remote sensing of fire - earth online - esaearth.esa.int/landtraining07/d4l1-pereira.pdf · remote...

Remote Sensing of FireJosé M.C. Pereira

Thursday 6 September, D4L1

6 September 2007 Lecture D4L1 Fires and burnt area detection José Pereira 2

"It was the month of bush fires, smoky skies, black hills, fleeing animals; the season of haze and hawks."

Paul Theroux, Sir Vidia's Shadow


• vegetation burning is a global scale environmental phenomenon.

• fires occur from the tundra to the tropical savanna, and from the desert to the evergreen rainforest.

• 3.5 x 106 km2 are estimated to have burned during the year 2000, resulting in the combustion of 2580 Tg of biomass, and in the emission of 4054 tg CO2. These are relatively low values, since 2000 was a La Niña year.

• the large extent of the area affected by fire, the very dynamic nature of the process, and the low accessibility of many key fire regions, make remote sensing an indispensable research and monitoring tool.

introduction | spectral signal | persistence | clouds - compositing | smoke | understory | spatial pattern | examples |


• fire produces 4 types of spectral signal observable from space:

• direct radiation from the flame front (heat & light)

• aerossols (smoke)

• solid residue (char & ash)

• altered vegetation structure (scar)

• active fire analysis relies on the thermal signal.

• burned area analysis is based on detecting the char and scar signals.



• active fires: the thermal signal is quite specific, unequivocal, especially when using instruments that do not saturate at low temperature. Confusion may remain with signals form oil refineries and volcanic eruptions, for example.

• one of the limitations of active fires is that they represent a snapshot, an instantaneous overview at the time of satellite overpass, because the thermal signal is very short-lived.

• burned area: the charcoal residue and the vegetation scar produce longer lasting spectral signals that allow the estimation of the area affected by fire. The burned area signal is, therefore, more adequate to assess ecological and economic damages, and to estimate atmospheric emissions, for example.

• however, these signals are prone to confusion with other types of land surface, with other land cover change processes, or evenwith external effects such as shadows.



• remotely sensing burned areas involves a series of problems:

• target variability / spectral separability

• signal persistence

• clouds

• smoke

• vegetion canopy / understory fires

• target spatial pattern / fragmentation



• the spectral signal resulting from surface darkening due to charcoal deposition is quite a specific consequence of vegetation burning.

• however, it is relatively short-lived. It is attenuated by wind scattering, or washed out by rain, in a period of a few weeks tosome months after the fire.

• the vegetion scar signal is more persistent, lasting from a few weeks in tropical savannas, to several years in the boreal forest.

• but the vegetation scar signal is less specific. Partial or total vegetation removal may result from harvesting, grazing, windthrow, or defoliation by pathogenic agents.



scars of variable age (≠ years) in the Siberian boreal forest.

Recently burned area and attenuated signal, a few days old, in the Colombian savanna.



• in the visible (0.4-0.7 μm), charcoal is confused with dense dark vegetation, water, dark soils, wetlands, cloud and terrain shadows.

• some of these confusions also may occurr in the SWIR, especially at 2.0-2.5μm.

• however, an increase in SWIR reflectance is often observed, as a result of burning dense, dark vegetation.

• the NIR is, unquestionably, the best spectral region to discriminate between burned areas and other surfaces, such as vegetation and not-too-dark soils.

Pereira e Govaerts, 2001, EUMETSAT



• the best bi-spectral space to design an index to detect and map burned areas is the NIR-SWIR (GEMI3, NBR).

• the fire-induced spectral reflectance change is negative in the NIR and positive in the SWIR, generating a distinctive signal.

Pereira, 1996, TGARS



• the best color composites to view burns are of the kind R-SWIR G-NIRB-Vis (e.g. TM 743, or AVHRR 321).



• most burned area mapping work at regional/continental/global scale is based on multi-temporal, change detection approaches:

• burning the land surface typically causes:

• decrease in albedo (with exceptions...)

• increase in temperature (albedo , ET )

• loss of photosynthetic signal

• decrease in vegetation / soil moisture (with exceptions...)

• burned area detction algorithms use one or more of the spectral signals generated by these biophysical changes of the surface.



• but, the Devil lies in the details... The exceptions mentioned include, e.g.:

• arid zones in Australia, with relatively sparse dark vegetation, on bright soils. Vegetation burning leads to an albedo increase.

• boreal forests, where burning increase solar irradiance at the soil surface, contributes towards melting permafrost, leading to an increase in surface soil moisture.

• seasonally flooded areas (e.g. Zambeze headwater, southernAfrica), and rice paddies, may also lead to confusion.

• cereal harvesting in the dark soils (chernozem) of the Ukraine and Khazakstan also cause false alarms.

• leaf fall in deciduous forests may be problematic for relativelycrude algorithms.



• fire-induced spectral changes in the Vis and SWIR may seem ambiguous or contradictory.

• in most cases, burning darkens the surface, but in some cases it brightens the surface.

• the outcome depends on the pre-fire phenological condition of the vegetation:

• when pre-fire NDVI is low (e.g. dry grass, very bright)burning leads to darkening.

• when pre-fire NDVI is high and the vegetation is moderately dark (e.g. forest) surface albedo may increase, especially if bright soil becomes exposed.

• this effect may be incorporated in to an algorithm, making the interpreation of the spectral change dependent (a function of) pre-fire NDVI.




• relationship between fire-induced reflectance change in the red (a), NIR (b) and SWIR (c) and pre-fire NDVI

•SPOT-VGT data fromsouthern Africa, Siberia e Iberian Peninsula.

Silva et al., 2004, IJRS


• the charcoal spectral signal is short-lived, especially when the fuel burned is very fine, such as in a grassy tropical savanna.



• post-fire spectral reflectance (ρ) dynamics of a burned area in an hydromorphic grassland (dambo) in the Western Province, Zambia, year 2000. bottom line, August 25; center line, August 28; top line, September 1.

• at 800nm reflectance increased from 0.15 to 0.25 (66%) in 1week, due to charcoal removal by wind, exposing bright sandy soil.

Pereira, 2003, IJWF



recently burned

1-2 days old

1 week old

• after a short time, the spectral signal of a burned savanna becomes dominated by the underlying soil signature. In a forest, the signal tends to be more persistent, because more biomass is burnt and fuel particles are larger, harder to scatter.



• the mean probability of seeing clouds when observing the Earth from space is 0.62 in the N hemisphere and 0.53 in the S hemisphere (WMO).

• vegetation burning takes place during the dry season, when these probabilities are lower. Still, some regions are problematic.

• problems with the obtention of cloud-free data led to the development of algorithms to creat multi-temporal composite images. The MaxNDVI was the 1st, and is the most used.

• however, MaxNDVI prefers more highly vegetated pixels, i.e. Pre-fire pixels. This delays detection of the burned area over a period of time that depends on the length of the compositing period.

• in the meantime, we’re losing the signal...



EXPRESSORCA 1996AVHRR

MNDVI M4 m2M4

m2 mAlb mAlbM4

Sousa et al., 2003, IJRS



(a) MNDVI, (b) MNDVI_mSWIR, (c) mRed, (d) NIRm3, and (e) darkm3.

• for SPOT-VGT, no thermal channel.Cabral et al., 2003, IJRS



• view zenith angle (VZA) histograms for each composite: (a) MNDVI, (b) MNDVI_mSWIR, (c) mRed, (d) NIRm3, and (e) darkm3.

Cabral et al., 2003, IJRS



• thus, algorithms developed to maximize the green vegetation signal are inadequate to map burned areas. Actually the maxNDVI is one of the worse.

• compositing is easier when thermal data are available, becasue they facilitate removal of clouds and cloud shadows.

• when the sensor lacks a thermal channel, algorithms based on the persistence of dark surfaces are appropriate, since cloud shadows are occasional.



• in temperate and boreal forests and shrublands, where the charcoal spectral signal lasts longer, one can wait until the end of the fire season to map burned areas.

• in tropical savannas and temperate grasslands, where the signal is ephemeral, mapping needs to be performed at shorter intervals during the dry season, simultaneously with the burning.

• at that time the atmosphere is contaminated by smoke aerosols, which affect surface observation and reduce the spectral contrast between distinct land cover types and surface conditions.



• smoke aerosol particles measure from 0.01μm to 1.0μm and are very efficient solar radiation scatterers.

• biomass burning smoke is also an absorbing aerosol, because it contains large amounts of black carbon (soot).

• the magnitude of the interference with surface observation

can be quantified by the aerosol transmittance, τaλ .



• α: Ängstrom parameter, characterizes aerosol particle size distribution. • β: Ängstrom turbidity coefficient, vertical measure of the amount of aerosol in the atmosphere.• κaλ: aerosol optical depth, measures the attenuation of radiation propagating through the atmosphere. • λ: radiation wavelength.

Amazonia, September 1995

Zambia, September1997

Pereira, 2003, IJWF



• in the Amazon case, aerosol transmittance in the visible range is <0.1, meaning the surface is totally obscured by smoke.

• in the SWIR range, transmittance goes up to values of 0.6 –0.8.

• during the dry season, in a very smokey atmosphere, the channels in the visible spectral domain will be useless for Earth surface observation.

• on the contrary, the SWIR spectral domain is much less affected by the smoke aerosol, allowing for a reasonably unperturbed surface observation.

• this is one more reason to favour the NIR-SWIR bi-spectral space over the Vis-NIR bispectral space, for monitoring and mapping burned areas.



• surface fires, which do not consume canopy fuels, predominate in tropical forests and savannas.

• canopy radiation interception and shadow casting may interfere with the detection of understory burns.

• problems with understory burn detection increase with tree canopy cover and leaf area index.

• we analysed this issue for the “miombo” (semi-deciduous savanna woodlands) of southern Africa.

• the results we obtained ought to extrapolate well to other savanna woodlands (e.g. in South America and Australia), but probably not to the evergreen rainforests, which are denser and have a more complex vertical structure.



• “miombo” woodlands in southern hemisphere Africa.



• simulation analysis using the HGORT model.

• in situ dendrometric data:stand density, crown dimensions, leaf area index.

• in situ spectroradiometric data: spectral signatures of bare soil, green grass, dry grass, burned surface, and tree leaves (reflectance and transmittance).

Pereira et al., 2004, RSE



• modeled spectral signatures, R (θI, θV, ΦR), for various points along the 99th percentile regression line. a) and b) early dry season; c) and d) late dry season. The JM distances between 1-month old burn and unburned are (a) 1.250; (b) 1.248; (c) 1.251; (d) 1.250. The JM distances between unburned and recent burn reach the maximum value of 1.414 in all cases.

Pereira et al., 2004, RSE



• recently burned areas in the miombo understory are clearly sperabale form unburned areas, using spectral data in the green-red-NIR. It is also possible to discriminate older burns (a few weeks old), but with somewhat lower accuracy.

• simulation results are very insensitive to stand structure variations, for the range of values analysed. Results depend almost exclusively on surface level spectral properties, becausethe tree crown layer is very transmissive (“transparent”).

• analysis of illumination/observation geometry scenarios showed that the spectral detectability of understory burns increases as the geometry approaches hotspot conditions.



• at the global scale there is very large variability between biomes and ecoregions in the extent of burning, spatial patterns of fires, and fire size distributions.

• the low spatial resolution data used in continental/global scaleanalyses are likely to introduce biases in fire maps and burned area estimates.

• the sign and magnitude of such biases depends on the extent and fragmentation pattern of burning, namely on the relative importance of small vs large burns. Thus, mapping accuracy is also expected to vary significantly in space.



• most algorithms used to create continental/global scale burned area maps are trained to capture relatively large, compact burns.

• heterogeneous fire patterns, resulting from mosaic-burning fire regimes are problematic for most current burned area detection and mapping methodologies.

• some tropical savannas, and most agricultural areas display these problematic space-time burning patterns. The bias induced by coarse spatial resolution imagery may lead to substantial underestimation of total area burned, or “shift” the seasonal profile of burning in mosaic-burning regions.



• agreement between Landsat and SPOT-VGT area burned estimates varies as a function of fire spatial fragmentation and size distribution.

• small, fragmented burning results in large underestimation of true burned area, at 1km resolution.

• in areas of large, compact fire scars, the SPOT-VGT 1km estimates are quite accurate, and may even lead to slight overstimation.

Dindar, Sudan

Okavango, Botswana

Mongu, Zambia

ETM+ (30m) SPOT-VGT (1 km)

Silva et al., 2005, RSE



Laris, 2005, RSE

• the large extent of area burned by the end of the season results from the coalescence of many small burns.

• if mapping is done frequently, there will smaller burned areas, and larger omissions errors.



Laris, 2005, RSE

• in each period, there are few 1km pixels with a large fraction of area burned.

• many of them would escape classification as burned.

• in the final, cumulative image, the classification task is eaasier, because contiguous burned patches are larger.

• classification accuracy for the final image only will be higher, but at the cost of a decreased monitoring frequency.



• Pareto boundary: methodology based on statistical indices extracted from the classical error matrix, to evaluate the influence of spatial data coarse resolution on the accuaracy of the final mapped product, obtained with hard classificationmethods.

• use fo the Pareto boundary may reveal to what extent the limited classification accuracy of a low spatial resolution burned area map is due to poor performance of the image classification algorithm, or to the low resolution of the imagery.

• we have developed a set of some 70 burned area maps, based on Landsat 30m imagery, and degraded their spatial resolution toevaluate the Pareto optimum, i.e. the highest achievable classification accuracy, given the observed spatial pattern of burning and the coarse resolution imagery pixel size.



Boschetti et al., 2004, RSE



AVHRR 1, 4 e 8 km

MODIS 500 e 250m

• in a fire regime with large contiguous burn scars it is possible, in principle, to obtain very accurate classification results, even at coarse spatial resolution, if the classification algorithm is good.This is what happens in the savanna grasslands of southern Sudan.



AVHRR 8km

4km1km

MODIS 500mMODIS 250m

• very small burns spatial pattern in an agricultural area of Zimbabwe. Performance of the sensors with resolution ≤ 1km leads to large mapping errors, even assuming a perfect classification algorithm.



• our objective is to evaluate the accuracy limits that can be expected when classifying burned areas, using imagery from sensor with spatial resolution varying from 250m (MODIS) up to 8km (Pathfinder AVHRR Land).

• the study covers the main ecoregions of the Earth where fire is an important environmental process, and relies on 70 classified Landsat scenes acquired without a formal statistical sampling procedure.

• the Landsat-based burned area maps on which the simulations are based sample the North American and Siberian boreal forests,temperate regions in Europe and the USA, the Asian steppe, diverse agricultural regions, tropical savannas and arid regions in South America, Africa and Australia.



• the Pareto analysis allows for:

• a priori definition of burned area mapping/estimation accuracy limits, which will vary between ecoregions and land cover types.

• identification of fire regimes likely to be missed or severely underestimated, at relatively coarse spatial resolutions (e.g. agricultural fires, burns in fishbone deforestation areas.

• quantify the tradeoff between burned area mapping frequency and accuracy.

• separate the classification error proportions due to algorithm deficiency and to imagery pixel size, thus highlighting the potential need and room for algorithm improvement.



• we have been applying these concepts about remote sensing of fire in various studies, at scales ranging from regional to global.

• several of those studies support estimation of greenhouse gas and aerosol emissions from biomass burning.

• our work has relied primarily on data from NOAA/AVHRR, SPOT-Vegetation, ERS-2/ATSR, ENVISAT/AATSR and also Meteosat, with spatial resolution varying from 1km to 5km.

• our work in Portugal uses Landsat imagery to map fire scars contry-wide. We have developed annual burned area maps from 1975-2005 (2006 in progress).



• 1st continental, multi-annual vegetation burning analysis.

• AVHRR GAC- based burned area, biomass burning and emissions estimates for Africa, 1982 – 1991.

•Paulo Barbosa’s Ph.D. dissertation (JRC/ISA, 2000).

highlight: discovered higher fire frequency in N hemisphere Africa.

Barbosa et al., 1999, GBC



•Global Burned Areas 2000 project. Integrated in the UN MilleniumAssessment e led by JRC/IES. The DEF/ISA contributed with the most area mapped of all teams: Africa, tropical South America, Europe and collaboration in Australia.

• highlight: importance of agricultural burning in the Russia-Kazakhstan border region.

Tansey et al., 2004, JGR



• mapping monthly burned area in S hemisphere Africa, during the year 2000, for SAFARI2000 e GBA2000 projects.

• Ph.D. Work of João Silva and Ana Sá(DEF/ISA)

• highlight: extent of area burned in Angola and DR Congo, and revision of regional fire seasonality.

Silva et al., 2004, JGR



• GBA2000: area burned in Africa and in Australia.

• Ph.D. Work of João Silva and Daniela Stroppiana (DEF/ISA).

highlight: quantified confirmation of the predominance of tropical burning in Australia.

Tansey et al., 2004, JGR



• relative surface albedo decrease induced by biomass burning in N hemisphere Africa, during January and December 1996.

• highlight: concentration of highest values in Sudanian savanna, drier and with less woody vegetation than the Guinean savanna.

Govaerts et al., 2002



• area burned in the Brazilian Amazon during the 2000 dry season mapped with SPOT-VGT data. Ph.D. dissertation A. Sousa.




Peak fire bi-monthly period



Vegetation fire counts coefficient of variation (σ / μ)



• monthly 0.5º screened WFA fire counts, 1996 - 2006.


acknowledgments

• the research presented here has been funded by a variety of sources, including the European Commission, the Fundação para a Ciência e Tecnologia (FCT, Portugal), a European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the Luso-American Foundation (FLAD) and the Programa de Desenvolvimento Educativo para Portugal (PRODEP).

• thanks to all my collaborators and co-authors, especially to those whose scientific publications were mentioned.

remote sensing of fire - earth online - esaearth.esa.int/landtraining07/d4l1-pereira.pdf · remote...

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