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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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Black carbon aerosols in the ambient air of Gangotri Glacier valley of north-western Himalaya in India

TL;DR: For the first time, measurements on ambient EBC mass concentration were made at a high altitude site Chirbasa (3600 m, amsl), near Gangotri Glacier in the Indian Himalaya, during the year 2016.
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Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon.

TL;DR: The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas.
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Global fire season severity analysis and forecasting

TL;DR: In this article, a seasonal fire prediction methodology based on time series forecasting methods is proposed, which consists of dividing the studied area into grid cells and extracting time series of fire counts to fit the forecasting models to estimate the fire season severity (FSS) from each cell, here defined as the sum of the fire counts detected in a season.
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A First Case Study of CCN Concentrations from Spaceborne Lidar Observations

TL;DR: The first cloud condensation nuclei concentration profiles derived from measurements with the Cloud-Aerosol Lidar with Orthogonal Polarization aboard the CALIOP aboard the CALIPSO satellite Observations are presented.
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Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

TL;DR: In this paper, the authors summarize recent trends in the forest fire events prediction, detection, spread rate, and mapping of the burned areas, aiming to summarize the recent trends of forest fire occurrence, its spread, and its impact on the environment.
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.
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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.
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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.
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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.
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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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