AUTOMATIC BURNED AREA MAPPING SOFTWARE (ABAMS) PRELIMINARY DOCUMENTATION Version Beta 6

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1 AUTOMATIC BURNED AREA MAPPING SOFTWARE (ABAMS) PRELIMINARY DOCUMENTATION Version Beta 6 Aitor Bastarrika Izagirre Surveying Engineering Department University of Basque Country aitor.bastarrika@ehu.es

2 INDE 1. Introduction Burned area mapping with two phases Automatic Burned Area Algorithm Software (ABAMS) description Prerequisites and installation Using ABAMS software Load data to a new Database Load new data Image Catalogue Database Processing Images Execute Burned Area Mapping Algorithms Some notes about the pre-defined burned area algorithms References

3 1. Introduction Fires are one of the most disturbance factors in template ecosystems, as severe economic, ecologic and atmospheric effects are produced. It is very important to know the location of the burned areas at the end of the fire season to evaluate the damages and to plan conservation strategies to avoid deforestation and erosion processes. Remote sensing has become one of the most effective techniques to map burned lands, due to the spatial systematic coverage from the space platforms, and the ability of detecting not visible spectral spaces where burned areas are well discriminated (as Near and Short Infrared). Landsat TM and ETM+ data is one of the most used data to burned land mapping due to its good balance between spatial (30 m), spectral ( 7 bands covering visible, near, short and thermal infrared) and temporal (16 days) resolution. During the last decade, a very large number of Landsat TM and ETM+ images are been freely available to the science community, like U. S. of Geology Service (USGS) at Glovis ( or Earth Explorer ( Web pages. In addition, some countries are developing projects so a high number of these images are also released, as Spanish National Remote Sensing Plan. This document describes the Automatic Burned Area Algorithm Software (ABAMS), a tool focused to generate automatically burned area perimeters using Landsat TM or ETM+ data. 2. Burned area mapping with two phases Burned patches are relatively easy to discriminate visually but have a wide spatial and spectral diversity caused by the severity of the fire, the time elapsed since the fire was extinguished, and the type of vegetation. For this reason, the automatic discrimination of burned patches always presents uncertainties, and most of the previously proposed algorithms try to get a balance between false detections (commission errors) and 3

4 detection rate (omission errors). An alternative to solve the apparent contradiction between omission and commission errors in mapping burned areas is to apply a twophase approach. In the first one, the goal would be to reduce the commission errors by means of a severe criteria and aims to detect the more clearly burned pixels (seed pixels: core burned ), even at the cost of omitting many burned pixels within each burn patch. The second phase analyses only the vicinity of the seed pixels, applying looser criteria to extend the burned area up to the actual burned perimeter, and thus reducing omission errors [1]. This software implements a simple two phase algorithm and permits the user the configuration of the criterions for the seeding and the second phase. In addition, the software proposes three set of pre-configured criterions (see section 4). 3. Automatic Burned Area Algorithm Software (ABAMS) description 3.1. Prerequisites and installation This software is implemented using ArcObjects 9.2 / 9.3 libraries ( and requires an ArcInfo 9.2 / 9.3 and Spatial Analyst extension ( installed in the computer. The installation is done as other Windows programs, executing the Setup ABAMS Beta v6.exe program. The software is executed clicking in the ABAMS icon in Start- All the programs ABAMS Using ABAMS software The software is divided in two main sections. In the section 1 (Load data to New Database) the images are loaded into an Image Catalogue and processed (computing reflectance if necessary and auxiliary burned land variables). In the section 2 (Execute Burned Area Mapping Algorithms), using the previous loaded Image Catalogue, the burned area mapping algorithm is configured and executed. 4

5 Figure 1. Load data into the Image Catalogue or Execute the BAM Algorithms Section 1 Section 2 START START Set Input Data Load Image Catalogue Create Image Catalogue Database Data and Algorithm configuration Image processing Burned area mapping END END Figure 2. Organigram of the two main sections The idea behind this structure is to spend the main computation time processing the images and generating burned area mapping popular indexes, so testing diverse burned area mapping algorithm configurations is faster Load data to a new Database Load new data 5

6 Figure 3. Load New Data Configuration interface First, the name of an Image Catalogue Database (Access Database *.mdb) is required (a dialog window is loaded after clicking the icon). The Database will be created to load the metadata of the images. Then, the input data folder has to been established (browse the folder clicking in the icon). The software considerate two type of basic image: Glovis data format ( Glovis Landsat data is downloaded in compressed form (.tar.gz format). This data type must be decompressed maintaining the folder structure (each image in one folder). This folder names starts with the letters LT5 or LE7 depending on the sensor (TM and ETM+ respectively). It must contain the _MTL.txt file that contains the metadata. Figure 4. Glovis folder structure example 6

7 Other data format: The software is able to load all the raster formats supported by ArcGIS ( orted_raster_dataset_file_formats). All the raster must be 6 (avoiding the thermal band) or 7 layers band stacks, and have to be located in the same folder level. Figure 5. Other format folder structure It is possible to insert raw data, but also reflectance data. In the second case, it is possible to insert scaled values (a typical technique to save disk space using 8 byte pixel type to save reflectance values). For example, a scale of 1 means that no scale is applied (reflectances are already in the range 0-1). A scale of 400 (quite popular in USGS burned area images) means that reflectance have been coded multiplied with Image Catalogue Database The images contained in the input folder are read and the metadata is loaded into Images_input table in the Image Catalogue Database (the access database defined in the first step). Depending on the image type, this metadata is automatically loaded (see Table 1). This table contains the next information for each image: 7

8 Name of the Field Content Loaded automatically GLOVIS OTHER Input_Folder The input general folder Path The folder of each input image Name The file name of the input image Sensor The sensor type (only TM and ETM+ are considered) Acquisition_date Acquisiton date of the image (Access Date type field) Sun_Elevation Sun elevation (degrees) Wrs_Path Path of the WRS Grid Wrs_Row Row of the WRS Grid Band1_File_Name File name of the Band 1 Band1_Gain Gain of the Band 1 (only for ETM+ data) H = High ; L = Low Band2_File_Name Same for Band 2 Band2_Gain Same for Band 2 Band31_File_Name Same for Band 3 Band3_Gain Same for Band 3 Band4_File_Name Same for Band 4 Band4_Gain Same for Band 4 Band5_File_Name Same for Band 5 Band5_Gain Same for Band 5 Band6_File_Name Same for Band 6 Band6_Gain Same for Band 6 Band7_File_Name Same for Band 7 Band7_Gain Same for Band 7 Spatial_Reference Spatial reference label Resol Resolution on axis Data_Type Data Type GLOVIS = Glovis data OTHER_RAW = Not Glovis raw format OTHER_REFL = Already reflectances Refl_Scale Scale applied over reflectance (Output_Reflectance= 1/ Scale * Input_coded_reflectances) Table 1. Fields included in the Image Catalogue Input_images table In the case of Glovis data, all metadata fields are automatically loaded (using the _MTL.txt file). In other cases, the rest of the metadata has to be filled. You can use the Image Catalogue Managing" interface (Figure 6) to edit the required fields (Sensor, Acquisition_date, Sun elevation in degrees - only if it is raw data -, Path, Row, the Gain for each band in case of "ETM+" data and the calibration file). 8

9 Figure 6. Image Catalogue Managing interface If the database already exists, you may add new data to the project (clicking Yes in the dialog shown in Figure 7), or overwrite the Database (Clicking No). Figure 7. Options if the database is already created The calibration files for Glovis TM and ETM+ images are already introduced (Chander_TM_L5.cal and Chander_ETM+_L7.cal files located in the Calibration 9

10 folder) [2]. If the input images have other calibration parameters, you can insert them clicking in Maintain Calibration file. Note that ETM+ calibration parameters need to define Low Gain and High Gain parameters (in the same textbox using the delimiter ";") (Figure 8). If you work with a unique path-row, you can clip the images activating the Clip checkbox and inserting the region of interest boundary coordinates (, Y minimum and maximum coordinates). Figure 8. Interface to add or edit calibration files Processing Images The input data is processed to be used for the Burned Area Mapping algorithm. This process includes reflectance computing, projecting and burned area mapping variables generation. This new information is saved in a Subfolder named BA_Data hanging form the Input Data Folder. This data will be saved in 32 bytes floating GRID format. An estimation el 3.5 Gigabytes is done for each Landsat complete scene, so you must assure enough disk space. Inside Image Catalogue Access file, a new table named Images_output is created to save metadata of the processed images. 10

11 Reflectance computing: If input data is in raw mode, reflectance is computed using the calibration parameters defined for each image. No atmospheric neither topographic correction is applied. Projecting data: In case of the software detects different spatial references for the input data, it allows to select the Output Spatial Reference (you must browse in the ArcGis Coordinate system folder to select the correct *.prj file), and the output resolution. Only supports transformations that do not require a Datum change. Figure 9. Burned Area Indexes computed Output spatial reference selection interface The next burned area mapping indexes are computed: Index Author Equation NDVI [3] GEMI [4] BAI [5] NBR [6] 2 η = ρ NDVI = ρ 2 2 ( ρ ρ ) NIR η GEMI = BAI = NIR NIR ρ + ρ + 1.5ρ R R ( ρ + ρ + 0.5) R R NIR NIR + 0.5ρ ( η ) ( ρ R 0.125) ( 1 ρ ) 1 ( ρ 0.1) 2 + ( ρ 0. 06) 2 NIR ρ NBR = ρ NIR NIR ρ + ρ R R LSWIR LSWIR R 11

12 BAIM [7] BAIM L = 1 ( ρ 0.05) 2 + ( ρ 0. 2) 2 NIR LSWIR MIRBI [8] MIRBI Logistic Regression Unitemporal (LR_post) Logistic Regression Multitemporal (LR_multi) ρ ρ = LSWIR SSWIR [1] 100 ρ BLUE ρ [1] 1 + e 1 -( MIRBI sswir NBR) 1 + e 1 -( * NDVI_pre * NBR_post * NBR_pre * MIRBI_post * ρblue_post * ρsswir_post ) 100 where ρnir is the reflectance in the NIR, ρ R is the reflectance in the RED ρ LSWIR is the reflectance in the long SWIR (Landsat TM/ETM+ band 7), ρ SSWIR is the reflectance in the short SWIR (Landsat TM/ETM+ band 5) Execute Burned Area Mapping Algorithms Once the images are processed, the Burned Area Mapping Algorithm has to be configured. First, the software requires the Image Catalogue Database. A user interface is shown with the processed images list (Figure 10). At this moment, two important parameters must be configured: 12

13 Figure 10. Output Database Managing interface Selection: Identification of the images that are selected to run the process. By default all the images are selected, but any of them may be unselected if we don t want to be used. Previous Reference: The images that will be used as previous reference data (in change detection multitemporal focus). It is possible to define more than one previous reference. After this interface, the main interface of the Burned Area Mapping Algorithm is shown. 13

14 Figure 11. Burned Area Mapping Algorithm configuration interface Temporal strategy: Unitemporal or multitemporal algorithms may be applied. The multitemporal option is only available when Previous_reference image is defined. The temporal strategy limits the variables that can be used to define the criterions for the algorithm (Table 3). The _post variables are referred to all images checked as Selected" and Previous_reference unchecked, as _pre variables refer to Selected and Previous_reference checked images. The execution of the algorithm is done individually, considering the images in each Path-Row (see organigram in Figure 12) 14

15 Table 2. Variables Temporal strategy Unitemporal Multitemporal TM1_post TM2_post TM3_post TM4_post TM5_post TM7_post NDVI_post GEMI_post BAI_post NBR_post BAIM_post MIRBI_post LR_post TM1_pre TM2_pre TM3_pre TM4_pre TM5_pre TM7_pre NDVI_pre GEMI_pre BAI_pre NBR_pre BAIM_pre MIRBI_pre LR_multi Variables available for burned area mapping algorithm 15

16 Image Catalogue First/Next Path-Row Get First/Next Previous image Get First/Next Posterior image Execute BA Any more posterior images? YES NO YES Any more previous images? NO Any more Path-Row? YES END Figure 12. Multitemporal Burned Area Mapping Algorithm organigram The burned area mapping algorithm criterions are defined in an abstract way, using ESRI Map Algebra language ( t_map_algebra, and the variables included in the Table 2. Check that the criterions fulfil the Map Algebra language syntax (always maintain a blank 16

17 character between variables and operators). The software allows the configuration of four basic parameters: Seeding Map Algebra: String Map Algebra expression to generate the Seeds. Minimum size of the seeds: Minimum area, in ha, to consider a valid seed. Region Growing Map Algebra: String Map Algebra expression to create the Second Phase Variable Minimum size of the perimeters: Minimum area, in ha, to consider a valid burned perimeter. The user can set the criterions that consider adequate and it is possible to save the configuration with the Save BA Algorithm" button. These configurations must be saved in the BA_Algorithms folder that hangs from the folder where the executable is located. The two phase algorithm will be applied to all selected images, and the results are aggregated so only one output Shapefile result will be generated (the program asks the user to set the path for the result), with these attributes: PRE_IMAGE: Name of the image (Name field) used as previous reference PRE_DATE: First date (Acquisition_date field) of the image used as previous reference. INIT_IMAGE: Name of the image (Name field) where the perimeter (or part of it) is detected in the first time. INIT_DATE: First date (Acquisition_date field) where the perimeter (or part of it) is detected. LAST_IMAGE: Name of the image (Name field) where the perimeter (or part of it) is detected at last time. LAST_DATE: Last date where the perimeter (or part of it) is detected AREA_HA: The area of each burned patch in hectares 17

18 4. Some notes about the pre-defined burned area algorithms As well as user-defined two phase burned algorithms, three pre-defined burned area algorithms are included: one that follows a multitemporal strategy (based on change detection), another one with a unitemporal focus, and a mixed one (unitemporal seeding and multitemporal second phase). These pre-configured options have been set using a burned sample database (about pixels) extracted from 6 Mediterranean regions (see Figure 13) [1]. The intention of this work was to use as many images as available and applying quite conservative criteria to them. Generally, these values will not fit the requirements of all the users, especially when the user needs to detect all the fires in the images despite of increasing the commission error. This is common, for example, when burned area cartography is produced to validate low spatial resolution burned area products. Others users need to detect the largest fires only with high confidence and ensuring low commission errors. Figure 13. Burned sample database regions Some users, and specially the fire_cci project team ( are using ABAMS software on other ecosystems and fire conditions, and this requires modifying the thresholds in order to obtain reasonable burned perimeters. Currently, we are working in setting more stable variables and thresholds taking into account more heterogeneous fire conditions (to cover a wider range of ecosystems and fire signals). While this research is concluded, we would like to make some recommendations to establish valid threshold values in your work areas: 18

19 The multitemporal focus is more stable than the unitemporal one, and the same thresholds work better in different situations. If available, it is better to work with a fire previous reference image (or more than one to avoid ETM+ gaps). The burned area indexes and variables are saved in the BA_data folder within the data folder (Table 3). This means that after executing a pre-defined algorithm you can load the result and the variables used to compute both phases into ArcMap, and try to fit better the parameters to your area. The new criteria may be saved in the same manner as the pre-defined algorithms, so that you can use them in posterior executions. Variable Name Internal Variable TM1 TM2 TM3 TM4 TM5 TM7 NDVI GEMI BAI NBR BAIM MIRBI Logistic Regression Unitemporal (LR_post) Logistic Regression Multitemporal (LR_multi) MASK I number_b1r I number_b2r I number_b3r I number_b4r I number_b5r I number_b6r I number_ndvi I number_gemi I number_bai I number_nbr I number_baim I number_mirbi I number_lru I number_lrm I number_m_s Table 3. Internal variables in the BA_data folder You can identify each scene (renamed to I1, I2, ) in the image catalogue database, Images_output table, Name field (see Figure 14). 19

20 Figure 14. Images_output table to identify the images in the BA_data folder If the pre-defined algorithms omit fires, you have to relax the seeding criterions (first phase). If fires that are not real are detected (the patches detected are not burned), you must set more severe seeding criterions. If the detected fires are ok, but only a part of the fire is mapped, you must relax the second phase (usually lowering the value of LR_multi). The LR_multi variable has shown to be quite conservative and tends to omit weak burned signals. 5. References [1] A. Bastarrika, E. Chuvieco, and M. P. Martín, "Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: balancing omission and commission errors.," Remote Sensing of Environment, vol. 115, pp , [2] G. Chander, B. L. Markham, and D. L. Helder, "Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors.," Remote Sensing of Environment 113, vol. 113, pp , [3] J. W. Rouse, R. W. Haas, J. A. Schell, D. H. Deering, and J. C. Harlan, "Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation," NASA/GSFC, Greenbelt, MD. USA, Type III Final Report [4] B. Pinty and M. M. Verstraete, "GEMI: a non-linear index to monitor global vegetation from satellites," Vegetatio, vol. 101, pp , [5] M. P. Martín, "Cartografía e inventario de incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR," Doctoral thesis, Universidad de Alcalá, Alcalá de Henares,

21 [6] C. H. Key and N. C. Benson, "The Normalized Burn Ratio (NBR): A Landsat TM radiometric measure of burn severity," U.S. Department of the Interior,Northern Rocky Mountain Science Center., [7] M. P. Martín, I. Gómez, and E. Chuvieco, "Performance of a burned-area index (BAIM) for mapping Mediterranean burned scars from MODIS data," Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment., pp , [8] S. Trigg and S. Flasse, "An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah," International Journal of Remote Sensing, vol. 22, pp ,