VEGETATION MAPPING FOR LANDFIRE National Implementation.

Slides:



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

U.S. Department of the Interior U.S. Geological Survey USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests GLC2000-JRC.
Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
Utilization of Remotely Sensed Data for Targeting and Evaluating Implementation of Best Management Practices within the Wister Lake Watershed, Oklahoma.
ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON Roundup Benoit Parmentier.
Sistema de Monitoreo de la Cobertura del Suelo de América del Norte.
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
Urbanization and Land Cover Change in Dakota County, Minnesota Kylee Berger and Julia Vang FR 3262 Remote Sensing Section 001/002.
Has EO found its customers? Global Vegetation Monitoring Unit GLC2000 Land Cover Classification.
U.S. Department of the Interior U.S. Geological Survey Decision Trees for Land Cover Mapping Guilty Parties: B. Wylie, C. Homer, C. Huang, L. Yang, M.
Land Cover Mapping for the Southwest Regional GAP Analysis Project Tenth Biennial Forest Service Remote Sensing Applications Conference, RS-2004, Salt.
Land Cover Mapping Background: Training Data and Classification Methods Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE.
The following slides are intended to provide a few examples of some problems and issues that come up in Landcover mapping. This will be an ever-growing.
Data Merging and GIS Integration
Geog 458: Map Sources and Errors Uncertainty January 23, 2006.
John Lowry RS/GIS Laboratory College of Natural Resources Utah State University Resource Management Tools & Geospatial Conference, Phoenix, AZ April 18-22,
The Coeur d'Alene Tribe is learning the remote sensing methodology developed by LANDFIRE, and will be attempting to apply the methods to higher resolution.
Global Land Cover: Approaches to Validation Alan Strahler GLC2000 Meeting JRC Ispra 3/02.
GIS 2, Final Project: Creating a Dasymetric Map for Two Counties in Minnesota By: Hamidreza Zoraghein Melissa Cushing Caitlin Lee Fall 2013.
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
Methods of Validating Maps of Deforestation and Selective Logging Carlos Souza Jr. Instituto do Homem e Meio Ambiente da Amazônia—Imazon.
Co-authors: Maryam Altaf & Intikhab Ulfat
By: GeoTrek. Hunter Krenek: Remote Sensing analyst & GIS analyst Joe Dowling: Assistant Project Manager & GIS analyst Peter Vogt: Website Designer & GIS.
Chenghai Yang 1 John Goolsby 1 James Everitt 1 Qian Du 2 1 USDA-ARS, Weslaco, Texas 2 Mississippi State University Applying Spectral Unmixing and Support.
Utility of National Spatial Data for Conservation Design Projects Steve Williams Biodiversity and Spatial Information Center North Carolina State University.
Satellite Cross comparisonMorisette 1 Satellite LAI Cross Comparison Jeff Morisette, Jeff Privette – MODLAND Validation Eric Vermote – MODIS Surface Reflectance.
Saving the Chesapeake’s Great Rivers and Special Places High Resolution Land Cover Data in the Chesapeake Bay Chesapeake Conservancy.
Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection Randolph H. Wynne, Jared P. Wayman, Christine Blinn Virginia.
Image Classification Digital Image Processing Techniques Image Restoration Image Enhancement Image Classification Image Classification.
Thematic Workshop on Standardization and Exchange of Land Use and Cover Information Wednesday, April 27, 2005 Chicago, Illinois.
Lu Liang, Peng Gong Department of Environmental Science, Policy and Management, University of California, Berkeley And Center for Earth System Science,
Biophysical Gradient Modeling. Management Needs Decision Support Tools – Baseline Information Vegetation characteristics Forest stand structure Fuel loads.
Ex_Water Yield Model Data needs 1.Soil depth,an average soil depth value for each cell. The soil depth values should be in millimeters (Raster) Source:
A Forest Cover Change Study Gone Bad Lessons Learned(?) Measuring Changes in Forest Cover in Madagascar Ned Horning Center for Biodiversity and Conservation.
Map Units for LANDFIRE: Integrating Vegetation Classification and Map Legends.
Topographic correction of Landsat ETM-images Markus Törmä Finnish Environment Institute Helsinki University of Technology.
Preliminary Results of Mapping Carbon at the Pixel Level in East Kalimantan GCF Kaltim Project Global Observatory for Ecosystem Services, Department of.
Land Cover Characterization Program National Mapping Division EROS Data CenterU. S. Geological Survey The National Land Cover Dataset of the Multi- Resolution.
Overcoming Chance Agreement in Classification Tree Modeling: Predictor Variables, Training Data, and Spatial Autocorrelation Considerations Southwest Regional.
Outlier Analyses What is an outlier? data point unrepresentative of its general location or otherwise “difficult” to represent by a generalized NPP model.
Application of spatial autocorrelation analysis in determining optimal classification method and detecting land cover change from remotely sensed data.
LiDAR Remote Sensing of Forest Vegetation Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota.
DEMs Download from Seamless Server Project Mosaic Calculate Slope Create a DEM (ArcGIS)
Object-oriented Land Cover Classification in an Urbanizing Watershed Erik Nordman, Lindi Quackenbush, and Lee Herrington SUNY College of Environmental.
Citation: Moskal., L. M. and D. M. Styers, Land use/land cover (LULC) from high-resolution near infrared aerial imagery: costs and applications.
GLC 2000 Workshop March 2003 Land cover map of southern hemisphere Africa using SPOT-4 VEGETATION data Ana Cabral 1, Maria J.P. de Vasconcelos 1,2,
Updated Cover Type Map of Cloquet Forestry Center For Continuous Forest Inventory.
Land Cover Classification and Monitoring Case Studies: Twin Cities Metropolitan Area –Multi-temporal Landsat Image Classification and Change Analysis –Impervious.
APPLIED REMOTE SENSING TECHNOLOGY TO ANALYZE THE LAND COVER/LAND USE CHANGE AT TISZA LAKE By Yudhi Gunawan * and Tamás János ** * Department of Land Use.
Citation: Moskal, L. M., D. M. Styers, J. Richardson and M. Halabisky, Seattle Hyperspatial Land use/land cover (LULC) from LiDAR and Near Infrared.
Assessing the Tree Canopy in Jefferson County, WV Jarlath O’Neil-Dunne University of Vermont Spatial Analysis Laboratory.
High Spatial Resolution Land Cover Development for the Coastal United States Eric Morris (Presenter) Chris Robinson The Baldwin Group at NOAA Office for.
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
By: Reid Swanson Sam Soper. Goal: To describe land cover/use changes that have occurred in the Twin Cities Metro-Area from the 1991 to 2005 Quantifying.
US Croplands Richard Massey Dr Teki Sankey. Objectives 1.Classify annual cropland extent, Rainfed-Irrigated, and crop types for the US at 250m resolution.
26. Classification Accuracy Assessment
Term Project Presentation
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.
Factsheet # 12 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Land use/land cover (LULC) from high-resolution.
HIERARCHICAL CLASSIFICATION OF DIFFERENT CROPS USING
VegDRI History, Current Status, and Related Activities
Incorporating Ancillary Data for Classification
By Yudhi Gunawan * and Tamás János **
Evaluating Land-Use Classification Methodology Using Landsat Imagery
Paulo Gonçalves1 Hugo Carrão2 André Pinheiro2 Mário Caetano2
An Enhanced Canopy Cover Layer for Hydrologic Modeling
Supervised Classification
National Forest Inventory for Great Britain
Corn and Soybean Differentiation Using Multi-Spectral Landsat Data
Image Classification of the Upper South Fork Eel River Watershed
Presentation transcript:

VEGETATION MAPPING FOR LANDFIRE National Implementation

 Existing vegetation type data layers  % Canopy Cover (separate for tree, shrub, and herbaceous data layers; binned)  Vegetation height (separate for tree, shrub, and herbaceous data layers; binned) Vegetation Dataset Deliverables

LANDFIRE Vegetation Mapping Data Requirements at EDC n LANDSAT ETM+ data (mosaics of three image dates; from MRLC) n Digital elevation model data (and derivatives; from 30m Elevation Derivatives for National Applications (EDNA)) n Preliminary classification products (from MRLC/NLCD) n Percent forest canopy cover data (from MRLC/NLCD) n High-quality field data (Map Attribute Table plot data from FIA, SCA, GAP, others) n Biophysical gradients (select list) and Biophysical settings data Major Requirements

Step 1. QA/QC of Field Data by Mappers n Isolate 2% of sample plots for traditional accuracy assessment using 3x3 k, 2% block design (do not use for map generation) n Identification of questionable plots – Identify 1990’s-2000’s NDVI difference values likely to represent plots of change – Identify plots very close to roads – Identify plots that do not match NLCD life forms n Visually assess questionable plots on imagery n If still questionable, flag plots in data base and do not use for map development

Example of Overlay of Points onto Imagery for QA/QC

Zone 16 Plot QA/QC Results n Started with 7293 plots n 956 had no EVT information n 135 plots withheld for accuracy assessment n 6202 plots used for life form modeling n 1474 plots excluded for vegetation type mapping (about from EROS analysis) n 4728 plots used for vegetation type mapping (65% points used for analysis)

Step 2. Vegetation Type Mapping; Part 1 n Extract digital values from spatial data layers using field plots that passed QA/QC inspection process n Generate life-form data mask (tree, shrub, herbaceous) using decision tree (Life-form field included in LFRDB) n Inspect cross-validation values/error matrices of mask n Develop additional data layers as needed (e.g., wetlands, other vegetation groupings)

Step 2. Vegetation Type Mapping; Part 2 n Run decision tree models separately for forest, shrub, and herbaceous life forms using all appropriate data (imagery, DEM and derivatives, BpG, BpS, wetlands) n Generate and inspect life-form specific cross-validation error matrices as well as spatial outputs (QA) n Assess impact of rare classes (decide to drop or keep) n Apply water, urban, agriculture masks (from NLCD) to vegetation type data layers n Merge life-form specific cover types into a single vegetation type data layer

Biophysical Gradient Data Used for Vegetation Type Mapping n Soil Depth n Degree Days n Daily Precipitation n Relative Humidity n Shortwave Radiation Flux Density n Maximum Temperature n Minimum Temperature n Nighttime Average Temp n Incoming Shortwave Radiation n Maximum Projected LAI – Forest and Grass Models n Potential Evapotranspiration – Forest and Grass Models n Soil Water Fraction – Forest and Grass Models n Growing Season Water Stress – Forest and Grass Models n Actual Evapotranspiration – Forest and Grass Models n Soil Water Potential – Grass Model

Summer ETM+ Image Mosaic Lifeform Mask Created Using Imagery and DEM Comparison Between Imagery and Lifeform Mask

LANDFIRE Forest Class Cross-Validation Error Matrix Reference Data Classified As

Key Cross Validation Numbers; Utah Highlands n 3-Lifeform Classification: 92% n 6-Lifeform Classification: 89% n Forest Classes: 78% n Shrub Classes: 78% n Herbaceous Classes: 65%

Vegetation Type Map; Utah Highlands

Step 3a. Canopy Cover; Trees n Create training set of forest canopy cover using high res orthophoto or satellite imagery (NLCD) n Establish relationship between Landsat and training data using regression tree n Apply relationship to generate spatial per-pixel estimates for all pixels n Evaluate error (R) values n Recode tree canopy continuous cover data to cover classes as defined by the Vegetation Working Group n Ensure consistency with EVT; correct when needed n Apply land cover masks: water, urban, agriculture

Utah Highlands Binned Forest Canopy Corr. Coef. = 88% (From NLCD) Avg. Error = 9.0%

n Extract digital values from spatial layers using field plots that have shrub or herbaceous canopy values n Stratify to life form n Generate life-form specific error values (R) n If R is acceptable, apply regression tree model n Recode shrub/herbaceous canopy continuous cover data to cover classes as defined by the Vegetation Working Group n Ensure consistency with EVT; correct when needed n Apply land cover masks: water, urban, agriculture Step 3b. Canopy Cover; Shrubs/Herbaceous

Utah Highlands Binned Shrub Canopy Corr. Coef. = 70% Avg. Error = 11%

Utah Highlands Binned Herbaceous Canopy Corr. Coef. = 62% Avg. Error = 12%

Utah Highlands Canopy Cover Composited Using Three Lifeform Dataset

Step 4. Canopy Height n Assign life-form specific height classes to plots in modified MAT as defined by the Vegetation Working Group n Extract digital values from the spatial data layers, including life-form specific cover types n Run decision tree model separately for the three life forms n Generate life-form specific cross-validation error matrices for height classes n Generate life-form specific height class spatial data using decision tree n Check for errors in the three life form-specific height maps n Mask each height map with water, urban, and agriculture masks

Step 4. Canopy Height Height Classes (LANDFIRE Vegetation Working Group) Forest 0-5 Meters 5-10 Meters Meters Meters > 50 Meters Shrub Meters Meters Meters > 3.0 Meters Herbaceous Meters Meters > 1.0 Meters

Utah Highlands Structure Stages Tree: Height > 10m, Canopy > 40% Tree: Height > 10m, Canopy <= 40% Tree: Height 40% Tree: Height <= 10m, Canopy <= 40% Shrub: Height > 1m, Canopy > 40% Shrub: Height > 1m, Canopy <= 40% Shrub: Height 40% Shrub: Height <= 1m, Canopy <= 40% Herbaceous: Height > 0.2m, Canopy > 40% Herbaceous: Height > 0.2m, Canopy <= 40% Herbaceous: Height 40% Herbaceous: Height <= 0.2m, Canopy <= 40% Barren land Water Permanent snow and ice Agriculture Residential and commercial lands Utah Existing Structural Stages

Utah Highlands Canopy Structure Stage

Questions, Comments? For Further Information Visit: Thank You!