Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

Niroumand-Jadidi, Milad; Bovolo, Francesca; Bruzzone, Lorenzo; Gege, Peter

doi:10.3390/rs12152381

Cited by 67 publications

(65 citation statements)

References 61 publications

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“…Therefore, the focus of this study is on the exploration of 4-band PlanetScope imagery for surface water extraction and its accuracy assessment in the context of glacial lakes mapping and monitoring in the HKKH region. The PlanetScope imagery has previously been used in surface water mapping [18], water velocity computation [19], bathymetry [20,21] and benthic habitat monitoring [22]. Planet Labs offer limited free data access to researchers and academicians through their education and research program.…”

Section: Introductionmentioning

confidence: 99%

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Qayyum

Ghuffar

Ahmad

et al. 2020

IJGI

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Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

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Section: Introductionmentioning

confidence: 99%

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Qayyum

Ghuffar

Ahmad

et al. 2020

IJGI

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“…The current pandemic caused by the novel severe acute respiratory syndrome corona virus (SARS-COV2) and the coronavirus disease that first occurred in Wuhan, China, in late 2019 (COVID- 19) [1], has had a huge impact on modern societies [2][3][4][5][6][7][8][9][10][11] and changed human life dramatically in early 2020. Starting in Wuhan, China, in January 2020, several countries have imposed so-called "lockdowns" to parts or all of their territories.…”

Section: Introductionmentioning

confidence: 99%

“…During these lockdowns, private and social life as well as industrial and commercial activities were reduced to a minimum to break the exponential increase in the infection chain of the pandemic and reduce the strain on health systems and reduce the fatality rate accordingly. Besides the effects on the growth rates of virus infections [2,3], these lockdowns had several unintended effects [4][5][6][7][8][9][10][11]. These include a negative impact on the economy [4], on mental health and sleeping behavior [5] or the food intake of obese children [6], but also unintended positive effects on Measurements in 2020 were performed on 26 March, which was a few days after the start of the strongest lockdown in Germany.…”

Section: Introductionmentioning

confidence: 99%

Evidence That Reduced Air and Road Traffic Decreased Artificial Night-Time Skyglow during COVID-19 Lockdown in Berlin, Germany

Jechow

Hölker

2020

Remote Sensing

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Artificial skyglow, the brightening of the night sky by artificial light at night that is scattered back to Earth within the atmosphere, is detrimental to astronomical observations and has an impact on ecosystems as a form of light pollution. In this work, we investigated the impact of the lockdown caused by the COVID-19 pandemic on the urban skyglow of Berlin, Germany. We compared night sky brightness and correlated color temperature (CCT) measurements obtained with all-sky cameras during the COVID-19 lockdown in March 2020 with data from March 2017. Under normal conditions, we expected an increase in night sky brightness (or skyglow, respectively) and CCT because of the transition to LED. This is supported by a measured CCT shift to slightly higher values and a time series analysis of night-time light satellite data showing an increase in artificial light emission in Berlin. However, contrary to this observation, we measured a decrease in artificial skyglow at zenith by 20% at the city center and by more than 50% at 58 km distance from the center during the lockdown. We assume that the main cause for the reduction of artificial skyglow originates from improved air quality due to less air and road traffic, which is supported by statistical data and satellite image analysis. To our knowledge, this is the first reported impact of COVID-19 on artificial skyglow and we conclude that air pollution should shift more into the focus of light pollution research.

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“…8 ) according to satellite images is that Venice’s canals became clearer after one month of lockdown (ESA 2020b ). Niroumand-Jadidi et al ( 2020 ) reported an almost 50% reduction in total suspended matter (TSM) after lockdown using shallow-water inversion via Planet Scope imagery. Before the COVID-19 periods, the concentration of TSM was 3 g/m 3 on average; however, it was reduced to 1.4 g/m 3 during COVID-19 lockdown.…”

Section: Surface Water Quality and Wastewater Surveillancementioning

confidence: 99%

Positive environmental effects of the coronavirus 2020 episode: a review

Mousazadeh

Paital

Naghdali

et al. 2021

Environ Dev Sustain

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The outbreak of COVID-19 has made a global catastrophic situation that caused 1,039,406 deaths out of 35,347,404 infections, and it will also cause significant socioeconomic losses with poverty increasing from 17.1 to 25.9%. Although the spreading rate of COVID-19 is very high on October 6, 2020, the death rate is still less than 2.94%. Nonetheless, this review article shows that the lockdown has induced numerous positive impacts on the environment and on energy consumption. For instance, the lockdown has decreased the electricity demand by 30% in Italy, India, Germany, and the USA, and by 12-20% in France, Germany, Spain, India, and the UK. Additionally, the expenditure of the fuel supply has been decreased by 4% in 2020 as compared to the previous years (2012-2019). In particular, The global demand for coal fuel has been reduced by 8% in March and April 2020 as compared to the same time in 2019. In terms of harmful emissions, the lockdowns reduced the emissions of nitrous oxides by 20-30% in China, Italy, France, Spain, and by 77.3% in São Paulo, Brazil. Similarly, the particulate matter level has been reduced from 5-15% in Western Europe, to 200% in New Delhi, India, which in turn has enhanced the air quality in a never-seen manner in recent times. In some places, such as New York, USA, CO 2 emission was also reduced by 5-10%. The water quality, in several polluted areas, has also been remarkably enhanced, for example, the dissolved oxygen content in the Ganga River, India, has increased by about 80%. Traffic congestion has also been reduced worldwide, and in some areas, it has been reduced by 50%, such as New York and Los Angeles, USA. Overall, while the COVID-19 pandemic has shrinked the global economy by 13-32%, the pandemic has also clearly benefited to other sectors, which must be considered as the spotlight for the permanent revival of the global ecosystem.

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Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

Cited by 67 publications

References 61 publications

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Evidence That Reduced Air and Road Traffic Decreased Artificial Night-Time Skyglow during COVID-19 Lockdown in Berlin, Germany

Positive environmental effects of the coronavirus 2020 episode: a review

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