scholarly journals Missing Burns in the High Northern Latitudes: The Case for Regionally Focused Burned Area Products

2021 ◽  
Vol 13 (20) ◽  
pp. 4145
Author(s):  
Dong Chen ◽  
Varada Shevade ◽  
Allison E. Baer ◽  
Tatiana V. Loboda
Keyword(s):  
North America ◽  
Boreal Forests ◽  
The United States ◽  
Omission Error ◽  
Burned Area ◽  
Commission Error ◽  
Consistent Estimate ◽  
Burned Areas ◽  
Research Activities ◽  
Northern Latitudes

Global estimates of burned areas, enabled by the wide-open access to the standard data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), are heavily relied on by scientists and managers studying issues related to wildfire occurrence and its worldwide consequences. While these datasets, particularly the MODIS MCD64A1 product, have fundamentally improved our understanding of wildfire regimes at the global scale, their performance may be less reliable in certain regions due to a series of region- or ecosystem-specific challenges. Previous studies have indicated that global burned area products tend to underestimate the extent of the burned area within some parts of the boreal domain. Despite this, global products are still being regularly used by research activities and management efforts in the northern regions, likely due to a lack of understanding of the spatial scale of their Arctic-specific limitations, as well as an absence of more reliable alternative products. In this study, we evaluated the performance of two widely used global burned area products, MCD64A1 and FireCCI51, in the circumpolar boreal forests and tundra between 2001 and 2015. Our two-step evaluation shows that MCD64A1 has high commission and omission errors in mapping burned areas in the boreal forests and tundra regions in North America. The omission error overshadows the commission error, leading to MCD64A1 considerably underestimating burned areas in these high northern latitude domains. Based on our estimation, MCD64A1 missed nearly half the total burned areas in the Alaskan and Canadian boreal forests and the tundra during the 15-year period, amounting to an area (74,768 km2) that is equivalent to the land area of the United States state of South Carolina. While the FireCCI51 product performs much better than MCD64A1 in terms of commission error, we found that it also missed about 40% of burned areas in North America north of 60° N between 2001 and 2015. Our intercomparison of MCD64A1 and FireCCI51 with a regionally adapted MODIS-based Arctic Boreal Burned Area (ABBA) shows that the latter outperforms both MCD64A1 and FireCCI51 by a large margin, particularly in terms of omission error, and thus delivers a considerably more accurate and consistent estimate of fire activity in the high northern latitudes. Considering the fact that boreal forests and tundra represent the largest carbon pool on Earth and that wildfire is the dominant disturbance agent in these ecosystems, our study presents a strong case for regional burned area products like ABBA to be included in future Earth system models as the critical input for understanding wildfires’ impacts on global carbon cycling and energy budget.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

Fires can overwinter in boreal forests of North America

2020 ◽  
Author(s):  
Rebecca Scholten ◽  
Sander Veraverbeke
Keyword(s):  
North America ◽  
Boreal Forest ◽  
Northwest Territories ◽  
Boreal Forests ◽  
Fire Spread ◽  
Burned Area ◽  
Organic Soils ◽  
Boreal Winter ◽  
Large Fire ◽  
Lightning Strikes

<div>The boreal forest stores 35 % of the world’s soil carbon reserves. Wildfires burn frequently in the boreal forest of North America and drive the boreal forest carbon balance. Previously, lightning strikes and human activities were identified as the sole ignition sources for wildfires in the boreal regions of North America. In recent years however, fire managers in Alaska, USA and Northwest Territories, Canada have started reporting the occurrence of overwintering fires. Overwintering fires are fires, that survive the cold and wet boreal winter by smouldering in deep, carbon-rich soils and re-emerge early in the subsequent spring, when fire weather favours fire spread.</div><div>Using the location and ignition dates of 42 overwintering fires reported by fire managers in Alaska and Northwest Territories between 2002 and 2017, we developed an algorithm to identify these new ignition sources. Our algorithm detected 8 out of 9 additional reported fires we used for validation, and further identified 15 unreported overwintering fires. Even though overwintering fires make up only 0.5 % of the burned area in total, they can amount to up to more than 10 % of the annual burned area after exceptionally large fire years.</div><div>We found that overwintering of fires is facilitated by deep burning into the organic soils. Overwintering fires occur more frequently after large fire years in combination with subsequent mild winters and springs leading to an early snowmelt.</div><div>In a warming climate, the boreal forest is burning more frequently and more intensely. As a consequence, the burned area and burn depth are predicted to increase. Our results suggest that overwintering fires are closely tied to these conditions and will therefore occur more often in the future.</div>

scholarly journals Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

2010 ◽  
Vol 14 (17) ◽  
pp. 1-20 ◽  
Author(s):  
Mirco Boschetti ◽  
Daniela Stroppiana ◽  
Pietro Alessandro Brivio
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Expert Knowledge ◽  
Training Data ◽  
Burned Area ◽  
Mediterranean Ecosystem ◽  
Commission Error ◽  
Spectral Indices ◽  
Burned Areas ◽  
High Resolution Satellite Images

Abstract This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) > 90%, K > 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover.

scholarly journals ANALYSIS OF RESPONSE AND RECOVERY OF VEGETATION TO FOREST FIRE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1207-1212
Author(s):  
A. Gong ◽  
J. Li ◽  
Y. Yang ◽  
Y. Chen ◽  
T. Zeng ◽  
...  
Keyword(s):  
Forest Fire ◽  
Recovery Time ◽  
The United States ◽  
Forest Vegetation ◽  
Mitigation Strategies ◽  
Burn Severity ◽  
Burned Area ◽  
Area Index ◽  
Burned Areas ◽  
Response And Recovery

Abstract. To analyse the response and recovery characteristics of forest to forest fire, this paper selected the forest fire in the Greater Khingan Mountains (GKM) in China in 1987 and the forest fire in the Yellowstone National Park (YSP) in the United States in 1988. We first used Landsat-5 TM images before and after the fire to extract the burned area and calculate burn severity based on the Differential Normalized Burn Ratio (dNBR). Next, we analysed the response of forest vegetation to forest fire with different burn severity using the anomaly value of Leaf area index (LAI) derived from Global Land Surface Satellite (GLASS) products. And the recovery of forest vegetation after forest fire were revealed using time – series LAI data and MODIS Land cover data. The results showed that the LAI decreased rapidly after the forest fire, and the greater the burn severity, the higher the decreasing amplitude of LAI. The maximum decreasing amplitude of LAI in the burned areas with high burn severity were 1.3–3.8 times higher than that in low burn severity areas. The recovery time of LAI is affected by burn severity and manual interference. The recovery time of LAI in burned areas in the GKM is about 5–10 years, which in the burned areas with high burn severity is 2 times than that with low burn severity. The recovery time of LAI in the burned areas with low burn severity in the YSP is at least 20 years, while that with high burn severity will take longer time to recovery. And the manual interference accelerated the recovery of LAI in the GKM. Our research on the response and recovery of vegetation is helpful for formulating and implementing adaptation and mitigation strategies in response to forest fire.

scholarly journals Automated mapping of burned areas in semi-arid ecosystems using modis time-series imagery

2015 ◽  
Vol XL-7/W3 ◽  
pp. 811-814 ◽  
Author(s):  
L. A. Hardtke ◽  
P. D. Blanco ◽  
H. F. del Valle ◽  
G. I. Metternicht ◽  
W. F. Sione
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Regional Scale ◽  
Arid Ecosystems ◽  
Burned Area ◽  
Second Phase ◽  
Commission Error ◽  
Spatial And Temporal Patterns ◽  
Burned Areas ◽  
Semi Arid

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R<sup>2</sup> = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R<sup>2</sup> = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

scholarly journals Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3289
Author(s):  
Xiaohe Yu ◽  
David J. Lary
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
The United States ◽  
Classification Model ◽  
Omission Error ◽  
Validation Dataset ◽  
Commission Error ◽  
Learning Models ◽  
Cloud Classification ◽  
Machine Learning Models

Remote sensing imagery, such as that provided by the United States Geological Survey (USGS) Landsat satellites, has been widely used to study environmental protection, hazard analysis, and urban planning for decades. Clouds are a constant challenge for such imagery and, if not handled correctly, can cause a variety of issues for a wide range of remote sensing analyses. Typically, cloud mask algorithms use the entire image; in this study we present an ensemble of different pixel-based approaches to cloud pixel modeling. Based on four training subsets with a selection of different input features, 12 machine learning models were created. We evaluated these models using the cropped LC8-Biome cloud validation dataset. As a comparison, Fmask was also applied to the cropped scene Biome dataset. One goal of this research is to explore a machine learning modeling approach that uses as small a training data sample as possible but still provides an accurate model. Overall, the model trained on the sample subset (1.3% of the total training samples) that includes unsupervised Self-Organizing Map classification results as an input feature has the best performance. The approach achieves 98.57% overall accuracy, 1.18% cloud omission error, and 0.93% cloud commission error on the 88 cropped test images. By comparison to Fmask 4.0, this model improves the accuracy by 10.12% and reduces the cloud omission error by 6.39%. Furthermore, using an additional eight independent validation images that were not sampled in model training, the model trained on the second largest subset with an additional five features has the highest overall accuracy at 86.35%, with 12.48% cloud omission error and 7.96% cloud commission error. This model’s overall correctness increased by 3.26%, and the cloud omission error decreased by 1.28% compared to Fmask 4.0. The machine learning cloud classification models discussed in this paper could achieve very good performance utilizing only a small portion of the total training pixels available. We showed that a pixel-based cloud classification model, and that as each scene obviously has unique spectral characteristics, and having a small portion of example pixels from each of the sub-regions in a scene can improve the model accuracy significantly.

scholarly journals A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

2021 ◽  
Vol 10 (8) ◽  
pp. 546
Author(s):  
Daniela Stroppiana ◽  
Gloria Bordogna ◽  
Matteo Sali ◽  
Mirco Boschetti ◽  
Giovanna Sona ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Region Growing ◽  
Influential Factors ◽  
Weighted Averaging ◽  
Fire Season ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Fully Automatic

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa; the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context; estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046.

Magic and Politics

2020 ◽  
Vol 23 (4) ◽  
pp. 5-14
Author(s):  
Sabina Magliocco
Keyword(s):  
United States ◽  
North America ◽  
Human Behavior ◽  
Presidential Election ◽  
The United States ◽  
Right Wing ◽  
Special Issue ◽  
Far Right ◽  
Cultural Expression ◽  
Spirit World

This essay introduces a special issue of Nova Religio on magic and politics in the United States in the aftermath of the 2016 presidential election. The articles in this issue address a gap in the literature examining intersections of religion, magic, and politics in contemporary North America. They approach political magic as an essentially religious phenomenon, in that it deals with the spirit world and attempts to motivate human behavior through the use of symbols. Covering a range of practices from the far right to the far left, the articles argue against prevailing scholarly treatments of the use of esoteric technologies as a predominantly right-wing phenomenon, showing how they have also been operationalized by the left in recent history. They showcase the creativity of magic as a form of human cultural expression, and demonstrate how magic coexists with rationality in contemporary western settings.

Pentecostalism and African diaspora : a case study of the Redeemed Christian Church of God (RCCG), in North America

2020 ◽  
Vol 1 (2) ◽  
pp. 77-92
Author(s):  
Rotimi Williams Omotoye
Keyword(s):  
United States ◽  
North America ◽  
United States Of America ◽  
The United States ◽  
Church Of God ◽  
Sources Of Information ◽  
Christian Church ◽  
Secondary Sources ◽  
The Impact ◽  
The Church

Pentecostalism as a new wave of Christianity became more pronounced in 1970's and beyond in Nigeria. Since then scholars of Religion, History, Sociology and Political Science have shown keen interest in the study of the Churches known as Pentecostals because of the impact they have made on the society. The Redeemed Christian Church of God (RCCG) was established by Pastor Josiah Akindayomi in Lagos,Nigeria in 1952. After his demise, he was succeeded by Pastor Adeboye Adejare Enock. The problem of study of this research was an examination of the expansion of the Redeemed Christian Church of God to North America, Caribbean and Canada. The missionary activities of the church could be regarded as a reversed mission in the propagation of Christianity by Africans in the Diaspora. The methodology adopted was historical. The primary and secondary sources of information were also germane in the research. The findings of the research indicated that the Redeemed Christian Church of God was founded in North America by Immigrants from Nigeria. Pastor Adeboye Enock Adejare had much influence on the Church within and outside the country because of his charisma. The Church has become a place of refuge for many immigrants. They are also contributing to the economy of the United States of America. However, the members of the Church were faced with some challenges, such as security scrutiny by the security agencies. In conclusion, the RCCGNA was a denomination that had been accepted and embraced by Nigerians and African immigrants in the United States of America.

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