1. Rome, 15 May 2014 - 09.00 - 16.00 hrs 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Progress of the activities at DIAEE: towards a strategy for comparing the monitoring systems available within the consortium Pablo Marzialetti, DIAEE, pablomarzialetti@psm.uniroma1.itpablomarzialetti@psm.uniroma1.it Giancarlo Santilli, DIAEE, santilli@psm.uniroma1.itsantilli@psm.uniroma1.it

2. Introduction One of the tasks of the ODS3F project concerns the comparison between the different available systems. The comparison, to be meaningful and correct, should take into account the different environmental conditions (topography, vegetation type, weather, etc.) in which the systems operate. Expected results: The results of the activity regard: - the assessment of the advantage and disadvantage associated with each available system; - the evaluation of the accuracy, adequacy and completeness of the information provided by the system. 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome

3. Monitoring Systems Feasibility Limitations Optical Visibility Fog occurrences Contrasts analysis False Alarms Minimum smoke column heights Algorithm robustness Infrared Topographic barriers Flame energy Algorithm robustness Products Optical Contrast maps, in order to train identification and classification methods Slope Position classification and Topographic Position Index Fog Stability Index Visibility Index Fog/Low stratus cloud map Smoke Viewshed analysis Infrared Topographic barriers DSM vs DTM Binary Viewshed analysis  Our objective: identify main territorial characteristics in order to compare different monitoring systems in next summer tests. 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome

4. Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Bad weather conditions There are several environmental factors that potentially affects the visibility and that they changes depending on the territorial geography. FOG is often described as a stratus cloud resting near the ground. Its formation is complex and its occurrence is widely variable in space and time, forming under a wide range of meteorological circumstances. Valley fog forms where cold dense air settles into the lower parts of a valley condensing and forming fog. It is often the result of a temperature inversion with warmer air passing above the valley. Valley fog is confined by local topography and can last for several days in calm conditions during the winter. Fog Stability Index (FSI) The FSI (HOLTSLAG 2010, WANTUCH 2001) is an empirical method, developed by the US Air Force. Is calculated according to the following formula : FSI = 2 * (TS - T850 ) + 2 * ( TS - DP ) + W850

5. Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Fog Stability Index (FSI) FSI = 2 * (TS - T850 ) + 2 * ( TS - DP ) + W850 stability humidity wind speed FSI<31 indicates a high probability of fog formation, 31<FSI<55 implies moderate risk of fog, and FSI>55 suggests low fog risk. Fog formation is favored for high humidity (TS-DP small), the atmosphere is stable (weak mixing, TS-T850 is small) and low wind speed (no mixing, W850 is small) (HOLTSLAG 2010, FREEMAN 1998). In order to find a connection with visibility in (WANTUCH 2001) was made a comprehensive statistical analysis of direct measurements and derived physical quantities, finding that the best correlation was: Visibility = -1.33 + 0.45 * FSI

6. FSI 06:00 Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome 5 years Historical Serie of Fog Stability Index & Visibility Index (Jan-2009 Feb-2014) Spatial Resolution: 0.125º degrees Temporal Resolution: daily at 06:00,12:00 & 18:00 Source Meteo data: ECMWF European Centre for Medium-Range Weather Forecasts Multitemporal Pixel statistics (grouped by month) FSI 12:00 FSI 18:00VIS 06:00VIS 12:00VIS 18:00 distribution of daily ROIs. means

7. Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome 5 years Historical Serie of Fog Stability Index & Visibility Index (Jan-2009 Feb-2014) Source Meteo data: ECMWF European Centre for Medium-Range Weather Forecasts Seasonality of FSI & VIS index during the 5 years analysis. Maximum Constant Difference in FSI index from 06:00 to 12:00 products. Higher FSI values for Italy and Greece & Lower FSI values for Spain ROI at midday. During winter rather similar values between ROIs. at 06:00 AM. & at 06:00 PM, while during winter, at 06:00 AM, Spain ROI evidence greater FSI risk than the other ones.. JulyJanuary France & ROI Greece & ROI Italy & ROI Spain & ROI FSI July 12:00 PMFSI July 06:00 AM FSI January 06:00 AM FSI January 12:00 PM FSI January 06:00 PM  70  20

8. Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Terrain Illumination & Aspect, could affect feature extraction processing, improving or reducing potential contrasts. Visibility of objects depends among other things on the perception of luminance contrasts between the objects and their surroundings. The greater the contrast of an object with its background, the greater its visibility. Illumination, Sunset & Sunrise 9 different illumination layers were processed for each ROI. 1 represents a synthesis of the day.

9. Slope Position Classification A Topographic Position Index (WEISS 2001) was introduced in order to classify at different scales the ROIs landscapes into Slope Positions. Geoprocessing results put into evidence the preeminence of valley-class, where valley fogs could be present. Terrain characterization Slope Position Classification & FSI 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Topographic Position Index

10. Terrain characterization Viewshed Analysis 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Binary Viewshed Analysis Viewshed identifies the cells in an input raster that can be seen from one or more observation points or lines. Each cell in the output raster receives a value that indicates how many observer points can be seen from each location. If you have only one observer point, each cell that can see that observer point is given a value of one. All cells that cannot see the observer point are given a value of zero. The observer points feature class can contain points or lines. The nodes and vertices of lines will be used as observation points (ESRI ArcGIS Help). SORIA Binary ViewshedARTA Binary Viewshed Clear topographic differences, which evidence visible surfaces seen by cameras (main condition for thermal cameras location)

11. Terrain characterization Viewshed Analysis 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Smoke Viewshed Analysis When we talk about optical systems, we can go further the binary viewshed analysis detailed before. Smoke columns could go higher, becoming visible depending on Camera and DEM quotas. For that case, a Line of Sight (DEM-pixel to Tower) process was developed, in order to create a new viewshed, capable to quantify the minimum smoke column heights necessary to be seen by the camera. At left we can see deepest areas in dark-black, classified as not visible by Binary Viewshed analysis. While at right, we can see traditional Binary Viewshed analysis results in white, and in the opposite and in dark-red, smoke column minimum quota that need to be reached to be seen by the camera. PROVENCE smoke column viewshedPROVENCE DEM & Camera Camera

12. 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome The Smoke Viewshed Analysis, put into evidence local topography and its high impact, not only for thermal monitoring systems, but for optical ones. From the first examples below, significant information can be extracted, only viewing minimun quota magnitudes and distribution. MONTE CAVO Smoke Viewshed  Note: In further analysis, should be introduced precise heights of cameras to not underestimate critical areas. (covered area and minimum quotas change significantly at different cameras heights). PROVENCE Smoke Viewshed Terrain characterization Viewshed Analysis

13.  Products developed: – Topographic Index and Classification (roughness index)  visibility – Fog Stability and Visibility Index (historical data)  visibility – Relative Humidity (historical data)  visibility – Aspect and Illumination  contrast – Binary Viewshed analysis  visibility – Minimum smoke heights analysis  visibility – Distances from Camera (buffering zones at 500, 1000, 2500, 5000 and 10000 meters, and distance to seashores)  visibility Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome … under development Evapotranspiration  with potential influence on the visibility Fog/Low Stratus Cloud satellite product  visibility and contrast impacts Dispersion Index  visibility

14. Evapotranspiration (Penman-Monteith method) Known as the sum of evaporation and plant transpiration from the Earth's land and ocean surface to the atmosphere, will have potentially influence on the visibility. FAO Penman-Monteith method, with historical meteo data, (and potentially Local near real time data, because the Penman methods may require local calibration of the wind function to achieve satisfactory results). Through evapotranspiration, trees in cloud forests collect the liquid water in fog or low clouds onto their surface, which drips down to the ground. Fog/Low Stratus Cloud satellite product (Cermak 2008) This near real time product (temporal resolution of 15 minutes), this product could add information to improve knowledge of impacts on visibility and contrast. Dispersion Index (Lavdas 1995) Measure the atmosphere’s ability to ventilate smoke from areas of prescribed burning activity. It will be used to improve the visibility index based on FSI.  Further works will look to improve the Visibility index depending not only on FSI index but also on this new set and local measurements.  Next step – Terrain characterization Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome

15. Monitoring Systems Feasibility Terrain characterization We have exposed territoral characteristics that could enhance or impact on Fire Monitoring Systems performance. In the next step, we will make a terrain characterization approach, dividing each product within its main classes. 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome ProductClass 1Class 2Class 3Class 4Class 5Class 6 Illumination08:00 – 12:0012:00 – 16:0016:00 – 20:00 Aspect0315º-45º45º-135º135º-225º225º-315º Binary ViewshedVisibleNot visible Minimum Smoke Height 0< 10 mts.< 20 mts.< 50 mts.< 100 mts.> 100 mts. Visibility0< 500< 1000< 2500< 5000> 5000 Fog Stability Index lowmediumhight Slope Position Classification ValleyPlainRidge Distance500 mts.1000 mts.2500 mts.5000 mts.7500 mts.10000 mts. Corine Land CoverForestAgriculturalother

16. Monitoring Systems Feasibility Terrain characterization 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome characteristicevent #1event #2event #3...event # n IlluminationC1C2...C2 AspectC2C5C4...C1 Binary ViewshedC1 C2...C2 Minimum Smoke HeightC3C5C2...C2 VisibilityC4 C1...C3 Fog Stability IndexC3C2...C2 Slope Position Classification C3 C2...C3 DistanceC2C4C6...C4 Corine Land CoverC3C2C1...C1 event detectedX√√√ false alarmXX√X Omission errors (event Not Detected that is a not a False Alarm) >> Need to be reduced Commission errors (event Detected that is a False Alarm) >>Need to be reduced characteristics weighted according to events. potential influences of the territory. Training process Terrain characteristics Events & characteristics Note: Event = feature detected by the system, not necessarily a fire/smoke event

17. Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Some products exposed will be fully available in the next days to be consulted by EOSIAL Web Map Services http://eosial.psm.uniroma1.it:8081/geoserver Historical series of Fog Stability Index: http://eosial.psm.unirom1.it:8081/geoserver/FSI_ODS3F/wms? General Project (under development) : (DEM, Aspect, Illumination, CLC, Cameras, Buffers, Binary Viewsheds, Smoke Viewsheds, ROIs.limits, FOG/Low Stratus daily product, Dispersion Index, etc.) http://eosial.psm.unirom1.it:8081/geoserver/ODS3F/wms?

18. Monitoring Systems Feasibility 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome Conclusions & requirements Because of several difficulties exposed by partners to introduce an external image dataset, the algorithms could not be tested in exact equal conditions. Nevertheless, the products described will help us to evidence those terrain characteristics that could impact in detection systems.  High Spatial Resolution DEMs. for every area of interest (to improve the accuracy of products).  Precise heights of cameras to not underestimate viewshed analysis.  Dataset of events detected and their characteristics, events not detected and false alarms.

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20. Thank you for your attention 4rd ODS3F Progress Meeting, 15 - 16 May 2014, Rome