prefer 1 st annual review meeting, 5-6 dec 2013, milano-italy prefer wp 3.2 information support to...
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PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
PREFER WP 3.2
Information support to Recovery/Reconstruction
Task 7 Damage Severity Map
PREFER WP 3.2
Information support to Recovery/Reconstruction
Task 7 Damage Severity Map
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Remote sensing data for burn severity assessment
• The WP goal is to provide reliable information on fire effects over Mediterranean areas and do that in a way that is comparable from region to region and over time.
•Remote sensing data allows to evaluate the damages caused by fires even in remote or inaccessible zones.
LANDSATE 8 FALSE COLOR IMAGES (burned areas in red)
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
• Landsat and Sentinel satellites data are well suited for damage assessment. • The 30-meter spatial resolution is effective, and the spectral signals allow to detect burned areas. • Landsat and Sentinel provide continuous and repetitive coverage for most land areas of the world. This enables comparison of post-fire to pre-fire conditions. • Further, we plan to find a method for extracting burn severity through: hyperspectral and SAR images if they will be available, but the primary research activity is focused on multispectral methods.
Suited Remote Sensing Data
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Burn Severity Scale/CBINo Damage Low Medium High
0 0.5 1 1.5 2.0 2.5 3.0
• In Precedent Studies field-based indices have been introduced (CBI, geoCBI) based on a visual assessment of the quantity of fuel consumed. These indices were correlated to spectral indices based on multi-spectral images. (dNBR or the RdNBR). • Radiative Tansfer Model and burn severity simulation
State of art
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
A comprehensive approach to monitor burn severity on the landscape is composed of four interrelated elements:
• the definition of severity
• the algorithm for burn severity extraction
• the field measures to calibrate and/or validate remote sensing results;
• the implementation of a support chain which deliver product to users.
Each element influences the others, and PREFER attempts to integrate these in a unified system.
Objective
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
There is still some discrepancy in the way researchers and managers use the term “burn severity.”
We define BURN SEVERITY as the degree of environmental change caused by fire, or how much fire has affected the ecological community.
Fire Intensity and Burn Severity
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
•Initial Assessments post-event image required as soon after fire as possible. In this case we register many fire effects, but likely miss the bulk of green-up from plants that survived fire. • Extended Assessments post-event image acquired one growing season after include survivorship of plants that burned, and may be most relevant to the actual ecological severity of the burn.
Acquisition Time importance
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Burn severity at pixel level
• Pixel concept• Vertical variability• Horizontal variability
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
• How fire changes a real site in nature is very complex, many response variables can be measured in order to define the change.
• Moreover, the site may be structurally composed of many strata.
•Terefore, the severity detected at this level (30 m) is an aggregate of many variables over many components of the site.
•This concept of severity is what we attempt to capture by high-resolution images.
Conclusion
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
MULTISPECTRAL ALGORITHMMULTISPECTRAL ALGORITHM
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
PRE-FIRE----- Mean----- Mean+/- std----- Max/Min
POST-FIRE----- Mean----- Mean+/- std----- Max/Min
Burn severity and Multi-Spectral High Resolution Data
GOLFO ARANCI AREA OLI
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
1
-1 -1
1
NBR
2
2
SWIRNIR
SWIRNIRNBR
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
DNBR
firepostfirepre NBRNBRDNBR
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
1
Pre-Fire CIR
Post-Fire CIR
DNBR
-0.2
1
DNBR
SOUTH OF SARDINIA
OLI
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Damage Level No Damage: the area is indistinguishable
from pre-fire conditions.Low Damage: little change in cover and
mortality of structurally vegetationMedium Damage: mixture of effects
ranging in the pixel from low to high changeHigh Damage: Vegetation has high to
complete mortality.
Burn Severity Map
Kompsat RapidEye
PREFER BURNED AREA
GOOGLE EARTH
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
REDNIR
REDNIRNDVI
2
2
SWIRNIR
SWIRNIRNBR
1
1
SWIRNIR
SWIRNIRNDII
Improvement to Burn severity Map
•DNBR tend to saturate. • Our idea to improve DNBR consists in computing several indices, each one capable to assess different characteristics of the vegetation and possibly capable to evaluate the effect on it of fire.
3/)( DNBRDNDIIDNDVIBSI
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Golfo Aranci
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
San Basilio
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
HYPERSPECTRAL ALGORITHMHYPERSPECTRAL ALGORITHM
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
• The possibility provided by hyperspectral images to compute several indices
• In this study we want to base the severity of the damage based on physical measurements that can be measured in field and at the same time that can be estimated by hyperspectral satellite imagery.
• Spectral signatures were collected in field on two areas test representative of the typical Mediterranean vegetation.
Methodology
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Lunghezza d’onda
Rif
lett
anza
Field data analysis
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Field data analysis
•The spectra collected during the campaign have joined to a picture, this allows to visualize the examined vegetation damage level.
• Inverse radiative transfer model to extract biophysical characteristics from field spectral measures.
•The analysis results showed that: Cab chlorophyll content in µg.cm-2, Car carotenoid content µg.cm-2, Cbrown brown pigment content (%), Cw Equivalent Water Ticknes (cm), Cm Leaf Mass Area( LMA) in (g.cm-2 and Leaf Area Index ( LAI) leaf area index; are the best representative biophysical parameters for damage severity levels.
LAICabCwCar
CbrownCm
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
TRAINING SET
Field Data
Pre-processing
Modtran
Linear MixingRadiative Transfer Model
Sensor Transfer Function
Biophysicalparameter
Simulate image
Burned Area Simulation
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
Future Developments
Multispectral- Improve the algorithm for damage assessment- Develop an automatic method for downloading (if possible) and pre-processing Landsat 8 images and calculate Burn Severity.- Definition of the strategy for the extended assessment of damages.
Hyperspectral- Complete the algorithm for image simulation- Develop a method for burn severity assessment
PREFER 1st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy
THANK YOU