Copernicus data for wildfire mapping and monitoring in Ireland

Author(s):  
Fiona Cawkwell ◽  
Emma Chalencon ◽  
Thedmer Postma ◽  
Ned Dwyer ◽  
Beatriz Martin ◽  
...  

<p>Although wildfires in Ireland are not extensive, information on their impacts in terms of atmospheric emissions and pollutants, and habitat losses is essential.  Current ground-based wildfire data are limited by their incompleteness, inconsistency in reporting, and a lack of timeliness. Additional data on fire alerts are drawn from international satellite derived databases such as NASA’s Fire Information for Resource Management System (FIRMS) and the European Forest Fire Information System (EFFIS) to produce a more consistent national summary. However, these databases exploit thermal anomalies derived from low spatial resolution satellite imagery, which can result in a large number of omissions of small, short-lived fires, especially when extensive cloud-cover persists, as is common in Ireland. To overcome these limitations, a new approach is proposed whereby data from the Copernicus Atmosphere Monitoring Service (CAMS) are used to identify atmospheric pollutant anomalies that may be associated with a wildfire, with Sentinel-2 pre- and post-fire imagery providing a more detailed account of the area burned and the vegetation cover affected. An inventory of fire events in Ireland reported by local and social media and the FIRMS and EFFIS databases from 2015-2020 was compiled. The average hourly concentration of selected pollutants (CO, O3, PM2.5, PM10, SO2, NOx) was derived from the CAMS European air quality analysis product at the location of each fire shortly before, during, and after the event. The average concentrations for the same period from the years excluding the year of the fire being studied were compared to the pollutant concentrations observed during the event. Preliminary results suggest that the concentration of PM2.5, PM10, SO2, and NOx show the clearest deviations from the baseline during the occurrence of a fire. Clear-sky Sentinel-2 images preceding and after selected fires were identified, and a number of different indices (NBR, dNBR, RdNBR, dMIRBI) calculated and combined to delineate burn event areas. Post-processing was undertaken to remove errors due to water, shadow and cloud cover, and eliminate features less than 0.4ha in size. Preliminary results show that burn scars can be clearly distinguished and their areas calculated, including fire events omitted from the 2015-2020 inventory. However, false alarms arise from natural land cover change, especially agricultural activity, and attempts to exclude these are being explored using the national mapping agency’s object-oriented digital mapping data model, PRIME2. Further analysis of the Sentinel-2 imagery to map the habitats burned is in progress, with a particular focus on identifying the location of gorse (Ulex europaeus), which is highly flammable in dry summer conditions due to the presence of deadwood. Atmospheric chemistry colleagues are undertaking a field campaign during 2021 to monitor the air quality during a burn event, along with laboratory measurements in a burn chamber, from which emissions factors for gorse can be calculated. Subsequently, it is hoped that detailed estimates of emissions from upland wildfires can be derived leading to improved national GHG inventories, and an assessment of these events made in terms of atmospheric impacts on population centres and environmental impacts on habitats and biodiversity.</p>

2021 ◽  
Vol 13 (5) ◽  
pp. 1028
Author(s):  
Alber Hamersson Sanchez ◽  
Michelle Cristina A. Picoli ◽  
Gilberto Camara ◽  
Pedro R. Andrade ◽  
Michel Eustaquio D. Chaves ◽  
...  

In their comments about our paper, the authors remark on two issues regarding our results relating to the MACCS-ATCOR Joint Algorithm (MAJA). The first relates to the sub-optimal performance of this algorithm under the conditions of our tests, while the second corresponds to an error in our interpretation of MAJA’s bit mask. To answer the first issue, we acknowledge MAJA’s capacity to improve its performance as the number of images increases with time. However, in our paper, we used the images we had available at the time we wrote our paper. Regarding the second issue, we misread the MAJA’s bit mask and mistakenly labelled shadows as clouds. We regret our error and here we present the updated tables and images. We corrected our estimation and, consequently, there is an increment in MAJA’s accuracy in the detection of clouds and cloud shadows. However, these increments are not enough to change the conclusion of our original paper.


2021 ◽  
Vol 13 (15) ◽  
pp. 2961
Author(s):  
Rui Jiang ◽  
Arturo Sanchez-Azofeifa ◽  
Kati Laakso ◽  
Yan Xu ◽  
Zhiyan Zhou ◽  
...  

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.


Author(s):  
Eli G. Pale-Ramon ◽  
Luis J. Morales-Mendoza ◽  
Sonia L. Mestizo-Gutierrez ◽  
Mario Gonzalez-Leee ◽  
Rene F. Vazquez-Bautista ◽  
...  

2017 ◽  
Vol 200 ◽  
pp. 693-703 ◽  
Author(s):  
Jos Lelieveld

In atmospheric chemistry, interactions between air pollution, the biosphere and human health, often through reaction mixtures from both natural and anthropogenic sources, are of growing interest. Massive pollution emissions in the Anthropocene have transformed atmospheric composition to the extent that biogeochemical cycles, air quality and climate have changed globally and partly profoundly. It is estimated that mortality attributable to outdoor air pollution amounts to 4.33 million individuals per year, associated with 123 million years of life lost. Worldwide, air pollution is the major environmental risk factor to human health, and strict air quality standards have the potential to strongly reduce morbidity and mortality. Preserving clean air should be considered a human right, and is fundamental to many sustainable development goals of the United Nations, such as good health, climate action, sustainable cities, clean energy, and protecting life on land and in the water. It would be appropriate to adopt “clean air” as a sustainable development goal.


2020 ◽  
Author(s):  
Benjamin N. Murphy ◽  
Christopher G. Nolte ◽  
Fahim Sidi ◽  
Jesse O. Bash ◽  
K. Wyat Appel ◽  
...  

Abstract. Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow tends to be time-consuming, error-prone, inconsistent among model users and difficult to document while consuming increased computer storage space. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g. energy system models, reduced-form models).


2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.


2021 ◽  
Vol 1 (2) ◽  
pp. 89-95
Author(s):  
Uitumen Erdenezul

Air pollution is a problem that needs attention, especially pollution by heavy metals such as lead (Pb). This research was conducted to measure the levels of Pb in the blood of people who do a lot of daily activities on the highway in the Ulaanbaatar region, Mongolia, so that an overview of the level of exposure to Pb in the air is obtained. The study was conducted using an observational method by measuring the blood directly from the participants using an atomic absorption spectrophotometer. The participants involved were 20 people who met the criteria. The results showed that the average level of Pb in the blood of people who had daily activities on the highway was 8.97 ppm. Where the smallest level is 5.12 ppm and the highest level is 12.06 ppm. This value is far above the threshold value determined by WHO, which is 0.05 ppm. Therefore, it can be concluded that the air quality in the Ulaanbaatar area is in the poor category with a high level of Pb exposure.


Sign in / Sign up

Export Citation Format

Share Document