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

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The Team Jianyu Gu - BS Lanzshou U in GIS - PhD student at Beijing Normal U. in GIS Kamini Yadav - BS U. of Delhi, Zoology MS TERI (The Energy & Resource Institute, Delhi – ISRO (Indian Space Research Org) at RRSC Jodhpur - PhD Student - UNH 3

Our Activities To Date Orientation to Project Begin Search for High-Res Reference Data USGS NGA – no luck with account so far NASA – confused Error matrix software in R Draft Reference Data Collection Form Review mapping projects – write pape r 4

Uncertainty Analysis Method for evaluating the mapping process Allows assessment of where errors occur, magnitude of error, and ability to control error Will review previous mapping projects to understand their processes Will apply to our mapping process to produce best results 5

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Draft Uncertainty Analysis 7 N. Error contribution Potential Implementation Difficulty Implementation Priority 1SystematicLow5 1.1Spatial resolutionLow Spectral resolutionLow521 2NaturalMedium4 2.1AtmosphereMedium420 3Input dataMedium2 3.1 Temporal NDVIMedium Spectral bandsMedium318 4Ancillary dataLow2 4.1SARLow Regional land cover mapsMedium High resolution imagesLow215 5PreprocessingLow2 5.1Geometric correctionLow Atmospheric correctionLow Cloud mask computation Low water mask computation Medium Forest MaskMedium Snow maskMedium112 6Classification systemHigh3 6.1Classification schemeHigh35 6.2Training sitesHigh23 6.3Number of classesMedium37 6.4Classification methodMedium26 7Processing sequenceMedium18 8Accuracy assessmentHigh1 8.1Sampling schemeHigh11 8.2Reference dataHigh24 8.3Interpreters' skillHigh12 Note: Implementation difficulty- ranked from 1: not very difficult to 5: extremely difficult

Lessons Learned Do not start any work until have basics determined and a complete procedure manual approved by all Must determine certain basics NOW! Classification scheme with complete definitions What about mixtures? Standard definition for rainfed vs. irrigated MMU Same scheme must be used in labeling the image map and the reference data Consider issues with doing regional analysis and then merging together Joining edges? Agreement about how & when ancillary data used Masking issues – cropland extent? 8

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10 Goal of our Assessment Balance statistical validity with practical application. If it is not going to be valid, why do it? If can not afford to do is right, why do it? Must document your process!

Reference Data Issues Must compile what reference data we already have (Mutlu’s group) By date and by REGION Must determine COMMON FORMAT and apply to all data Must evaluate reference data to make sure it is valid Must augment reference data to insure adequate amount for assessment 11

More Reference Data Issues Use half the data for training, but must put other half aside and not seen by analysts Need each group to think about this now Complete the reference data collection form 12

13 A Final Favor Ground Truth Please use the term – “Reference Data” or “Ground data”

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