rome, 15 may 2014 - 09.00 - 16.00 hrs 4rd ods3f progress meeting, 15 - 16 may 2014, rome progress of...

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, [email protected] Santilli, DIAEE, [email protected]

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.


Monitoring Systems FeasibilityLimitations• 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.


Monitoring Systems Feasibility


Bad weather conditionsThere 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



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

FSI 06:00



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

MultitemporalPixel statistics(grouped by

month)

FSI 12:00 FSI 18:00

VIS 06:00 VIS 12:00 VIS 18:00

distribution of daily ROIs.

means



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



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.

Slope Position ClassificationA 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 characterizationSlope Position Classification & FSI


Topographic Position Index

Terrain characterizationViewshed Analysis


Binary Viewshed AnalysisViewshed 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 Viewshed ARTA Binary Viewshed

Clear topographic differences, which evidence

visible surfaces seen by cameras (main condition for thermal cameras location)



Smoke Viewshed AnalysisWhen 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


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


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



… under developmentEvapotranspiration

with potential influence on the visibilityFog/Low Stratus Cloud satellite product

visibility and contrast impactsDispersion Index

visibility

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 FeasibilityTerrain characterizationWe 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.


Product Class 1 Class 2 Class 3 Class 4 Class 5 Class 6

Illumination 08:00 – 12:00 12:00 – 16:00 16:00 – 20:00

Aspect 0 315º-45º 45º-135º 135º-225º 225º-315º

Binary Viewshed Visible Not visible

Minimum Smoke Height 0 < 10 mts. < 20 mts. < 50 mts. < 100 mts. > 100 mts.

Visibility 0 < 500 < 1000 < 2500 < 5000 > 5000

Fog Stability Index low medium hight

Slope Position Classification Valley Plain Ridge

Distance 500 mts. 1000 mts. 2500 mts. 5000 mts. 7500 mts. 10000 mts.

Corine Land Cover Forest Agricultural other

Monitoring Systems FeasibilityTerrain characterization


characteristic event #1 event #2 event #3 ... event # n

Illumination C1 C2 C2 ... C2

Aspect C2 C5 C4 ... C1

Binary Viewshed C1 C1 C2 ... C2

Minimum Smoke Height C3 C5 C2 ... C2

Visibility C4 C4 C1 ... C3

Fog Stability Index C3 C2 C2 ... C2

Slope Position Classification C3 C3 C2 ... C3

Distance C2 C4 C6 ... C4

Corine Land Cover C3 C2 C1 ... C1

event detected X √ √ √

false alarm X X √ 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



Some products exposed will be fully available in the next days to be consulted by EOSIAL Web Map Serviceshttp://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?



Conclusions & requirementsBecause 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.

some references


Thank you for your attention


rome, 15 may 2014 - 09.00 - 16.00 hrs 4rd ods3f progress meeting, 15 - 16 may 2014, rome progress of...

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