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Title: Pinus halepensis regeneration after a wildfire in a semiarid environment:
assessment using multitemporal Landsat images
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Sergio M. Vicente-Serrano1*, Fernando Pérez-Cabello2, and Teodoro Lasanta1
Running title: Pinus halepensis regeneration using Landsat images
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Additional keywords : NDVI, vegetation recovery, resilience, mediterranean, semiarid, Ebro
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Valley, Spain.
Instituto Pirenaico de Ecología, CSIC (Spanish Research Council), Campus de Aula Dei, P.O. Box
202, Zaragoza 50080, Spain
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Departamento de Geografía. Universidad de Zaragoza. C/ Pedro Cerbuna 12. 50009. Zaragoza.
Spain.
* svicen@ipe.csic.es
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Abstract. Aleppo pine (Pinus halepensis Miller) is an important vegetation component in
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Mediterranean ecosystems, and is a fire-prone species. We studied the spatial and temporal patterns
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of forest regeneration using a 24-year time series of Landsat images and the normalized difference
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vegetation index (NDVI) in a homogeneous P. halepensis forest, 3000 ha of which were extensively
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burned in 1995. We demonstrated a progressive slow and linear recovery in NDVI values, based on
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Landsat images between 1997 and 2007. The forest tended to recover to pre-disturbance conditions,
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both with respect to the magnitude of the NDVI and in terms of the spatial pattern. In the burnt area,
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the fire severity was slightly higher in areas with high pre-fire NDVI values, reflecting the high
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density of trees and biomass amount. However, we found that the spatial differences in the rates of
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NDVI recovery were not affected by the burn severity. Moreover, burn severity did not affect the
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rates of NDVI recovery after the fire. Although highly homogeneous P. halepensis regeneration
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was the dominant pattern in the study area (more than the 70% of the burn area showed positive and
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significant trends), some spatial differences in the magnitude of change were observed. The forest
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tended to recover the spatial pattern corresponding to the pre-fire condition, although it was difficult
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to establish whether terrain elevation or the previous tree size and density were the main governing
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factors, given the strong relationship between them. Other topographic factors, such as slope and
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the incoming solar radiation, did not clearly influence regeneration.
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Brief summary. We studied the spatial and temporal patterns of forest regeneration using a 24-year
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time series of Landsat images and the normalized difference vegetation index (NDVI) in a
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homogeneous P. halepensis forest, 3000 ha of which were extensively burned in 1995. The forest
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tended to recover to pre-disturbance conditions, both with respect to the magnitude of the NDVI
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and in terms of the spatial pattern.
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Introduction
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Fire has been an important ecological factor and management tool in Mediterranean ecosystems for
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thousands of years (Naveh, 1975; Trabaud, 1994). However, wildfire frequency has increased
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markedly in recent decades due to factors including increased fuel load as a consequence of
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vegetation regrowth following rural abandonment (Moreno et al., 1998; Vicente-Serrano et al.,
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2000; Viedma et al., 2006), unfavorable climatic conditions (Pausas, 2004), and deliberate fires
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(Martínez et al., 2008).
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Wildfires have economic and environmental impacts. Amongst the most important effects of
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wildfires on vegetation are disorganization of vegetation structure and alterations to successional
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patterns (Rodrigo et al., 2004). In Mediterranean ecosystems, however, the plant species are
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adapted to fires. Adaptive responses minimizing fire effects include active and passive mechanisms
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such as thick insulating bark, sprouting from underground storage organs, and seedling re-
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establishment. Thus, in spite of the negative short- and long-term effects of fire, these mechanisms
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enable the rapid recovery of most Mediterranean plant species, and the re-establishment of
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communities with similar characteristics to those prior to the fire. This is termed an auto-
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successional process (Hanes, 1971), and several studies have concluded that regeneration following
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fire is similar to an auto-successional process (Naveh, 1990; Trabaud, 2002). Therefore, fire
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temporarily alters the structure and composition of the burnt plant communities.
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Aleppo pine (Pinus halepensis Miller) is an important vegetation component in Mediterranean
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ecosystems, and is a fire-prone species. P. halepensis seedlings are highly tolerant to hydric stress
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(Zavala et al., 2000), which allows them to resist the harsh environmental conditions that occur in
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burnt areas, with high temperatures and bare soil favoring water loss. This tree species has an
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effective mechanism for fire-induced seed germination (Trabaud et al., 1987; Barbéro et al., 1998;
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Nathan & Ne’eman, 2000), and is one of the most important serotinous tree species in
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Mediterranean ecosystems (Barbéro et al., 1998). Serotinous species are characterized by long-term
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retention of seeds within mature closed cones which, when heated by fire, open to release seeds
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onto the soil. This behavior favors rapid tree regeneration, and a few years after a fire the P.
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halepensis coverage may be completely recovered (Daskalakou and Thanos, 1996; Pausas et al.,
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2004). However, the process may be spatially inhomogeneous as a consequence of topographic
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factors including exposure, but the occurrence of new fires may also affect the rate of P. halepensis
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regeneration, and may produce spatial differences in forest recovery (Tsitsoni, 1997; Broncano et
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al., 2005; Màrcia et al., 2006).
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Forest regeneration processes after fire have been widely monitored using remote sensing (e.g.
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Steyaert et al., 1997; Pérez-Cabello, 2002; Pérez-Cabello et al., 2006; Thompson et al., 2007).
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Satellite images facilitate assessment of large areas and the temporal recurrence of events, enabling
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analysis of regeneration processes with very high temporal resolution. The analysis of remote
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sensing data has allowed assessment of the frequency and surface extent of forest fires over long
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time periods (Díaz-Delgado and Pons, 2001; Díaz-Delgado et al., 2004), and has enabled analysis
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of forest regeneration rates (White et al., 1996; Viedma et al., 1997; Epting and Verbyla, 2005), the
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effect of topographic and geographic factors on the patterns of forest fires and vegetation recovery
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(Viedma et al., 2006; Wittenberg et al., 2007), and processes related to resilience and forest decline
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related to burn severity and fire recurrence (Díaz-Delgado et al., 2002; Thompson et al., 2007).
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Nevertheless, few studies have analyzed fire impacts and vegetation recovery in coniferous forests
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in semiarid areas near the limit of their ecological distribution, and very few studies focused on P.
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halepensis forests have been conducted using remote sensing data (e.g., Reyes et al., 2004;
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Clemente et al., 2009). Although there have been several reports concerning post-fire regeneration
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of P. halepensis forests using field-collected data (e.g. Kazanis and Arianoutsou, 2004; Broncano et
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al., 2005), there are few studies focused on this species over large areas representing a range of
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environmental conditions. The broad spatial coverage and the high temporal and spatial resolution
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of remote sensing facilitate this approach.
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In this study we investigated the spatial and temporal patterns of forest regeneration using a 24-year
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time series of Landsat images of a homogeneous P. halepensis forest in which 3000 ha were
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severely burnt in 1995. The forest is in a semiarid region of northeast Spain characterized by low
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and variable rainfall, and subject to frequent droughts. The main hypothesis of the research was that
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the rates of tree colonization after the fire are sufficiently high to guarantee the forest recovery after
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few years of a severe fire without the need of a human intervention in a well developed semiarid
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forest and that recovery follows a coherent spatial pattern controlled by environmental or fire
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factors. For the purpose of testing this hypothesis, multi-temporal Landsat TM and Landsat ETM+
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data have been used
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Study area
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The studied P. halepensis forest is about 35 km from Zaragoza, in the middle of the Ebro River
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Depression (northeast Spain) in the Montes de Castejón region. The forest is on a structural
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platform developed on Miocene carbonate and marl sediments, arranged on horizontal strata. Marls
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are the predominant lithology, with the limestone and gypsum content producing greybrown limy
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soils with some rendsines and xerorendsines in marly sectors (CSIC, 1970). The climate is
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Mediterranean with semiarid features. The mean annual rainfall is approximately 400 mm. The
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nearest weather stations (at Zaragoza and Zuera) are at a lower altitude and have an average annual
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precipitation of 314 mm and 363 mm, respectively (Cuadrat et al., 2007). Severe droughts are
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frequent in the region (Vicente-Serrano and Cuadrat, 2007) and periods of more than 50 days with
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no precipitation are common (Vicente-Serrano and Beguería, 2003). A combination of the low
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rainfall, the drying effect of the prevailing NW wind (which blows for almost one-third of the year;
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109 days on average in Zaragoza), and the high temperatures during summer (mean maximum
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temperature of 31ºC in Zaragoza; Cuadrat et al., 2007) poses extreme challenges to the
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development of vegetation in the area.
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The landscape is dominated by winter cereal crops (wheat and barley) with small patches of steppe
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vegetation and forest with thermophilic species such as P. halepensis and Quercus coccifera
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(Braun-Blanquet and Bolós, 1987). The studied pine forest (Pinares de Zuera) is a relict covering
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about 17,273 ha, and is comprised mostly of P. halepensis with underlying vegetation dominated by
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Buxus sempervirens, Juniperus communis and Q. coccifera. In the most degraded areas, mainly on
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steep slopes, thermophilic shrubs occur, dominated by Rosmarinus officinalis, Thymus sp. and
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Genista scorpius. Communities dominated by Ononis tridentata, Gypsophila hispanica and
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Helianthemum squamatum occur on some gypsum outcrops. The area is included in the Natura
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2000 as a place of special interest to the European Community, because it is the only semiarid forest
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within a landscape dominated by steppe vegetation. Commercial exploitation of the forest is
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marginal as the traditional extraction of wood for construction, firewood and resin has been
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abandoned several decades ago. There is some recreational use of the forest because of its proximity
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to the city of Zaragoza (population 700,000), a factor that significantly increases the risk of forest
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fires.
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Methods
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Field evaluation of forest recovery after fire
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As a consequence of the climatic conditions and the characteristics of the vegetation, the Zuera hills
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are subject to recurrent forest fires. The most serious of these affected about 3000 ha of P.
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halepensis forest in 24th June 1995, mainly in the eastern areas of the Zuera hills and covering a
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diversity of elevation, aspect and slope conditions. The trees were logged following the fire, and the
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forest was then abandoned to natural regeneration. In 2008, 13 years after the fire, the majority of
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the burnt area exhibited complete coverage of P. halepensis with a dense underbrush, in which the
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dominant species are Q. coccifera, R. officinalis and G. scorpius. As a consequence of the sprouting
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characteristics of Q. coccifera, in the winter following the fire and in the spring of 1996 there was a
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dense coverage (to 2050 cm) of this and other species (Pérez and Pérez, 2001). . In 2008 the
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pattern within the fire perimeter was a very dense coverage of P. halepensis (up to 235,000 trees/ha
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in some areas) 1113 years of age, 1.53 m high, and with basal diameters 39 cm. In some trees
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early cones were apparent. Although the present coverage of P. halepensis is generally within the
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fire perimeter, there are some differences in coverage, height and thickness of the trees, which
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indicates different post-fire recovery rates.
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Spectral signatures of vegetation components
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A critical factor in monitoring forest regeneration is the feasibility of using remote sensing to
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identify P. halepensis growth in a complex mosaic of bare soil and charcoal remains, Q. coccifera
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and other shrubs, and herbaceous species. To assess the potential use of remote sensing to monitor
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this process, in June and August 2008 we made spectrophotometric measurements in the burnt area
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using an instrument consisting of two spectrometers (AvaSpec-2048 and AvaSpec-NIR256-1,7) in a
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AvaSpec Multichannel platform. The spectrometers made measurements in the 4001750 nm
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spectral range with an optical resolution of 2.46 nm. To improve the signal to noise ratio, spectral
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data were calculated as the mean of 20 individual spectra on days with a clear sky and no haze. To
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avoid problems related to variations in incident radiation, measurements were restricted to the
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period about 2 h before solar noon, where the angle of incidence is close to the vertical. Spectral
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measurements were performed with a hand-held radiometer pointed vertically downward (nadir)
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from approximately 0.5 m above the plant canopy. Spectral measurements of a high albedo
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“spectralon” reflectance panel (30  30 cm PTFE calibration panel) were made immediately before
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each surface measurement. We obtained a sample of 60 continuous spectral signatures from each of
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the following species: P. halepensis, Q. coccifera, R. officinales, G. scorpius, Brachipodium sp.,
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bare soil and dry matter within the fire perimeter, and also for ashes in a neighboring forest area
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affected by fire in August 2008. Figure 1 shows the average spectral signatures obtained for
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different components of the vegetation in August 2008. The spectral signature in the visible region
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obtained for P. halepensis was similar to that for Q. coccifera and the other shrubs. However, there
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were marked differences in the infrared region of the spectrum, where the magnitude of reflectance
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for P. halepensis was approximately double that of the shrubs, indicating much higher vegetation
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activity in the conifers than in the other cover classes. Thus, the field observations seem to borne
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out the use of Landsat images for analysis of the regeneration of P. halepensis in relation to the
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other components of the vegetation. Landsat TM spectral bands were simulated using the
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continuous spectral signatures obtained in the field, taking into account the spectral responsivity of
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each Landsat TM band (Teillet et al., 2001), and the normalized difference vegetation index
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(NDVI; Rousse, 1973) was calculated from the simulated bands. The average NDVI for Q.
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coccifera was 0.73, for P. halepensis was 0.82, for the other shrubs was 0.67, and for ashes and
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charcoal was 0.13.
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Therefore, based on the recovery process observed over 13 years, the spectral information collected
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in the field, and the large differences in the vegetation activity between P. halepensis and other
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components of the vegetation, it can be assumed that the baseline vegetation photosynthetic activity
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after the fire would be lower than that of the original P. halepensis forest.
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Landsat database
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We used a database of Landsat TM and Landsat ETM+ images for the period 19842007. The
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database comprised 28 images, 16 of which were from a summer time series and 12 from a spring
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time series. The two time series were used to identify possible differences in forest recovery as a
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function of seasonal differences in vegetation activity, and to assess with more robustness any
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spatial and temporal patterns in the recovery process. Table 1 shows the dates of the images used in
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each time series. The database was processed using a procedure that included calibration and cross-
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calibration of the TM and ETM+ images, atmospheric correction using a radiative transfer model
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(6S) that included external atmospheric information, a non-Lambertian topographic correction to
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avoid errors caused by the differences in illumination conditions, and a relative normalization
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between dates. The procedure allowed accurate measurements of physical surface reflectance units
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to be obtained. The correction applied to the images guaranteed the temporal homogeneity of the
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dataset, the absence of artificial noise caused by sensor degradation and atmospheric conditions,
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and spatial comparability between different areas, given the accurate topographic normalization
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applied. Details of the correction procedure applied to the images, and a complete description of the
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data set and its validation can be found in Vicente-Serrano et al. (2008).
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Estimation of burn severity from Landsat images
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Burn severity can be obtained from remote sensing data using the spectral information in the images
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(Brewer et al., 2005; Cocke et al., 2005). Much scientific effort has been devoted to this analysis,
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and methods of varying complexity can be applied to derive an estimate of the level of damage
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caused by fire (De Santis and Chuvieco, 2007). A band combination approach can provide a good
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estimate of the burn severity (Epting et al., 2005), and among the possible band combinations the
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normalized burnt ratio (NBR) index has provided good estimates of burn severity in several areas
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(e.g. Epting et al., 2005; Loboda et al., 2007). The index integrates (albeit in opposite ways) the two
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bands most responsive to burning: near infrared (NIR; 0.760.90 µm) and mid-infrared (SWIR;
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2.082.35 µm) (Key and Benson, 2002) according to:
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NBR 
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To provide a quantitative measure of change, the ∆NBR is obtained by subtracting the NBR dataset
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derived after burning from the NBR dataset derived before burning. It is widely accepted that the
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∆NBR image is correlated to the environmental changes caused by fire (Key and Benson, 2004).
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Numerous studies have found correlations between changes in NBR and field-based severity
NIR  SWIR 
NIR  SWIR 
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metrics in different forest types (Van Wagtendonk et al., 2004). We calculated the ∆NBR using the
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images for August 1993 and 1995, the latter image recorded two months after the forest fire.
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Analysis of forest recovery from Landsat images
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The Normalized Difference Vegetation Index (NDVI) has been the most frequently used tool for
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monitoring, analyzing and mapping temporal and spatial post-fire forest recovery (e.g. Viedma et
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al., 1997; Díaz-Delgado et al., 2002 and 2003) as it integrates two of the most important bands for
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vegetation discrimination; the NIR (particularly responsible for the amount of vegetal biomass) and
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the visible red reflectance (R; 0.630.69 µm). There are some shortcomings in the use of NDVI for
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monitoring vegetation status. The relationships among vegetation parameters (leaf area index,
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vegetation cover, green biomass, and NDVI) are often nonlinear (Choudhury et al., 1994; Gillies et
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al., 1997) as the NDVI signal saturates before the maximum biomass is reached (Carlson et al.,
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1990). Moreover, the NDVI is affected by background soil properties (e.g. surface soil color) that
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introduce errors (Huete 1988), and the NDVI does not distinguish among vegetation types. Despite
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these shortcomings, numerous studies have reported a close relationship between the NDVI and
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several ecological parameters. The NDVI measures the fractional absorbed photosynthetically
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active radiation (FPAR) (Myneni et al., 1995), and exhibits a strong relationship with vegetation
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parameters such as green leaf area index (Baret and Guyot, 1991; Carlson and Ripley, 1997), green
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biomass (Tucker et al., 1983; Gutman 1991), and fractional vegetation cover (Gillies et al., 1997).
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Moreover, the common problem of NDVI saturation corresponding to the same vegetation coverage
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or LAI levels does not affect our study, as it concerned the first stages of vegetation recovery,
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which is clearly recognized in the NDVI (Salvador et al., 2000). To analyze the spatial and
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temporal patterns of forest regeneration after the fire we calculated the NDVI for each available
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image in the March and August series.
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Statistical analysis
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Spatial and temporal patterns in the NDVI
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To determine the post-fire rates of vegetation recovery and its spatial pattern we used the two NDVI
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time series (August and March) for each of the 32,243 pixels of 900 m2 burnt in 1995. To analyze
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the statistical significance of the NDVI trends after the fire, we used a non-parametric coefficient
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(Rho-Spearman), as it is less affected by the presence of outliers and the non-normality of the series
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than are parametric coefficients (Lanzante, 1996). The analysis was conducted between 1995 (the
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first image following the forest fire) and 2006 (the last image in the dataset) for the August time
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series, and between 1997 and 2007 for the March series. The Spearman coefficient identifies
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statistically significant trends but it does not identify the magnitude of change; for this purpose we
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calculated for both time series the regression slope between each of the 32,243 series and the series
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of years. This parameter indicated the rate of increase or decrease of the NDVI per year.
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To determine the spatial pattern of the NDVI before and after the fire we applied two principal
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component analyses (for the March and August series) using the NDVI of the 32,243 pixels for the
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March and August series as variables. The PCA identifies common features, and also allows
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particular local characteristics to be determined (Richman, 1986; Eastman and Fulk, 1993). A T-
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mode PCA was applied to identify the general spatial patterns. Periods in which each component is
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represented can be identified by temporal evolution of the factorial loadings. The number of
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components was selected according the criteria of an eigenvalue > 1, and the components were
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rotated (Varimax) to redistribute the final explained variance, and to obtain more stable and robust
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patterns (Richman, 1986; Hair et al., 1998).
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The role of environmental factors
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We analyzed the role of a variety of environmental factors in explaining the spatial differences in
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post-fire forest regeneration. For this purpose we used a 1-m digital elevation model (DEM)
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obtained from pairs of stereo aerial photographs, and re-sampled at 30 m to match the spatial
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resolution of the NDVI data. From the DEM we derived the terrain slope (degrees) using MiraMon
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software (Pons, 2008), as some studies have shown that this factor plays a major role in explaining
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rates of vegetation recovering after fire (Díaz-Delgado et al., 2002; Wittenberg et al., 2007). Instead
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of using a categorical variable to define the terrain aspect we used a continuous model of incoming
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solar radiation (Ra), as it provides more spatial details (northern and southern slopes have low and
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high Ra values, respectively). Ra values were modeled from a DEM with a cell size of 30 m. For
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this purpose we used an algorithm that includes the effects of terrain complexity (shadowing and
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reflection) and the daily solar position (Pons and Ninyerola, 2008); this is also implemented in the
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MiraMon GIS. Ra was measured in MJ m−2 day−1. We also analysed the role of climate parameters
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in the post-fire forest regeneration. For this purpose, we used the digital layers of annual
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precipitation and potential evapotranspiration (PET) obtained from the Digital Climatic Atlas of
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Aragón (http://www.opengis.uab.es/wms/aragon/). Layers were obtained by means of regression-
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based interpolation considering the 1970-2000 average climatologies, and PET using the
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Hargreaves equation (Cuadrat et al., 2007).
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To assess the significance of the relationships between the the environmental variables and the
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spatial patterns of post-fire forest regeneration taking into account the large dataset (32,243 points),
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we used a bootstrap approach considering the analysis of 1074 independent samples of 30 random
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points for each one until complete the totality of the points. We used two different statistical
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analyses to assess the influence of the variables. On the one hand, we used standard Ordinary Least
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Square (OLS) regression. Nevertheless, given that the values of each variable are not commonly
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spatially independent and, therefore, the presence of spatial autocorrelation can severely affect the
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results, we have also applied a Generalized Least Square model (GLS), in which spatial
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autocorrelation is accounted for. The method improves other statistical procedures to consider
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spatial autocorrelation in the data (Beguería and Pueyo, 2009). GLS was performed using the nlme
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package implemented in R software. In the GLS models we specified the correlation structure
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defining an exponential semivariogram model with a single (range) parameter, which was
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determined automatically. Analysis of variance (ANOVA) was applied for each one of the 1074
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OLS and GLS models corresponding to each variable. The purpose was to determine if spatial
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autocorrelation in the datasets is affecting noticeably the results and if significant differences are
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obtained using OLS or GLS in the influence of each environmental variable on the vegetation
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recovery after the fire. Given the large number of analysis performed using independent 30 point
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samples, we considered a significant influence of each variable on post-fire forest regeneration
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when more than 80% of the samples showed a significant relationship (for the OLS and GLS
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methods) at the 95% significance level.
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Results
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Spatial patterns of fire severity
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Figure 2 shows the spatial distribution of the NBR, which indicates the fire severity in the burnt
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area 41 days (approximately two months) following the fire. At this time there was a predominance
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of areas with moderately high and high fire severity (65.3% of the study area). The maximum
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severity was recorded on southern and northern slopes of the main ravines, which are orientated
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eastwest. There was no clear topographic or climatic relationship with the fire severity since no
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significant relationship (> 80% of the samples at 95% significance level) has been found between
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the spatial distribution of the NBR and the topographic and climatic variables.
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Spatial and temporal patterns of vegetation recovery after fire
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Figure 3 shows the evolution of average and standard deviation values for the NDVI in the burnt
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area for the August and March time series. In both series there was a clear difference between the
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pre-fire period, in which the NDVI generally showed low temporal variability, and the post-fire
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period, in which the time series showed a general post-fire trend of a progressive increase in the
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NDVI values. In the August series the NDVI between 1984 and 1993 showed similar average
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values and very low temporal variability, indicating stability in the coniferous canopy. The
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consistency of values could be associated with problems of signal saturation, although this is
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unlikely, as in March the NDVI values were slightly higher than in August. A more likely
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explanation is that the forest was well adapted to the limiting environmental conditions, which
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included droughts that affected the region in some years; a drought in 1992 was associated with a
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slight decrease in the average NDVI. Immediately after the fire there was an abrupt decrease in the
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NDVI, from an average of approximately 0.6 to 0.4 in 1995 and 1997. The high NDVI baseline
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after the 1995 fire, relative to the expected NDVI, was mainly a consequence of the reduction in
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temporal variance through use of the relative radiometric correction method. This phenomenon is
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common with the application of relative correction techniques, which reduce the variability related
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to the most extreme values but provide a higher temporal consistency and homogeneity to the
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resulting trends (Vicente-Serrano et al., 2008). In the following years there was a progressive
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increase in the NDVI to an average of approximately 0.54 between 2004 and 2006, which was
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similar to the pre-fire NDVI values. The standard deviation decreased markedly after the fire,
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indicating a general spatial homogenization of the NDVI within the burnt area. After the fire the
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standard deviation increased to approximately 0.1 between 1999 and 2002, and continued to
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increase thereafter, with maximum standard deviations (0.13 in 2004, 0.12 in 2005 and 0.14 in
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2006) much higher than during the pre-fire period (0.080.11). This indicates that natural forest
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regeneration was accompanied by an increase in spatial variability in the NDVI, which suggests
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spatial differences in forest regeneration.
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The March series showed an abrupt decrease in the NDVI after the 1995 fire, even more marked
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than for the August time series, although no images were available for the year immediately
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following the fire (1996). The rate of recovery in the NDVI in the March time series was much
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more rapid than for the August series. For the last two images the average NDVI was similar (0.66
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and 0.65 for 2005 and 2007, respectively) to the pre-fire average NDVI value. Spatial variability in
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NDVI values, indicated by the standard deviation, also showed a similar pattern to that observed in
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the August series, indicating a decrease in variability immediately after the fire, and a progressive
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increase after 2000.
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Figure 4 shows the spatial distribution of NDVI values corresponding to years before and after the
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fire. In 1993 the vegetation activity was higher in March than August over the whole study area,
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although the spatial patterns of vegetation activity were highly related (R = 0.71 between the spatial
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distribution of the NDVI in March and August 1993). The smaller magnitude of the index and the
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greater variability of the values in August could be related to a contribution of herbaceous species,
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which are much more sensitive to the drought conditions in summer. The same pattern was
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observed for the years before the fire (R = 0.56 in 1997, R = 0.72 in 2005). This indicates there was
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no seasonal effect on the pattern and rate of vegetation recovery, and suggests the results are robust.
353
The effect of the forest fire was evident in 1997, with a sharp decrease in the NDVI values over the
354
whole study area in both the March and August series. Five years after the fire, in 2000, there was
355
partial recovery in the NDVI value, but there were some spatial differences; in both the March and
356
August series the highest NDVI values were recorded in the northeast, whereas NDVI values in the
357
east and southeast were more similar to those in the immediate post-fire period. The last available
358
images, in 2006 and 2007, show that the NDVI was similar to that in the pre-fire period over the
359
majority of the burnt area, with a similar pattern for the August and March series. The exception
360
was a small area in the southwest, which was burnt in winter 2007 and had a NDVI value below 0.6
361
in March 2007.
362
Temporal analysis of the NDVI after the fire showed a general positive trend toward higher values
363
over most of the study area. The spatial pattern of recovery was very homogeneous for both the
364
August and March series. Of the area burnt in 1995, 71.6% showed a positive and significant trend
15
365
(p < 0.05) in the August NDVI values between 1995 and 2006, and 73% showed a similar trend for
366
the March series between 1997 and 2007. There were no areas with negative and significant trends
367
in the NDVI during the analyzed post-fire period, and 28.4% (August series) and 27% (March
368
series) of the area showed no significant changes during the analyzed period.
369
Although an increase in the NDVI was the general pattern over the burnt area, there were marked
370
spatial differences in the rates of vegetation recovery and, therefore, in the magnitude of the NDVI
371
changes throughout the post-fire period. Figure 5 shows the NDVI change per year based on the
372
spatial distribution of regression slopes between the series of years (independent variable) and the
373
NDVI series (dependent variable). The assumption of a linear recovery appears reasonable given
374
the evolution for the study area (Fig. 3). The range of vegetation recovery ranged from a minor
375
decrease in NDVI values per year (< 0.005 units per year) to a substantial increase (> 0.045 units).
376
The magnitude of the NDVI increase was higher for the March series than the August series, and
377
this was not affected by the period used to calculate the regression slopes: the magnitude of the
378
NDVI change and the spatial pattern was similar (data not shown) for the August series slopes
379
calculated over the 19972006 period. Although the rate of NDVI increase was higher in the March
380
time series, the spatial pattern was similar, with strong agreement between the patterns of NDVI
381
recovery in both time series. Thus, there was a significant correlation (R = 0.57) between the spatial
382
distribution of the NDVI rates per year in the March and August time series showing that patterns in
383
the rates of vegetation recovering after the fire are well identified using Landsat images
384
independently of the season used for monitoring.
385
Figure 6 shows the spatial pattern of the three components obtained from the T-mode PCA for the
386
NDVI time series for March and August. Figure 7 shows the similarity of each NDVI image to the
387
three patterns as a plot of the component loadings (correlation between the patterns and the NDVI
388
for each date) to identify which periods are representative of each pattern. The first three
389
components explained 77% of the variance for the August series, and 59% for the March series.
16
390
This indicates there was higher temporal variability in the NDVI spatial patterns in the August
391
series. The first component explained more than 30% of the variance for both the March and
392
August series. Both the August and March components showed similar spatial patterns with few
393
differences. The first component had a very homogeneous pattern of high values over the most
394
elevated areas. The lower NDVI values were at the margin of the forest, mainly in the lower areas
395
of the south and southeast, where the greatest aridity was recorded. Figure 6 also indicates the high
396
spatial homogeneity of the P. halepensis forest prior to the fire, as this pattern was representative of
397
the pre-fire period for both the March and August series; this pattern is characteristic of a mature
398
forest in which the differences in vegetation activity are determined by varying environmental
399
conditions. After the fire, the spatial organization of the NDVI changed markedly relative to the
400
pre-fire period. The spatial configuration of the NDVI in the years immediately following the fire is
401
represented in both time series by the second component. This shows a pattern of the highest NDVI
402
values occurring in the northwest of the study area, and the lowest values in the east and southeast.
403
The temporal evolution of the pattern showed some differences between the March and August
404
series: although the loading values were maximum after the fire in both series, the pattern was
405
evident for more years (19972001) for the August series than for the March series (only 1997 and
406
1998). These results indicate that vegetation recovery was favored by the higher moisture
407
conditions common in spring, making the pattern less consistent. In contrast, with the high water
408
stress in summer, the pattern would be more consistent. The third component showed the general
409
pattern for the last years in the August time series, whereas for the March series the pattern was
410
clearly dominant in 1999 and 2000; the following years showed some similarity to the second
411
component, but also with the pre-fire conditions represented by the first component, mainly for the
412
March time series. These results suggest that in the two years following the fire, the spatial
413
configuration of vegetation activity was different from the previous conditions, and that it was
414
determined by other factors, such as the previous vegetation cover or the spatial pattern of fire
17
415
severity. As the regeneration processes progressed, the spatial pattern would tend to be more similar
416
to the pre-fire conditions.
417
418
Factors affecting spatial patterns of vegetation recovery after fire
419
Figure 8 shows the relationship between the rates of vegetation recovery after the fire (NDVI
420
increase per year, derived from the regression slopes between each of the 32,243 NDVI series and
421
the time series) and some topographical and climatic variables (elevation, slope, Ra, precipitation
422
and PET), the pre-fire and immediately post-fire NDVI, and the NBR for the August series. The
423
results found for the March series are very similar and they are not shown. The percentage of the
424
1074 independent analysis in which the relationship between the rate of forest recovery after the fire
425
and each variable was significant using the OSL and GLS methods is shown in Table 2. The results
426
are quite similar considering OLS and GLS approaches, and the ANOVA analysis does not show
427
large percentage of cases in which OLS and GLS significantly provide different results regarding
428
the role of the environmental variables on the forest recovery after the fire. Both procedures show
429
that the rates of vegetation recovery were directly related to the elevation and the spatial distribution
430
of the pre-fire NDVI.
431
The slope and exposure (solar radiation) did not explain the spatial pattern of forest regeneration.
432
Precipitation and PET show a direct and inverse relationship with forest regeneration, respectively
433
although the percentage of analysis with a significant relationship was very low (35% and 0.13%
434
for precipitation in August and March, respectively, and 21% and 10% for PET considering the
435
GLS method). Generally, it is interesting to note that the influence of the climate variables on
436
vegetation recovery is stronger in summer, when water stress is higher. Although the fire severity
437
(NBR) had a positive relationship with the rates of forest regeneration, the influence was only
438
significant for the 38% and 27% of the models for August and March, respectively. The post-fire
439
NDVI pattern (NDVI-95) was not significantly related to the spatial pattern of forest regeneration
18
440
but the relationship was positive in both seasons (R = 0.35 and R= 0.33, for August and March,
441
respectively), which indicates that post-fire forest regeneration could be favored by the patterns of
442
post-fire tree survival. The variable that contributed most to explaining the spatial patterns of forest
443
regeneration rates was the pre-fire NDVI pattern (92% and 75% of the models showing a significant
444
influence for the August and March series, respectively), again indicating the tendency of the forest
445
to recover to pre-fire conditions.
446
447
Discussion and conclusions
448
This study analyzed the impact of forest fire on the spatial and temporal patterns of vegetation
449
activity in a homogeneous P. halepensis forest in a semiarid region subject to limiting
450
environmental conditions. The hypothesis formulated at the beginning of the study has been
451
confirmed. Thanks to the use of long time series of satellite imagery we have shown that in a semi-
452
arid region with no human intervention after the fire disturbance, the rates of tree colonization after
453
the fire are sufficiently high to guarantee the forest recovery 13 years after a severe fire. Moreover,
454
the results also indicate that the spatial pattern of the forest growth tended to recover to the pre-
455
disturbance conditions.
456
The use of two seasonal remote sensing series conferred a high degree of reliability on the results,
457
and also demonstrated that the use of remote sensing data to monitor the fire consequences and the
458
regeneration processes was not affected by the season selected for analysis. Although the rates of
459
vegetation recovery were slightly greater in spring than summer, the spatial patterns of forest
460
recovery were quite similar, as were the roles of the various topographic and environmental factors
461
analyzed.
462
The close relationship between the results obtained for the summer and spring series also indicated
463
the robustness of the use of the NDVI to monitor the growth of P. halepensis in relation to other
464
species in the canopy. The forest regeneration observed in the field indicates that the first available
19
465
satellite images after the fire (1997) reflected very diverse vegetation cover, mainly composed of
466
various shrub and herbaceous species and bare soil, but dominated by Q. coccifera because of its
467
capacity for rapid sprouting. At this stage the P. halepensis density was low, although the first trees
468
had appeared a few months after the fire. Using field radiometry we showed that P. halepensis has a
469
much higher vegetation activity and NDVI value than the other components of the canopy. This
470
would explain why two years after the fire, despite the vegetation cover being high over the
471
majority of the study area (average about 50%), the NDVI fell dramatically relative to the pre-fire
472
conditions characterized by a dense P. halepensis forest with high vegetation activity.
473
The vegetation recovery observed in the field was consistent with the progressive recovery of the
474
NDVI value, as determined from the Landsat images for 19972007. It is widely accepted that
475
Mediterranean ecosystems affected by fire show direct regeneration, which tends to be
476
characterized by restoration of the community that was present immediately prior to the disturbance
477
(Trabaud and Lepart, 1980; Trabaud, 1994). The high capacity of P. halepensis forests to recover
478
after fire is well known, due to the large seed bank stored in the canopy (Tapias et al., 2001). This
479
capacity was highlighted by Broncano et al. (2005), who showed that monospecific forests of P.
480
halepensis have a high probability of returning to their original composition after fire, whereas the
481
resilience of mixed forests is much lower. For the Zuera hills we showed that the forest tended to
482
recover to the pre-disturbance conditions, both with respect to the magnitude of the NDVI and the
483
spatial pattern. This pattern could be related to two factors acting individually or in combination.
484
On the one hand, in dense areas the canopy seed bank should favor the development of a denser
485
coverage of trees after fire. This hypothesis is consistent with the finding of Pausas et al. (2004) that
486
the recovery P. halepensis forests of southeast Spain was related to the height of the trees prior to
487
disturbance.
488
In the burnt area, the fire severity was slightly higher in areas with previously high NDVI values,
489
indicative of the high density of trees and the amount of biomass. This is a common pattern which
20
490
has been identified using remote sensing data under a variety of environmental conditions (e.g.
491
Epstein and Verbyla, 2005; Thompson et al., 2007). Generally speaking, high-severity burnt areas
492
register higher rates of soil loss and lower rates of vegetation recovery, due to the higher destruction
493
of the forest floor and shallow buried seeds, and destruction of the canopy (DeBano et al., 1998).
494
The corollary is that a rapid return to pre-fire conditions is expected in low-severity burnt areas.
495
Nevertheless, in the Zuera forest we found that the spatial differences in the rates of NDVI recovery
496
were not affected by the burn severity. In contrast to other studies showing that the largest decline
497
in the NDVI corresponded to the highest burn severity (Díaz-Delgado et al., 2003; Epstein and
498
Verbyla, 2005), in the Zuera forest the spatial distribution of NDVI values two years after the fire
499
(1997) was not correlated to the burn severity. Moreover, the burn severity did not affect the rates
500
of NDVI recovery after the Zuera forest fire. These patterns could be related to the homogeneity of
501
the fire severity that affected the Zuera forest, as evidenced by the NBR values. Good regeneration
502
of forests affected by high burn severity is not rare (Donato et al., 2006). Pausas et al. (2003)
503
showed better regeneration of P. halepensis forests in sites of high fire severity in southeast Spain.
504
Pausas et al. (2003) suggested that high fire severity favored seeding species over sprouting species,
505
as a consequence of higher mortality among the latter due to the fire severity. This pattern is in
506
agreement with the observed recovery of vegetation after the fire in the Zuera forest; although Q.
507
coccifera was the dominant species in the canopy during the first years after the fire, its maximum
508
coverage was only 40% in the most favorable areas (Pérez and Pérez, 2001), and several years after
509
the fire P. halepensis was again the dominant species.
510
Although homogeneous P. halepensis regeneration has been the dominant pattern in the Zuera
511
forest (more than the 70% of the burnt area showed positive and significant trends), some spatial
512
differences in the magnitude of change were observed. However, as noted previously, the forest
513
tended to recover to the pre-fire spatial pattern, and it was difficult to establish whether differences
514
in the elevation (affecting climate conditions) or the previous tree size and density were the main
21
515
governing factors, given the strong relationship between these factors in the forest preceding the
516
fire. Other topographic factors, such as slope and exposure, did not clearly influence regeneration.
517
Although regeneration of vegetation since the Zuera forest fire has been successful with high
518
regeneration rates in few years without the need of human intervention, the present high tree density
519
poses risks to forest development due to high mortality and high dry biomass accumulation, but also
520
because of high water stress coupled with the low capacity of the trees to develop root systems.
521
Thus, if a new forest fire occurs in these immature stages of the forest, the consequences could be
522
serious, as the success of recovery is mainly dependent on the canopy seed bank to enable rapid and
523
dense post-fire natural revegetation. The production of seeds by immature trees is low, and
524
regeneration after a new fire may have a low probability of success. It is well known that recurrent
525
wildfires have caused a decline in growth and decreased the recovery capacity of Mediterranean
526
forests (Díaz-Delgado et al., 2002; Màrcia et al., 2006). Consequently, it is essential that forest
527
management strategies be put in place to diminish the fire risk and to favor the development of
528
mature forests similar to those of pre-fire conditions.
529
530
Acknowledgements
531
This work has been supported by the research projects CGL2008-01189/BTE, CGL2006-
532
11619/HID and CGL2008-1083/CLI financed by the Spanish Commission of Science and
533
Technology and FEDER, EUROGEOSS (FP7-ENV-2008-1-226487) and ACQWA (FP7-ENV-
534
2007-1- 212250) financed by the VII Framework Programme of the European Commission, “Las
535
sequías climáticas en la cuenca del Ebro y su respuesta hidrológica” and “La nieve en el Pirineo
536
aragonés: Distribución espacial y su respuesta a las condiciones climática” Financed by “Obra
537
Social La Caixa” and the Aragón Government and “Programa de grupos de investigación
538
consolidados” financed by the Aragón Government.
539
540
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733
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30
735
Table legends
736
Table 1. Dates of the Landsat5 -TM and Landsat7 -ETM+ images used in this study.
737
Table 2. Percentage of the 1074 OLS and GLS models in which the spatial distribution of the
738
variables has a significant influence (p < 0.05) on the rates of post-fire regeneration in the March
739
and August series. Two last columns show the percentage of the 1074 models in which the analysis
740
of variance (ANOVA) indicates significant differences in the results between the OLS and GLS
741
methods.
742
Figure captions
743
Figure 1: Average spectral signatures of Pinus halepensis, Quercus coccifera, ashes and charcoal,
744
and various other shrub and herbaceous cover, determined in the field in the Zuera forest in August
745
2008.
746
Figure 2. Spatial distribution of the NBR in the burnt areas, and histogram of NBR values
747
within the fire perimeter. 50 m. contour lines are included in the figure.
748
Figure 3. Average and standard deviation values for the NDVI for the March and August time series
749
for areas within the 1995 fire perimeter.
750
Figure 4. Spatial distribution of NDVI values in August and March for different years.
751
Figure 5. Spatial distribution of the rates of NDVI recovery per year (regression slopes) after the
752
1995 fire (19952006 for the August time series, and 19972007 for the March time series).
753
Figure 6. Spatial distribution of the T-mode Rotated PC-scores corresponding to the August and
754
March NDVI series.
755
Figure 7. Temporal evolution of the T-mode Rotated PC loadings obtained from the August and
756
March NDVI series. The percentage of the explained variance by each component is shown.
757
Figure 8. Relationship between the spatial distribution of the rates of NDVI regeneration after the
758
fire for the August series, quantified using the regression slopes, the NDVI before and after fire, the
759
topographic and climatic variables and the NBR.
760
31
761
762
Table 1.
March
Date
03/11/1989
03/30/1990
03/06/1993
03/09/1994
03/28/1995
03/17/1997
03/20/1998
03/23/1999
03/17/2000
03/10/2003
03/07/2005
03/13/2007
Sensor
TM
TM
TM
TM
TM
TM
TM
TM
ETM+
ETM+
TM
TM
August
Date
08/20/1984
08/07/1985
08/13/1987
08/02/1989
08/24/1991
08/10/1992
08/29/1993
08/03/1995
08/24/1997
08/14/1999
08/08/2000
07/26/2001
08/30/2002
08/27/2004
08/14/2005
08/01/2006
Sensor
TM
TM
TM
TM
TM
TM
TM
TM
TM
TM
ETM+
ETM+
ETM+
TM
TM
TM
763
764
32
765
766
Table 2.
NDVI-95
NDVI-93
Elevation
Slope
Solar
Radiation
NBR
Pecipitation
PET
OLS
August
47
95
84
2
March
51
80
82
1
GLS
August
39
92
78
8
March
42
75
75
6
ANOVA
August
18
11
11
34
March
24
22
18
37
0
29
46
0
2
21
21
0
16
38
35
21
9
27
13
10
35
38
22
29
36
41
37
38
767
768
33
769
0 .3 5
0 .3 0
P in u s h a le p e n s is
R eflectance
0 .2 5
Q u e rc u s c o c c ife ra
0 .2 0
0 .1 5
S h ru b s
0 .1 0
A s h e s a n d d ry m a tte r
0 .0 5
0 .0 0
400
770
771
772
773
500
600
700
800
900
1000
W a v e le n g h t ( m )
Figure 1.
34
774
775
776
777
778
779
780
Figure 2.
35
781
Fire
0.8
0.7
NDVI
0.6
0.5
0.4
0.3
August
0.2
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
0.8
0.7
NDVI
0.6
0.5
0.4
0.3
March
782
783
784
785
0.2
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Figure 3.
36
786
787
788
Figure 4.
37
789
790
791
792
793
Figure 5.
38
794
795
796
797
798
799
800
Figure 6.
39
August
March
0.8
1.0
PC 1 (35%)
0.6
0.4
0.2
R-Pearson
R-Pearson
1.0
0.8
PC 2 (26%)
0.6
0.4
0.2
0.0
0.2
0.8
PC 2 (16%)
0.6
0.4
0.2
1.0
PC 3 (17%)
0.6
0.4
0.2
0.0
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
R-Pearson
R-Pearson
801
802
803
804
805
0.4
0.0
1.0
0.8
PC 1 (32%)
0.6
0.0
1.0
R-Pearson
R-Pearson
0.0
1.0
0.8
0.8
PC 3 (11%)
0.6
0.4
0.2
0.0
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Figure 7.
40
806
807
808
809
810
811
Figure 8.
41