Remote Sensing of Soil and Vegetation Moisture from Space for Monitoring Drought and Forest Fire Events

2012 ◽  
pp. 507-536
Author(s):  
Lingli Wang ◽  
John Qu ◽  
Xianjun Hao
Keyword(s):  
Remote Sensing ◽  
Forest Fire

The new generation of remote sensing services for operational forest fire-fighting within GMES

2006 ◽  
Author(s):  
Fernando Valcarce ◽  
Jesús Gonzalo ◽  
Joaquín Ramírez ◽  
Abel Calle Montes ◽  
Emilio Chuvieco
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Fire Fighting ◽  
New Generation

scholarly journals Evaluating forest fire probability under the influence of human activity based on remote sensing and GIS

2020 ◽  
Author(s):  
Wei Yang ◽  
Xiaoli Jiang
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Human Life ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Operating Characteristics ◽  
Distance Analysis ◽  
Comprehensive Method ◽  
Proposed Model ◽  
Probability Prediction

Abstract. Fires are an important factor involved in the disturbance of forest ecosystems, causing resource damage and the loss of human life. Evaluating forest fire probability can provide an effective method to minimize these losses. In this study, a comprehensive method that integrates remote-sensing data and geographic information systems is proposed to evaluate forest fire probability. In our analysis, we selected four probability indicators: drought index, vegetation condition, topographical factors and anthropogenic factors. To evaluate the influence of anthropogenic factors on fire probability, a distance analysis from fire locations to settlements or roads was conducted to see which distance was associated with a higher probability. The forest fire probability index (FFPI) was calculated to assess the probability level in Heilongjiang Province, China. According to the FFPI, five classes were identified: very low, low, moderate, high, and very high. A receiver operating characteristics (ROC) curve was used as the validation method, and the results of the ROC analysis showed that the proposed model performed well in terms of forest fire probability prediction. The results of this study provide a technical framework for the Department of Forest Resource Management to predict occurrence of fires.

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