Burnt area detection in PDN using two post ... - Earth-prints · Burnt area detection in PDN using...
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Burnt area detection in PDN using two post fire images 1. Istituto Nazionale di Geofisica e Vulcanologia, Italy [email protected] , [email protected] 2. The University of Manchester, UK 3. Istituto di Fisica Applicata Nello Carrara Background Stefania Amici 1 2 , Leonardo Santurrii 3 , Alessandro Piscini 1 •The dynamic of open fire phenomena makes remote sensing from satellite a good tool for both fire detection and burn area assessment. • A multi-temporal approach rely on pre and post fire multispectral images to derive vegetation maps (e.g. Normalized Differential Vegetation Index-NDVI and Normalized Burn Ratio-NBR) •The Peak District National Park was affected by two major fires on Dovestones and Stalybridge area - 10 th April 2016, Fire1 and Stainery fire occurred on 8-9 th May 2016, Fire2. Methods RESULTS Conventional Approach •Pixels affected by fire trivially show a vegetation decrease; such pixel shows high positive dNDVI values (white areas in (b) ) •The dNDVI histogram shows show a relevant separation between burned area pixels and the unburned area pixels. •The two classes can be therefore separated by a simple thresholding technique, generating a low number of classification errors The proposed alternative method to be used when an image acquired before the fire is not available shows interesting results •The re-growth of vegetation in a burned pixel is supposed to be higher than that characterizing a pixel not affected by fire •Pixels characterized by high re-growth show lower values of dNDVIp •The dNDVIp histograms (l) and (o) shows that burned area pixels are still separated by the unburned ones. The They can be therefore separated by a thresholding technique •Time interval among the two acquisitions is a key parameter •It should be sufficiently large to allow a significant re-growth in the burned area, however, a too large period could originate error because of significant changes in the unburned vegetation •Histograms coming from Sentinel 2A show a lower separabilty because of a higher time extent among the image and because of the lower resolution of Landsat data (resulting in lower spatial frequencies) •The proposed technique based on dNDVIp values could be useful adopted also in conjunction with other procedures such as SAR data . Work in progress is based on NBR index study WHAT WE LEARNED •A multi-temporal approach rely on pre and post fire images to derive vegetation maps (g) Proposed multitemporal approach based on images acquired after the fire event (h) dNDVIp (a-posteriori dNDVI) map from sentinel data: black area is formed by pixels with lower values related to a vegetation regrowth after the fire (i) Burned area map obtained by a simple thresholding of dNDVIp map in (h) R.Carlà, L. Santurri, L.Bonora, C. Conese. “Multitemporal burnt area detection methods based on a couple of images acquired after the fire event” In Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, SPIE Remote Sensing 2009. Berlin, Germany References REMARKS Acknowledgements •Two post fire images can be used to detect burn area •Vegetation phenology plays key role Fire1 Fire2 •The authors thanks Prof. Julia McMoprrow for valuable feedback and support. •A special thanks to Dr. Gail Millin-Chalabi for organizing the field trip, taking pictures and describing the vegetation species. •Catherine Porter from PDNP is thanked for information about occurred fires. •SNAP by ESA and ENVI-HARRIS have been used to process the data •Sentinel playground visualization tool by synergize http://apps.sentinel-hub.com/sentinel-playground/ •For helping us to have a quick look of available data Landsat 8 4 May 2016 Landsat 8 5 June 2016 Fire 1 Fire 2 Sentinel 2A 20 April 2016 Sentinel 2A 6 June 2016 Sentinel 2A 19 July2016 Visualization in R (SWIR2), G (SWIR1) and B (Green) wavelength allows to visually identifying burned areas •dNDVIp values histograms show good separabilty •Phenology impact on Sentinel 2A data needs to be further analysis Landsat 8- Band 4 0.665 μm Band 5 0.865 μm Sentinel 2A- Band 4 0.665 μm Band 8 0.842 μm dNDVI derived by using Landsat 8 pair (a) Usual multitemporal approach based on a couple of images acquired one before and one after the fire (b) dNDVI map from Landsat data: White area is formed by pixel with high dNDVI values related to a vegetation decrease : (c) Burned area map obtained by a simple thresholding of dNDVI map in (b) (d) Histogram of dNDVI burned area pixels (yellow) and not burned area pixel s(light blue) (e) The same of ( c ) starting from Sentinel data (f) Histogram of dNDVI burned area pixel (blue) and not burned area pixel (red) coming from map (e) (b) (c) (d) (f) (g) NDVI is an index used measure of healthy, green vegetation in remote sensing data •The differential version of NDVI, named dNDVI is commonly used in assessment of vegetation change; if defined as dNDVI = NDVI T3 – NDVI T2 high dNDVI values are related to a vegetation decrease, Pixels affected by fire trivially show a vegetation decrease; such pixel shows high positive dNDVI values «a posteriori» dNDVIp •When data before the fire are not available, an «a- posteriori» dNDVI is defined, based on data acquired after the event dNDVIp = NDVI T3 – NDVI T4 Pixels affected by fire usually show a high re-growth of vegetation; in such case they show negative high modulus values of the dNDVIp (black areas in dNDVIp map) (h) (l) (i) (n) (o) dNDVIp dNDVI (e) Google Earth map of affected area 2016 dNDVI map using Landsat 8 May – 5 July images (l) Histogram of dNDVIp burned area pixel (purple) and not burned area pixel (pink) (m) Pic of a detail of the burned area (n) The same of (i) starting from Sentinel data (o) Histogram of dNDVIp burned area pixel (green) and not burned area pixel (purple) coming from map (n) (m) Conventional approach Proposed alternative approach Picture taken byDr. Gail Millin-Chalabi (a)
Transcript of Burnt area detection in PDN using two post ... - Earth-prints · Burnt area detection in PDN using...
Burnt area detection in PDN using two post fire images 1. Istituto Nazionale di Geofisica e Vulcanologia, Italy [email protected], [email protected]
2. The University of Manchester, UK 3. Istituto di Fisica Applicata Nello Carrara
Background
• .
Stefania Amici 1 2, Leonardo Santurrii3, Alessandro Piscini 1
•The dynamic of open fire phenomena makes remote sensing from satellite a good tool for
both fire detection and burn area assessment.
• A multi-temporal approach rely on pre and post fire multispectral images to derive
vegetation maps (e.g. Normalized Differential Vegetation Index-NDVI and Normalized Burn
Ratio-NBR)
•The Peak District National Park was affected by two major fires on Dovestones and
Stalybridge area - 10th April 2016, Fire1 and Stainery fire occurred on 8-9th May 2016, Fire2.
What we learned
Methods
RESULTS
Conventional Approach •Pixels affected by fire trivially show a vegetation decrease; such pixel shows high positive dNDVI values (white areas in (b) ) •The dNDVI histogram shows show a relevant separation between burned area pixels and the unburned area pixels. •The two classes can be therefore separated by a simple thresholding technique, generating a low number of classification errors The proposed alternative method to be used when an image acquired before the fire is not available shows interesting results •The re-growth of vegetation in a burned pixel is supposed to be higher than that characterizing a pixel not affected by fire •Pixels characterized by high re-growth show lower values of dNDVIp •The dNDVIp histograms (l) and (o) shows that burned area pixels are still separated by the unburned ones. The They can be therefore separated by a thresholding technique •Time interval among the two acquisitions is a key parameter •It should be sufficiently large to allow a significant re-growth in the burned area, however, a too large period could originate error because of significant changes in the unburned vegetation •Histograms coming from Sentinel 2A show a lower separabilty because of a higher time extent among the image and because of the lower resolution of Landsat data (resulting in lower spatial frequencies) •The proposed technique based on dNDVIp values could be useful adopted also in conjunction with other procedures such as SAR data . Work in progress is based on NBR index study
WHAT WE LEARNED
•A multi-temporal approach rely on pre and post fire images to derive vegetation maps
(g) Proposed multitemporal approach based on images acquired after the fire event
(h) dNDVIp (a-posteriori dNDVI) map from sentinel data: black area is formed by pixels with lower values related to a vegetation regrowth after the fire
(i) Burned area map obtained by a simple thresholding of dNDVIp map in (h)
R.Carlà, L. Santurri, L.Bonora, C. Conese. “Multitemporal burnt area detection methods based on a couple of images acquired after the fire event” In Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, SPIE Remote Sensing 2009. Berlin, Germany
References
REMARKS
Acknowledgements
•Two post fire images can be used to detect burn area •Vegetation phenology plays key role
Fire1
Fire2
•The authors thanks Prof. Julia McMoprrow for valuable feedback and support. •A special thanks to Dr. Gail Millin-Chalabi for organizing the field trip, taking pictures and describing the vegetation species. •Catherine Porter from PDNP is thanked for information about occurred fires. •SNAP by ESA and ENVI-HARRIS have been used to process the data •Sentinel playground visualization tool by synergize http://apps.sentinel-hub.com/sentinel-playground/ •For helping us to have a quick look of available data
Landsat 8 4 May 2016
Landsat 8 5 June 2016
Fire 1
Fire 2
Sentinel 2A 20 April 2016
Sentinel 2A 6 June 2016
Sentinel 2A 19 July2016
Visualization in R (SWIR2), G (SWIR1) and B (Green) wavelength allows to visually identifying burned areas
•dNDVIp values histograms show good separabilty •Phenology impact on Sentinel 2A data needs to be further analysis
Landsat 8- Band 4 0.665 µm
Band 5 0.865 µm Sentinel 2A- Band 4 0.665 µm
Band 8 0.842 µm
dNDVI derived by using Landsat 8 pair
(a) Usual multitemporal approach based on a couple of images acquired one before and one after the fire
(b) dNDVI map from Landsat data: White area is formed by pixel with high dNDVI values related to a vegetation decrease :
(c) Burned area map obtained by a simple thresholding of dNDVI map in (b)
(d) Histogram of dNDVI burned area pixels (yellow) and not burned area pixel s(light blue)
(e) The same of ( c ) starting from Sentinel data (f) Histogram of dNDVI burned area pixel (blue) and not burned
area pixel (red) coming from map (e)
(b) (c) (d)
(f)
(g)
NDVI is an index used measure of healthy, green vegetation in remote sensing data
•The differential version of NDVI, named dNDVI is commonly used in assessment of vegetation change; if defined as dNDVI = NDVIT3 – NDVIT2 high dNDVI values are related to a vegetation decrease, Pixels affected by fire trivially show a vegetation decrease; such pixel shows high positive dNDVI values
«a posteriori» dNDVIp •When data before the fire are not available, an «a- posteriori» dNDVI is defined, based on data acquired after the event dNDVIp = NDVIT3 – NDVIT4
Pixels affected by fire usually show a high re-growth of vegetation; in such case they show negative high modulus values of the dNDVIp (black areas in dNDVIp map)
(h) (l)
(i)
(n)
(o)
dNDVIp
dNDVI
(e)
Google Earth map of affected area 2016 dNDVI map using Landsat 8 May – 5 July images
(l) Histogram of dNDVIp burned area pixel (purple) and not burned area pixel (pink) (m) Pic of a detail of the burned area (n) The same of (i) starting from Sentinel data (o) Histogram of dNDVIp burned area pixel (green) and not burned area pixel (purple) coming from map (n)
(m)
Conventional approach
Proposed alternative approach
Pictu
re taken b
yDr. G
ail Millin
-Ch
alabi
(a)
