Elena Aragoneses , Mariano García , Hao Tang , Emilio Chuvieco
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Here, we use a multi-sensor approach integrating satellite Light Detection and Ranging (LiDAR) observations from the Global Ecosystems Dynamics Investigation (GEDI) sensor, with other remote sensing imagery and biophysical variables to provide spatially-explicit estimates of two key descriptors of crown fire behaviour – canopy fuel load (CFL) and canopy bulk density (CBD) – over the entire European territory at 1 km<sup>2</sup> grid resolution.</div><div>GEDI L1B and L2A level footprints were used to estimate Leaf Area Density, from which CFL and CBD were subsequently derived. The approach was assessed by applying it to regions of the United States, where bioclimatic conditions are similar to those in Europe, and for which LANDFIRE CBD maps are available (CBD <em>r</em> = 0.6–0.86 and RMSE = 33.1–59.6 %). We then extrapolated the estimates to European areas not covered by GEDI using machine learning models with multispectral (Landsat 8) and radar (Phased Array L-band Synthetic Aperture Radar sensor – PALSAR) imagery, and biophysical variables (CFL <em>r</em> = 0.85 and RMSE = 12.98 %; CBD <em>r</em> = 0.75 and RMSE = 21 %). Pixel-level uncertainty for the spatial extrapolation was also estimated.</div><div>The new wall-to-wall maps of crown fuel properties (<span><span>https://doi.org/10.21950/Z6BWQG</span><svg><path></path></svg></span>) provide new insights into the potential for fire risk prevention in Europe, which together with climate and socio-economic models, would greatly improve the prioritisation of management areas and the targeting of mitigation measures in strategic areas to reduce wildfire risk.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114578"},"PeriodicalIF":11.4000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-sensor approach allows confident mapping of forest canopy fuel load and canopy bulk density to assess wildfire risk at the European scale\",\"authors\":\"Elena Aragoneses , Mariano García , Hao Tang , Emilio Chuvieco\",\"doi\":\"10.1016/j.rse.2024.114578\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the increasing influence of climate and socio-economic changes, crown fires are becoming the main concern of fire managers and civil protection authorities in Europe. Evaluating and mitigating the negative impacts of these fires requires better tools to identify high-risk areas. Prevention and management strategies for crown fires require accurate and cost-effective tools that can parameterise fuel properties. Here, we use a multi-sensor approach integrating satellite Light Detection and Ranging (LiDAR) observations from the Global Ecosystems Dynamics Investigation (GEDI) sensor, with other remote sensing imagery and biophysical variables to provide spatially-explicit estimates of two key descriptors of crown fire behaviour – canopy fuel load (CFL) and canopy bulk density (CBD) – over the entire European territory at 1 km<sup>2</sup> grid resolution.</div><div>GEDI L1B and L2A level footprints were used to estimate Leaf Area Density, from which CFL and CBD were subsequently derived. The approach was assessed by applying it to regions of the United States, where bioclimatic conditions are similar to those in Europe, and for which LANDFIRE CBD maps are available (CBD <em>r</em> = 0.6–0.86 and RMSE = 33.1–59.6 %). We then extrapolated the estimates to European areas not covered by GEDI using machine learning models with multispectral (Landsat 8) and radar (Phased Array L-band Synthetic Aperture Radar sensor – PALSAR) imagery, and biophysical variables (CFL <em>r</em> = 0.85 and RMSE = 12.98 %; CBD <em>r</em> = 0.75 and RMSE = 21 %). Pixel-level uncertainty for the spatial extrapolation was also estimated.</div><div>The new wall-to-wall maps of crown fuel properties (<span><span>https://doi.org/10.21950/Z6BWQG</span><svg><path></path></svg></span>) provide new insights into the potential for fire risk prevention in Europe, which together with climate and socio-economic models, would greatly improve the prioritisation of management areas and the targeting of mitigation measures in strategic areas to reduce wildfire risk.</div></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":\"318 \",\"pages\":\"Article 114578\"},\"PeriodicalIF\":11.4000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425724006047\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724006047","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
随着气候和社会经济变化的影响越来越大,树冠火灾正成为欧洲消防管理人员和民防当局关注的主要问题。评估和减轻这些火灾的负面影响需要更好的工具来识别高风险地区。树冠火灾的预防和管理策略需要精确且具有成本效益的工具,这些工具可以参数化燃料特性。在这里,我们使用了一种多传感器方法,将来自全球生态系统动力学调查(GEDI)传感器的卫星光探测和测距(LiDAR)观测与其他遥感图像和生物物理变量相结合,以1平方公里的网格分辨率为整个欧洲领土提供树冠火灾行为的两个关键描述符-冠层燃料负荷(CFL)和冠层容重(CBD)的空间显式估计。利用GEDI L1B和L2A水平足迹估算叶面积密度,进而得到CFL和CBD。通过将该方法应用于生物气候条件与欧洲相似的美国地区,并且可以获得LANDFIRE CBD地图(CBD r = 0.6-0.86, RMSE = 33.1 - 59.6%),对其进行了评估。然后,我们使用多光谱(Landsat 8)和雷达(相控阵l波段合成孔径雷达传感器- PALSAR)图像的机器学习模型,以及生物物理变量(CFL r = 0.85, RMSE = 12.98%),将估计外推到GEDI未覆盖的欧洲地区;CBD r = 0.75, RMSE = 21%)。对空间外推的像素级不确定性也进行了估计。皇冠燃料属性的新全面地图(https://doi.org/10.21950/Z6BWQG)为欧洲预防火灾风险的潜力提供了新的见解,再加上气候和社会经济模型,将大大改善管理领域的优先次序,并在战略领域确定减轻措施的目标,以减少野火风险。
A multi-sensor approach allows confident mapping of forest canopy fuel load and canopy bulk density to assess wildfire risk at the European scale
With the increasing influence of climate and socio-economic changes, crown fires are becoming the main concern of fire managers and civil protection authorities in Europe. Evaluating and mitigating the negative impacts of these fires requires better tools to identify high-risk areas. Prevention and management strategies for crown fires require accurate and cost-effective tools that can parameterise fuel properties. Here, we use a multi-sensor approach integrating satellite Light Detection and Ranging (LiDAR) observations from the Global Ecosystems Dynamics Investigation (GEDI) sensor, with other remote sensing imagery and biophysical variables to provide spatially-explicit estimates of two key descriptors of crown fire behaviour – canopy fuel load (CFL) and canopy bulk density (CBD) – over the entire European territory at 1 km2 grid resolution.
GEDI L1B and L2A level footprints were used to estimate Leaf Area Density, from which CFL and CBD were subsequently derived. The approach was assessed by applying it to regions of the United States, where bioclimatic conditions are similar to those in Europe, and for which LANDFIRE CBD maps are available (CBD r = 0.6–0.86 and RMSE = 33.1–59.6 %). We then extrapolated the estimates to European areas not covered by GEDI using machine learning models with multispectral (Landsat 8) and radar (Phased Array L-band Synthetic Aperture Radar sensor – PALSAR) imagery, and biophysical variables (CFL r = 0.85 and RMSE = 12.98 %; CBD r = 0.75 and RMSE = 21 %). Pixel-level uncertainty for the spatial extrapolation was also estimated.
The new wall-to-wall maps of crown fuel properties (https://doi.org/10.21950/Z6BWQG) provide new insights into the potential for fire risk prevention in Europe, which together with climate and socio-economic models, would greatly improve the prioritisation of management areas and the targeting of mitigation measures in strategic areas to reduce wildfire risk.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.