Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles

ArtículoThe evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.S

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site

Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery

P. 205-217Multispectral imagery is a widely used source of information to address post-fire ecosystem management. The aim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assess initial burn severity (overall, on vegetation and on soil) in fire-prone pine forests along the Mediterranean-Transition-Oceanic climatic gradient in the Mediterranean Basin. We selected four large wildfires which affected pine forests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain field values of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices to match these three field burn severity metrics was compared and their transferability along the climatic gradient assessed using linear regression models. Specifically, we analysed the performance of 12 indices previously used for burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The results showed that Landsat spectral indices have a greater ability to determine site and vegetation burn severity than soil burn severity. We found large differences in indices performances among the three different climatic regions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanic one. In general, the dNBR-EVI showed the best fit to site, vegetation and soil burn severity in the three regions, demonstrating broad transferability along the entire climatic gradient.S

Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

[EN] The evaluation at detailed spatial scale of soil status after severe fires may provide useful information on the recovery of burned forest ecosystems. Here, we aimed to assess the potential of combining multispectral imagery at different spectral and spatial resolutions to estimate soil indicators of burn severity. The study was conducted in a burned area located at the northwest of the Iberian Peninsula (Spain). One month after fire, we measured soil burn severity in the field using an adapted protocol of the Composite Burn Index (CBI). Then, we performed soil sampling to analyze three soil properties potentially indicatives of fire-induced changes: mean weight diameter (MWD), soil moisture content (SMC) and soil organic carbon (SOC). Additionally, we collected post-fire imagery from the Sentinel-2A MSI satellite sensor (10–20 m of spatial resolution), as well as from a Parrot Sequoia camera on board an unmanned aerial vehicle (UAV; 0.50 m of spatial resolution). A Gram-Schmidt (GS) image sharpening technique was used to increase the spatial resolution of Sentinel-2 bands and to fuse these data with UAV information. The performance of soil parameters as indicators of soil burn severity was determined trough a machine learning decision tree, and the relationship between soil indicators and reflectance values (UAV, Sentinel-2 and fused UAV-Sentinel-2 images) was analyzed by means of support vector machine (SVM) regression models. All the considered soil parameters decreased their value with burn severity, but soil moisture content, and, to a lesser extent, soil organic carbon discriminated at best among soil burn severity classes (accuracy = 91.18 %; Kappa = 0.82). The performance of reflectance values derived from the fused UAV-Sentinel-2 image to monitor the effects of wildfire on soil characteristics was outstanding, particularly for the case of soil organic carbon content (R2 = 0.52; RPD = 1.47). This study highlights the advantages of combining satellite and UAV images to produce spatially and spectrally enhanced images, which may be relevant for estimating main impacts on soil properties in burned forest areas where emergency actions need to be applied.S

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site

Causal Fire Relationships in Sonoma County & Identification of Suitable Fire Management Strategies

Acknowledging recent wildfires that have set historic fire size records in both Northern and Southern California, it is important to understand what impacts can be expected and how variations in regional characteristics can influence fire severity. Combining a long history of droughts alongside periodic rain events this paper considers the causal fire relationships and fire management strategies necessary for implementation for future fire size and fire severity reduction in Sonoma County. Throughout this paper a subset of questions are answered regarding fire severity, precipitation, erosion and sediment response. Methods include the examination of peer-reviewed articles and grey sources to identify fire characteristics in relation to severity and region, habitat resilience and ability for self-restoration, and impacts that have been seen in areas following a fire. A comparative analysis was then made to assess the correlation between topography, soil burn severity, precipitation, vegetative cover, and acreage burned in order to determine the most suitable fire management practices to reduce severity and future fire occurrence. Data collected has been synthesized and presented throughout the paper in synthesis tables for additional examination and understanding. Further research included within this paper is an examination of various post-fire management measures and strategies to identify those most suitable for Sonoma County. In Sonoma County regional characteristics influence post-fire management practices, while the relationships between fire severity, soil composition, habitat and precipitation shape impacts. Analysis of average annual rainfall, average elevation, average elevation change, vegetative cover and soil burn severity were completed for seven fires. No correlation was identified between average annual rainfall and acreage burned. For both average elevation and average elevation change, acreage burned increased with increasing elevation and elevational change. All fires evaluated were in chaparral regions. Soil burn severity analysis found that severity tends to be distributed similarly among fires, with the greatest percentage of land having unburned or low severity, followed by moderate and high burn severity. Recommendations include the utilization of community education and awareness programs, wildland fire use and prescribed fire as fire management strategies to protect and prevent against large, severe and economically burdening fires

Using Unmanned Aerial Vehicles (UAV) for forest damage monitoring in south-western Europe

P. 1-8Prescribed burns are being considered as a management tool for the prevention of forest fires in many countries that have important firefighting problems. Knowledge of fire intensity and eliminated vegetation fuel are of great interest to evaluate their effectiveness. Both parameters are directly related to burn severity, so their evaluation is fundamental to predict the post-fire evolution of burned area. In this study we evaluated two prescribed burnings carried out in North of Spain during October 2017 by using multispectral data from an Unmanned Aerial Vehicle (UAV). In particular, four surface reflectance images were obtained in green (550 nm), red (660 nm), red-edge (735 nm) and near infrared (790 nm) at very high spatial resolution (GSD 20 cm) from which different spectral indexes were computed. Additionally, vegetation and soil burn severity was measured in 153 field plots and an analysis of variance (ANOVA) between each spectral index and burn severity (both in vegetation and soil) was performed. A Fisher’s least significant difference test determined that three vegetation burn severity levels and two soil burn severity levels could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that multispectral data from UAVs may be considered as a valuable indicator of burn severity for prescribed burnings.S

Near-infrared spectroscopy for estimating soil burn severity

Mestrado em Estudos AmbientaisForest fires are a natural phenomenon and occurred long before human kind was around, serving important ecosystem functions. In the past decades, however, some parts of the world have seen marked increases in the frequency and spatial extent of wildfires. This includes Portugal, where forest fires have, on average, affected 100.000 ha of rural lands per year since the mid1970s. In general, the direct and indirect effects of fires depend strongly on the temperatures to which vegetation and soil are exposed. In the case of wildfires – as opposed to prescribed burning or experimental fires - these temperatures can hardly ever be measured. Therefore, wildfire impacts are commonly assessed using proxies based on the consumption of the vegetation and the colour of the ashes deposited on the soil surface. These so-called burn severity indices typically provide qualitative estimates, distinguishing between low, medium and high severity. Recently, however, near- infrared (NIR) reflectance spectroscopy was successfully applied to estimate the maximum temperatures reached (MTR) by soils heated under laboratory conditions. The present study wanted to explore the potential of NIR for estimating MTR in soils burnt by wildfires. To this end, the work addressed two main topics: (i) spatial variability in the relationships between soil heating temperatures in a muffle and the corresponding NIR-based MTR estimates, both between and within study sites; (ii) the importance of this spatial variability in estimating MTRs of wildfire-burnt soil samples. A number of NIR-based models was constructed and used to predict the known MTR of laboratory-heated soil samples. One of the two long-unburnt study sites revealed marked variability over short distances, whereas the other did not. The models based on larger sample numbers, however, provided robust MTR predictions, even when these models involved samples from the two study sites. This probably reflected the sites comparable parent materials, soils and land cover (eucalypt plantations in schist soils). The best achieved models were used to estimate MTR by soil samples from a wildfire occurred in the central-north of Portugal, in the year 2010.. According to the index proposed in this work and the maximum temperatures reached estimations, the soil burn severity of the studied sites was moderate to high in surface samples, and low to moderate in the sub-surface samples

2013 Wildfire Season: An Overview, Southwestern U.S.

Ecological restoration is a practice that seeks to heal degraded ecosystems by reestablishing native species, structural characteristics, and ecological processes. The Society for Ecological Restoration International defines ecological restoration as "an intentional activity that initiates or accelerates the recovery of an ecosystem with respect to its health, integrity and sustainability...Restoration attempts to return an ecosystem to its historic trajectory" (Society for Ecological Restoration International Science & Policy Working Group 2004). Most frequent-fire forests throughout the Intermountain West have been degraded during the last 150 years. Many of these forests are now dominated by unnaturally dense thickets of small trees, and lack their once diverse understory of grasses, sedges, and forbs. Forests in this condition are highly susceptible to damaging, stand-replacing fires and increased insect and disease epidemics. Restoration of these forests centers on reintroducing frequent, low-severity surface fires--often after thinning dense stands--and reestablishing productive understory plant communities. The Ecological Restoration Institute at Northern Arizona University is a pioneer in researching, implementing, and monitoring ecological restoration of frequent-fire forests of the Intermountain West. By allowing natural processes, such as low-severity fire, to resume self-sustaining patterns, we hope to reestablish healthy forests that provide ecosystem services, wildlife habitat, and recreational opportunities. The Southwest Fire Science Consortium (SWFSC) is a way for managers, scientists, and policy makers to interact and share science. SWFSC's goal is to see the best science used to make management decisions and scientists working on the questions managers need answered. The SWFSC tries to bring together localized efforts to develop scientific information and to disseminate that to practitioners on the ground through an inclusive and open process. This publication would not have been possible without funding from the USDA Forest Service and the Southwest Fire Science Consortium. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the opinions or policies of the United States Government. Mention of trade names or commercial products does not constitute their endorsement by the United States Government or the ERI