Journal ArticleDOI
Mapping burned areas using dense time-series of Landsat data
Todd J. Hawbaker,Melanie K. Vanderhoof,Yen Ju Beal,Joshua D. Takacs,Gail L. Schmidt,Jeff T. Falgout,Brad Williams,Nicole M. Fairaux,M. K. Caldwell,Joshua J. Picotte,Stephen M. Howard,Susan Stitt,John L. Dwyer +12 more
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This paper used gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors.About:
This article is published in Remote Sensing of Environment.The article was published on 2017-09-01. It has received 155 citations till now.read more
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Rapid growth of the US wildland-urban interface raises wildfire risk
Volker C. Radeloff,David P. Helmers,H. Anu Kramer,Miranda H. Mockrin,Patricia M. Alexandre,Avi Bar-Massada,Van Butsic,Todd J. Hawbaker,Sebastián Martinuzzi,Alexandra D. Syphard,Susan I. Stewart +10 more
TL;DR: The wildland-urban interface (WUI) is the area where houses and wildland vegetation meet or intermingle, and where wildfire problems are most pronounced, and grew rapidly from 1990 to 2010, making it the fastest-growing land use type in the conterminous United States.
Journal ArticleDOI
Current status of Landsat program, science, and applications
Michael A. Wulder,Thomas R. Loveland,David P. Roy,Christopher J. Crawford,Jeffrey G. Masek,Curtis E. Woodcock,Richard G. Allen,Martha C. Anderson,Alan Belward,Warren B. Cohen,John L. Dwyer,Angela Erb,Feng Gao,Patrick Griffiths,Dennis L. Helder,Txomin Hermosilla,Txomin Hermosilla,James D. Hipple,Patrick Hostert,M. Joseph Hughes,Justin L. Huntington,David M. Johnson,Robert E. Kennedy,Ayse Kilic,Zhan Li,Leo Lymburner,Joel McCorkel,Nima Pahlevan,Ted Scambos,Crystal B. Schaaf,John R. Schott,Yongwei Sheng,James C. Storey,Eric Vermote,James E. Vogelmann,Joanne C. White,Randolph H. Wynne,Zhe Zhu,Zhe Zhu +38 more
TL;DR: The programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs are described and the key trends in Landsat science are presented.
Journal ArticleDOI
Historical background and current developments for mapping burned area from satellite Earth observation
Emilio Chuvieco,Florent Mouillot,Guido R. van der Werf,Jesús San Miguel,Mihai Tanasse,Nikos Koutsias,Mariano García,Marta Yebra,Marc Padilla,Ioannis Z. Gitas,Angelika Heil,Todd J. Hawbaker,Louis Giglio +12 more
TL;DR: In this article, the authors explore the physical basis to detect burned areas from satellite observations, describes the historical trends of using satellite sensors to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales).
Journal ArticleDOI
Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa
TL;DR: In this paper, a locally adapted multitemporal two-phase burned area (BA) algorithm has been developed using as inputs Sentinel-2 MSI reflectance measurements in the short and near infrared wavebands plus the active fires detected by Terra and Aqua MODIS sensors.
Journal ArticleDOI
A review of machine learning applications in wildfire science and management
Piyush Jain,Piyush Jain,Sean C. P. Coogan,Sriram Subramanian,Mark Crowley,Steve W. Taylor,Mike D. Flannigan +6 more
TL;DR: A scoping review of ML in wildfire science and management, identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms.
References
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
Classification and regression trees
TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.