Satellite Remote Sensing contributions to Wildland Fire Science and Management

No funding was received for this particular review, but support research was funded by the European Space Agency’s Climate Change Initiative Programme to Dr. Chuvieco.This paper reviews the most recent literature related to the use of remote sensing (RS) data in wildland fire management. Recent Findings Studies dealing with pre-fire assessment, active fire detection, and fire effect monitoring are reviewed in this paper. The analysis follows the different fire management categories: fire prevention, detection, and post-fire assessment. Extracting the main trends from each of these temporal sections, recent RS literature shows growing support of the combined use of different sensors, particularly optical and radar data and lidar and optical passive images. Dedicated fire sensors have been developed in the last years, but still, most fire products are derived from sensors that were designed for other purposes. Therefore, the needs of fire managers are not always met, both in terms of spatial and temporal scales, favouring global over local scales because of the spatial resolution of existing sensors. Lidar use on fuel types and post-fire regeneration is more local, and mostly not operational, but future satellite lidar systems may help to obtain operational products. Regional and global scales are also combined in the last years, emphasizing the needs of using upscaling and merging methods to reduce uncertainties of global products. Validation is indicated as a critical phase of any new RS-based product. It should be based on the independent reference information acquired from statistically derived samples. The main challenges of using RS for fire management rely on the need to improve the integration of sensors and methods to meet user requirements, uncertainty characterization of products, and greater efforts on statistical validation approaches.European Space Agenc

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak

Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impactFunding for this study was provided by CONAFOR/CONACYT Projects “CO2-2014-3-252620” and “CO-2018-2-A3-S-131553” for the development and enhancement of a Forest Fire Danger Prediction System for Mexico, funded by the Sectorial Fund for forest research, development and technological innovation “Fondo Sectorial para la investigación, el desarrollo y la innovación tecnológica forestal”S

Deep Learning Approach to Improve Spatial Resolution of GOES-17 Wildfire Boundaries using VIIRS Satellite Data

The rising severity and frequency of wildfires in recent years in the U.S. have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellites provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite has a high temporal (5 min) but a low spatial resolution (2 km), and VIIRS polar orbiter satellite has a low temporal (~12 h) but high spatial resolution (375 m). This study aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution wildfire monitoring tool.This study considers the problem of increasing the spatial resolution of low resolution satellite data using high resolution satellite. An Autoencoder DL model is proposed to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and architectures are implemented and tested to predict both the area of fire and corresponding fire radiance values. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

Can fire spread simulations contribute to support decisions in a fire suppression context ? An evaluation using MaxEnt, FARSITE and satellite active fire data

Mestrado em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia / ULThis work is of an exploratory nature and describes and evaluates a method to simulate fire spread which, in an operational context, has the potential to be used as a decision-support tool for fire management and suppression. The use of fire spread models has, for the most part, followed a deterministic approach, which does not account for predictions uncertainty. However, fire spread models are subject to assumptions and limitations that inherently produce errors during simulations and so should be integrated in the simulations themselves. For that matter uncertainty was propagated through Farsite fire behavior model by randomly defining 100 different independent combinations of some of the most important input variables. The simulations were run with three different fuel maps, one standardized and two customized. For the evaluation of the fire spread predictions a qualitative and a quantitative analysis were made. Both analyses used MaxEnt derived reference perimeters, and active fire data was used on the qualitative analyses to add temporal depth to the evaluation. Results showed that uncertainties in wind speed and direction, location of ignitions (spatial and temporal), fuel model assignment and typology may have major impact on prediction accuracy. Overall, fuel models presented better results when compared with the standard model and generally showed higher Kappa and burned class agreement values and lower omission errors. This thesis suggests that this method has major potential to optimize fuel management practices, especially if simulations are run with fuel maps derived Portuguese landcover mapsN/

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

NASA 2014 The Hyperspectral Infrared Imager (HyspIRI) - Science Impact of Deploying Instruments on Separate Platforms

The Hyperspectral Infrared Imager (HyspIRI) mission was recommended for implementation by the 2007 report from the U.S. National Research Council Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, also known as the Earth Science Decadal Survey. The HyspIRI mission is science driven and will address a set of science questions identified by the Decadal Survey and broader science community. The mission includes a visible shortwave infrared (VSWIR) imaging spectrometer, a multispectral thermal infrared (TIR) imager and an intelligent payload module (IPM). The IPM enables on-board processing and direct broadcast for those applications with short latency requirements. The science questions are organized as VSWIR-only, TIR-only and Combined science questions, the latter requiring data from both instruments. In order to prepare for the mission NASA is undertaking pre-phase A studies to determine the optimum mission implementation, in particular, cost and risk reduction activities. Each year the HyspIRI project is provided with feedback from NASA Headquarters on the pre-phase A activities in the form of a guidance letter which outlines the work that should be undertaken the subsequent year. The 2013 guidance letter included a recommendation to undertake a study to determine the science impact of deploying the instruments from separate spacecraft in sun synchronous orbits with various time separations and deploying both instruments on the International Space Station (ISS). This report summarizes the results from that study. The approach taken was to evaluate the impact on the combined science questions of time separations between the VSWIR and TIR data of <3 minutes, <1 week and a few months as well as deploying both instruments on the ISS. Note the impact was only evaluated for the combined science questions which require data from both instruments (VSWIR and TIR). The study concluded the impact of a separation of <3 minutes was minimal, e.g. if the instruments were on separate platforms that followed each other in a train. The impact of a separation of <1 week was strongly dependent on the question that was being addressed with no impact for some questions and a severe impact for others. The impact of a time separation of several months was severe and in many cases it was no longer possible to answer the sub-question. The impact of deploying the instruments on the ISS which is in a precessive (non-sun synchronous) orbit was also very question dependent, in some cases it was possible to go beyond the original question, e.g. to examine the impact of the diurnal cycle, whereas in other cases the question could not be addressed for example if the question required observations from the polar regions. As part of the study, the participants were asked to estimate, as a percentage, how completely a given sub-question could be answered with 100% indicating the question could be completely answered. These estimations should be treated with caution but nonetheless can be useful in assessing the impact. Averaging the estimates for each of the combined questions the results indicate that 97% of the questions could be answered with a separation of < 3 minutes. With a separation of < 1 week, 67% of the questions could be answered and with a separation of several months only 21% of the questions could be answered

The use of GIS for the development of a fully embedded predictive fire model

Fire is very important for maintaining balance in the ecosystems and is used by fire management across the world to regulate growth of vegetation in natural conservation areas. However, improper management of fire may lead to hazardous behaviour. Fire modelling tools are implemented to provide fire managers with a platform to test and plan fire management activities. Fire modelling occurs in two parts: fire behaviour models and fire spread models, where fire behaviour models account for the behaviour of fires that is used in fire spread models to model the propagation of a fire event. Since fire is a worldwide phenomenon a number of fire modelling approaches have been developed across the world. Most existing fire models only model either fire behaviour or fire spread, but not both, hence full integration of fire models into GIS is not completely implemented. Full integration of environmental modelling in GIS refers to the case where an environmental model such as a fire model is implemented within a GIS environment, without requiring any transfer of data from other external environments. Most existing GIS based fire spread models account for fire propagation in the direction of prevailing winds (or defined fire channels) as opposed to full fire spread in all directions. The purpose of this study is to illustrate the role of GIS in fire management through the development of a fully integrated, predictive, wind driven, surface fire model. The fire model developed in this study models both the risk of fire occurring (fire behaviour model), and the propagation of a fire in case of an ignition incident (fire spread model), hence full integration of fire modelling in a GIS environment. The fire behaviour model is based on prevailing meteorological conditions, the type of vegetation in an area, and the topography. The spread of a fire in this model is determined by the transfer of heat energy and rate of spread of fire, and is developed based on the Cellular Automata (CA) modelling approach. This model considers the spread of fire in all directions instead of the forward wind direction only as is the case in most fire spread models. The fire behaviour model calculates fire intensity and rate of spread which are used in the fire spread model, hence demonstrating the full integration of fire modelling in GIS. No external data exchange with the model occurs except for acquisition of input data such as measured values of environmental conditions. v This cellular automata based fire spread model is developed in the ArcGIS ModelBuilder geoprocessing environment, and requires the development of a custom geoprocessing function tool to facilitate the fast and effective performance of the model. The test study area used in this research is the Kruger National Park because of frequent fire activity that occurs in the park, as a result of management activities and accidental fires, and also because these fires are recorded by park fire ecologists. Validation of the model is achieved by comparison of simulated fire areas after a certain period of time with known location of the fire at that particular time. This is achieved by the mapping of fire scars and active fire areas acquired from MODIS Terra and Aqua images, fire scars are also acquired from the Kruger National Park Scientific Services. Upon evaluation, the results of the fire model show successful simulation of fire area with respect to time. The implementation of the model within the ArcGIS environment is also performed successfully. The study thus concludes that GIS can be successfully used for the development of a fully integrated (embedded) fire model