scholarly journals Dynamics of arid landscapes burning in Russia and adjacent territories based on active fire data

Author(s):  
S.S. Shinkarenko ◽  
◽  
V.V. Doroshenko ◽  
A.N. Berdengalieva ◽  
I.A. Komarova ◽  
...  
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2021 ◽  
Vol 11 (1) ◽  
Author(s):  
R. Libonati ◽  
J. M. C. Pereira ◽  
C. C. Da Camara ◽  
L. F. Peres ◽  
D. Oom ◽  
...  

AbstractBiomass burning in the Brazilian Amazon is modulated by climate factors, such as droughts, and by human factors, such as deforestation, and land management activities. The increase in forest fires during drought years has led to the hypothesis that fire activity decoupled from deforestation during the twenty-first century. However, assessment of the hypothesis relied on an incorrect active fire dataset, which led to an underestimation of the decreasing trend in fire activity and to an inflated rank for year 2015 in terms of active fire counts. The recent correction of that database warrants a reassessment of the relationships between deforestation and fire. Contrasting with earlier findings, we show that the exacerbating effect of drought on fire season severity did not increase from 2003 to 2015 and that the record-breaking dry conditions of 2015 had the least impact on fire season of all twenty-first century severe droughts. Overall, our results for the same period used in the study that originated the fire-deforestation decoupling hypothesis (2003–2015) show that decoupling was clearly weaker than initially proposed. Extension of the study period up to 2019, and novel analysis of trends in fire types and fire intensity strengthened this conclusion. Therefore, the role of deforestation as a driver of fire activity in the region should not be underestimated and must be taken into account when implementing measures to protect the Amazon forest.


Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.


2021 ◽  
Vol 93 ◽  
pp. 107216
Author(s):  
Akashdeep Sharma ◽  
Harish Kumar ◽  
Kapish Mittal ◽  
Sakshi Kauhsal ◽  
Manisha Kaushal ◽  
...  

2007 ◽  
Vol 69 (3) ◽  
pp. 400-409 ◽  
Author(s):  
J.A. Martínez ◽  
I. Zuberogoitia ◽  
J.E. Martínez ◽  
J. Zabala ◽  
J.F. Calvo
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2021 ◽  
Vol 12 (7) ◽  
pp. 715-726
Author(s):  
Andrew Pagan ◽  
John Rogan ◽  
Birgit Schmook ◽  
Zachary Christman ◽  
Florencia Sangermano
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Author(s):  
Andrey Karpachevskiy ◽  
Sergey Lednev ◽  
Ivan Semenkov ◽  
Anna Sharapova ◽  
Sultan Nagiyev ◽  
...  

Author(s):  
Pamela Ochungo ◽  
Ruan Veldtman ◽  
Rahab Kinyanjui ◽  
Elfatih M. Abdel-Rahman ◽  
Eliud Muli ◽  
...  

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