Urbanization by Watersheds

Slides:

Advertisements

Similar presentations

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

Advertisements

Northeast Landscape Planning Committee Meeting #7 – Ecological Policy Framework (cont’d) Resource review Landcover data Watershed data FIA data.

Urbanization and Land Cover Change in Dakota County, Minnesota Kylee Berger and Julia Vang FR 3262 Remote Sensing Section 001/002.

Issues Urban pollution Agricultural water use Linking the rivers of India Conservation.

Global Land Cover: Approaches to Validation Alan Strahler GLC2000 Meeting JRC Ispra 3/02.

? ? How much resource is there now? Where is the resource located? What will the resource be in the future? Mississippi Institute for Forest Inventory.

Accuracy Assessment. 2 Because it is not practical to test every pixel in the classification image, a representative sample of reference points in the.

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

Ten State Mid-Atlantic Cropland Data Layer Project Rick Mueller Program Manager USDA/National Agricultural Statistics Service Remote Sensing Across the.

Co-authors: Maryam Altaf & Intikhab Ulfat

The Dying Dead Sea Assessing the decline of the Dead Sea area in relation to irrigated agriculture Noel Peterson and Zach Tagar FR 5262.

Ann Krogman Twin Cities Urban Lakes Project. Background Information… 100 lakes throughout the Twin Cities Metro Area Sampled in 2002 Land-use around each.

Investigating Land Cover Change In Crow Wing County Emily Smoter and Michael Palmer Remote Sensing of Natural Resources and the Environment University.

Classification & Vegetation Indices

Exercise #5: Supervised Classification. Step 1. Delineating Training Sites and Generating Signatures An individual training site is delineated as an “area.

Team: Andrea Claassen and Thomas Bales FR 5262, 12/13/2011 Instructor: Joe Knight.

Conversion of Forestland to Agriculture in Hubbard County, Minnesota By: Henry Rodman Cory Kimball 2013.

Source Water Protection Management Strategies for Source Water Protection Plan’s Implementation Contact Information: Lisa Vollbrecht City of St. Cloud.

Seto, K.C., Woodcock, C.E., Song, C. Huang, X., Lu, J. and Kaufmann, R.K. (2002). Monitoring Land-Use change in the Pearl River Delta using Landsat TM.

EROS Data Center 1.br.ini. 5/11/95 GLC-2000 North America: Classification and Results Validation Chandra Giri SAIC, EROS Data Center Zhiliang Zhu USGS,

Water a single resource. Watershed Area that contributes water to a particular point on a stream or other surface-water feature, such as a lake Based.

Hurricane Katrina Damage Analysis Alex Stern and William Tran.

Where the Missouri River Meets the Mississippi River Edward "Ted" and Pat Jones-Confluence Point State Park.

Imagine there’s no tree canopy…....it’s easy if you try.

Change Detection in the Metro Area Michelle Cummings Laura Cossette.

Hill Country Associates Pedernales River analysis Team: Kelly Blanton, Erica Tice, William Weldon, and Paul Starkel.

 Up to what level of classification can we perform on LISSIII/LISSIV data?  Is any advantage of high spectral resolution of LISSIII over LISSIV. If.

Landscape Analysis: 1.Determine 1993 and 2001 Land Cover of the Sougahatchee Basin Sougahatchee Basin 2. Compare 2001 Land Cover to 1993 Land Cover 3.

Land Use / Land Cover Change in the Phoenix Metropolitan Area Lori Krider & Melinda Kernik

By: Katie Blake and Paul Walters.  To analyze land cover changes in the Twin Cities Metro Area from 1984 to 2005 Image difference and Thematic Change.

February 25-26, 2003 San Diego, California Inventory & Monitoring Technology Development USDA Forest Service, Remote Sensing Application Center,

Remote Sensing Classification Accuracy

Object-oriented Land Cover Classification in an Urbanizing Watershed Erik Nordman, Lindi Quackenbush, and Lee Herrington SUNY College of Environmental.

An Analysis of Land Use/Land Cover Changes and Population Growth in the Pedernales River Basin Kelly Blanton-Project Manager Paul Starkel-Analyst Erica.

1 Estimation of Diffused pollution loads declination by purchasing Land of Riparian buffer zone assigned to Dae-cheong water resource area using Remote.

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.

By:Nick Severson Brian Trick Land Cover Change of Twin Cities Metro and Scott County ______________________________FR Fall 2013.

Accuracy Assessment Accuracy Assessment Error Matrix Sampling Method

LAND USE/LAND COVER CHANGE IN BEXAR COUNTY, TEXAS Maryia Bakhtsiyarava FNRM 5262.

Detecting Land Cover Land Use Change in Las Vegas Sarah Belcher & Grant Cooper December 8, 2014.

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.

Detect geographic and temporal patterns of land use and land cover change in China from 1982 to 2012 Yaqian He, Eungul Lee, and Timothy A. Warner May 5.

26. Classification Accuracy Assessment

Jakobshavn Isbrae Glacial Retreat

Flint Hills Map Exhibit Unit: Flint Hills Region Map Activities

Quantifying Urbanization with Landsat Imagery in Rochester, Minnesota

26. Classification Accuracy Assessment

HYDRAULIC FRACKING & ITS EFFECTS ON LAND COVER CHANGE

Frank Falzone Ross Meyer FR December.2012

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

By Yudhi Gunawan * and Tamás János **

Stainless Steel Water Well Casing.

Evaluating Land-Use Classification Methodology Using Landsat Imagery

Erosion Patterns in Austin Streams

REMOTE SENSING ANALYSIS OF URBAN SPRAWL IN BIRMINGHAM, ALABAMA:

Name: _________________ Class: ________

Hill Country Associates Pedernales River analysis

Effect of Land Use and Land Cover changes on Salim Ali Bird Sanctuary

Supervised vs. unsupervised Learning

MRV & Reporting Status & Related Space Data Needs

Hill Country Associates Progress Report

Жирэмслэлт ба тархины харвалт

Research Question Study Area = Twin Cities 7 County Metro

Remote Sensing Landscape Changes Before and After King Fire 2014

Image Classification of the Upper South Fork Eel River Watershed

Calculating land use change in west linn from

Christina Konnaris (Jake Brenner)

Change in Land Cover in Elk Grove

Presentation of Jordan Case Study First Management Board Meeting

Presentation transcript:

Urbanization by Watersheds 1984 – 2009 Twin Cities Metropolitan Area Urbanization by Watersheds Tobias Fimpel & Alex Steele

Objective Identify land cover changes within the 8 primary watersheds in the Twin Cities metro area from 1984 – 2009

Study Area

Data Landsat images Watersheds in the Twin Cities Metropolitan Area 8-15-1984 7-25-2009 Watersheds in the Twin Cities Metropolitan Area Shapefile,DNR Level 08 Catchments from the Metropolitan Council.

Data

Procedures – Training Areas Classification Scheme: Water Urban Undeveloped Agriculture

Procedures – Supervised Classification

Procedures – Clipping

Procedures – Accuracy Assessments Stratified Random Samples Reference Data: Landsat Overall accuracies of 72% and 84%

Procedures – Change Detection

Procedures – Clip Watersheds

Results

Results

Mississippi River – Rush – Vermillion Results Mississippi River – Rush – Vermillion North Fork – Crow River From-To Change Area (Hectacres) Percent Change Ag to Ag 19453.1 22.9529145 Ag to Undev 1014.03 1.196464517 Ag to Urban 14063.6 16.59378754 Ag to Water 108.63 0.128173664 Undev to Ag 24758.8 29.21316497 Undev to Undev 3143.61 3.709178051 Undev to Urban 16420.82 19.37509587 Undev to Water 206.82 0.244029064 Urban to Ag 1442.7 1.702256697 Urban to Undev 76.23 0.089944568 Urban to Urban 3553.92 4.193307076 Urban to Water 36.99 0.043644885 Water to Ag 86.94 0.102581408 Water to Undev 112.41 0.132633725 Water to Urban 169.11 0.199534643 Water to Water 104.49 0.123288835 Total 84752.2 100 From-To Change Area (Hectacres) Percent Change Ag to Ag 1963.35 16.46104509 Ag to Undev 669.87 5.616298812 Ag to Urban 1908.09 15.99773628 Ag to Water 94.59 0.793057914 Undev to Ag 2861.28 23.98943596 Undev to Undev 1179 9.884927372 Undev to Urban 2431.8 20.38860592 Undev to Water 108.18 0.906998679 Urban to Ag 126.36 1.05942275 Urban to Undev 49.14 0.411997736 Urban to Urban 231.57 1.941520468 Urban to Water 8.55 0.071684588 Water to Ag 40.59 0.340313148 Water to Undev 80.37 0.673835125 Water to Urban 64.98 0.544802867 Water to Water 109.53 0.918317299 Total 11927.25 100

Results

Results

Urbanization