气候变化领域集成服务门户(CCPortal): Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests

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DOI	10.1016/j.rse.2020.112025
	Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests
	Montorio R.; Pérez-Cabello F.; Borini Alves D.; García-Martín A.
发表日期	2020
ISSN	00344257
卷号	249
英文摘要	Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed. © 2020 Elsevier Inc.
英文关键词	Fire severity; Landsat-8; Linear spectral mixing; Machine learning; Post-fire ground covers; Sentinel-2
语种	英语
scopus关键词	Database systems; Decision trees; Ecosystems; Fires; Machine learning; Mapping; Maps; Predictive analytics; Random forests; Regression analysis; Remote sensing; Vegetation; Classification maps; Management strategies; Non-photosynthetic vegetation; Predictive modeling; Remote sensing approaches; Short wave infrared; Simulation approach; Variable importances; Classification (of information); algorithm; cartography; database; ecosystem response; forest management; machine learning; model validation; multispectral image; remote sensing; satellite imagery; spectral analysis; wildfire; Spain
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179161
作者单位	Department of Geography and Spatial Management, University of Zaragoza, C/Pedro Cerbuna 12, Zaragoza, 50009, Spain; GEOFOREST-IUCA research group, Environmental Sciences Institute (IUCA), University of Zaragoza, C/Pedro Cerbuna 12, Zaragoza, 50009, Spain; Lab of Vegetation Ecology, Instituto de Biociências, Universidade Estadual Paulista (UNESP), Avenida 24-A 1515, Rio Claro, 13506-900, Brazil; Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. Huesca s/n, Zaragoza, 50090, Spain
推荐引用方式 GB/T 7714	Montorio R.,Pérez-Cabello F.,Borini Alves D.,et al. Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests[J],2020,249.
APA	Montorio R.,Pérez-Cabello F.,Borini Alves D.,&García-Martín A..(2020).Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests.Remote Sensing of Environment,249.
MLA	Montorio R.,et al."Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests".Remote Sensing of Environment 249(2020).