BackgroundThe burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May–June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution satellite imagery and compared estimates to Civil Registry office records.MethodsAfter identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020) and thereby compute excess burials. We also analysed death notifications to the Civil Registry office over the same period.ResultsWe collected 78 observations from 11 cemeteries. In all but one, a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020.DiscussionTo our knowledge, this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

Abstract:Mangroves are an important bulkhead against climate change: they afford protection for coastal areas from tidal waves and cyclones, and are among the most carbon-rich forests in the tropics. As such, protection of mangroves is an urgent priority. This work provides some new information on patterns of degradation in the Sundarbans, the largest contiguous mangrove forest in the world, which are home to more than 35 reptile species, 120 commercial fish species, 300 bird species and 32 mammal species. Using radar imagery, we contrast and quantify the recent impacts of cyclone Sidr and anthropogenic degradation on this ecosystem. Our results, inferred from changes in radar backscatter, confirm already reported trends in coastline retreat for this region, with areas losing as much as 200 m of coast per year. They also suggest rapid changes in mangrove dynamics for Bangladesh and India, highlighting an overall decrease in mangrove health in the east side of the Sundarbans, and an overall increase in this parameter for the west side of the Sundarbans. As global environmental change takes its toll in this part of the world, more detailed, regular information on mangroves' distribution and health is required: our study illustrates how different threats experienced by mangroves can be detected and mapped using radar-based information, to guide management action.

This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

Background While the impact of the COVID-19 pandemic has been well documented in high-income countries, less is known about the health effects in Somalia, where health systems are weak and vital registration is underdeveloped. Methods We used remote sensing and geospatial analysis to quantify burial numbers from January 2017 to September 2020 in Mogadishu. We imputed missing grave counts using surface area data. Simple interpolation and a generalised additive mixed growth model were used to predict actual and counterfactual burial rates by cemetery and across Mogadishu during the most likely period of COVID-19 excess mortality and to compute excess burials. We undertook a qualitative survey of key informants to determine the drivers of COVID-19 excess mortality. Results Burial rates increased during the pandemic, averaging 1.5-fold and peaking at a 2.2-fold increase on pre-pandemic levels. When scaled to plausible range of baseline crude death rates, the excess death toll between January and September 2020 was 3200–11 800. Compared with Barakaat Cemetery Committee's burial records, our estimates were lower. Conclusions Our study indicates considerable underestimation of the health effects of COVID-19 in Banadir and an overburdened public health system struggling to deal with the increasing severity of the epidemic in 2020.

(ENGLISH) Background The burden of COVID-19 in low-income and conflict-affected countries is still unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May-June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate in Yemen (population approximately one million) by analysing very high-resolution satellite imagery, and compared estimates to Civil Registry office records from the city. Methods After identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020), and thereby compute excess burials. We also analysed death notifications to the Civil Registry office during April-July 2020 and in previous years. Results We collected 78 observations from 11 cemeteries, of which 10 required imputation from burial surface area. Cemeteries ranged in starting size from 0 to 6866 graves. In all but one a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈ 1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020. Discussion To our knowledge this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate, and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

Habitat evaluation is considered an essential step for assessing the potential for an area to support a viable reintroduced population. Remote sensing techniques can support such investigations, by greatly enhancing the temporal and spatial coverage of habitat assessments. This study makes use of freely available earth observation data to evaluate the suitability of the Ouadi Rim e-Ouadi Achim Game Reserve in central Chad for the reintroduction of the Scimitar-horned Oryx. The reserve was the last stronghold of the species within its historical range, prior to extinction in the wild in the 1980s, providing a basis for reintroduction. Results show that since the 1980s, there has been a steady increase in average annual precipitation and satellite-derived estimates of primary production. The spatial downscaling of the average trend in primary production showed that the north of the protected area exhibited a drying trend over the period 1982-2008, while the south was associated with intense greening. As a result, the subdesert transition zone preferred by oryx is currently narrowing. If this is correct, this implies a potential reduction of favourable habitat for the oryx, which could have detrimental effects on the success of establishing a self-sustaining reintroduced population.

Background While the impact of the COVID-19 pandemic has been well documented in high-income countries, much less is known about its impact in Somalia where health systems are weak and vital registration is under developed. Methods We used remote sensing and geospatial analysis to quantify the number of burials from January 2017 to September 2020 in Mogadishu. We imputed missing grave counts using surface area data. Simple interpolation and a generalised additive mixed growth model were used to predict both actual and counterfactual burial rates by cemetery and across Mogadishu during the most likely period of COVID-19 excess mortality and to compute excess burials. We also undertook a qualitative survey of key informants to determine the drivers of COVID-19 excess mortality. Results Burial rates increased during the pandemic period with a ratio to pre-pandemic levels averaging 1.5-fold and peaking at 2.2-fold. When scaled to plausible range of baseline Crude Death Rates (CDR), excess death toll between January and September 2020 ranged between 3,200 and 11,800. When compared to burial records of the Barakaat Cemetery Committee our estimates were found to be lower. Conclusions Our study points to considerable under estimation of COVID-19 impact in Banadir and an overburdened public health system struggling to deal with the increasing severity of the epidemic in 2020.

Introduction In countries with weak surveillance systems confirmed COVID-19 deaths are likely to underestimate the death toll of the pandemic. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data on burial patterns in Mogadishu, Somalia during 2020 to estimate the date of introduction, transmissibility and other epidemiologic characteristics of SARS-CoV-2 in this low-income, crisis-affected setting. Methods We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions in Mogadishu up to September 2020. Results Under the assumption that excess deaths in Mogadishu February-September 2020 were directly attributable to SARS-CoV-2 infection we arrived at median estimates of October-November 2019 for the date of introduction and low R0 estimates (1.3-1.5) stemming from the early and slow rise of excess deaths. The effect of control measures on transmissibility appeared small. Conclusion Subject to study assumptions, a very early SARS-CoV-2 introduction event may have occurred in Somalia. Estimated transmissibility in the first epidemic wave was lower than observed in European settings.