Topic Editors

Dr. Alfonso Fernández-Manso
Applied Ecology and Remote Sensing Group, Agrarian Science and Engineering Department, University of León, Av. Astorga s/n, 24400 Ponferrada, Spain
Dr. Carmen Quintano
1. Electronic Technology Department, School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain
2. Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology (INIA), 34004 Palencia, Spain

The Future of Wildfires: Latest Geo-Technologies Applied to Fires and a New Perspective of Forest Resilience

Abstract submission deadline
closed (31 October 2021)
Manuscript submission deadline
closed (31 March 2022)
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Topic Information

Dear Colleagues,

The aim of this article collection is to contribute to the study of the future of wildfires, using update geo-technologies (including, but not limited to, Remote Sensing, LiDAR, UAVs, IA) and rethinking resilience to fire. This topic encourages interdisciplinary studies to provide ideas for developing ways in which we can improve the way we live with wildfires, from a multidisciplinary approach.

Wildfires have severe consequences for human health and wellbeing, biodiversity, and economies around the world. Understanding of forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, severity and resilience (capacity of an ecosystem to recuperate to its pre-disturbance situation). Geo-technologies may be seen as a tool to study forest cycle management (risk, suppression, damage and regeneration analysis). While more importantly, the use of methodologies based on geo-technologies can be of great help in allowing researchers to redefine wildfire resilience.

This topic entitled “The Future of Wildfires: Latest Geo-Technologies Applied to Fires and a New Perspective of Forest Resilience” is focused on advances in geo-technologies such as new remote sensing technologies, new sensors, big data collections, and processing methodologies, applied to fire cycle analysis, fire resilience redefinition, and to guide management of fire-prone ecosystems affected by large fires. We welcome submissions from Forests, Remote Sensing, Fire and Geomatics journals that cover, but are not limited to:

  • Innovative geo-technology for wildfire suppression, operational planning, or research methods;
  • Rethinking wildfire resilience and adaptation to global change vulnerability (impact on forest fire severity and consequences for ecosystem recovery);
  • Incorporation of wildfire processes within the earth-system, socio-economic and landscape fields;
  • Monitoring fire risk/danger using new geospatial data and technologies;
  • Fire effects on vegetation, soils, and hydrology using geo-technologies;
  • Improved methods of modelling image time-series of fire disturbance recovery;
  • Impact of fires and environmental consequences of large fires from remote sensing, big data, unmanned aerial vehicles (UAV), LiDAR, radar, and machine learning algorithms;
  • Time series analysis for forest resilience study.

Dr. Alfonso Fernández-Manso
Dr. Carmen Quintano
Topic Editor-in-Chief

Keywords

  • fire
  • LiDAR
  • machine learning algorithms
  • remote sensing
  • geomatics
  • forest resilience
  • fire risk
  • geo-technologies

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Forests
forests
3.282 4.0 2010 18.3 Days 2000 CHF
Geomatics
geomatics
- - 2021 15.8 Days 1000 CHF
Remote Sensing
remotesensing
5.349 7.4 2009 19.7 Days 2500 CHF
Fire
fire
2.726 4.9 2018 13.9 Days 1800 CHF

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Published Papers (12 papers)

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Article
Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil
Remote Sens. 2022, 14(13), 3141; https://doi.org/10.3390/rs14133141 - 30 Jun 2022
Viewed by 1032
Abstract
MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) [...] Read more.
MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life. Full article
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Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices
Remote Sens. 2022, 14(12), 2941; https://doi.org/10.3390/rs14122941 - 20 Jun 2022
Viewed by 917
Abstract
Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load [...] Read more.
Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations. Full article
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Article
Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan
Remote Sens. 2022, 14(8), 1918; https://doi.org/10.3390/rs14081918 - 15 Apr 2022
Cited by 2 | Viewed by 1360
Abstract
As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we [...] Read more.
As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation. Full article
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Article
Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China
Forests 2022, 13(3), 423; https://doi.org/10.3390/f13030423 - 08 Mar 2022
Cited by 1 | Viewed by 1620
Abstract
Climate determines the spatiotemporal distribution pattern of forest fires by affecting vegetation and the extent of drought. Thus, analyzing the dynamic change of the forest fire season and its response to climate change will play an important role in targeted adjustments of forest [...] Read more.
Climate determines the spatiotemporal distribution pattern of forest fires by affecting vegetation and the extent of drought. Thus, analyzing the dynamic change of the forest fire season and its response to climate change will play an important role in targeted adjustments of forest fire management policies and practices. In this study, we studied the spatiotemporal variations in forest fire occurrence in Fujian Province, China using the Mann–Kendall trend test and correlation analysis to analyze Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 and meteorological data. The results show that forest fire occurrence rose first and then declined over the years, but the proportion of forest fires during the fire prevention period decreased. The forest fires increased significantly in spring and summer, exceeding the forest fires occurring in the fire prevention period in 2010. The spatial distribution of forest fires decreased from northwest to southeast coastal areas, among which the number of forest fires in the northwest mountainous areas was large in autumn and winter. The fire risk weather index was strongly and positively correlated with forest fire occurrence across various sites in the province. The findings accentuate the need for properly adjusting the fire prevention period and resource allocation, strengthening the monitoring and early warning of high fire risk weather, and publicizing wildfire safety in spring and summer. As the forest fire occurrence frequency is high in the western and northwest mountainous areas, more observation towers and forest fire monitoring facilities should be installed. Full article
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Article
Hydrological Response of Burned Soils in Croplands, and Pine and Oak Forests in Zagros Forest Ecosystem (Western Iran) under Rainfall Simulations at Micro-Plot Scale
Forests 2022, 13(2), 246; https://doi.org/10.3390/f13020246 - 06 Feb 2022
Cited by 1 | Viewed by 693
Abstract
The post-fire hydrological processes depend on both land use and soil condition (burned or not). This study aims at understanding the variability of the water infiltration, surface runoff and erosion in burned soils under different land uses (forestland and cropland) in comparison to [...] Read more.
The post-fire hydrological processes depend on both land use and soil condition (burned or not). This study aims at understanding the variability of the water infiltration, surface runoff and erosion in burned soils under different land uses (forestland and cropland) in comparison to unburned sitesTo this aim, infiltration, runoff and soil losses after a wildfire in two pine and oak forests, and a cropland are evaluated in Zagros forests (Western Iran) using a portable rainfall simulator. This area represents one of the lands with the highest biodiversity and naturalistic value of the entire Middle East, but no similar hydrological evaluations have been conducted so far. The difference in infiltration between the burned and unburned sites under the three land uses was not significant (on the average less than 10%). The runoff and erosion due to the wildfire noticeably increased in the forestland (+95% and 60%, respectively) and slightly decreased in the cropland (−16% and −20%) in comparison to the unburned sites. In the burned croplands erosion requires much attention, because the soil loss is on an average 30-fold compared to the values measured in the forestland. This increase may be even higher, since the rainsplash erosion could be underestimated and the rill or gully erosion was not considered due to the use of a portable rainfall simulator. Therefore, the study suggests the adoption of suitable strategies in croplands of the Zagros forests, in order to limit the negative impacts of high-intensity fires and hydrogeological events. Overall, the study has provided an insight to improve the knowledge on soil hydrology under different land uses and soil conditions. This evaluation helps landscape planners to select the most suitable anti-erosive actions against erosion in fire-affected areas without any needs of long monitoring field campaigns or model implementation. Full article
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Article
Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery
Remote Sens. 2022, 14(3), 688; https://doi.org/10.3390/rs14030688 - 31 Jan 2022
Cited by 3 | Viewed by 2483
Abstract
Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km2. This [...] Read more.
Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km2. This program also provides daily data of active fire (fires spots) from a methodology that uses MODIS (Aqua and Terra) sensor data as reference satellites, which presents limitations mainly when dealing with small active fires. Remote sensing researches on active fire dynamics have contributed to wildfire comprehension, despite generally applying low spatial resolution data. Convolutional Neural Networks (CNN) associated with high- and medium-resolution remote sensing data may provide a complementary strategy to small active fire detection. We propose an approach based on object detection methods to map active fire in the Pantanal. In this approach, a post-processing strategy is adopted based on Non-Max Suppression (NMS) to reduce the number of highly overlapped detections. Extensive experiments were conducted, generating 150 models, as five-folds were considered. We generate a public dataset with 775-RGB image patches from the Wide Field Imager (WFI) sensor onboard the China Brazil Earth Resources Satellite (CBERS) 4A. The patches resulted from 49 images acquired from May to August 2020 and present a spatial and temporal resolutions of 55 m and five days, respectively. The proposed approach uses a point (active fire) to generate squared bounding boxes. Our findings indicate that accurate results were achieved, even considering recent images from 2021, showing the generalization capability of our models to complement other researches and wildfire databases such as the current program Queimadas in detecting active fire in this complex environment. The approach may be extended and evaluated in other environmental conditions worldwide where active fire detection is still a required information in fire fighting and rescue initiatives. Full article
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Article
Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection
Remote Sens. 2021, 13(23), 4790; https://doi.org/10.3390/rs13234790 - 26 Nov 2021
Cited by 5 | Viewed by 1485
Abstract
This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on [...] Read more.
This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans. Full article
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Communication
Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
Remote Sens. 2021, 13(21), 4226; https://doi.org/10.3390/rs13214226 - 21 Oct 2021
Cited by 1 | Viewed by 851
Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high [...] Read more.
The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate. Full article
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Article
Adaptive Forest Fire Spread Simulation Algorithm Based on Cellular Automata
Forests 2021, 12(11), 1431; https://doi.org/10.3390/f12111431 - 20 Oct 2021
Cited by 2 | Viewed by 1218
Abstract
The popular simulation process that uses traditional cellular automata with a fixed time step to simulate forest fire spread may be limited in its ability to reflect the characteristics of actual fire development. This study combines cellular automata with an existing forest fire [...] Read more.
The popular simulation process that uses traditional cellular automata with a fixed time step to simulate forest fire spread may be limited in its ability to reflect the characteristics of actual fire development. This study combines cellular automata with an existing forest fire model to construct an improved forest fire spread model, which calculates a speed change rate index based on the meteorological factors that affect the spread of forest fires and the actual environment of the current location of the spread. The proposed model can adaptively adjust the time step of cellular automata through the speed change rate index, simulating forest fire spread more in line with the actual fire development trends while ensuring accuracy. When used to analyze a forest fire that occurred in Mianning County, Liangshan Prefecture, Sichuan Province in 2020, our model exhibited simulation accuracy of 96.9%, and kappa coefficient of 0.6214. The simulated fire situation adapted well to the complex and dynamic fire environment, accurately depicting the detailed fire situation. The algorithm can be used to simulate and predict the spread of forest fires, ensuring the accuracy of spread simulation and helping decision makers formulate reasonable plans. Full article
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Article
Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China
Forests 2021, 12(10), 1299; https://doi.org/10.3390/f12101299 - 23 Sep 2021
Cited by 8 | Viewed by 1642
Abstract
Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as [...] Read more.
Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%. Full article
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Article
A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation
Forests 2021, 12(7), 933; https://doi.org/10.3390/f12070933 - 16 Jul 2021
Cited by 3 | Viewed by 2000
Abstract
Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to [...] Read more.
Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to build relationships between the input drivers (weather, fuel and site characteristics) and observed DFMC. The latter attempts to simulate the processes that occur in the fuel with energy and water balance conservation equations. However, empirical models lack explanations for physical processes, and process-based models may provide an incomplete representation of DFMC. To combine the benefits of empirical and process-based models, here we introduced the Long Short-Term Memory (LSTM) network and its combination with an effective physics process-based model fuel stick moisture model (FSMM) to estimate DFMC. The LSTM network showed its powerful ability in describing the temporal dynamic changes of DFMC with high R2 (0.91), low RMSE (3.24%) and MAE (1.97%). When combined with a FSMM model, the physics-guided model FSMM-LSTM showed betterperformance (R2 = 0.96, RMSE = 2.21% and MAE = 1.41%) compared with the other models. Therefore, the combination of the physics process and deep learning estimated 10-h DFMC more accurately, allowing the improvement of wildfire risk assessments and fire simulating. Full article
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Article
Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data
Remote Sens. 2021, 13(6), 1189; https://doi.org/10.3390/rs13061189 - 20 Mar 2021
Cited by 3 | Viewed by 1624
Abstract
Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually [...] Read more.
Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation. Full article
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