D
D.K. McIver
Researcher at Boston University
Publications - 13
Citations - 2827
D.K. McIver is an academic researcher from Boston University. The author has contributed to research in topics: Land cover & Statistical classification. The author has an hindex of 7, co-authored 13 publications receiving 2595 citations. Previous affiliations of D.K. McIver include Oracle Corporation.
Papers
More filters
Journal ArticleDOI
Global land cover mapping from MODIS: algorithms and early results
Mark A. Friedl,D.K. McIver,J.C.F. Hodges,Xiaoyang Zhang,D. Muchoney,Alan H. Strahler,Curtis E. Woodcock,Sucharita Gopal,Annemarie Schneider,Amanda Cooper,Alessandro Baccini,Feng Gao,Crystal B. Schaaf +12 more
TL;DR: This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP, and a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data.
Journal ArticleDOI
Using prior probabilities in decision-tree classification of remotely sensed data
D.K. McIver,Mark A. Friedl +1 more
TL;DR: A method for incorporating prior probabilities in remote-sensing-based land cover classification using a supervised decision-tree classification algorithm that allows robust probabilities of class membership to be estimated from nonparametric supervised classification algorithms using a technique known as boosting.
Journal ArticleDOI
Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods
D.K. McIver,Mark A. Friedl +1 more
TL;DR: The authors present a method to estimate pixel-scale land cover classification confidence using nonparametric machine learning methods based on recent theoretical developments from the domains of statistics and machine learning that explain the machine learning technique known as "boosting" as being equivalent to additive logistic regression.
Journal ArticleDOI
Characterization of North American land cover from NOAA‐AVHRR data using the EOS MODIS Land Cover Classification Algorithm
TL;DR: In this article, the authors describe the compilation and assessment of a new map of North American land cover produced through the application of advanced pattern recognition techniques to multitemporal satellite data.
Book ChapterDOI
An Overview of Uncertainty in Optical Remotely Sensed Data for Ecological Applications
TL;DR: In this paper, the authors focus on optical remote sensing technologies, including airborne radar, video imaging systems, and satellite instruments with high spatial and spectral resolution, which provide detailed information regarding topography and vegetation structure in three dimensions.