Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard

Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies

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.European Space AgencyMinisterio de Educación, Cultura y Deport

Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis of the contribution of multitemporal information from multispectral satellite images for the automatic land cover classes’ discrimination. The outcomes show that multispectral information contributes more significantly than multitemporal information for the automatic classification of land cover types. In the sequence, we review some of the most important steps that constitute a standard protocol for the automatic land cover mapping from satellite images. Moreover, we delineate a methodological approach for the production and assessment of land cover maps from multitemporal satellite images that guides us in the production of a land cover map with high thematic accuracy for the study area. Finally, we develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation indices time series from satellite images for numerous land cover classes. The simplified multitemporal information retrieved with the model proves adequate to describe the main land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals

A high-resolution fuel type mapping procedure based on satellite imagery and neural networks: Updating fuel maps for wildfire simulators

A major limitation in the simulation of forest fires involves the proper characterization of the surface vegetation over the study area, based on land cover maps. Unfortunately, these maps may be outdated, with areas where vegetation is either not documented or inaccurately portrayed. These limitations may impair the predictions of wildfire simulators or the design of risk maps and prevention plans. This study proposes a complete procedure for fuel type classification using satellite imagery and fully-connected neural networks. Specifically, our work is based on pixel- based processing cells, generating high-resolution maps. The field study is located in the Northeast of Castilla y Leo ́n, a central Spanish region, and the Rothermel criteria was followed for the fuel classification. The results record an accuracy of close to 78% on the test sets for the two studied settings, improving on the results reported in previous studies and ratifying the robustness of our approach. Additionally, the confusion matrix analysis and the per-class statistics computed confirm good reliability for all fuel types in a cross-validation framework. The predicted maps can be used on wildfire simulators through GIS tools.BERC 2022–2025 PID2019-107685RB-I00 European Regional Development Fund (ERDF) and the Department of Education of the regional government, the Junta of Castilla y Leo ́n, (Grant contract SA089P20); the European Union’s Horizon 2020 – Research and Innovation Framework Program under Grant agreement ID 101036926

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Validation of the moderate-resolution satellite burned area products across different biomes in South Africa

Biomass burning in southern Africa has brought significant challenges to the research society as a fundamental driver of climate and land cover changes. Burned area mapping approaches have been developed that generate large-scale low and moderate resolution products made with different satellite data. This consequently afford the remote sensing community a unique opportunity to support their potential applications in e.g., examining the impact of fire on natural resources, estimating the quantities of burned biomass and gas emissions. Generally, the satellite-derived burned area products produced with dissimilar algorithms provide mapped burned areas at different levels of accuracy, as the environmental and remote sensing factors vary both spatially and temporally. This study focused on the inter-comparison and accuracy evaluation of the 500-meter Moderate Resolution Imaging Spetroradiomter (MODIS) burned area product (MCD45A1) and the Backup MODIS burned area product (hereafter BMBAP) across the main-fire prone South African biomes using reference data independently-derived from multi-temporal 30-meter Landsat 5 Thematic Mapper (TM) imagery distributed over six validation sites. The accuracy of the products was quantified using confusion matrices, linear regression and subpixel burned area measures. The results revealed that the highest burned area mapping accuracies were reported in the fynbos and grassland biomes by the MCD45A1 product, following the BMBAP product across the pine forest and savanna biomes, respectively. Further, the MCD45A1 product presented higher subpixel detection probabilities for the burned area fractions 50% of a MODIS pixel. Finally the results demonstrated that the probability of identifying a burned area within a MODIS pixel is directly related to the proportion of the MODIS pixel burned and thus, highlights the relevance of fractional burned area during classification accuracy assessment of lower resolution remotely-sensed products using data with higher spatial resolution.Dissertation (MSc)--University of Pretoria, 2011.Geography, Geoinformatics and Meteorologyunrestricte

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem