Evaluation of spectral indices for estimating burn severity in semiarid grasslands

2016 ◽  
Vol 25 (2) ◽  
pp. 147 ◽  
Author(s):  
Bing Lu ◽  
Yuhong He ◽  
Alexander Tong
Keyword(s):  
Native Species ◽  
Landsat Imagery ◽  
Recovery Process ◽  
Fire Disturbance ◽  
Burn Severity ◽  
Vegetation Recovery ◽  
Grass Species ◽  
Burned Area ◽  
Spectral Indices ◽  
Landsat Images

Using Landsat imagery, this study was conducted to evaluate a fire disturbance that occurred in Canada’s Grasslands National Park on 27 April 2013. We used spectral indices (e.g. Normalised Burn Ratio (NBR) and Mid-infrared Burn Index (MIRBI)) derived from Landsat images to evaluate burn severity and to analyse the vegetation recovery process. A field survey was conducted to assess burn severity, which we used to evaluate the performance of spectral indices. Responses of the vegetation community to the fire disturbance were also investigated during the field campaign. Results show that the selected spectral indices performed differently for evaluating burn severity, but MIRBI performed best, likely due to its ability to discriminate post-fire residuals. Severely burned areas were distributed along a river where a larger amount of senesced biomass had accumulated before the fire. The semiarid grasslands showed a strong resilience to fire disturbance, and vegetation recovery was likely influenced by burn severity and water availability. Different vegetation types (e.g. grass, trees and shrubs) had distinct recovery rates and, thus, fire influences plant community development. The fire disturbance changed the composition of grass species in the burned area and also promoted invasion by non-native species.

scholarly journals Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 43 ◽  
Author(s):  
Sathishkumar Samiappan ◽  
Lee Hathcock ◽  
Gray Turnage ◽  
Cary McCraine ◽  
Jonathan Pitchford ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cost Effective ◽  
Recovery Process ◽  
Turnaround Time ◽  
Coastal Marsh ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Surface Model ◽  
Unmanned Aerial System

Wildfires can be beneficial for native vegetation. However, wildfires can impact property values, human safety, and ecosystem function. Resource managers require safe, easy to use, timely, and cost-effective methods for quantifying wildfire damage and regeneration. In this work, we demonstrate an approach using an unmanned aerial system (UAS) equipped with a MicaSense RedEdge multispectral sensor to classify and estimate wildfire damage in a coastal marsh. We collected approximately 7.2 km2 of five-band multispectral imagery after a wildfire event in February 2016, which was used to create a photogrammetry-based digital surface model (DSM) and orthomosaic for object-based classification analysis. Airborne light detection and ranging data were used to validate the accuracy of the DSM. Four-band airborne imagery from pre- and post-fire were used to estimate pre-fire health, post-fire damage, and track the vegetation recovery process. Immediate and long-term post-fire classifications, area, and volume of burned regions were produced to track the revegetation progress. The UAS-based classification produced from normalized difference vegetation index and DSM was compared to the Landsat-based Burned Area Reflectance Classification. Experimental results show the potential of using UAS and the presented approach compared to satellite-based mapping in terms of classification accuracies, turnaround time, and spatial and temporal resolutions.

scholarly journals Generating annual estimates of forest fire disturbance in Canada: the National Burned Area Composite

2020 ◽  
Vol 29 (10) ◽  
pp. 878 ◽  
Author(s):  
R. J. Hall ◽  
R. S. Skakun ◽  
J. M. Metsaranta ◽  
R. Landry ◽  
R.H. Fraser ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Fire Disturbance ◽  
Automated System ◽  
Primary Data ◽  
Burned Area ◽  
Average Accuracy ◽  
Mapping System

Determining burned area in Canada across fire management agencies is challenging because of different mapping scales and methods. The inconsistent removal of unburned islands and water features from within burned polygon perimeters further complicates the problem. To improve the determination of burned area, the Canada Centre for Mapping and Earth Observation and the Canadian Forest Service developed the National Burned Area Composite (NBAC). The primary data sources for this tool are an automated system to derive fire polygons from 30-m Landsat imagery (Multi-Acquisition Fire Mapping System) and high-quality agency polygons delineated from imagery with spatial resolution ≤30m. For fires not mapped by these sources, the Hotspot and Normalized Difference Vegetation Index Differencing Synergy method was used with 250–1000-m satellite data. From 2004 to 2016, the National Burned Area Composite reported an average of 2.26 Mha burned annually, with considerable interannual variability. Independent assessment of Multi-Acquisition Fire Mapping System polygons achieved an average accuracy of 96% relative to burned-area data with high spatial resolution. Confidence intervals for national area burned statistics averaged±4.3%, suggesting that NBAC contributes relatively little uncertainty to current estimates of the carbon balance of Canada’s forests.

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 Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

2018 ◽  
Vol 10 (4) ◽  
pp. 2015-2031 ◽  
Author(s):  
Emilio Chuvieco ◽  
Joshua Lizundia-Loiola ◽  
Maria Lucrecia Pettinari ◽  
Ruben Ramo ◽  
Marc Padilla ◽  
...  
Keyword(s):  
Near Infrared ◽  
Thermal Anomaly ◽  
Detection Algorithm ◽  
Fire Disturbance ◽  
Spatial Distance ◽  
Burned Area ◽  
Detection Accuracy ◽  
Landsat Images ◽  
Full Resolution ◽  
Omission Errors

Abstract. This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001–2016) of the Terra-MODIS archive. The BA detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one targeting to reduce omission errors by applying contextual analysis around the seed pixels. This product was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA files: monthly full-resolution continental tiles and biweekly global grid files at a degraded resolution of 0.25∘. Each set of products includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and atmospheric emission models. Average annual burned area from this product was 3.81 Mkm2, with maximum burning in 2011 (4.1 Mkm2) and minimum in 2013 (3.24 Mkm2). The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The validation indicates an overall accuracy of 0.9972, with much higher errors for the burned than the unburned category (global omission error of BA was estimated as 0.7090 and global commission as 0.5123). These error values are similar to other global BA products, but slightly higher than the NASA BA product (named MCD64A1, which is produced at 500 m resolution). However, commission and omission errors are better compensated in our product, with a tendency towards BA underestimation (relative bias −0.4033), as most existing global BA products. To understand the value of this product in detecting small fire patches (<100 ha), an additional validation sample of 52 Sentinel-2 scenes was generated specifically over Africa. Analysis of these results indicates a better detection accuracy of this product for small fire patches (<100 ha) than the equivalent 500 m MCD64A1 product, although both have high errors for these small fires. Examples of potential applications of this dataset to fire modelling based on burned patches analysis are included in this paper. The datasets are freely downloadable from the Fire_cci website (https://www.esa-fire-cci.org/, last access: 10 November 2018) and their repositories (pixel at full resolution: https://doi.org/cpk7, and grid: https://doi.org/gcx9gf).

scholarly journals Utilizing Multi-Sensor Fire Detections to Map Fires in the United States

2014 ◽  
Vol XL-1 ◽  
pp. 161-166 ◽  
Author(s):  
S. M. Howard ◽  
J. J. Picotte ◽  
M. J. Coan
Keyword(s):  
United States ◽  
Landsat Imagery ◽  
Local Level ◽  
The United States ◽  
Burn Severity ◽  
Burned Area ◽  
Fire Occurrence ◽  
Cooperative Effort ◽  
The Us ◽  
Occurrence Records

In 2006, the Monitoring Trends in Burn Severity (MTBS) project began a cooperative effort between the US Forest Service (USFS) and the U.S.Geological Survey (USGS) to map and assess burn severity all large fires that have occurred in the United States since 1984. Using Landsat imagery, MTBS is mandated to map wildfire and prescribed fire that meet specific size criteria: greater than 1000 acres in the west and 500 acres in the east, regardless of ownership. Relying mostly on federal and state fire occurrence records, over 15,300 individual fires have been mapped. While mapping recorded fires, an additional 2,700 "unknown" or undocumented fires were discovered and assessed. It has become apparent that there are perhaps thousands of undocumented fires in the US that are yet to be mapped. Fire occurrence records alone are inadequate if MTBS is to provide a comprehensive accounting of fire across the US. Additionally, the sheer number of fires to assess has overwhelmed current manual procedures. To address these problems, the National Aeronautics and Space Administration (NASA) Applied Sciences Program is helping to fund the efforts of the USGS and its MTBS partners (USFS, National Park Service) to develop, and implement a system to automatically identify fires using satellite data. In near real time, USGS will combine active fire satellite detections from MODIS, AVHRR and GOES satellites with Landsat acquisitions. Newly acquired Landsat imagery will be routinely scanned to identify freshly burned area pixels, derive an initial perimeter and tag the burned area with the satellite date and time of detection. Landsat imagery from the early archive will be scanned to identify undocumented fires. Additional automated fire assessment processes will be developed. The USGS will develop these processes using open source software packages in order to provide freely available tools to local land managers providing them with the capability to assess fires at the local level.

scholarly journals Post-fire vegetation recovery in Portugal based on spot/vegetation data

2010 ◽  
Vol 10 (4) ◽  
pp. 673-684 ◽  
Author(s):  
C. Gouveia ◽  
C. C. DaCamara ◽  
R. M. Trigo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Surface ◽  
Recovery Process ◽  
Vegetation Recovery ◽  
Fire Season ◽  
Burned Area ◽  
Vegetation Density ◽  
Fire Event ◽  
Central Portugal

Abstract. A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI), with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation indices.

scholarly journals Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

2018 ◽  
Author(s):  
Emilio Chuvieco ◽  
Joshua Lizundia-Loiola ◽  
M. Lucrecia Pettinari ◽  
Ruben Ramo ◽  
Marc Padilla ◽  
...  
Keyword(s):  
Near Infrared ◽  
Error Rates ◽  
Fire Disturbance ◽  
Spatial Distance ◽  
Burned Area ◽  
Landsat Images ◽  
Thermal Anomalies ◽  
Global Dynamic ◽  
Fire Modelling ◽  
Area Product

Abstract. This paper presents a new global burned area (BA) product, generated from the MODIS red (R) and near infrared (NIR) reflectances and thermal anomalies data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001–2016) of the MODIS archive. The BA detection 20 algorithm was based on temporal composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one aiming to reduce omission errors by applying contextual analysis around the seed pixels. The product was developed within the European Space Agency's (ESA) Climate Change Initiative programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA products: monthly full-resolution continental tiles (http://doi.org/cpk7) and biweekly global grid files at a degraded resolution of 0.25 degrees (http://doi.org/gcx9gf). Each one includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and emission models. The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The estimated commission and omission error rates of the pixel product was 0.512 (0.020) and 0.708 (0.030), respectively, lower 30 than previous ESA products but higher than the latest NASA MCD64A1 BA dataset. Examples of potential applications of this product to fire modelling based on burned patches analysis are included in this paper. They show greater sensitivity of our product to small burn patch detection than existing BA products.

scholarly journals Discovering Vegetation Recovery and Landslide Activities in the Wenchuan Earthquake Area with Landsat Imagery

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5243
Author(s):  
Cheng Zhong ◽  
Chang Li ◽  
Peng Gao ◽  
Hui Li
Keyword(s):  
Wenchuan Earthquake ◽  
Landsat Imagery ◽  
Vegetation Recovery ◽  
Landsat Images ◽  
Ecosystem Recovery ◽  
Landslide Stability ◽  
The Wenchuan Earthquake ◽  
Mixed Pixels ◽  
Seismic Landslide ◽  
Earthquake Area

Post-seismic vegetation recovery is critical to local ecosystem recovery and slope stability, especially in the Wenchuan earthquake area where tens of thousands of landslides were triggered. This study executed a decadal monitoring of post-seismic landslide activities all over the region by investigating landslide vegetation recovery rate (VRR) with Landsat images and a (nearly) complete landslide inventory. Thirty thousand landslides that were larger than nine pixels were chosen for VRR analysis, to reduce the influence of mixed pixels and support detailed investigation within landslides. The study indicates that about 60% of landslide vegetation gets close to the pre-earthquake level in ten years and is expected to recover to the pre-earthquake level within 20 years. The vegetation recovery is significantly influenced by topographic factors, especially elevation and slope, while it is barely related to the distance to epicenter, fault ruptures, and rivers. This study checked and improved the knowledge of vegetation recovery and landslide stability in the area, based on a detailed investigation.

Time series of high-resolution images enhances efforts to monitor post-fire condition and recovery, Waldo Canyon fire, Colorado, USA

2018 ◽  
Vol 27 (10) ◽  
pp. 699 ◽  
Author(s):  
Melanie K. Vanderhoof ◽  
Clifton Burt ◽  
Todd J. Hawbaker
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Vegetation Index ◽  
Burn Severity ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Object Based ◽  
Fire Condition ◽  
Normalised Difference Vegetation Index ◽  
High Resolution Images

Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011–2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, Worldview-3 and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery, resprouting vigorously within 1 year, whereas 4 years post-fire, areas previously dominated by conifers were divided approximately equally between being classified as dominated by quaking aspen saplings with herbaceous species in the understorey or minimally recovered. Relative to using a pixel-based Normalised Difference Vegetation Index (NDVI), our object-based approach showed higher rates of revegetation. High-resolution imagery can provide an effective means to monitor post-fire site conditions and complement more prevalent efforts with moderate- and coarse-resolution sensors.

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