AA_Vis_1. Students examine both the “true color” And “false-color Infrared” images provided by GLOBE AA_Vis_2.

Slides:



Advertisements
Similar presentations
Step 1: Create a folder with your name on your computers desktop to save downloaded materials in. Step 2: Download Multispec software
Advertisements

Mapping Burn Severity. Burned Area Reflectance Classification (BARC)
GLOBE Land Cover Measurements Manual Mapping Land Cover Sample Sites Accuracy Assessment MultiSpec Computer assisted land cover mapping Accuracy Assessment.
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
Utilization of Remotely Sensed Data for Targeting and Evaluating Implementation of Best Management Practices within the Wister Lake Watershed, Oklahoma.
PRODUCT DESCRIPTION Sabie Environmental Consulting TITLE: Remote sensed land cover classifcation REQUIRED BY: Biological Science DepartmentPRODUCT # 34.
Accuracy Assessment of Thematic Maps
Leila Talebi, Anika Kuczynski, Andrew Graettinger, and Robert Pitt
Urbanization and Land Cover Change in Dakota County, Minnesota Kylee Berger and Julia Vang FR 3262 Remote Sensing Section 001/002.
VALIDATION OF REMOTE SENSING CLASSIFICATIONS: a case of Balans classification Markus Törmä.
Lecture 14: Classification Thursday 18 February 2010 Reading: Ch – 7.19 Last lecture: Spectral Mixture Analysis.
AA_Vis_1. Using the software “MultiSpec,” students follow protocols to prepare a “clustered” image of their GLOBE Study Site.
Lecture 14: Classification Thursday 19 February Reading: “Estimating Sub-pixel Surface Roughness Using Remotely Sensed Stereoscopic Data” pdf preprint.
Section 10.3 – The Inverse of a Matrix No Calculator.
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
Accuracy Assessment. 2 Because it is not practical to test every pixel in the classification image, a representative sample of reference points in the.
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Using Remote Sensing Imagery By: J.Verplanke,
Chapter 9 Accuracy assessment in remotely sensed categorical information 遥感类别信息精度评估 Jingxiong ZHANG 张景雄 Chapter 9 Accuracy assessment in remotely sensed.
Methods of Validating Maps of Deforestation and Selective Logging Carlos Souza Jr. Instituto do Homem e Meio Ambiente da Amazônia—Imazon.
An Object-oriented Classification Approach for Analyzing and Characterizing Urban Landscape at the Parcel Level Weiqi Zhou, Austin Troy& Morgan Grove University.
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.
Investigating Land Cover Change In Crow Wing County Emily Smoter and Michael Palmer Remote Sensing of Natural Resources and the Environment University.
U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.
Classification & Vegetation Indices
Forest stratification of REDD pilot sites, using VHR data. Vincent Markiet, Johannes Reiche¹, Samuela Lagataki², Akosita Lewai², Wolf Forstreuter³ 1) Wageningen.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
FAO Actions Related to GFOI Components. FAO history in forest monitoring and assessment Began in 1946 focused on commercial timber Activities involving.
Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.7 Estimation of uncertainties.
 The textbook GIS methods section: Provides basic understanding of GIS concepts What is RS? How can we use RS for GIS, when, where and why?
Forest Cover at Pisgah State Park. Size Class 1 Seedlings Under 1 inch DBH.
Accuracy Assessment Having produced a map with classification is only 50% of the work, we need to quantify how good the map is. This step is called the.
Height Growth [m/3yr] An example: Stand biomass estimation by LiDAR.
Land Cover Change Monitoring change over time Ned Horning Director of Applied Biodiversity Informatics
Land Cover Change Monitoring change over time Ned Horning Director of Applied Biodiversity Informatics
Future Discussion Introduction MethodologyResultsAbstract There are three types of data used in the project. They are IKONOS, ASTER, and Landsat TM, representing.
Environmental Modeling Advanced Weighting of GIS Layers.
Remote Sensing Classification Accuracy
Application of spatial autocorrelation analysis in determining optimal classification method and detecting land cover change from remotely sensed data.
BOT / GEOG / GEOL 4111 / Field data collection Visiting and characterizing representative sites Used for classification (training data), information.
Remote Sensing 13/10/2009 Dr. Ahmad BinTouq URL: GEO.
Comparing Three Great Lakes Research Projects By Mary Bresee.
Background: The Center for International Forestry Research (CIFOR) together with the International Centre for Research in Agroforestry (ICRAF) and the.
H51A-01 Evaluation of Global and National LAI Estimates over Canada METHODOLOGY LAI INTERCOMPARISONS LEAF AREA INDEX JUNE 1997 LEAF AREA INDEX 1993 Baseline.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
1 Estimation of Diffused pollution loads declination by purchasing Land of Riparian buffer zone assigned to Dae-cheong water resource area using Remote.
Mapping Canada’s Rangeland and Forage Resources using Earth Observation Emily Lindsay MSc Candidate – Carleton University Supervisors: Doug J. King & Andrew.
Environmental Modeling Validating GIS Models. 1. A Habitat Model Issues: ► Mapping Florida Scrub Jay habitat in the Kennedy Space Center in the Kennedy.
Case Study 1: The North- Eastern Fringes of Bukit Barisan Selatan National Park, Lampung, Sumatra Background The site is mountainous with an elevation.
Housekeeping –5 sets of aerial photo stereo pairs on reserve at SLC under FOR 420/520 –June 1993 photography.
The Global Land Cover Facility BRAZIL ARGENTINA PARAGUAY Forest Nonforest Deforestation Water Protected Area Cloud Forest Cover Change Eastern Paraguay.
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.
LANDSAT EVALUATION OF TRUMPETER SWAN (CYGNUS BUCCINATOR) HISTORICAL NESTING SITES IN YELLOWSTONE NATIONAL PARK Laura Cockrell and Dr. Robert B. Frederick.
Accuracy Assessment Accuracy Assessment Error Matrix Sampling Method
Accuracy Assessment of Thematic Maps THEMATIC ACCURACY.
Remote Sensing Dr. Ahmad BinTouq GEO440: GIS for Urban & Regional Planning.
26. Classification Accuracy Assessment
Assessing the climate impacts of land cover and land management using an eddy flux tower cluster in New England Earth Systems Research Center Institute.
26. Classification Accuracy Assessment
Accuracy Assessment of Thematic Maps
Feature Extraction “The identification of geographic features and their outlines in remote-sensing imagery through post-processing technology that enhances.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
National Forest Inventory for Great Britain
Housekeeping 5 sets of aerial photo stereo pairs on reserve at SLC under FOR 420/520 June 1993 photography.
Planning a Remote Sensing Project
Section 3.3 – The Inverse of a Matrix
Land Cover Investigation
Remote Sensing Landscape Changes Before and After King Fire 2014
Evaluating the Ability to Derive Estimates of Biodiversity from Remote Sensing Kaitlyn Baillargeon Scott Ollinger, Andrew Ouimette,
Calculating land use change in west linn from
Christina Konnaris (Jake Brenner)
Presentation transcript:

AA_Vis_1

Students examine both the “true color” And “false-color Infrared” images provided by GLOBE AA_Vis_2

Students construct a Land Cover Map keyed to the MUC System AA_Vis_3

Students gather data From 90 x 90 m Qualitative Land Cover Samples to “validate” their Map. AA_Vis_4

1222 Site 1 92 Site Site 3 92 Site 4 At each of these sites, The MUC value is determined, a GPS reading is taken, and photographs are taken. These are Validation Data. 91 Site Site 6 Site Site AA_Vis_5

Warner’s Woods X UPS Complex √ State Park Area √ Truck Terminal √ Clark’s Loop X Dale’s Grove √ School Woodlot X Town Grove √ The Accuracy Assessment Data Work Sheet is filled out for all sites AA_Vis_6

Map Data (What we expected to find) Validation Data (What we actually found) The Difference/Error Matrix is Constructed AA_Vis_7

Total Map Data (What we expected to find) Validation Data (What we actually found) The Difference/Error Matrix is Constructed AA_Vis_7

Total Map Data (What we expected to find) Validation Data (What we actually found) Map and Validation Data Are Entered on the Matrix | | | | | AA_Vis_8

Total Map Data (What we expected to find) Validation Data (What we actually found) | | | | | Totals Are Calculated for Rows and Columns AA_Vis_9

Overall Accuracy is Calculated Overall Accuracy = x 100 Overall Accuracy = x 100 = 63% Sum of Major Diagonal Tallies Total Number of Samples 5 8 AA_Vis_10

Total More Qualitative Sites are visited, and the Matrix is Developed for All Classes on the Map Total AA_Vis_11

Overall Accuracy is Calculated Overall Accuracy = X 100 = 0.74 X 100 = 74% AA_Vis_12

REMOTELY SENSED FOREST VEGETATION MAPS: RASTER MAPS CREATED FROM LANDSAT TM IMAGERY July 1996 Landsat TM Image Pawtuckaway Study Area, NH, USA Forest Cover Type Map from Classification of July 1996 Landsat TM Image Pawtuckaway Study Area, NH, USA Legend Class Names White Pine White Pine-Hemlock Other Conifer Hemlock Oak Red Maple Mixed Forest Nonforest Beech Other Scale Kilometers AA_Vis_13

ERROR MATRICES AND ESTIMATES OF ACCURACY AA_Vis_15