Journal ArticleDOI
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Mark A. Friedl,Damien Sulla-Menashe,Bin Tan,Annemarie Schneider,Navin Ramankutty,Adam Sibley,Xiaoman Huang +6 more
TLDR
The datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4, are described, with a four-fold increase in spatial resolution and changes in the input data and classification algorithm.About:
This article is published in Remote Sensing of Environment.The article was published on 2010-01-15. It has received 2713 citations till now. The article focuses on the topics: Land cover & Ancillary data.read more
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An assessment of the global impact of 21st century land use change on soil erosion.
Pasquale Borrelli,David A. Robinson,Larissa R. Fleischer,Emanuele Lugato,Cristiano Ballabio,Christine Alewell,Katrin Meusburger,Sirio Modugno,Brigitta Schütt,Vito Ferro,Vincenzo Bagarello,Kristof Van Oost,Luca Montanarella,Panos Panagos +13 more
TL;DR: An unprecedentedly high resolution global potential soil erosion model is presented, using a combination of remote sensing, GIS modelling and census data, that indicates a potential overall increase in global soil erosion driven by cropland expansion.
Journal ArticleDOI
The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning
Christine Wiedinmyer,S. K. Akagi,Robert J. Yokelson,Louisa K. Emmons,J. A. Al-Saadi,John J. Orlando,Amber J. Soja +6 more
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
Global land cover mapping at 30 m resolution: A POK-based operational approach
Jun Chen,Jin Chen,AnPing Liao,Xin Cao,LiJun Chen,Xuehong Chen,Chaoying He,Gang Han,Shu Peng,Miao Lu,WeiWei Zhang,Xiaohua Tong,Jon P. Mills +12 more
TL;DR: In this article, an approach based on the integration of pixel-and object-based methods with knowledge (POK-based) has been developed to handle the classification process of 10 land cover types, i.e., firstly each class identified in a prioritized sequence and then results are merged together.
Journal ArticleDOI
Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Peng Gong,Jie Wang,Le Yu,Yongchao Zhao,Yuanyuan Zhao,Lu Liang,Zhenguo Niu,Xiaomeng Huang,Haohuan Fu,Shuang Liu,Congcong Li,Xueyan Li,Wei Fu,Caixia Liu,Yue Xu,Xiaoyi Wang,Qu Cheng,Luanyun Hu,Wenbo Yao,Han Zhang,Peng Zhu,Ziying Zhao,Haiying Zhang,Yaomin Zheng,Luyan Ji,Yawen Zhang,Han Chen,An Yan,JianHong Guo,Liang Yu,Lei Wang,Xiaojun Liu,Tingting Shi,Menghua Zhu,Yanlei Chen,Guangwen Yang,Ping Tang,Bing Xu,Chandra Giri,Nicholas Clinton,Zhiliang Zhu,Jin Chen,Jun Chen +42 more
TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.
Journal ArticleDOI
Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4)
TL;DR: The Global Fire Emissions Database (GFED4) as discussed by the authors provides global monthly burned area at 0.25°m spatial resolution from mid-1995 through the present and daily burned area for the time series extending back to August 2000.
References
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Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
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Global Consequences of Land Use
Jonathan A. Foley,Ruth DeFries,Gregory P. Asner,Carol C. Barford,Gordon B. Bonan,Stephen R. Carpenter,F. Stuart Chapin,Michael T. Coe,Michael T. Coe,Gretchen C. Daily,Holly K. Gibbs,Joseph H. Helkowski,Tracey Holloway,Erica A. Howard,Christopher J. Kucharik,Chad Monfreda,Jonathan A. Patz,I. Colin Prentice,Navin Ramankutty,Peter K. Snyder +19 more
TL;DR: Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity.
Journal ArticleDOI
Human Domination of Earth's Ecosystems
TL;DR: Human alteration of Earth is substantial and growing as discussed by the authors, between one-third and one-half of the land surface has been transformed by human action; the carbon dioxide concentration in the atmosphere has increased by nearly 30 percent since the beginning of the Industrial Revolution; more atmospheric nitrogen is fixed by humanity than by all natural terrestrial sources combined; more than half of all accessible surface fresh water is put to use by humanity; and about one-quarter of the bird species on Earth have been driven to extinction.
Journal ArticleDOI
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.