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Open AccessJournal ArticleDOI

The collection 6 MODIS active fire detection algorithm and fire products.

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TLDR
Improvements made to the fire detection algorithm and swath-level product that were implemented as part of the Collection 6 land-product reprocessing, which commenced in May 2015, indicated targeted improvements in the performance of the collection 6 activeFire detection algorithm compared to Collection 5, with reduced omission errors over large fires, and reduced false alarm rates in tropical ecosystems.
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This article is published in Remote Sensing of Environment.The article was published on 2016-06-01 and is currently open access. It has received 810 citations till now. The article focuses on the topics: Fire detection & Moderate-resolution imaging spectroradiometer.

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Citations
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Journal ArticleDOI

Transport of biomass burning products from Siberian wildfires into the Arctic

TL;DR: In this paper , a study of the long-range transport of biomass burning products from Siberian wildfires into the Arctic atmosphere during the period of 2000-2019 is presented, which is characterized by a statistically significant linear trend of 1.7 ± 1.0% / year.
Proceedings ArticleDOI

Graph-based semi-supervised classification for similar wildfire dynamics

TL;DR: In this paper , the authors proposed a framework combining complex networks and machine learning to classify similar wildfire regions, and extracted graph-based features to characterize spatiotemporal fire regimes from different tropical/temperate/boreal forests around the globe.
Journal ArticleDOI

Location-Aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger Forecasting

TL;DR: Shams et al. as discussed by the authors proposed a 2-D/3-D two-branch convolutional neural network with a location-aware adaptive normalization (LOAN) layer, which modulates the dynamic features conditional on their geographical locations.
Journal ArticleDOI

Wildfire Incidence throughout the Brazilian Pantanal Is Driven by Local Climate Rather Than Bovine Stocking Density

TL;DR: In this article , the authors examined the covariation between the presence and density of cattle and the incidence of fires across the Brazilian Pantanal and found that fire foci are related to climatic variables, such as lower annual precipitation and higher annual drought indices (SPI) rather than to cattle stocking rates.
References
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Journal ArticleDOI

An Enhanced Contextual Fire Detection Algorithm for MODIS

TL;DR: An improved replacement detection algorithm is presented that offers increased sensitivity to smaller, cooler fires as well as a significantly lower false alarm rate.
Journal ArticleDOI

The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning

TL;DR: The Fire Inventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning as discussed by the authors.
Journal ArticleDOI

An overview of MODIS Land data processing and product status

TL;DR: In this article, the authors provide a summary of the MODIS instrument performance and status, the data production system, the products, their status and availability for land studies, and a partnership between Science Team members and MODIS Science Data Support Team is producing data sets of unprecedented volume and number for the land research and applications.
Journal ArticleDOI

Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm

TL;DR: The first results of the MODIS vegetation continuous field algorithm's global percent tree cover are presented in this article, where a supervised regression tree algorithm is used to estimate tree cover per 500m MODIS pixel.
Journal ArticleDOI

Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power

TL;DR: The Global Fire Assimilation System (GFASv1.0) as mentioned in this paper calculates biomass burning emissions by assimilating Fire Radiative Power (FRP) observations from the MODIS instruments onboard the Terra and Aqua satellites.
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