Using UAV ‐based remote sensing to assess grapevine canopy damage due to fire smoke

2020 ◽  
Vol 100 (12) ◽  
pp. 4531-4539
Author(s):  
Elena Brunori ◽  
Mauro Maesano ◽  
Federico V Moresi ◽  
Adriano Antolini ◽  
Andrea Bellincontro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Smoke ◽  
Canopy Damage

Forest Fire Smoke Exposure Assessment: Use of Remote Sensing Data in a Pollution Dispersion Model

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S203
Author(s):  
S B. Henderson ◽  
B Burkholder ◽  
M Brauer ◽  
P L. Jackson ◽  
B Klinkenberg ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Exposure Assessment ◽  
Forest Fire ◽  
Dispersion Model ◽  
Remote Sensing Data ◽  
Smoke Exposure ◽  
Pollution Dispersion ◽  
Sensing Data ◽  
Fire Smoke

scholarly journals Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery

2021 ◽  
Vol 14 (1) ◽  
pp. 45
Author(s):  
Zewei Wang ◽  
Pengfei Yang ◽  
Haotian Liang ◽  
Change Zheng ◽  
Jiyan Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Satellite Data ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Wildfire Smoke ◽  
Fire Smoke ◽  
Remote Sensing Satellite

Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.

scholarly journals PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2260
Author(s):  
Jialei Zhan ◽  
Yaowen Hu ◽  
Weiwei Cai ◽  
Guoxiong Zhou ◽  
Liujun Li
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Forest Fire ◽  
Spatial Domain ◽  
Small Scale ◽  
Small Target ◽  
Remote Sensing Images ◽  
Fire Smoke ◽  
Feature Pyramid ◽  
Small Forest

The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods.

Remote sensing of cytotype and its consequences for canopy damage in quaking aspen

2021 ◽  
Author(s):  
Benjamin Blonder ◽  
Philip G. Brodrick ◽  
James A. Walton ◽  
K. Dana Chadwick ◽  
Ian K. Breckheimer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quaking Aspen ◽  
Canopy Damage

Direct radiative effects of smoke aerosol during the extreme 2019/2020 Australian wildfire season

2021 ◽  
Author(s):  
Bernd Heinold ◽  
Holger Baars ◽  
Matthew Christensen ◽  
Anne Kubin ◽  
Kevin Ohneiser ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southern Hemisphere ◽  
Climate Model ◽  
Global Climate ◽  
Radiation Budget ◽  
Climate Modelling ◽  
Radiative Effects ◽  
Smoke Aerosol ◽  
Fire Smoke ◽  
Sensitivity Studies

<p>Record wildfires affected Australia from December 2019 to early 2020. Massive plumes of fire pollutants were lifted into the upper troposphere and even into the stratosphere by pyro-convection triggered by the intense heat of the fires. Subsequently the smoke aerosol was transported over thousands of kilometres eastwards at above 20 km altitude as Lidar observations in South America and satellite imagery show. Space and ground-based remote sensing of aerosol optical thickness indicate a temporary substantial increase in aerosol loading over large parts of the Southern Hemisphere, which offset the usual hemispheric contrast in aerosol. In addition to the massive impact on air quality at Australia’s east coast, this had important effects on the hemisphere-wide radiation budget.</p><p>We investigate the dispersal of the fire smoke aerosol and its radiative effects with the global aerosol-climate model ECHAM6.3-HAM2.3. Biomass burning emissions are prescribed by daily satellite-based estimates from the Global Fire Assimilation System (GFAS). As the horizontal model resolution is too coarse to explicitly resolve convection, the injection height of Australian fire smoke is set to heights between 5 and 15 km and varied in terms of sensitivity studies. The model results for late 2019 and early 2020 are evaluated with ground and satellite remote sensing measurements, as well as contrasted with smoke results for years with low Australian wildfire emissions. The sensitivity results show how the fire injection heights affect the evolution of the smoke plume but also what role radiatively induced self-lifting plays. According to the model, the 2019/20 Australian wildfires considerably perturbed the radiation budget of the Southern Hemisphere. Due to large transport heights relative to clouds and a long lifetime of smoke particles in the stratosphere, the solar irradiance at ground averaged from January to March 2020 decreased by more than 1 W m<sup>-2</sup> for the Southern Hemisphere, which corresponds roughly to the short-term cooling caused by a large volcanic eruption, while the elevated smoke layers experienced significant absorptive heating.</p><p>Considering the recent series of extreme wildfires globally and their probably further increasing occurance in a changing climate,  these results indicate a need for larger attention to pyro-convection in global climate modelling.</p>

Phased Arrays for Radio Astronomy, Remote Sensing, and Satellite Communications

2018 ◽  
Author(s):  
Karl F. Warnick ◽  
Rob Maaskant ◽  
Marianna V. Ivashina ◽  
David B. Davidson ◽  
Brian D. Jeffs
Keyword(s):  
Remote Sensing ◽  
Radio Astronomy ◽  
Phased Arrays ◽  
Satellite Communications
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