An Enhanced Contextual Fire Detection Algorithm for MODIS

By Louis Giglioa, Jacques Descloitresa, Christopher O. Justicec, Yoram J. Kaufmanb. 2003

1,843 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/f2376acb71a89f5fe4f2c4dbb46b50f6524a6442

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. David P. Roy
2. Luigi Boschetti
3. Svetlana Turubanova
4. Matthew C. Hansen
5. C. Justice
6. Brian R Zutta
7. Alexander Krylov
8. Peter V. Potapov

In Global and Planetary Change

1. Matthew C. Hansen
2. David P. Roy
3. Peter V. Potapov
4. Alexandra Tyukavina
5. Svetlana Turubanova
6. Luigi Boschetti
7. C. Justice
8. Yoram J. Kaufman

In Safety, Risk, Reliability and Quality

1. C. Justice
2. David P. Roy

Citations in the corpus

• The collection 6 MODIS active fire detection algorithm and fire products (2016, background): “More detailed descriptions can be found in Giglio et al. (2003).”
• Satellite Image Analysis for Disaster and Crisis-Management Support (2007, methodology): “Global gazetteers such as the GEOnet Names Server [17] can be used for labeling settlements and physiographical features like rivers and mountains.”
• Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data (2005, methodology): “Satellite data have been used to monitor fire globally for more than two decades using active fire detection algorithms developed for different sensors to take advantage of the elevated radiance signal of hot fires (Arino & Rosaz, 1999; Dozier, 1981; Elvidge et al., 2001; Giglio et al., 2003; Matson”
• Humid tropical forest disturbance alerts using Landsat data (2016, background): “In some cases, such as with the official MODerate resolution Imaging Spectroradiometer (MODIS) active fire algorithm (Giglio et al 2003), national agencies are able to implement algorithms directly for local decision support activities.”
• Remote sensing of fire severity: assessing the performance of the normalized burn ratio (2006, methodology): “MODIS active fire algorithm are smaller than the 1 km MODIS observation [19] and so changes in reflectance due to fire effects are likely to be smaller.”
• Fire Information for Resource Management System: Archiving and Distributing MODIS Active Fire Data (2009, background): “active fire detection algorithm that uses a multispectral contextual approach to exploit the strong emission of midinfrared radiation from fires allowing subpixel fire detection [21]–[23].”
• A method to derive smoke emission rates from MODIS fire radiative energy measurements (2005, background): “MODIS detects fires at a spatial resolution of 1 km at nadir [15], [30], [32].”