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Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index
Abstract
Several remote sensing studies have discussed the potential of satellite imagery as an alternative for extensive field sampling to quantify fire–vegetation impact over large areas. Most studies depend on Landsat image availability with infrequent image acquisition dates and consequently are limited for assessing intra-annual fire–vegetation dynamics or comparing different fire plots and dates. The control pixel based regeneration index (pRI) derived from SPOT-VEGETATION (VGT) normalized difference vegetation index (NDVI) is used in this study as an alternative to the traditional bi-temporal Landsat approach based on the normalized burn ratio (NBR). The major advantage of the pRI is the use of unburnt control plots which allow the expression of the intra-annual variation due to regeneration processes without external influences. In the comparison of Landsat and VGT data, (i) the inter-annual differences between the bi-temporal and control plot approach were contrasted and (ii) metrics of pRI were derived and compared with the inter-annual dynamics of both VGT and Landsat data. Results of these comparisons, demonstrate the overall similarity between NBR and NDVI data, stress the importance of the elimination of external influences (e.g., phenological variations), and emphasize the failure of including post-fire vegetation responses in bi-temporal Landsat assessments, especially in quickly recovering ecotypes with a strong annual phenological cycle such as savanna. This highlights the importance of using high frequency multi-temporal approaches to estimate fire–vegetation impact in temporally dynamic vegetation types.
... Therefore, the application of the differencing method in continuous Remote Sens. 2018, 10, 1904 3 of 27 time series requires methodological improvements [47,48]. Lhermitte et al. [49] tried to solve this problem by selecting control pixels surrounding the unburned area based on time series similarity. This approach considers the spatial context (neighboring pixels) to minimize the external influences and phenological variations [50]. ...
... Remote Sens. 2018, 10, x FOR PEER REVIEW 3 of 28 continuous time series requires methodological improvements [47,48]. Lhermitte et al. [49] tried to solve this problem by selecting control pixels surrounding the unburned area based on time series similarity. This approach considers the spatial context (neighboring pixels) to minimize the external influences and phenological variations [50]. ...
... The normalization procedure is very promising because it does not require neighboring control pixels, considering only the time series of the pixel. This method is different from that proposed by the research of Diáz-Delgado et al. [50] and Lhermitte et al. [49], because it is fast and simple data processing restricted to the pixel, which generates an image that emphasizes the burning points in different environments. ...
Fires associated with the expansion of cattle ranching and agriculture have become a problem in the Amazon biome, causing severe environmental damages. Remote sensing techniques have been widely used in fire monitoring on the extensive Amazon forest, but accurate automated fire detection needs improvements. The popular Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64 product still has high omission errors in the region. This research aimed to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso (State of Pará) and to determine their accuracy in the different types of land use/land cover during the period 2000-2014. The burned area mapping from 8-day composite products, compared the following data: near-infrared (NIR) band; spectral indices (Burnt Area Index (BAIM), Global Environmental Monitoring Index (GEMI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR), variation of Normalized Burn Ratio (NBR2), and Normalized Difference Vegetation Index (NDVI)); and the seasonal difference of spectral indices. Moreover, we compared the time series normalization methods per pixel (zero-mean normalization and Z-score) and the seasonal difference between consecutive years. Threshold-value determination for the fire occurrences was obtained from the comparison of MODIS series with visual image classification of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the overall accuracy. The best result considered the following factors: NIR band and zero-mean normalization, obtaining the overall accuracy of 98.99%, commission errors of 32.41%, and omission errors of 31.64%. The proposed method presented better results in burned area detection in the natural fields (Campinarana) with an overall accuracy value of 99.25%, commission errors of 9.71%, and omission errors of 27.60%, as well as pasture, with overall accuracy value of 99.19%, commission errors of 27.60%, and omission errors of 34.76%. Forest areas had a lower accuracy, with an overall accuracy of 98.62%, commission errors of 23.40%, and omission errors of 49.62%. The best performance of the burned area detection in the pastures is relevant because the deforested areas are responsible for more than 70% of fire events. The results of the proposed method were better than the burned area products (MCD45, MCD64, and FIRE-CCI), but still presented limitations in the identification of burn events in the savanna formations and secondary vegetation.
... Using a 22 yr pre-and post-wildfire data set, we hypothesized that, at the landscape scale, forested catchments in SDWC recover to pre-wildfire conditions (fuel biomass) much faster than has been previously reported (Brown 1972). Additionally, we hypothesized that the impact of even the most severe burn has minimal medium-term (decadal) effect on the recovery on eucalypt forests and woodlands dominated by resprouter vegetation communities in the Sydney Basin (Shakesby et al. 2007 Fox et al. 2008, Lhermitte et al. 2011Fox et al. 2008), and the Normalized Difference Vegetation Index (NDVI; Díaz-Delgado et al. 2002, Fox et al. 2008, Hernandez-Clemente et al. 2009, Jacobson 2010. The NDVI was selected to compare pre-and post-wildfire healthiness of forested catchments in our study area because that index has a strong relationship with aboveground biomass and is widely used to detect vegetation change and investigate post-fire vegetation recovery (Fox et al. 2008, Gouveia et al. 2010, van Leeuwen et al. 2010, Lhermitte et al. 2011, Gitas et al. 2012. ...
... Additionally, we hypothesized that the impact of even the most severe burn has minimal medium-term (decadal) effect on the recovery on eucalypt forests and woodlands dominated by resprouter vegetation communities in the Sydney Basin (Shakesby et al. 2007 Fox et al. 2008, Lhermitte et al. 2011Fox et al. 2008), and the Normalized Difference Vegetation Index (NDVI; Díaz-Delgado et al. 2002, Fox et al. 2008, Hernandez-Clemente et al. 2009, Jacobson 2010. The NDVI was selected to compare pre-and post-wildfire healthiness of forested catchments in our study area because that index has a strong relationship with aboveground biomass and is widely used to detect vegetation change and investigate post-fire vegetation recovery (Fox et al. 2008, Gouveia et al. 2010, van Leeuwen et al. 2010, Lhermitte et al. 2011, Gitas et al. 2012. It is a ratio-based index using the red band of the spectral region, which strongly absorbs visible red light for use in photosynthesis and transpiration. ...
... Landsat (30 m 2 pixels) is readily available satellite imagery and has been providing coverage of the Earth's surfaces since 1972. Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) have been extensively used to investigate vegetation change by using various vegetation indices (Vescovo and Gianelle 2008;Lhermitte et al. 2011). At the time of this study, both Landsat 5 and Landsat 7 crossed the study area every 16 days. ...
Monitoring landscape-scale vegetation responses of resprouter species to wildfire is helpful in explaining post-wildfire recovery. Several previous Australian studies have investigated the temporal recovery of eucalypt obligate- seeder communities (which have a significantly delayed revegetation response), but little research has been conducted for resprouter communities. In this study, we found that eucalypt dominated resprouter communities in Sydney’s drinking water supply catchments (SDWC) have a rapid post-wildfire response and recovery rate. This study was designed to detect inter-annual landscape-scale changes in vegetation response using a 22 yr pre- and post-wildfire time series of Landsat satellite-derived Australian summer images (1990/91 to 2011/12). Four burned subcatchments and three unburned subcatchments were analyzed. The temporal change in eucalypt forest and woodland vegetation communities was examined within the subcatchments using the Normalized Differenced Vegetation Index (NDVI) to assess their health. A new spectral index, differenced Recovery Index (dRI), was developed to quantify the difference between the pre- and post-wildfire NDVI values. We found that, spectrally, at the landscape scale, vegetation communities recovered to near pre-wildfire conditions within five to seven years post wildfire. These results demonstrate the resilience of resprouter vegetation communities in the Sydney Basin to large-area disturbance events at the landscape scale.
... In previous studies, Landsat NDVI trajectories were normalized for precipitation effects using synchronous NDVI values from unburned control sites (cp. Riaño et al., 2002;Hope et al., 2007;Lhermitte et al., 2011). We elected not to utilize this normalization for the following reasons: (1) sites in southern California that were unburned in the study period are remarkably sparse (cf. ...
... We elected not to utilize this normalization for the following reasons: (1) sites in southern California that were unburned in the study period are remarkably sparse (cf. Storey et al., 2016); (2) substantial error can stem from inter-site differences in phenology (Lhermitte et al., 2011);and (3) no substantial improvement in accuracy of chaparral change detection was shown to result from this normalization (Storey et al., 2019). Deriving multi-annual trajectories from time periods that were consistent for the entire study region helped to control for temporal variations in precipitation. ...
Regrowth after fire is critical to the persistence of chaparral shrub communities in southern California, which has been subject to frequent fire events in recent decades. Fires that recur at short intervals of 10 years or less have been considered an inhibitor of recovery and the major cause of ‘community type-conversion’ in chaparral, primarily based on studies of small extents and limited time periods. However, recent sub-regional investigations based on remote sensing suggest that short-interval fire (SIF) does not have ubiquitous impact on postfire chaparral recovery. A region-wide analysis including a greater spatial extent and time period is needed to better understand SIF impact on chaparral.
This study evaluates patterns of postfire recovery across southern California, based on temporal trajectories of Normalized Difference Vegetation Index (NDVI) derived from June-solstice Landsat image series covering the period 1984–2018. High spatial resolution aerial images were used to calibrate Landsat NDVI trajectory-based estimates of change in fractional shrub cover (dFSC) for 294 stands. The objectives of this study were (1) to assess effects of time between fires and number of burns on recovery, using stand-aggregate samples (n = 294) and paired single- and multiple-burn sample plots (n = 528), and (2) to explain recovery variations among predominant single-burn locations based on shrub community type, climate, soils, and terrain. Stand-aggregate samples showed a significant but weak effect of SIF on recovery (p < 0.001; R² = 0.003). Results from paired sample plots showed no significant effect of SIF on dFSC among twice-burned sites, although recovery was diminished due to SIF at sites that burned three times within 25 years. Multiple linear regression showed that annual precipitation and temperature, chaparral community type, and edaphic variables explain 28% of regional variation in recovery of once-burned sites. Many stands that exhibited poor recovery had burned only once and consist of xeric, desert-fringe chamise in soils of low clay content.
... Resistance reflects the capacity for a system to absorb a disturbance (Pimm 1984;Tilman and Downing 1994;Tilman 1996). Resilience (in terms of engineering resilience) quantifies the post-disturbance recovery of an ecosystem property to its equilibrium state, and is often measured as a rate (Pimm 1984;Tilman and Downing 1994;Lhermitte et al. 2011). These are shown in Figure 1. ...
... Satellite-derived vegetation indices are rarely used within the literature to estimate ecosystem stability, although a handful of studies do exist, which look at recovery after known disturbance events including climatic events (Washington-Allen et al. 2008;De Keersmaecker et al. 2016) and wildfires (Goetz et al. 2006;Lhermitte et al. 2011;Spasojevic et al. 2016). In all these cases, there is a known period of pre-disturbance and post-disturbance, and also the presence of undisturbed (e.g. an unburnt control plot) areas, for comparison. ...
To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to satellite remote sensing. Here we present a methodology to extract four components of ecosystem functioning stability from satellite‐derived time series of Enhanced Vegetation Index (EVI) data. The four stability components are as follows: variability, resistance, recovery time and recovery rate in ecosystem functioning. We apply our method to the island of Ireland to demonstrate the use of remotely sensed data to identify large disturbance events in productivity. Our method uses stability measures that have been established at the field‐plot scale to quantify the stability of ecosystem functioning. This makes our method consistent with previous small‐scale stability research, whilst dealing with the unique challenges of using remotely sensed data including noise. We encourage the use of remotely‐sensed data in assessing the stability of ecosystems at a scale that is relevant to conservation and management practices. This paper presents a new methodological approach to measure multiple stability components of ecosystem functioning at large spatial scales using remotely sensed data. The measures of resistance, recovery time, recovery rate and temporal variability are comparable to those previously established at small scales whilst accounting for inherent noise within the data.
... Burned area data are critical in estimating biomass emissions, localizing fire impacts on vegetation, wildlife habitats and human settlements and creating of respective remedial strategies (DeBano et al., 1998;Goldammer and De Ronde, 2004;Roy et al., 2002). Resource managers also increasingly demand detailed spatio-temporal burned area information to assess trends and changes in extent burned areas and associated impacts (Laris, 2005;Lhermitte et al., 2011;Thackway et al., 2013). Such requirements can be met by seasonal burned area mapping using moderate spatial resolution imagery such as Landsat. ...
... Multitemporal analyses provides an efficient way of deriving spatiotemporal burned area information as images can be analyzed together (Coppin et al., 2004;Gillanders et al., 2008;Hu et al., 2018;Huang et al., 2010;Mallet et al., 2015;Wehmann and Liu, 2015;Zhang et al., 2019) Because factors that confound burned area mapping such as phenology vary with time, mapping using multiple images over time can also significantly overcome such confusion in mapping burned areas than the use of one or two images (Lhermitte et al., 2011;Lu et al., 2004;Pereira, 2003). However, few studies exploit such approaches with moderate spatial resolution datasets such as Landsat mainly due to the low temporal resolution. ...
Monitoring of environmental change can benefit from the increasing availability of multitemporal satellite imagery, and efficient and effective analysis tools are needed to generate relevant spatio-temporal land cover datasets. We present a data driven approach for automatic training sample selection to support supervised spatio-temporal mapping of seasonally burned areas in the semi-arid savannas of Southern Africa. Our approach leveraged the distinctive spectral-temporal trajectories associated with areas on the landscape burned at different times or areas remaining unburned over time. Using fuzzy c-means clustering, we extracted distinctive trajectories from the multitemporal mid-infrared burn index (MIRBI) data derived from Landsat data and characterized them based on empirically developed labeling rules. The selected training trajectories captured both the burn condition (burned or unburned) and if burned, the timeframe of the burn event. We assessed the approach by training a Random Forests model using over 2500 automatically selected training data and validated the model against ground truth for years 2009 and 2014. Based on over 1000 validation points in each year, we obtained overall accuracies above 90% showing reliable and consistent training data were supplied by our automatic training sample selection approach. The method provides a data driven and automatic approach which can reduce the time-consuming and expensive training task, enabling quicker generation of relevant burned area information that can support fire monitoring programs and climate change research.
... The Normalized Burn Ratio (NBR) is the most commonly applied index for measuring fire severity (Eidenshink et al., 2007, French et al., 2008, Parsons, 2003, which has been used in a variety of ecosystems including tundra (Kolden & Rogan, 2013), forests (Cocke et al., 2005), and shrublands (Garcia & Caselles, 1991, Lhermitte et al., 2011. The NBR is the normalized difference ratio of near-infrared (NIR) and shortwave-infrared (SWIR) bands, typically applied with Landsat Terrestrial Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Observing Land Imager (OLI), or downscaled Moderate-resolution Imaging reflectance since 1984, high-latitude image acquisition has been inconsistent (French et al., 2008, Intrieri et al., 2002. ...
... A range of multispectral indices (e.g., Huete, 1988, Tucker, 1979, Lhermitte et al., 2011, multivariate transformations (e.g. Koutsias et al., 2009, Huang et al., 2002, Rogan & Yool, 2001, spectral unmixing algorithms (Meng et al., 2017, Quintano et al., 2013, Veraverbeke and Hook, 2013, and radiative transfer models (Chuvieco et al., 2006, De Santis et al., 2009 have been used to evaluate VNIR-derived fire severity. ...
Tundra fires are projected to increase with anthropogenic climate change, yet our ability to assess key wildfire metrics such as fire severity remains limited. The Normalized Burn Ratio (NBR) is the most commonly applied index for fire severity mapping. However, the computation of NBR depends on short-wave infrared (SWIR) data, which are not commonly available from historical and contemporary high-resolution (≤4 m) optical imagery. The increasing availability of visible near-infrared (VNIR) measurements from proximal to spaceborne sensors/platforms has the potential to advance our understanding of the spatiotemporal patterns of fire severity within tundra fires. Here we systematically assess the feasibility of using VNIR data for fire severity mapping in ten Alaskan tundra fires (cumulatively burned ~1700 km2). We compared the accuracy of 10 published VNIR-based fire indices using both uni-temporal (post-fire image) and bi-temporal (pre-fire and post-fire image difference) assessments against ground-based fire severity data (Composite Burn Index, CBI) at 109 tundra sites. The Global Environmental Monitoring Index (GEMI) had the highest correspondence with CBI (R2 = 0.77 uni-temporal; R2 = 0.85 bi-temporal), with similar performance to NBR (R2 = 0.77 uni-temporal; R2 = 0.83 bi-temporal). Tundra vegetation types affected NBR but not GEMI, as SWIR reflectance was influenced to a greater extent in shrub than graminoid tundra. We applied GEMI to contemporary high-resolution (i.e. Quickbird 2) and historical meso-resolution imagery (i.e. Landsat Multispectral Scanner) to demonstrate the capability of GEMI for resolving fine-scale patterns of fire severity and extending fire severity archives. Results suggest that GEMI accurately captured the heterogeneous patterns of tundra fire severity across fire seasons, ecoregions, and vegetation types.
... Thus, the relativized RdNBR index, which provides information on the changes induced by fire regardless of prefire land cover (Miller and Thode, 2007), may more accurately predict burn severity in heterogeneous landscapes (Safford et al., 2008;Miller et al., 2009). Further, mono-temporal NBR may help provide a more accurate burn severity assessment in heterogeneous systems, likely due to an attenuation of errors associated with differences in vegetation phenology and cover (Epting et al., 2005;Lhermitte et al., 2011). Abovementioned correlation patterns of individual NBR-based indices were similar for both Landsat 8 OLI and Sentinel-2 MSI data. ...
... Consequently, the dNDVI index may substitute NBR-based indices for assessing site and soil burn severity when imagery with a SWIR band is unavailable. Nevertheless, similar to dNBR patterns, the dNDVI index showed a weaker correlation with vegetation burn severity, probably due to the effect of the heterogeneity of pre-fire vegetation types in terms of chlorophyll content and canopy cover (Todd and Hoffer, 1998;Lhermitte et al., 2011). Moreover, dNDVI from Deimos-1 data poorly correlated with field burn severity. ...
The development of improved spatial and spectral resolution sensors provides new opportunities to assess burn severity more accurately. This study evaluates the ability of remote sensing indices derived from three remote sensing sensors (i.e., Landsat 8 OLI/TIRS, Sentinel-2 MSI and Deimos-1 SLIM-6-22) to assess burn severity (site, vegetation and soil burn severity). As a case study, we used a megafire (9,939 ha) that occurred in a Mediterranean ecosystem in northwestern Spain. Remote sensing indices included seven reflective, two thermal and four mixed indices, which were derived from each satellite and were validated with field burn severity metrics obtained from CBI index. Correlation patterns of field burn severity and remote sensing indices were relatively consistent across the different sensors. Additionally, regardless of the sensor, indices that incorporated SWIR bands (i.e., NBR-based indices), exceed those using red and NIR bands, and thermal and mixed indices. High resolution Sentinel-2 imagery only slightly improved the performance of indices based on NBR compared to Landsat 8. The dNDVI index from Landsat 8 and Sentinel-2 images showed relatively similar correlation values to NBR-based indices for site and soil burn severity, but showed limitations using Deimos-1. In general, mono-temporal and relativized indices better correlated with vegetation burn severity in heterogeneous systems than differenced indices. This study showed good potential for Landsat 8 OLI/TIRS and Sentinel-2 MSI for burn severity assessment in fire-prone heterogeneous ecosystems, although we highlight the need for further evaluation of Deimos-1 SLIM-6-22 in different fire scenarios, especially using bi-temporal indices.
... Based on field experience, the degradation of grasslands often initiates from relatively small patches [9], which do not manifest on larger scales of pixels due to their relatively limited spatial scope [10], especially when there is an increasing trend in the surrounding pixels. In addition, it has been confirmed that green herb leaf cover shows a robust yearly pattern with significant fluctuations in magnitude [11], especially in the QLB. High altitudes profoundly affect vegetation phenology [12], causing alpine vegetation to turn green and wither within 1-2 weeks in the QLB. ...
The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin.
... Satellite images provide spatially explicit information for the investigation of fire severity, and they have long been regarded as a valuable data source for monitoring biomass burning from the local to global scales, along with its dynamic characteristics [8,9]. Over the past half-century, wildfire researchers have developed various qualitative and quantitative methods for fire severity based on field surveys and the remote sensing monitoring of burn areas [10]. Currently, one of the most commonly used methods of assessing fire severity is the comprehensive use of satellite remote sensing spectral indices in combination with the Composite Burn Index (CBI), which was designed as an operational field-based methodology for burn severity assessment that can rate the average burn severity within sample areas [11,12]. ...
Quantitative assessment of forest fire severity is significant for understanding the change of eco-logical processes from fire disturbances. As a novel spectral index derived from the mul-ti-objective optimization algorithm, the Analytic Burned Area Index (ABAI) is originally designed for mapping burned areas. However, the performance of ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (Composite Burn Index, CBI) to validate the effectiveness of ABAI in detecting fire severity. First, the effectiveness of ABAI for forest fire severity was validated using uni-temporal images of Sen-tinel-2 and Landsat 8 OLI. Second, the fire severity accuracy derived from ABAI with bi-temporal images of both sensors was evaluated. Finally, the performance of ABAI was tested with different sensors and compared with representative spectral indices. The results show: 1) ABAI demon-strates significant advantages in terms of accuracy and stability in assessing fire severity, particu-larly in areas with large amounts of terrain shadows and severe burn regions; 2) ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and performed almost as well as dNBR in bi-temporal images. 3) ABAI outperforms commonly used indices on both Sentinel-2 and Landsat 8 data, indicating that ABAI is generally more generalizable and powerful and provides an optional spectral index for fire severity evalu-ation.
... Trigg and Flasse 70 used field-measured spectra to demonstrate that most changes occur on the first 15 days after fire, thus introducing large uncertainties when using images with a revisit time longer than that. 63,71 Harmonization of Landsat and Sentinel-2 images thus provides an important opportunity to monitor tropical savanna postfire dynamics in detail, 72 assisting to overcome the impacts of image acquisition lag-time in the analysis remote sensing time series. ...
... 'Resilience' measures the speed of recovery after the disturbance (engineering resilience), or the magnitude of disturbance that can be absorbed before the ecosystem's structure changes (ecological resilience; Walker et al., 1981;Holling, 1996). Engineering resilience can be quantified by measuring the time required to return to the biomass state that existed before the stress (Tilman, 1996;Lhermitte et al., 2010Lhermitte et al., , 2011a, or by autocorrelation or the persistence of trends within a time-series of vegetation characteristics (Simoniello et al., 2008;Dakos et al., 2012). Large-scale ecological resilience of tropical savannas and forests, conversely, has been quantified through the probability that forest, savanna or treeless cover will switch states (Hirota et al., 2011). ...
... respectively). In coniferous forest types, only minor or no improvements in performance have been demonstrated when using relativized forms of the dNBR (Hudak et al. 2007;Miller and Thode 2007;Wulder et al. 2007;Soverel, Perrakis and Coops 2010;Lhermitte et al. 2011;Parks, Dillon and Miller 2014;Tanase, Kennedy and Aponte 2015). Although Miller and Thode (2007) suggest that relativization can improve identification of high fire severity areas, we found only minor improvement in accuracy when using the RBR to classify high severity fire (N classes = 2; Table 2). ...
Spectral indices derived from satellite optical remote sensing data have typically been used for fire severity estimation, although other remote sensing systems such as Light Detection and Ranging (LiDAR) are increasingly applied. Despite a multitude of remotely sensed fire severity estimation methods, comparisons of method performance are few. Insights into the merits and limitations of remotely sensed fire severity methods help develop appropriate spatial tools for the management of fire-affected areas. We evaluated the performance of seven passive (optical) and active (LiDAR) remotely sensed fire severity estimation methods in classifying and explaining variation in a field-estimated modified Composite Burn Index (MCBI) for a recent large wildfire in south-eastern Australia. Our evaluation included three commonly applied indices; the differenced Normalized Burn Ratio (dNBR), Relative dNBR (RdNBR) and Relative Burn Ratio (RBR). We compared these NBR indices against two recently proposed fire severity estimation methods that have not previously been evaluated with CBI field data–the Vegetation Structure Perpendicular Index (VSPI) spectral index and the LiDAR point cloud-derived Profile Area Change (PAC), along with experimental relativized forms of these indices (RVSPI and RPAC, respectively). The RVSPI (κ = 0.47) demonstrated similar overall classification accuracy (N classes = 4) to the PAC (κ = 0.48), however both indices had lower classification accuracy than the dNBR (κ = 0.59), RdNBR (κ = 0.59) and RBR (κ = 0.61). The VSPI and PAC were unable to accurately represent non-structural changes caused by lower severity fire. Application of these optical and LiDAR indices should consider their discussed limitations in relation to the objectives of their application. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
... Los incendios forestales generan pérdida de biodiversidad y tienen la capacidad de influir y alterar diferentes procesos ecológicos, principalmente, al eliminar parcial o completamente la capa vegetacional (Petropoulose et al., 2014). Por otro lado, los incendios forestales también pueden alterar las características del suelo, tanto a escala espacial como temporal (Lhermitte et al., 2011), modificando procesos físicos, químicos y microbianos subterráneos (Lentile, 2006). También y dependiendo de la escala espacial, actualmente los incendios forestales pueden ejercer una gran influencia a nivel de bioma y clima (Running, 2008). ...
Reporte técnico que considera el análisis de severidad de un incendio forestal ocurrido en un predio en la provincia de Ultima Esperanza en la región de Magallanes y propuestas de restauración ecológica acorde a la clasificación y zonificación de daño.
... Yet, the above-mentioned vegetation indices may not fully reflect the actual hydrological cycle process, previous studies have shown that the Soil-Adjusted Vegetation Index (SAVI) can accurately estimate the actual evaporation capacity, so is therefore used as a remote sensing indicator of evapotranspiration in this study (Jodar et al., 2018;Mokhtari et al., 2018;Ren and Zhou, 2014). In addition, most of studies have mainly concentrated on the ability of ecosystems to resist and recover from particular disturbance types, such as fires and droughts, which are the two most commonly studied disturbances (Goetz et al., 2006;Lhermitte et al., 2011;Lloret et al., 2007). However, measuring a specific type of disturbance based on remote sensing is imperfect for ecosystem monitoring, because ecosystem disturbances can occur constantly, and a single ecosystem might be affected by multiple disturbances simultaneously. ...
Large-scale quantification of ecosystem stability in multiple dimensions is crucial to understanding underlying ecological processes and informing ecological management decision-making. However, historically this field has been limited to spatiotemporal scales based on the use of discrete ground-based measurements. The objective of this study was to quantify ecosystem stability in multiple dimensions in a karst peak cluster depression region in southwest China, using dense Landsat time series from 1988 to 2018. Three components of ecosystem stability, namely resistance, resilience, and variability, were derived by applying a time decomposition algorithm, and then the correlations of each component were analyzed to explore the dimensionality of ecosystem stability. Our results revealed following: (1) of the entire karst area, 87.97% of the pixels were disturbed in the past 31 years, the majority of the maximum disturbance events occurred during 2004 in the northeast and mid-west of Guangxi. (2) Only 0.03% of the pixels showed high resistance, whereas a wide part of the study area showed low resilience, with 72.95 % of the pixels had low recovery rate and 39.27% of the pixels being able to be restored to their original state after the disturbance. The south area showed lower variance compared to other areas in karst regions. (3) Correlations between different ecosystem stability indicators obtained from dense remote sensing time series were only weakly correlated or uncorrelated, which provided satellite‐scale evidence that it is necessary to conduct a multi-dimensional evaluation of ecosystem stability, and the effective dimensionalities of ecosystem stability were significantly influenced by different disturbance intensities. These findings expand the understanding of the internal self-maintenance and self-recovery of the ecosystem in response to disturbances, and provide a theoretical basis for ecological engineering construction and regional environmental governance assessment.
... Several methodologies have been proposed for vegetation assessment, namely, methodologies based on monitoring the vegetation state from spectral indices [17]; for example, the Normalized Difference Vegetation Index (NDVI) [29][30][31], the Soil-Adjusted Vegetation Index (SAVI) [32][33][34], the Leaf Area Index (LAI), the Fractional Vegetation Cover (FVC) [35,36], the Regeneration Index [37][38][39], the Normalized Difference Infrared Index (NDII) [34,[40][41][42], and Spectral Mixture Analysis (SMA) [43,44]. Other spectral indices, such as the Normalized Burn Ratio (NBR) and Enhanced Vegetation Index (EVI), which combine and extract useful information from several spectral bands [30], have also been widely used to study fire-induced vegetation changes, including burn severity [45] and regeneration dynamics [27,30,32,46,47]. ...
Vegetation recovery after the large wildfires that occurred in central Portugal in 2017 is assessed in the present study. These wildfires had catastrophic consequences, among which were human losses and a vast extent of forest devastation. Landsat 8 OLI images were used to obtain the land use and cover (LUC) classification and to determine the Normalized Burned Ratio index (NBR) for different times. NBR results were used to determine the difference between the NBR (dNBR) before the fire (pre-fire) and after the fire (post-fire), and the results obtained were cross-checked with the LUC. The dNBR results were cross-referenced with biophysical data to identify the characteristics of the most important burned areas in need of vegetative recovery. The results showed the spatial differentiation in vegetation recovery, highlighting different factors in this process, in particular the type of vegetation (the predominant species and bank of seeds available), the biophysical characteristics of burned areas (for example, the soil type in burned areas), the continentality gradient, and the climate conditions. The vegetation recovery was differentiated by time according to the species present in the burned areas pre-fire. In general, shrubland recovery was faster than that of tree species, and the recovery was more marked for species that were regenerated by the rhizomes after fire. The recovery process was also influenced by the season in the study area. It was more efficient in the spring and at the beginning of the summer, highlighting the importance of optimal conditions needed for vegetation regeneration, such as the temperature and precipitation (soil humidity and water availability for growing plants). The results of this research are important to forest planning: the definition of the strategies for the ecosystems’ recovery, the adoption of preventive measures to avoid the occurrence of large wildfires, the modification of anthropogenic practices, etc.
... Deleterious effects caused by HTAs include intensifying emission of greenhouse gases (GHGs) (Schwietzke et al., 2016), degradation of ecosystem services MacDougall et al., 2013;Pellegrini et al., 2017), and damage to public health and property (Moritz et al., 2014;Schoennagel et al., 2017). Positive effects of HTAs include the acceleration of soil nutrient transformation/recycling (Cochrane, 2003), regeneration of ecosystems (Cochrane et al., 1999;Lhermitte et al., 2011), and supply of heat and energy for economic production (Bowman et al., 2011). Therefore, accurate and efficient detection of HTAs is crucial for local authorities and other stakeholders for fire prevention, suppression, post-fire restoration, and the global scientific community, which requires systematic information to better understand Earth's system, such as fire regime characterization (Giglio, 2007;Chen et al., 2020), emergency management (Maffei and Menenti, 2019;Adagbasa et al., 2020), ecological responses (Bowman et al., 2009), GHG emissions (van der Werf et al., 2008, and volcanic activities (Blackett, 2015;Coppola et al., 2015;Ganci et al., 2011;Gouhier et al., 2016;Wright et al., 2002;Wright et al., 2004). ...
High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites.
... However, in most studies, the use of remote sensing refers to forest areas. Only two works (Lhermitte et al. 2010;Lhermitte et al. 2011) out of 60 from the Gitas et al. (2012) review (data on 1991 À 2012) are devoted to arid lands. ...
In arid lands of Central Kazakhstan, fires commonly occur due to both man-made (e.g. space rocket launches) and natural factors. Remote sensing is the best method to identify burned landscapes and to assess their long-term dynamics. In this paper, we assessed total areas and seasonal features of fires using Landsat 8 OLI data of high spatial and temporal resolution, corrected for atmospheric effects. We defined data requirements, considered various spectral indices, and evaluated their suitability for automated delineation of burned areas. Spectral indices included special burn indices (NBRT, MIRBI, NBR, NBR2, BAI) and vegetation indices (MSAVI, MSAVI2, MTVI2, GEMI3) that allowed us to determine the boundaries and severity of fires indirectly based on the state of vegetation. Our study showed that burned areas were most accurately identified using indices of NBR2 (normalized burn ratio 2) and MSAVI2 (modified soil adjusted vegetation index 2). They were extracted using image segmentation and natural breaks classification methods. We have assessed MSAVI2 and NBR2 segmentation results with machine learning metrics, i.e. for Soyuz drop zone (landing area of rocket stages) precision of NBR segmentation equaled 99.5%, recall − 99.5%, accuracy − 98.5%. For Proton drop zone NBR segmentation precision equaled 99.3%, recall − 99.7%, accuracy − 99.4%. MSAVI2 results are less accurate. The accuracy of segmentation significantly depended on the vegetation state which was predetermined by the frequency and severity of previous fires within certain territory.
... Regardless of the spatial distribution, both maps produced the same estimation of proportion of area burned. Similarity in the spatial patterns of NDVI-and NBR-derived severity estimates was detected by Lhermitte et al. [75] in African savannas, who found differences when comparing severity maps generated using the bi-temporal approach and the control pixel method. Comparing maps allows for a demonstration of the impact of methodology in the thematic presentation of burn severity and, thus, can identify the limitations of the generated information. ...
The severity of forest fires derived from remote sensing data for research and management has become increasingly widespread in the last decade, where these data typically quantify the pre- and post-fire spectral change between satellite images on multi-spectral sensors. However, there is an active discussion about which of the main indices (dNBR, RdNBR or RBR) is the most adequate to estimate the severity of the fire, as well about the adjustment model used in the classification of severity levels. This study proposes and evaluates a new technique for mapping severity as an alternative to regression models, based on the use of the maximum likelihood estimation (MLE) automatic learning algorithm, from GeoCBI field data and spectral indices dNBR, RdNBR and RBR applied to Landsat TM, ETM+ Images, for two fires in central Spain. We compare the severity discrimination capability on dNBR, RdNBR and RBR, through a spectral separability index (M) and then evaluated the concordance of these metrics with field data based on GeoCBI measurements. Specifically, we evaluated the correspondence (R2) between each metric and the continuous measurement of fire severity (GeoCBI) and the general precision of the regression and MLE models, for the four categorized levels of severity (Unburned, Low, Moderate, and High). The results show that the RBR has more spectral separability (average between two fires M = 2.00) that the dNBR (M = 1.82) and the RdNBR (M=1.80), additionally the GeoCBI has a better adjustment with the RBR of (R2 = 0.73), than the RdNBR (R2 = 0.72), and dNBR (R2 = 0.71). Finally, the overall classification accuracy achieved with the MLE (Kappa = 0.65) has a better result than regression models (Kappa = 0.58) and higher accuracy of individual classes.
... Wildfire events are one of the most extended ecological disturbances in natural ecosystems. They affect a variety of temporal and spatial scales the dynamic of the vegetation layer because of their removal (Lhermitte et al., 2011). Although wildfire events have been a modeling agent of the Chilean landscape, especially in semi-arid ecosystems, currently and as in several parts of the world, the fire regimes have increased at an alarming pace (Úbeda & Sarricolea, 2016). ...
Because of climate change, wildfire events have increased in the semi-arid ecosystems of the world. In this context, it is necessary to count with efficient tools for the rehabilitation or restoration of those systems. Plant propagation is crucial to obtain plants for rehabilitation
programs, and it becomes a limiting factor in the success of those initiatives. For this, it is necessary to know how relevant factors affect seed germination in plants of interest. Peumus boldus is a dominant plant species of the Mediterranean area of Chile that has been severely affected by wildfire events. Consequently, in this investigation, the joint effect of pregermination treatments such as epicarp and mesocarp removal with the use of gibberellic acid on the germination of semimature fruits of P. boldus was studied. The results show that only the removal of the epicarp and the mesocarp had a significant effect on seed germination, reaching a proportion of 51.4%, which is the highest value reported so far in a published study with a controlled setting for P. boldus. Therefore, to obtain a high proportion of P. boldus plantlets for use in rehabilitation programs; seeds should be collected in December and exposed to the pre-germination treatment of pericarp and mesocarp removal without using gibberellic acid in doses lower than or equal to 10 g L-1.
... The opening of the Landsat archive in 2008, now available geometrically and radiometrically corrected, provided new opportunities for improved understanding of the mechanisms of forest changes [14,15]. Several studies have addressed the spatial and temporal analysis of post-fire vegetation dynamics through different forest ecosystems: Mediterranean [16,17], boreal [18,19], Siberian [20,21], temperate [22], tropical [23], savannah [24] or across different ecozones at the regional or national scale [25][26][27][28]. ...
Wildfires constitute the most important natural disturbance of Mediterranean forests, driving vegetation dynamics. Although Mediterranean species have developed ecological post-fire recovery strategies, the impacts of climate change and changes in fire regimes may endanger their resilience capacity. This study aims at assessing post-fire recovery dynamics at different stages in two large fires that occurred in Mediterranean pine forests (Spain) using temporal segmentation of the Landsat time series (1994–2018). Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) was used to derive trajectory metrics from Tasseled Cap Wetness (TCW), sensitive to canopy moisture and structure, and Tasseled Cap Angle (TCA), related to vegetation cover gradients. Different groups of post-fire trajectories were identified through K-means clustering of the Recovery Ratios (RR) from fitted trajectories: continuous recovery, continuous recovery with slope changes, continuous recovery stabilized and non-continuous recovery. The influence of pre-fire conditions, fire severity, topographic variables and post-fire climate on recovery rates for each recovery category at successional stages was analyzed through Geographically Weighted Regression (GWR). The modeling results indicated that pine forest recovery rates were highly sensitive to post-fire climate in the mid and long-term and to fire severity in the short-term, but less influenced by topographic conditions (adjusted R-squared ranged from 0.58 to 0.88 and from 0.54 to 0.93 for TCA and TCW, respectively). Recovery estimation was assessed through orthophotos, showing a high accuracy (Dice Coefficient ranged from 0.81 to 0.97 and from 0.74 to 0.96 for TCA and TCW, respectively). This study provides new insights into the post-fire recovery dynamics at successional stages and driving factors. The proposed method could be an approach to model the recovery for the Mediterranean areas and help managers in determining which areas may not be able to recover naturally.
... Wildfires act as a triggering factor for ecosystems changes, by affecting ecosystems' structure and vegetation patterns and consequently their overall functions and processes (Knorr et al. 2011;Lhermitte et al. 2011;Karamesouti et al. 2016). On a global scale, about 350 million hectares of land are annually affected by fire events (van der Werf et al. 2006) and wildfires play an important role in the evolution, organization and distribution of ecosystems (Ireland et al. 2015). ...
Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management.This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and robability area based on the NBR differencebetween pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, intowhich other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to mapforest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired tosupport a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in anarea based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found tobe the most suitable option, since it required less computational time and resources in comparison to the GIS-based modelingapproach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provideinformation that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.
... Regardless of the spatial distribution, both maps produced the same estimation of proportion of area burned. Similarity in the spatial patterns of NDVI-and NBR-derived severity estimates was detected by Lhermitte et al. [75] in African savannas, who found differences when comparing severity maps generated using the bi-temporal approach and the control pixel method. Comparing maps allows for a demonstration of the impact of methodology in the thematic presentation of burn severity and, thus, can identify the limitations of the generated information. ...
Burn severity, which can be reliably estimated by validated spectral indices, is a key element for understanding ecosystem dynamics and informing management strategies. However, in North Patagonian forests, where wildfires are a major disturbance agent, studies aimed at the field validation of spectral indices of burn severity are scarce. The aim of this work was to develop a field validated methodology for burn-severity mapping by studying two large fires that burned in the summer of 2013-2014 in forests of Araucaria araucana and other tree species. We explored the relation between widely used spectral indices and a field burn-severity index, and we evaluated index performance by examining index sensitivity in discriminating burn-severity classes in different vegetation types. For those indices that proved to be suitable, we adjusted the class thresholds and constructed confusion matrices to assess their accuracy. Burn severity maps of the studied fires were generated using the two most accurate methods and were compared to evaluate their level of agreement. Our results confirm that reliable burn severity estimates can be derived from spectral indices for these forests. Two severity indices, the delta normalized burn ratio (dNBR) and delta normalized difference vegetation index (dNDVI), were highly related to the fire-induced changes observed in the field, but the strength of these associations varied across the five different vegetation types defined by tree heights and tree and tall shrub species regeneration strategies. The thresholds proposed in this study for these indices generated classifications with global accuracies of 82% and Kappa indices of 70%. Both the dNBR and dNDVI classification approaches were more accurate in detecting high severity, but to a lesser degree for detecting low severity burns. Moderate severity was poorly classified, with producer and user errors reaching 50%. These constraints, along with detected differences in separability, need to be considered when interpreting burn severity maps generated using these methods.
... While reducing data scatter, normalization did not substantially affect the slopes or intercepts of the trajectories, derived from best-fit linear functions. It is possible that utility of normalization would be enhanced by pixel-specific control site associations, as proposed by Lhermitte et al. (2011). Nonetheless, our results suggest that effects of surface condition variations are sufficiently reduced by selection of anniversary-date (summer solstice) images and computation of best-fit trajectories, as to produce useful estimates of postfire recovery in chaparral. ...
Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial variation in shrub type, soil substrate, or illumination differences associated with topography. We evaluate the effects of these variables in Landsat-derived metrics of FSC and postfire recovery, based upon three chaparral sites in southern California which contain shrub community ecotones, complex terrain, and soil variations. Detailed validations of prefire and postfire FSC are based on high spatial resolution ortho-imagery; cross-stratified random sampling is used for variable control. We find that differences in the composition and structure of shrubs (inferred from ortho-imagery) can substantially influence FSC-NDVI relations and impact recovery metrics. Differences in soil type have a moderate effect on the FSC-NDVI relation in one of the study sites, while no substantial effects were observed due to variation of terrain illumination among the study sites. Arithmetic difference recovery metrics – based on NDVI values that were not normalized with unburned control plots – correlate in a moderate but significant manner with a change in FSC (R² values range 0.47–0.59 at two sites). Similar regression coefficients resulted from using Landsat visible reflectance data alone. The lowest correlations to FSC resulted from Soil-Adjusted Vegetation Index (SAVI) and are attributed to the effects of the soil-adjustment factor in sparsely vegetated areas. The Normalized Burn Ratio and Normalized Burn Ratio 2 showed a moderate correlation to FSC. This study confirms the utility of Landsat NDVI data for postfire recovery evaluation and implies a need for stratified analysis of postfire recovery in some chaparral landscapes.
... Wildfires also have great influences on long-term interannual trends in both LSP timing and greenness. The abrupt LSP changes by biomass burning and the post-fire recovery processes could alter the long-term LSP time series (Di-Mauro et al., 2014;Lhermitte et al., 2011;Meng et al., 2015;Wang and Zhang, 2017). However, many studies on LSP trends have not paid attention to the potential interruption of wildfire or other land disturbances (Julien and Sobrino, 2009;Li et al., 2019;Piao et al., 2014;Zeng et al., 2011;Zhang et al., 2007), while some analyzed the LSP by excluding the disturbed areas (Jönsson et al., 2018;Melaas et al., 2016). ...
Land surface phenology (LSP) characterizes the timing and greenness of seasonal vegetation growth in satellite pixels and it has been widely used to associate with climate change. However, wildfire, causing considerable land surface changes, exerts abrupt changes on the LSP magnitudes and great influences on the LSP long-term trends, which are poorly investigated. This study for the first time conducted a systematic analysis of the wildfire impacts on LSP by investigating 838 forest wildfires occurred from 2002 to 2014 across the western United States. Specifically, we derived three LSP timing metrics that are the start (SOS), end (EOS), and length (LOS) of growing season and two LSP greenness metrics that are seasonal greenness maximum (GMax) and minimum (GMin) from daily time series of 250-m MODIS two-band enhanced vegetation index (EVI2) during 2001-2015. Burned area and burn severity were obtained from the Monitoring Trends in Burn Severity project. The results showed GMax and GMin were decreased at an extent of 0.063 and 0.074 EVI2, respectively. LSP timings presented diverse responses to wildfire occurrences. Absolute abrupt shift of > 2 days in SOS appeared in 73% of burned areas with 40% advances and 33% delays, the shift in EOS occurred in 80% of burned areas with 33% advances and 47% delays, and the shift in LOS occurred in 85% of the burned areas with 36% shortening and 49% lengthening. Moreover, the LSP changes were significantly influenced by burn severity with the largest impact on LSP timing at the moderate burn severity and on LSP greenness at the high burn severity. Finally, the phenological trends from 2001 to 2015 differed significantly between burned and unburned reference areas and the trend difference varied with the wildfire occurrence year. Overall, this study demonstrated that wildfires exert complex and diverse impacts on LSP timing and greenness metrics and significantly influence LSP trends associating with climate change. The approach developed in this study provides a prototype to investigate LSP responses to other land disturbances associated with natural processes and human activities on the landscape.
... Lagged values of EVI are included because vegetation has "memory" such that its current state reflects the residual effects of previous conditions. For this work, we used a similar approach pursued in several previous studies that quantified resilience by measuring the time or rate of biomass recovery to a state that existed prior to disturbance (Tilman, 1996;Lhermitte et al., 2011;De Keersmaecker et al., 2015). Hence, this model considers standardized anomalies for both short-term precipitation effects and grassland system memory. ...
Pasturelands are globally extensive, sensitive to climate, and support livestock production systems that provide an essential source of food in many parts of the world. In this paper, we integrate information from remote sensing, global climate, and land use databases to improve understanding of the resilience and resistance of this ecologically vulnerable and societally critical land use. To characterize the effect of climate on pastureland productivity at global scale, we analyze the relationship between satellite‐derived enhanced vegetation index data from MODIS and gridded precipitation data from CHIRPS at 3‐ and 6‐month time lags. To account for the effects of different production systems, we stratify our analysis by agroecological zones and by rangeland versus mixed crop‐livestock systems. Results show that 14.5% of global pasturelands experienced statistically significant greening or browning trends over the 15‐year study period, with the majority of these locations showing greening. In arid ecosystems, precipitation and lagged vegetation index anomalies explain up to 69% of variation in vegetation productivity in both crop‐livestock and rangeland‐based production systems. Livestock production systems in Australia are least resistant to contemporaneous and short‐term precipitation anomalies, while arid livestock production systems in Latin America are least resilient to short‐term vegetation greenness anomalies. Because many arid regions of the world are projected to experience decreased total precipitation and increased precipitation variability in the coming decades, improved understanding regarding the sensitivity of pasturelands to the joint effects of climate change and livestock production systems is required to support sustainable land management in global pasturelands.
... Fernández-Manso et al., 2016a;Lu et al., 2015;Stroppiana et al., 2012). The Normalized Burn Ratio (NBR, Key and Benson, 2006), in particular, its differenced version (dNBR, Key and Benson, 2006) or relativized versions (RdNBR, Miler and Thode, 2007), have become a standard means to assess burn severity from satellite data (see Fernández-García et al., 2018;Lhermitte et al., 2011;McCarley et al., 2017, among others). ...
All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR.
... Methodologies such as the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithms that perform time series analysis of this rich image archive have become popular for vegetation trend analyses (Huang et al. 2010, Kennedy andYang 2010;Verbesselt et al. 2010;Banskota et al. 2014). Numerous studies have described post-fire vegetation recovery with multispectral time series analysis in Mediterranean ecosystems (Viedma et al. 1997;Díaz-Delgado 2001;Díaz-Delgado et al. 2002;Riaño et al. 2002;Díaz-Delgado and Lloret 2003;Malak 2006;Hope and Tague 2007;Wittenberg et al. 2007;Röder et al. 2008;Minchella et al. 2009;Gouveia and DaCamara 2010;Solans Vila 2010;Vicente-Serrano and Pérez-Cabello 2011;Veraverbeke et al. 2012;Fernandez-Manso and Quintano 2016;Lanorte et al. 2014;Meng et al. 2014;Petropoulos et al. 2014;Yang et al. 2017) and boreal ecosystems (Hicke et al. 2003;Epting 2005;Goetz and Fiske 2006;Cuevas-González et al. 2009;Jin et al. 2012;Frazier and Coops 2015;Bartels et al. 2016;Liu 2016;Pickell et al. 2016;White et al. 2017;Yang et al. 2017;Frazier et al. 2018); other forest types have been less studied (Idris and Kuraji 2005;Lhermitte et al. 2011;Sever and Leach 2012;Chen et al. 2014;Chompuchan 2017;Yang et al. 2017;Hislop et al. 2018), with only a few studies conducted in ponderosa pine and mixed conifer forests of western North America (White et al. 1996;van Leeuwen 2008;van Leeuwen et al. 2010;Chen et al. 2011;Meng et al. 2015). Among these studies, the Normalized Difference Vegetation Index (NDVI) has most frequently been applied to indicate vegetation greenness. ...
Few studies have examined post-fire vegetation recovery in temperate forest ecosystems with Landsat time series analysis. We analyzed time series of Normalized Burn Ratio (NBR) derived from LandTrendr spectral-temporal segmentation fitting to examine post-fire NBR recovery for several wildfires that occurred in three different coniferous forest types in western North America during the years 2000 to 2007. We summarized NBR recovery trends, and investigated the influence of burn severity, post-fire climate, and topography on post-fire vegetation recovery via random forest (RF) analysis. NBR recovery across forest types averaged 30 to 44% five years post fire, 47 to 72% ten years post fire, and 54 to 77% 13 years post fire, and varied by time since fire, severity, and forest type. Recovery rates were generally greatest for several years following fire. Recovery in terms of percent NBR was often greater for higher-severity patches. Recovery rates varied between forest types, with conifer−oak−chaparral showing the greatest NBR recovery rates, mixed conifer showing intermediate rates, and ponderosa pine showing slowest rates. Between 1 and 28% of patches had recovered to pre-fire NBR levels 9 to 16 years after fire, with greater percentages of low-severity patches showing full NBR recovery. Precipitation decreased and temperatures generally remained the same or increased post fire. Pre-fire NBR and burn severity were important predictors of NBR recovery for all forest types, and explained 2 to 6% of the variation in post-fire NBR recovery. Post-fire climate anomalies were also important predictors of NBR recovery and explained an additional 30 to 41% of the variation in post-fire NBR recovery. Landsat time series analysis was a useful means of describing and analyzing post-fire vegetation recovery across mixed-severity wildfire extents. We demonstrated that a relationship exists between post-fire vegetation recovery and climate in temperate ecosystems of western North America. Our methods could be applied to other burned landscapes for which spatially explicit measurements of post-fire vegetation recovery are needed.
... Actually, the second constraint would also help to exclude non-vegetation with high NDVI, because the decline of NDVI, in the absence of vegetation variation, commonly would not meet the constraint. The change of NBR in pre-fire and post-fire images, defined as delta NBR or dNBR, has proven to be a good indicator of burn severity and vegetation regrowth (the higher the severity, the greater the dNBR) [49,50]. It was suggested that a dNBR greater than 0.1 commonly indicates a burn of low severity [23]; thus, we chose T dNBR = 0.1. ...
Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.
... In the real situation, besides forest clear cutting (case 1 in Fig. 11), forest disturbances, such as forest fire (case 2 in Fig. 11), can also result in a reduction of forest cover. However, unlike forest clear cutting, some forest fire can leave the complete trunks of trees, which make recovery to large trees in the next year possible (Chu and Guo, 2014;Lhermitte et al., 2011). To find out the cause of forest cover change (in the red oval) during 2012-2014 for the study site of Russia, the corresponding annual Google Earth images were illustrated. ...
... Wildfires act as a triggering factor for ecosystems changes, by affecting ecosystems' structure and vegetation patterns and consequently their overall functions and processes (Knorr et al. 2011;Lhermitte et al. 2011;Karamesouti et al. 2016). On a global scale, about 350 million hectares of land are annually affected by fire events (van der Werf et al. 2006) and wildfires play an important role in the evolution, organization and distribution of ecosystems (Ireland et al. 2015). ...
Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.
... Thus, they represent a tool of high value due to the possibility of obtaining information in large areas of difficult access (BOSCHETTI et al., 2010). The use of remote sensing has been very useful and effective in the estimation of areas affected by fire, as well as in the evaluation and monitoring of forest fires at different scales (LHERMITTE et al., 2011;SANTOS et al., 2018). Its use enables the advancement of studies on the dynamics of forest fires by monitoring changes in the surface. ...
Obtaining information on burned areas has been studied and improved in the last decades, and the biggest question is the acquisition of consistent and detailed information about the occurrence of burnings in a simple and effective way. In view of this, remote sensing is a very interesting tool because it allows obtaining information in large areas of difficult access. The identification of areas burned by orbital data is directly related to their spectral behavior. The objective of this study was to analyze the performance of spectral indices in the identification of burned area in OLI/Landsat-8 satellite images. The indices for the before and after fire images were calculated using bands of red and near infrared: NDVI, MSAVI, SAVI, and GEMI, and bands of near infrared and short wave infrared: NBR, BAIMmod, and MIRBImod. The difference between pre and post-fire index was also calculated: dNDVI, dMSAVI, dSAVI, dGEMI, dNBR, dBAIMmod, and dMIRBImod. From these indices, six different compositions (RGB) were created and later they were segmented and classified in a non-supervised way and soon after made the extraction of the area of interest. The results of this classification were validated with the reference data obtained through the visual interpretation of the image. The methods had shown a good quality of classification, with a percentage of accuracy ranging from 85.54 to 92.46% and Kappa value of 0.70 to 0.89. The best method was the dNBR, NBRpost-fire, and dMIRBImod indices in the RGB composite.
... Actually, the second constraint would also help to exclude nonvegetation with high NDVI, because the decline of NDVI, in the absence of vegetation variation, commonly wouldn't meet the constraint. The change of NBR in pre-fire and post-fire images, defined as delta NBR or dNBR, has proved to be a good indicator of burn severity and vegetation regrowth (higher the severity, greater the dNBR)(Miller and Thode, 2007;Lhermitte et al., 2011). It was suggested dNBR greater than 0.1 commonly indicates burn of low severity(Lutes et al., 2006), thus we chose T dN BR = 0.1. ...
Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery as well as the high-performance computing capacity of Google Earth Engine, we proposed an automated pipeline for generating 30-meter resolution global-scale annual burned area map from time-series of Landsat images, and a novel 30-meter resolution global annual burned area map of 2015 (GABAM 2015) is released. GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with recent Fire_cci version 5.0 BA product found a similar spatial distribution and a strong correlation () between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission error of GABAM 2015 are 13.17% and 30.13%, respectively.
... Wide-coverage periodic satellite images provide useful information for wildfire study. However, to examine long-term vegetation regeneration, periodic field data are often lacking, and studies are generally based on remotely sensed data alone (Hope, Tague, and Clark 2007;Lhermitte et al. 2011;Ireland and Petropoulos 2015). ...
Wildfires are a major natural hazard with tremendous implications for the Earth’s ecosystems. Investigating fire regimes and fire–vegetation dynamics using remote-sensing techniques is becoming increasingly common because of their large-scale coverage and data availability. However, there is still scarce study to compare vegetation regeneration between different ecosystems after wildfires due to lack of data. This study used time series of Landsat images to explore and compare post-fire vegetation recovery in a Mediterranean (Witch Creek Fire) and tundra (Anaktuvuk River Fire) ecosystem. After 8 years of disturbance, the vegetation in the Mediterranean ecosystem had still not yet recovered, whereas the tundra ecosystem recovered in just 3 years. Higher degree burning leads to quicker vegetation recovery rate. However, ecological retrogression was also detected. Spatial heterogeneity in post-fire vegetation recovery observed in both sites can be attributed to topographic factors, soil water availability and the thermokarst process.
... Low resilience is considered as an early-warning signal for critical transitions, and ecosystem with low resilience has a higher probability to shift suddenly to an alternative state (Scheffer et al. 2009). The most straightforward measurements of resilience are the recovery rate after perturbation, the recovery time required to return to equilibrium, and the autocorrelation of vegetation characteristics within a certain phase (Simoniello et al. 2008;Lhermitte et al. 2011). The global pattern of vegetation cover can also be used to indicate resilience which is defined as the probability of the occurrence of a vegetation cover type at the local climate condition computed by logistic regression models (Hirota et al. 2011). ...
Climate variability and climate extremes are important climatic determinants of plant growth, species distribution and net primary productivity. A comprehensively quantitative analysis of the sensitivity and resilience of ecosystems to climate variability is vital to identify which regions and species are most in danger in response to future climate change. Here, we proposed an empirical approach to assess the relative sensitivity and resilience of ecosystems to short-term climate variability in the Heihe River Basin (HRB) which is the second largest inland river basin in the northern China and contains ecosystems of semi-arid, arid and hyper-arid types. Based on the monthly time series of normalized difference vegetation index (NDVI), land surface temperatures (LST) and the ratio of actual evapotranspiration to potential evapotranspiration (AET/PET) derived from MODIS sensor from 2000 to 2013, we developed a multiple linear regressive and autoregressive model to determine the sensitivity of NDVI anomalies to climate variability indicated by monthly LST anomalies (temperature variability) and monthly AET/PET anomalies (water variability). We included 1 month time lag of NDVI anomalies in order to reflect ecosystem resilience. We found that the sensitivity and resilience to climate variability were different in the upper, middle and lower reaches of the HRB. Temperature variability dominantly controlled vegetation anomalies in the upper reach, but water variability was the dominant climatic factor in the middle and lower reaches. The different responses of semi-arid to hyper-arid ecosystems to climate variability depended much on the distinct climatic conditions and diverse vegetation types. Ecosystems in drier condition tended to show higher sensitivity to water variability, and ecosystems with colder climate were likely to be more sensitive to temperature variability. The most sensitive vegetation type to water variability and temperature variability in the HRB was crop and meadow, respectively. Grass had been proved to have the lowest resilience. Our research on the sensitivity and resilience of semi-arid to hyper-arid ecosystems is helpful for formulating and implementing adaptation and mitigation strategies in response to climate change.
... The combination of its normalized difference formulation and the use of the higher absorption and reflectance regions of chlorophyll make this index robust in different conditions [9]. In contrast to other vegetation index such as Enhanced Vegetation Index (EVI), the NDVI is easier to calculate and has been widely applied to study vegetation regeneration [14,21,[49][50][51]. ...
Wildfires cause disturbances in ecosystems and generate environmental, economic, and social costs. Studies focused on vegetation regeneration in burned areas acquire interest because of the need to understand the species dynamics and to apply an adequate restoration policy. In this work we intend to study the variables that condition short-term regeneration (5 years) of three species of the genus Pinus in the Mediterranean region of the Iberian Peninsula. Regeneration modelling has been performed through multiple regressions, using Ordinary Least Squares (OLS) and Geographic Weight Regression (GWR). The variables used were fire severity, measured through the Composite Burn Index (CBI), and a set of environmental variables (topography, post-fire climate, vegetation type, and state after fire). The regeneration dynamics were measured through the Normalized Difference Vegetation Index (NDVI) obtained from Landsat images. The relationship between fire severity and regeneration dynamics showed consistent results. Short-term regeneration was slowed down when severity was higher. The models generated by GWR showed better results in comparison with OLS (adjusted R 2 = 0.77 for Pinus nigra and Pinus pinaster; adjusted R 2 = 0.80 for Pinus halepensis). Further studies should focus on obtaining more precise variables and considering new factors which help to better explain post-fire vegetation recovery.
... A pesar de esto, es un aspecto metodológico que hasta la fecha ha recibido poca atención en la literatura. En los trabajos pioneros en el tema de Lhermitte et al. (2010Lhermitte et al. ( , 2011), los autores consideran la selección de parcelas control estudiando un periodo pre-incendio de un año. Sin embargo, dicho periodo no permite asegurar que las parcelas referencia y control respondan con idéntico patrón de actividad fotosintética ante ...
Resumen: Los regímenes naturales de incendios han sufrido modificaciones; consecuentemente, es indispensable disponer de herramientas robustas para el seguimiento post-fuego de la vegetación. Los satélites de alta resolución temporal permiten construir series temporales de índices de vegetación para monitorear la recuperación post-fuego. Una de las técnicas utilizadas consiste en comparar la serie temporal de una parcela quemada con la de una parcela control no quemada. Sin embargo, para su implementación es necesario seleccionar parcelas control que antes del incendio tengan una vegetación con igual estructura y funcionamiento que la parcela quemada. Un estudio previo definió criterios biológicos para localizar parcelas quemadas y control con idéntico funcionamiento pre-incendio. Para testearlos se propuso una rutina de test no paramétricos de baja potencia estadística, analizando el cociente QVI (Quotient Vegetation Index) de las series temporales de NDVI (Normalized Difference Vegetation Index) de parcelas control y quemadas. Sin embargo, actualmente existen técnicas de análisis autorregresivas con mayor potencia estadística. Los objetivos del presente trabajo fueron proponer seis nuevas rutinas basadas en test autorregresivos y comparar el desempeño de éstas contra la rutina no paramétrica. Seleccionamos 13.700 parcelas de bosque y extrajimos las series temporales NDVI MODIS entre 2002 y 2005. Aleatoriamente seleccionamos 43 parcelas de referencia. A través de las rutinas planteadas comparamos la serie temporal de referencia con cada una de las 13.657 series restantes. Estimamos el desempeño midiendo la distancia euclidiana entre la serie de temporal de la parcela de referencia y las series temporales de las parcelas aceptadas por cada rutina. También, medimos la calidad y contabilizamos la cantidad de las series temporales QVI seleccionadas por cada rutina. Las rutinas autorregresivas tuvieron mejor desempeño, ya que seleccionaron parcelas control con series temporales de NDVI con la máxima similitud con respecto a las parcelas de referencia y series QVI de mayor calidad. Palabras clave: selección de parcelas control, ecología del fuego, monitoreo post-incendio, NDVI MODIS, análisis de series de tiempo de NDVI. To cite this article: Landi, M. A., Ojeda, S., Di Bella, C. M., Salvatierra, P., Argañaraz, J. P., Bellis, L. M. 2017. Control plot selection for studies of post-fire dynamics: performance of non-parametric and autoregressive routines. Revista de Teledetección, 49, 79-90.
... A pesar de esto, es un aspecto metodológico que hasta la fecha ha recibido poca atención en la literatura. En los trabajos pioneros en el tema de Lhermitte et al. (2010Lhermitte et al. ( , 2011), los autores consideran la selección de parcelas control estudiando un periodo pre-incendio de un año. Sin embargo, dicho periodo no permite asegurar que las parcelas referencia y control respondan con idéntico patrón de actividad fotosintética ante ...
p>Natural fire regimes have been modified; therefore robust post-fire monitoring tools are needed to understand the post-fire recovery process. Satellites with high temporal resolution allow us to build time series of vegetation indices for monitoring post-fire vegetation recovery. One of the techniques used is to compare the time series of a burned plot with that of an unburned control plot. However, for its implementation it is necessary to select control plots in which the vegetation has the same structure and functioning than the plot burned before the fire. Previous study defined biological criteria to detect burned and unburned control plots with identical pre-fire vegetation functioning. Moreover, a non-parametric test routine of low statistical power was proposed to test them, this was based on the analysis of the QVI (Quotient Vegetation Index), calculated between NDVI (Normalized Difference Vegetation Index) time series of the burned and control site. However, currently there are autoregressive analysis techniques with greater statistical power. Therefore the aims were to propose six new statistical routines based on autoregressive test, and compare the performance of these with the non-parametric routine. We selected 13,700 forest plots and extracted the NDVI MODIS time series between 2002 and 2005. We randomly selected 43 reference plots, and through each routine, we compared each reference time series with the other 13,657 time series. We estimated the performance of the routines measuring the euclidian distance between the time series of the reference plot and the time series of the plots accepted for each routine. We also measured the quality and the amount of the QVI time series selected by each routine. Autoregressive routines showed better performance than the non-parametric routine, since they selected control plots with NDVI time series with greatest similarity with respect to the reference plots and QVI series with highest quality.</p
... The consistent differences observed in fire heterogeneity and shape aggregation/disaggregation based on the spatial resolution of the imagery may be more or less relevant depending on the application of the burned area dataset. Differences in total area burned can influence estimates of total greenhouse gas emissions [6,7], while differences in distance to nearest unburned patch will influence predictions of recovery rate due to seed dispersal [75][76][77] and could be relevant to guiding ground-based recovery measures. Alternatively, the additional detail may be less relevant in deriving relationships between burned area and climate, as most climate and other geographical data layers are rarely offered at a resolution finer than 30 m. ...
The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.
... For vegetation cover, various studies have shown the usefulness of the Normalized Difference Vegetation Index (NDVI) for detecting changes in vegetation cover (e.g., Lunetta et al. 2006, Wang et al. 2009, Lhermitte et al. 2011). Among these techniques, NDVI image differencing has been one of the most popular change detection methods, and proven to be a valuable approach for the detection of change in vegetation cover (e.g., Sinha & Kumar 2012, Mancino et al. 2014). ...
Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes on vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000-2013 exhibits much higher increase compared to 1985-2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long term linkage between the changes in soil salinity and vegetation cover.
The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.
Debido a la alta incidencia de incendios forestales en el país, el gran número de extensiones afectadas, la inaccesibilidad de algunas zonas forestales y a los escasos recursos económicos, de tiempo y personal técnico capacitado, se debe optar por explorar alternativas de evaluación de los incendios forestales de forma remota.
El uso de imágenes satelitales y la aplicación de índices espectrales es una metodología práctica que se puede implantar en el estudio y evaluación de los incendios forestales sin el requerimiento de realizar en recorridos de campo. Este folleto da un panorama general a la aplicación y manejo de imágenes satelitales para la evaluación de los incendios forestales.
Wildfires have significant environmental and socio-economic impacts, affecting ecosystems and people worldwide. Over the coming decades, it is expected that the intensity and impact of wildfires will grow depending on the variability of climate parameters. Although Bulgaria is not situated within the geographical borders of the Mediterranean region, which is one of the most vulnerable regions to the impacts of temperature extremes, the climate is strongly influenced by it. Forests are amongst the most vulnerable ecosystems affected by wildfires. They are insufficiently adapted to fire, and the monitoring of fire impacts and post-fire recovery processes is of utmost importance for suggesting actions to mitigate the risk and impact of that catastrophic event. This paper investigated the forest vegetation recovery process after a wildfire in the Ardino region, southeast Bulgaria from the period between 2016 and 2021. The study aimed to present a monitoring approach for the estimation of the post-fire vegetation state with an emphasis on fire-affected territory mapping, evaluation of vegetation damage, fire and burn severity estimation, and assessment of their influence on vegetation recovery. The study used satellite remotely sensed imagery and respective indices of greenness, moisture, and fire severity from Sentinel-2. It utilized the potential of the landscape approach in monitoring processes occurring in fire-affected forest ecosystems. Ancillary data about pre-fire vegetation state and slope inclinations were used to supplement our analysis for a better understanding of the fire regime and post-fire vegetation damages. Slope aspects were used to estimate and compare their impact on the ecosystems’ post-fire recovery capacity. Soil data were involved in the interpretation of the results.
Soil is a vital resource for feeding the burgeoning global population, and it is also essential for realizing most of the "United Nations Sustainable Development Goals (SDGs)." For example, it is vital to realizing the "Zero hunger (SDG2), Good health and well‐being (SDG3), Clean water and sanitation (SDG6), and Life on land (SDG15)". Excess salts present in the soil make it saline, and it poses a significant threat to agricultural production and environmental health. Soil salinity is an extensive problem and spreads over one billion hectares extended over 100 countries. This paper presents a comprehensive review of global soil salinity management through the applications of remote sensing and GIS. All possible sources of relevant and current literature were accessed and more than 260 publications were collected and carefully analyzed, for this review. The rationale and severity of the salinity problem are provided. The impact of salinity on plant yield and the effect of climate change on soil salinity are detailed. The salinity indicators and salinity monitoring and mapping are provided. And the global cases of soil salinity management through remote sensing and GIS applications under different agro‐hydro‐climatic environments are discussed, followed by a summary of conclusions and challenges along with future research directions. The analysis of past investigations showed that remote sensing strategy might be a practical approach to adequately assess plant response such as evapotranspiration under diverse salinity environments, yet it additionally has various difficulties. The lower spatial and temporal resolution of imagery may reason errors because of subpixel heterogeneity. However, with the improvement of the better‐resolution thermal infrared remote method, there is the possibility to spot spatial variations at a smaller scale.
Current predictions of global change effects in the Mediterranean Basin include an increase in the number, severity and recurrence of wildfires that will affect post-fire recovery of forest ecosystems, altering the provision of public goods and services on which many local populations depend. Mediterranean pine forests have been greatly affected by large wildfires, mainly in recent decades, including Pinus pinaster and P. halepensis as two common and important species in these ecosystems. Derived products from satellite images are becoming the most effective tool for analyzing and mapping post-fire damage, and for monitoring post-fire forest recovery. However, remote sensing based fire damage maps should be validated with field data, mainly using the Composite Burn Index (CBI). Thus, spatial tools based on remote sensing are essential for developing adequate post-fire management strategies, in order to reduce soil erosion and facilitate the recovery of these forests. This chapter reviews the remote sensing techniques currently used for mapping post-fire damage and for monitoring vegetation recovery in Mediterranean pine forest ecosystems, including close-range, airborne and satellite approaches (thermal and optical multispectral), RADAR and LiDAR techniques.
Background:
Climate change is an important factor driving vegetation changes in arid areas. Identifying the sensitivity of vegetation to climate variability is crucial for developing sustainable ecosystem management strategies. The Irtysh River is located in the westerly partition of China, and its vegetation cover is more sensitive to climate change. However, previous studies rarely studied the changes in the vegetation coverage of the Irtysh River and its sensitivity to climate factors from a spatiotemporal perspective.
Methods:
We adopted a vegetation sensitivity index based on remote sensing datasets of high temporal resolution to study the sensitivity of vegetation to climatic factors in the Irtysh River basin, then reveal the driving mechanism of vegetation cover change.
Results:
The results show that 88.09% of vegetated pixels show an increasing trend in vegetation coverage, and the sensitivity of vegetation to climate change presents spatial heterogeneity. Sensitivity of vegetation increases with the increase of coverage. Temperate steppe in the northern mountain and herbaceous swamp and broadleaf forest in the river valley, where the normalized difference vegetation index is the highest, show the strongest sensitivity, while the desert steppe in the northern plain, where the NDVI is the lowest, shows the strongest memory effect (or the strongest resilience). Relatively, the northern part of this area is more affected by a combination of precipitation and temperature, while the southern plains dominated by desert steppe are more sensitive to precipitation. The central river valley dominated by herbaceous swamp is more sensitive to temperature-vegetation dryness index. This study underscores that the sensitivity of vegetation cover to climate change is spatially differentiated at the regional scale.
The mountainous grassland ecosystem in Golden Gate National Park (South Africa) has post-fire ecological resilience. However, vegetation species composition and structure can alter when the ecosystem continually has uncontrolled fires. This study developed a vegetation response-ability model by integrating environmental factors (elevation, aspect, rainfall, land surface temperature, soil, and fire severity), adaptive strategies (flowering months, water requirements, resprouter/seeders) and ecological status (increaser or decreaser) for the park. Vegetation recovery index derived from pre- and post-fire normalised difference vegetation index (NDVI) was used with correlation and regression analysis to validate the model. The results showed that amongst the environmental variables, elevation and fire were the most important factors influencing vegetation response-ability followed by soil, aspect, rainfall, and land surface temperature. Almost half (48%) of the park had a high vegetation response-ability, 43% medium, and 9% low. On the other hand, the vegetation recovery index showed 34% of the park fully recovered to pre-fire conditions, while 61% and 5% largely and slightly recovered, respectively. There was a strong correlation between vegetation response-ability and vegetation recovery index. The regression analysis showed a good relationship between vegetation response-ability and vegetation recovery index (R = 0.91) with 83.34% coefficient of determination.
In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.
Forest fires are the main disturbance factor for natural ecosystems, especially in boreal forests. Monitoring of the regeneration dynamics of vegetation cover in the post-fire period of ecosystem recovery is crucial for both estimation of forest stands and forest management. In this study, on the example of burnt areas of 2010 wildfires in Kuyarski forest district of Mari Zavolzhje forests we estimated the post-fire dynamics of different classes of vegetation cover between 2011-2016 years by use of Landsat and Canopus-B time series satellite images. To validate the newly obtained thematic maps we used 80 test sites with independent field data, as well as Canopus-B high spatial resolution images. For the analysis of the satellite images we referred to Normalized Differenced Vegetation Index (NDVI) and Tasseled Cap transformation. The research revealed that in the post-fire period, the area of thematic classes "Reforestation of the middle and low density" has maximum cover (44%) on the investigated burnt area. On the burnt areas of 2010, there is an active ongoing process of grass overgrowing (up to 20%), also there are thematic classes of deadwood (15%) and open spaces (10%). The overall unsupervised classification accuracy is more than 70% which shows high degree of consistency between the thematic map and the ground truth data. The results indicate that there is mostly natural regeneration of tree species pattern corresponding to the pre-fire condition. Forest plantations cover only 2% of the overall burnt area. By 2016, the NDVI parameters of young vegetation cover had recovered to the pre-fire level as well. The research results can be employed in long-term succession monitoring on the lands disturbed by fire and management plan development for the reforestation activities in Mari Zavolzhje.
Normalized Burn Ratio (NBR), a satellite-derived index widely used to map the burned area and to assess burn severity level, was reconceptualized to propose the indices of post-fire recovery condition and resilience. Time series Landsat imagery during 1994–2015 were used to observe the forest recovery of Wu-Ling fire scars in Taiwan. Burn Recovery Ratio (BRR) was newly developed as the indicator to better clarify the forest recovery status. Results show that BRR coupled with dNBR (bi-temporal NBR) could quantitatively describe the level of forest recovery through the heterogeneity of forest landscape which is confirmed by field investigation. Time of complete recovery (tc), indicator of post-fire resilience, were predicted using curve-fitting of forest recovery trajectories to the exponential decay function. The spatial distribution of tc could reveal the patterns of post-fire recovery across the fire scars. For wildfire prevention, the issue of fire recurrence should be concerned at the areas of fire-adapted species with low tc value. For areas of deterioration sites with high tc value, the rehabilitation project should be implemented to accelerate forest restoration.
In this paper we present a first approach to evaluate the plant regeneration processes after wildfires. Ten burnt areas were selected and their NDVI variations were monitored throughout the post-fire period. The main objective was to recognise the different regeneration patterns of each burnt area. Several variables (such as the amount of rain, lithology, slope, aspect, etc.) were considered in order to analyse their possible relationship with the recovery process. Some of these variables showed a significant effect over the regeneration time, although further analyses seem still needed.
Burned forested areas have patterns of varying burn severity as a consequence of various topographic, vegetation, and meteorological factors. These patterns are detected and mapped using satellite data. Other ecological information can be abstracted from satellite data regarding rates of recovery of vegetation foliage and variation of burn severity on different vegetation types. Middle infrared wavelengths are useful for burn severity mapping because the land cover changes associated with burning increase reflectance in this part of the electromagnetic spectrum. Simple stratification of Landsat Thematic Mapper data define varying classes of burn severity because of changes in canopy cover, biomass removal, and soil chemical composition. Reasonable maps of burn severity are produced when the class limits of burn severity reflectance are applied to the entire satellite data. Changes in satellite reflectance over multiple years reveal the dynamics of vegetation and fire severity as low burn areas have lower changes in reflectance relative to high burn areas. This results as a consequence of how much the site was altered due to the burn and how much space is available for vegetation recovery. Analysis of change in reflectance across steppe, riparian, and forested vegetation types indicate that fires potentially increase biomass in steppe areas, while riparian and forested areas are slower to regrow to pre-fire conditions. This satellite-based technology is useful for mapping severely burned areas by exploring the ecological manifestations before and after fire.
Upwelling radiance from savanna woodlands may originate from two separate layers: (1) the field layer, which is a mixture of soil, litter and herbs, and (2) the tree layer composed of woody parts and leaves. Unless detailed field data are available for a particular savanna location, it is unknown how the individual layers may influence the red and near-infrared signals and whether radiative interactions between layers are important. We employed an existing radiative transfer model (SAIL) in conjunction with a simple, single-scattering model to analyse the variation in Advanced Very High Resolution Radiometer (AVHRR) channel 1 and 2 response as well as NDVI for savanna-woodland vegetation in eastern Zambia. Linear fits between predicted and observed values of reflectance and NDVI were significant ( p 0.05) in the red and in NDVI, however, the model failed to explain a high proportion of the variation in near-infrared. Red and NDVI in sites where tree cover was high were also poorly modelled, which suggests that multiple interactions between canopy layers make a single-scattering model unreliable, particularly in the near-infrared. Modelled results were also compared to aircraft radiometer measurements provided by the integrated camera and radiometer instrument (ICAR). Simulations parameterized with field data suggest that the model may be used to infer tree and field layer influences at different points during the seasonal cycle. Results also suggest that the field layer dominated the signal in our savanna woodland sites throughout most points of the seasonal cycle, which is consistent with other canopy radiative-transfer studies. Simulations indicated that the tree layer was a relatively more important component of NDVI during the dry season when the field layer was largely senescent, accounting for 20-40 per cent of the satellite signal.
In this paper we analyse the interactions between fire severity (plant damage) and plant regeneration after fire by means of remote sensing imagery and a field fire severity map. A severity map was constructed over a large fire (2692 ha) occurring in July 1994 in the Barcelona province (north-east of Spain). Seven severity classes were assigned to the apparent plant damage as a function of burning intensity. Several Landsat TM and MSS images from dates immediately before and after the fire were employed to monitor plant regeneration processes as well as to evaluate the relationship with fire severity observed in situ . Plant regeneration was monitored using NDVI measurements (average class values standardized with neighbour unburned control plots). Pre-fire NDVI measurements were extracted for every plant cover category (7), field fire severity class (7), and spatial cross-tabulation of both layers (33) and compared to post-fire values. NDVI decline due to fire was positively correlated with field fire severity class. Results show different patterns of recovery for each dominant species, severity class and combination of both factors. For all cases a significant negative correlation was found between damage and regeneration ability. This work leads to a better understanding of the influence of severity, a major fire regime parameter on plant regeneration, and may aid to manage restoration on areas burned under different fire severity levels.
Runoff and erosion rates are known to increase substantially after a major forest fire. Erosion control measures therefore need to be put into place quickly after a large fire, and determining where to locate the measures requires accurate mapping of post fire erosion risk. Burn severity can be determined from field observations, but these are costly and time consuming. Satellite imagery is an alternative to quickly map burn severity for erosion mapping purposes. Post fire erosion decreases as forest vegetation recovers and this is related to both pre fire vegetation characteristics and soil properties. The objectives of this study were to test the use of SPOT multispectral images for mapping burn severity, pre fire vegetation density, and longer term (2.5 years) vegetation recovery. Indices tested include the Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Differenced Normalized Burn Ratio (dNBR), and Differenced Normalized Difference Vegetation Index (dNDVI). Indices were compared to field data gathered immediately after the fire and about 2.5 years later. The multi-temporal indices (dNBR and dNDVI) were more useful for burn severity mapping in the heterogeneous forest–scrubland–vineyard environment where bedrock and vineyard surfaces were confused with burned areas when using single images (NBR and NDVI). All of the post fire indices showed traces of the fire 2.5 years later; this was confirmed using Analysis of Variance where differences in indices were related to original ground cover (pine forest, mixed forest, mostly bedrock and vineyards) and the burn scar. Pre fire vegetation cover was less successfully mapped using the NDVI according to the initial field observations. Similarly, none of the post fire indices were able to distinguish differences in N–S vegetation recovery revealed by field measurements of understory vegetation height and cover; N facing slopes had deeper finer soils and these more favourable conditions led to greater vegetation growth than on S facing slopes and topslope convexities. It is suggested that relationships between topography and soil properties can be useful for mapping both soil erodibility and post fire vegetation recovery.
Although the regeneration index based on control plots provides a valuable tool to quantify fire impact and subsequent vegetation regrowth, the practical implementation at large scale levels remains limited due to the need for detailed reference maps. The objective of this research therefore was the development of an image-based selection approach for control pixels based on time series similarity (TSS). The TSS approach allows the computation of a perpixel regeneration index on regional to global scale without the need for reference maps. Evaluation of the control plot selection approaches based on un-burnt focal pixels confirmed the validity of the TSS approach and showed optimal results for the TSSRMSD approach with and due to beneficial averaging effects and minimal window size effects. As such, the effects of spatial heterogeneity and noise are minimized and a preliminary quality indicator can be derived
Techniques based on multi-temporal, multi-spectral, satellite-sensor- acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches,digital change,detection from,three angles. First, the different perspectives from which the variability in ecosystems and the change,events have been dealt with are summarized.,Change,detection between pairs of images,(bi-temporal) as well as between,time profiles of imagery,derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change,detection,methods,themselves,are categorized,in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological,aspects are being,fine-tuned as this review,is being written, and we summarize the new developments that can be expected in the near future. The review,highlights the high complementarity,between,different change,detection methods.
Vegetation response and burn severity were examined following eight large wildfi res that burned in 2003 and 2004: two wildfi res in California chaparral, two each in dry and moist mixed-conifer forests in Montana, and two in boreal forests in interior Alaska. Our research objectives were: 1) to characterize one year post-fi re vegetation recovery relative to initial fi re effects on the soil surface that could potentially serve as indicators of vegetation response (and thus, ultimately longer- term post-fi re ecosystem recovery), and 2) to use a remotely-sensed indicator of burn severity to describe landscape patterns in fi re effects. We correlated one-year post-fi re plant species richness and percent canopy cover to burn severity and to soil surface cover immediately after the fi res. For all eight wildfi res, plant canopy cover and species richness were low and highly variable one year post-fi re. We found a greater number of forbs when compared to other plant life forms, independent of burn severity. Plant cover was dominated by grasses in chaparral systems, by forbs in mixed-conifer forests, and by shrubs in boreal forests, similar to the unburned vegetation. Fine- scale variability in post-fi re effects on soils, the diversity of pre-fi re vegetation, and the resilience of plants to fi re likely explain the high variation observed in post-fi re vegetation responses across sites and burn severities. On most low and moderate burn severity sites, >30% of the soil surface was covered with organic material immediately post-fi re, and one year later, the canopy cover of understory vegetation averaged 10% or more, suggesting low risk to post-fi re erosion. In California chaparral and the two Montana mixed conifer sites, 5% or less of the area within the fi re perimeter burned with high severity, while in Alaska, 58% was mapped as high burn severity; we think this is characteristic in Alaska, but uncharacteristic of chaparral fi res, especially given the high proportion of non-native species post-fi re in our chaparral sites. All fi res had a mosaic of different burn severities (as indicated by delta Normalized Burn Ratio, dNBR) with highly variable patch size (mean 1.3 ha to 14.4 ha, range from
The first results of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field algorithm's global percent tree cover are presented. Percent tree cover per 500-m MODIS pixel is estimated using a supervised regression tree algorithm. Data derived from the MODIS visible bands contribute the most to discriminating tree cover. The results show that MODIS data yield greater spatial detail in the characterization of tree cover compared to past efforts using AVHRR data. This finer-scale depiction should allow for using successive tree cover maps in change detection studies at the global scale. Initial validation efforts show a reasonable relationship between the MODIS-estimated tree cover and tree cover from validation sites.
The scientific community dealing with modelling of emissions of greenhouse gases and aerosols from anthropogenic sources demands reliable and quantitative information on the magnitude of biomass burning at a global scale. It is in this context that the Global Burnt Area — 2000 (GBA2000) initiative has been launched. The specific objectives of this initiative are to produce a map of the areas burnt globally for the year 2000, using the medium resolution (1.1 km) Système Pour l'Observation de la Terre (SPOT) 4-VEGETATION (SPOT-VGT) satellite imagery and to derive statistics of area burnt per country, per month and per main type of vegetation cover. A series of regional algorithms has been developed and incorporated into a data processing system designed to yield monthly estimates of areas burnt at a global scale. The map data will then be transformed into quantitative information and made publicly available over the World Wide Web at a range of spatial and temporal resolutions to satisfy some of the requirements of the atmospheric and climate change modelling community.
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 2 = 0.65. 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.
The area burned in southern Africa during the 2000 dry season was mapped on a monthly basis from May to November using SPOT-VEGETATION (VGT) satellite imagery at 1 km spatial resolution. Burned areas were identified with a classification tree that relied only on the near-infrared channel of VGT. The classification tree algorithm yielded very accurate results (Kappa = 0.93). However, when compared with burned area maps derived from 30 m resolution Landsat Enhanced Thematic Mapper (ETM+) imagery, the VGT 1 km burned area maps reveal variable accuracy, dependent on vegetation type and on the spatial pattern of the burned areas. Fire incidence was higher in the northern part of the study area, especially in Wetter Zambezian Miombo Woodland, Mosaic of Guineo-Congolian Lowland Forest and Secondary Grassland, Edaphic and Secondary Grassland on Kalahari Sand, Drier Zambezian Miombo Woodland, and Undifferentiated North-Zambezian Miombo Woodland. Fire incidence was lower in the eastern part of the study area and almost absent from the western and southern semiarid and desert regions. The most extensively burned countries were, in decreasing order, the Democratic Republic of Congo, Angola, Mozambique and Zambia. The total area burned was estimated at 959480 km2.
1] Thematic maps of active fires or burned areas derived from low resolution remotely sensed data are widely used as an input for the estimation of the atmospheric emissions due to biomass burning. The present work considers three of the global products available for year 2000: two burned area datasets (GBA2000 and GlobScar) and one active fires dataset (World Fire Atlas) and analyses the relative agreements and disagreements in terms of areal extents and geographic location of the fire activity. The intercomparison of the three datasets shows that, while there is generally a good spatial agreement, the disagreement in terms of areal estimates is major. Without a quantitative validation and calibration using high resolution data, the uncertainty of the areal estimates significantly increases the variance of the estimation of atmospheric emissions. (2004), Lessons to be learned from the comparison of three satellite-derived biomass burning products, Geophys. Res. Lett., 31, L21501, doi:10.1029/2004GL021229.
Burn severity strongly influences post-fire vegetation succession, soil erosion, and wildlife populations in the fire-adapted boreal forest and tundra ecosystems of Alaska. Therefore, satellite-derived maps of burn severity in the remote Alaskan landscape are a useful tool in both fire and resource management practices. To assess satellite-derived measures of burn severity in Alaska we calculated the Normalized Burn Ratio (NBR) from pre-and post-fire Landsat TM/ETM+ data. We established 289 composite burn index (CBI) plots in or near four national park areas between 2001 and 2003 in order to compare ground-based measurements of burn severity with satellite-derived values of burn severity. Within the diverse vegetation types measured, a strong linear relationship between a differenced Normalized Burn Ratio (dNBR) and CBI for eight out of the nine fire assessments was found; R 2 values ranged from 0.45 to 0.88. The variations in severity among four pre-fire vegetation types were examined and a significant difference in the average dNBR and average CBI values among the vegetation types was found. Black spruce forests overall had the strongest relationship with dNBR, while the high severity white spruce forests had the poorest fit with dNBR. Deciduous forests and tall shrub plots had the lowest average remotely sensed burn severity (dNBR), but not the lowest ground severity among the vegetation types sampled. The tundra vegetation sampled had the lowest ground severity. Finally, a significant difference was detected between initial and extended assessments of dNBR in tundra vegetation types. The results indicated that the dNBR can be used as an effective means to map burn severity in boreal forest and tundra ecosystems for the climatic conditions and fire types that occurred in our study sites.
The maximum solar elevation is typically less than 50 degrees in the Alaskan boreal region and solar elevation varies substantially during the growing season. Because of the relatively low solar elevation at boreal latitudes, the effect of topography on spectral reflectance can influence fire severity indices derived from remotely sensed data. We used Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data to test the effect of changing solar elevation and topography on the Normalized Burn Ratio (NBR) and the differenced Normalized Burn Ratio (dNBR). When a time series of unburned pixels from black spruce forests was examined, we found that NBR values consistently decreased from June through September. At the stand level, dNBR-derived values from similar unburned and burned black spruce stands were substantially higher from September imagery relative to July or August imagery. Within the Boundary burn, we found mean post-fire NBR to consistently vary owing to topographic control of potential solar radiation. To minimise spectral response due to topographic control of vegetation and fire severity, we computed a dNBR using images from August and September immediately after a June–July wildfire. There was a negative bias in remotely sensed fire severity estimates as potential solar radiation decreased owing to topography. Thus fire severity would be underestimated for stands in valley bottoms dominated by topographic shading or on steep north-facing slopes oriented away from incoming solar radiation. This is especially important because highly flammable black spruce stands typically occur on such sites. We demonstrate the effect of changing pre-and post-fire image dates on fire severity estimates by using a fixed NBR threshold defining 'high severity'. The actual fire severity was constant, but owing to changes in phenology and solar elevation, 'high severity' pixels within a burn ranged from 56 to 76%. Because spectral reflectance values vary substantially as solar elevation and plant phenology change, the use of reflectance-based indices to assess trends in burn severity across regions or years may be especially difficult in high-latitude areas such as the Alaskan boreal forest.
Monitoring the post-burn recovery condition of chaparral vegetation in southern California is important for managers to determine the appropriate time to conduct controlled burns. Due to the di culty of monitoring post-® re recovery over large areas and the absence of detailed ® re records in many areas, we examined the possibility of using satellite observations to establish the post-® re recovery stage of chamise chaparral stands in this region. SPOT XS data collected on three dates between 1986 and 1992 were analysed to determine if temporal changes in a spectral vegetation index tracked the expected post-® re recovery trajectory of the above-ground biomass of chamise chaparral stands of varying post-® re ages. Results of the study indicated that neither the normalized di erence vegetation index nor the soil adjusted vegetation index followed the expected post-® re recovery patterns in these stands. These ® ndings are explained by interannual variations in precipitation having a larger than expected e ect on the growth of this drought-resistant evergreen community, with changes in green leaf area dominating the temporal variations in the spectral vegetation indices.
________________________________________________________ Monitoring and inventory to assess the effects of wildland fire is critical for 1) documenting fire effects, 2) assessing ecosystem damage and benefit, 3) evaluating the success or failure of a burn, and 4) appraising the potential for future treatments. However, monitoring fire effects is often difficult because data collection requires abundant funds, resources, and sampling experience. Often, the reason fire monitoring projects are not implemented is because fire management agencies do not have scientifically based, standardized protocols for inventorying pre-and postfire conditions that satisfy their monitoring and management objectives. We have developed a comprehensive system, called the Fire Effects Monitoring and Inventory System (FIREMON), which is designed to satisfy fire management agencies' monitoring and inventory requirements for most ecosystems, fuel types, and geographic areas in the United States. FIREMON consists of standardized sampling methods and manuals, field forms, database, analysis program, and an image analysis guide so that fire managers can 1) design a fire effects monitoring project, 2) collect and store the sampled data, 3) statistically analyze and summarize the data, 4) link the data with satellite imagery, and 5) map the sampled data across the landscape using image processing. FIREMON allows flexible but comprehensive sampling of fire effects so data can be evaluated for significant impacts, shared across agencies, and used to update and refine fire management plans and prescriptions. FIREMON has a flexible structure that allows the modification of sampling methods and local code fields to allow the sampling of locally important fire effects evaluation criteria.
Satellite remotely sensed data of fire disturbance offers important information; however, current methods to study fire severity may need modifications for boreal regions. We assessed the potential of the differenced Normalized Burn Ratio (dNBR) and other spectroscopic indices and image transforms derived from Landsat TM/ETM+ data for mapping fire severity in Alaskan black spruce forests (Picea mariana) using ground measures of severity from 55 plots located in two fire events. The analysis yielded low correlations between the satellite and field measures of severity, with the highest correlation (R 2 adjusted = 0.52, P < 0.0001) between the dNBR and the composite burn index being lower than those found in similar studies in forests in the conterminous USA. Correlations improved using a ratio of two Landsat shortwave infrared bands (Band 7/Band 5). Overall, the satellite fire severity indices and transformations were more highly correlated with measures of canopy-layer fire severity than ground-layer fire severity. High levels of fire severity present in the fire events, deep organic soils, varied topography of the boreal region, and variations in solar elevation angle may account for the low correlations, and illustrate the challenges faced in developing approaches to map fire and burn severity in high northern latitude regions.
Several recent papers have suggested replacing the terminology of fire intensity and fire severity. Part of the problem with fire intensity is that it is sometimes used incorrectly to describe fire effects, when in fact it is justifiably restricted to measures of energy output. Increasingly, the term has created confusion because some authors have restricted its usage to a single measure of energy output referred to as fireline intensity. This metric is most useful in understanding fire behavior in forests, but is too narrow to fully capture the multitude of ways fire energy affects ecosystems. Fire intensity represents the energy released during various phases of a fire, and different metrics such as reaction intensity, fireline intensity, temperature, heating duration and radiant energy are useful for different purposes. Fire severity, and the related term burn severity, have created considerable confusion because of recent changes in their usage. Some authors have justified this by contending that fire severity is defined broadly as ecosystem impacts from fire and thus is open to individual interpretation. However, empirical studies have defined fire severity operationally as the loss of or change in organic matter aboveground and belowground, although the precise metric varies with management needs. Confusion arises because fire or burn severity is sometimes defined so that it also includes ecosystem responses. Ecosystem responses include soil erosion, vegetation regeneration, restoration of community structure, faunal recolonization, and a plethora of related response variables. Although some ecosystem responses are correlated with measures of fire or burn severity, many important ecosystem processes have either not been demonstrated to be predicted by severity indices or have been shown in some vegetation types to be unrelated to severity. This is a critical issue because fire or burn severity are readily measurable parameters, both on the ground and with remote sensing, yet ecosystem responses are of most interest to resource managers.
Problematic aspects of fire in tropical savannas are reviewed, from the standpoint of their impact on the detection and mapping of burned areas using remotely sensed data. Those aspects include: the heterogeneity of savanna—resulting in heterogeneity of fire-induced spectral changes; fine fuels and low fuel loadings—resulting in short persistence of the char residue signal; tropical cloudiness—which makes multitemporal image compositing important; the frequent presence of extensive smoke aerosol layers during the fire season—which may obscure fire signals; and the potential problem of detecting burns in the understory of woody savannas with widely variable tree stand density, canopy cover and leaf area index. Finally, the capabilities and limitations of major satellite remote sensing systems for pan-tropical burned area mapping are addressed, considering the spatial, spectral, temporal and radiometric characteristics of the instruments.
There has been considerable interest in the recent literature regarding the assessment of post-fire effects on forested areas within the North American boreal forest. Assessing the physical and ecological effects of fire in boreal forests has far-reaching implications for a variety of ecosystem processes – such as post-fire forest succession – and land management decisions. The present paper reviews past assessments and the studies presented in this special issue that have largely been based on the Composite Burn Index and differenced Normalized Burn Ratio (dNBR). Results from relating and mapping fire/burn severity within the boreal region have been variable, and are likely attributed, in part, to the wide variability in vegetation and terrain conditions that are characteristic of the region. Satellite remote sensing of post-fire effects alone without proper field calibration should be avoided. A sampling approach combining field and image values of burn condition is necessary for successful mapping of fire/burn severity. Satellite-based assessments of fire/burn severity, and in particular dNBR and related indices, need to be used judiciously and assessed for appropriateness based on the users' need. Issues unique to high latitudes also need to be considered when using satellite-derived information in the boreal forest region.
Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.
Mediterranean landscapes are dynamic systems that undergo temporal changes in composition and structure in response to disturbances,
such as fire. Neither landscape patterns nor driving factors that affect them are evenly distributed in space. Accordingly,
disturbances and biophysical factors interact in space through time. The aim of this paper is to assess the relative influence
of topography and fire on the landscape patterns of a large forested area located in Sierra de Gredos (Central Spain) through
time. A series of Landsat MSS images from 1975 to 1990, and a digital elevation model (DEM) were used to map fires, assess
topographical complexity and evaluate changes in landscape composition and structure. Functional regions across the entire
landscape were identified using different classification criteria (i.e., percentage burned area and topographic properties)
to model topographic and fire impacts at regional scales. A canonical variance partition method, with a time series split-plot
design, quantified the relative influence and co-variation of topography and fire on land cover patterns through time. Main
results indicated that analyzing portions of the landscape under similar environmental conditions and fire histories, the
effects of different fire regimes on the spatio-temporal dynamics of main land covers can be highlighted. However, the impact
of fire on landscape patterns was high variable among regions due to the different regeneration abilities of main land covers,
the topographic constraints and the fire histories of each region. Hence, broad patterns of fire related variance and co-variation
with topography emerged across the entire area due to the different conditions of each landscape portion in which this large
Mediterranean landscape was divided.