An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

编号 030033201

推送时间 20220228

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

来源期刊 REMOTE SENSING

期 第332期

发表时间 20220304

关键词 forests;  fires;  fuel map;  hyperspectral;  LUCAS;  machine learning;  PRISMA;  pseudolabels;  SVM classifier;  sample generation; 

摘要 Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input.

服务人员 付贺龙

服务院士 唐守正

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