Forest fires are one of the main causes of destruction of natural resources and this is observed on a global scale. Many countries worldwide are affected by this calamity, which is induced by nature and human activities. Using satellite data it is revealed that in last decade nearly 350 million hectares of land was affected by vegetation fires worldwide.

Remote sensing and GIS technologies are useful disciplines in studying land features. It assists in monitoring and knowing the causes of forest fires and understanding how to reduce its affects and find solution to numerous issues attached to it. Many researchers, for obtaining appropriate solutions have been working for more than last two decades. Jia et al. (2006) and Arroyo et al. (2008) used remote sensing techniques for fuel mapping; Fraser et al. (2003) and Maingi (2005) applied remote sensing for mapping burned land surfaces, whereas Flannigan and VonderHarr (1986) used the techniques for monitoring forest fires. In the literature it can be found that many workers studied on post-fire vegetation regeneration and fire management. Among them, Solan-Vila and Barbosa (2010); Diaz-Delgado and Pons (2001); Duncan et al. (2009); and Keramitsoglou et al. (2008) have cited good examples on post-fire studies using remote sensing approaches. Spectral signatures of Earth’s features have unique characteristics for identifying feature objects. Healthy living vegetation reflects radiation in near infrared (NIR) and absorbs red light in the visible region of the spectrum. Whereas, burnt areas reflect more radiation in the visible and short-wave infra red (SWIR) region and absorb more radiation in the NIR. Thus, features like forest burnt areas can be depicted easily using spectral signature technology and using specific algorithms that finds indices. Using indices has more advantage over using original bands of satellite data for interpretation because it helps in reducing anisotropic as well as atmospheric effect (Chuvieco et al., 2002).

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Thermal Mapping of Forest Fires (TMFF) subsystem

This system is connected to the USGS website (http://e4ft101.cr.usgs.gov) to download MODIS (MOD14A1 and MYD14A1) fire products. MOD14A1 and MYD14A2 correspond respectively to the Terra and the Aqua satellite platforms of the MODIS sensors. These products are produced twice a day (one from Terra and one from Aqua) and are in raster hdf format, tiled and projected on a conical projection. The consisting pixels refer to the possibility for each area to be fire. The TMFF mosaics, resamples and reprojects these data. A user has only to give the spatial extend (location) and dates; the TMFF will download the corresponding MODIS products. For TMFF system a number of algorithms are written, in this work, to make the system automatic. The algorithms are written in Interactive Data Language (IDL), which traces and maps forest fire areas in shape files. The pixels that have the highest possibility of being of fires events are selected and vectorized. These vectors are imported to a GIS and the Regions of Interests (ROIs) are extracted.

Index Mapping for Image Analysis (IMIA)

This system retrieves automatically LANDSAT 7 ETM images from the database using the information of ROIs, which comes from the TMFF as final output. The database is created from the Glovis website (http://glovis.usgs.gov) and is connected to IMIA system. The ROIs are used to determine the time series of LANDSAT 7 ETM images and to extract the images. The extracted images are automatically processed in IMIA system to produce post fire maps and its statistics and useful information about index temporal variations.

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For each fire event the system searches the geo-database for images that will include the specific region. The numbers of images that are retrieved depend on the temporal resolution. Each of these images is processed independently and the Normalized Difference Vegetation Index (NVDI) , Normalized Difference Moisture Index (NDMI) and Normalized Burned Ratio Index (NBRI) are calculated. Thus, if n images are retrieved then that means 3n index images are calculated. 


                        NDVI = ([band 4] – [band 3])/([band 4] + [band 3])                                (1)


                        NDMI = ([band 4] – [band 5])/([band 4] + [band 5])                               (2)


                        NBRI = ([band 4] – [band 7])/([band 4] + [band 7])                                (3)


            Healthy vegetation like forests has a high content of chlorophyll and that always show higher NDVI values. Whereas burnt areas will show very low values with negative or zero values. For studying forest fire areas near-infrared, mid-infrared and thermal bands are found to be sensitive to burn magnitude changes and are frequently used. NDMI contrasts the near-infrared (NIR) band 4, which is sensitive to the reflectance of leaf chlorophyll content to the mid-infrared (MIR) band 5, which is sensitive to the absorbance of leaf moisture. NBRI is very useful in discriminating the burned areas from the unburned areas.

                Index images are used in producing post fire maps and its statistics and the mean index temporal variation graphs. The post fire maps are produced using a master image and the slave images. A master image is the nearest image in time before the fire event, and all images after the fire event are called slave images. Each post fire map is produced by thresholding the difference between the master NBRI image and the slave NBRI image. The result is a binary mask indicating the spatial extent of the burned area. The mean temporal variations are calculated using the earliest post fire map. Based on the master NBRI image and the first slave NBRI image, the burned area of first fire event is determined. In the similar manner the post fire maps for subsequent images are produced. For each subsequent slave image the mean temporal variation values are calculated for the burnt area. Thus, after the first fire event, how forest recovery progresses can be monitored using subsequent post-fire maps.

Written by

I'm a Remote Sensing and a Surveying Engineer. I received my degree from NTUA in 2010, where I also received my Ph.D. in hyperspectral remote sensing in 2016. From graduation in 2010, my career started as a Researcher Associate and Teaching Associate in the Laboratory of Remote Sensing of NTUA. From that time I also worked at several private companies as a Remote Sensing Expert and Geospatial Analyst. From the beginning of 2015 I was positioned as Senior Earth Observation Expert. During these years, I have participated in more than 20 funded European Commission and European Space Agency projects, have over 16 peer reviewed scientific publications in the field of Remote Sensing, and have an international patent in hyperspectral data compression.My main research and professional interests are in the optical remote sensing area, where I specialize in data (images, point measurements) processing and algorithm design and development. Some of the software tools that I operate to accomplish my research and business dreams are SNAP, ENVI, IDL, QGIS, ERDAS Imagine, ArcGIS, and Python. I have been working with these tools since 2008.

Dimitris Sykas

Remote Sensing Expert

Dimitris Sykas