Elevation in wildfire frequencies with respect to the climate change

Mansoor, Sheikh; Farooq, Iqra; Kachroo, M. Mubashir; Mahmoud, Alaa El Din; Fawzy, Manal; Popescu, Simona Mariana; Alyemeni, Mohammed Nasser; Sonne, Christian; Rinklebe, Jörg; Ahmad, Parvaiz

doi:10.1016/j.jenvman.2021.113769

Cited by 126 publications

(47 citation statements)

References 40 publications

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“…The global climate change is also affecting the hydrological cycle and mediated the amount of precipitation of its distribution in various parts of the world. Increasing global temperature is enhancing the evaporation rate as well as water stress in crop plants (Mansoor et al, 2022 ). It can lower the plant nutrient uptake pattern and crop yield (Meena et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Reuse of poor-quality water for sustainable crop production in the changing scenario of climate

Dotaniya

Meena

Saha

et al. 2022

Environ Dev Sustain

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The availability of freshwater is limited for agriculture systems across the globe. A fast-growing population demands need to enhance the food grain production from a limited natural resources. Therefore, researchers and policymakers have been emphasized on the production potential of agricultural crops in a sustainable manner. On the challenging side, freshwater bodies are shrinking with the pace of time further limiting crop production. Poor-quality water may be a good alternative for fresh water in water scarce areas. It should not contain toxic pollutants beyond certain critical levels. Unfortunately, such critical limits for different pollutants as well as permissible quality parameters for different wastewater types are lacking or poorly addressed. Marginal quality water and industrial effluent used in crop production should be treated prior to application in crop field. Hence, safe reuse of wastewater for cultivation of food material is necessary to fulfil the demands of growing population across the globe in the changing scenario of climate.

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Section: Introductionmentioning

confidence: 99%

Reuse of poor-quality water for sustainable crop production in the changing scenario of climate

Dotaniya

Meena

Saha

et al. 2022

Environ Dev Sustain

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“…With respect to climate in Chile and Argentina, fire history studies have linked forest fire activity in the southern range of the Andes to warm and dry conditions associated with El Niño Southern Oscillation ENSO (Holz and Veblen, 2012;Mundo et al, 2013;Holz et al, 2017). It is also expected that wildfire activity will increase in most WUIs during the 21st century (Mansoor et al, 2022), and in the western province of Chubut in Patagonia the tendency is to have maximum and minimum increased between 1 and 2 • C in the next 50 years (SMN, 2019). Catastrophic fires have already occurred in Andean WUI in northwestern Chubut in Patagonia during the hot and dry 2010-2011 and 2011-2012 fire seasons (Defossé et al, 2015), but even more catastrophic fires have occurred in the 2020-2021 fire season, causing three fatalities and hundreds of burned houses.…”

Section: Introductionmentioning

confidence: 99%

Forty Years of Wildland Urban Interface Growth and Its Relation With Wildfires in Central-Western Chubut, Argentina

Godoy

Martinuzzi²,

Masera³

et al. 2022

Front. For. Glob. Change

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Wildland Urban Interface (WUI) areas are rapidly expanding worldwide. In many regions of the world, this expansion could be explained by the increasing possibilities of telecommuting and developing home-office work, while at the same time living in, or surrounded by wilderness areas of magnificent beauty. However, growth and development of these WUIs are still not well dimensioned and regulated, especially in fire prone ecosystems. Over the last two decades, an increasingly number of megafires occurred in these WUI areas in different regions of the world. Recently, big megafires occurred in WUIs located around towns in the western Andean-Patagonian region of Argentina. In this study, we analyzed the evolution of the WUI and its relationship with wildfires around cities and towns located in the northwestern part of Chubut province of Argentina. The studied region covers 324,823 ha and includes the cities of Esquel and Trevelin, and other small villages. Our objectives were to (i) map the current WUI, (ii) quantify changes occurred in the WUI in the last 40 years around the main cities of the region, and (iii) analyze the relationships among WUIs, vegetation types, and wildfire occurrence. We mapped the distribution of WUI using spatially explicit information on housing density derived from censuses and high-resolution imagery from 1981/1982 and 2021, and land cover data. The current WUI covered 8% of the study area, however it has 97% of the houses. Between 1981 and 2021, the WUI area increased by 80%, especially in the southern part of our study region. Finally, information on wildfires distribution revealed that 65% of the ignition points of the recent fires in the region occurred within the WUI. Most of the vegetation burned were herbs, sub-shrubs and woodlands. WUI expansion in our study area appears to be constrained by terrain features (slopes and accessibility), but not by vegetation characteristics. Our study suggests that continuing, unplanned housing expansion in wilderness areas without appropriate vegetation management will likely increases wildfire risk and human environmental conflicts further. Effective land use planning and wildfire management are crucial for sustainable housing expansion in western Patagonia.

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“…The devasting wildfires in Portugal set most of the regions in the country on high alert for fires. Several factors contribute to wildfires [3]; factors like human negligence or other biomes due to lack of awareness toward the environment appear to be some predictable causes of wildfires [4]. Some unpredictable factors include lightning, high atmospheric temperature, or dryness due to global warming with rapid industry growth and air pollution [1].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Harkat

Nascimento

Bernardino³

et al. 2022

Remote Sensing

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Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the detection outputs. As a result, the time necessary for data processing is drastically reduced, as the time required rises exponentially with the size of the captured pictures. In a real-time fire emergency, it is critical to notice the fire pixels and warn the firemen as soon as possible to handle the problem more quickly. The present study addresses the challenge mentioned above by implementing an on-site detection system that detects fire pixels in real-time in the given scenario. The proposed approach is accomplished using Deeplabv3+, a deep learning architecture that is an enhanced version of an existing model. However, present work fine-tuned the Deeplabv3 model through various experimental trials that have resulted in improved performance. Two public aerial datasets, the Corsican dataset and FLAME, and one private dataset, Firefront Gestosa, were used for experimental trials in this work with different backbones. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%.

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Elevation in wildfire frequencies with respect to the climate change

Cited by 126 publications

References 40 publications

Reuse of poor-quality water for sustainable crop production in the changing scenario of climate

Reuse of poor-quality water for sustainable crop production in the changing scenario of climate

Forty Years of Wildland Urban Interface Growth and Its Relation With Wildfires in Central-Western Chubut, Argentina

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

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