the Clinch/Hidden Valley study site were used in this mountainous classification.

Similar documents
Note: This validation is designed to test the accuracy of the mapping process (i.e. the map models) not the accuracy of the map itself.

Estimating Probability of Success Rate

CHAPTER 1 THE UNITED STATES 2001 NATIONAL LAND COVER DATABASE

Appendix E: Cowardin Classification Coding System

2011 Land Use/Land Cover Delineation. Meghan Jenkins, GIS Analyst, GISP Jennifer Kinzer, GIS Coordinator, GISP

BIODIVERSITY CONSERVATION HABITAT ANALYSIS

SIF_7.1_v2. Indicator. Measurement. What should the measurement tell us?

NR402 GIS Applications in Natural Resources. Lesson 9: Scale and Accuracy

Comparing CORINE Land Cover with a more detailed database in Arezzo (Italy).

DIFFERENTIATING A SMALL URBAN AREA FROM OTHER LAND COVER CLASSES EMPLOYING LANDSAT MSS

Land accounting in Québec: Pilot project for a sub-provincial area

Lesson 6: Accuracy Assessment

Sub-pixel regional land cover mapping. with MERIS imagery

Information Paper. Kansas City District. Missouri River Fish and Wildlife Mitigation Project Jim and Olivia Hare Wildlife Area, MO

Development of statewide 30 meter winter sage grouse habitat models for Utah

Impacts of sensor noise on land cover classifications: sensitivity analysis using simulated noise

Land cover classification methods

Evaluating Wildlife Habitats

High Resolution Land Cover Mapping in the Lower Columbia River Estuary. Prepared by: Sanborn Map Company Lower Columbia River Estuary Partnership

Fri. Apr. 20, Today: Review briefly Ch. 11 (Mineral Exploration) Summarize Ch Land use classification

Name Hour. Chapter 4 Review

Investigation of the Effect of Transportation Network on Urban Growth by Using Satellite Images and Geographic Information Systems

Extent. Level 1 and 2. October 2017

Role of GIS and Remote Sensing to Environment Statistics

An Internet-based Agricultural Land Use Trends Visualization System (AgLuT)

Southwest LRT Habitat Analysis. May 2016 Southwest LRT Project Technical Report

Resolving habitat classification and structure using aerial photography. Michael Wilson Center for Conservation Biology College of William and Mary

Wetland Mapping. Wetland Mapping in the United States. State Wetland Losses 53% in Lower US. Matthew J. Gray University of Tennessee

Phase 6 Land Use Database version 2

Online publication date: 22 January 2010 PLEASE SCROLL DOWN FOR ARTICLE

Evaluating Urban Vegetation Cover Using LiDAR and High Resolution Imagery

OBJECT-BASED CLASSIFICATION USING HIGH RESOLUTION SATELLITE DATA AS A TOOL FOR MANAGING TRADITIONAL JAPANESE RURAL LANDSCAPES

STUDY ON FOREST VEGETATION CLASSIFICATION BASED ON MULTI- TEMPORAL REMOTE SENSING IMAGES

Chapter 7 Part III: Biomes

Lab#8: Working With Geodatabases. create a geodatabase with feature datasets, tables, raster datasets, and raster catalogs

Capabilities and Limitations of Land Cover and Satellite Data for Biomass Estimation in African Ecosystems Valerio Avitabile

o 3000 Hannover, Fed. Rep. of Germany

Accuracy Assessment of Image Classification Algorithms

Lesson 9: California Ecosystem and Geography

Land cover/land use mapping and cha Mongolian plateau using remote sens. Title. Author(s) Bagan, Hasi; Yamagata, Yoshiki. Citation Japan.

Principals and Elements of Image Interpretation

RESULT. 4.1 Temporal Land use / Land Cover (LULC) inventory and Change Analysis

79 International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 ISSN

Object-Oriented Oriented Method to Classify the Land Use and Land Cover in San Antonio using ecognition Object-Oriented Oriented Image Analysis

Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification

LOCAL KNOWLEDGE BASED MOOSE HABITAT SUITABILITY ASSESSMENT FOR THE SOUTH CANOL REGION, YUKON. Prepared by: Tess McLeod and Heather Clarke

Houston-Galveston Area Council 2002 Land Cover Image Processing and Accuracy Assessment Protocol

Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

Analysis of Land Cover Change within Historically Abandoned and Reclaimed Mine Land Surrounding Centralia, Pennsylvania

Object Based Imagery Exploration with. Outline

Geologic History. Earth is very, very old

Targeted LiDAR use in Support of In-Office Address Canvassing (IOAC) March 13, 2017 MAPPS, Silver Spring MD

CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION

Houston-Galveston Area Council 2008 Land Cover Image Processing Protocol. Prepared by: Roger Meyer 811 Nth 9 th Ave East Duluth, MN 55805

TAKING THE " " OUT OF "GROUND TRUTH": OBJECTIVE ACCURACY ASSESSMENT. Timothy B. Hill GIS/Remote Sensing Analyst Geographic Resource Solutions, USA

UK NEA Economic Analysis Report Cultural services: Mourato et al. 2010

ISO Land Cover for Agricultural Regions of Canada, Circa 2000 Data Product Specification. Revision: A

Using MERIS and MODIS for Land Cover Mapping in the Netherlands

Outline: Introduction - Data used - Methods - Results

The elevations on the interior plateau generally vary between 300 and 650 meters with

Urban land cover and land use extraction from Very High Resolution remote sensing imagery

Land cover classification methods. Ned Horning

Land cover research, applications and development needs in Slovakia

7.1 INTRODUCTION 7.2 OBJECTIVE

Chapter 1: America s Land Lesson 1: Land and Climate

LAND COVER CATEGORY DEFINITION BY IMAGE INVARIANTS FOR AUTOMATED CLASSIFICATION

System of Environmental-Economic Accounting. Advancing the SEEA Experimental Ecosystem Accounting. Extent Account (Levels 1 and 2)

This is trial version

Wetland Mapping Methods for the Arrowhead Region of Minnesota

StreBanD DSS: A Riparian Buffer Decision Support System for Planners

The Effects of Haze on the Accuracy of. Satellite Land Cover Classification

A COMPARISON OF INTER-ANALYST DIFFERENCES IN THE CLASSIFICATION OF A LANDSAT ETM+ SCENE IN SOUTH-CENTRAL VIRGINIA

Proceedings - AutoCarto Columbus, Ohio, USA - September 16-18, Dee Shi and Xiaojun Yang

DEVELOPMENT OF DIGITAL CARTOGRAPHIC DATABASE FOR MANAGING THE ENVIRONMENT AND NATURAL RESOURCES IN THE REPUBLIC OF SERBIA

PRINCIPLES OF PHOTO INTERPRETATION

Chapter 6, Part Colonizers arriving in North America found extremely landscapes. It looked different to region showing great.

Exercise 6: Working with Raster Data in ArcGIS 9.3

Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon

Critical Area Mapping Update Project St. Mary s County Town Hall April 8th, :30 p.m.

International Journal of Wildland Fire

Regional Ecosystems of West-Central Yukon

BIOMES. Definition of a Biome. Terrestrial referring to land. Climatically controlled sets of ecosystems. Characterized by distinct vegetation

Land Cover Classification Over Penang Island, Malaysia Using SPOT Data

Land cover classification of the Lake of the Woods/ Rainy River Basin by object-based image analysis of Landsat and lidar data

METHODS. have a brief description of the Commonwealth in mind. Virginia is 102,830 km 2 in size.

Mapping Willow Distribution Across the Northern Range of Yellowstone National Park

One of the many strengths of a GIS is that you can stack several data layers on top of each other for visualization or analysis. For example, if you

Land Surface Processes and Land Use Change. Lex Comber

2.1.2 Land cover data

Geospatial technology for land cover analysis

GOVERNMENT GUIDELINES ON PROTECTED AREAS

Appendix 1. Supplementary information on methodology and chorus projections Location matters: evaluating Greater Prairie-Chicken (Tympanuchus cupido)

New Jersey Digital Land Dataset Comparison and Integration Analysis January 2003

Effects of input DEM data spatial resolution on Upstream Flood modeling result A case study in Willamette river downtown Portland

How does the physical environment influence communities and ecosystems? Hoodoos in Cappadocia, Turkey

Harrison 1. Identifying Wetlands by GIS Software Submitted July 30, ,470 words By Catherine Harrison University of Virginia

APPENDIX. Normalized Difference Vegetation Index (NDVI) from MODIS data

A comparison of pixel and object-based land cover classification: a case study of the Asmara region, Eritrea

Wonders of the Rainforest Resource Book

Using geographically weighted variables for image classification

Transcription:

the Clinch/Hidden Valley study site were used in this mountainous classification. Deciduous forest was present on over half of the image (Table 97). Coniferous forest and herbaceous were the only other categories found on more than 15% of the land area. Following the 3x3 homogeneity filter, 47.5% of the original area was still assessable (Table 98). This number was reduced to 24.4% following the 5x5 filter (Table 99). Two hundred-seven reference points were used in the every-pixel accuracy assessment (Table 100). One hundred-fourteen were available for the 3x3 level assessment (Table 102), while 72 were used in the 5x5 level (Table 104) assessment. The overall accuracy increased from 58.9% at the every-pixel level (Table 101), to 77.2% at the 3x3 level(table 103), and finally to a relatively high 90.3% at the 5x5 level (Table105). Deciduous producer s, both coniferous, and herbaceous user s accuracies were all 100% following the 5x5 evaluation. However, shrub producer s and disturbed producer s had zero accuracies at the same level. As expected, the Kappa was highest after the 5x5 filter procedure. Scene 1834 Scene 1834 (Figure 22) is the only scene that contained significant portions of the Appalachian Plateau physiographic province, predominate in the northcentral portion of this scene. The Clinch Mountain/Hidden Valley was the main training dataset for this heavily shadowed mountain image. Almost 70% of this area was classified as deciduous forest (Table 106). Coniferous forest and herbaceous land cover were each found to be on over 10% of the area. 162

Figure 21. Classification output for scene 1735 full, 3x3 homogeneous, and 5x5 homogeneous. 163

Table 97. Total area and proportion of each land cover type for scene 1735 when analyzed at the every-pixel level. Pixels Hectares Acres Total Area Deciduous 3,041,294 273,716.0 676,353.0 0.525 Coniferous 1,385,616 124,705.0 308,147.0 0.239 Mixed 48,754 4,387.9 10,842.4 0.008 Shrub/Scrub 332,018 29,881.6 73,837.5 0.057 Herbaceous 885,231 79,670.8 196,867.0 0.153 Open Water 48,594 4,373.5 10,806.8 0.008 Disturbed 48,316 4,348.4 10,745.0 0.008 Coastal Wetland 0 0 0 0.000 Total 5,789,823 521,084.0 1,287,599.0 1.000 Table 98. Results of the 3x3 homogeneous filter on land cover types within scene 1735 in area, proportion of total area, and proportion remaining from the 1735 everypixel level. Pixels Hectares Acres Total Area Every-Pixel Deciduous 1,846,650 166,199.0 410,676.0 0.671 0.607 Coniferous 461,672 41,550.5 102,671.0 0.168 0.333 Mixed 1,552 139.7 345.1 0.001 0.032 Shrub/Scrub 30,263 2,723.7 6,730.2 0.011 0.091 Herbaceous 396,536 35,688.2 88,185.6 0.144 0.448 Open Water 5,979 538.1 1,329.7 0.002 0.123 Disturbed 9,680 871.2 2,152.7 0.004 0.200 Coastal Wetland 0 0 0 0.000 0.000 Total 2,752,332 247,710.0 612,091.0 1.000 0.475 Table 99. Results of the 5x5 homogeneous filter on land cover types within scene 1735 in area, proportion of total area, and proportion remaining from 1735 everypixel level. Pixels Hectares Acres Total Area Every-Pixel Deciduous 1,108,997 99,809.7 246,630.0 0.786 0.365 Coniferous 137,106 12,339.5 30,491.0 0.097 0.099 Mixed 26 2.3 5.8 0.000 0.001 Shrub/Scrub 1,462 131.6 325.1 0.001 0.004 Herbaceous 159,539 14,358.5 35,479.9 0.113 0.180 Open Water 808 72.7 179.7 0.001 0.017 Disturbed 2,260 203.4 502.6 0.002 0.047 Coastal Wetland 0 0 0 0.000 0.000 Total 1,410,198 126,917.8 313,614.0 1.000 0.24356 164

Table 100. Error matrix for scene 1735 at the every-pixel level. Reference data (aerial videography points) are shown as column headings, while actual pixel values are displayed in the left column as row headings. The number of points classified correctly in each cover-type is in bold. Deciduous Coniferous Mixed Shrub/ Scrub Herbaceous Water Open Disturbed Coastal Wetland Deciduous 109 7 9 11 2 138 Coniferous 14 3 6 1 5 2 31 Mixed 1 2 3 Shrub/Scrub 1 1 6 1 9 Herbaceous 1 10 3 14 Open Water 2 1 1 4 Disturbed 1 1 Coastal Wetland 0 Other 1 1 2 1 2 7 Total 128 7 16 13 34 0 9 0 207 Total 165

Table 101. User s and producer s accuracy for each land cover class, along with overall accuracy and the Kappa statistic, for scene 1735 at the every-pixel level. User s Producer s Deciduous 0.790 0.852 Coniferous 0.097 0.429 Mixed 0.000 0.000 Shrub/Scrub 0.000 0.000 Herbaceous 0.714 0.294 Open Water 0.000 Disturbed 0.000 0.000 Coastal Wetland Overall Accuracy 0.589 Kappa 0.276 166

Table 102. Error matrix for scene 1735 at the 3x3 homogeneous level. Reference data (aerial videography points) are shown as column headings, while actual pixel values are displayed in the left column as row headings. The number of points classified correctly in each cover-type is in bold. The number of reference points for each category that falls on non-homogeneous pixels is shown in the last row. Deciduous Conifer Mixed Shrub/ Scrub Herbaceous Open Water Disturbed Coastal Wetland Total Deciduous 80 4 6 4 1 95 Coniferous 6 2 2 1 1 12 Mixed 0 Shrub/Scrub 1 1 Herbaceous 6 6 Open Water 0 Disturbed 0 Coastal Wetland 0 Other 0 Total 86 2 6 6 11 0 3 0 114 Non- Homogeneous 42 5 10 7 23 6 93 167

Table 103. User s and producer s accuracy for each category, along with overall accuracy and Kappa statistic, for scene 1735 at the 3x3 homogeneous level. User s Producer s Deciduous 0.842 0.930 Coniferous 0.167 1.000 Mixed 0.000 Shrub/Scrub 0.000 0.000 Herbaceous 1.000 0.545 Open Water Disturbed 0.000 Coastal Wetland Overall Accuracy 0.772 Kappa 0.373 168

Table 104. Error matrix for scene 1735 at the 5x5 homogeneous level. Reference data (aerial videography points) are shown as column headings, while actual pixel values are displayed in the left column as row headings. The number of points correctly classified in each cover-type is in bold. The number of reference points for each category that falls on non-homogeneous pixels is shown in the last row. Deciduous Coniferous Mixed Shrub/ Shrub Herbaceous Open Water Disturbed Coastal Wetland Deciduous 60 3 3 1 67 Coniferous 1 1 Mixed 0 Shrub/Scrub 0 Herbaceous 4 4 Open Water 0 Disturbed 0 Coastal Wetland 0 Other 0 Total 60 1 0 3 7 0 1 0 72 Non- Homogeneous 68 6 16 10 27 8 135 Total 169

Table 105. User s and producer s accuracy for each land cover class, along with overall accuracy and Kappa statistic, for scene 1735 at the 5x5 homogeneous level. User s Producer s Deciduous 0.896 1.000 Conifer 1.000 1.000 Mixed Shrub/Scrub 0.000 Herbaceous 1.000 0.571 Open Water Disturbed 0.000 Coastal Wetland Overall Accuracy 0.903 Kappa 0.556 170