An Innovative Approach To Integrating SAS Macros With GIS . - USDA
An Innovative Approach to Integrating SAS Macros with GIS Software Products toProduce County-Level Accuracy AssessmentsAudra ZakzeskiUSDA NASSRobert SeffrinUSDA NASS
Introducing the Cropland Data LayerAgriculture by crop type and location 9 billion pixels!Cropland Data Layer Free to the public A decade of history Updated annually 130 different landcover categoriesA sample:Resources 6 remote sensinganalysts/statisticians 5-6 states peranalyst during growingseason
Satellite Imagery to Land Cover MapProcessing Steps1. Gather satellite imagery from April - October2. Gather farm/crop data from Farm ServiceAgencySatellite Imagery3. Take sample of satellite pixels for eachdifferent type of land cover and developseasonal profile of the land4. Construct decision tree of all land coverprofiles5. Apply decision tree to all satellite imagepixels and areas of the land resulting in finalCropland Data Layer6. Derive accuracy assessments for all states& types of land cover using validation data.Cropland Data Layer
Accuracy AssessmentsSave your eyes – Don’t try to read that – it’s just a visualThings to measure for each crop per state1.Producer Accuracy/Omission Error Probability a validation pixel is correctly mapped. Occurs when a pixel from the validation is excluded from thecorrect category2.User Accuracy/Commission Error Probability a CDL pixel matches the validation pixel Occurs when a pixel is included in the incorrect category3.Confidence Value 4.Measure of how easy it was to classify a pixel – calculated inERDAS ImagineAcreage Estimate & Coefficient of Variation 5.Calculated in SAS using Linear RegressionPixel Count Acreage Count of pixels for each land cover
Step 1: Batch file to run ERDAS ImagineCalculating producer accuracy, user accuracy, confidencevalue, and pixel counts happens in ERDAS Imagine.Use SAS to create batch files calling ERDAS Imagine andaccuracy calculations%LET DirEst File path for Estimate file;%LET DirCnty File path for state/county outlines;%LET IMGWork ERDAS Model .exe file path;%LET Model Preexisting ERDAS .pmdl model file path;%LET Zone1 &DirCnty/Specific State Outline;%LET Zone2 File path for CDL File #1;%LET Class File path for CDL File #2;%LET MTXout File path for preexisting matrix file (.mtx);%LET IMGlaunch &IMGwork &Model –s –m &Zone1 &Zone2 &Class &MTXout;OPTIONS NOXWAIT XSYNC;Define file path locationsDATA NULL ;FILE “File location to be created batch file” LRECL 600;PUT ‘Set Imagine Batch Run 1”;PUT ‘ECHO off ‘;PUT ‘TITLE ‘ “&StYr2. &Tabulated”;PUT ‘COLOR 79 ‘;PUT ‘ECHO Mosaic running from N:\Estimates\Acreage\Temp\Tabulate.bat”;PUT ‘TIME /t’;PUT %unquote(%str(%’)&IMGLanch %str(%));RUN;Code to create batch files torun ERDAS Imagine
Step 2: Reformat Matrix File The resulting matrix file created by the batch file isconverted to column formatted data.DATA NULL ;SET MtxColNames;LENGTH List Rename 2000;ARRAY Class(*) &ClassRng;DO i 1 to HBOUND (Class);List Trim(List) ’ c’ PUT(Class[i],z3.);Rename Trim(Rename) ’ c’ PUT(i-1,z4.) ’c ’ PUT(Class[i],z3.);END;CALL SYMPUT(‘ClassList’,List);CALL SYMPUT(‘Rename’, Rename);RUN;County ID666666666666PROC DATASETS LIBRARY Work NODETAILS NOLIST;MODIFY MtxMain;RENAME &Rename;QUIT;County66661 (Corn)2 (Cotton)3 (Rice)4 (Sorghum)1 (Corn)152325012 (Cotton)5468416883 (Rice)7445842254 (Sorghum)123695751ValidationCategoryClassified CDLCategoryPixelCount1 (Corn)1 (Corn)1 (Corn)1 (Corn)1 (Corn)2 (Cotton)3 (Rice)4 (Sorghum)1 (Corn)2 (Cotton)3 (Rice)4 (Sorghum)1 (Corn)2 (Cotton)3 (Rice)4 (Sorghum)15232 (Cotton)2 (Cotton)2 (Cotton)2 (Cotton)3 (Rice)3 (Rice)3 (Rice)3 (Rice)2501546841688744584225These values are used to calculateproducer and user accuracy
Step 3: Creating the TemplateReferenceMapProducer AccuracyChoropleth MapMap ofPixel CountsUser AccuracyChoropleth MapBar Chart/ScatterplotLegendBar Chart/ScatterplotLegend*All data to be displayed is for displaypurposes only.Confidence ValueChoropleth Map
Accuracy Assessment Dashboard*All data displayed isfor displaypurposes only.
Step 4: Constructing Pieces Bar Chart/Scatterplots Template CreationPROC TEMPLATE;DEFINE STATGRAPH Bar Chart county;DYNAMIC yAxis “Category var” xAxis “Continuous var”;MVAR Title “Title” SF Counts “StatFinal count”;begingraph;entrytitle Title;layout overlay /x2axisopts xaxisopts yaxisopts REFERENCELINE Y yAxis / datatransparency 0.9;barchart x yAxis / options scatterplot y yAxis x Pixel Acres / options scatterplot y yAxis x Estimate / options scatterplot y yAxis x Conf Mean / options Cscatterplot y yAxis x Conf Mean / options Pscatterplot y yAxis x Conf Mean / options UENDLAYOUT;ENDGRAPH;END;RUN;*All data displayed is for display purposes only.
Step 5: Assembling HTML Columns Column 1 – 2 maps, 1 legendfilename htmlpath “C:\Temp”;ods listing close;ods tagsets.htmlpanel nogtitle path htmlPathfile ”Final.html” style Pairedoptions(panelborder ’1’ panelcolumns ’4 3’)ods tagsets.htmlpanel event row panel(start);ods tagsets.htmlpanel event column panel(start);DATA Anno Map ASD TITLE1 “County FIPS and Districts”;PROC GANNO ANNOTATE Anno Map ASD;TITLE “Pixels merged”;PROC GMAP DATA My anno goptions xpixel 240 ypixel 300;PROC GANNO ANNOTATE My anno;Defines html panel4 rows, 3 columnsStart constructionRow 1, Column 1Annotated CountyMapPixel Counts byCountyLegend – createdmanually, stored aspicture
Step 5: Assembling HTML Columns Bar Chart / Scatterplots Placementods tagsets.htmlpanel event column panel(finish);ods tagsets.htmlpanel event column panel(start);ods graphics / reset noborder width 750px height 1000pximagename ”&File” imagefmt png noscale;PROC SGENDER DATA RevGraph.ND12 TEMPLATE Bar Chart county;DYNMAIC yAxis ”StAsdCty” xAxis ”Estimate”;FORMAT Estimate Pixel Acres Thous. StAsdCty SAC Fips.;WHERE GeoLevel ’Cy’ AND ssYYmmmv ”&SSyyMMMv” andCatName04 ”Swht’;RUN;Calling ODS tagsets starts editing column 2.Inserts the data into the bar chart/scatterplots templatecreated in previous step and sizes it appropriately
Step 5: Assembling HTML Columns Column 3 – 3 maps, 1 legendods tagsets.htmlpanel event column panel(finish);ods tagsets.htmlpanel event column panel(start);Starts editingColumn 3TITLE “Accuracy, Producer”;PROC GMAP Choropleth Map ofProducer AccuacyTITLE “Accuracy, User”;PROC GMAP Choropleth Map ofUser AccuacyData My anno Goptions xpixels 240 ypixels 200;TITLE;PROC GANNO ANNOTATE My anno;Legend – createdmanually, stored aspictureTitle “Confidence, Mean”;PROC GMAP Choropleth Map ofConfidenceodsodsodsODStagsets.htmlpanel event column panel(finish);tagsets.htmlpanel event row panel(finish);all close;LISTING;Closes ODS
Using the Accuracy Assessment Dashboard*All data displayed isfor displaypurposes only.
Audra ZakzeskiUSDA – NASS3251 Old Lee Hwy, Room 305Fairfax, VA 22030(703) 877 - 8000audra zakzeski@nass.usda.govRobert SeffrinUSDA – NASS3251 Old Lee Hwy, Room 305Fairfax, VA 22030(703) 877 - 8000robert seffrin@nass.usda.gov
PROC GANNO ANNOTATE Anno_Map_ASD; TITLE "Pixels merged"; PROC GMAP County DATA My_anno goptions xpixel 240 ypixel 300; PROC GANNO ANNOTATE My_anno; 4 rows, 3 columns Start construction Row 1, Column 1 Annotated County Map Pixel Counts by Legend - created manually, stored as picture Step 5: Assembling HTML Columns
Integrating Cisco CallManager Express and Cisco Unity Express Prerequisites for Integrating Cisco CME with Cisco Unity Express 2 † Configuration Examples for Integrating Cisco CME with Cisco Unity Express, page 33 † Additional References, page 39 Prerequisites for Integrating Cisco CME with
3.1 Integrating Sphere Theory 3 3.2 Radiation Exchange within a Spherical Enclosure 3 3.3 The Integrating Sphere Radiance Equation 4 3.4 The Sphere Multiplier 5 3.5 The Average Reflectance 5 3.6 Spatial Integration 5 3.7 Temporal Response of an Integrating Sphere 6 4.0 Integrating Sphere Design 7 4.1 Integrating Sphere Diameter 7
work/products (Beading, Candles, Carving, Food Products, Soap, Weaving, etc.) ⃝I understand that if my work contains Indigenous visual representation that it is a reflection of the Indigenous culture of my native region. ⃝To the best of my knowledge, my work/products fall within Craft Council standards and expectations with respect to
The modern approach is fact based and lays emphasis on the factual study of political phenomenon to arrive at scientific and definite conclusions. The modern approaches include sociological approach, economic approach, psychological approach, quantitative approach, simulation approach, system approach, behavioural approach, Marxian approach etc. 2 Wasby, L Stephen (1972), “Political Science .
Integrating Sphere Theory and Applications 1.0 IntegratIng Sphere theory The integrating sphere is a simple, yet often misunderstood device for measuring optical radiation. The function of an integrating sphere is to spatially integrate radiant flux. Before one can optimize a sphere design for a particular
1.0 Integrating Sphere Theory The following section discusses of the theory and technical background of integrating sphere performance. 1.1 Materials and Spheres: An integrating sphere in essence is an enclosure to contain and diffuse input light so that it is evenly spread over the entire surface area of the sphere. This diffusion is
from inside an integrating sphere. This allows us to study and quantify properties of realistic ports of non-negligible length, as opposed to the common thin-port assumption used in most theoretical treatments, where . anisms defines the properties of the integrating sphere. The basic theory of empty integrating spheres was laid out almost .
Integrating Sphere - Theory and application Based upon the principle of multiple diffuse reflection (resulting from the Lambertian coating), the integrating sphere is used to spatially integrate radiant flux, either from an external or an internal source of radiation. The efficiency of an integrating sphere is determined
