Zhiliang Zhu, Ph.D. (Former Employee)
Science and Products
Filter Total Items: 94
Land cover mapping of North and Central America—Global Land Cover 2000 Land cover mapping of North and Central America—Global Land Cover 2000
The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The...
Authors
Rasim Latifovic, Zhi-Liang Zhu
Exploration of satellite-measured vegetation seasonality for Landfire land cover Exploration of satellite-measured vegetation seasonality for Landfire land cover
The purpose of this study is to explore the use of satellite data and other sources of spatial data for large area classification in the western United States to support research on potential fire hazards. Extensive field information was made available to this project from two sources: Forest Inventory and Assessment (FIA) and Utah State University. Seasonal spectral patterns of...
Authors
James Vogelmann, Chengquan Huang, Brian L. Tolk, Gretchen G. Moisen, Zhiliang Zhu
Predictive modeling of forest cover type and tree canopy height in the central Rocky Mountains of Utah Predictive modeling of forest cover type and tree canopy height in the central Rocky Mountains of Utah
Maps of forest cover type and canopy height are needed for LANDFIRE, a multi-scale fire risk assessment project designed to generate intermediate-resolution data of vegetation and fire fuel characteristics for the U.S. Here we describe an evaluation study in the central Rockies of Utah, comparing tree-based methods, multivariate adaptive regression splines (MARS), and a hybrid method for...
Authors
Gretchen G. Moisen, T.S. Frescino, Chengquan Huang, James Vogelmann, Zhiliang Zhu
Deriving annual integrated NDVI greenness at 30 m spatial resolution Deriving annual integrated NDVI greenness at 30 m spatial resolution
Temporal greenness matrics have been found useful for characterizing vegetation phenology, and have been used to discriminate vegetation cover types and to estimate key vegetation attributes including percent cover and green biomass. So far, however, such matrics have been calculated only from coarse resolution satellite data. Intermediate spatial resolution satellites like Landsat...
Authors
Chengquan Huang, Brian L. Tolk, James Vogelmann, Michelle L. Knuppe, Zhiliang Zhu
Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping
FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for deciduous/evergreen/mixed...
Authors
Chengquan Huang, Limin Yang, Collin G. Homer, Michael Coan, Russell P. Rykhus, Zheng Zhang, Bruce Wylie, K. Hegge, Zhiliang Zhu, Andrew Lister, Michael Hoppus, Ronald Tymcio, Larry DeBlander, William Cooke, Ronald McRoberts, Daniel Wendt, Dale Weyermann
Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data
Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically...
Authors
George Z. Xian, Zhiliang Zhu, Michael Hoppus, Michael Fleming
Science and Products
Filter Total Items: 94
Land cover mapping of North and Central America—Global Land Cover 2000 Land cover mapping of North and Central America—Global Land Cover 2000
The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The...
Authors
Rasim Latifovic, Zhi-Liang Zhu
Exploration of satellite-measured vegetation seasonality for Landfire land cover Exploration of satellite-measured vegetation seasonality for Landfire land cover
The purpose of this study is to explore the use of satellite data and other sources of spatial data for large area classification in the western United States to support research on potential fire hazards. Extensive field information was made available to this project from two sources: Forest Inventory and Assessment (FIA) and Utah State University. Seasonal spectral patterns of...
Authors
James Vogelmann, Chengquan Huang, Brian L. Tolk, Gretchen G. Moisen, Zhiliang Zhu
Predictive modeling of forest cover type and tree canopy height in the central Rocky Mountains of Utah Predictive modeling of forest cover type and tree canopy height in the central Rocky Mountains of Utah
Maps of forest cover type and canopy height are needed for LANDFIRE, a multi-scale fire risk assessment project designed to generate intermediate-resolution data of vegetation and fire fuel characteristics for the U.S. Here we describe an evaluation study in the central Rockies of Utah, comparing tree-based methods, multivariate adaptive regression splines (MARS), and a hybrid method for...
Authors
Gretchen G. Moisen, T.S. Frescino, Chengquan Huang, James Vogelmann, Zhiliang Zhu
Deriving annual integrated NDVI greenness at 30 m spatial resolution Deriving annual integrated NDVI greenness at 30 m spatial resolution
Temporal greenness matrics have been found useful for characterizing vegetation phenology, and have been used to discriminate vegetation cover types and to estimate key vegetation attributes including percent cover and green biomass. So far, however, such matrics have been calculated only from coarse resolution satellite data. Intermediate spatial resolution satellites like Landsat...
Authors
Chengquan Huang, Brian L. Tolk, James Vogelmann, Michelle L. Knuppe, Zhiliang Zhu
Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping
FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for deciduous/evergreen/mixed...
Authors
Chengquan Huang, Limin Yang, Collin G. Homer, Michael Coan, Russell P. Rykhus, Zheng Zhang, Bruce Wylie, K. Hegge, Zhiliang Zhu, Andrew Lister, Michael Hoppus, Ronald Tymcio, Larry DeBlander, William Cooke, Ronald McRoberts, Daniel Wendt, Dale Weyermann
Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data
Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically...
Authors
George Z. Xian, Zhiliang Zhu, Michael Hoppus, Michael Fleming