August 20, 2015 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Mutlu Ozdogan Center for Sustainability.

Slides:

Advertisements

Similar presentations

GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) LAND COVER MAP OF.

Advertisements

North American Croplands Richard Massey & Dr. Teki Sankey.

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Author/Presenter MRV & Reporting Status & Related Space Data.

Accuracy Assessment Error Matrix Program Kamini Yadav and Russ Congalton.

Biometrics & Security Tutorial 5. 1 (a) Understand two stages (Enrollment and Authentication) in a fingerprint system. (P6: 12)

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

VALIDATION OF REMOTE SENSING CLASSIFICATIONS: a case of Balans classification Markus Törmä.

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

Original image: 512 pixels by 512 pixels. Probe is the size of 1 pixel. Picture is sampled at every pixel ( samples taken)

A Novel 2D To 3D Image Technique Based On Object- Oriented Conversion.

AA_Vis_1. Using the software “MultiSpec,” students follow protocols to prepare a “clustered” image of their GLOBE Study Site.

Validation of the GLC2000 products Philippe Mayaux.

Land Use/Land Cover Assessment of Dane County, Wisconsin: Contemporary Trend and Future Projections By Eric Fabian.

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

A Statistically Valid Method for Using FIA Plots to Guide Spectral Class Rejection in Producing Stratification Maps Mike Hoppus & Andrew Lister USDA-Forest.

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.

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

Transforming a sample design for taking into account new statistical needs, new information or new technological instruments for data collection Elisabetta.

By Phoebe Oduor.  Countries are required to report on their GHG emissions to UNFCCC every four years (mandatory) and every two years (intermediary) 

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,

Lu Liang, Peng Gong Department of Environmental Science, Policy and Management, University of California, Berkeley And Center for Earth System Science,

March 19, 2015 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Prof. Mutlu Ozdogan Nelson Institute.

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.

Line detection Assume there is a binary image, we use F(ά,X)=0 as the parametric equation of a curve with a vector of parameters ά=[α 1, …, α m ] and X=[x.

The study area is the Sub-Saharian Africa. According to the IGBP vegetation map the major vegetation types present in the area include savanna and woody.

Selection of Multi-Temporal Scenes for the Mississippi Cropland Data Layer, 2004 Rick Mueller Research and Development Division National Agricultural Statistics.

Height Growth [m/3yr] An example: Stand biomass estimation by LiDAR.

U.S. Department of the Interior U.S. Geological Survey Monthly Progress for Africa GCEV2 Jun Xiong, Prasad, Pardha 25 September, 2014.

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.

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

Remote Sensing Classification Accuracy

Mixture of Gaussians This is a probability distribution for random variables or N-D vectors such as… –intensity of an object in a gray scale image –color.

Validation of MODIS Snow Mapping Algorithm Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.

Reference Data Standard Protocols Kamini Yadav Dr. Russ Congalton.

Accuracy Assessment: Building Global Cropland Reference Data Updates for March 2015 Kamini Yadav and Russ Congalton.

U.S. Department of the Interior U.S. Geological Survey Monthly Progress for Africa GCEV2 Jun Xiong, Prasad, Pardha 23 October, 2014.

Kussul Nataliia, Shelestov Andrii, Skakun Sergii Space Research Institute of NAS of Ukraine and SSA of Ukraine Kyiv National University of Environmental.

1 Global Mapping, Approaches, Issues, and Accuracies Russ Congalton & Kamini Yadav University of New Hampshire January 16, 2014 Menlo Park, CA.

February 19, 2015 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Prof. Mutlu Ozdogan Nelson Institute.

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.

Reference data & Accuracy Assessment Dr. Russ Congalton Kamini Yadav.

Cropland mapping in South America

Cropland Extent Mapping in South America Global Food Security - Support Analysis m Chandra Giri, Ying Zhong January 19 th, 2016.

Landsat-based cropland mapping in Europe and North Africa Aparna Phalke and Mutlu Ozdogan.

November 20, 2014 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Prof. Mutlu Ozdogan Nelson Institute.

U.S. Department of the Interior U.S. Geological Survey Chandra Giri Ying Zhong 7/15/2015 Cropland Extent Mapping in South America.

Dr. Russ Congalton & Kamini Yadav GFSAD30 Meeting, Menlo Park 19 th -21 st January, 2016 Reference Data Collection & Accuracy Assessment: Some Results.

Part 4: Contextual Classification in Remote Sensing * There are different ways to incorporate contextual information in the classification process. All.

NMF Demo: Lee, Seung Bryan Russell Computer Demonstration.

Accuracy Assessment Accuracy Assessment Error Matrix Sampling Method

US Croplands Richard Massey Dr Teki Sankey. Objectives 1.Classify annual cropland extent, Rainfed-Irrigated, and crop types for the US at 250m resolution.

April 23, 2015 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Prof. Mutlu Ozdogan Nelson Institute.

ACCURACY ASSESSMENT SOUTH AMERICA, NORTH AMERICA KAMINI YADAV DR. RUSSELL CONGALTON.

26. Classification Accuracy Assessment

Gofamodimo Mashame*,a, Felicia Akinyemia

GEO-XIII Plenary, 8/10/2016 St Petersburg, Russian Federation

26. Classification Accuracy Assessment

Classification of unlabeled data:

The e-sensing architecture for big Earth observation data analytics

1) Brazil Greece 33⁰ 13⁰.

Chapter 10 Image Segmentation.

Bryan Russell Computer Demonstration

Assessment of data quality

Urbanization by Watersheds

network of simple neuron-like computing elements

The Image The pixels in the image The mask The resulting image 255 X

Mediterranean Action Plan/Barcelona Convention Current status and challenges for establishment of PRTR systems in Southern Mediterranean Countries Third.

Presentation transcript:

August 20, 2015 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Mutlu Ozdogan Center for Sustainability and the Global Environment (SAGE) Nelson Institute for Environmental Studies University of Wisconsin - Madison

Updates on following Stratification of study area Developing New LDA models at three different levels according to new strata Scaling of data Area Based Accuracy

ZoneCore_SceneRandom_Scene zone1177p39r (Egypt)171p39r 182p38r(EgyptR1/Libya)177p40r 194p36r 198p38r 201p38r zone2174p35r (Syria)171p35r 172p34r (Turkey)175p33r 179p33r(bulgariaR3) zone3200p33r (Spain)196p35r 201p37r (Morocco)202p34r 193p35r(Algeria)200p31r 192p35r(Tunisia) zone4181p26r (Ukraine)174p28r 176p26r 173p25r zone5182p30r (Bulgaria)190p26r 187p27r(Hungary)183p27r 182p29r(Romania)185p28r 184p30r zone6199p26r(France)196p26r 202p24r (UK)197p27r 200p27r 207p22r zone7189p24r(Poland)187p19r 193p24r(Germany)182p24r 182p21r(ukraineR1/Russia and some part Belarus)183p25r 185p24r 186p22r 188p25r 192p23r 196p23r 194p25r 175p23r zone8203p33r(Portugal)191p27r 192p29r(Italy)189p31r 196p29r(algeriaR1/france)192p30r 200p29r 202p31r 201p32 193p27r Total eight zones covering 23 core footprints and 36 random footprints

New own LDA model coefficients meansdmaxminvarrangecountslopeelevationcutoffpoint egypt(177p39r) libya(182p38r) syria(174p35r) turkey_west(172p34r) turkey_east(179p33r) spain morocco algeria tunisia ukraine (181p26r) p26r 173p25r bulgaria (182p30r) hungary(187p27r) romania(182p29r) france(199p26r) UK(202p24r) poland(189p24r) germany(193p24r) Russia and some part Belarus(182p21r) portugal(203p33r) italy(192p29r) franceORalgeriaR1(196 p29r)

New zonal LDA model coefficients meansdmaxminvarrangecountslopeelevationcutoffpoint zone zone zone zone zone zone zone

New segment-based (Own-based accuracy) Overall accuracy producer_accuracy_cro p.i. producer_accurac y_noncrop.i. user_accuracy_crop.i. user_accuracy_n oncrop.i. egypt(177p39r)84.93%90.61%69.65%89.03%72.95% libya(182p38r)87.55%44.63%95.52%64.57%90.30% syria(174p35r)76.77%75.74%78.07%78.26%75.40% turkey_west(172p34r ) 87.32%87.59%86.91%92.66%78.73% turkey_east(179p33r ) 78.30%70.94%85.32%81.91%75.68% spain83.23%82.76%84.27%91.19%71.18% morocco77.68%64.57%90.00%85.65%73.15% algeria74.93%62.86%87.42%83.57%69.64% tunisia72.45%66.92%78.71%77.69%68.14% ukraine (181p26r)70.08%69.43%70.68%65.57%74.12% 176p26r 173p25r bulgaria (182p30r)79.36%77.96%80.98%80.24%78.60% hungary(187p27r)71.04%83.49%55.65%70.08%72.94% romania(182p29r)78.52%85.54%67.58%80.68%74.56% france(199p26r)76.77%87.39%65.45%73.16%82.71% UK(202p24r)63.24%71.46%50.23%69.62%52.37% poland(189p24r)72.25%89.73%40.73%73.29%68.54% germany(193p24r)74.28%78.23%70.89%70.59%78.37% Russia and some part Belarus(182p21r) 67.12%21.01%94.25%68.12%67.01% portugal(203p33r)76.98%59.00%88.64%76.77%77.09% italy(192p29r)81.08%80.35%81.89%80.07%82.04% franceORalgeriaR1( 196p29r) 76.81%53.25%89.49%72.54%78.32% zone egypt(177p39r)86.07% libya(182p38r)85.61% syria(174p35r)73.78% turkey_west(17 2p34r) 84.60% turkey_east(17 9p33r) 75.84% spain83.98% morocco75.89% algeria67.74% tunisia69.80% ukraine (181p26r) 176p26r 173p25r bulgaria (182p30r) 77.30% hungary(187p2 7r) 73.01% romania(182p2 9r) 80.00% france(199p26r)71.87% UK(202p24r)57.81% poland(189p24r ) 73.18% germany(193p2 4r) 68.07% Russia and some part Belarus(182p21 r) 83.25% portugal(203p3 3r) 62.88% italy(192p29r)72.99% franceORalgeri aR1(196p29r) 77.94%

Area Based Accuracy Traditional pixel based approach accuracy: where π is overall accuracy, Ci is equal to 1 or 0 if the validation sample unit i is correctly classified (yes and no, respectively), and n is the number of validation units collected. The accuracy of a map created using OBIA should be computed using: where N is the total number of segments in the image, and Si is the area of a single sample unit i.

Reference: Meghan MacLean, Russell Congalton, “Map accuracy assessment issues when using an object based oriented approach” Dissertation, University of New Hampshire.

Thank you