CROSS-CORRELATION BETWEEN WORLD FIRE ATLAS AND ENVIRONMENTAL CLASSIFICATIONS

Transcription

1 CROSS-CORRELATION BETWEEN WORLD FIRE ATLAS AND ENVIRONMENTAL CLASSIFICATIONS Mrs. Diane Defrenne 1 and Dr Olivier Arino 2 1 SERCO, via Galileo Galilei, Frascati, Italy. diane.defrenne.goncalves@esa.int, 2 ESA, via Galileo Galilei, Frascati, Italy. : olivier.arino@esa.int Abstract The European Space Agency World Fire Atlas project provides a global fire monitoring service using data acquired by the ATSR-2 and AATSR sensors from the Agency ERS-2 and ENVISAT satellites. With a temporal continuity raging from November 1995 until present day, and with the global coverage of the satellite data, this service could consistently support a global correlation study between the hot spots detected and vast degree of environmental observations. As such, a study was initiated that probes the correlation elements between land cover, meteorology and atmospheric conditions with the project s global 10-year mapping of fire. The initial datasets selected were the meteorological data provided by the project ERA-40* (from the European Center for Medium range Weather Forecasting ECMWF), the vegetation classification (Global Land Cover classification for the year 2000 from the Joint Research Centre) and the atmospheric chemistry observations from Tropospheric Emission Monitoring Internet Service (TEMIS from KNMI). First results were very promising. Globally the correlation between the meteorological parameter and the WFA are good as expected and very good results were also obtained with the NO2 tropospheric column concentration. 1 Introduction The World Fire Atlas is a global detection of the hot spots using the Along Track Scanning Radiometer (ATSR) of ERS-2 from November 1995 to December 2002 and extended using the Advanced ATSR since the beginning of 2003 to present. The WFA has the big advantage to include almost 10 years of data and to be global. Since the data has proved their reliability to compose statistic studies (REF), it is possible to try to extract the maximum information on the fire behaviour and consequences locally or globally. Worldwide information about the current status of the environment at the time of the fires has been chosen to try to extract some relevant information as the vegetation, the meteorological parameter and the atmospheric analyse at the hot spot detection time. Then a pre-processing has been applied to all global matrixes in order to have all data in the same format with respect to the resolution of each data set and to facilitate the coding of an automatic system using Visual Basic combined with a free Geographical Information System (GIS) object library named InovaGIS. This system permits not only to cross-correlate the WFA data with others chosen classifications but also to limit the study geographically and temporarily. Seeing the preliminary reports, it has been decided to focus the study on two directions. First, a study to approach the fire behaviour rule which is a group of rules between the fire and the meteorological rate in a region. Thus, according to a vegetation status, some cross-correlation between Vegetation, WFA and Meteorological data has been realized over the Amazone, West Africa and Siberia. Second, the cross-correlation of hot spots detected and the NO2 tropospheric column concentration has been done on a large part of Africa in order to find the parameter leading the NO2 emission with the fire in a region having a vegetation homogeneous. This paper will present first the data used for the cross-correlation and the method, then the results for the two studies and at least the future potential initiated by the cross-correlation studies. 2 Data and Method 2.1 DATA The World Fire Atlas forms a unique time series of global hot spots location and timing available for 10 years of data from November 1995 to present. The processing consists of extracting the hot spots greater than 312ºK (Algorithm 1) or than 308ºK (Algorithm 2) on the brightness temperature in the 3.7µm channel from the data

2 recorded at night-time by the second Along Track Scanning Radiometer (ATSR-2) on ERS-2 and the Advanced Along Track Scanning Radiometer (AATSR) on ENVISAT. The monthly record generated by this processing is giving for each hot spot detected the date, the time, the longitude and the latitude with a 1km resolution and with a measurement frequency of about 3 days. All monthly data set is an ASCII file available on the European Space Agency (ESA) World Fire Atlas website ( Worldwide information about the current status of the environment at the time and the place of the fires has been chosen to try to extract some relevant information. Thus, a first study of feasibility implies the vegetation classification GLC2000 (Global Land Cover 2000) [2] of JRC (JOINT RESEARCH CENTRE) to define timelessly the current vegetation status, the monthly mean temperature and mean precipitation from ERA40* (ECMWF 40 Years Re-Analysis) [5] of the ECMWF (European Centre for Medium-Range Weather Forecasts) for the meteorological status of the region around the hot spot detected and at least the NO2 air pollution concentration from TEMIS (Tropospheric Emission Monitoring Internet Service) from KNMI (Koninklijk Nederlands Meteorologisch Instituut) to analyse the NO2 concentration status over the region of the hot spots. All classifications might be almost global and must cover at least 5 years in common with the WFA so as to be able to perform statistic and to observe the possible correlation having often a seasonal or annual cycle. In the following table, it is possible to observe the difference between the data sets: Name Parameter Resolution Frequency Possible improvement WFA Hot spot detected by the ATSR 1km Every 3 days average Filter gas flare and volcanoes eruption. GLC2000 Vegetation Index 30 sec 1 data set for the Use the data set from year 2000 Globcover (available by the end of 2007) giving a monthly vegetation classification for the whole ERA 40 * Temperature 2.5 Monthly Mean Using the daily field data. Precipitation TEMIS NO2 tropospheric column concentration 2.5 Monthly Mean 15 min Monthly Mean Using the daily field data. Seeing the various resolutions of the classifications used in the studies, it is important to adapt the dimension of the region of interest. For example, a cross-correlation between the meteorological data and the WFA must be done only on a geographical tile having at the minimum the dimension of the resolution of the ERA model. WFA statistic are applied only on monthly field and since measurement of the ERA 40 fields and of the TEMIS fields are monthly, they can only be compared with a statistical monthly unit. A big improvement will be done when the WFA will be cross-correlate with daily data field that exist for the meteorology and the atmospheric concentration. Regarding the classification used for the vegetation, the GLC2000 presents the advantage of the high resolution but the inconvenience of being the vegetation status of the world for the year 2000 which introduces an approximation for the vegetation status of the other years. Others global vegetation classification are in development and particularly the Globecover project that is going to produce monthly vegetation field for the whole year WFA TOOL Combined the several data field together are not easy and limit the study in a defined region as a continent, a country or a bounding box is either more complex. For the WFA user, a tool has been developed to discriminate the WFA data geographically and temporally. This tool was coded in Visual Basic combined with the free GIS object library INOVAGIS. In the tool, the GIS library and a current shape file defining the political boundaries of the world allow producing various hot spots shape files that belong to one or more chosen country or continent. Either, the discrimination in time and algorithm is coded to permit to have exactly the data belonging to a region between a start date and an end date and detected by one of the two WFA algorithms. From the code of this tool has been developed the system allowing the cross-correlation study.

3 2.3 PRE-PROCESSING THE DATA FIELDS In the following table, we can see the various format of the data field. It seems that each institution or research centre takes a different option. Taking the data as they are, complicates a lot the cross-correlation work whereas converting all the data in an homogeneous format simplifies hugely the code seeing that the various part of the code touching a field is not taking in consideration the data format. Name Format Dimension Number of Time coverage files WFA ASCII ~50 Kb 1/month From November 1995 to present. Temperature* Netcdf 22 Kb 1/month From September 1957 to August Precipitation* Netcdf 22 Kb 1/month From September 1957 to August Vegetation Avl 659,352,960 Kb 1 Unique data set for NO2 concentration grd 5,064 Kb 1/month From April 1996 to present. In consequences, all the data except the WFA file were converted in a matrix raster image *.img linked to the characteristic of the data which are in the *.doc file. 2.4 METHOD TO CROSS-CORRELATEAND EXTRACT SIGNIFICANT MEANING The first method to cross-correlate the various data field was to select the fire and to observe what was the pixel values on the classifications. So, we could have an idea of the status of the hot spot region but considering the only pixel of 1kmx1km of the possible fire won t give significant information because the statistics are dealing with monthly data field. First results were presented [1] and showed the evidence. Fire occurs more with a certain type of vegetation, more in the dry zone and more with a defined quantity of precipitation. They were the first step before a clear definition of the objective of the cross-correlation between WFA and global classification and it permits to have a vision of the possibility of this study. Even if these results are not defining any behaviour rules, they showed the real existence of correlation between varieties of classifications and increased the WFA confidence in his statistic use Nov-95 Mar-96 Jul-96 Nov-96 Mar-97 Jul-97 Nov-97 Mar-98 Jul-98 Nov-98 M ar-99 Jul-99 Nov-99 Mar-00 Jul-00 N ov-00 Mar-01 Jul-01 Nov-01 Mar-02 Jul-02 Nov-02 M ar-03 Jul-03 Nov-03 C lass 1 to 8 Class 9 to 10 Class 11 to 15 Class 16 to 18 Figure 1: Temporal distribution of fire according to GLC2000 vegetation class. Global Land Cover Class from GLC2000 [2] 1 Tree Cover, broadleaved, evergreen 2 Tree Cover, broadleaved, deciduous, closed 3 Tree Cover, broadleaved, deciduous, open 4 Tree Cover, needle-leaved, evergreen 5 Tree Cover, needle-leaved, deciduous 6 Tree Cover, mixed leaf type 7 Tree Cover, regularly flooded, fresh 8 Tree Cover, regularly flooded, saline (daily variation) 9 Mosaic: Tree Cover /Other natural vegetation 10 Tree Cover, burnt 11 Shrub Cover, closed-open, evergreen 12 Shrub Cover, closed-open, deciduous 13 Herbaceous Cover, closed-open 14 Sparse Herbaceous or sparse shrub cover 15 Regularly flooded shrub and/or herbaceous cover 16 Cultivated and managed areas 17 Mosaic: Cropland/Tree Cover /Other natural vegetation 18 Mosaic: Cropland/ Shrub and/or herbaceous cover 19 Bare area 20 Water Bodies (Natural or artificial) It is highly difficult to cross-correlate more than 2 classifications because if the number of classes is about n i for a defined field i than the number of cases between two classifications will be n i *n j then for three classifications n i *n j *n p and so on. Considering two major classifications of which the WFA, the others should be consider as an auxiliary information. For example, the cross-correlation between the number of hot spots and the meteorological data, in this instance the mean cumulated precipitation or the mean temperature, will consider the local status of the vegetation repartition as secondary information to join the regional studies.

4 Seeing the preliminary results (figure 2 and 3), it has been decided to orientate the study on two directions in order to obtain, first an approach to the fire behaviour rules using the meteorological data and second, to define an emission prediction factor for the NO2 using the TEMIS data. Figure 2: Temporal distribution between January 1996 and August 2002 of number of hot spot detected by month (in red), the monthly mean cumulated precipitation (in blue) and the monthly mean temperature (in yellow) in the bounding box [18S,12S][56W,50W]. Figure 3: Temporal distribution between April 1996 and August 2005 of number of hot spot detected by month (in blue) and the NO2 monthly mean tropospheric column concentration (in red) over the Angola. Both studies use almost the same method that is to observe the repartition of the vegetation index in a tile having a dimension depending of the resolution of the classification correlated with the WFA (5º*5º for ECMWF data and 4º*4º for NO2 data), then group the tile having the same characteristics and next observe the possible correlation to extract behaviour scheme between fires and environment. Of course, to extract rules, it is necessary to exclude the industrial or constant fire as gas flares, volcanoes or any non-vegetation fires. In this paper, it has been surveyed only region with almost hot spots that are deriving from vegetation fires. For the NO2 Emission Predictor Factor, the study was going up till generating some law by linear regression instead of the Fire Behaviour Rules from meteorological data which closed before emitting any conclusion but that showed the capability of the cross-correlation to define dependence between both fields. 3 Results 3.1 WORLD FIRE ATLAS CORRELATED WITH METEOROLOGICAL DATA FROM ECMWF IN ORDER TO ESTABLISH SOME FIRE BEHAVIOUR RULES. Some studies have been done over various parts of the globe to try to establish rules from the precipitation and the temperature linked the number of hot spots detected. It has been chosen two types of results, one that shows that the number of fires is essentially dependant of the temperature in Siberia and another one showing that the fire are relative this time to the precipitation in West Africa. One each example, the region of interest is divided by tile and there is the repartition of the vegetation by GLC200 (see table figure1) and a graph presenting the temporal distribution of the mean temperature, the mean cumulated precipitation and the number of hot spots detected in the tile. SIBERIA: From the figure 4a, we can conclude that the two tiles have almost the same vegetation but from the figures 4b, it is obvious that the big event on the west tile ([60N,65N][70E,75E]) is due to the prolongation of the dry season between July and September The fires frequency follows the temperature. Nevertheless the temperature is not monitoring completely the fire because it might be possible to define a precipitation threshold for the beginning of the fire season which might be around 5cm. These results might be detailed using daily data for the meteorological parameters and the hot spots. The daily fields exist and must help to observe the frequency and the volume of data and that permits to extract the fire behaviour rules of this region having a temporal unit of one day instead of one month that is not adequate.

5 Figure 4a: Distribution of vegetation by GLC2000 class over the region of interest in Siberia (see index class on figure 1) Figure 4b: Temporal distribution between January 1996 and August 2002 of number of hot spot detected by month (in red), the monthly mean cumulated precipitation (in blue) and the monthly mean temperature (in yellow) in the bounding boxes [60N,65N][70E,75E] on the left and [60N,65N][75E,80E] on the right. WEST AFRICA: Figure 5a: Distribution of vegetation by GLC2000 class over the region of interest West Africa (see index class on figure 1) Figure 5b: Temporal distribution between January 1996 and August 2002 of number of hot spot detected by month (in red), the monthly mean cumulated precipitation (in blue) and the monthly mean temperature (in yellow) in the bounding boxes corresponding in the map of the figure 5a This case is completely different than the Siberia region because it is possible to observe in general that the fire season is highly dependant of the rain period. In the two tiles in the north, the vegetation is almost inexistent (figure 5a) but we can observe some fire events coinciding to a little rainy period before (two graphs in the top of the figure 5b). Fires occur when some vegetation is present, so around three months after the rain. It will be interesting to compare these results with others bare area in different part of the world. The situation is completely different in two tiles in the south that has vegetation. There is a biomass to burn and the fire season began systematically after the rain season. One more time these results might be refined daily to really define some fire behaviour rules. Nevertheless the graphs present a first result to define rules between fire and rain as the time duration between the rain and the fire, the amplitude of the fire in function of the length of the rain period, the rain threshold that give the start of the fire, To conclude, the graph on these two regions, the Siberia and the West Africa showed that a refining study might be done and it will be surely useful to define the fire behaviour rule by region. Another point is very important is

6 the vegetation classification is fixed to the year 2000.and nowadays GLC2000 is the only vegetation classification global and having 30arcsecond resolution. 3.2 CROSS-CORRELATION BETWEEN NO2 CONCENTRATION AND HOT SPOTS DETECTED IN ORDER TO FIND AN EMISSION PREDICTION FACTOR. Some assumptions were done to realize the study about the NO2 prediction factor from vegetation fires. First, the vegetation classification is fixed, so as it is said before the evolution due to biomass already burnt or the natural or human activities can change hugely the vegetation and second the study ignore completely the natural NO2 emission cycle from the vegetation [4] (see figure 6). In order to include the vegetation as a secondary information, we divided the region of interret in small tile of 5 x5 to group them and define globally the zones then it has been performed a correlation between the number of fires by month and the No2 mean concentration in a month from march 1996 to may 2005 (without Jan 1998 and November 2003 because data from TEMIS are not available or complete) for each sub-region selected. Finally a linear regression has been perform to try to discriminate the emission due to the fires and the one due to the natural NO2 cycle. Figure 6: Annual NO2 cycle measurement using satellite data [4] Figure 7a: regions of interest selected in Africa Figure 7b: correlation of the fire(red) and the NO2 tropospheric column (blue) in the tile [6N,10N][18E,22E] Figure 7c: NO2 tropospheric column calculated(red) and observed (yellow) and their difference (blue) in the tile The same study has been done in each tile and where the number of hot spots is enough and the correlation is good as it is in the example, it is possible to observe in the residual (Fig 7c) the NO2 natural annual cycle. The objective is, in the future, to discriminate this natural cycle from the vegetation burnt emission. References [1] The ATSR-2 and AATSR World Fire Atlas products: Validation, consistency, and relationship with climate variables. Olivier Arino, Stephen Plummer and Diane Defrenne. Envisat symposium septembre 2004 [2] Fritz S., & al (JRC), Harmonisation, mosaicing and production of the Global Land Cover 2000 database (beta version). EUR EN Version 1 update. [3] O. Arino, H. Trebossen, F. Achard, M. Leroy, C. Brockman, P. Defourny, R. Witt, J. Latham, C. Schmullius. S. Plummer, H. Laur, P. Goryl, N. Houghton, The Globcover initiative. MERIS and AATSR workshop, sept [4] Savage, N.H., K.S. Law, J.A. Pyle, A. Richter, H. Nüß, J.P. Burrows, Using GOME NO2 satellite data to examine regional differences in TOMCAT model performance, Atmos. Chem. Phys., , [5] Kållberg, P., A. Simmons, S. Uppala and M. Fuentes, The ERA-40 archive, ERA-40 Project Report Series, n 17, sept *ECMWF ERA-40 data used in this study have been obtained from the ECMWF data server.