NLCD and MODIS Landuse Processing Tools and Projection Issues in Modeling Limei Ran and Alison Eyth Center for Environmental Modeling for Policy Development.

Slides:

Advertisements

Similar presentations

Limei Ran 1, Uma Shankar 1, Ellen Cooter 2, Aijun Xiu 1, Neil Davis 1 1 Center for Environmental Modeling for Policy Development, Institute for the Environment,

Advertisements

The MODIS Land Cover Product MODIS Land Cover Team Boston University GLC 2000 – “FIRST RESULTS” WORKSHOP JRC – Ispra, March 2002.

Spatial Resolution in Digital Images Digital Images –“Size” can change easily (zoom, subset, mosaic) –Pixel size is assumed not to change within an image.

Has EO found its customers? Global Vegetation Monitoring Unit GLC2000 GLOBAL LEGEND GLC 2000 – “FIRST RESULTS” WORKSHOP JRC – Ispra, March 2002.

GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) LAND COVER MAP OF.

Production of land cover map of Asia, Central Asia, and Middle East with emphasis of the development of ground truth database Ryutaro Tateishi, Hiroshi.

Xiangming Xiao Department of Botany and Microbiology, College of Arts and Sciences Center for Spatial Analysis, College of Atmospheric.

Utility of National Spatial Data for Conservation Design Projects Steve Williams Biodiversity and Spatial Information Center North Carolina State University.

Tom Craven Matt Simon Anne Trainor.  Construct a RHESSys ecohydrologic model for Sandhills Region.

Snapshot of Northeast Ohio Environment. Overview Topography Watersheds Parks and Conservation Areas Land Cover (developed areas, forests, wetlands, agriculture)

DETERMINING LAND DEVELOPMENT EFFECT ON SURFACE RUNOFF USING GIS.

NUTRIENT RUNOFF EFFECTS ON JORDAN LAKE Brianna Young, Jennifer Jackson and Emily Nurminen.

Sistema de Monitoreo de la Cobertura del Suelo de América del Norte.

C-CAP The Coastal Change Analysis Program Overview of C-CAP Products, Vision, Status, and Related Tools from the Coastal Services Center.

Limei Ran 1,2, Robert Gilliam 3, Jonathan Pleim 3, Adel Hanna 1, William Benjey 3 1 Center for Environmental Modeling for Policy Development, Institute.

Spatial Processing in R Luke Winslow. Task Calculate a buffer around a lake Create a Shapefile for just the buffer Extract Land/Cover Land/Use for buffer.

Multi-Resolution Land Characteristics (MRLC) Consortium Great Lakes Regional Data Exchange Land Cover / Land Use Chicago, IL April 27, 2005 Steve Raber.

Has EO found its customers? Global Vegetation Monitoring Unit GLC2000 Land Cover Classification.

Working with Raster Grids and Map Algebra Katherine Paybins, USGS.

The Role of RS Techniques in European Land Use Database Construction Centre for Geo-Information 1 The Role of RS Techniques in European Land.

Detect and Simulate Vegetation, Surface Temperature, Rainfall and Aerosol Changes: From Global to Local Examples from EOS MODIS remote sensing Examples.

Introduction to ArcGIS for Environmental Scientists Module 2 – GIS Fundamentals Lecture 5 – Coordinate Systems and Map Projections.

Classification Advantages of Visual Interpretation: 1. Human brain is the best data processor for information extraction, thus can do much more complex.

Surface Reflectivity from OMI using MODIS to Eliminate Clouds: Effects of Snow on UV-Vis Trace Gas Retrievals Gray O’Byrne, 1 Randall V. Martin, 1,2 Aaron.

Bukola Johnson LUMA-GIS, Sweden.  Objectives  Background  Datasets  Methodology  Results  Conclusion.

Utility of National Spatial Data for Conservation Design Projects Steve Williams Biodiversity and Spatial Information Center North Carolina State University.

NATHAN FOSTER WARM SEASON WORKSHOP 5/2/12 The Flash Flood Potential Index (FFPI) Project at BTV.

Saving the Chesapeake’s Great Rivers and Special Places High Resolution Land Cover Data in the Chesapeake Bay Chesapeake Conservancy.

Land_Cover_CCI – CMUG Co-location Meeting, Reading, March 2011 Land Cover_CCI Pierre Defourny et al. Univ.cath. de Louvain.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data Michael D. King, 1 Eric G. Moody, 1,2 Crystal B. Schaaf, 3 and Steven Platnick.

Land Processes Group, NASA Marshall Space Flight Center, Huntsville, AL Response of Atmospheric Model Predictions at Different Grid Resolutions Maudood.

ORNL DAAC Spatial Data Access Tool (SDAT): Internet tools to access and visualize land-based data National Aeronautics and Space Administration

Thematic Workshop on Standardization and Exchange of Land Use and Cover Information Wednesday, April 27, 2005 Chicago, Illinois.

1 The MODIS Land Cover Product The MODIS Land Cover/Land Cover Dynamics Product: Status and Validation A.H. Strahler (PI), Mark Friedl, Xiaoyang Zhang,

Lu Liang, Peng Gong Department of Environmental Science, Policy and Management, University of California, Berkeley And Center for Earth System Science,

Page 1. U.S. Department of the Interior U.S. Geological Survey Landsat at 40: The Nation’s oldest Earth-observing satellite program Landsat at 40: The.

Development and Land Use Change: Central Potomac River Watershed

Remote Sensing Classification Systems

Global map layers Additional global data sets such as Hydrology data (Hydrosheds), new and updated Landcover data (Globcover), demographic data and others.

Development and Preliminary Results of Image Processing Tools for Meteorology and Air Quality Modeling Limei Ran Center for Environmental Modeling for.

An Adaptive Management Model for the Red River Basin of the North.

North American Carbon Program Sub-pixel Analysis of a 1-km Resolution Land-Water Mask Source of Data: The North American sub-pixel water mask product is.

Land Cover Characterization Program National Mapping Division EROS Data CenterU. S. Geological Survey The National Land Cover Dataset of the Multi- Resolution.

The MIMS Spatial Allocator: A Tool for Generating Emission Surrogates without a Geographic Information System* Alison M. Eyth, Kimberly Hanisak Carolina.

Land cover mapping of Asia ， Central Asia, and Middle East for GLC2000 project Ryutaro Tateishi, Hiroshi Sato, and Zhu Lin Center for Environmental Remote.

Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.

Linkages between Southern Pine Beetle Outbreaks and Forest Density Dahl Winters December 6, 2006.

CEOS Data Cube Open Source Software Status Brian Killough CEOS Systems Engineering Office (SEO) WGISS-40 Harwell, Oxfordshire, UK September 30, 2015 (remote.

The Biosphere. Biosphere = anywhere on or above the surface of the earth where organisms live. -2 parts -Terrestrial -Aquatic Climate = the weather conditions.

Roger Sayre, PhD Senior Scientist for Ecosystems Land Change Science Program U.S. Geological Survey and GEO Task Lead GI-14 Commonalities in Global Land.

Terrain Susceptibility Kyle Renner GIS in Water Resources 2015

Bi-national Land Cover Change for the Great Lakes Basin.

Tom Kutcher Natural Resources Specialist Narragansett Bay NERR Selection of an Effective Classification Scheme.

Limei Ran 1, Ellen Cooter 2, Verel Benson 3, Dongmei Yang 1, Robert Gilliam 2, Adel Hanna 1, William Benjey 2 1 Center for Environmental Modeling for Policy.

Advances in Support of the CMAQ Bidirectional Science Option for the Estimation of Ammonia Flux from Agricultural cropland Ellen Cooter U.S. EPA, National.

Science Review Panel Meeting Biosphere 2, Tucson, AZ - January 4-5, 2011 Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite.

Spatial Allocator Version 4.3

Global Landcover and Disturbance Analysis NRSM 532 BIOS 534

Land Use in Regional Climate Modeling

Impacts of Land Use Changes From Urban Development on Future Air Quality in Kansas City 15th CMAS Conference 10/26/2016 Yuqiang Zhang, Jesse Bash, Shawn.

Best Management Practices Implemented in Lower Bear River

Paraguay Landscape Analysis

Nutrient Runoff Effects on Jordan Lake

GLC-2000-Continental Southeast Asia

Limei Ran and Alison Eyth

GIS Aspect We used GIS to extract the watershed basin we were going to focus on so that we could obtain the NLCD landcover data for the catchments the.

Paraguay Landscape Analysis

WP05: Turkey Land Use Change Model

Presentation of Jordan Case Study First Management Board Meeting

Presentation transcript:

NLCD and MODIS Landuse Processing Tools and Projection Issues in Modeling Limei Ran and Alison Eyth Center for Environmental Modeling for Policy Development Institute for the Environment University of North Carolina at Chapel Hill

Outline of the Presentation Landuse Data New Release of Spatial Allocator (SA) 3.5 Projection Issues in Modeling Future Enhancements for SA Acknowledgements

Landuse Data in WRF and MM5 USGS Global Land Cover Characteristics (GLCC) 30-second (around 1km) landuse data are used in: WRF GEOGRID MM5 TERRAIN GLCC database was developed from: NOAA 1km AVHRR satellite images spanning 04/1992 through 03/1993

2001 NLCD and MODIS Landuse Data Two new land cover data sets are available: m NLCD for US (Landsat 7 and 5 TM images) km MODIS for the Globe (TERRA MODIS satellite images) 30m NLCD data can be obtained from two places: USGS for US 21-classes USGS Land cover data Imperviousness Tree Canopy NOAA Coastal Change Analysis Program (C-CAP) for coast regions 30-classes USGS Land cover data MODIS land cover database can be obtained from: –Department of Geography, Boston University 20-classes IGBP land cover data

2001 NLCD Data USGS NOAA

2001 NLCD Classification 11 - Open Water 12 - Perennial Ice/Snow 21 - Developed - Open Space 22 - Developed - Low Intensity 23 - Developed - Medium Intensity 24 - Developed - High Intensity 31 - Barren Land (Rock/Sand/Clay) 32 - Unconsolidated Shore 41 - Deciduous Forest 42 - Evergreen Forest 43 - Mixed Forest 51 - Dwarf Scrub 52 - Shrub/Scrub 71 - Grassland/Herbaceous 72 - Sedge/Herbaceous 73 - Lichens 74 – Moss 75 - Tundra 81 - Pasture/Hay 82 - Cultivated Crops 90 - Woody Wetlands 91 - Palustrine Forested Wetland 92 - Palustrine Scrub/Shrub Wetland 93 - Estuarine Forested Wetland 94 - Estuarine Scrub/Shrub Wetland 95 - Emergent Herbaceous Wetlands 96 - Palustrine Emergent Wetland 97 - Estuarine Emergent Wetland 98 - Palustrine Aquatic Bed 99 - Estuarine Aquatic Bed

MODIS 2001 Land Cover Data

MODIS Land Cover IGBP Classification 0 - water 1 - evergreen needleleaf forest 2 - evergreen broadleaf forest 3 - deciduous needleleaf forest 4 - deciduous broadleaf forest 5 - mixed forests 6 - closed shrublands 7 - open shrublands 8 - woody savannas 9 - savannas 10 - grasslands 11 - permanent wetlands 12 - croplands 13 - urban and built-up 14 - cropland/natural vegetation mosaic 15 - permanent snow and ice 16 - barren or sparsely vegetated 17 - IGBP Water Bodies (recoded to 0 for MODIS Land Product consistency) unclassified fill value

New Release of Spatial Allocator 3.5 New release is organized into three parts: Vector Tools to process shapefiles Raster Tools to process NLCD/MODIS landuse data Surrogate Tools to compute surrogates Uses PROJ4.6 to project between spherical earth R and ellipsoid:.PROJ4.6: no datum transformation. Matches WRF,MM5 projections..PROJ4.5 or older versions: performs datum transformation automatically and results in mismatches.

NLCD and MODIS 2001 Data Processing Two steps to generate modeling grid land cover data from NLCD and MODIS data: 1.Pre-process original NLCD data sets to get rid of overlaps. 2.Compute modeling grid land cover information from: USGS NLCD land cover USGS NLCD imperviousness USGS canopy NOAA coastal NLCD land cover NASA MODIS land cover data Two output files produced: WRF landuse NetCDF file CSV text file

NC 1km Grids, NLCD and MODIS

NLCD and WRF Land Cover NC 1km Grids

NLCD and WRF Land and Water

Projection Issues in Modeling Map projection can be:Map projection can be: Forward:P(x,y) = F { Projection, Parms(Earth_Model), P(long,lat) } Reverse:P(long,lat) = F { Projection, Parms(Earth_Model), P(x,y) } Projection: LCC, AEAC, UTM Earth model (datum): sphere R, ellipsoid WGS84 or NAD83, NAD27 Datum transformation is needed when projecting a GIS data set to a projection that uses a different earth model.Datum transformation is needed when projecting a GIS data set to a projection that uses a different earth model. NLCD AEAC with NAD83 to LCC with R= m Forward projection to x/y file in LCC with R transform datum? Transform to lat/long file in sphere R Reverse projection to lat/long file in NAD83 Yes No

Conclusion: 1.Make sure that spatial data are in WGS84 datum (USGS GLCC data). 2.No Datum transformation from WGS84 to WRF or MM5 sphere (PROJ4.6).

Future Enhancements for Spatial Allocator Develop programs to extract preprocessed GOES satellite data for a modeling grid. User defined domain to extract GOES data Output in WRF NetCDF format Add variable grid option to: Surrogate computation BELD3 data processing Land cover computation

Acknowledgements This project is fully funded by US EPA under the Contract No. EP-W , “Operation of the Center for Community Air Quality Modeling and Analysis”. We gratefully acknowledge the support of William Benjey, Jonathan Pleim, and Robert Gilliam from US EPA.