-fold improvement in spatial sampling. Finally, we discuss the potential of TROPOMI to map other important vegetation parameters at a global scale with moderate spatial resolution and short revisit time. Those include leaf photosynthetic pigments and proxies for canopy structure, which will complement SIF retrievals for a self-contained description of vegetation condition and functioning.

This paper presents a random forest-based machine learning algorithm to automatically detect satellite oscillator anomalies using dual- or triple-frequency GPS carrier phase measurements. The algorithm can distinguish satellite oscillator anomalies from other GPS carrier phase disturbances including ionospheric scintillation and receiver oscillator anomalies. Carrier phase power spectral density and carrier phase ratios between carriers are extracted from measurements and applied as input features to the random forest algorithm. The method is trained using data collected at seven GNSS monitoring stations located in Alaska, Ascension Island, Greenland, Hong Kong, Peru, Puerto Rico, and Singapore. The overall detection accuracies of 98.4% and 99.0% are achieved for dual- and triple-frequency signals, respectively. The method outperforms other machine learning algorithms. The preliminary detection results demonstrate that the method presented can be employed on a global satellite oscillator anomaly monitoring system.

This paper focuses on events linked to controlled underwater explosions of World War 2 (WW2) ordnances which were included in the Reviewed Event Bulletin (REB). Data used for the study were provided by seismic stations of the International Monitoring System (IMS) in 2020. Examined events were triggered by devices of different charge size and took place in several locations in Europe. There were also other, previously detected WW2 ordnance underwater explosions which could be compared to events in 2020. It is shown that these relatively small underwater explosions listed in the REB, with good coupling to the ground, are located by the IMS network within 20 km from the ground truth. Charge size of explosive material was related to event magnitude. Results were compared to magnitudes published for underwater explosions of larger sizes. The conclusion is that an in-water explosion will result in seismic waves with amplitudes equivalent to the amplitudes of seismic waves from an in-ground explosion with 17.2 times the yield in kT.