Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations

Labenski, Pia; Ewald, Michael J.; Schmidtlein, Sebastian; Heinsch, Faith Ann; Fassnacht, Fabian Ewald

doi:10.1016/j.rse.2023.113711

Cited by 10 publications

(3 citation statements)

References 71 publications

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“…The findings of this study align harmoniously with the investigations conducted by Labenski [37]. In their work, they embarked on modeling and mapping surface fuels in mixed forests, employing the amalgamation of airborne LiDAR and Sentinel-2 data.…”

Section: Estimation Accuracy Of Machine Learning Model Based On Rando...supporting

confidence: 82%

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Deng,

Yan,

Jiang

et al. 2023

Remote Sensing

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Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Traditional methods of estimating forest fuel load typically involve ground surveys combined with remote sensing, which can be costly and inefficient. Therefore, low-cost, large-scale FFL estimation remains challenging. In this study, Sentinel-2A satellite imagery from the Changsha forest region was used as the data source. Firstly, different feature variables were constructed based on false-color (B843), true-color (B432), four-band (B8432) combinations, and the Normalized Difference Water Index (NDWI). Subsequently, a machine learning approach based on random convolution was employed to estimate FFL. This study also included accuracy assessments of the estimation results and the creation of FFL maps for the study area. The results showed that the FFL estimation based on the B8432 band combination achieved the highest accuracy, with RMSE and R2 values of 5.847 t·hm−2 and 0.656, respectively. FFL estimation results based on false-color imagery followed, with true-color imagery and NDWI index-based estimation results exhibiting lower accuracy. This study offers critical FFL insights using random convolution techniques applied to Sentinel-2A imagery, enhancing the ability to monitor and manage forest fuel conditions effectively, thereby facilitating more informed regional wildfire risk management strategies.

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Section: Estimation Accuracy Of Machine Learning Model Based On Rando...supporting

confidence: 82%

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Deng,

Yan,

Jiang

et al. 2023

Remote Sensing

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“…Future works could refine the mapping of vegetation types and the related fuel models using spatially explicit local information and more accurate datasets. For instance, LiDAR-derived data and metrics could be used to improve the assessment and mapping of canopy fuels, which are Salis et al 10.3389/ffgc.2023.1241378 Frontiers in Forests and Global Change 16 frontiersin.org crucial for assessing the occurrence of crown fires, as well as for estimating canopy understory (Gonzalez-Ferreiro et al, 2017;Mauro et al, 2021;Marino et al, 2022;Labenski et al, 2023). Likewise, more accurate mapping of herbaceous fuels (i.e., considering their level of agricultural management or grazing regimes) and shrublands (i.e., determining height, density, canopy cover) would improve the assignment of specific fuel models, with relevant effects in the fuel map quality and reliability for several fire-prone contexts (García et al, 2011;Marino et al, 2016;Huesca et al, 2019;Bright et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Assessing cross-boundary wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area

Salis,

Del Giudice,

Alcasena-Urdiroz

et al. 2023

Front. For. Glob. Change

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The growing threats posed by wildfires in Southern Europe are calling for the development of comprehensive and sound management and risk assessment strategies. In this work, we present the application of wildfire simulation modeling based on the minimum travel time (MTT) algorithm to assess fine-scale (100-m resolution) wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area (Sardinia, Corsica, Tuscany, Liguria and Provence-Alpes-Côte d’Azur), which cover about 72,000 km2 of land. We simulated thousands of wildfires considering the current landscape and characterized and measured fine-scale wildfire risk factors and profiles by taking into account historical fire regimes, fuels, winds and fuel moisture conditions associated with the occurrence of the largest wildfires (>100 ha) that affected the study area in the last 20 years. Individual fires were simulated at 100-m resolution, consistent with the input files. Modeled annual burn probability and ignition probability revealed that Sardinia was the Region most affected by wildfires. The wildfire simulation outputs were then combined with main land uses and building footprint locations to characterize wildfire transmission and exposure to communities, and were summarized for main vegetation types and Regions. This study presents a cross-boundary and standardized approach based on wildfire spread modeling to analyze and quantify wildfire risk profiles in Southern Europe. The stochastic wildfire modeling systems we implemented used harmonized sets of data for a vast, fire-prone Mediterranean area, where previous similar studies were conducted at coarser resolutions and covered lower extent of lands. The approach presented in this work can be used as a reference pillar for the development and implementation of a common wildfire risk monitoring, management, and governance plan in the study area. The methods and findings of this study can be replicated in neighboring Mediterranean and other regions threatened by wildfires.

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“…In terms of forest fuel mapping and the estimation of biomass, several RaDaR detection systems such as Airborne SAR (AIRSAR), GeoSAR, and Intermap Technology Corporation are available for spatial monitoring [33,34]. A recent study has shown that combining both passive and active sensors improves the results of fuel mapping in contrast to using only a single data source [35]. While multispectral passive sensors can be used to estimate species composition based on spectral response, LiDAR active sensors can extract the vertical forest structure, and therefore, the combined use of both types of sensors provides a novel and unique approach to mapping fuel types [7,[36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Comparing Forest Understory Fuel Classification in Portugal Using Discrete Airborne Laser Scanning Data and Satellite Multi-Source Remote Sensing Data

Mihajlovski,

Fernandes,

Pereira

et al. 2023

Fire

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Wildfires burn millions of hectares of forest worldwide every year, and this trend is expected to continue growing under current and future climate scenarios. As a result, accurate knowledge of fuel conditions and fuel type mapping are important for assessing fire hazards and predicting fire behavior. In this study, 499 plots in six different areas in Portugal were surveyed by ALS and multisource RS, and the data thus obtained were used to evaluate a nationwide fuel classification. Random Forest (RF) and CART models were used to evaluate fuel models based on ALS (5 and 10 pulse/m2), Sentinel Imagery (Multispectral Sentinel 2 (S2) and SAR (Synthetic Aperture RaDaR) data (C-band (Sentinel 1 (S1)) and Phased Array L-band data (PALSAR-2/ALOS-2 Satellite) metrics. The specific goals of the study were as follows: (1) to develop simple CART and RF models to classify the four main fuel types in Portugal in terms of horizontal and vertical structure based on field-acquired ALS data; (2) to analyze the effect of canopy cover on fuel type classification; (3) to investigate the use of different ALS pulse densities to classify the fuel types; (4) to map a more complex classification of fuel using a multi-sensor approach and the RF method. The results indicate that use of ALS metrics (only) was a powerful way of accurately classifying the main four fuel types, with OA = 0.68. In terms of canopy cover, the best results were estimated in sparse forest, with an OA = 0.84. The effect of ALS pulse density on fuel classification indicates that 10 points m−2 data yielded better results than 5 points m−2 data, with OA = 0.78 and 0.71, respectively. Finally, the multi-sensor approach with RF successfully classified 13 fuel models in Portugal, with moderate OA = 0.44. Fuel mapping studies could be improved by generating more homogenous fuel models (in terms of structure and composition), increasing the number of sample plots and also by increasing the representativeness of each fuel model.

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Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations

Cited by 10 publications

References 71 publications

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Assessing cross-boundary wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area

Comparing Forest Understory Fuel Classification in Portugal Using Discrete Airborne Laser Scanning Data and Satellite Multi-Source Remote Sensing Data

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