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

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.

scholarly journals A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

2020 ◽  
Vol 12 (5) ◽  
pp. 858 ◽  
Author(s):  
Alfonso Fernández-Manso ◽  
Carmen Quintano
Keyword(s):  
Maximum Entropy ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Hyperspectral Data ◽  
Burned Area ◽  
Burned Areas ◽  
Mediterranean Countries ◽  
Burned Area Mapping

Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.

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.

Remote sensing of burned areas in tropical savannas

2003 ◽  
Vol 12 (4) ◽  
pp. 259 ◽  
Author(s):  
José M. C. Pereira
Keyword(s):  
Remote Sensing ◽  
Canopy Cover ◽  
Stand Density ◽  
Fire Season ◽  
Burned Area ◽  
Tropical Savannas ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Major Satellite ◽  
Burned Areas

Problematic aspects of fire in tropical savannas are reviewed, from the standpoint of their impact on the detection and mapping of burned areas using remotely sensed data. Those aspects include: the heterogeneity of savanna—resulting in heterogeneity of fire-induced spectral changes; fine fuels and low fuel loadings—resulting in short persistence of the char residue signal; tropical cloudiness—which makes multitemporal image compositing important; the frequent presence of extensive smoke aerosol layers during the fire season—which may obscure fire signals; and the potential problem of detecting burns in the understory of woody savannas with widely variable tree stand density, canopy cover and leaf area index. Finally, the capabilities and limitations of major satellite remote sensing systems for pan-tropical burned area mapping are addressed, considering the spatial, spectral, temporal and radiometric characteristics of the instruments.

scholarly journals DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5138
Author(s):  
Seyd Teymoor Seydi ◽  
Mahdi Hasanlou ◽  
Jocelyn Chanussot
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Burned Area ◽  
High Resolution Data ◽  
Burned Areas ◽  
Heterogeneous Datasets ◽  
Burned Area Mapping ◽  
Net Framework ◽  
Sentinel 2

Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

scholarly journals REMOTE SENSING & GIS INTEGRATION FOR MONITORING THE AREAS AFFECTED BY FOREST FIRES: A CASE STUDY IN IZMIR, TURKEY

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 165-170 ◽  
Author(s):  
E. Çolak ◽  
A. F. Sunar
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Landsat 8 ◽  
Area Index ◽  
Burned Areas ◽  
Digital Elevation ◽  
Elevation Model ◽  
Normalized Burn Ratio ◽  
Sentinel 2

<p><strong>Abstract.</strong> A forest fire is stated as an ecological disaster whether it is man-made or caused naturally. İzmir is one of the regions where forest fires are most intensified in Turkey. The study area located at Aegean region of Turkey suffered two forest fires in 2017; Menderes and Bayındır areas. This study presents the integration of remote sensing (Sentinel 2 and Landsat 8 satellite images) and GIS data to map and evaluate the forest burned areas due to both forest fires. For this purpose, different indexes such as Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) Burn Index are applied besides different classification algorithms. The results showed that different vegetation types/zones are being affected. Sentinel 2 and Landsat 8 data are integrated to the GIS established with fieldwork data to analyse and also validate the results. Digital Elevation Model (DEM) data produced from ASTER satellite is also overlaid to the outcomes to emphasize the destructed forest areas. The efficiency of using two different satellites are outlined by comparing the accuracy of forest fire maps produced.</p>

scholarly journals Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

2021 ◽  
Vol 13 (1) ◽  
pp. 432
Author(s):  
Aru Han ◽  
Song Qing ◽  
Yongbin Bao ◽  
Li Na ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Change ◽  
Fire Severity ◽  
Dynamic Change ◽  
Burned Area ◽  
Fire Event ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

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 Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

2019 ◽  
Vol 11 (22) ◽  
pp. 2695
Author(s):  
Peng Wang ◽  
Lei Zhang ◽  
Gong Zhang ◽  
Benzhou Jin ◽  
Henry Leung
Keyword(s):  
Objective Function ◽  
Multispectral Imaging ◽  
Accurate Determination ◽  
Super Resolution ◽  
Burned Area ◽  
Landsat 8 ◽  
Random Walker ◽  
Burned Areas ◽  
Burned Area Mapping ◽  
Super Resolution Mapping

Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

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