Wildfires have increased in size and frequency in recent decades in many biomes, but have they also become more severe? This question remains under-examined despite fire severity being a critical ...aspect of fire regimes that indicates fire impacts on ecosystem attributes and associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than 1000 ha in south-eastern Australia to examine the extent and spatial pattern of high-severity burned areas between 1987 and 2017. High-severity maps were generated from Landsat remote sensing imagery. Total and proportional high-severity burned area increased through time. The number of high-severity patches per year remained unchanged but variability in patch size increased, and patches became more aggregated and more irregular in shape. Our results confirm that wildfires in southern Australia have become more severe. This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in south-eastern Australia.
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In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned ...area (BA) mapping combining radar and optical datasets acquired by Sentinel-1 and Sentinel-2 on-board sensors, respectively. Combining active and passive datasets into a seamless wall-to-wall cloud cover independent mapping algorithm significantly improves existing methods based on either sensor type. Five areas were used to determine the optimum model settings and sensors integration, whereas five additional ones were utilised to validate the results. The optimum CNN dimension and data normalisation were conditioned by the observed land cover class and data type (i.e., optical or radar). Increasing network complexity (i.e., number of hidden layers) only resulted in rising computing time without any accuracy enhancement when mapping BA. The use of an optimally defined CNN within a joint active/passive data combination allowed for (i) BA mapping with similar or slightly higher accuracy to those achieved in previous approaches based on Sentinel-1 (Dice coefficient, DC of 0.57) or Sentinel-2 (DC 0.7) only and (ii) wall-to-wall mapping by eliminating information gaps due to cloud cover, typically observed for optical-based algorithms.
•Convolutional Neural Networks were used to map burned areas in several ecosystems.•Optimum CNN configuration depended on land cover and input data.•Combining SAR and optical datasets improved accuracy based on single datasets.•Combining SAR and optical images provided wall-to-wall burned area maps.•The most accurate maps were observed over forests, the least over grasslands.
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Spectral indices derived from optical remote sensing data have been widely used for fire-severity classification in forests from local to global scales. However, comparative analyses of multiple ...indices across diverse forest types are few. This represents an information gap for fire management agencies in areas like temperate south-eastern Australia, which is characterised by a diversity of natural forests that vary in structure, and in the fire-regeneration strategies of the dominant trees. We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in south-eastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 7.9M ha region’s forest area. Forest types were aggregated into six forest groups based on their fire-regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index performance was evaluated for each forest type and forest group by examining its sensitivity to four fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, separability, and optimality). For the best-performing indices, we calculated index-specific thresholds (by forest types and groups) to separate between the four severity classes, and evaluated the accuracy of fire-severity classification on independent samples. Our results indicated that the best-performing indices of fire severity varied with forest type and group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values ranged from 0.33 (fair agreement) to 0.77 (substantial agreement), depending on the forest group and index. Fire severity in resprouter open forests and woodlands was most accurately mapped using the delta Normalised Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference vegetation index) performed best for open forests with mixed fire responses (resprouters and seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate seeder closed forests. Our analysis highlighted the low sensitivity of all indices to fire impacts in Rainforest. We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes.
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Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by wind and insect outbreaks are crucial for ...guiding management decisions. To this end, past studies relied mostly on passive sensors (e.g., optical), and active sensors (i.e., radar) were rarely used. This study used L-band space-borne synthetic aperture radar (SAR) within a change-detection framework to delineate forested areas affected by wind and insect disturbances. The results showed that changes in backscatter relate to damage caused by wind and insect outbreaks. Overall accuracies of 69–84% and 65–88% were obtained for delineation of areas affected by wind damage and insect outbreaks, respectively, depending on the acquisition date and environmental conditions. Areas susceptible to insect outbreaks or experiencing the initial outbreak phase (green) were detected with lower accuracies (64–74%). It is expected that L-band space-borne SAR data can be applied over larger areas and ecosystem types in the temperate and boreal regions to delineate and detect damaged areas.
•A first analysis of insect outbreaks using L-band data is established.•SAR-based change detection is adopted, adapted, and used over affected areas.•Accuracies up to 90% were observed depending on disturbance agent and timing.•Thresholding and machine learning classification results were similar.•Early detection of insect outbreaks (one-year lead time) was demonstrated.
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This paper presents a burned area mapping algorithm based on change detection of Sentinel-1 backscatter data guided by thermal anomalies. The algorithm self-adapts to the local scattering conditions ...and it is robust to variations of input data availability. The algorithm applies the Reed-Xiaoli detector (RXD) to distinguish anomalous changes of the backscatter coefficient. Such changes are linked to fire events, which are derived from thermal anomalies (hotspots) acquired during the detection period by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Land cover maps were used to account for changing backscatter behaviour as the RXD is class dependent. A machine learning classifier (random forests) was used to detect burned areas where hotspots were not available. Burned area perimeters derived from optical images (Landsat-8 and Sentinel-2) were used to validate the algorithm results. The validation dataset covers 21 million hectares in 18 locations that represent the main biomes affected by fires, from boreal forests to tropical and sub-tropical forests and savannas. A mean Dice coefficient (DC) over all studied locations of 0.59 ± 0.06 (± confidence interval, 95%) was obtained. Mean omission (OE) and commission errors (CE) were 0.43 ± 0.08 and 0.37 ± 0.06, respectively. Comparing results with the MODIS based MCD64A1 Version 6, our detections are quite promising, improving on average DC by 0.13 and reducing OE and CE by 0.12 and 0.06, respectively.
•A novel, C-band SAR time-series based burned area mapping algorithm is proposed.•The Reed-Xiaoli detector was used to delineate anomalous SAR changes.•The most accurate detections were observed over forests, the least over grasslands.•The results were compared with those from available global burned area products.•Dual-pass SAR data availability, topography, and soil moisture influenced accuracy.
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•Mortalities of fire-tolerant eucalypts increased after high-severity wildfire.•Near loss of 10 to 20-cm diameter cohort after high-severity wildfire.•Eucalypt seedlings but not basal ...resprouts increased after high-severity wildfire.•Percentage mortalities of small eucalypts increased with recent prescribed fire.•Study indicates fire-tolerant eucalypts are not perpetually resistant to all fires.
Mixed-species eucalypt forests of temperate Australia are assumed tolerant of most fire regimes based on the impressive capacity of the dominant eucalypts to resprout. However, empirical data to test this assumption are rare, limiting capacity to predict forest tolerance to emerging fire regimes including more frequent severe wildfires and extensive use of prescribed fire. We quantified tree mortality and regeneration in mixed-species eucalypt forests five years after an extensive wildfire that burnt under extreme fire weather. To examine combined site-level effects of wildfire and prescribed fire, our study included factorial replications of three wildfire severities, assessed as crown scorch and understorey consumption shortly after the wildfire (Unburnt, Low, High), and two times since last preceding fire (<10years since prescribed fire, >30years since any fire). Our data indicate that while most trees survived low-severity wildfire through epicormic resprouting, this capacity was tested by high-severity wildfire. Five years after the wildfire, percentage mortalities of eucalypts in all size intervals from 10 to >70cm diameter were significantly greater at High severity than Unburnt or Low severity sites, and included the near loss of the 10–20cm cohort (93% mortality). Prolific seedling regeneration at High severity sites, and unreliable basal resprouting, indicated the importance of seedling recruitment to the resilience of these fire-tolerant forests. Recent prescribed fire had no clear effect on forest resistance (as tree survival) to wildfire, but decreased site-level resilience (as recruitment) by increasing mortalities of small stems. Our study indicates that high-severity wildfire has the potential to cause transitions to more open, simplified stand structures through increased tree mortality, including disproportionate losses in some size cohorts. Dependence on seedling recruitment could increase vulnerabilities to subsequent fires and future climates, potentially requiring direct management interventions to bolster forest resilience.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil ...moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.
Prescribed burning is a technique commonly applied to reduce fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity and rate of spread. Such ...conditions deviate from those encountered during the fire season. As a result, prescribed burns mostly affect surface fuels and understorey vegetation, an outcome markedly different when compared to wildfires.
Information on prescribed burning outcomes is important for evaluating the extent to which land management objectives have been achieved. This study focused on the ability of Radar Burn Ratio (RBR), a Synthetic Aperture Radar (SAR)-based multi-temporal index, to appraise the outcome of prescribed burns in eucalypt forests of Western Australia. L-band datasets acquired by the Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) sensor were used to relate changes in radar backscatter coefficient with the effects of prescribed burning. Two modelling approaches based on post- to pre-fire ratios were used to evaluate prescribed burn impacts. Post-fire tree survival in areas affected by high fire impacts was inferred by assessing the recovery rates of the backscatter coefficient for the first year after the burn.
The impact of prescribed burns was estimated with relatively high overall accuracy (79%) using cross-polarized backscatter (HV) under dry conditions. The HH polarization and the Normalized Difference Backscatter Intensity (NDBI) showed certain ability to detect burned areas under wet conditions. The results suggest that L-band backscatter coefficient may contribute to evaluating the success of prescribed burns in Western Australian eucalypt forests through its sensitivity to changes in vegetation structure. In addition, tree survival (as opposed to mortality) and recovery towards the pre-fire state might be inferred by analysing short-term post-fire trends of the backscatter coefficient in areas affected by high fire impacts.
•Prescribed burns efficiency can be estimated from L-band SAR data.•HV polarization and dry conditions resulted in the most accurate estimates.•Tree survival and recovery can be inferred from short-term backscatter trends.•Post-fire SAR signal recovery was substantial after one year in eucalypt forests.
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The backscatter predicted by three common surface scattering models (the Integral Equation Model (IEM), the Dubois, and the Oh models) was evaluated against fully polarized L-band airborne ...observations. Before any site-specific calibration, the Oh model was found to be the most accurate among the three, with mean errors between the simulated and the observed backscatter of 1.2 dB ( ±2.6 dB standard deviation of the error) and -0.4 dB ( ±2.4 dB) for HH and VV polarizations, respectively, while the IEM and Dubois presented larger errors, with a maximum of 4.5 dB ( ±2 dB) for the IEM in VV polarization. The backscatter errors were observed to be related to surface roughness, another major factor determining the electromagnetic scattering at the soil surface. An existing semiempirical calibration of the surface roughness correlation length was therefore applied to improve the mismatch between modeled and observed backscatters. The application of the semiempirical calibration led to a significant improvement of the backscatter prediction for the IEM. After calibration, the IEM outperformed the Oh model, resulting in a mean backscatter error of -0.3 dB ( ±1.1 dB) and -0.2 ( ±1.2 dB) for HH and VV polarizations, respectively. To test the robustness of the semiempirical calibration, calibration functions derived from an independent data set were applied and shown to also improve the (uncalibrated) IEM performance, suggesting that the calibration procedure is relatively robust for global application.
This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical ...Africa. Depending on the site, the burned area was mapped at different time points during the 2015–2016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence) and the detection method. Algorithm performance was compared by evaluating the detected BA agreement with reference fire perimeters independently derived from medium resolution optical imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that might have been boosted by the use of optical datasets when generating the reference fire perimeters. The analysis suggested that optical-based algorithms provide for a significant increase in accuracy over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors may provide a complementary data source for wall to wall BA detection.
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