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Fires in the Sardinia Island are one of the most important environmental factors controlling the ecosystem´s function and structure. The evaluation of fire effects by means of remote sensing is economically and practically the best way to assess fire damage, before going to the field. The use of alternative techniques for fire effects assessment is needed, in particular to characterize the biomass loss at the regional level. Radar remotely sensed data can provide great advantages with respect to optical sensors. The paper is devoted to show the results obtained by applying a semi-automatic algorithm to the images of the L-band SAR sensor PALSAR, on board of the ALOS satellite, for the estimate of the burned area. To assess the quality of the estimate, the radar based results have been compared with those obtained from optical data and ground based information.
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European J ournal of Remote Sensi ng - 2012, 45: 233-241
doi: 10.5721/EuJRS20124521
Fire Damage Assessment in Sardinia: the use of
ALOS/PALSAR data for post re effects management
Nicolas Mari1,2*, Giovanni Laneve3, Enrico Cadau4 and Ximena Porcasi1
1 Instituto Gulich - Comisión Nacional de Actividades Espaciales, Centro Espacial Teólo Tabanera,
Córdoba, Argentina
2 Instituto de Clima y Agua - Instituto de Nacional Tecnología Agropecuaria (CIRN-INTA) Hurlingham,
Buenos Aires, Argentina
3 Universita di Roma “La Sapienza”, Centro de Ricerca Progetto San Marco, Roma, Italy
4 Serco S.p.A. Frascati (RM), Italy
*Corresponding author, e-mail address:
Fires in the Sardinia Island are one of the most important environmental factors controlling
the ecosystem´s function and structure. The evaluation of re effects by means of remote
sensing is economically and practically the best way to assess re damage, before going
to the eld. The use of alternative techniques for re effects assessment is needed, in
particular to characterize the biomass loss at the regional level. Radar remotely sensed data
can provide great advantages with respect to optical sensors. The paper is devoted to show
the results obtained by applying a semi-automatic algorithm to the images of the L-band
SAR sensor PALSAR, on board of the ALOS satellite, for the estimate of the burned area.
To assess the quality of the estimate, the radar based results have been compared with those
obtained from optical data and ground based information.
Keywords: SAR, Forest Fires, burned area, Mediterranean, optical data.
During year 2009, the Island of Sardinia, suffered 684 res of important size, affecting 37104
ha of which 12270 ha were wooded vegetation, as reported by the JRC 2009. According to
historical data, the island is burned every year with important impacts in the loss of wooded
vegetation. It is thought that longer dry weather periods, can negatively contribute to the
biomass loss, as fuels gets drier and more susceptible to burn [Bajocco et al., 2007]. Related
to this, there is a growing concern in the conservation of wooded areas as sinks of carbon
and, on the other hand, to avoid high frequency res, as they contribute to the release of extra
greenhouse gas emissions to the atmosphere. Therefore the estimation of burned areas and
the quantication of biomass loss is a critical parameter to understand how res contribute
Mari et al. Fire Damage as sessment in Sardinia i sland with SAR data.
to the consumption of different vegetation types, and how this is related to the loss of carbon
stocks and its release to the atmosphere. Some research papers reported the utility of Synthetic
Aperture Radar (SAR) data for providing information on patterns of disturbance by detecting
re scars [Bourgeau-Chavez et al., 1997; Cahoon et al., 1994, Kasischke et al., 1993; Kasischke
et al., 1997], and for studying re effects in forested areas [Tanase et al., 2010]. The ability to to
detect burned areas with SAR, as for any ecological process, will depend rstly on dening
the optimal system parameters, including microwave frequency, polarization, incidence angle,
resolution, and sampling frequency [Kasischke et al., 1997]. These characteristics will determine
the type of backscatter mechanisms over forested terrains, and related to these, the type of effects
produced by the re disturbance. The ALOS/PALSAR sensor is a L-band microwave frequency
(23cm) radar which has a deep penetration capability under the canopy, interacting with large
branches, tree stems, and even has some interaction with the ground [Le Toan et al., 1992]. It is
expected that the effect of re on the reduction of the canopy structure, can produce a decrease of
the backscatter signal, depending on the polarization mode. Optimal polarization in burned area
detection was found to be the HV cross-polarized conguration (Transmit horizontal, receive
vertical) [Keiko et al., 2009], since exhibits good sensitivity to biomass, and being least affected
by forest types and ground conditions [Le Toan et al., 2004]. The incidence observation angle
of the radar beam will also affect on the proportion of the backscatter signal, as different angles
result in exposure of different branch structures and orientations. Therefore, the overall pattern
of the post-re canopy structure, and the optimal SAR system parameters, will determine the
interpretation of the associated re effects. For Mediterranean pine forest, Tanase reported good
agreements for L-band and HV polarization to burn severity assessment. In this work, we intend
to demonstrate the ability of ALOS/PALSAR data to estimate the effect of re over different
Mediterranean vegetation types and land covers, and to develop a methodology for burned area
estimation from SAR data, validated by using traditional optical burned area estimation methods
and ground truth data.
Material and Methods
Study Area
The Island of Sardinia is characterized by Mediterranean climate with hot and dry summers,
and a rainy season concentrated during winter and spring seasons. The heterogeneity of
topographic forms comprises hilly regions and interior valleys. Along the coast and river
valleys, vegetation is dominated by sclerophyllous shrubs, thermo Mediterranean Quercus
ilex forests, and agricultural lands. Inland areas are characterized by forest stands combined
with pastures and shrublands. The principal forest formations include mesomediterranean
Quercus ilex and Quercus suber forests. At higher elevations the sclerophyllous oak forests
merge with broadleaved forests of Quercus congesta and Quercus ichnusae [Bajoco et
al., 2007]. In the Sardinia Island wild res are one of the most important environmental
controls affecting the ecosystems functioning and structure, being responsible for the actual
shape of landscapes and vegetation forms. Nevertheless, changes in the natural re regime
have led to a higher frequency and intensity of res, representing a factor of degradation,
with signicant impacts on forest stands. In this work we analyzed a re occurred the 23d
of July of 2009 in Sardinia island, in the locality of Pardu along the municipalities of Pau,
Villaurbana, Usellus and Villa Verde (lat: 39 49 35.3 N lon: 8 48 02.8 E). The re affected
a total of 2242 ha. as reported by the reports of the CFVA (Corpo Forestale e di Vigilanza
European J ournal of Remote Sensi ng - 2012, 45: 233-241
Ambientale) of the Regione Autonoma della Sardegna, from which 50% of the area was
wooded vegetation (Fig. 1).
Figure 1 - The island of Sardinia and the Study area with detail of the burned perimeter. ALOS
image represented in RGB: HH, HV, HH.
SAR data
The SAR data used in this study corresponds to the Advanced Land Observing Satellite
(ALOS) Phased Array Type L-band SAR (PALSAR) (Fig. 2). Images were acquired in the
pre and post re dates of the re event, the 22 of June 2009 and 22 of September 2009
respectively. The pre-re date is 31 days prior to the re, and the post re image was acquired
61 days after the event. ALOS images were calibrated by using NEST software* in order to
retrieve σ0 (dB), and then co-registered on a common reference (Geographic WGS-84).
SAR -Statistical Analysis
Mean dB values of pre and post re sites were extracted for the different vegetation types
affected by re. Statistical analysis was made separately for each of both HH and HV
polarization modes. Negative differences in dB between the pre and post re events for
each vegetation types were interpreted to be related to re effect (Student’s Test).
SAR-Burned Area estimation
The analysis was made using a subset of the entire image around the burned perimeter (The
ground truth perimeter was obtained from Regione Autonoma della Sardegna). We calculated
the difference between pre and post re imagery only for HV polarization mode (∆HV). The
resulting image was ltered with an “Enhanced Frost” adaptive lter with an 11x11 window size,
with the purpose of isolating positive values related to higher damage levels. Subsequent positive
values were selected as burned and transformed to a binary map (re /non re) (Fig. 2).
Optical data
We use an optical SPOT-4 image of the post re event (27/07/2009). A simple threshold
methodology was developed to retrieve the burned perimeter based on Normalized Difference
Vegetation Index (NDVI), Normalized Burn Ratio (NBR) and Near Infrared (NIR) band 3
as main inputs (Fig. 3). Optimal thresholds were obtained from visual interpretation for the
3 inputs independently, avoiding commission errors as much as possible.
Mari et al. Fire Damage as sessment in Sardinia i sland with SAR data.
Figure 2 - ALOS/PALSAR data, Pre-re event at top left, Post-re event, top right, Difference in
HV polarization, down left, and the Burned area estimated from positive threshold values from the
∆HV SAR data.
Complementary data
Burned vegetation types were recognized with the Corine Land Cover 2006 map**.
Data sample extraction was assessed with an Ortofoto image mosaic obtained on line
by a WMS server. Four Vegetation types and land covers were identified to be affected
by the Pardu fire: Coniferous forest, Natural Grasslands, Sclerophyllous vegetation
and Agricultural areas.
Results and Conclusions
HH Polarization exhibits a similar and continuous effect of increased backscatter over the
different vegetation types for the post re event (Fig. 4b). It has been proved that the effect
of the increased backscattering response for the post re event with HH polarization is
attributed to local dielectric properties of the scatterers, with higher soil moisture content
[Tanase et al., 2010]. Nevertheless, for the HV polarization it is observed mayor variability
in the response of backscatter signals for the different vegetation types, with a more evident
reduction for the woody vegetation types (Fig. 4a).
Assuming dB data is normally distributed; sclerophyllous vegetation presented the greater
reduction in mean dB values (Mean reduction= 3.4dB, n=1412, p< 0.001), suggesting a
more structural damage in comparison to the other vegetation types.
European J ournal of Remote Sensi ng - 2012, 45: 233-241
Figure 3 - SPOT4 Optical data, Normalized Burn Ratio (NBR) top left, Normalized Difference
Vegetation Index (NDVI) top right, Near Infrared (NIR) down left, and the Burned area estimated
from thresholds of the indices and spectral NIR data.
Figure 4a - Interactions between pre and post re SAR data with HV cross polarized mode.
Mari et al. Fire Damage as sessment in Sardinia i sland with SAR data.
Figure 4b - Interactions between pre and post re SAR data with HH polarized mode.
Coniferous forest also showed a signicant reduction in mean dB values (Mean reduction=
1.2dB, n=951, P<0.001), but with a less mean difference in comparison to Sclerophyllous
vegetation (Tab. 1).
Table 1 - Statistical Analysis for the pre and post re events (Student’s Test), considering the effect
of different polarizations over diverse vegetation types.
Post-re Pre-re Post-re Pre-re
Types Mean dB S.D Mean dB S.D Mean dB S.D Mean dB S.D
Areas -13.66897 3.032 -15.131 1.909 *** -21.267 2.996 -21.882 3.419
Forest -1081376 1.854 -11.465 2.206 -17.858 2.412 -16.619 2.616 ***
Grasslands -14.64136 3.279 -15.235 3.173 *** -22.179 2.752 -22.276 3.031
Vegetation 12.81717 1.740 -13.098 1.724 *** -21.084 1.857 -17.687 1.883 ***
Natural Grasslands were insensitive to the signal of HV polarization, meaning that there
is probably no utility in using HV polarization mode to verify re effects on herbaceous
types for these typical Mediterranean regions. Nevertheless it either could be also possible,
that grasslands recovery is faster after 60 days, and structural damage is almost repaired.
Agricultural Areas presented a similar increasing behavior as seen with the HH polarization
European J ournal of Remote Sensi ng - 2012, 45: 233-241
mode, remaining less clear what are the principal interactions involved. The burned area
estimation methodology was based on the fact that HV polarization resulted to be more
sensitive to re effects in wooded vegetation. The difference in HV (Pre-Post) evidenced a
spatial correlation with the wooded vegetation types, with higher dark dB values related to
structural damage (Fig. 3). The application of the enhanced frost adaptive lter was done
in a trial and error basis, reaching the 11x11 window which showed the better separability
between burned/unburned values (Fig. 3). Results show an agreement between 70 and 80%
of the burned area for Coniferous and Sclerophyllous vegetation as compared to the ground
truth perimeter. The Agricultural and Grassland areas presented a low agreement between
25-35% (Fig. 5). Other studies in tropical forest showed similar results for Agricultural
mixed areas [Hoekman, 1999]. Omission errors were evaluated for each vegetation type,
according to their relative proportion inside the ground truth perimeter: Agricultural
Areas had the higher omission error (0.74) followed by Coniferous Forest (0.65). Natural
Grasslands (0.27) and Sclerophyllous types (0.21). There were no commission errors
according to the information of the ground truth perimeter. It is conclusive that the shrubby
vegetation type can be more exposed to re ames, with the consequent total destruction
of the structural components of the canopy, more suitable to be detected with SAR data
(See example in Figure 6). The less reduction of mean dB values for Coniferous forest in
comparison with the sclerophyllous types may be related to the height of the trees, which
are in less contact with re ames. These results indicate the possibility of using ALOS/
PALSAR L-HV imagery for re Damage Assessment in forested Mediterranean Regions,
especially under challenging weather conditions. A mixed optical-SAR data methodology
could be explored for better discriminations of all types of vegetation structures.
Figure 5 - Total agreement for the different vegetation types from optical reectance data and SAR
Mari et al. Fire Damage as sessment in Sardinia i sland with SAR data.
Figure 6 - Burned Vegetation Types from Monti Arci area.
This work has been nanced by Comision Nacional de Actividades Espaciales (CONAE)
and the Agenzia Spaciale Italiana (ASI) in a convening with Centro di Riccerca Projetto
San Marco (CEPSM). The PALSAR data and Spot data were obtained as a courtesy of the
European Space agency (ESA). The on-ground pictures have been taken by Dario Secci.
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Received 03/10/2011, accepted 16/03/2012
... For example, Lucas et al. (2010) examined the relationship between PALSAR HH and HV backscattering coefficients and above ground biomass and concluded that the retrieval algorithms ideally needed to consider differences in surface moisture conditions and vegetation structure. Mari et al. (2012) used PALSAR backscatter information to estimate the effect of fire over different Mediterranean vegetation types. The results suggested that HH polarization exhibited a similar and continuous effect of increased backscatter after fire over different vegetation types, while for the HV polarization, a significant reduction was observed for sclerophyllous tree and coniferous forest. ...
... fire, deforestation), especially a stand clearing event. Under that circumstance, there will be a sudden and considerable increase of Sigma0 in HH and decrease of Sigma0 in HV due to the absence of woody stems and large branches and presence of ground layer which could cause the increase of surface scattering and reduction of volume scattering (Isoguchi et al. 2009;Mari et al. 2012). ...
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Post-fire forest regeneration is crucial to forest management. Three different restoration treatments including natural regeneration (NR), artificial regeneration (AR), and artificial promotion (AP), were adopted in the Greater Hinggan Mountain area of China after a serious fire occurred on May 6, 1987. NR is a control treatment where recovery occurs naturally without intervention, AR comprises salvage logging followed by planting, while AP includes regeneration by removing dead trees, weeding, and tidying to promote seed germination. In this study, the objective was to detect and compare the effects of the three restoration treatments using radar indices derived from ALOS/PALSAR data. Four time-series SAR images were pre-processed to acquire the backscattering coefficients. Then the coefficients in both HH and HV polarization were examined and two radar vegetation indices were derived and evaluated, based on which, the post-fire forest dynamics under different restoration treatments were detected and compared. The results showed that the forests under NR presented a completely different recovery trajectory compared to those under the other two treatments. This difference could be characterized by both the backscattering intensity in HH and HV polarization and two radar indices. This study indicated the effects of different restoration treatments, as well as demonstrated the applicability and efficiency of radar remote sensing techniques in forest monitoring and management.
... No existe una literatura muy profusa en torno al uso de datos SAR en el monitoreo de áreas quemadas, sin embargo, algunos autores han estudiado la utilidad de diversos sensores SAR en la detección de áreas quemadas (Huang et al., 2004, Mari et al., 2012, Gimeno et al., 2004, Bourgeau-Chavez et al., 2002, así como la sensibilidad de distintas configuraciones de adquisición, frecuencias y polarizaciones a la severidad de dichas áreas quemadas (Tanase et al., 2010b). También podemos encontrar literatura asociada a las características polarimetricas de áreas quemadas (Tanase et al., 2013), usando varios tipos de descomposiciones de los blancos. ...
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... Immediately after a severe fire event and for the first post-fire year, oppositely to the perceptible increase already described for VV backscatter, a noticeable decrease in the VH backscatter is observable (Figure 4). This opposite behaviour of the two polarizations accords with other studies (Imperatore et al. 2017;Mari et al. 2017;. The burn of stems and large branches reduces the volumetric backscattering contribution of these structural vegetation components (i.e., scatterers), to which the cross-polarized signal is sensitive. ...
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In the present study, the temporal and spatial dynamics of the post-fire recovery of different Mediterranean vegetation types during the three years after the fire event were analyzed, according to different fire severity categories, integrating the use of Synthetic Aperture Satellite Radar (SAR) (Sentinel-1) and optical (Sentinel-2) image time series. The results showed that Mediterranean forest species and shrub/herbaceous species are adapted to fire, with high efficiency in restoring the vegetation cover. Differently, the ecological vulnerability of non-native eucalyptus plantations was found in a lower recovery trend during the observation period. The use of optical short-wave infrared (SWIR) and SAR C-band-based data revealed that some ecological characteristics, such as the woody biomass and structure, recovered at slower rates, comparing to those suggested by using near-infrared (NIR) and red-edge data. An optimized burn recovery ratio (BRR) was proposed to estimate and map the spatial distribution of the degree of vegetation recovery.
... Change detection (CD) from remote sensing images identifies changes by analyzing multitemporal images acquired over the same geographical area at different times, such as land use/land cover, damages due to earthquakes, floods and fires, changes of roads, cities and plants (Chen, Moriya, Sakai, Koyama, & Cao, 2014;Lu, Mausel, Brondizio, & Moran, 2004;Mari, Laneve, Cadau, & Porcasi, 2012). In the past three decades, lots of CD methods have been proposed to automatically achieve accurate CD results (Hao, Shi, Zhang, & Li, 2014;Moser, Angiati, & Serpico, 2011). ...
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Several studies have taken advantage of polarimetric synthetic aperture radar (PolSAR) to monitor forest disturbance caused by wildfire given its higher sensitivity to forest structure compared to single polarization SAR. This letter explores the capability of a simple volume scattering model (SVSM) to characterize burned forested area caused by wildfire. The SVSM considers a shape factor and geometric randomness to model a nth cosine probability density function (PDF) assumption of the rotation angle with respect to the line of sight. The shape factor describes the shape of elements that constitute the forest canopy, while the geometric randomness represents the variance of the PDF. Two quad polarization L-band PALSAR-2 data acquired over Fort McMurray, AB, Canada, in 2015 and 2016 before and after a severe wildfire are used for this exploration. The ability of the shape factor is evaluated first for the coniferous and broadleaf tree classification, which achieves an overall accuracy as high as 77.41% and kappa of 0.55. The simple linear regression between the burn classes and geometric randomness change shows that the geometric randomness change has a high potential for the light, modest, and severe burn classes estimation.
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This article focuses on the mapping of fire burn scars, fire severity and soil erosion susceptibility using multi-sensoral satellite data. An automatic procedure for the mapping of fire affected areas and for the estimation of fire severity using Sentinel-2 data is presented. The Sentinel-2 based classification results are compared to a burn scar derived by a semi-automatic object-based approach using Sentinel-1 amplitude and coherence time-series data systematically processed by the Sentinel-1 InSAR Browse service implemented on the Geohazard Exploitation Platform (GEP) of ESA. Further, a transferable approach to compute a soil erosion susceptibility index based on Pléiades data is presented. The SAR- and optical-data based methods are applied in a test area near Marseille/Vitrolles, France, which was affected by severe forest fires in August 2016.
Optical and radar satellite remote sensing have proven to provide essential crisis information in case of natural disasters, humanitarian relief activities and civil security issues in a growing number of cases through mechanisms such as the Copernicus Emergency Management Service (EMS) of the European Commission or the International Charter ‘Space and Major Disasters’. The aforementioned programs and initiatives make use of satellite-based rapid mapping services aimed at delivering reliable and accurate crisis information after natural hazards. Although these services are increasingly operational, they need to be continuously updated and improved through research and development (R&D) activities. The principal objective of ASAPTERRA (Advancing SAR and Optical Methods for Rapid Mapping), the ESA-funded R&D project being described here, is to improve, automate and, hence, speed-up geo-information extraction procedures in the context of natural hazards response. This is performed through the development, implementation, testing and validation of novel image processing methods using optical and Synthetic Aperture Radar (SAR) data. The methods are mainly developed based on data of the German radar satellites TerraSAR-X and TanDEM-X, the French satellite missions Pléiades-1A/1B as well as the ESA missions Sentinel-1/2 with the aim to better characterize the potential and limitations of these sensors and their synergy. The resulting algorithms and techniques are evaluated in real case applications during rapid mapping activities. The project is focussed on three types of natural hazards: floods, landslides and fires.
A contrast-sensitive Potts model (CSP) custom-designed for change detection is presented using remotely sensed images. In traditional Potts model, a constant penalty coefficient is used, which results in ignorance of significant details and excessively homogenous patches during change detection using the difference image generated from multitemporal images. In the proposed CSP, the difference image is divided into unchanged, uncertainty and changed regions. Then different linear functions are introduced instead of the constant penalty coefficient for different regions. Two experiments were carried on optical satellite images, and the results indicate that the proposed CSP produces more accurate change maps than some state-of-the-art methods.
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Forest fires are in the focus of the ����� �i�ot �ro�ect ��nte�rate�� ��ste�� for Fire �is�� ����� �i�ot �ro�ect ��nte�rate�� ��ste�� for Fire �is�� Mana�e��ent) fun��e�� b� the �ta�ian ��ace A�enc� �A��). The �ro�ect, starte�� �ate in November 2008. It is due for completion by end-2011. The EO �art of the �ro�ect is centre�� on ��) �A� borne obser�ation in the �, �� an�� the �� is centre�� on ��) �A� borne obser�ation in the �, �� an�� the �� ban��s, fro�� A�� an�� E�A ��atfor��s ��os��o�������e�� an�� En�isat; �2) on T��/M��/�W��/ NIR-and Red, where appropriate-observation by opto-electronic payloads operating at all spatial resolutions from 200�� onwards (SE�IRI, M�DIS, �R�IR, �RG, TM, ASTER, from 200�� onwards (SE�IRI, M�DIS, �R�IR, �RG, TM, ASTER, ��������) an�� �3) u�on �A� �er� hi�h reso�ution ���os��o ����Me��) an�� V�N�� obser�ation by new commercial or dual-use satellites. The system, of which the appointed user is the Italian Department of Civil Protection (DPC), is ex�ecte�� to ��ea� at once with �aw enforce��ent �burn scar ��a��in�), �re�are��ness �ris�� ��a��in� an�� urban interface fire contin�enc� ��annin�) an�� o�erationa� issues �fire ��etection and propagation prediction). It will be demonstrated in three operational theaters (northern Italy – Liguria, southern Italy – Calabria, and the island of Sardinia), all characterized by hi�h frequenc� of occurrence of fires, but �reat�� ��ifferin� in ter��s of fires st��e.
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This paper addresses the use of radar remote sensing to map forest above-ground biomass, and discusses the use of biomass maps to test a dynamic vegetation model that identifies carbon sources and sinks and predicts their variation over time. For current radar satellite data, only the biomass of young/sparse forests or regrowth after disturbances can be recovered. An example from central Siberia illustrates that biomass can be measured by radar at a continental scale, and that a significant proportion of the Siberian forests have biomass values less than 50 tonnes/ha. Comparison between the radar map and calculations by the Sheffield Dynamic Global Vegetation Model (SDGVM) indicates that the model considerably overestimates biomass; under-representation of managed areas, disturbed areas and areas of low site quality in the model are suggested reasons for this effect. A case study carried out at the Bdingen plantation forest in Germany supports the argument that inadequate representations of site quality and forest management may cause model overestimates of biomass. Comparison of the calculated biomass of stands planted after 1990 with biomass estimates by radar allows identification of forest stands where the growth conditions assumed by the model are not valid. This allows a quality check on model calculations of carbon fluxes: only calculations for stands where there is good agreement between the data and the model predictions should be accepted. Although the paper only uses the SDGVM model, similar effects are likely in other dynamic vegetation models, and the results show that model calculations attempting to quantify the role of forests as carbon sources or sinks could be qualified and potentially improved by exploiting remotely sensed measurements of biomass.
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The objective of this paper is to identify land-cover types where fire incidence is higher (preferred) or lower (avoided) than expected from a random null model. Fire selectivity may be characterized by the number of fires expected in a given land-cover class and by the mean surface area each fire will burn. These two components of fire pattern are usually independent of each other. For instance, fire number is usually connected with socioeconomic causes whereas fire size is largely controlled by fuel continuity. Therefore, on the basis of available fire history data for Sardinia (Italy) for the period 2000–2004 we analyzed fire selectivity of given land-cover classes keeping both variables separate from each other. The results obtained from analysis of 13,377 fires show that for most land-cover classes fire behaves selectively, with marked preference (or avoidance) in terms of both fire number and fire size. Fire number is higher than expected by chance alone in urban and agricultural areas. In contrast, in forests, grasslands, and shrublands, fire number is lower than expected. In grasslands and shrublands mean fire size is significantly larger than expected from a random null model whereas in urban areas, permanent crops, and heterogeneous agricultural areas there is significant resistance to fire spread. Finally, as concerns mean fire size, in our study area forests and arable land burn in proportion to their availability without any significant tendency toward fire preference or avoidance. The results obtained in this study contribute to fire risk assessment on the landscape scale, indicating that risk of wildfire is closely related to land cover.
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Synthetic aperture radar (SAR) data at X-, C-, and L-bands have been investigated to determine the relationship between backscatter and forest burn severity over three sites in Spain. The dependence of SAR backscatter on local incidence angle and environmental conditions has been analyzed. At HH and VV polarizations, the backscatter increased with burn severity for X- and C-bands, whereas it decreased for L-band. Cross-polarized (HV) backscatter decreased with burn severity for all frequencies. Determination coefficients were used to quantify the relationship between radar backscatter and burn severity for given intervals of local incidence angle. For X- and C-band copolarized data, higher determination coefficients were observed for slopes oriented toward the sensors, whereas for cross-polarized data, the determination coefficients were higher for slopes oriented away from the sensor. At L-band, the association strength of cross-polarized data to burn severity was high for all local incidence angles. C- and L-band cross-polarized backscatter showed better potential for burn severity estimation in the Mediterranean environment when the local incidence angle is accounted for. The small dynamic range observed for X-band data could hinder its use in forests affected by fires.
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Meteorological conditions, extremely conducive to fire development and spread in the spring of 1987, resulted in forest fires burning over extremely large areas in the boreal forest zone in northeastern China and the southeastern region of Siberia. The great China fire, one of the largest and most destructive forest fires in recent history, occurred during this period in the Heilongjiang Province of China. Satellite imagery is used to examine the development and areal distribution of 1987 forest fires in this region. Overall trace gas emissions to the atmosphere from these fires are determined using a satellite-derived estimate of area burned in combination with fuel consumption figures and carbon emission ratios for boreal forest fires.
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Fires represent one of the main factors of degradation and destruction of the Mediterranean forest heritage. According to fire-fighting agencies, a satellite-based fire-detection system can be considered operationally useful for Mediterranean countries when fires with a minimum extent of 1500 m<sup>2</sup> can be detected with a temporal resolution of 30 min. In fact, such a system should be able to detect fires at their first stage when it is possible to extinguish them more easily. The Centro di Ricerca Progetto San Marco has been analyzing for several years the possibility of using images acquired by the Spinning Enhanced Visible and Infrared Imager sensor onboard the geostationary satellite Meteosat Second Generation for this purpose. A new processing approach exploiting the increase in both spatial and temporal resolution (15 min) with respect to previous meteosat systems is described in this paper. The idea is based on the use of a change-detection technique to maximize the detection capabilities of the system in spite of its limited spatial resolution. This technique consists of comparing two or more images acquired at 15-min intervals, for which any temperature change can be attributed to fast dynamic phenomena, such as fires, when natural changes are modeled and removed. An assessment of the performances of this algorithm is carried out comparing its results with the report made available by Italian fire-fighting agencies and with fire products based on higher resolution sensors such as the Moderate Resolution Imaging Spectroradiometer
The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.
Normalized difference vegetation index (NDVI) composite image data, produced from AVHRR data collected in 1990, were evaluated for locating and mapping the areal extent of wildfires in the boreal forests of Alaska during that year. A technique was developed to map forest fire boundaries by subtracting a late-summer AVHRR NDVI image from an early summer scene. The locations and boundaries of wildfires within the interior region of Alaska were obtained from the Alaska Fire Service, and compared to the AVHRR-derived fire-boundary map. It was found that AVHRR detected 89.5% of all fires with sizes greater than 2000 ha with no false alarms and that, for most cases, the general shape of the fire boundary detected by AVHRR matched those mapped by field observers. However, the total area contained within the fire boundaries mapped by AVHRR were only 61 % of those mapped by the field observers. However, the AVHRR data used in this study did not span the entire time period during which fires occurred, and it is believed the areal estimates could be improved significantly if an expanded AVHRR data set were used.
At the behest of NASA's Mission to Planet Earth, the National Research Council recently conducted a review on the current status and future directions for earth science information provided by spaceborne synthetic aperture radars. As part of this process, a panel of 16 scientists met to review the utility of SAR for monitoring ecosystem processes. The consensus of this ecology panel was that the demonstrated capabilities of imaging radars for investigating terrestrial ecosystems could best be organized into four broad categories: 1) classification and detection of change in land cover; 2) estimation of woody plant biomass; 3) monitoring the extent and timing of inundation; and 4) monitoring other temporally-dynamic processes. The major conclusions from this panel were: 1) Multichannel radar data provide a means to classify land-cover patterns because of its sensitivity to variations in vegetation structure and vegetation and ground-layer moisture. The relative utility of data from imaging radars versus multispectral scanner data has yet to be determined in a rigorous fashion over a wide range of biomes for this application. 2) Imaging radars having the capability to monitor variations in biomass in forested ecosystems. This capability is not consistent among different forest types. The upper levels of sensitivity for L-band and C-band systems such as SIR-C range between <100 t ha−1 for complex tropical forest canopies to ∼250 t ha−1 for simpler forests dominated by a single tree species. Best performance for biomass estimation is achieved using lower frequency (P- and L-band) radar systems with a cross-polarized (HV or VH) channel. 3) Like-polarized imaging radars (HH or VV) are well suited for detection of flooding under vegetation canopies. Lower frequency radars (P- and L-band) are most optimal for detecting flooding under forests, whereas higher frequency radars (C-band) work best for wetlands dominated by herbaceous vegetation. 4) It has been shown that spaceborne radars that have been in continuous operation for several years [such as the C-band (VV) ERS-1 SAR] provide information on temporally dynamic processes, such as monitoring a) variations in flooding in nonwooded wetlands, b) changes in the frozen/thawed status of vegetation, and c) relative variations in soil moisture in areas with low amounts of vegetation cover. These observations have been shown to be particularly important in studying ecosystems in high northern latitudes.