1. Landsat vs. Rapideye TREE box S7 E39 (in dash line) 1 3 4 2 5 Landsat 17/05/2010 Rapideye 02/07/2010.

Slides:



Advertisements
Similar presentations
Sampling Design, Spatial Allocation, and Proposed Analyses Don Stevens Department of Statistics Oregon State University.
Advertisements

Mixed Designs: Between and Within Psy 420 Ainsworth.
DDDAS: Stochastic Multicue Tracking of Objects with Many Degrees of Freedom PIs: D. Metaxas, A. Elgammal and V. Pavlovic Dept of CS, Rutgers University.
Local Linearization (Tangent Line at a point). When the derivative of a function y=f(x) at a point x=a exists, it guarantees the existence of the tangent.
Supplementary Figure S1 Distribution of observed (blue) and Poisson expected (red) standard deviation of human-chimpanzee divergence over different window.
HMM-BASED PATTERN DETECTION. Outline  Markov Process  Hidden Markov Models Elements Basic Problems Evaluation Optimization Training Implementation 2-D.
Accuracy Assessment Chapter 14. Significance Accuracy of information is surprisingly difficult to address We may define accuracy, in a working sense,
7-1 Chapter Seven SAMPLING DESIGN. 7-2 Sampling What is it? –Drawing a conclusion about the entire population from selection of limited elements in a.
CHAPTER 3 Community Sampling and Measurements From: McCune, B. & J. B. Grace Analysis of Ecological Communities. MjM Software Design, Gleneden Beach,
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.
Mapping of mountain pine beetle red-attack forest damage: discrepancies by data sources at the forest stand scale Huapeng Chen and Adrian Walton.
Geo479/579: Geostatistics Ch13. Block Kriging. Block Estimate  Requirements An estimate of the average value of a variable within a prescribed local.
Moving on From Experimental Approaches to Advancing National Systems for Measuring and Monitoring Forest Degradation Across Asia Moving on From Experimental.
Sampling in i-Tree Concepts, techniques and applications.
Factor trees.
Inventory Presentation to VFR Regional Management Team July 2001 Regional TEM and VRI Status and Issues Arrowsmith TSA Inventory Update Audit IKONOS Satellite.
Lecture 14 Dustin Lueker. 2  Inferential statistical methods provide predictions about characteristics of a population, based on information in a sample.
Hiroshi Sasakawa Ph. D. Japan Forest Technology Association Remote sensing expert JICA Project in Gabon International Symposium on Land Cover Mapping for.
Imagery Data  Landsat imagery Apr May 2000 Apr  RapidEye Dec  Fisheye ground survey images Oct Spring 2013 Spring 2014.
Image Classification 영상분류
Imagine there’s no tree canopy…....it’s easy if you try.
Assessing Urban Forests Top-down Bottom-up. Assessing Urban Forests Top-down Produces good cover estimates Can detail and map tree and other cover locations.
Normalized Difference Fraction Index (NDFI): a new spectral index for enhanced detection of forest canopy damage caused by selective logging and forest.
Use of Aerial Videography in Habitat Survey and Computers as Observers Leonard Pearlstine University of Florida.
Data Hiding in Image and Video Part I: Fundamental Issues and Solutions ECE 738 Class Presentation By Tanaphol Thaipanich
Copyright in Modern Times $$$ ™ vs ©
Winter Injury in American chestnut James Sharpe Rebecca Stern April 20, 2015 Stat 231 James Sharpe Rebecca Stern April 20, 2015 Stat 231.
Challenges of monitoring natural disturbance processes using remotely sensed data in North Coast and Cascades Network: comparison of approaches Natalya.
PSY 1950 t-tests, one-way ANOVA October 1, vs.
Sampling for Part Based Object Models Daniel Huttenlocher September, 2006.
Chuvieco and Huete (2009): Fundamentals of Satellite Remote Sensing, Taylor and Francis Emilio Chuvieco and Alfredo Huete Fundamentals of Satellite Remote.
NTTS 2011 Brussels February 22, Joint Research Centre (JRC) Sampling Very High Resolution Images for Area Estimation
Estimating Products and Quotients
Canopy Forest Cover Change in Fanjingshan National Nature Reserve: “What measurable environmental changes have taken place after implementing payment for.
Evaluation of Landscape Vegetation Inventory 2014 Forest Analysis & Inventory Branch (FAIB)
The Effects of Spatial Patterns on Canopy Cover Estimated by FVS (Forest Vegetation Simulator) A Thesis Defense by Treg Christopher Committee Members:
Impacts of Landuse Management and Climate Change on Landslides Susceptibility over the Olympic Peninsula of Washington State Muhammad Barik and Jennifer.
Citation: Moskal., L. M. and D. M. Styers, Land use/land cover (LULC) from high-resolution near infrared aerial imagery: costs and applications.
Observations about Error in Integral Approximations The Simplest Geometry.
Citation: Moskal, L. M., D. M. Styers, J. Richardson and M. Halabisky, Seattle Hyperspatial Land use/land cover (LULC) from LiDAR and Near Infrared.
1 We will illustrate the heteroscedasticity theory with a Monte Carlo simulation. HETEROSCEDASTICITY: MONTE CARLO ILLUSTRATION 1 standard deviation of.
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.
Why Study Land Cover?. Our GPS readings are accurate to about ± 16 meters. Satellite ground tracks do not exactly cover any one 30 m x 30 m site If.
Potential impacts of map error on land cover change detection Nick Cuba Clark University 2/25/12 1.
Images were sourced from the following web sites: Slide 2:commons.wikimedia.org/wiki/File:BorromeanRing...commons.wikimedia.org/wiki/File:BorromeanRing...
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Pairwise comparisons: Confidence intervals Multiple comparisons Marina Bogomolov and Gili Baumer.
Counting the trees in the forest
26. Classification Accuracy Assessment
Joonghoon Shin Oregon State University
More on Inference.
What’s new in FUSION? Bob McGaughey
Great Barrier Reef Report Card 2015 – Burdekin: Ground Cover
Introduction to Hypothesis Test – Part 2
Factsheet # 12 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Land use/land cover (LULC) from high-resolution.
A Comparative Analysis of Urban Tree Canopy Assessment Methods in Minnesota Remote Sensing of Natural Resources and Environment | FR 5262 | University.
Monitoring Surface Area Change in Iowa's Water Bodies
More on Inference.
Agrostatistics Directorate Ministry of Agriculture and Forestry
Slug Tests Under- versus Overestimation of Aquifer
Chapter 10 Image Segmentation.
Corn and Soybean Differentiation Using Multi-Spectral Landsat Data
The What’s-It Tree All images cleared for re-use for non-commercial purposes.
Satellite Eyes Slide Show Assessment.
Diagnostics and Remedial Measures
Bootstrap Segmentation Analysis and Expectation Maximization
Image Classification of the Upper South Fork Eel River Watershed
Diagnostics and Remedial Measures
Computed Tomography (C.T)
Presentation transcript:

1. Landsat vs. Rapideye TREE box S7 E39 (in dash line) Landsat 17/05/2010 Rapideye 02/07/2010

Site1

Site2

Site3

Site 4

Site5

2. Change detection Landsat 17/5/2010 Rapideye 2/7/2010 Landsat 30/6/2000

Landsat 1/7/2010Rapideye 2/7/2010

3. Forest cover detection Landsat 1/7/2010 Rapideye 2/7/2010 VHR image 05/10/2009 Object-based segmentation

Rapideye vs. Landsat Landsat 2010 Rapideye 2010 Analysis of no-change polygons

Landsat overestimation of logged area VHR LS RE

VHR LS RE Non detection of small forest blocks

82% forest cover 81% forest cover 47% forest cover VHR LS RE Underestimation of forest cover

Rapideye vs. Landsat In fragmented landscapes, Landsat fails to detect forest in many areas with forest cover of 20-90% (errors of omission) In homogeneous landscapes, Landsat fails to detect small canopy openings (errors of forest commission) Landsat forest estimates have high variance in those areas with forest cover higher than 90% (inaccuracy and lack of precision).

Rapideye vs. Landsat Landsat fails to detect forest in many areas smaller than 1 ha.

Actual distribution of forest cover within polygons from VHR data (just for comparison with the previous slide).

Landsat overestimation of logged area VHR LS SE_S

VHR LS SE_S Non detection of small forest blocks

82 % forest cover 76 % forest cover 47 % forest cover VHR LS SE_S Underestimation of forest cover