Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries.

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Comportamiento histórico de los incendios forestales en la provincia de Pinar Del Río, Cuba

FLORESTA ◽

10.5380/rf.v28i12.2304 ◽

1998 ◽

Vol 28 (12) ◽

Author(s):

MARCOS PEDRO RAMOS RODRÍGUEZ ◽

RONALDO VIANA SOARES

Keyword(s):

Data Base ◽

Forest Fires ◽

Vegetation Type ◽

Integrated System ◽

Burned Area ◽

Prevention Measures ◽

Planted Forests ◽

Burned Areas ◽

Del Rio ◽

Pinus Spp

El conocimiento del comportamiento histórico de los incendios forestales ocurridos en un territorio es de gran utilidad para la planificación eficiente de las medidas de prevención apropiadas para cada territorio, pues permite establecer la tendencia de ocurrencias de los incendios y de sus afectaciones, los períodos del día y del año con mayor riesgo de surgimiento y propagación, las causas del surgimiento, los tipos de especies y los tipos de bosques de acuerdo a su origen, más afectados, a la vez que es posible analizar la efectividad del servicio de protección contra los incendios. El presente trabajo tiene la finalidad de analizar el comportamiento histórico de los incendios forestales en la provincia de Pinar del Río durante el período de 1975 a 1996 con el fin de contribuir al aumento de la efectividad de la prevención contra estos fenómenos, para lo que se utilizó la Base de Datos sobre Incendios Forestales de la Provincia de Pinar del Río a la que se accedió con el Sistema Integrado para el Manejo de Base de Datos sobre Incendios Forestales (SIMBDIF). Entre otros resultados se pueden mencionar que en los bosques de Pinus spp, Eucalyptus spp y Casuarina spp ocurrió el 93,28 % de los incendios y a ellos correspondió el 94,44 % de las afectaciones. La mayor causa de surgimiento fue el rayo, con un porcentaje del 47,46 %. El 83,69 % de los incendios y el 92,41 % de las afectaciones se presentaron de Marzo a Agosto, ocurriendo desde las 13:00 y hasta las 16:00 horas el 69,43 % de los incendios. Se obtuvo diferencia significativa tanto para las medias del número de incendios ocurridos en bosques naturales y plantados como para las medias del número de hectáreas afectadas en los mismos. History of forest fires in Pinar Del Río province, Cuba Abstract The knowledge of the forest fires historical behavior in a territory is of great importance for the efficient planning of the appropriate prevention measures, since it permits to establish the occurrence trend of the fires and the burned areas, the periods of time (days and months) with greater occurrence an propagation risks, the cause of the fires, and the amount of fires and respective burned areas for each vegetation type. This paper relates the historical behavior of the forest fires in the Pinar del Río province from 1975 to 1996 in order to contribute for the increase of efficiency in forest fire prevention. The data base of forest fires in Pinar del Río, that is part of the Integrated System of the Data Base Managing on Forest Fires (SIMBDIF), was used. Plantations of Pinus spp, Eucalyptus spp, and Casuarina spp registered 93.28% of the fires and 94.44% of the burned areas. The main cause of the fires was lightning, with 47.46% of the occurrences. About 83.69% of the fires and 92.41% of the burned area were recorded from March to August, and 69.43% of the occurrences were registered between 1:30 and 4:00 PM. Significant statistical difference was detected for the number of fires and burned areas between natural and planted forests.

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

Fire ◽

10.3390/fire4030034 ◽

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.

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Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain

Forests ◽

10.3390/f10060518 ◽

2019 ◽

Vol 10 (6) ◽

pp. 518 ◽

Cited By ~ 4

Author(s):

Natalia Quintero ◽

Olga Viedma ◽

Itziar R. Urbieta ◽

José M. Moreno

Keyword(s):

Time Series ◽

Forest Fires ◽

Fire Hazard ◽

Sustainable Use ◽

Google Earth ◽

Burned Area ◽

Change Metrics ◽

Google Earth Engine ◽

The Stability

Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard.

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MAPPING OF BURNED AREA USING PRESENCE AND BACKGROUND LEARNING FRAMEWORK ON THE GOOGLE EARTH ENGINE PLATFORM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w8-37-2019 ◽

2019 ◽

Vol XLII-3/W8 ◽

pp. 37-41

Author(s):

D. Attaf ◽

K. Djerriri ◽

D. Mansour ◽

D. Hamdadou

Keyword(s):

Remote Sensing ◽

Classification Problem ◽

Google Earth ◽

The Other ◽

Burned Area ◽

Support Vector ◽

Classification Techniques ◽

Burned Areas ◽

Google Earth Engine ◽

One Class Classification

Abstract. Mapping of burned areas caused by forest fires was always a main concern to researchers in the field of remote sensing. Thus, various spectral indices and classification techniques have been proposed in the literature. In such a problem, only one specific class is of real interest and could be referred to as a one-class classification problem. One-class classification methods are highly desirable for quick mapping of classes of interest. A common used solution to deal with One-Class classification problem is based on oneclass support vector machine (OC-SVM). This method has proved useful in classification of remote sensing images. However, overfitting problem and difficulty in tuning parameters have become the major obstacles for this method. The new Presence and Background Learning (PBL) framework does not require complicated model selection and can generate very high accuracy results. On the other hand the Google Earth Engine (GEE) portal provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. Therefore, this study mainly aims to investigate the possibility of using the PBL framework within the GEE platform to extract burned areas from freely available Landsat archive in the year 2015. The quality of the results obtained using PBL framework was assessed using ground truth digitized by qualified technicians and compared to other classification techniques: Thresholding burned area spectral Index (BAI) and OC-SVM classifiers. Experimental results demonstrate that PBL framework for mapping the burned areas shows the higher classification accuracy than the other classifiers, and it highlights the suitability for the cases with few positive labelled samples available, which facilitates the tedious work of manual digitizing.

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Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

Revista de Teledetección ◽

10.4995/raet.2018.8618 ◽

2018 ◽

pp. 61 ◽

Cited By ~ 1

Author(s):

J.A. Anaya ◽

W.F. Sione ◽

A.M. Rodriguez-Montellano

Keyword(s):

Accuracy Assessment ◽

Global Scale ◽

Google Earth ◽

Burned Area ◽

Amazon Forest ◽

Burned Areas ◽

Use Of Time ◽

Chaco Region ◽

Relative Form ◽

Normalized Burn Ratio

There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.

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Myths, misconceptions, othering and stigmatizing responses to Covid-19 in South Africa: A rapid qualitative assessment

PLoS ONE ◽

10.1371/journal.pone.0244420 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0244420

Author(s):

Tenielle Schmidt ◽

Allanise Cloete ◽

Adlai Davids ◽

Lehlogonolo Makola ◽

Nokubonga Zondi ◽

...

Keyword(s):

South Africa ◽

South African ◽

Qualitative Data ◽

Group Discussion ◽

Civil Society Organizations ◽

Qualitative Assessment ◽

Western Cape ◽

Kwazulu Natal ◽

High Level ◽

Study Participants

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a new strain of virus in the Coronavirus family that has not been previously identified. Since SARS-CoV-2 is a new virus, everyone is at risk of catching the Coronavirus disease 2019 (Covid-19). No one has immunity to the virus. Despite this, misconceptions about specific groups of people who are immune to Covid-19 emerged with the onset of the pandemic. This paper explores South African communities’ misconceptions about who is most vulnerable to Covid-19. A rapid qualitative assessment was conducted remotely in Gauteng, KwaZulu-Natal and the Western Cape provinces of South Africa. Recruitment of study participants took place through established relationships with civil society organizations and contacts made by researchers. In total, 60 key informant interviews and one focus group discussion was conducted. Atlas.ti.8 Windows was used to facilitate qualitative data analysis. The qualitative data was coded, and thematic analysis used to identify themes. The results show a high level of awareness and knowledge of the transmission and prevention of SARS-CoV-2. Qualitative data revealed that there is awareness of elderly people and those with immunocompromised conditions being more vulnerable to catching Covid-19. However, misconceptions of being protected against the virus or having low or no risk were also evident in the data. We found that false information circulated on social media not only instigated confusion, fear and panic, but also contributed to the construction of misconceptions, othering and stigmatizing responses to Covid-19. The study findings bring attention to the importance of developing communication materials adapted to specific communities to help reduce misconceptions, othering and stigmatization around Covid-19.

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Effects of prescribed forest burning on carabid beetles (Coleoptera: Carabidae): a case study in south-eastern Norway

Entomologica Fennica ◽

10.33338/ef.84352 ◽

2006 ◽

Vol 17 (3) ◽

Cited By ~ 5

Author(s):

Konstantin Gongalsky ◽

Fred Midtgaard ◽

Hans Overgaard

Keyword(s):

Forest Fires ◽

Prescribed Burning ◽

Abundant Species ◽

Carabid Beetles ◽

Burned Area ◽

Burned Areas ◽

Clear Cut ◽

Se Norway ◽

Eastern Norway

The influence of prescribed burning on ground beetles was studied in a single 12 ha stand that was partially clear-cut, selectively-cut and retained (= standing forest), and was compared to an unburned stand in 2002 in SE Norway. Thirty-two species were collected using Barber pitfall traps. Carabids were more numerous and more diverse in the burned area, compared to the unburned forest. Overall abundance was highest in the selectively-cut treatment, followed by the clear-cut and standing forest. Species diversity tended to increase in the sequence unburned forest – burned standing forest – burned selectively-cut – burned clearcut. Species composition differed little between the burned treatments. Pterostichus adstrictus, a species associated with open habitats and which frequently colonizes burned areas, was the most abundant species collected. It was most common in the burned area, particularly in the selectively-cut treatment. Our results suggest that burning of a single stand may support some carabid species, even endangered ones, although larger forest fires are probablymore effective for conservation purposes.

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AN UNSUPERVISED METHOD BASED ON FIRE INDEX ENHANCEMENT AND GRNN FOR AUTOMATED BURNED AREA MAPPING FROM SINGLE-PERIOD REMOTE SENSING IMAGERY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-37-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 37-42

Author(s):

Q. Zhang ◽

Y. Xiao

Keyword(s):

Remote Sensing ◽

Forest Fires ◽

Current Situation ◽

General Regression Neural Network ◽

Burned Area ◽

Area Index ◽

Remote Sensing Imagery ◽

Burned Areas ◽

Unsupervised Method ◽

Burned Area Mapping

Abstract. In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires.

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

10.5194/egusphere-egu21-8562 ◽

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

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 CO2 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.

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Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon

Remote Sensing ◽

10.3390/rs12233864 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3864

Author(s):

Ana Carolina M. Pessôa ◽

Liana O. Anderson ◽

Nathália S. Carvalho ◽

Wesley A. Campanharo ◽

Celso H. L. Silva Junior ◽

...

Keyword(s):

Land Cover ◽

Forest Fires ◽

Carbon Emission ◽

Regional Scale ◽

National Level ◽

Burned Area ◽

Land Cover Changes ◽

Burned Areas ◽

Great Divergence ◽

Global Emissions

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.

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