C
C. Dimiceli
Researcher at University of Maryland, College Park
Publications - 20
Citations - 4187
C. Dimiceli is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Land cover & Moderate-resolution imaging spectroradiometer. The author has an hindex of 14, co-authored 20 publications receiving 3836 citations.
Papers
More filters
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
Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data
Matthew C. Hansen,Stephen V. Stehman,Peter Potapov,Thomas R. Loveland,John R. Townshend,Ruth DeFries,Kyle Pittman,Belinda Arunarwati,Fred Stolle,Marc K. Steininger,Mark L. Carroll,C. Dimiceli +11 more
TL;DR: Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics.
Journal ArticleDOI
Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error
Joseph O. Sexton,Xiao-Peng Song,Min Feng,Praveen Noojipady,Anupam Anand,Chengquan Huang,Do-Hyung Kim,K. M. Collins,Saurabh Channan,C. Dimiceli,John R. Townshend +10 more
TL;DR: A global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.
Journal ArticleDOI
Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data
TL;DR: In this paper, a regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multitemporal metrics and a root mean square error (rmse) of 9.06% tree cover was found from the global training data set.
Journal ArticleDOI
Detection of land cover changes using MODIS 250 m data
Xiwu Zhan,R. Sohlberg,John R. Townshend,C. Dimiceli,Mark L. Carroll,J.C Eastman,Matthew C. Hansen,Ruth DeFries +7 more
TL;DR: In this article, the authors used decision trees for updating the look-up tables required by the Vegetative Cover Conversion (VCC) product and evaluated the relative performance of each of the five change detection methods used as VCC algorithms.