Search

Forest fires are considered to be an important part of numerous terrestrial ecosystems and vegetation types, being also a significant factor of ecosystem disruption. In this sense, fires play an important role in the structure and function of the ecosystems. Biomes are characterized by a specific type of fire regime, which is a synergy of the climate conditions and the characteristics of the vegetation types dominating each biome. The assessment of burned areas and the identification of the fire regimes can be implemented with freely available low- to high-resolution satellite data as those of Landsat and Sentinel-2. Moreover, the biomes are characterized by the phenology, a useful component for vegetation monitoring, especially when time series of satellite images are used. Both the identification of fire regime by reconstructing the fire history and the monitoring of the post-fire evolution of burned areas were studied with remote sensing methods. Specifically, the present paper is a pilot study implemented in a Mediterranean biome, aimed at establishing the methodological framework to (i) define fire regimes, (ii) characterize the phenological pattern of the vegetation (pre-fire situation) of the fire-affected areas, and (iii) compare the phenology of the recovered fire-affected areas with the corresponding one of the pre-fire situation. At the global level, based on MODIS fire perimeters, we found that fires are occurring at 70% in the tropical and subtropical grasslands, savannas, and shrublands, followed by fires at tropical and subtropical moist broadleaf forests by 7% and by fires at deserts and xeric shrublands by 6.5%. [ABSTRACT FROM AUTHOR]

Background: Singular value decomposition (SVD), as an alternative solution to principal components analysis (PCA), may enhance the spectral profile of burned areas in satellite image composites. Methods: In this regard, we combine the pre-processing options of centering, non-centering, scaling, and non-scaling the input multi-spectral data, prior to the matrix decomposition, and treat their combinations as four different SVD-based PCA versions. Using both unitemporal and bi-temporal data sets, we test all four combinations to derive principal components. We assess the effects of the transformations based on multiresponse permutation procedures and quantify the enhanced spectral separability between burned areas and other major land cover classes via the Jeffries-Matusita metric. Lastly, we evaluate visually and numerically all principal components and select a subset of interest. Results: The best transformation for the subset of selected components, is the uncentered-unscaled one. Conclusions: The results indicate that an uncentered and unscaled SVD may improve the spectral separability of burned areas in some of the higher order components. [ABSTRACT FROM AUTHOR]

There is a long history of the use of Landsat data in burned land mapping mainly due to certain characteristics of the Landsat imagery including the spatial, spectral, and temporal data resolution, the low cost (Landsat data are now freely available), and the existence of an almost 35-year historical archive (excluding Landsat 1–3). Landsat 8 (Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)) was launched on 11 February 2013 and it captures data in three new bands along with two additional thermal bands. However, is the spectral signal of burned surfaces in satellite remote-sensing data of Landsat series consistent and robust enough to allow the successful application of the techniques developed so far for Landsat 8? In this article, we compare the spectral signal of burned surfaces between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 OLI sensors using five case studies that correspond to five large fire events in different biophysical environments in Greece, for which both Landsat 7 ETM+ and Landsat 8 OLI data were available. From the comparative analysis using histogram data plots of burned (post-fire image) and vegetated (pre-fire image) areas, spectral signature plots and separability indices of certain land-cover types, estimated using the same sampling areas over both satellite images, a general consistency was observed between the two sensors. Slight differences between the sensors were attributed to differences in the acquisition dates and were related to the type of vegetation rather than the sensors used to record the satellite images. Neither sensor provided improved discrimination over the other. [ABSTRACT FROM PUBLISHER]

This study presents a new semi-automatic method to map burned areas by using multi-temporal Land Remote Sensing Satellite Program (Landsat) Thematic Mapper (TM) and Enhanced TM Plus (ETM+) images. The method consists of a set of rules that are valid especially when the post-fire satellite image has been captured shortly after the fire event. The overall accuracy of the method when applied to two case studies in Mt Parnitha and Samos Island in Greece were 95.69% and 93.98%, respectively. The commission and omission errors for Mt Parnitha were 6.92% and 10.24%, while those for Samos Island were 3.97% and 8.80%, respectively. Between the two types of error, it is preferred to minimize omission errors, since commission errors can be easily identified as part of product quality assessment and algorithm tuning procedures. The rule-based approach minimizes human interventions and makes it possible to run the mapping algorithm for a series of images that would otherwise need extensive time investment. In case of failure to capture burned areas correctly, it is possible either to make some adjustments by modifying the thresholding coefficients of the rules, or to discard some of the rules, since some editing is usually required to correct errors following the automated extraction procedures. When this method was applied to a series of US Geological Survey (USGS) Landsat TM and ETM+ archived satellite images covering the periods 1984–1991 and 1999–2009, a total of 1773 fires were identified and mapped from six different scenes that covered Attica and the Peloponnese in Greece. The majority of uncaptured burned areas corresponded to fires with size classes of 0–1 ha and 1–5 ha, where the loss in capturing fire scars is generally significant. This was expected since it is possible that small fires, identified and recorded by forest authorities, may not have been captured by satellite data due to limitations arising either from the spatial resolution of the sensor or imposed by the temporal series, which do not systematically cover the full period. [ABSTRACT FROM PUBLISHER]

Abstract: The aim of our study was to explore the spectral properties of fire-scorched (burned) and non fire-scorched (vegetation) areas, as well as areas with different burn/vegetation ratios, using a multisource multiresolution satellite data set. A case study was undertaken following a very destructive wildfire that occurred in Parnitha, Greece, July 2007, for which we acquired satellite images from LANDSAT, ASTER, and IKONOS. Additionally, we created spatially degraded satellite data over a range of coarser resolutions using resampling techniques. The panchromatic (1m) and multispectral component (4m) of IKONOS were merged using the Gram-Schmidt spectral sharpening method. This very high-resolution imagery served as the basis to estimate the cover percentage of burned areas, bare land and vegetation at pixel level, by applying the maximum likelihood classification algorithm. Finally, multiple linear regression models were fit to estimate each land-cover fraction as a function of surface reflectance values of the original and the spatially degraded satellite images. The main findings of our research were: (a) the Near Infrared (NIR) and Short-wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas; (b) when the bi-spectral space consists only of NIR and SWIR, then the NIR ground reflectance value plays a more significant role in estimating the percent of burned areas, and the SWIR appears to be more important in estimating the percent of vegetation; and (c) semi-burned areas comprising 45–55% burned area and 45–55% vegetation are spectrally closer to burned areas in the NIR channel, whereas those areas are spectrally closer to vegetation in the SWIR channel. These findings, at least partially, are attributed to the fact that: (i) completely burned pixels present low variance in the NIR and high variance in the SWIR, whereas the opposite is observed in completely vegetated areas where higher variance is observed in the NIR and lower variance in the SWIR, and (ii) bare land modifies the spectral signal of burned areas more than the spectral signal of vegetated areas in the NIR, while the opposite is observed in SWIR region of the spectrum where the bare land modifies the spectral signal of vegetation more than the burned areas because the bare land and the vegetation are spectrally more similar in the NIR, and the bare land and burned areas are spectrally more similar in the SWIR. [Copyright &y& Elsevier]

Maps depicting the spatially explicit fire history of an area, including variables such as fire frequency and fire return interval, are important tools promoting the understanding of processes associated with wildfires (fire ignition and spread), the assessment of the impacts of wildland fires on landscape dynamics, and decisions on appropriate management practices. Remote sensing is a cost- and time-effective alternative to automatically assess a vast amount of spatial information and produce various thematic maps. The aim of this study was to reconstruct the recent fire history of Attica region (Greece), in a spatially explicit mode by means of remote sensing techniques using a series of Landsat images acquired from 1984 to 2011. The results show that the fire scar perimeters were captured with high accuracy. Regression modelling shows that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained (86.3% of the variance) by the number of satellite images used (standardized coefficient 0.752) followed by the date of the first image (standardized coefficient 0.705). The use of satellite data as the basic source of information alongside automated classification methods should be promoted for the creation of fire history maps. The latter is further supported when considering the long history of data capture from Landsat satellites, which provide a huge, global historical archive of repeat images of the Earth's surface. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, principal component analysis (PCA), a dimensionality reduction method, has been applied successfully as an image enhancement technique to improve the spectral signal of burnt surfaces. Forward/backward PCA (F/B PCA) and image differencing, which the proposed method consists of, creates a new spectral space that preserves the original spectral patterns while enhancing particular structures of the original satellite data. Burnt surfaces constitute a spectrally enhanced feature after selective removal of spectral information from the original Landsat-7 Enhanced Thematic Mapper data. Improvement of the spectral separability of burnt surfaces is most evident in spectral channels ETM+4 and ETM+7, where burnt surfaces already compose distinct spectral objects, and channels ETM+2 and ETM+5. This improvement is reasonable since the third PC axis, which is not considered in the back-transformation, is composed mainly of the spectral information in these channels. Another benefit of the technique is a reduction of interband correlation in the satellite data. No clear differences between the standardized and non-standardized F/B PCA were identified to recommend the use of one over the other. Both methods show advances in certain aspects. Finally, an increase of the separability value between burnt areas and dry vegetated areas from 0.473 to 1.06 and 1.31 was obtained with the use of the standardized and non-standardized F/B PCA, respectively. [Copyright &y& Elsevier]

Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%. [ABSTRACT FROM AUTHOR]

Abstract: A multi-scale, object-based analysis of a Quickbird satellite image has been carried out to delineate forest vegetation polygons in a natural forest in Northern Greece. Following a multi-resolution segmentation, a classification tree was developed and compared using a nearest neighbour classifier for the assignment of image segments to classes. Additionally, texture images derived from local indicators of spatial association were calculated and used to improve the classification. The best results were obtained when texture images were considered in the classification sequence, however, the accuracy of the final map did not exceed 80%. The classification tree yielded better results than the nearest neighbour algorithm. Overall, the object-based classification approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping Mediterranean forest ecosystems. [Copyright &y& Elsevier]

This study presents an improvement of an old rule-based semi-automatic method to map burned areas by using multi-temporal Landsat images. The rule-based approach consists of a set of rules developed based on spectral properties of burned areas as compared to the pre-fire unburned vegetation and to the spectral signatures of other land cover types found in post-fire satellite scene. Actually, the spectral properties based on which the rules have been developed are presented in two graphs, one that corresponds to spectral signatures plots and the second that corresponds to the histogram data plots. The spectral patterns based on which the rule-based approach has been developed are not always the same. For example, depending on the type of the fire-affected vegetation (e.g. dry vegetation instead of green) the spectral pattern of the SWIR channel that correspond to channel 7 in Landsat 4-7 and 8 is not valid. Instead, there is a similar spectral behaviour but in the SWIR channel that correspond to channel 5 in Landsat 4-7, or channel 6 in Landsat 8. Additionally, the threshold value of 0.10-0.25 of the second rule seems not to be sufficient to cover all variability since there are cases that this value should be higher. Two characteristic examples of the insufficiencies found on the old-rules are concerned in the current analysis, one that presents limitations concerning the rule 5 (Serifos) and one that represents limitations concerning the rule 2 (Portugal).To revise the old set of rules and create new rules we followed the same methodology as in the original paper from Koutsias et al. (2013). Therefore, sampling plots that correspond to burned surfaces were located on the post-fire Landsat TM images. Additionally, other important land cover types were delineated on the satellite images using the pan-European CORINE 2000 Land Cover database. Radiometric values of the sampling plot pixels were extracted, and simple statistics were generated to characterize the spectral properties of the various land cover categories including the burned area class. Histogram and signature data plots were created to graphically represent the spectral profiles for all land cover types including burned areas. It was also used to compare them with patterns of unburned vegetation when using multi-temporal satellite images and with other land cover types when using only single-date post-fire satellite images. [ABSTRACT FROM AUTHOR]

Abstract: Land cover/land use (LCLU) transitions and landscape changes were mapped and interpreted in a deltaic Natura 2000 habitat site in northern Greece. Multi-temporal maps created over a period of about 65 years and based on panchromatic aerial photographs and high spatial resolution satellite data, helped to assess LCLU transformations. These changes were associated with human interference and major socio-economic processes occurring in the area during the study period. In 1945, forests and wetlands dominated the delta area, occupying 63% of the total land cover. Subsequently, these land cover types severely reduced in coverage, and agricultural areas dominated the landscape as a result of policies introduced to accommodate the settlement of refugees from Asia Minor. This transition increased landscape fragmentation in 1960. Due to relative increase of swap changes, the landscape became less heterogeneous in 1992. Finally, in 2002, agricultural areas exhibit a small decrease in coverage having converted back to natural areas following the introduction of EU Common Agricultural Policy. About 77% of the deltaic ecosystem experienced LCLU changes during the first period of the analysis (1945–1960). Almost two thirds of this change is attributed to intercategorical transitions, while the rest is related to swap changes. During the second period of the analysis (1960–1992), half the study area was transformed due to net and swap changes. Finally, in the third, most recent time period (1992–2002), 85% of the landscape showed persistence while net changes were twice as frequent as swap changes. Human interference was the key factor beyond the landscape transformations and intercategorical transitions observed in the delta. During the previous century natural habitats were pressured by major socio-economic processes that transformed their composition and structure. Assessing these changes is critical to develop area-specific policies that minimize negative influences and provide a framework for restoring habitats to their natural conditions. [Copyright &y& Elsevier]