A synergetic approach to burned area mapping using maximum entropy modeling trained with hyperspectral data and VIIRS hotspots

Producción CientíficaSouthern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.Ministerio de Economía, Industria y Competitividad (grant AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?

Producción CientíficaForest managers rely on accurate burn severity estimates to evaluate post-fire damage and to establish revegetation policies. Burn severity estimates based on reflective data acquired from sensors onboard satellites are increasingly complementing field-based ones. However, fire not only induces changes in reflected and emitted radiation measured by the sensor, but also on energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo (LSA) are greatly affected by wildfires. In this study, we examine the usefulness of these elements of energy balance as indicators of burn severity and compare the accuracy of burn severity estimates based on them to the accuracy of widely used approaches based on spectral indexes. We studied a mega-fire (more than 450 km2 burned) in Central Portugal, which occurred from 17 to 24 June 2017. The official burn severity map acted as a ground reference. Variations induced by fire during the first year following the fire event were evaluated through changes in ET, LST and LSA derived from Landsat data and related to burn severity. Fisher’s least significant difference test (ANOVA) revealed that ET and LST images could discriminate three burn severity levels with statistical significance (uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate (κ = 0.63 and 0.57, respectively), similar to the accuracy of the estimate based on dNBR (κ = 0.66). We conclude that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting as useful indicators of burn severity for mega-fires in Mediterranean ecosystems, may provide critical information about how energy balance changes due to fireMinisterio de Economía, Industria y Competitividad (project AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

Vegetation and soil fire damage analysis based on species distribution modeling trained with multispectral satellite data

Producción CientíficaForest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers.Ministerio de Economía, Industria y Competitividad (project 559 AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17)Ministerio de Educación, Cultura y Deporte (grants PRX17/00234 and PRX17/00133

Comparison of physical-based models to measure forest resilience to fire as a function of burn severity

Producción CientíficaWe aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R2 > 0.75), followed by MESMA (R2 > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity.Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional (FEDER) - (project AGL2017-86075-C2-1-R)Junta de Castilla y León - (project LE005P20)British Ecological Society - (project SR22-100154

Remote sensing applied to the study of fire regime attributes and their influence on post-fire greenness recovery in pine ecosystems

Producción CientíficaWe aimed to analyze the relationship between fire regime attributes and the post-fire greenness recovery of fire-prone pine ecosystems over the short (2-year) and medium (5-year) term after a large wildfire, using both a single and a combined fire regime attribute approach. We characterized the spatial (fire size), temporal (number of fires, fire recurrence, and return interval), and magnitude (burn severity of the last fire) fire regime attributes throughout a 40-year period with a long-time series of Landsat imagery and ancillary data. The burn severity of the last fire was measured by the dNBR (difference of the Normalized Burn Ratio) spectral index, and classified according to the ground reference values of the CBI (Composite Burn Index). Post-fire greenness recovery was obtained through the difference of the NDVI (Normalized Difference Vegetation Index) between pre- and post-fire Landsat scenes. The relationship between fire regime attributes (single attributes: fire recurrence, fire return interval, and burn severity; combined attributes: fire recurrence-burn severity and fire return interval-burn severity) and post-fire greenness recovery was evaluated using linear models. The results indicated that all the single and combined attributes significantly affected greenness recovery. The single attribute approach showed that high recurrence, short return interval and low severity situations had the highest vegetation greenness recovery. The combined attribute approach allowed us to identify a wider variety of post-fire greenness recovery situations than the single attribute one. Over the short term, high recurrence as well as short return interval scenarios showed the best post-fire greenness recovery independently of burn severity, while over the medium term, high recurrence combined with low severity was the most recovered scenario. This novel combined attribute approach (temporal plus magnitude) could be of great value to forest managers in the development of post-fire restoration strategies to promote vegetation recovery in fire-prone pine ecosystems in the Mediterranean Basin under complex fire regime scenarios.Ministerio de Economía y Competitividad, y el Fondo Europeo de Desarrollo Regional (FEDER), en el marco del GESFIRE (AGL2013-48189-C2-1-R) y proyectos FIRESEVES (AGL2017-86075-C2-1-R)Junta de Castilla y León en el marco de los proyectos FIRECYL (LE033U14) y SEFIRECYL (LE001P17)Ministerio de Educación (FPU14/00636

Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Producción CientíficaPrescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

Evaluation of composite burn index and land surface temperature for assessing soil burn severity in mediterranean fire-prone pine ecosystems

Producción CientíficaWe analysed the relationship between burn severity indicators, from remote sensing and field observations, and soil properties after a wildfire in a fire-prone Mediterranean ecosystem. Our study area was a large wildfire in a Pinus pinaster forest. Burn severity from remote sensing was identified by studying immediate post-fire Land Surface Temperature (LST). We also evaluated burn severity in the field applying the Composite Burn Index (CBI) in a total of 84 plots (30 m diameter). In each plot we evaluated litter consumption, ash colour and char depth as visual indicators. We collected soil samples and pH, soil organic carbon, dry aggregate size distribution (MWD), aggregate stability and water repellency were analysed. A controlled heating of soil was also carried out in the laboratory, with soil from the control plots, to compare with the changes produced in soils affected by different severity levels in the field. Our results shown that changes in soil properties affected by wildfire were only observed in soil aggregation in the high severity situation. The laboratory-controlled heating showed that temperatures of about 300 ◦C result in a significant reduction in soil organic carbon and MWD. Furthermore, soil organic carbon showed a significant decrease when LST values increased. Char depth was the best visual indicator to show changes in soil properties (mainly physical properties) in large fires that occur in Mediterranean pine forests. We conclude that CBI and post-fire LST can be considered good indicators of soil burn severity since both indicate the impact of fire on soil properties.Ministerio de Economía y Competitividad y el Fondo Europeo de Desarrollo Regional (FEDER), en el marco del proyecto GESFIRE (AGL2013-48189-C2-1-R)Junta de Castilla y León en el marco del proyecto SEFIRECYL (LE001P17

Burn severity and post-fire land surface albedo relationship in Mediterranean forest ecosystems

Producción CientíficaOur study explores the relationship between land surface albedo (LSA) changes and burn severity, checking whether the LSA is an indicator of burn severity, in a large forest fire (117.75 km2, Spain). The LSA was obtained from Landsat data. In particular, we used an immediately-after-fire scene, a year-after-fire scene and a pre-fire one. The burn severity (three levels) was assessed in 111 field plots by using the Composite Burn Index (CBI). The potentiality of remotely sensed LSA as an indicator for the burn severity was tested by a one-way analysis of variance, correlation analysis and regression models. Specifically, we considered the total shortwave, visible, and near-infrared LSA. Immediately after the fire, we observed a decrease in the LSA for all burn severity levels (up to 0.631). A small increase in the LSA was found (up to 0.0292) a year after the fire. The maximum adjusted coefficient of determination (R2adj) of the linear regression model between the immediately post-fire LSA image and the CBI values was approximately 67%. Fisher’s least significance difference test showed that two burn severity levels could be discriminated by the immediately post-fire LSA image. Our results demonstrate that the magnitude of the changes in the LSA is related to the burn severity with a statistical significance, suggesting the potentiality of immediately-after-fire remotely sensed LSA for estimating the burn severity as an alternative to other satellite-based methods. However, the persistency of these changes in time should be evaluated in future research.Ministerio de Economía, Industria y Competitividad (project 559 AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17