Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm

2011 ◽  
Vol 20 (4) ◽  
pp. 487 ◽  
Author(s):  
Tatiana V. Loboda ◽  
Elizabeth E. Hoy ◽  
Louis Giglio ◽  
Eric S. Kasischke
Keyword(s):  
Land Surface ◽  
High Performance ◽  
Wildland Fire ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
Burned Areas ◽  
Data Limitations ◽  
Burned Area Mapping ◽  
Moderate Resolution ◽  
Area Product

With the recently observed and projected trends of growing wildland fire occurrence in high northern latitudes, satellite-based burned area mapping in these regions is becoming increasingly important for scientific and fire management communities. Coarse- and moderate-resolution remotely sensed data products are the only viable source of comprehensive and timely estimates of burned area in remote, sparsely populated regions. Several MODIS (Moderate Resolution Imaging Spectroradiometer)-based burned area products for Alaska are currently available. However, our research shows that the existing burned area products underestimate the extent of the effect of fire by 15–70%. Environmental conditions limit the effective observation of land surface in Alaska to the period between May and September. These limitations are particularly noticeable in mapping late-season fires. Here we present an ecosystem-based modification to a previously developed burned area mapping approach designed to enhance the algorithm performance in Alaska. The mapping results show a consistently high performance of the adjusted algorithm in mapping burned areas in Alaska during large (2004 and 2005) and small (2006 and 2007) fire years. The adjusted burned area product maps burned areas identified by the Monitoring Trends in Burn Severity products with the overall accuracy of 90–93% and Kappa of 0.67–0.75%.

scholarly journals MODIS Reflectance and Active Fire Data for Burn Mapping in Colombia

2011 ◽  
Vol 15 (10) ◽  
pp. 1-17 ◽  
Author(s):  
Silvia Merino-de-Miguel ◽  
Federico González-Alonso ◽  
Margarita Huesca ◽  
Dolors Armenteras ◽  
Carol Franco
Keyword(s):  
Terrestrial Ecosystem ◽  
Fire Detection ◽  
Cost Effective ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
Burnt Area ◽  
Ecosystem Monitoring ◽  
Active Fire ◽  
Burned Area Mapping ◽  
Area Product

Abstract Satellite-based strategies for burned area mapping may rely on two types of remotely sensed data: postfire reflectance images and active fire detection. This study uses both methods in a synergistic way. In particular, burned area mapping is carried out using MCD43B4 [Moderate Resolution Imaging Spectrometer (MODIS); Terra + Aqua nadir bidirectional reflectance distribution function (BRDF); adjusted reflectance 16-day L3 global 1-km sinusoidal grid V005 (SIN)] postfire datasets and MODIS active fire products. The developed methodology was tested in Colombia, an area not covered by any known MODIS ground antenna, using data from 2004. The resulting burned area map was validated using a high-spatial-resolution Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image and compared to two global burned area products: L3JRC (terrestrial ecosystem monitoring global burnt area product) and MCD45A1 (MODIS Terra + Aqua burned area monthly global 500-m SIN grid V005). The results showed that this method would be of great interest at regional to national scales because it proved to be quick, accurate, and cost effective.

Accuracy and spatiotemporal distribution of fire in the Brazilian biomes from the MODIS burned-area products

2020 ◽  
Vol 29 (10) ◽  
pp. 907 ◽  
Author(s):  
Nickolas Castro Santana ◽  
Osmar Abílio de Carvalho Júnior ◽  
Roberto Arnaldo Trancoso Gomes ◽  
Renato Fontes Guimarães
Keyword(s):  
Confusion Matrix ◽  
Burned Area ◽  
Accuracy Analysis ◽  
Visual Interpretation ◽  
Burned Areas ◽  
Active Fire ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Area Product ◽  
Area Monitoring

The Moderate Resolution Imaging Spectroradiometer (MODIS) products are the most used in burned-area monitoring, on regional and global scales. This research aims to evaluate the accuracy of the MODIS burned-area and active-fire products to describe fire patterns in Brazil in the period 2001–2015. The accuracy analysis, in the year 2015, compared the MODIS products (MCD45/MCD64) and the burned areas extracted by the visual interpretation of the LANDSAT/Operational Land Imager (OLI) images from the confusion matrix. The accuracy analysis of the active-fire products (MOD14/MYD14) in the year 2015 used linear regression. We used the most accurate burned-area product (MCD64), in conjunction with environmental variables of land use and climate. The MCD45 product presented a high error of commission (>36.69%) and omission (>77.04%) for the whole country. The MCD64 product had fewer errors of omission (64.05%) compared with the MCD45 product, but increased errors of commission (45.85%). MCD64 data in 2001–2015 showed three fire domains in Brazil determined by the climatic pattern. Savanna and grassy areas in semi-humid zones are the most prone areas to fire, burning an average of 25% of their total area annually, with a fire return interval of 5–6 years.

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.

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 Global analysis of radiative forcing from fire-induced shortwave albedo change

2015 ◽  
Vol 12 (2) ◽  
pp. 557-565 ◽  
Author(s):  
G. López-Saldaña ◽  
I. Bistinas ◽  
J. M. C. Pereira
Keyword(s):  
Land Surface ◽  
Radiative Forcing ◽  
Global Analysis ◽  
Weather Prediction ◽  
Global Scale ◽  
Burned Area ◽  
Albedo Change ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Environmental Prediction ◽  
Area Product

Abstract. Land surface albedo, a key parameter to derive Earth's surface energy balance, is used in the parameterization of numerical weather prediction, climate monitoring and climate change impact assessments. Changes in albedo due to fire have not been fully investigated on a continental and global scale. The main goal of this study, therefore, is to quantify the changes in instantaneous shortwave albedo produced by biomass burning activities and their associated radiative forcing. The study relies on the MODerate-resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned-area product to create an annual composite of areas affected by fire and the MCD43C2 bidirectional reflectance distribution function (BRDF) albedo snow-free product to compute a bihemispherical reflectance time series. The approximate day of burning is used to calculate the instantaneous change in shortwave albedo. Using the corresponding National Centers for Environmental Prediction (NCEP) monthly mean downward solar radiation flux at the surface, the global radiative forcing associated with fire was computed. The analysis reveals a mean decrease in shortwave albedo of −0.014 (1σ = 0.017), causing a mean positive radiative forcing of 3.99 Wm−2 (1σ = 4.89) over the 2002–20012 time period in areas affected by fire. The greatest drop in mean shortwave albedo change occurs in 2002, which corresponds to the highest total area burned (378 Mha) observed in the same year and produces the highest mean radiative forcing (4.5 Wm−2). Africa is the main contributor in terms of burned area, but forests globally give the highest radiative forcing per unit area and thus give detectable changes in shortwave albedo. The global mean radiative forcing for the whole period studied (~0.0275 Wm−2) shows that the contribution of fires to the Earth system is not insignificant.

scholarly journals Fire Activity and Their Relationship with the Global Fire Weather Index Database Components in Guinea

2019 ◽  
Vol 8 (2) ◽  
pp. 18
Author(s):  
Mamadou Baïlo Barry ◽  
Daouda Badiane ◽  
Saïdou Moustapha Sall ◽  
Moussa Diakhaté ◽  
Habib Senghor
Keyword(s):  
Statistical Analysis ◽  
Fire Behavior ◽  
Burned Area ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Burned Areas ◽  
Active Fire ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The relationships between the Canadian Fire Weather Index (FWI) System components and the monthly burned area as well as the number of active fire which has taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua/TERRA were investigated in 32 Guinean stations between 2003 and 2013. A statistical analysis based on a multi-linear regression model was used to estimate the skills of FWI components on the predictability of burned area and active fire. This statistical analysis gave performances explaining between 16 to 79% of the variance for the burned areas and between 29 and 82% of the variance for the number of fires (P<0.0001) at lag 0. Respectively 16 to 79 % and 29 to 82 % of the variance of the burned areas and variance for the number of fires (P<0.0001) at lag0 can be explained based on the same statistical analysis. All the combinations used gave significant performances to predict the burned areas and active fire on the monthly timescale in all stations excepted Fria and Yomou where the predictability of the burned areas was not obvious. We obtained a significant correlation between the average over all of the stations of burned areas, active fires and FWI composites with percentage of variance between (75 to 84% and 29 to 77%) for active fires and burned areas at lag0 respectively. While for burned area peak (January), the skill of the predictability remains significant only one month in advance, for the active fires, the model remains skilful 1 to 3 months in advance. Results also showed that active fires are more related to fire behavior indices while the burned areas are related to the fine fuel moisture codes. These outcomes have implications for seasonal forecasting of active fire events and burned areas based on FWI components, as significant predictability is found from 1 to 3 months and one month before respectively.

scholarly journals LIVVkit 2.1: automated and extensible ice sheet model validation

2019 ◽  
Vol 12 (3) ◽  
pp. 1067-1086 ◽  
Author(s):  
Katherine J. Evans ◽  
Joseph H. Kennedy ◽  
Dan Lu ◽  
Mary M. Forrester ◽  
Stephen Price ◽  
...  
Keyword(s):  
Land Surface ◽  
High Performance ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Earth System ◽  
Remotely Sensed Data ◽  
Surface Mass ◽  
Community Earth System Model ◽  
Southwest Region ◽  
Temperature Radiation

Abstract. A collection of scientific analyses, metrics, and visualizations for robust validation of ice sheet models is presented using the Land Ice Verification and Validation toolkit (LIVVkit), version 2.1, and the LIVVkit Extensions repository (LEX), version 0.1. This software collection targets stand-alone ice sheet or coupled Earth system models, and handles datasets and analyses that require high-performance computing and storage. LIVVkit aims to enable efficient and fully reproducible workflows for postprocessing, analysis, and visualization of observational and model-derived datasets in a shareable format, whereby all data, methodologies, and output are distributed to users for evaluation. Extending from the initial LIVVkit software framework, we demonstrate Greenland ice sheet simulation validation metrics using the coupled Community Earth System Model (CESM) as well as an idealized stand-alone high-resolution Community Ice Sheet Model, version 2 (CISM2), coupled to the Albany/FELIX velocity solver (CISM-Albany or CISM-A). As one example of the capability, LIVVkit analyzes the degree to which models capture the surface mass balance (SMB) and identifies potential sources of bias, using recently available in situ and remotely sensed data as comparison. Related fields within atmosphere and land surface models, e.g., surface temperature, radiation, and cloud cover, are also diagnosed. Applied to the CESM1.0, LIVVkit identifies a positive SMB bias that is focused largely around Greenland's southwest region that is due to insufficient ablation.

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 OPTIMIZATION OF A RANDOM FOREST CLASSIFIER FOR BURNED AREA DETECTION IN CHILE USING SENTINEL-2 DATA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 337-342
Author(s):  
E. Roteta ◽  
P. Oliva
Keyword(s):  
Random Forest ◽  
Visual Assessment ◽  
Random Forest Classifier ◽  
Burned Area ◽  
National Forest ◽  
Central Chile ◽  
Burned Areas ◽  
Area Product ◽  
Sentinel 2

Abstract. Due to the high variability of biomes throughout the country, the classification of burned areas is a challenge. We calibrated a random forest classifier to account for all this variability and ensure an accurate classification of burned areas. The classifier was optimized in three steps, generating a version of the burned area product in each step. According to the visual assessment, the final version of the BA product is more accurate than the perimeters created by the Chilean National Forest Corporation, which overestimate large burned areas because it does not consider the inner unburned areas and, it omits some small burned areas. The total burned surface from January to March 2017 was 5,000 km2 in Chile, 20 % of it belonging to a single burned area in the Maule Region, and with 91 % of the total burned surface distributed in 6 adjacent regions of Central Chile.

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