Sander Veraverbeke

Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece

A vast area (more than 100u2009000u2009ha) of forest, shrubs and agricultural land burned on the Peloponnese peninsula in Greece during the 2007 summer. Three pre- and post-fire differenced Landsat Thematic Mapper (TM)-derived spectral indices were correlated with field data of burn severity for these devastating fires. These spectral indices were the Normalised Difference Vegetation Index (NDVI), the Normalised Difference Moisture Index (NDMI) and the Normalised Burn Ratio (NBR). The field data consist of 160 Geo Composite Burn Index (GeoCBI) plots. In addition, indices were evaluated in terms of optimality. The optimality statistic is a measure for the index’s sensitivity to fire-induced vegetation depletion. Results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderately high R2u2009=u20090.65 whereas the correlation between field data and the differenced NDMI (dNDMI) and the differenced NDVI (dNDVI) was clearly lower (respectively R2u2009=u20090.50 and R2u2009=u20090.46). The dNBR also outperformed the dNDMI and dNDVI in terms of optimality. The resulting median dNBR optimality equalled 0.51 whereas the median dNDMI and dNDVI optimality values were respectively 0.50 and 0.40 (differences significant for Pu2009<u20090.001). However, inaccuracies observed in the spectral indices approach indicate that there is room for improvement. This could imply improved preprocessing, revised index design or alternative methods.

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well-illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing a small image-to-image difference in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p < 0.001). The original c-correction method improved the optimality of badly illuminated pixels while deteriorating the optimality of well-illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination–reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that deviated up to two months from the ideal anniversary date scheme the modified c-correction technique resulted in a more reliable change detection.

Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains

Abstract Spatio-temporal variability in energy fluxes at the earths surface implies spatial and temporal changes in observed land surface temperatures (LST). These fluxes are largely determined by variation in meteorological conditions, surface cover and soil characteristics. Consequently, a change in these parameters will be reflected in a different temporal LST behavior which can be observed by remotely sensed time series. Therefore, the objective of this paper is to perform a quantitative analysis on the parameters that determine this variability in LST to estimate the impact of changes in these parameters on the surface thermal regime. This study was conducted in the Russian Altay Mountains, an area characterized by strong gradients in meteorological conditions and surface cover. Spatio-temporal variability in LST was assessed by applying the fast Fourier transform (FFT) on 8 year of MODIS Aqua LST time series, herein considering both day and nighttime series as well as the diurnal difference. This FFT method was chosen as it allows to discriminate significant periodics, and as such enables distinction between short-term weather components, and strong, climate related, periodic patterns. A quantitative analysis was based on multiple linear regression models between the calculated, significant Fourier components (i.e. the annual and average component) and five physiographic variables representing the regional variability in meteorological conditions and surface cover. Physiographic predictors were elevation, potential solar insolation, topographic convergence, vegetation cover and snow cover duration. Results illustrated the strong inverse relationship between averaged daytime and diurnal difference LST and snow duration, with a R adj 2 of 0.85 and 0.60, respectively. On the other hand, nocturnal LST showed a strong connection with elevation and the amount of vegetation cover. Amplitudes of the annual harmonic experienced both for daytime and for nighttime LST similar trends with the set of physiographic variables – with stronger relationships at night. As such, topographic convergence was found to be the principal single predictor which demonstrated the importance of severe temperature inversions in the region. Furthermore, limited contribution of the physiographic predictors to the observed variation in the annual signal of the diurnal difference was retrieved, although a significant phase divergence was noticed between the majority of the study region and the perennial snowfields. Hence, this study gives valuable insights into the complexity of the spatio-temporal variability in LST, which can be used in future studies to estimate the ecosystems’ response on changing climatic conditions.

Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra

Significance Rising arctic temperatures could mobilize reservoirs of soil organic carbon trapped in permafrost. We present the first quantitative evidence for large, regional-scale early winter respiration flux, which more than offsets carbon uptake in summer in the Arctic. Data from the National Oceanic and Atmospheric Administration’s Barrow station indicate that October through December emissions of CO2 from surrounding tundra increased by 73% since 1975, supporting the view that rising temperatures have made Arctic ecosystems a net source of CO2. It has been known for over 50 y that tundra soils remain unfrozen and biologically active in early winter, yet many Earth System Models do not correctly represent this phenomenon or the associated CO2 emissions, and hence they underestimate current, and likely future, CO2 emissions under climate change. High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO2) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO2 fluxes across Alaska during 2012–2014. We find that tundra ecosystems were a net source of CO2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO2 emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO2 in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate.

A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBRMT)

Abstract Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsats differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R2xa0=xa00.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBRMT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBRMT estimates resulted in a moderate-high coefficient of determination R2xa0=xa00.54. dNBRMT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBRMT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODISs repeated temporal sampling, the dNBRMT accounts for both first- and second-order fire effects.

Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (APEX)

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation

Abstract Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation–substrate environments which makes spectral mixture analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R2 of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R2xa0=xa00.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R2xa0=xa00.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the models performance for other soil types, with other image data and at different post-fire timings.

Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper

In this study several pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment are evaluated. GeoCBI (Geo Composite Burn Index) field data of burn severity were correlated with remotely sensed measures, based on the NBR (Normalized Burn Ratio), the NDMI (Normalized Difference Moisture Index) and the NDVI (Normalized Difference Vegetation Index). In addition, the strength of the correlation was evaluated for specific fuel types and the influence of the regression model type is pointed out. The NBR was the best remotely sensed index for assessing burn severity, followed by the NDMI and the NDVI. For this case study of the 2007 Peloponnese fires, results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderate–high R 2u2009=u20090.65. Absolute indices outperformed their relative equivalents, which accounted for pre-fire vegetation state. The GeoCBI–dNBR relationship was stronger for forested ecotypes than for shrub lands. The relationship between the field data and the dNBR and dNDMI (differenced NDMI) was nonlinear, while the GeoCBI–dNDVI (differenced NDVI) relationship appeared linear.

Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data

High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14)asinputtoakriginginterpolationtoderivecontinuousmapsofthetimingofburntareafor16largewildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixels time of burning varied between four and six among the fires studied. The median standarderrorfromkrigingrangedbetween0.80and3.56daysandthemedianstandarderrorfromgeolocationuncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour. Additional keywords: carbon emissions, fire growth, fire propagation, fire spread.

Advances in Remote Sensing of Post-Fire Vegetation Recovery Monitoring - A Review

Ioannis Gitas1, George Mitri2, Sander Veraverbeke3,4 and Anastasia Polychronaki1 1Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, Thessaloniki, 2Biodiversity Program, Institute of the Environment, University of Balamand and Department of Environmental Sciences, Faculty of Science, University of Balamand, 3Department of Geography, Ghent University, Ghent 4Jet Propulsion Laboratory, California Institute of Technology,Pasadena, CA, 1Greece 2Lebanon 3Belgium 4USA