Latest algorithms and scientific developments for forest fire detection and monitoring using MSG/SEVIRI and MODIS sensors

Calle, A.; Casanova, J. L.; Moclán, C.; Romo, A.; Cisbani, E.; Costantini, Marco; Zavagli, Massimo; Greco, B.

doi:10.1109/rast.2005.1512547

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analyzing satellite imagery collected from multiple sensors and/or multiple platforms is a common technique that has been used to increase the sampling frequency of fire observations (Kelhä et al, 2003), to validate the performance of active fire detection algorithms Csiszar et al, 2006;Calle et al, 2008;Schroeder et al, 2008), and to cross-calibrate measurements such as fire radiative power (Wooster et al, 2003;Roberts et al, 2005;Roberts and Wooster, 2008). Rather than splicing or comparing active fire products, however, the ability to enhance fire detection and characterization through a symbiotic synthesis of polar orbiting and geostationary data (e.g., Calle et al, 2005) remains relatively unexplored. This paper directly addresses this topic by suggesting that frequency density (f-D) distributions of fire radiative power (FRP) can serve as thermal signatures of fire activity.…”

Section: Introductionmentioning

confidence: 99%

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

Freeborn

Wooster

Roberts

et al. 2009

Remote Sensing of Environment

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

Freeborn

Wooster

Roberts

et al. 2009

Remote Sensing of Environment

View full text Add to dashboard Cite

“…Wooster et al [19] demonstrated that MIR can be used to estimate the entire radiant energy from fire, while Calle et al [20] showed that a multi-temporal fire radiant energy calculation based on MIR radiance can be used to detect fires at sub-pixel level. Based on the above, the proposed algorithm depends only on the MIR channel to detect thermal anomalies.…”

Section: Mir Conditionmentioning

confidence: 99%

Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8

et al. 2016

View full text Add to dashboard Cite

Satellite remote sensing is regularly used for wildfire detection, fire severity mapping and burnt area mapping. Applications in the surveillance of wildfire using geostationary-based sensors have been limited by low spatial resolutions. With the launch in 2015 of the AHI (Advanced Himawari Imaginer) sensor on board Himawari-8, ten-minute interval imagery is available covering an entire earth hemisphere across East Asia and Australasia. Existing active fire detection algorithms depend on middle infrared (MIR) and thermal infrared (TIR) channels to detect fire. Even though sub-pixel fire detection algorithms can detect much smaller fires, the location of the fire within the AHI 2 × 2 km (400 ha) MIR/TIR pixel is unknown. This limits the application of AHI as a wildfire surveillance and tracking sensor. A new multi-spatial resolution approach is presented in this paper that utilizes the available medium resolution channels in AHI. The proposed algorithm is able to map firelines at a 500 m resolution. This is achieved using near infrared (NIR) (1 km) and RED (500 m) data to detect burnt area and smoke within the flagged MIR (2 km) pixel. Initial results based on three case studies carried out in Western Australia shows that the algorithm was able to continuously track fires during the day at 500 m resolution. The results also demonstrate the utility for wildfire management activities.

show abstract

“…The need for an operator to continually observe a region from a tower poses many labor difficulties and hazards that can be eliminated by autonomous systems [17]. An alternative to the human operated tower is satellite based detection described in [2], [23] and [24]. While satellite imagery may be ideal for large scale damage assessment of fire aftermath, the spatial resolutions limited by their ground sampling distance and the temporal resolutions limited by their passing coverage over a given area are not sufficient solutions to early detection [4].…”

Section: Early Forest Fires Detection and Current Systemsmentioning

confidence: 99%

Early Forest Fire Detection using Texture Analysis of Principal Components from Multispectral Video

Davenport¹

View full text Add to dashboard Cite

The aim of this study is to incorporate the spectral, temporal and spatial attributes of a smoke plume for Early Forest Fire Detection. Image processing techniques are used on multispectral (red, green, blue, mid-wave infrared, and long-wave infrared) video to segment and indentify the presence of a smoke plume within a scene. The temporal and spectral variance of a smoke plume is captured through Principal Component Analysis (PCA) where the Multispectral-Multitemporal PCA is performed on a sequence of video frames simultaneously. The presence of a plume existing in one of the higher order principal components is determined by the texture of its spatial content. The texture is characterized by statistical descriptors derived from the principal component"s joint probability density distribution of intensities occurring within a spatial relationship, known as a Gray Level Co-Occurrence Matrix (GLCM). Initial analysis is performed on selected frames where only a subset of time is considered. Once the parameters are chosen from the static analysis, the algorithms are executed on video through time to validate the method. The results show that a smoke plume is readily segmented via PCA. Based on the five spectral bands over 3 seconds sampled at 1 second, the plume exists in the 7 th principal component. Within these principal components, the smoke"s presence is best identified by the correlation texture descriptor. The smoke is very spatially correlated compared to the scene at large. Therefore a spike in the spatial correlation of the principal components is all that is needed to identify the start of the smoke plume.

show abstract

Latest algorithms and scientific developments for forest fire detection and monitoring using MSG/SEVIRI and MODIS sensors

Cited by 7 publications

References 11 publications

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8

Early Forest Fire Detection using Texture Analysis of Principal Components from Multispectral Video

Contact Info

Product

Resources

About