The overall goal of our proposed research is the continuing prototype development of a
framework for estimating local-scale carbon stocks and future carbon sequestration
potential for the State of Maryland using remote sensing and ecosystem modeling.
Specifically, we will address the following objectives:
(1) Improve our existing methodology for carbon stock estimation and uncertainty
and assess its efficacy across an expanded range of environmental and forest conditions;
(2) Provide local-scale estimates of carbon stocks and their uncertainties for the entire
state of Maryland representing Eastern U.S. forest types;
(3) Initialize and run a prognostic ecosystem model to estimate carbon stocks and
their changes, and to estimate carbon sequestration potential;
(4) Provide detailed validation of national biomass maps using FIA data and localscale biomass maps.(5) Demonstrate new data acquisition technology (single photon counting) for lowcost, rapid carbon assessments.
Our proposed work will greatly expand our coverage from 2 to 24 Maryland counties and
extends from the tidewater forests of the Chesapeake Bay through the coastal plains and
uplands, to the mountainous forests of Western Maryland and the Appalachians. This
gradient in land use, topographic, edaphic, and climatic conditions enables an appropriate
expansion of methods, models, data, and assessments consistent with the goals of the
second phase of CMS.
Our objectives build from our Phase 1 work and lead to a clear set of tasks for the
proposed effort. These are divided into seven categories of activities traceable to this
framework: (1) Remote sensing data acquisition and processing; (2) Field data collection
and analysis; (3) Algorithm development and image processing; (4) Statistical and
machine learning model development; (5) County biomass and uncertainty map
generation, and end-to-end error analysis; (6) Prognostic ecosystem modeling, and; (7)
national biomass map validations. An additional element of our proposed work is a
coordinated outreach effort to county and state agencies to inform and promote their
activities in CMS and includes a transfer of technology to the State of Vermont. To
promote this outreach we will also implement a new, web-based data visualization, query
and delivery system, Grid^Intel Online (GIO) that allows any user to call up lidar data,
associated imagery, biomass and error estimates for arbitrary map areas.
Deliverables for this project expand upon those from Phase 1. In addition to the
developed framework the project will produce the following CMS products: (1) tiled and
mosaicked canopy height and forest/non-forest maps at 2 m and 30 m resolution for
Maryland; (2) AGBM maps at 30 m resolution with associated uncertainty maps; (3) EDmodel based carbon and carbon-flux maps at 90 m resolution; (4) ED-model maps of
carbon sequestration potential; (5) web-based data visualization and query system; (6)
map of canopy structure and biomass derived from wall-to-wall single photon lidar for
Alleghany county; (7) assessment of main sources of error and proposed strategies for
reducing errors in future deployment of an operational CMS.
Contact Support to request an email list of project participants.
Project URL(s):
None provided.
Data Products:
Product Title: Prognostic ecosystem model (ED) based maps of carbon stocks and flux.
Time Period: Updates forthcoming
Description: - Develop a framework for estimating local-scale, high-resolution carbon stocks and future carbon sequestration potential using remote sensing and ecosystem modeling.
Intercomparison Efforts/Gaps: Comparisons between Lidar and FIA biomass maps and ED modeled biomass at local scale. Use of local scale maps to validate national scale maps (e.g. Kellndorfer, CMS Phase 1 National Map, Blackard (FIA map) )
Application Areas: - Land management ; - Forest inventory
Relevant Policies/Programs: FIA, Federal Land Policy and Management Act (FLPMA), Maryland Greenhouse Gas Emissions Reduction Act Plan, Maryland Climate Action Plan, Chesapeake Bay TMDL, Maryland Forest Preservation Act, Maryland No Net Forest Loss Act
Potential Users: Maryland Department of Natural Resources (DNR) Forest Service, DOE, EPA, private landowners, county GIS departments, national and global entities that want to validate top down products
Stakeholders:
Current Application Readiness Level: 8,9
Start Application Readiness Level: 5
Target Application Readiness Level: 8,9
Future Developments: Updates forthcoming
Limitations: Updates forthcoming
Date When Product Available: By 2017
Assigned Data Center: ORNL DAAC
Metadata URL(s):
Data Server URL(s):
Archived Data Citation:
Bounding Coordinates:
West Longitude:
0.00000
East Longitude:
0.00000
North Latitude:
0.00000
South Latitude:
0.00000
Product Title: Single photon Lidar canopy height and derived biomass maps.
Time Period: Updates forthcoming
Description: - Develop a framework for estimating local-scale, high-resolution carbon stocks and future carbon sequestration potential using remote sensing and ecosystem modeling.
Spatial Resolution: Canopy height at 1m and biomass at 30 m
Temporal Frequency:
Input Data Products: Single Photon Lidar (Sigma Space Corporation instrument called High Resolution Quantum Lidar System (HRQLS)): Area of Lidar data acquisition is 170,000 ha in a 12-hour-coverage.
Application Areas: - Land management ; - Forest inventory
Relevant Policies/Programs: FIA, Federal Land Policy and Management Act (FLPMA), Maryland Greenhouse Gas Emissions Reduction Act Plan, Maryland Climate Action Plan, Chesapeake Bay TMDL, Maryland Forest Preservation Act, Maryland No Net Forest Loss Act
Potential Users: Maryland Department of Natural Resources (DNR) Forest Service, DOE, EPA, private landowners, county GIS departments, national and global entities that want to validate top down products
Stakeholders:
Current Application Readiness Level: 8,9
Start Application Readiness Level: 5
Target Application Readiness Level: 8,9
Future Developments: Updates forthcoming
Limitations: Updates forthcoming
Date When Product Available:
Assigned Data Center: ORNL DAAC
Metadata URL(s):
Data Server URL(s):
Archived Data Citation:
Bounding Coordinates:
West Longitude:
0.00000
East Longitude:
0.00000
North Latitude:
0.00000
South Latitude:
0.00000
Product Title: Web-based data visualization and query system.
Description: - Provide an easy-to-access platform for obtaining data products.
Status: Planned
CMS Science Theme(s): Decision Support; Land Biomass
Keywords: Evaluation & User Interfaces
Spatial Extent:
Spatial Resolution:
Temporal Frequency:
Input Data Products:
Algorithm/Models Used:
Evaluation:
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories:
Application Areas: - Land management ; - Forest inventory
Relevant Policies/Programs: FIA, Federal Land Policy and Management Act (FLPMA), Maryland Greenhouse Gas Emissions Reduction Act Plan, Maryland Climate Action Plan, Chesapeake Bay TMDL, Maryland Forest Preservation Act, Maryland No Net Forest Loss Act
Potential Users: Maryland Department of Natural Resources (DNR) Forest Service, DOE, EPA, private landowners, county GIS departments, national and global entities that want to validate top down products
Stakeholders:
Current Application Readiness Level: 8,9
Start Application Readiness Level: 5
Target Application Readiness Level: 8,9
Future Developments: Updates forthcoming
Limitations: Updates forthcoming
Date When Product Available:
Assigned Data Center: ORNL DAAC
Metadata URL(s):
Data Server URL(s):
Archived Data Citation:
Bounding Coordinates:
West Longitude:
0.00000
East Longitude:
0.00000
North Latitude:
0.00000
South Latitude:
0.00000
Product Title: CMS: LiDAR-derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011
Start Date: 01/2011End Date: 12/2011 (Variable based on Lidar acquisition dates (2004-2012))
Description: This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements. The field-based estimates were related to LiDAR height and volume metrics through random forests regression models across three physiographic regions of Maryland.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords: Carbon Stocks (; terrestrial); ; Uncertainties & Standard Errors
Spatial Extent: Maryland (all 24 counties) and Addison County of Vermont
Spatial Resolution: 30 m
Temporal Frequency:
Input Data Products: Updates forthcoming
Algorithm/Models Used: Updates forthcoming
Evaluation: Biomass from field measurements and allometry. Comparisons between empirical and modeled biomass
Intercomparison Efforts/Gaps: Comparisons between Lidar and FIA biomass maps and ED modeled biomass at local scale. Use of local scale maps to validate national scale maps (e.g. Kellndorfer, CMS Phase 1 National Map, Blackard (FIA map) )
Uncertainty Estimates: - Pixel-level uncertainty estimates for local scale biomass map. ; - Improved methodology for estimating FIA biomass estimates in 'non-forest' lands and plot-pixel level comparisons with Lidar biomass maps.
Application Areas: - Land management ; - Forest inventory
Relevant Policies/Programs: FIA, Federal Land Policy and Management Act (FLPMA), Maryland Greenhouse Gas Emissions Reduction Act Plan, Maryland Climate Action Plan, Chesapeake Bay TMDL, Maryland Forest Preservation Act, Maryland No Net Forest Loss Act
Potential Users: Maryland Department of Natural Resources (DNR) Forest Service, DOE, EPA, private landowners, county GIS departments, national and global entities that want to validate top down products
Archived Data Citation: Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, K. Johnson, H. Tang, J.O. Dunne, and G.C. Hurtt. 2016. CMS: LiDAR-derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1320
Bounding Coordinates:
West Longitude:
-79.71000
East Longitude:
-74.82000
North Latitude:
39.95000
South Latitude:
37.69000
Product Title: LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Region, V2
Start Date: 01/2004End Date: 12/2014
Description: This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation. Field-based estimates were then related to LiDAR height and volume metrics through random forest regression models across three physiographic regions. Spatial errors were estimated at the pixel level using standard prediction intervals to assess the accuracy of the modeling approach. Estimates of biomass were further validated against the permanent network of FIA plots and compared with existing coarse resolution national biomass maps.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords:
Spatial Extent: Maryland, Pennsylvania, Delaware
Spatial Resolution: 30 and 90 m resolution
Temporal Frequency: Each county had a one time lidar sampling done between 2004 and 2014
Archived Data Citation: Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, K. Johnson, H. Tang, J.O. Dunne, and G.C. Hurtt. 2018. LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Region, V2. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1538
Bounding Coordinates:
West Longitude:
-81.23000
East Longitude:
-74.02000
North Latitude:
42.71000
South Latitude:
37.80000
Product Title: Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA
Start Date: 01/2011End Date: 12/2011 (Variable based on Lidar acquisition dates (2004-2012))
Description: This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables from airborne lidar, the National Agriculture Imagery Program (NAIP) and the National Land Cover Database (NLCD), and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.
Application Areas: - Land management ; - Forest inventory
Relevant Policies/Programs: FIA, Federal Land Policy and Management Act (FLPMA), Maryland Greenhouse Gas Emissions Reduction Act Plan, Maryland Climate Action Plan, Chesapeake Bay TMDL, Maryland Forest Preservation Act, Maryland No Net Forest Loss Act
Potential Users: Maryland Department of Natural Resources (DNR) Forest Service, DOE, EPA, private landowners, county GIS departments, national and global entities that want to validate top down products
Archived Data Citation: Hurtt, G.C., M. Zhao, R. Sahajpal, A. Armstrong, R. Birdsey, E. Campbell, K. Dolan, R.O. Dubayah, J.P. Fisk, S. Flanagan, C. Huang, W. Huang, K. Johnson, R. Lamb, L. Ma, R. Marks, D. O'Leary III, J. O'Neil-Dunne, A. Swatantran, and H. Tang. 2019. Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1660
Bounding Coordinates:
West Longitude:
-79.52000
East Longitude:
-75.05000
North Latitude:
39.81000
South Latitude:
37.83000
Product Title: LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015
Start Date: 01/2010End Date: 12/2015 (2010-2015)
Description: This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords:
Spatial Extent: Connecticut, New Hampshire, Massachusetts, Maine, Rhode Island, Vermont, U.S.
Spatial Resolution: 30 m
Temporal Frequency: : Annual and for the nominal year 2015
Archived Data Citation: Tang, H., L. Ma, A.J. Lister, J. O'Neil-Dunne, J. Lu, R. Lamb, R.O. Dubayah, and G.C. Hurtt. 2021. LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1854
Bounding Coordinates:
West Longitude:
-74.80000
East Longitude:
-66.36000
North Latitude:
46.76000
South Latitude:
39.96000
Product Title: CMS: Tree Canopy Cover at 0.5-meter resolution, Vermont, 2016
Start Date: 07/2016End Date: 09/2016 (2016-07-27 to 2016-09-13)
Description: This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.
Archived Data Citation: O'Neil-Dunne, J., E. Buford, S. Macfaden, and A. Royar. 2022. CMS: Tree Canopy Cover at 0.5-meter resolution, Vermont, 2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2072
Bounding Coordinates:
West Longitude:
-73.49000
East Longitude:
-71.46000
North Latitude:
45.02000
South Latitude:
42.70000
Publications:
Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O'Neil-Dunne, J., Dubayah, R., Hurtt, G. 2019. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters. 14(9), 095002. DOI: 10.1088/1748-9326/ab2917
O'Neil-Dunne J, MacFaden S, Royar A, Reis M., Dubayah R. and Swatantran A. (2014) An Object-Based Approach to Statewide Land Cover Mapping. Proceedings of the 2014 ASPRS Annual Conference. Louisville, KY http://www.asprs.org/a/publications/proceedings/Louisville2014/ONeilDunne.pdf
Tang, H., Ma, L., Lister, A., O'Neill-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R., Hurtt, G. 2021. High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA. Environmental Research Letters. 16(3), 035011. DOI: 10.1088/1748-9326/abd2ef
Tang, H., Swatantran, A., Barrett, T., DeCola, P., Dubayah, R. 2016. Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar. Remote Sensing. 8(9), 771. DOI: 10.3390/rs8090771
Finley, A. O., Banerjee, S., Zhou, Y., Cook, B. D., Babcock, C. 2017. Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment. 190, 149-161. DOI: 10.1016/j.rse.2016.12.004
Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. On nearest-neighbor Gaussian process models for massive spatial data. WIREs Computational Statistics. 8(5), 162-171. DOI: 10.1002/wics.1383
Salazar, E., Hammerling, D., Wang, X., Sanso, B., Finley, A. O., Mearns, L. O. 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change. 138(1-2), 55-69. DOI: 10.1007/s10584-016-1722-1
Swatantran, A., Tang, H., Barrett, T., DeCola, P., Dubayah, R. 2016. Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar. Scientific Reports. 6(1). DOI: 10.1038/srep28277
Huang, W., Swatantran, A., Johnson, K., Duncanson, L., Tang, H., O'Neil Dunne, J., Hurtt, G., Dubayah, R. 2015. Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA. Carbon Balance and Management. 10(1). DOI: 10.1186/s13021-015-0030-9
Johnson, K. D., Birdsey, R., Cole, J., Swatantran, A., O'Neil-Dunne, J., Dubayah, R., Lister, A. 2015. Integrating LIDAR and forest inventories to fill the trees outside forests data gap. Environmental Monitoring and Assessment. 187(10). DOI: 10.1007/s10661-015-4839-1
Johnson, K. D., Birdsey, R., Finley, A. O., Swantaran, A., Dubayah, R., Wayson, C., Riemann, R. 2014. Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance and Management. 9(1). DOI: 10.1186/1750-0680-9-3
Archived Data Citations:
Hurtt, G.C., M. Zhao, R. Sahajpal, A. Armstrong, R. Birdsey, E. Campbell, K. Dolan, R.O. Dubayah, J.P. Fisk, S. Flanagan, C. Huang, W. Huang, K. Johnson, R. Lamb, L. Ma, R. Marks, D. O'Leary III, J. O'Neil-Dunne, A. Swatantran, and H. Tang. 2019. Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1660
Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, K. Johnson, H. Tang, J.O. Dunne, and G.C. Hurtt. 2016. CMS: LiDAR-derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1320
Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, K. Johnson, H. Tang, J.O. Dunne, and G.C. Hurtt. 2018. LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Region, V2. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1538
Tang, H., L. Ma, A.J. Lister, J. O'Neil-Dunne, J. Lu, R. Lamb, R.O. Dubayah, and G.C. Hurtt. 2021. LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1854
O'Neil-Dunne, J., E. Buford, S. Macfaden, and A. Royar. 2022. CMS: Tree Canopy Cover at 0.5-meter resolution, Vermont, 2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2072
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
Fusing Next-generation Active Remote Sensing Data for Improved Forest Height and Structure Mapping
-- (Wenlu Qi, Ralph Dubayah)
[abstract]
Integrating Lidar Canopy Height and Landsat-based Forest Disturbance History with Ecosystem Demography Model for Carbon Change Estimation, A Case in Charles County, Maryland
-- (Maosheng Zhao, Chengquan Huang, George Hurtt, Ralph Dubayah, Justin Fisk, Anu Swatantran, Wenli Huang, Hao Tang)
[abstract]
Integrating LIDAR and Forest Inventories to Fill the Trees Outside Forests Data Gap
-- (Kristofer Johnson, Richard Birdsey, Jason Cole, Anuradha Swatantran, Jarlath O'Neil-Dunne, Ralph Dubayah, Andrew J. Lister)
[abstract]
A High-Resolution Carbon Monitoring System by Combining Ecosystem Demography Model with Remotely Sensed Land Cover and Canopy Height -- (Maosheng Zhao, George Hurtt, Ralph Dubayah, Justin Fisk, Amanda Armstrong, Anuradha Swatantran, Naiara Pinto) [abstract]
A High-Resolution Carbon Monitoring System by Combining Ecosystem Demography Model with Remotely Sensed Land Cover and Canopy Height -- (Maosheng Zhao, George Hurtt, Ralph Dubayah, Justin Fisk, Amanda Armstrong, Anuradha Swatantran, Naiara Pinto, Oliver Rourke, Larry Flanagan) [abstract]
A High-Resolution Carbon Monitoring System by Combining Ecosystem Demography Model with Remotely Sensed Land Cover and Canopy Height -- (Maosheng Zhao, George Hurtt, Ralph Dubayah, Justin Fisk, Amanda Armstrong, Anuradha Swatantran, Naiara Pinto, Oliver Rourke, Steve Flanagan) [abstract]
2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
High-Resolution Ecosystem Modeling as part of Robust Carbon Monitoring System
-- (Maosheng Zhao, George Hurtt, Ralph Dubayah, Justin Fisk, Amanda Armstrong, Anuradha Swatantran, Naira Pinto, Oliver Rourke, Steve Flanagan, Chengquan Huang)
[abstract]
Forest Structure and Biomass Mapping Using Time Series Landsat Observations, Small Footprint Lidar, and Field Inventory Data in North Carolina
-- (Chengquan Huang)
[abstract]