A new algorithm for mapping burned areas in Colombia (original) (raw)
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Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term per- spective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l’Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km 2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Addi- tionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R 2 = 0.01–0.28, while our algorithm performed showed a stronger correlation coefficient (R 2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.
A Landsat-TM/OLI algorithm for burned areas in the Brazilian Cerrado: preliminary results
Advances in forest fire research, 2014
Accurate burned area information is required and of particular interest for the scientific communities dealing with land use and climate changes. Currently, due to the very broad spatial extent and the limited accessibility of some of the largest regions affected by fire, instruments on-board satellites provide the only available operational systems capable to collect cost-effective burned area data. This paper presents the initial results of an algorithm for automatic extraction of burned area scars using Landsat TM and OLI imagery in the Cerrado (savannah) biome of Brazil. Development and validation tests were conducted for the "Jalapão" region, which has been intensely affected by fire in the last years; during the 2010 dry season, it accounted for 60% of all active fire pixels detected in the Cerrado. A series of Landsat TM and /OLI 52 scenes (path/row: 221/067) covering the period of 2000-2013 was used. Input images were accepted only with cloud cover up to 10%, and the maximum period of time between consecutive scenes was up to 1 month. Composite images with differences in NDVI (dNDVI) and NBRL (dNBRL) of consecutive scenes were used to identify fire scars. The algorithm computes and filters the rate of change in dNDVI and dNBRL indexes, relative to the pre-fire condition. The value of the dNBRL change is then used in the calculation of the burned area mask. Results of the automatic extraction were evaluated against maps of burned scar produced by visual photo interpretation of the composite images for the reference period of 2004-2010. Omission and commission errors were obtained, and the reliability of the algorithm and the burned area match levels were calculated for the image series. Omission Errors ranged from 4.8% to 21.0%, and Commission Errors from 2.3% to 24.1%. Reliability of the Algorithm and Burned Area Match varied from 75.8 % to 97.2%, and from 79 to 95.2%, respectively. These values are comparable to the best reported in the literature for other regions. Commission errors were associated mainly to clouds and their shadows in the images; agricultural practices were another source of error. Detailed error analysis and results are included in the text. The algorithm developed is currently being implemented for operational and automatic generation of VII International Conference on Forest Fire Research D. X. Viegas (Ed.), 2014 2 burned scar maps at a regional scale, particularly for conservation areas.
GLOBAL BURNED-LAND ESTIMATION IN LATIN AMERICA USING MODIS COMPOSITE DATA
Ecological Applications, 2008
This paper presents results of the AQL2004 project, which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLatif). The project intended to obtain monthly burned-land maps of the entire region, from Mexico to Patagonia, using MODIS (moderate-resolution imaging spectroradiometer) reflectance data. The project has been organized in three different phases: acquisition and preprocessing of satellite data; discrimination of burned pixels; and validation of results. In the first phase, input data consisting of 32-day composites of MODIS 500-m reflectance data generated by the Global Land Cover Facility (GLCF) of the University of Maryland (College Park, Maryland, USA) were collected and processed. The discrimination of burned areas was addressed in two steps: searching for ''burned core'' pixels using postfire spectral indices and multitemporal change detection and mapping of burned scars using contextual techniques. The validation phase was based on visual analysis of Landsat and CBERS (China-Brazil Earth Resources Satellite) images. Validation of the burned-land category showed an agreement ranging from 30% to 60%, depending on the ecosystem and vegetation species present. The total burned area for the entire year was estimated to be 153 215 km 2 . The most affected countries in relation to their territory were Cuba, Colombia, Bolivia, and Venezuela. Burned areas were found in most land covers; herbaceous vegetation (savannas and grasslands) presented the highest proportions of burned area, while perennial forest had the lowest proportions. The importance of croplands in the total burned area should be taken with reserve, since this cover presented the highest commission errors. The importance of generating systematic products of burned land areas for different ecological processes is emphasized.
An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 µm MODIS Imagery
Remote Sensing, 2015
The Brazilian Cerrado is significantly affected by anthropic fires every year, which makes the region an important source of pyrogenic emissions. This study aims at generating improved 1 km monthly burned area maps for Cerrado based on remote-sensed information. The algorithm relies on a burn-sensitive vegetation index based on MODIS daily values of near and middle infrared reflectance and makes use of active fire detection from multiple sensors. Validation is performed using reference burned area (BA) maps derived from Landsat imagery. Results are also compared with MODIS standard BA products. A monthly BA database for the Brazilian Cerrado is generated covering the period 2005-2014. Estimated value of BA is 1.3 times larger than the value derived from reference data, making the product suitable for applications in fire emission studies and ecosystem management. As expected the intra and inter-annual variability of estimated BA over the Brazilian Cerrado is in agreement with the regime of precipitation. This work represents the first step towards setting up a regional database of BA for Brazil to be developed in the
Remote Sensing of Environment, 2011
Maps of burned area have been obtained from an automatic algorithm applied to a multitemporal series of Landsat TM/ETM+ images in two Mediterranean sites. The proposed algorithm is based on two phases: the first one intends to detect the more severely burned areas and minimize commission errors. The second phase improves burned patches delimitation using a hybrid contextual algorithm based on logistic regression analysis, and tries to minimize omission errors. The algorithm was calibrated using six study sites and it was validated for the whole territory of Portugal (89,000 km 2 ) and for Southern California (70,000 km 2 ). In the validation exercise, 65 TM/ETM+ scenes for Portugal and 35 for California were used, all from the 2003 fire season. A good agreement with the official burned area perimeters was shown, with kappa values close to 0.85 and low omission and commission errors (b 16.5%). The proposed algorithm could be operationally used for historical mapping of burned areas from Landsat images, as well as from future medium resolution sensors, providing they acquire images in two bands of the Short Wave Infrared (1.5-2.2 μm).
Assessment of the potential of SAC-C/MMRS imagery for mapping burned areas in Spain
Remote Sensing of Environment, 2004
Early and detailed information regarding the location and extension of areas affected by forest fires is a critical issue for assessing their effects at several scales. Remote sensing is a valuable tool for burned area mapping, providing spatially explicit information on the scorched areas, even for remote regions.
Mapping Burned Areas in Latin America from Landsat-8 with Google Earth Engine
2018
A burned area product was generated from Landsat 8 OLI based three-month composites for the whole territory of Latin America using the processing power of Google earth engine. The product covers the year 2016, and it is guided by thermal anomalies recorded by the, Suomi-NPP satellite Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. The BA algorithm takes into account the spectral contrast between the fire affected areas and the unburned background, using a dedicated thresholding technique. Several spectral indices/bands were used to detect burned pixels by using the spectral signatures extracted at the hotspots. Results showed ~518K km 2 of burned areas for 2016, in comparison with the ~318K km 2 of the MODIS MCD64A1 c6 product. The comparison of the automatic method with manually obtained burned perimeters in 40 validation sites showed a very high overall accuracy. Aggregated commission and omission errors for the burned category were found below 20%. The most problematic areas were found related to the low temporal resolution of the sensor and the persistence of the cloud cover, as well as to the confusion with croplands, particularly with regards to commission errors.
Remote Sensing, 2014
The objective of this study was to analyze the spatial and temporal distribution of burned areas in Rondônia State, Brazil during the years 2000 to 2011 and evaluate the burned area maps. A Linear Spectral Mixture Model (LSMM) was applied to MODIS surface reflectance images to originate the burned areas maps, which were validated with TM/Landsat 5 and ETM+/Landsat 7 images and field data acquired in August 2013. The validation presented a correlation ranging from 67% to 96% with an average value of 86%. The lower correlation values are related to the distinct spatial resolutions of the MODIS and TM/ETM+ sensors because small burn scars are not detected in MODIS images and higher spatial correlations are related to the presence of large fires, which are better identified in MODIS, increasing the accuracy of the mapping methodology. In addition, the 12-year burned area maps of Rondônia indicate that fires, as a general pattern, occur in areas that have already been converted to some land use, such as vegetal extraction, large animal livestock areas or diversified permanent crops. Furthermore, during the analyzed period, land use conversion associated with climatic events significantly influenced the occurrence of fire in Rondônia and amplified its impacts. OPEN ACCESS Remote Sens. 2014, 6 8003
Validation of the burned area “(V,W)” Modis algorithm in Brazil
Advances in forest fire research, 2014
This work presents an automated regional algorithm that allows detecting burned areas in Brazil based on information from TERRA/AQUA MODIS data. The procedure relies on the so-called W burning index, that requires daily reflectance from the 1km MODIS Level 1B calibrated radiance from bands 2 (near infrared) and 20 (middle infrared). Burned pixels are first identified as those located in the neighbourhood of active fires and associated to values of W and temporal changes in W larger than a fixed threshold. Pixels in the neighbourhood of the previously identified ones are then tested as burned ones based on contextual tests performed on associated values of W and temporal changes in W. Validation of results was performed over Cerrado region using high resolution burned area maps derived from Landsat imagery, paying special attention to the omission and commission errors. For comparison, validation of NASA/MODIS burned area products MCD45A1 and MCD64A1 is also carried out over the same area. Results from the new algorithm present considerably lower omission error when compared to NASA/MODIS products. The two NASA products present very low commission errors (ranging from 2 to 10%) but they are affected by very high occurrence of omission errors (greater than 60% in almost all cases analysed). The new product has larger commission errors (ranging from 20 to 40%) but a large fraction of those (more than 40%) occur at the borders of the scars and may therefore not be strictly viewed as false alarms; there is also a clear reduction of the omission cases (below 40% in all cases).
Journal of Geophysical Research, 2006
This analysis concerns an estimation of burned area and fire severity levels in an area affected by a large wildfire that took place in the south of Spain in July 2004. Fire severity is defined in this work as the impact of fire on the vegetation. The objective was to find an efficient method for quick fire severity mapping based on remote sensing techniques that can be useful for postfire forest management. Several methods for image analysis (Linear Spectral Unmixing, Matched Filtering and Normalized Burn Ratio Index) were applied to postfire Landsat 5-TM, Envisat-MERIS, and Terra-MODIS images. Maps depicting fire severity of three levels of an acceptable reliability were obtained using a small amount of field data and following a simple method of processing. Linear spectral unmixing produced the best classifications for MERIS and MODIS images, while the matched filtering technique produced the most accurate classification for the TM image. These preliminary results show that short-term fire severity maps can be obtained by means of high-to medium-resolution postfire remote sensing data, in order to evaluate the situation after a forest fire and plan forest restoration works.