USE OF EUROPEAN SATELLITE PRODUCTS FOR DATA COLLECTION (FOREST FIRE MAPPING) - Forcip+
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USE OF EUROPEAN SATELLITE PRODUCTS FOR DATA COLLECTION (FOREST FIRE MAPPING) Rémi SAVAZZI / Jean-Luc Kicin - ONF Agence DFCI Méditerranée Sadri HAOUET / Mathilde Caspard – laboratoire ICube SERTIT FORCIP+ Final Conference 7 - 8 June 2017, Valladolid, Spain
Plan of presentation • Introduction • Use of SENTINEL and PLEIADES for contour and severity mapping • Use of PLEIADES images for precise cartography • Use of SENTINEL for risk mapping • Conclusion 2
Plan of presentation • Introduction • Use of SENTINEL and PLEIADES for contour and severity mapping • Use of PLEIADES images for precise cartography • Use of SENTINEL for risk mapping • Conclusion 3
SERTIT • Sertit is an ICube Laboratory Platform, University of Strasbourg • Operational 24/7/365 rapid mapping service – SERTIT’s Rapid Mapping Service • Activities on a national/international scale • International Charter Space and Major Disasters • Copernicus Emergency Management Service – Mapping (EMS) • Rapid (6-12hrs) provision, processing and analysis of remote sensing data in case of : – natural and environmental disasters – humanitarian relief activities – civil security issues • Support to decision makers : – Civil security services – Humanitarian Institutions, NGOs – Prevention and forecasting services • Anticipation, prevention, crisis, feedback, mitigation 4
• ONF (National Forestry Board) is a State Public Body, under the joint supervision of the Ministries of Forestry and the Environment • Is responsible for forest management for both the state and local government, and has a public service remit to aid the State and local authorities, in particular in the role of protecting forests against fire • Has a specialized agency in the Midi-Mediterranean area to protect forests against fires that performs the following tasks: Operational - monitoring, detection, first response, support to control activities Development and maintenance work - roads, water points, areas cleared of undergrowth Expertise support - hazard and fire mapping and risk of fire management plans, mapping equipment… Project management 5
SENTINEL-2 Sentinel-2 is an Earth observation mission developed by ESA as part of the Copernicus Programme to perform terrestrial observations in support of services such as forest monitoring, land cover changes detection, and natural disaster management. It consists of two identical satellites, Sentinel-2A and Sentinel-2B launched in 2015 and 2017 • Multi-spectral data with 13 bands in the visible, near infrared, and short wave infrared part of the spectrum • Revisiting every 5 days. • Spatial resolution of 10 m, 20 m and 60 m • 290 km field of view
PLEIADES The Pléiades constellation is composed of two very- high-resolution optical Earth-imaging satellites. Pléiades-HR 1A and Pléiades-HR 1B. Designed as a dual civil/military system, Pléiades will meet the space imagery requirements of European defence as well as civil and commercial needs. • Multi-spectral (including near infrared) and panchromatic data / stereoscopic images • Revisiting every 26 days. • Spatial resolution of 50cm and 2 m • 100 km field of view
Illustrations on Rognac forest fire near Marseille August 10th , 2016 (area burnt more than 2600 ha) Photography by cosmonaut Oleg Skripochka from ISS
Plan of presentation • Introduction • Use of SENTINEL and PLEIADES for contour and severity mapping • Use of PLEIADES images for precise cartography • Use of SENTINEL for risk mapping • Conclusion 9
Contour mapping : Pléiades data Contour digitized by photo- interpretation 7/12/2017 10
Contour mapping : Sentinel-2 data Burnout : fully automatic tool for burn scar mapping on Sentinel-2 and Landsat-8 data. FULL TILE PROCESSED >90% accuracy 7/12/2017 11
Severity : Sentinel-2 ΔNBR Burn Severity − < 0.1 Unburned NBR = NBR pre + 0.1 to 0.27 Low-severity burn Moderate-low 0.27 to 0.44 NBR pre ; NBR post ; dNBR severity burn Moderate-high 0.44 to 0.66 severity burn > 0.66 High-severity burn All calculated by Burnout tool dNBR = NBR(pre-fire) - NBR(post-fire) dNBR NBR post 7/12/2017 12
Volume : burned vegetation The map presents an indicator of vegetation volume loss after the August 2016 fires. It’s derived from Pléiades stereoscopic data acquired on the 13th August 2016 and 21st April 2015. The method is currently under development at ICube- SERTIT. 7/12/2017 13
Contour and severity mapping : Sentinel-2 data • Method of calculation using the Near InfraRed (NIR) and Shortwave Infrared bands (SWIR) of images from the Sentinel 2 satellite (EU Copernicus Project) made available by ESA and CNES • Resolution of pixel images of 20m x 20m • 10 days between 2 images with 1 satellite (Sentinel 2A) but will be reduced to 5 days in 2017 with Sentinel 2B − NBR = + NBR post fire NBR pre fire Post-fire image of 13/08/2016 Pre-fire image 03/08/2016 Sentinel 2A © ESA 2016 © CNES 2016 Sentinel 2A © ESA 2016 © CNES 2016 dNBR = NBR(pre-fire) - NBR(post-fire) 14
Contour mapping methodology Result of the dNBR calculation (raster format) Value > 0.1 Vectorization without smoothing pixels 15
Severity mapping methodology • The Severity of the fire on the vegetation is defines as being the loss of aerial and subterranean organic matter due to the fire, by combustion or mortality. • The classes of the severity index are defined from US fires, but can be used as first approximation to interpret the dNBR in Mediterranean conditions. ΔNBR Burn Severity Categories (USGS Firemon) ΔNBR Burn Severity < 0.1 Unburned 0.1 to 0.27 Low-severity burn 0.27 to 0.44 Moderate-low severity burn 0.44 to 0.66 Moderate-high severity burn > 0.66 High-severity burn Map of severity index Map of severity index (continue datas from value -2 to +2) (datas with US classification) 16
Evaluation of the impacts of the fire on the vegetation using severity index • Fields measurements on the Rognac fire indicate that: – For a same severity index, the impacts on the vegetation vary with the type of vegetation in place before fire: • Wooded type (forest) : Végétation > 3m • Brush type (moor, garrigue, maquis) végétation < 3m • Herbaceous type – For a same severity index, the impacts on the vegetation vary with the density of the vegetal cover – Test of a composite index of impact on the vegetation function of the type of végetation and of the severity index Sévérité : Moyenne Densité / Mélange houppier couvert de roussi ou Sévérité du feu sur la végétation (dNBR) la partiellement roussi. Présence de sujet végétation vert possible Faible Moyen Fort Houppier vert ou Etage arborée légèrement roussi sur la Houppier totalement roussi totalement ou Forêt partie inférieure (présence de quelques partiellement brulé (Sous étage et litière sujets encore vert possible) (feu de cime) brulée) Source : Google Earth (mode bâtiment 3D) Source : ONF 2016 Agence DFCI Sévérité : Moyenne Faible Végétation totalement brulé Houppier roussi Arbustif Moyenne Strate arbustive roussie Végétation totalement brulée Strate arbustive verte et Strate arbustive brulée et Végétation Dense roussie (en mélange) roussi (en mélange) totalement brulé Végétation rase Végétation haute Herbacée totalement brulée totalement brulée Source : Google Earth (mode Street View) Source : ONF 2016 Agence DFCI 17
Comparison of the results • Shapes of the fire slightly differents • Contour digitized by photo- interpretation of Pleaides by SERTIT • Automatic burn scar mapping on dNBR of Sentinel-2 for ONF 18
Comparison of the results • Severity index:equivalents results • Generalisation and vectorisation by SERTIT (less variability) • Calculation and mapping by pixel by ONF (maintained variability) Severity Index ONF Severity Index SERTIT 19
Plan of presentation • Introduction • Use of SENTINEL and PLEIADES for contour and severity mapping • Use of PLEIADES images for precise cartography • Use of SENTINEL for risk mapping • Conclusion 20
PLEIADES image 8/13/2016 (no treatment) (Image supplied by SERTIT) (Image supplied by SERTIT) 21
Treatment of PLEIADES image 8/13/2016 (NDVI) NDVI = normalized difference vegetation index (NDVI) VIS and NIR stand for the spectral reflectance measurements acquired in the visible (red) and near-infrared regions 22
PLEIADES images 8/13/2016 vs 21/04/2015 (visual comparison) (Image supplied by SERTIT) (Image supplied by SERTIT) 23
Treatment of PLEIADES images 8/13/2016 vs 21/04/2015 (DNDVI) 24
Contour mapping : Pléiades data difficulties – Perturbations in dNDVI due to the too large delay between the 2 images and differents seasons – Problems with drop shadows due to the pixel resolution (50cm) – => automatic treatment of the burn scar very difficult Pre fire Post fire (unburned) Drop shadow DNDVI 25
This images allow to sharpen the outline of the burnt area in complex interfaces (Image supplied by SERTIT) (Image supplied by SERTIT) 26
Drawing the outline can only be made manually, by visual analysis on different displays (Image supplied by SERTIT) 27
These results are very useful for return of experience The analysis can enhance different ways of propagation Propagation by little leaps (30-40m) Propagation by long leaps (500m) Propagation by long leaps Propagation by (350m) « wick effect » along banks and edges 28
Truck garage and highway (photo B. Horvart / AFP) 29
Truck garage and highway before fire (Image supplied by SERTIT) 30
Truck garage and highway after fire (Image supplied by SERTIT) 31
Truck garage and highway DNDVI treatment 32
Plan of presentation • Introduction • Use of SENTINEL and PLEIADES for contour and severity mapping • Use of PLEIADES images for precise cartography • Use of SENTINEL for risk mapping • Conclusion 33
RISK mapping These images make it possible to obtain many different types of vegetation or land use. For this purpose the images are combined with other sources of information on the nature of the forest stands, the agricultural occupation of the territory or the distribution of urbanization. This types are traduced in a level of combustibility then combined to slopes and wind simulations to calculate the power of the flame front
Satellite image
Vegetation /land use
Power of the flame front
Plan of presentation • Introduction • Use of SENTINEL and PLEIADES for contour and severity mapping • Use of PLEIADES images for precise cartography • Use of SENTINEL for risk mapping • Conclusion 38
Conclusions and perspectives • Different images for different uses • Big progress in precision • further developments – ONF • Pleiades in WUI (before and after fire) • Mapping indicator of impacts on vegetation after fire (vegetation typology x severity) • Contour mapping and severity for hivernal fires in mountain (surface fires) • Post fire erosion indicator (DEM + field+ satellite images + soil datas) – SERTIT • Mapping from RADAR data available at SERTIT for rapid recognition of burn scar area • Indicator of vegetation volume loss after fire • Post fire erosion indicator 39
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