Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

5 p.Nowadays Earth observation satellites, in particular Landsat, provide a valuable help to forest managers in post-fire operations; being the base of post-fire damage maps that enable to analyze fire impacts and to develop vegetation recovery plans. Sentinel-2A MultiSpectral Instrument (MSI) records data in similar spectral wavelengths that Landsat 8 Operational Land Imager (OLI), and has higher spatial and temporal resolutions. This work compares two types of satellite-based maps for evaluating fire damage in a large wildfire (around 8000 ha) located in Sierra de Gata (central-western Spain) on 6–11 August 2015. 1) burn severity maps based exclusively on Landsat data; specifically, on differenced Normalized Burn Ratio (dNBR) and on its relative versions (Relative dNBR, RdNBR, and Relativized Burn Ratio, RBR) and 2) burn severity maps based on the same indexes but combining pre-fire data from Landsat 8 OLI with post-fire data from Sentinel-2A MSI data. Combination of both Landsat and Sentinel-2 data might reduce the time elapsed since forest fire to the availability of an initial fire damage map. Interpretation of ortho-photograph Pléiades 1 B data (1:10,000) provided us the ground reference data to measure the accuracy of both burn severity maps. Results showed that Landsat based burn severity maps presented an adequate assessment of the damage grade (κ statistic = 0.80) and its spatial distribution in wildfire emergency response. Further using both Landsat and Sentinel-2 MSI data the accuracy of burn severity maps, though slightly lower (κ statistic = 0.70) showed an adequate level for be used by forest managersS

Cross-diffusion based filtering as pre-processing step for remote sensing procedures

A new methodology combining 2 × 2 cross-diffusion systems of nonlinear partial differential equations (CDS) with classical image classification procedures is proposed in the present paper. Such a kind of mathematical models (CDS) have been theoretically studied in previous works in the context of image processing, however here they are tested and stressed in very practical instances. In particular, the main contribution of this paper is the improvement of the classification of satellite images when they are previously filtered by means of a CDS model. This conclusion is based on a wide and costly experimentation with satellite images of areas damaged by forest fires and surface coal mining, all of them located in Mediterranean areas. The efficiency of our metho- dology is not only in terms of the classification improvement but also in terms of the runtime saving since CDS based filtering is much less costly than other classical partial differential equations based filtering mathematical models as for example anisotropic models or higher order ones, always within the framework of nonlinear partial differential equations

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

Sinergias Entre el Modelo de Mezclas Espectrales y el Análisis de Imágenes Basado en Objetos en el Estudio de Incendios ForestalesSynergies Between Linear Spectral Mixture Analysis and Object-Based Image Analysis to Study Forest Fires

Las metodologías unitemporales habitualmente utilizadas para cartografiar el área afectada por un incendio forestal se basan en la clasificación de una imagen NDVI post-incendio; sin embargo, presentan algunas limitaciones. Este trabajo propone una metodología basada en el empleo conjunto del Modelo de Mezclas Espectrales (Spectral Mixture Analysis - SMA) y el análisis de imágenes basado en objetos (Object-Based Image Analysis - OBIA), con la finalidad de minimizar estos problemas. Por una parte, SMA, que trabaja a nivel subpíxel, posibilita minimizar las confusiones originadas por la influencia del suelo; y, por otra, OBIA, trabajando a nivel suprapíxel, permite considerar características no espectrales tales como la forma y la textura. La imagen fracción vegetación quemada, obtenida al descomponer espectralmente una imagen Landsat Enhanced Thematic Mapper Plus, ETM+, posterior al incendio considerado, fue la entrada de un clasificador orientado a objetos que empleó dos niveles de segmentación. La precisión de los resultados obtenidos utilizando esta metodología en el incendio ocurrido entre los días 13 y 17 de septiembre de 1998 en Tabuyo del Monte – LeónEspaña (3.309 ha), es muy prometedora, indicando sinergias entre ambos métodos, y con un gran potencial para la cartografía de áreas quemadas y estimación de niveles de severidad.Abstract Unitemporal methodologies used to mapping burned area are usually based on post fire NDVI image classification; nevertheless they present several limitations. In order to minimize these deficiencies, this work shows a methodology based on Spectral Mixture Analysis (SMA) and Object-Based Image Analysis (OBIA). On the one hand, SMA, working at subpixel level, permits minimise the errors due to soil influence. On the other hand, OBIA, working at suprapixel scale, allows to consider not only spectral characteristics but also form and texture. The burned vegetation fraction image, obtained by unmixing a Landsat Enhanced Thematic Mapper Plus (ETM+) post fire image, was the input to a two segmentation levels OBIA. The results acuracy obtained applying this methodology on the study of the forest fire occurred between 13th-17th September 1998 in Tabuyo del Monte (León-Spain) (3,309 ha) was very promising, showing synergies between SMA and OBIA and a great potential to burned area mapping and severity estimation

Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature

14 p.Forest fires are incidents of great importance in Mediterranean environments. Landsat data have proven to be suitable for evaluating post-fire vegetation damage and determining different levels of burn severity, which is crucial for planning post-fire rehabilitation. This study assessed the utility of combined Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images and Land Surface Temperature (LST) to accurately map burn severity. We studied a large convection- dominated wildfire, which occurred on 19–21 September 2012 in Spain, in a zone dominated by Pinus pinaster Ait. Burn severity degree (low, moderate, and high) was measured 2–3 months after fire in 111 field plots using the Composite Burn Index (CBI). Four fraction images were generated using MESMA from the reflective bands of a post-fire Landsat 7 Enhanced Thematic Mapper (ETM +) image: 1.-char, 2.-green vegetation (GV), 3.-non-photosynthetic vegetation and soil (NPVS) and 4.-shade. The thermal band was converted to LST using a single channel algorithm. Next, Multinomial Logistic Regression (MLR) was used to obtain the probability of each burn severity level from MESMA fraction images and LST. Finally, a burn severity map was generated from the probability images and independently validated using an error matrix, producer and user accuracies per class, and κ statistic. MLR identified the char fraction image and LST as the only significant explanatory variables when burn severity acted as the response variable. Two burn severity degrees (low-moderate and high) were finally considered to build the final burn severity map. In this way, we reached a higher accuracy (κ = 0.79) than using the original three burn severity levels (κ = 0.66). Our study demonstrates the validity of combining fraction images and LST from Landsat data to map burn severity accurately in Mediterranean countriesS

Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data

P. 102-118All 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.S

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

14 p.Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999–2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forestsS

Aplicaciones de la geoinformación en el desarrollo de proyectos turísticos en espacios forestales: el ejemplo de "La Mirada Circular"Geoinformation applications for tourist development projects in forest areas: the example of "La Mirada Circular"

ResumenEl trabajo presenta y analiza el proyecto de geoturismo denominado La Mirada Circular, un importante proyecto de desarrollo socio-económico y conservación de los recursos forestales en la comarca de El Bierzo de la Comunidad Autónoma de Castilla y León (España). La comarca de El Bierzo es una importante área natural con dos espacios declarados por la UNESCO como Patrimonio de la Humanidad (Las Médulas) y como Reserva de la Biosfera (Los Ancares). Además la comarca cuenta con importantes espacios integrados en la Red Natura 2000 de la Unión Europea. El geo-portal del proyecto (www.lamiradacircular.com) es la gran apuesta de investigación y utilización de las Geotecnologias de la Información y el Conocimiento (Geo-TIC) en dos de los sectores más importantes del estado español: los sectores turístico y forestal. El proyecto, a partir de la aplicación exhaustiva de las Geo-TIC, desarrolla los conceptos de geoturismo y turismo inteligente, implementando novedosas aplicaciones (GPS, Geopodcasting, Mobile Mapping, Códigos QR, realidad virtual, etc. ) que conviertan el territorio forestal en un verdadero museo interpretativo tanto en una dimensión real como virtual. AbstractThe presents paper analyzes the geo-tourism project called “La Mirada Circular” (the Circular Look), a major project of socio-economic and forest resource conservation in the region of El Bierzo region of Castilla y León (Spain). The region of El Bierzo is an important natural area with two sites identified by UNESCO as a World Heritage Site (The Médulas) and as a Biosphere Reserve (The Ancares). In addition the county has important integrated areas in the Natura 2000 network of the European Union. The project website (www.lamiradacircular.com) is the great challenge of research and use of Geo Information and Knowledge (Geo-ICT) in two of the largest Spanish sectors of the Spanish state: the tourism sector and the forest sectors. The draft from the comprehensive implementation of the Geo- ICT develops the concept of geo-tourism and intelligent tourism implementing innovative application (GPS, Geopodcasting, Mobile Mapping, QR codes, virtual reality, etc..) which converted forest land into a real museum that interprets both real and virtual dimensions

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

RS-EducA©: aplicación de las TICs en el desarrollo de una plataforma multiaprendizaje para la innovación educativa en la enseñanza de la Teledetección

Accésit 2012[ES] Dentro del marco del Espacio Europeo para la Educación Superior (EEES) (Duran et al., 2009), aún en proceso de implantación en España, tanto las instituciones europeas como las españolas instan a migrar a nuevos paradigmas de enseñanza-aprendizaje, centrados en el aprendizaje autónomo durante toda la vida (LifeLong Learning, LLL). Cuando los estudiantes reflexionan de forma crítica, realizan investigaciones y desarrollan métodos para explorar nuevos temas, potencialmente ordenan sus propias ideas y fomentan nuevas conexiones entre las mismas. Esta experiencia puede preparar a los estudiantes para tomar conciencia de la nueva información científica y a integrarla con su anterior conocimiento a largo plazo (Lee et al., 2010). De esta forma, en los antiguos planes de estudios el papel protagonista era asumido por el profesor, mientras que en los actuales planes el alumno es el protagonista de su aprendizaje. Algunos trabajos que efectúan un análisis crítico de las mejoras obtenidas con la implantación de los nuevos Grados son Sánchez et al., (2008) y García et al., (2008), entre otros