scholarly journals Burned-Area Detection in Amazonian Environments Using Standardized Time Series Per Pixel in MODIS Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1904 ◽  
Author(s):  
Níckolas Santana ◽  
Osmar de Carvalho Júnior ◽  
Roberto Gomes ◽  
Renato Guimarães
Keyword(s):  
Time Series ◽  
Seasonal Difference ◽  
Thematic Mapper ◽  
Burned Area ◽  
Amazon Forest ◽  
Spectral Indices ◽  
Area Index ◽  
Commission Errors ◽  
Normalized Burn Ratio ◽  
Omission Errors

Fires associated with the expansion of cattle ranching and agriculture have become a problem in the Amazon biome, causing severe environmental damages. Remote sensing techniques have been widely used in fire monitoring on the extensive Amazon forest, but accurate automated fire detection needs improvements. The popular Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64 product still has high omission errors in the region. This research aimed to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso (State of Pará) and to determine their accuracy in the different types of land use/land cover during the period 2000–2014. The burned area mapping from 8-day composite products, compared the following data: near-infrared (NIR) band; spectral indices (Burnt Area Index (BAIM), Global Environmental Monitoring Index (GEMI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR), variation of Normalized Burn Ratio (NBR2), and Normalized Difference Vegetation Index (NDVI)); and the seasonal difference of spectral indices. Moreover, we compared the time series normalization methods per pixel (zero-mean normalization and Z-score) and the seasonal difference between consecutive years. Threshold-value determination for the fire occurrences was obtained from the comparison of MODIS series with visual image classification of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the overall accuracy. The best result considered the following factors: NIR band and zero-mean normalization, obtaining the overall accuracy of 98.99%, commission errors of 32.41%, and omission errors of 31.64%. The proposed method presented better results in burned area detection in the natural fields (Campinarana) with an overall accuracy value of 99.25%, commission errors of 9.71%, and omission errors of 27.60%, as well as pasture, with overall accuracy value of 99.19%, commission errors of 27.60%, and omission errors of 34.76%. Forest areas had a lower accuracy, with an overall accuracy of 98.62%, commission errors of 23.40%, and omission errors of 49.62%. The best performance of the burned area detection in the pastures is relevant because the deforested areas are responsible for more than 70% of fire events. The results of the proposed method were better than the burned area products (MCD45, MCD64, and FIRE-CCI), but still presented limitations in the identification of burn events in the savanna formations and secondary vegetation.

scholarly journals Sensitivity of Spectral Indices on Burned Area Detection using Landsat Time Series in Savannas of Southern Burkina Faso

2021 ◽  
Vol 13 (13) ◽  
pp. 2492
Author(s):  
Jinxiu Liu ◽  
Eduardo Eiji Maeda ◽  
Du Wang ◽  
Janne Heiskanen
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Reference Data ◽  
Vegetation Indices ◽  
Model Fitting ◽  
Burned Area ◽  
Spectral Indices ◽  
Harmonic Model ◽  
Area Index ◽  
Burned Area Mapping

Accurate and efficient burned area mapping and monitoring are fundamental for environmental applications. Studies using Landsat time series for burned area mapping are increasing and popular. However, the performance of burned area mapping with different spectral indices and Landsat time series has not been evaluated and compared. This study compares eleven spectral indices for burned area detection in the savanna area of southern Burkina Faso using Landsat data ranging from October 2000 to April 2016. The same reference data are adopted to assess the performance of different spectral indices. The results indicate that Burned Area Index (BAI) is the most accurate index in burned area detection using our method based on harmonic model fitting and breakpoint identification. Among those tested, fire-related indices are more accurate than vegetation indices, and Char Soil Index (CSI) performed worst. Furthermore, we evaluate whether combining several different spectral indices can improve the accuracy of burned area detection. According to the results, only minor improvements in accuracy can be attained in the studied environment, and the performance depended on the number of selected spectral indices.

Assessment of different indices from Landsat time series in burned area mapping

2020 ◽  
Author(s):  
Jinxiu Liu
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Burned Area ◽  
Free Access ◽  
Landsat Images ◽  
Area Index ◽  
Burned Area Mapping

<p>Fire is recognized as an important land surface disturbance, as it influences terrestrial carbon cycle, climate and biodiversity. Accurate and efficient mapping of burned area is beneficial for social and environmental applications. Remote sensing plays a key role in detecting burned areas and active fires from reginal to global scales. Due to the free access to the Landsat archive, studies using dense time series of Landsat imagery for burned area mapping are appearing and increasing. However, the performance of Landsat time series when using different indices for burned area mapping has not been assessed. In this study, the objective was to identify which indices can detect burned area better when using Landsat time series in savanna area of southern Burkina Faso. We selected Burned Area Index (BAI), Normalized Burned Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI) for comparison as they are commonly used indices for burned area detection. The algorithm was based on breakpoint identification and burned pixel detection using harmonic model fitting with different indices Landsat time series. It was tested in savanna area in southern Burkina Faso over 16 years with 281 Landsat images ranging from October 2000 to April 2016.The same reference data was used to evaluate the performance of burned area detection with different indices Landsat time series. The result demonstrated that BAI was the most accurate in burned area detection from Landsat time series, followed by NBR, GEMI and NDVI.</p>

scholarly journals FUZZY LOGIC BASED BURNED SEVERITY CLASSIFICATION AND MAPPING WITH LANDSAT-8 DATA

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 259-265
Author(s):  
B. Valipour Shokouhi ◽  
M. Eslami
Keyword(s):  
Fuzzy Logic ◽  
Urban Infrastructure ◽  
Training Data ◽  
Burn Severity ◽  
Burned Area ◽  
Landsat 8 ◽  
Area Index ◽  
Average Accuracy ◽  
Multi Temporal ◽  
Normalized Burn Ratio

<p><strong>Abstract.</strong> Wildfire has a strong effect on both land use and land cover so that every year, thousands of hectares of forests, farms, and urban infrastructure are destroyed. Mapping and estimation of damages are crucial for planning and decision making. The aim of this study is classification and mapping burn severity using multi-temporal Landsat data and well-known burn severity indices including Normalized Burn Ratio (NBR) and Burned Area Index (BAI) calculated for pre- and post- Landsat 8 images. Subtracted images such as dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio) and dBAI (Difference Burned Area Index) were produced on the bases of indices as classification input. Among classification methods, the fuzzy supervised classification was utilized with three classes. The result shows the strong performance of the Fuzzy Logic system (FLS) in the detection of the area with the limited number of training data so that the average accuracy of the classes is 85%; plus, from the human logic perspective, the result was meaningful so that recognition of the features and changes visually were understandable.</p>

scholarly journals Estimativa de Albedo e Índice de Área Foliar na Amazônia (Albedo Estimate and Leaf Area Index in Amazonia)

2011 ◽  
Vol 4 (1) ◽  
pp. 22 ◽  
Author(s):  
Ailton Marcolino Liberato
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Scientific Literature ◽  
Thematic Mapper ◽  
Amazon Forest ◽  
Geometric Correction ◽  
Landsat Images ◽  
Area Index ◽  
Landsat 5 Tm

Propôs-se, neste trabalho, estimar dados de albedo e Indice de Área Foliar (IAF) à superfície terrestre usando-se o sensor Thematic Mapper (TM) do satélite Landsat 5 e compará-lo com valores disponíveis na literatura científica. A região de estudo esta localizada no estado de Rondônia. Para a realização do estudo obtiveram-se quatro imagens orbitais do satélite Landsat 5 – TM, na órbita 231 e ponto 67, nas datas 13/07/2005, 13/05, 30/06 e 16/07 do ano de 2006, a que correspondem os dias Juliano 194, 133, 181 e 197, respectivamente. As correções geométricas para as imagens foram realizadas e geradas as cartas de albedo e IAF. O algoritmo SEBAL estimou satisfatoriamente os valores de albedo e IAF de superfícies sobre áreas de floresta (exceto para IAF) e pastagem.Palavras-chave: sensoriamento remoto, vegetacao, Floresta da Amazonia. Albedo Estimate and Leaf Area Index in Amazonia ABSTRACTThis study objectives the assessment of albedo and Leaf Area Index (LAI) data at surface using  images from Thematic Mapper (TM) sensor onboard Landsat 5 satellite, and  compare the results with values available in the scientific literature. The study area is located in the State of Rondônia. To carry out the study four orbital TM - Landsat images were obtained in the path 231 and row  67, for the dates of 07/13/2005, 06/30 and 07/16 of  2006 year, which correspond to the days 194, 181 and 197, respectively. The geometric correction for images was performed and maps of albedo and IAF were generated. The algorithm SEBAL estimated, satisfactorily, the values of albedo and IAF on the surface pasture and forest (except for LAI).Keywords: remote sensing, vegetation, Amazon Forest.

scholarly journals Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1862 ◽  
Author(s):  
Daniela Smiraglia ◽  
Federico Filipponi ◽  
Stefania Mandrone ◽  
Antonella Tornato ◽  
Andrea Taramelli
Keyword(s):  
Decision Making ◽  
Satellite Data ◽  
Burned Area ◽  
Decision Making Process ◽  
Large Set ◽  
Spectral Indices ◽  
Commission Errors ◽  
Burned Area Mapping ◽  
Optical Satellite Data ◽  
Sentinel 2

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

1984–2010 trends in fire burn severity and area for the conterminous US

2016 ◽  
Vol 25 (4) ◽  
pp. 413 ◽  
Author(s):  
Joshua J. Picotte ◽  
Birgit Peterson ◽  
Gretchen Meier ◽  
Stephen M. Howard
Keyword(s):  
Time Series ◽  
Vegetation Classification ◽  
Cumulative Distribution ◽  
Burn Severity ◽  
Burned Area ◽  
Detectable Change ◽  
Historical Trends ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio ◽  
In Fire

Burn severity products created by the Monitoring Trends in Burn Severity (MTBS) project were used to analyse historical trends in burn severity. Using a severity metric calculated by modelling the cumulative distribution of differenced Normalized Burn Ratio (dNBR) and Relativized dNBR (RdNBR) data, we examined burn area and burn severity of 4893 historical fires (1984–2010) distributed across the conterminous US (CONUS) and mapped by MTBS. Yearly mean burn severity values (weighted by area), maximum burn severity metric values, mean area of burn, maximum burn area and total burn area were evaluated within 27 US National Vegetation Classification macrogroups. Time series assessments of burned area and severity were performed using Mann–Kendall tests. Burned area and severity varied by vegetation classification, but most vegetation groups showed no detectable change during the 1984–2010 period. Of the 27 analysed vegetation groups, trend analysis revealed burned area increased in eight, and burn severity has increased in seven. This study suggests that burned area and severity, as measured by the severity metric based on dNBR or RdNBR, have not changed substantially for most vegetation groups evaluated within CONUS.

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

2018 ◽  
pp. 61 ◽  
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

<p>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.</p>

scholarly journals Operational implementation of the burned area component of the Copernicus Climate Change Service: from MODIS 250 m to OLCI 300 m data

2021 ◽  
Author(s):  
Joshua Lizundia-Loiola ◽  
Magí Franquesa ◽  
Martin Boettcher ◽  
Grit Kirches ◽  
M. Lucrecia Pettinari ◽  
...  
Keyword(s):  
Climate Change ◽  
Near Infrared ◽  
Burned Area ◽  
Landsat 8 ◽  
Full Time ◽  
Temporal Consistency ◽  
Climate Data ◽  
Thermal Anomalies ◽  
Commission Errors ◽  
Omission Errors

Abstract. This paper presents a new global, operational burned area (BA) product at 300 m, called C3SBA10, generated from Sentinel-3 Ocean and Land Colour Instrument (OLCI) near-infrared (NIR) reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data. This product was generated within the Copernicus Climate Change Service (C3S). Since C3S is a European service, it aims to use extensively the European Copernicus satellite missions, named Sentinels. Therefore, one of the components of the service is adapting previous developed algorithms to the Sentinel sensors. In the case of BA datasets, the precursor BA dataset (FireCCI51), which was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI), was based on the 250 m-resolution NIR band of the MODIS sensor, and the effort has been focused on adapting this BA algorithm to the characteristics of the Sentinel-3 OLCI sensor, which provides similar spatial and temporal resolution to MODIS. As the precursor BA algorithm, the OLCI's one combines thermal anomalies and spectral information in a two-phase approach, where first thermal anomalies with a high probability of being burned are selected, reducing commission errors, and then a contextual growing is applied to fully detect the BA patch, reducing omission errors. The new BA product includes the full time-series of S3 OLCI data (2017–present). Following the specifications of the FireCCI project, the final datasets are provided in two different formats: monthly full-resolution continental tiles, and monthly global files with aggregated data at 0.25-degree resolution. To facilitate the use by global vegetation dynamics and atmospheric emission models several auxiliary layers were included, such as land cover and cloud-free observations. The C3SBA10 product detected 3.77 Mkm2, 3.59 Mkm2, and 3.63 Mkm2 of annual BA from 2017 to 2019, respectively. The quality and consistency assessment of C3SBA10 and the precursor FireCCI51 was done for the common period (2017–2019). The global spatial validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 14–22 % and omission errors from 50 to 53 %, similar to those presented by the FireCCI51 product. The temporal reporting accuracy was also validated using 4.7 million active fires. 88 % of the detections were made within 10 days after the fire by both products. The spatial and temporal consistency assessment performed between C3SBA10 and FireCCI51 using four different grid sizes (0.05º, 0.10º, 0.25º, and 0.50º) showed global, annual correlations between 0.93 and 0.99. This high consistency between both products ensures a global BA data provision from 2001 to present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository (DOI: https://doi.org/10.24381/cds.f333cf85, Lizundia-Loiola et al. (2020a)).

scholarly journals Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

2021 ◽  
Vol 13 (24) ◽  
pp. 5131
Author(s):  
Jinxiu Liu ◽  
Du Wang ◽  
Eduardo Eiji Maeda ◽  
Petri K. E. Pellikka ◽  
Janne Heiskanen
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Spatial Information ◽  
Majority Vote ◽  
Burned Area ◽  
Harmonic Model ◽  
Fire Dynamics ◽  
Area Index ◽  
Burned Areas ◽  
Straw Burning

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.

scholarly journals Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018 ◽  
Vol 10 (8) ◽  
pp. 1196 ◽  
Author(s):  
Davide Fornacca ◽  
Guopeng Ren ◽  
Wen Xiao
Keyword(s):  
Time Lag ◽  
Burned Area ◽  
Time Interval ◽  
Mountainous Region ◽  
Spectral Indices ◽  
Area Index ◽  
Burn Scar ◽  
Mountainous Regions ◽  
Class Separability ◽  
Northwest Yunnan

Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

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