Land Cover Mapping for the Southwest Regional GAP Analysis Project Tenth Biennial Forest Service Remote Sensing Applications Conference, RS-2004, Salt.

Slides:

Advertisements

Similar presentations

The Ecology Core Dataset: Information to Support Effective Management in the Pacific Northwest The Ecology Core Dataset: Information to Support Effective.

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.

September 5, 2013 Tyler Jones Research Assistant Dept. of Geology & Geography Auburn University.

Classification and Regression Trees for Land Cover Mapping Lecture materials taken from a presentation by: B. Wylie, C. Homer, C. Huang, L. Yang, M. Coan.

VEGETATION MAPPING FOR LANDFIRE National Implementation.

Utilization of Remotely Sensed Data for Targeting and Evaluating Implementation of Best Management Practices within the Wister Lake Watershed, Oklahoma.

FIA Data and Data Gaps Elizabeth LaPoint - NRS FIA Durham, NH June 2011.

Chapter 7 – Classification and Regression Trees

Chapter 7 – Classification and Regression Trees

Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.

Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,

Southwest Regional Gap Analysis Project: An Overview of Project Goals and Organization Southwest Regional Gap Analysis Project Arizona, Colorado, Nevada,

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 Background: Training Data and Classification Methods Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE.

Lessons Learned: Land Cover Mapping for the Southwest Regional Gap Analysis Project John Lowry Utah Project Coordinator SWReGAP College of Natural Resources.

Data Merging and GIS Integration

Ensemble Learning: An Introduction

SEReGAP Land Cover Mapping Summary and Results Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada:

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.

Three kinds of learning

Classification of Remotely Sensed Data General Classification Concepts Unsupervised Classifications.

Methods of Validating Maps of Deforestation and Selective Logging Carlos Souza Jr. Instituto do Homem e Meio Ambiente da Amazônia—Imazon.

Ramesh Gautam, Jean Woods, Simon Eching, Mohammad Mostafavi, Scott Hayes, tom Hawkins, Jeff milliken Division of Statewide Integrated Water Management.

USDA Forest Service Remote Sensing Applications Center Forest Inventory and Analysis New Technology How is FIA integrating new technological developments.

Co-authors: Maryam Altaf & Intikhab Ulfat

earthobs.nr.no Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images Arnt-Børre Salberg and.

U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.

Satellite Cross comparisonMorisette 1 Satellite LAI Cross Comparison Jeff Morisette, Jeff Privette – MODLAND Validation Eric Vermote – MODIS Surface Reflectance.

Benjamin Blandford, PhD University of Kentucky Kentucky Transportation Center Michael Shouse, PhD University of Southern Illinois.

Using spectral data to discriminate land cover types.

Land Cover Classification Defining the pieces that make up the puzzle.

Optimal use of new satellite resources. Research funded by NERC/CEH and JNCC. Rapid Land Cover Mapping.

Chuvieco and Huete (2009): Fundamentals of Satellite Remote Sensing, Taylor and Francis Emilio Chuvieco and Alfredo Huete Fundamentals of Satellite Remote.

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

STRATIFICATION PLOT PLACEMENT CONTROLS Strategy for Monitoring Post-fire Rehabilitation Treatments Troy Wirth and David Pyke USGS – Biological Resources.

Biophysical Gradient Modeling. Management Needs Decision Support Tools – Baseline Information Vegetation characteristics Forest stand structure Fuel loads.

Image Classification 영상분류

Gradient Modeling Spatial layers of environmental gradients (predictor variables) known to govern rust propagation were compared to percent rust infection.

U.S. Department of the Interior U.S. Geological Survey Topographic Data Update IGOL: Rome September 13-15, 2004 Doug Muchoney USGS.

Canada Centre for Remote Sensing Field measurements and remote sensing-derived maps of vegetation around two arctic communities in Nunavut F. Zhou, W.

Vegetation Mapping An Interagency Approach The California Department of Forestry and Fire Protection and the USDA Forest Service Mark Rosenberg: Research.

Role of Spatial Database in Biodiversity Conservation Planning Sham Davande, GIS Expert Arid Communities Technologies, Bhuj 11 September, 2015.

Topographic correction of Landsat ETM-images Markus Törmä Finnish Environment Institute Helsinki University of Technology.

Accuracy of Land Cover Products Why is it important and what does it all mean Note: The figures and tables in this presentation were derived from work.

Map of the Great Divide Basin, Wyoming, created using a neural network and used to find likely fossil beds See:

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

Wetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis. Nathan Torbick Spring 2003 Update on current.

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

14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.

Overcoming Chance Agreement in Classification Tree Modeling: Predictor Variables, Training Data, and Spatial Autocorrelation Considerations Southwest Regional.

LANDSCAPE SCALE MONITORING OF FOREST TREATMENTS AND DISTURBANCE S.E. Sesnie 1,2, A.D. Olsson 1, B.G. Dickson 1, A. Leonard 3, V. Stein Foster 3 and C.

Interfacing Vegetation Databases with ecological theory and practical analysis. Mike Austin, Margaret Cawsey and Andre Zerger CSIRO Sustainable Ecosystems.

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,

Mapping Canada’s Rangeland and Forage Resources using Earth Observation Emily Lindsay MSc Candidate – Carleton University Supervisors: Doug J. King & Andrew.

High Spatial Resolution Land Cover Development for the Coastal United States Eric Morris (Presenter) Chris Robinson The Baldwin Group at NOAA Office for.

26. Classification Accuracy Assessment

Classification and Regression Trees (CART)

Quantifying Urbanization with Landsat Imagery in Rochester, Minnesota

Factsheet # 19 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Hyperspectral Remote Sensing of Urban.

HIERARCHICAL CLASSIFICATION OF DIFFERENT CROPS USING

VegDRI History, Current Status, and Related Activities

A Comparative Analysis of Urban Tree Canopy Assessment Methods in Minnesota Remote Sensing of Natural Resources and Environment | FR 5262 | University.

Relationships between lake water levels and Habitat at Bear Lake,UT

Map of the Great Divide Basin, Wyoming, created using a neural network and used to find likely fossil beds See:

Incorporating Ancillary Data for Classification

Evaluating Land-Use Classification Methodology Using Landsat Imagery

Image Information Extraction

The Pagami Creek Wildfire

Ensemble Methods: Bagging.

Presentation transcript:

Land Cover Mapping for the Southwest Regional GAP Analysis Project Tenth Biennial Forest Service Remote Sensing Applications Conference, RS-2004, Salt Lake City, Utah John Lowry and R. Douglas Ramsey Remote Sensing/GIS Laboratory Utah State University Logan, Utah

Presentation Overview Project Background & Objectives Mapping Methodology Training Data Collection Approach Current Status & Preliminary Results

State-based vegetation classification systems (cover type legends) State-based mapping methods State-based mapping area I.Project Background & Objectives

40 Mapping zones Spectrally consistent Eco-regionally distinct Labor divided among 5 state teams

NVC Formation NVC Alliance NVC Association Gap Analysis Program MRLC 2000 Proposal ~1,800 units National Park Mapping ~ NVC Class/Subclass ~10 units NatureServe Ecological Systems ~5,000 units ~700 units (Natural/Semi-natural types) ~300 units (Slide Courtesy Pat Comer, Nature Serve) Thematic Target Legend Developed with NatureServe

Groups of plant communities and sparsely vegetated habitats unified by similar ecological processes, substrates, and/or environmental gradients...and spectral characteristics. Ecological Systems

Elevation Landform Predictor Datasets: DEM derived

July-AugSept-Oct ETM Bands 5, 4, 3 Predictor Datasets: Imagery Derived

Data-mining software for decision-making and exploratory data analysis Identifies complex relationships between multiple independent variables to predict a single categorical class Predictor variables may be categorical or continuous Recursively “splits” the predictor data to create prediction rules or a decision tree. Software packages available: See5, SPLUS, CART II.Mapping Methods: Classification Trees

Mining the Predictor Layers Fall Brightness Summer NDVI Elevation Landform Etc…. Output table SAMPLE SITES Imagery: Landsat 7 ETM ( ) for spring, summer & fall NDVI, SAVI, Brightness,Greeness, Wetness, Landsat 7 Bands DEM: Elevation, Aspect, Slope, Landform Vector: Geology, Soils Meteorological : DAYMET

Simplified Example: Splits on 2 variables

Simplified Example: Tree output for 2 variables

Example: Rules Output See5 [Release 1.17] Wed Apr 23 13:42: Options: Rule-based classifiers Class specified by attribute `dep' Read 7097 cases (10 attributes) from t3.data Rules: Rule 1: (17, lift 45.4) band01 = 1 band03 > 115 band03 <= 122 band05 <= 81 band06 <= > class 1 [0.947] Rule 2: (9, lift 43.6) band01 = 1 band02 <= 102 band03 > 115 band03 <= 118 band04 <= 117 band06 <= > class 1 [0.909] Rule 3: (6, lift 42.0) band01 = 13 band03 <= 110 band05 <= 73 band07 = 4 | Generated with cubistinput by EarthSat | Training samples : | Validation samples: 2565 | Minimum samples : 0 | Sample method : Random | Output format : See5 dep.|h:/mgzn_5/trainingdata/mrgpts1.img(:Layer_1) Xcoord:ignore. Ycoord:ignore. band01:1,2,-30 |h:/mgzn_5/img_files/sum30cl.img(:Layer_1) band02:continuous.|h:/mgzn_5/img_files/subrt.img(:Layer_1) band03:continuous.|h:/mgzn_5/img_files/sundvi.img(:Layer_1) band04:continuous.|h:/mgzn_5/img_files/fandvi.img(:Layer_1) band05:continuous.|h:/mgzn_5/img_files/fabrt.img(:Layer_1) band06:continuous.|h:/mgzn_5/img_files/elev.img(:Layer_1) band07:0,1,2,3,4,5,6,7,8,9,10. |h:/mgzn_5/img_files/landf.img(:Layer_1) dep:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20. |h:/mgzn_5/trainingdata/mrgpts1

Boosting (iterative tree’s try to account for previous tree’s errors)—C5 Different over-fitting issues associated with each tree tend to be averaged out. Multiple Tree Approaches VOTEVOTE (Slide Courtesy Bruce Wylie, USGS EDC)

Imagine CART Module (USGS Eros Data Center)—See5-Imagine Integration

III.Training Data Collection Opportunistic, ground-based sampling, stratified by digital landform model

Percent ground cover by dominant species is recorded through ocular estimation. Only the top 4 species of each of 4 life forms are recorded

~3000 Air Photo Interpretation Sites from USFS Photos

Regional Total ~ 93,000

IV. Current Status & Preliminary Results

Edge-matching between three mapping areas

Considered correctly classified if majority of pixels agree with sample polygon Accuracy Assessment with 20% withheld data:

Accuracy Assessment with 20% withheld data: Southern Wasatch Range

1995 GAP 30 M2004 GAP 30 M1995 GAP Pub.1KM

Summary Approximately 100 Ecological Systems and 10 NLCD Land Use classes Generalized to 1 acre MMU Delivered via NBII data node Anticipated completion: 1 September, 2004

Acknowledgements