scholarly journals Spatio-temporal patterns of wildfires in the Niassa Reserve –Mozambique, using remote sensing data

2020 ◽  
Author(s):  
Eufrásio Nhongo ◽  
Denise Fontana ◽  
Laurindo Guasselli
Keyword(s):  
Forest Fires ◽  
Forest Cover ◽  
Fire Regime ◽  
Spatial Dynamics ◽  
Remote Sensing Data ◽  
Fire Frequency ◽  
Ecosystem Change ◽  
Burned Area ◽  
Burned Areas ◽  
Active Fire

AbstractWildfires are among the biggest factors of ecosystem change. Knowledge of fire regime (fire frequency, severity, intensity, seasonality, and distribution pattern) is an important factor in wildfire management. This paper aims to analyze the spatiotemporal patterns of fires and burned areas in the Niassa Reserve between 2002-2015 using MODIS data, active fire product (MCD14ML) and burned area product (MCD64A1). For this, the annual and monthly frequencies, the trend of fires and the frequency by types of forest cover were statistically analyzed. For the analysis of the spatial dynamics of forest fires we used the Kernel density (Fixed Method). The results show a total of 20.449 forest fires and 171.067 km2 of burned areas in the period 2002-2015. Fire incidents were highest in 2015, while the largest burned areas were recorded in 2007. The relationship between increased fires and burned areas is not linear. There was a tendency for fires to increase, while for burnt areas there was stabilization. Forest fires start in May and end in December. August-October are the most frequent period, peaking in September. Fires occur predominantly in deciduous forests and mountain forests because of the type of vegetation and the amount of dry biomass. There is a monthly spatial dynamics of wildfires from east to west in the reserve. This behavior is dependent on vegetation cover type, fuel availability, and senescence.

A view of recent forest fire in Australia by satellite derived indices

2021 ◽  
Author(s):  
Kim-Anh Nguyen ◽  
Yuei-An Liou ◽  
Le-Thu Ho
Keyword(s):  
Air Quality ◽  
Optical Sensors ◽  
Forest Fires ◽  
Remote Sensing Data ◽  
Heat Index ◽  
Burned Area ◽  
Landsat 8 ◽  
Burned Areas ◽  
High Severity ◽  
Sentinel 2

<p>Bushfire is one of the dangerous natural manmade hazards. It can cause great damges to the air quality, human health, environment and bio-diversity. In addition, forest fires may be a potential and signigicant source of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans. In early 2020, Australia experienced serious bushfires with over an area of estimated 18.6 million hectares burned, over 5,900 buidlings (including 2, 779 homes) destroyed, and at least 34 people (including three fire fighters) and billion animals and some endangered species killed. Subsequently, air quality was degraded to hazardous levels. It was estimated that about 360 million tonnes of CO<sub>2</sub> was emitted as of 2 Jan. 2020 by NASA. Remote sensing data has been instrumental for the environmental monitoring in particular the bushfire. Many methods and algorithms have been proposed to detect the burned areas in the forest. However, it is challenging or even infeasible to routinely apply them by non-experts due to a chain of sophisticated schemes during their implementation. Here, we present a simple and effective method for mapping a burned area. The performances of different optical sensors and indices are conducted. Sentinel-2 MSI and Landsat 8 data are ultilized for the comparison of burned forest by analyzing different indices (including NDVI, NDBR and newly development index Nomarlized Difference Laten Heat Index (NDLI)). The forest damages are estimated over the Katoombar, Austrialia and the burning severity map is generated and classified into eight levels (none, high regrowth, lowregrowth, unburned, low severity, moderate low severity, moderate high severity, and high severity). The comparision in results from Sentinel-2 MSI data and Landsat image is performed and presented.</p>

scholarly journals USE OF MULTISPECTRAL IMAGING IN THE EVALUATION OF BURNINGS AND FOREST FIRES IN KRAHÔLANDIA INDIGENOUS LAND (2003-2014)

FLORESTA ◽  
2015 ◽  
Vol 45 (4) ◽  
pp. 853
Author(s):  
Lawrence Nóbrega de Oliveira ◽  
Gustavo Maximiano Junqueira Lazzarini ◽  
Antonio Carlos Batista ◽  
Kaio Cesar Cardoso de Lima Fonseca Alves ◽  
Marcos Giongo
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
Multispectral Imaging ◽  
Remote Sensing Data ◽  
Indigenous Communities ◽  
Burned Area ◽  
Land Area ◽  
Burned Areas ◽  
Gis Tools ◽  
Indigenous Land

AbstractHuman actions change the natural occurrences of wildfire. The indigenous communities, during their time of occupation of the Cerrado, probably utilized fire to manipulate the landscape and its resources. In this study, we mapped and analyzed the spatial distribution of burned areas of the Kraholândia Indigenous Land, from 2003 to 2014, using Remote Sensing resources and GIS tools. During the assessed period, the total burned area extended across 1,516,873 ha, representing 4.94 times the sum of Kraholândia Indigenous Land area (306,871 ha). The average annual burned area was 126,406 ha (41.19%), with the year of the largest burned area recorded at 185,297 ha (60.4%) and the year of the smallest burned area was 71,764 ha (23.4%). There were 29,764 ha (9.7%) that had never been burned during the 12 years, and 1,693 ha (0.6%) that had been burned every year of the period. Moreover, the areas that recorded the highest frequency of fire occurrence and burnings were surprisingly not those that produced the largest burned areas over the period. The remote sensing data, allied with methodology employed, succeeded in identifying the frequency of burnings and wildfire in the Krahôlandia Indigenous Land.ResumoUtilização de imagens multispectrais na avaliação das ocorrências de queimadas e incêndios florestais na Terra Indígena Krahôlandia (2003-2014). As ações humanas alteram as ocorrências naturais dos incêndios e queimadas. Os povos indígenas, quando da ocupação do Cerrado, provavelmente usavam o fogo para manipular a paisagem e os seus recursos em várias épocas do ano. Este trabalho teve por objetivo analisar e mapear a distribuição espacial de áreas queimadas na Terra Indígena Krahôlandia, no período de 2003 a 2014, utilizando ferramentas de sensoriamento remoto e SIG. Nos 12 anos avaliados, a área queimada total foi de 1.516.872,51 ha, que representa 4,94 vezes a área total da TI Krahôlandia (306.871,02 ha). A média anual de área queimada foi de 126.406,04 ha (41,19%) com o ano da maior área queimada com 185,297 ha (60,4%) e o ano da área menor com 71,764 ha (23,4%). Houve 29.764 ha (9,7%) que nunca tinham sido queimadas durante os 12 anos, e 1.693 ha (0,6%) que tinham sido queimados todos os doze anos. Além disso, as áreas que registraram a maior frequência de ocorrência de incêndios e queimadas não foram surpreendentemente aquelas que produziram as maiores áreas queimadas ao longo do período. Os dados de sensoriamento remoto aliados com metodologia empregada conseguiu identificar a frequência de ocorrência de queimadas e incêndios florestais na terra indígena Krahôlandia.Palavras-chave: Cerrado; recorrência de fogo.

scholarly journals Multi-Sensor, Active Fire-Supervised, One-Class Burned Area Mapping in the Brazilian Savanna

2021 ◽  
Vol 13 (19) ◽  
pp. 4005
Author(s):  
Allan A. Pereira ◽  
Renata Libonati ◽  
Julia A. Rodrigues ◽  
Joana Nogueira ◽  
Filippe L. M. Santos ◽  
...  
Keyword(s):  
Fire Regime ◽  
Training Sample ◽  
Burned Area ◽  
Mapping Accuracy ◽  
Mapping Algorithm ◽  
Burned Areas ◽  
Active Fire ◽  
Landsat Satellite ◽  
Omission Errors

Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.

scholarly journals Assessing Wildfire Regimes in Indigenous Lands of the Brazilian Savannah-Like Cerrado

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 34
Author(s):  
Pedro Melo ◽  
Javier Sparacino ◽  
Daihana Argibay ◽  
Vicente Sousa Júnior ◽  
Roseli Barros ◽  
...  
Keyword(s):  
Forest Fires ◽  
Fire Regime ◽  
Management Strategies ◽  
Fire Regimes ◽  
Google Earth ◽  
Dry Season ◽  
Burned Area ◽  
Burned Areas ◽  
Brazilian Savannah ◽  
Indigenous Lands

The Brazilian savannah-like Cerrado is classified as a fire-dependent biome. Human activities have altered the fire regimes in the region, and as a result, not all fires have ecological benefits. The indigenous lands (ILs) of the Brazilian Cerrado have registered the recurrence of forest fires. Thus, the diagnosis of these events is fundamental to understanding the burning regimes and their consequences. The main objective of this paper is to evaluate the fire regimes in Cerrado’s indigenous lands from 2008 to 2017. We used the Landsat time series, at 30 m spatial resolution, available in the Google Earth Engine platform to delineate the burned areas. We used precipitation data from a meteorological station to define the rainy season (RS), early dry season (EDS), middle dry season (MDS), and late dry season (LDS) periods. During 2008–2017, our results show that the total burned area in the indigenous lands and surrounding area was 2,289,562 hectares, distributed in 14,653 scars. Most fires took place between June and November, and the annual burned area was quite different in the years studied. It was also possible to identify areas with high fire recurrence. The fire regime patterns described here are the first step towards understanding the fire regimes in the region and establishing directions to improve management strategies and guide public policies.

Characterization of Fire Regime Descriptors in Botswana Using Remotely Sensed Data

2019 ◽  
pp. 1034-1048
Author(s):  
John Isaac Molefe
Keyword(s):  
Inverse Relationship ◽  
Fire Regime ◽  
Fire Frequency ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
Fire Activity ◽  
Conditional Statements ◽  
Fire Information ◽  
Cluster Mapping

Despite its role and relevance in environmental management at all scales the use of fire has been contentious. The absence of information on fire parameters compounds the situation. This study derives fire parameter information for Botswana by analyzing MODIS fire data for (2001-2012), using conditional statements, and cluster mapping in ArcGIS. The study also related the fire information to other variables to examine how they interact with fire. The results of the study indicates that over the 12 year period the burned area has exhibited an upward trend. It has also shown that most of the fire in the country occur over the late dry season when the fires are potentially destructive. A south-north transect of fire frequency is observed, accompanied by an inverse relationship between frequency and intensity. Of all the factors, rainfall (0.638) and biomass(NDVI) (0.355) were the most significant contributors to the fire activity. The study demonstrated the utility of the MODIS fire data in characterizing the fire regime of the country and thus contribute to the policy process.

scholarly journals Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

2020 ◽  
Vol 12 (12) ◽  
pp. 2061 ◽  
Author(s):  
Carlos Ivan Briones-Herrera ◽  
Daniel José Vega-Nieva ◽  
Norma Angélica Monjarás-Vega ◽  
Jaime Briseño-Reyes ◽  
Pablito Marcelo López-Serrano ◽  
...  
Keyword(s):  
Real Time ◽  
Forest Fires ◽  
Large Scale ◽  
Infrared Imaging ◽  
Activity Patterns ◽  
Burned Area ◽  
Management Decision ◽  
Coarse Scale ◽  
Early Evaluation ◽  
Active Fire

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact.

scholarly journals A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2422
Author(s):  
Lisa Knopp ◽  
Marc Wieland ◽  
Michaela Rättich ◽  
Sandro Martinis
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Economic Consequences ◽  
Validation Dataset ◽  
Burned Area ◽  
Burned Areas ◽  
Spectral Bands ◽  
Sentinel 2

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.

Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA)

2009 ◽  
Vol 18 (4) ◽  
pp. 404 ◽  
Author(s):  
Federico González-Alonso ◽  
Silvia Merino-de-Miguel
Keyword(s):  
Forest Fires ◽  
Burned Area ◽  
Wide Field ◽  
Imaging Spectrometer ◽  
Area Index ◽  
North West ◽  
Active Fire ◽  
Moderate Resolution ◽  
Field Sensor ◽  
Speed Accuracy

The present paper presents an algorithm that synergistically combines data from four different parts of the spectrum (near-, shortwave, middle- and thermal infrared) to produce a reliable burned-area map. It is based on the use of a modified version of the BAIM (MODIS – Moderate Resolution Imaging Spectrometer – Burned Area Index) together with active fire information. The following study focusses in particular on an image from the AWiFS (Advanced Wide Field Sensor) sensor dated 21 August 2006 and MODIS active fires detected during the first 20 days of August as well as ancillary maps and information. The methodology was tested in Galicia (north-west Spain) where hundreds of forest fires occurred during the first 20 days of August 2006. Burned area data collected from the present work was compared with official fire statistics from both the Spanish Ministry of the Environment and the Galician Forestry Service. The speed, accuracy and cost-effectiveness of this method suggest that it would be of great interest for use at both regional and national levels.

scholarly journals Current and future estimates for the fire frequency and the fire rotation period in the main woodland types of peninsular Spain: a case-study approach

2015 ◽  
Vol 24 (2) ◽  
pp. e031 ◽  
Author(s):  
Antonio Vázquez ◽  
José M. Climent ◽  
Luis Casais ◽  
José R. Quintana
Keyword(s):  
Forest Fires ◽  
Fire Regime ◽  
Rotation Period ◽  
Fire Frequency ◽  
Large Variability ◽  
Rotation Periods ◽  
In Fire ◽  
Climatic Scenario ◽  
Peninsular Spain ◽  
Reference Areas

<p><em>Aim of study</em>. Fire regimes are frequently dynamic and change as a function of the interactions between the three main fire drivers: fuels, ignitions and climatic conditions. We characterized the recent period (1974-2005) and performed estimates for the future fire regime</p><p><em>Area of study</em>. We have considered five pine and another four woodland types by means of the analyses of 100 reference areas in peninsular Spain.</p><p><em>Material and methods</em>. The estimates of the expected alterations in fire frequency and the fire rotation period were based on models previously developed for the climatic scenarios SRES A2 and B2.</p><p><em>Main results</em>. The results point to the large variability in fire frequency and rotation periods between the woodland types as defined, and also among the reference areas delimited for each of them. Fire frequencies will increase for all woodland types while very relevant shortenings of the fire rotation periods are expected. For the 32 yr period analysed, rotation periods longer than 500 yr were obtained in 54% of the reference areas while this percentage would decrease to 31% in the B2 and to 29% in the A2 climatic scenario. In the most affected woodland type, <em>P. pinaster</em>, from a median rotation period of 83 yr it would decrease to 26 yr in the B2 and to 20 yr in the A2 climatic scenario.</p><p><em>Research highlights</em>. We conclude that the predicted increases in fire activity will have adverse effects on some of the main Spanish woodland types due to the expected future disruptions in the fire regime.  </p><p><strong>Keywords: </strong>Forest fires; fire regime; fire frequency; fire rotation period; climatic change.</p><p><strong>Abbreviations used: </strong>SRES: Special Report on Emissions Scenarios; IPCC: Intergovernmental Panel on Climate Change; RA: Reference Areas.</p>

scholarly journals STUDY OF THE STATE OF FORESTS OF THE ROSTOV REGION WITH THE USE OF MODERN GEOINFORMATION TECHNOLOGIES

2021 ◽  
Vol 2 (6) ◽  
pp. 100-109
Author(s):  
M.M. Streltsova ◽  
◽  
, O.E. Arkhipova ◽  
Keyword(s):  
Forest Fires ◽  
Forest Cover ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Steppe Zone ◽  
The State ◽  
Forest Change ◽  
Space Images ◽  
Rostov Region ◽  
Geoinformation Technologies

The work is devoted to the study of the forests of the Rostov region, the determination of the spatio-temporal dynamics of the area of the territory covered with forest, using remote sensing data and geoinformation systems. The relevance of the study is due to the active anthropogenic impact on forests in the steppe zone, in a region with a forest deficit cover. The purpose of the study is to study the state of forests based on the use of modern geoinformation technologies, to assess the dynamics of forest cover in the forest fund of the Rostov region. The object of research is one of the most wooded areas of the Rostov region – the Verkhnedonsky. To study the state of the forests of the Rostov region, satellite images obtained using the Sentinel-2 spacecraft and data from the Global Forest Change application were used. Earth Engine. The efficiency of application of various methods of classification of space images has been investigated. It was revealed that despite the forest fires that affect the forests of the region due to climatic and natural factors, the area of gum massifs since 2015, in accordance with the classification carried out, has increased by about 300 hectares.

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